diff --git a/local_evaluation.py b/local_evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..bbddd811914e11091cbc816dad4defb4c536af49 --- /dev/null +++ b/local_evaluation.py @@ -0,0 +1,145 @@ +import numpy as np +import time +import os +import pandas as pd +from tqdm.auto import tqdm +from PIL import Image +from sklearn.model_selection import train_test_split +from sklearn.metrics import f1_score + +""" +This is only a reference script provided to allow you +to do local evaluation. The evaluator **DOES NOT** +use this script for orchestrating the evaluations. +""" + +from my_models.user_model import SubmissionModel + +IOU_THRESHOLD = 0.75 +MAX_IMAGES = 100000000000 +COL_NAME = 'img_fName' # 'bb_fName' # 'img_fName' + + +def iou_single(w, h, bbox_gt, bbox_pred): + # bbox format - xtl, ytl, xbr, ybr + gt = np.zeros((w, h), dtype=np.uint8) + pred = np.zeros((w, h), dtype=np.uint8) + gt[bbox_gt[0]:bbox_gt[2], bbox_gt[1]:bbox_gt[3]] = 1 + pred[bbox_pred[0]:bbox_pred[2], bbox_pred[1]:bbox_pred[3]] = 1 + union = np.bitwise_or(gt, pred) + intersection = np.bitwise_and(gt, pred) + iou = np.sum(intersection)/(np.sum(union)+1) + return iou + + +def iou_values(gt, pred): + all_iou = [] + name_idx_map = {name: idx for name, idx in zip(pred[COL_NAME], pred.index)} + for ri, row in tqdm(gt.iterrows(), total=len(gt)): + bbox_gt = int(row['bbx_xtl']), int(row['bbx_ytl']), int(row['bbx_xbr']), int(row['bbx_ybr']) + prow = pred.iloc[ name_idx_map[row[COL_NAME]] ] + bbox_pred = int(prow['bbx_xtl']), int(prow['bbx_ytl']), int(prow['bbx_xbr']), int(prow['bbx_ybr']) + iou = iou_single(w=row['img_w'], + h=row['img_h'], + bbox_gt=bbox_gt, + bbox_pred=bbox_pred, + ) + all_iou.append(iou) + return all_iou + + +def evaluate(config): + print("Starting local evaluation") + + labels_df = pd.read_csv(config.labels_path) + + if config.partial_eval: + + if config.fold is not None: + filename = os.path.join("../data", "train_bb_with_4folds_v3.parquet" if COL_NAME == 'bb_fName' else "train_with_4folds_v3.parquet") + print("Loading validation from:", filename) + labels_df = pd.read_parquet(filename) + labels_df = labels_df[labels_df["sgkf_fold_s42"] == config.fold] + labels_df = labels_df.reset_index(drop=True) + else: + print( + "Warning: Selecting 5 percent of the data for eval, " + "the underrepresented classes might have very few samples" + ) + _, labels_df = train_test_split(labels_df, test_size=0.05, random_state=42, + stratify=labels_df['class_label']) + + model_time_elapsed = 0 + + model = SubmissionModel() + + pred_dict = { + COL_NAME: [], + "class_label": [], + "bbx_xtl": [], + "bbx_ytl": [], + "bbx_xbr": [], + "bbx_ybr": [] + } + + # Model Predictions + for img_name in tqdm(labels_df[COL_NAME]): + img_path = os.path.join(config.data_dir, img_name) + image = np.array(Image.open(img_path)) + + pred_start = time.perf_counter() + preds = model.predict(image) + model_time_elapsed += time.perf_counter() - pred_start + + assert len(preds) == 2, "Should be tuple of (class_label, bbox)" + class_label, bbox = preds + pred_dict[COL_NAME].append(img_name) + pred_dict['class_label'].append(class_label) + pred_dict['bbx_xtl'].append(bbox[0]) + pred_dict['bbx_ytl'].append(bbox[1]) + pred_dict['bbx_xbr'].append(bbox[2]) + pred_dict['bbx_ybr'].append(bbox[3]) + + preds_df = pd.DataFrame(pred_dict) + + # Scoring + all_iou = iou_values(labels_df, preds_df) + iou_filtered = np.array(all_iou) < IOU_THRESHOLD + + classes = {name: idx for idx, name in enumerate(list(labels_df['class_label'].unique()))} + dummy = len(classes) + + gt_classes = labels_df['class_label'].map(classes).values + pred_classes = preds_df['class_label'].map(classes).replace(np.nan, dummy).values + pred_classes_filtered = pred_classes.copy() + pred_classes_filtered[np.where(iou_filtered)] = dummy + + macro_f1 = np.mean(f1_score(y_true=gt_classes, y_pred=pred_classes_filtered, average=None)[:len(classes)]) + mean_iou = np.mean(all_iou) + macro_f1_nofilter = np.mean(f1_score(y_true=gt_classes, y_pred=pred_classes, average=None)[:len(classes)]) + num_iou_filtered = np.sum(iou_filtered) + + results = { + "macro_f1": float(macro_f1), + "mean_iou": float(mean_iou), + "macro_f1_nofilter": float(macro_f1_nofilter), + "num_iou_filtered": int(num_iou_filtered), + } + + print("=========================Completed=========================") + + print(f"Total time taken by model: {model_time_elapsed}s") + print("Results", results) + + +if __name__ == '__main__': + class Config: + # data_dir = '../data/images/' + data_dir = '../data/images_boxes/' if COL_NAME == 'bb_fName' else '../data/images/' + labels_path = '../data/phase2_train_v0.csv' + partial_eval = True # Runs on 5 % of the dataset + fold = None # 3 # 0 # None + + config = Config() + + evaluate(config) diff --git a/my_models/README.md b/my_models/README.md new file mode 100644 index 0000000000000000000000000000000000000000..6ed9908425ae49eee6f29fd11ca31e361374071d --- /dev/null +++ b/my_models/README.md @@ -0,0 +1,11 @@ +# Add your models here + +Your models need to implement a class that contains the `predict` function. This will recieve a single input image and output the classification and bouding box coordinates. + +Your model needs to predict the result for each image in `1 second` + +# Regarding YOLOv5 code + +Since AIcrowd submissions need to run without internet. The code for YOLOv5 is copied locally in `my_models/torch_hub_cache/yolov5/`, the commit hash used is `94e943e609f296fc2b0eddf32f3f9b28ad1da106`. + +Full credit goes to `https://github.com/ultralytics/yolov5/` \ No newline at end of file diff --git a/my_models/__init__.py b/my_models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold0/stage1/best.pt b/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold0/stage1/best.pt new file mode 100644 index 0000000000000000000000000000000000000000..2314de05feb8a617a4bd08eef50f4f12acd5bf6b --- /dev/null +++ b/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold0/stage1/best.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ebfaf5594f930f90abea5c648f8a178c3e208b93fee49dff5d1f3de4b2e40a74 +size 82818289 diff --git a/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold0/stage1/config.json b/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold0/stage1/config.json new file mode 100644 index 0000000000000000000000000000000000000000..f7e380299db8d03a10ca77df9ab3c63f7b889648 --- /dev/null +++ b/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold0/stage1/config.json @@ -0,0 +1 @@ +{"version": "4.0.0", "seed": 42, "folds": 4, "folds_seed": 42, "imgsz": 384, "ar": null, "center_crop": null, "crop_ratio": null, "image_size": 384, "backbone": "tiny_vit_21m_384", "global_pool": "avg", "num_classes": 7, "pretrained": true, "max_pixel": 255.0, "IMG_MEAN": [0.485, 0.456, 0.406], "IMG_STD": [0.229, 0.224, 0.225], "epochs": 96, "batch_size": 32, "val_batch_size": 32, "accumulate_grad_batches": 1, "gradient_clip_val": null, "cutmix_prob": 0.5, "cutmix_alpha": 1.0, "mixup_prob": 0.5, "mixup_alpha": 0.2, "optimizer": "AdamW", "lr0": 0.0001, "lrf": 0.0, "scheduler": "cos_lr", "dropout": 0.0, "swa_lrs": null, "ema": 0.999, "save_top_k": 5, "label_smoothing": 0.1, "sampler": null, "batch_sampler": null, "batch_sampler_alpha": 0.25, "precision": "16-mixed", "device": "gpu", "deterministic": true, "num_workers": 8, "pruning": null} \ No newline at end of file diff --git a/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold1/stage1/best.pt b/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold1/stage1/best.pt new file mode 100644 index 0000000000000000000000000000000000000000..d3383fdb2d4e2f6ef56df1b85bd7a894c4e0092a --- /dev/null +++ b/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold1/stage1/best.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ee285f6597b9d735dfa7f1033024d975a89631c33a3b793dff7d3bf73dd940f1 +size 82818289 diff --git a/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold1/stage1/config.json b/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold1/stage1/config.json new file mode 100644 index 0000000000000000000000000000000000000000..f7e380299db8d03a10ca77df9ab3c63f7b889648 --- /dev/null +++ b/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold1/stage1/config.json @@ -0,0 +1 @@ +{"version": "4.0.0", "seed": 42, "folds": 4, "folds_seed": 42, "imgsz": 384, "ar": null, "center_crop": null, "crop_ratio": null, "image_size": 384, "backbone": "tiny_vit_21m_384", "global_pool": "avg", "num_classes": 7, "pretrained": true, "max_pixel": 255.0, "IMG_MEAN": [0.485, 0.456, 0.406], "IMG_STD": [0.229, 0.224, 0.225], "epochs": 96, "batch_size": 32, "val_batch_size": 32, "accumulate_grad_batches": 1, "gradient_clip_val": null, "cutmix_prob": 0.5, "cutmix_alpha": 1.0, "mixup_prob": 0.5, "mixup_alpha": 0.2, "optimizer": "AdamW", "lr0": 0.0001, "lrf": 0.0, "scheduler": "cos_lr", "dropout": 0.0, "swa_lrs": null, "ema": 0.999, "save_top_k": 5, "label_smoothing": 0.1, "sampler": null, "batch_sampler": null, "batch_sampler_alpha": 0.25, "precision": "16-mixed", "device": "gpu", "deterministic": true, "num_workers": 8, "pruning": null} \ No newline at end of file diff --git a/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold2/stage1/best.pt b/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold2/stage1/best.pt new file mode 100644 index 0000000000000000000000000000000000000000..75de467fa5fc2bc145c6da74f27a5e7075dc1d42 --- /dev/null +++ b/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold2/stage1/best.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:034731482047514d62af50cbaba5af5598489e060b303299aa48d219685ff764 +size 82818289 diff --git a/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold2/stage1/config.json b/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold2/stage1/config.json new file mode 100644 index 0000000000000000000000000000000000000000..f7e380299db8d03a10ca77df9ab3c63f7b889648 --- /dev/null +++ b/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold2/stage1/config.json @@ -0,0 +1 @@ +{"version": "4.0.0", "seed": 42, "folds": 4, "folds_seed": 42, "imgsz": 384, "ar": null, "center_crop": null, "crop_ratio": null, "image_size": 384, "backbone": "tiny_vit_21m_384", "global_pool": "avg", "num_classes": 7, "pretrained": true, "max_pixel": 255.0, "IMG_MEAN": [0.485, 0.456, 0.406], "IMG_STD": [0.229, 0.224, 0.225], "epochs": 96, "batch_size": 32, "val_batch_size": 32, "accumulate_grad_batches": 1, "gradient_clip_val": null, "cutmix_prob": 0.5, "cutmix_alpha": 1.0, "mixup_prob": 0.5, "mixup_alpha": 0.2, "optimizer": "AdamW", "lr0": 0.0001, "lrf": 0.0, "scheduler": "cos_lr", "dropout": 0.0, "swa_lrs": null, "ema": 0.999, "save_top_k": 5, "label_smoothing": 0.1, "sampler": null, "batch_sampler": null, "batch_sampler_alpha": 0.25, "precision": "16-mixed", "device": "gpu", "deterministic": true, "num_workers": 8, "pruning": null} \ No newline at end of file diff --git a/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold3/stage1/best.pt b/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold3/stage1/best.pt new file mode 100644 index 0000000000000000000000000000000000000000..fffdfeb5269237be12ce03957a7ce49381fa3a6e --- /dev/null +++ b/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold3/stage1/best.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e62e8e110ed21a2e6712544fa0d16a5c1732eb52c4610ae8ce9a31acd19de703 +size 82818289 diff --git a/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold3/stage1/config.json b/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold3/stage1/config.json new file mode 100644 index 0000000000000000000000000000000000000000..f7e380299db8d03a10ca77df9ab3c63f7b889648 --- /dev/null +++ b/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold3/stage1/config.json @@ -0,0 +1 @@ +{"version": "4.0.0", "seed": 42, "folds": 4, "folds_seed": 42, "imgsz": 384, "ar": null, "center_crop": null, "crop_ratio": null, "image_size": 384, "backbone": "tiny_vit_21m_384", "global_pool": "avg", "num_classes": 7, "pretrained": true, "max_pixel": 255.0, "IMG_MEAN": [0.485, 0.456, 0.406], "IMG_STD": [0.229, 0.224, 0.225], "epochs": 96, "batch_size": 32, "val_batch_size": 32, "accumulate_grad_batches": 1, "gradient_clip_val": null, "cutmix_prob": 0.5, "cutmix_alpha": 1.0, "mixup_prob": 0.5, "mixup_alpha": 0.2, "optimizer": "AdamW", "lr0": 0.0001, "lrf": 0.0, "scheduler": "cos_lr", "dropout": 0.0, "swa_lrs": null, "ema": 0.999, "save_top_k": 5, "label_smoothing": 0.1, "sampler": null, "batch_sampler": null, "batch_sampler_alpha": 0.25, "precision": "16-mixed", "device": "gpu", "deterministic": true, "num_workers": 8, "pruning": null} \ No newline at end of file diff --git a/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold99/stage1/config.json b/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold99/stage1/config.json new file mode 100644 index 0000000000000000000000000000000000000000..f7e380299db8d03a10ca77df9ab3c63f7b889648 --- /dev/null +++ b/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold99/stage1/config.json @@ -0,0 +1 @@ +{"version": "4.0.0", "seed": 42, "folds": 4, "folds_seed": 42, "imgsz": 384, "ar": null, "center_crop": null, "crop_ratio": null, "image_size": 384, "backbone": "tiny_vit_21m_384", "global_pool": "avg", "num_classes": 7, "pretrained": true, "max_pixel": 255.0, "IMG_MEAN": [0.485, 0.456, 0.406], "IMG_STD": [0.229, 0.224, 0.225], "epochs": 96, "batch_size": 32, "val_batch_size": 32, "accumulate_grad_batches": 1, "gradient_clip_val": null, "cutmix_prob": 0.5, "cutmix_alpha": 1.0, "mixup_prob": 0.5, "mixup_alpha": 0.2, "optimizer": "AdamW", "lr0": 0.0001, "lrf": 0.0, "scheduler": "cos_lr", "dropout": 0.0, "swa_lrs": null, "ema": 0.999, "save_top_k": 5, "label_smoothing": 0.1, "sampler": null, "batch_sampler": null, "batch_sampler_alpha": 0.25, "precision": "16-mixed", "device": "gpu", "deterministic": true, "num_workers": 8, "pruning": null} \ No newline at end of file diff --git a/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold99/stage1/last-EMA.pt b/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold99/stage1/last-EMA.pt new file mode 100644 index 0000000000000000000000000000000000000000..13303a8959e8b21f98264cc0f569c37cfcd4ef18 --- /dev/null +++ b/my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold99/stage1/last-EMA.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e9003fa61e467c866e8afe8ac19b7cc541078562390861d9045d8d58d717f764 +size 82818289 diff --git a/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold0/stage1/best.pt b/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold0/stage1/best.pt new file mode 100644 index 0000000000000000000000000000000000000000..53bd7f285c7f8ffa16d8a5e9be328a99f85934f1 --- /dev/null +++ b/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold0/stage1/best.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:14f45833f2f6d51be44a74d541404e03c07ba3b7b87e6d0c8d9455deccee6be2 +size 81649957 diff --git a/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold0/stage1/config.json b/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold0/stage1/config.json new file mode 100644 index 0000000000000000000000000000000000000000..3b9c1c69d35675bad694b142f7a3e2260260ce29 --- /dev/null +++ b/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold0/stage1/config.json @@ -0,0 +1 @@ +{"version": "4.1.0", "seed": 42, "folds": 4, "folds_seed": 42, "imgsz": 512, "ar": null, "center_crop": null, "crop_ratio": null, "image_size": 512, "backbone": "tf_efficientnetv2_s", "global_pool": "avg", "num_classes": 7, "pretrained": true, "max_pixel": 255.0, "IMG_MEAN": [0.485, 0.456, 0.406], "IMG_STD": [0.229, 0.224, 0.225], "epochs": 96, "batch_size": 32, "val_batch_size": 32, "accumulate_grad_batches": 1, "gradient_clip_val": null, "cutmix_prob": 0.5, "cutmix_alpha": 1.0, "mixup_prob": 0.5, "mixup_alpha": 0.2, "optimizer": "AdamW", "lr0": 0.001, "lrf": 0.0, "scheduler": "cos_lr", "dropout": 0.0, "swa_lrs": null, "ema": 0.999, "save_top_k": 5, "label_smoothing": 0.1, "sampler": null, "batch_sampler": null, "batch_sampler_alpha": 0.25, "precision": "16-mixed", "device": "gpu", "deterministic": true, "num_workers": 8, "pruning": null} \ No newline at end of file diff --git a/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold1/stage1/best.pt b/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold1/stage1/best.pt new file mode 100644 index 0000000000000000000000000000000000000000..bfd4a71282b3e3514a058846e823fc236535bfe3 --- /dev/null +++ b/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold1/stage1/best.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f1423cfdc1efca53792c95302f6b3c1bf1f480e645390cb9482733c7abe6a150 +size 81649957 diff --git a/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold1/stage1/config.json b/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold1/stage1/config.json new file mode 100644 index 0000000000000000000000000000000000000000..3b9c1c69d35675bad694b142f7a3e2260260ce29 --- /dev/null +++ b/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold1/stage1/config.json @@ -0,0 +1 @@ +{"version": "4.1.0", "seed": 42, "folds": 4, "folds_seed": 42, "imgsz": 512, "ar": null, "center_crop": null, "crop_ratio": null, "image_size": 512, "backbone": "tf_efficientnetv2_s", "global_pool": "avg", "num_classes": 7, "pretrained": true, "max_pixel": 255.0, "IMG_MEAN": [0.485, 0.456, 0.406], "IMG_STD": [0.229, 0.224, 0.225], "epochs": 96, "batch_size": 32, "val_batch_size": 32, "accumulate_grad_batches": 1, "gradient_clip_val": null, "cutmix_prob": 0.5, "cutmix_alpha": 1.0, "mixup_prob": 0.5, "mixup_alpha": 0.2, "optimizer": "AdamW", "lr0": 0.001, "lrf": 0.0, "scheduler": "cos_lr", "dropout": 0.0, "swa_lrs": null, "ema": 0.999, "save_top_k": 5, "label_smoothing": 0.1, "sampler": null, "batch_sampler": null, "batch_sampler_alpha": 0.25, "precision": "16-mixed", "device": "gpu", "deterministic": true, "num_workers": 8, "pruning": null} \ No newline at end of file diff --git a/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold2/stage1/best.pt b/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold2/stage1/best.pt new file mode 100644 index 0000000000000000000000000000000000000000..92a3e679ecfff384968c529262bc81d176e52775 --- /dev/null +++ b/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold2/stage1/best.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9ee5623570b62e7ad2668801db75c796eba29dd8fab726aa72c21f81b9c5cf19 +size 81649957 diff --git a/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold2/stage1/config.json b/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold2/stage1/config.json new file mode 100644 index 0000000000000000000000000000000000000000..3b9c1c69d35675bad694b142f7a3e2260260ce29 --- /dev/null +++ b/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold2/stage1/config.json @@ -0,0 +1 @@ +{"version": "4.1.0", "seed": 42, "folds": 4, "folds_seed": 42, "imgsz": 512, "ar": null, "center_crop": null, "crop_ratio": null, "image_size": 512, "backbone": "tf_efficientnetv2_s", "global_pool": "avg", "num_classes": 7, "pretrained": true, "max_pixel": 255.0, "IMG_MEAN": [0.485, 0.456, 0.406], "IMG_STD": [0.229, 0.224, 0.225], "epochs": 96, "batch_size": 32, "val_batch_size": 32, "accumulate_grad_batches": 1, "gradient_clip_val": null, "cutmix_prob": 0.5, "cutmix_alpha": 1.0, "mixup_prob": 0.5, "mixup_alpha": 0.2, "optimizer": "AdamW", "lr0": 0.001, "lrf": 0.0, "scheduler": "cos_lr", "dropout": 0.0, "swa_lrs": null, "ema": 0.999, "save_top_k": 5, "label_smoothing": 0.1, "sampler": null, "batch_sampler": null, "batch_sampler_alpha": 0.25, "precision": "16-mixed", "device": "gpu", "deterministic": true, "num_workers": 8, "pruning": null} \ No newline at end of file diff --git a/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold3/stage1/best.pt b/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold3/stage1/best.pt new file mode 100644 index 0000000000000000000000000000000000000000..d1bb476e54fee88c4f726cfe0fd242c3040c5f02 --- /dev/null +++ b/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold3/stage1/best.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b2bb340e41853bed8ed1d64c7dd6ce475de05a7f9b220d84c4b5fefe41fe12ee +size 81649957 diff --git a/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold3/stage1/config.json b/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold3/stage1/config.json new file mode 100644 index 0000000000000000000000000000000000000000..3b9c1c69d35675bad694b142f7a3e2260260ce29 --- /dev/null +++ b/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold3/stage1/config.json @@ -0,0 +1 @@ +{"version": "4.1.0", "seed": 42, "folds": 4, "folds_seed": 42, "imgsz": 512, "ar": null, "center_crop": null, "crop_ratio": null, "image_size": 512, "backbone": "tf_efficientnetv2_s", "global_pool": "avg", "num_classes": 7, "pretrained": true, "max_pixel": 255.0, "IMG_MEAN": [0.485, 0.456, 0.406], "IMG_STD": [0.229, 0.224, 0.225], "epochs": 96, "batch_size": 32, "val_batch_size": 32, "accumulate_grad_batches": 1, "gradient_clip_val": null, "cutmix_prob": 0.5, "cutmix_alpha": 1.0, "mixup_prob": 0.5, "mixup_alpha": 0.2, "optimizer": "AdamW", "lr0": 0.001, "lrf": 0.0, "scheduler": "cos_lr", "dropout": 0.0, "swa_lrs": null, "ema": 0.999, "save_top_k": 5, "label_smoothing": 0.1, "sampler": null, "batch_sampler": null, "batch_sampler_alpha": 0.25, "precision": "16-mixed", "device": "gpu", "deterministic": true, "num_workers": 8, "pruning": null} \ No newline at end of file diff --git a/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold99/stage1/config.json b/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold99/stage1/config.json new file mode 100644 index 0000000000000000000000000000000000000000..3b9c1c69d35675bad694b142f7a3e2260260ce29 --- /dev/null +++ b/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold99/stage1/config.json @@ -0,0 +1 @@ +{"version": "4.1.0", "seed": 42, "folds": 4, "folds_seed": 42, "imgsz": 512, "ar": null, "center_crop": null, "crop_ratio": null, "image_size": 512, "backbone": "tf_efficientnetv2_s", "global_pool": "avg", "num_classes": 7, "pretrained": true, "max_pixel": 255.0, "IMG_MEAN": [0.485, 0.456, 0.406], "IMG_STD": [0.229, 0.224, 0.225], "epochs": 96, "batch_size": 32, "val_batch_size": 32, "accumulate_grad_batches": 1, "gradient_clip_val": null, "cutmix_prob": 0.5, "cutmix_alpha": 1.0, "mixup_prob": 0.5, "mixup_alpha": 0.2, "optimizer": "AdamW", "lr0": 0.001, "lrf": 0.0, "scheduler": "cos_lr", "dropout": 0.0, "swa_lrs": null, "ema": 0.999, "save_top_k": 5, "label_smoothing": 0.1, "sampler": null, "batch_sampler": null, "batch_sampler_alpha": 0.25, "precision": "16-mixed", "device": "gpu", "deterministic": true, "num_workers": 8, "pruning": null} \ No newline at end of file diff --git a/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold99/stage1/last-EMA.pt b/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold99/stage1/last-EMA.pt new file mode 100644 index 0000000000000000000000000000000000000000..3cc6113cd3e2a38ce4f110d6c9ff90af0307d45c --- /dev/null +++ b/my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold99/stage1/last-EMA.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e7ae1111cf10a6ea4c3b20aed5875ca8af39858a0d33f258d893230d3ed29cad +size 81649957 diff --git a/my_models/user_model.py b/my_models/user_model.py new file mode 100644 index 0000000000000000000000000000000000000000..6f0860aa3971a2a085713a7124722531542b5149 --- /dev/null +++ b/my_models/user_model.py @@ -0,0 +1,7 @@ +from my_models.yolo.combo import ComboModel + +################################################################### +##### Specify your model here ##### +################################################################### + +SubmissionModel = ComboModel diff --git a/my_models/utils/__init__.py b/my_models/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/my_models/utils/torch.py b/my_models/utils/torch.py new file mode 100644 index 0000000000000000000000000000000000000000..f123837b005edcbff44f3fb6548be60eadf7c8d2 --- /dev/null +++ b/my_models/utils/torch.py @@ -0,0 +1,72 @@ +import numpy as np +import os, random +import torch +import json + + +def seed_everything(seed): + """ + Seeds basic parameters for reproducibility of results. + Args: + seed (int): Number of the seed. + """ + random.seed(seed) + os.environ["PYTHONHASHSEED"] = str(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + +SEEDS = [42] + +FOLDS = 4 + +MAP_CLASSES = { + 'aegypti': 0, # 0.47% # yellow fever + 'albopictus': 1, # 44.44% # Asian tiger + 'anopheles': 2, # 0.78% + 'culex': 3, # 44.16% # common genus + 'culiseta': 4, # 6.13% + 'japonicus-koreicus': 5, # 4.00% + #'japonicus/koreicus': 5, # 4.00% +} + +MAP_LABELS_LIST = [k for k, v in MAP_CLASSES.items()] +MAP_CLASSES_LIST = [v for k, v in MAP_CLASSES.items()] +MAP_CLASSES_REVERSE = {v: k for k, v in MAP_CLASSES.items()} + + +class Config: + """ + Placeholder to load a config from a saved json + """ + + def __init__(self, dic): + for k, v in dic.items(): + setattr(self, k, v) + + +def save_config(config, path): + """ + Saves a config as a json + Args: + config (Config): Config. + path (str): Path to save at. + """ + dic = config.__dict__.copy() + if dic.get("__doc__") is not None: + del dic["__doc__"] + if dic.get("__module__") is not None: + del dic["__module__"] + if dic.get("__dict__") is not None: + del dic["__dict__"] + if dic.get("__weakref__") is not None: + del dic["__weakref__"] + + with open(path, "w") as f: + json.dump(dic, f) + + return dic + diff --git a/my_models/yolo/__init__.py b/my_models/yolo/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/my_models/yolo/combo.py b/my_models/yolo/combo.py new file mode 100644 index 0000000000000000000000000000000000000000..0490b3f655e7d76b37ff0728cdc58b6a3c2cde14 --- /dev/null +++ b/my_models/yolo/combo.py @@ -0,0 +1,544 @@ +import numpy as np +import pandas as pd +import time +import os, sys, gc, random +import cv2 +import torch + +from my_models.utils.torch import * +from ensemble_boxes import * +from ultralytics import YOLO +from ultralytics import RTDETR +import timm +import albumentations as A +from albumentations.pytorch import ToTensorV2 +import json + +OPENVINO = True # True # False # True +ONNX = False +DEVICE = "cpu" +BBX_TTA = False # False # False +CLS_TTA = False # True # True +DEBUG = False # True +BBX_IMAGE_SIZE = 768 +MARGIN = None # 0.05 # None + +prefix = "PT" +if ONNX: + prefix = "ONNX" +elif OPENVINO: + prefix = "OPENVINO" + + +class Config: + def __init__(self, dic): + for k, v in dic.items(): + setattr(self, k, v) + + +def seed_everything(seed): + random.seed(seed) + os.environ["PYTHONHASHSEED"] = str(seed) + np.random.seed(seed) + + +def get_bbx_model(arch, weights): + if arch == "YOLO": + model = YOLO(weights, task='detect') + elif arch == "RTDETR": + model = RTDETR(weights) + else: + raise Exception("Model not found", arch) + return model + + +def get_cls_model(arch, weights, model_config): + model = None + + if arch == "MosquitoModel": + class MosquitoModel(torch.nn.Module): + def __init__(self, config): + super().__init__() + + self.config = config + + self.backbone = timm.create_model(self.config.backbone, pretrained=False, + num_classes=config.num_classes, global_pool=config.global_pool) + self.head = None + + def forward(self, x): + batch_size, channels, width, height = x.size() + # Features + x = self.backbone(x) + # Classifier + x = self.head(x) if self.head is not None else x + # return logits + return x + + model = MosquitoModel(model_config) + model_dump = torch.load(weights, map_location=torch.device(DEVICE)) + model.load_state_dict(model_dump["state_dict"]) + model.eval() + else: + raise Exception("Model not found", arch) + return model + + +def load_model(debug=False): + bbx_models, cls_models = [], [] + + # Boxes + BBX_MODELS = { + "yolo8n_768_fold0": { + "arch": "YOLO", + "pt": "my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold0_1.4/best.pt", + "ov": "my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold0_1.4/best_openvino_model/" + }, + "yolo8n_768_fold3": { + "arch": "YOLO", + "pt": "my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold3_1.4/best.pt", + "ov": "my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold3_1.4/best_openvino_model/" + }, + "yolo8n_768_fold1": { + "arch": "YOLO", + "pt": "my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold1_1.4/best.pt", + "ov": "my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold1_1.4/best_openvino_model/" + }, + "yolo8n_768_fold2": { + "arch": "YOLO", + "pt": "my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold2_1.4/best.pt", + "ov": "my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold2_1.4/best_openvino_model/" + }, + + } + + for name, model_info in BBX_MODELS.items(): + ckpt_file = model_info.get("ov") if OPENVINO else model_info.get("pt") + print(name, "... loading:", ckpt_file) if debug is True else None + if os.path.exists(ckpt_file): + bbx_model = get_bbx_model(model_info.get("arch"), ckpt_file) + bbx_models.append(bbx_model) + else: + raise Exception("Weights not found: %s" % ckpt_file) + + # Classifier + CLS_MODELS = { + + # ---------- classifier_384_tiny_vit_21m folds ---------- + + # Best fold + # "vit_tiny_384_hard_cutmix_mixup_bg_imgnet_ls_ema_ext17k_yolov8n_oof_fold0": { + # "arch": "MosquitoModel", + # "pt": "my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold0/stage1/", + # }, + + # "vit_tiny_384_hard_cutmix_mixup_bg_imgnet_ls_ema_ext17k_yolov8n_oof_fold1": { + # "arch": "MosquitoModel", + # "pt": "my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold1/stage1/", + # }, + + # "vit_tiny_384_hard_cutmix_mixup_bg_imgnet_ls_ema_ext17k_yolov8n_oof_fold2": { + # "arch": "MosquitoModel", + # "pt": "my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold1/stage1/", + # }, + + # "vit_tiny_384_hard_cutmix_mixup_bg_imgnet_ls_ema_ext17k_yolov8n_oof_fold3": { + # "arch": "MosquitoModel", + # "pt": "my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold3/stage1/", + # }, + + # ---------- classifier_384_tiny_vit_21m full fit ---------- + + "vit_tiny_384_hard_cutmix_mixup_bg_imgnet_ls_ema_ext17k_yolov8n_oof_fold99": { + "arch": "MosquitoModel", + "pt": "my_models/classifiers_model_weights/classifier_384_tiny_vit_21m_384_4.0.0/seed42/fold99/stage1/", + }, + + + # ---------- classifier_512_tf_efficientnetv2_s_4.1.0 folds ---------- + + # "classifier_512_tf_efficientnetv2_s_4.1.0_hard_cutmix_mixup_bg_imgnet_ls_ema_ext26k_yolov8n_oof_fold0": { + # "arch": "MosquitoModel", + # "pt": "my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold0/stage1/", + # }, + + # "classifier_512_tf_efficientnetv2_s_4.1.0_hard_cutmix_mixup_bg_imgnet_ls_ema_ext26k_yolov8n_oof_fold1": { + # "arch": "MosquitoModel", + # "pt": "my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold1/stage1/", + # }, + + # "classifier_512_tf_efficientnetv2_s_4.1.0_hard_cutmix_mixup_bg_imgnet_ls_ema_ext26k_yolov8n_oof_fold2": { + # "arch": "MosquitoModel", + # "pt": "my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold2/stage1/", + # }, + + # Best fold + # "classifier_512_tf_efficientnetv2_s_4.1.0_hard_cutmix_mixup_bg_imgnet_ls_ema_ext26k_yolov8n_oof_fold3": { + # "arch": "MosquitoModel", + # "pt": "my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold3/stage1/", + # }, + + # ---------- classifier_512_tf_efficientnetv2_s_4.1.0 full fit ---------- + + "classifier_512_tf_efficientnetv2_s_4.1.0_hard_cutmix_mixup_bg_imgnet_ls_ema_ext26k_yolov8n_oof_fold99": { + "arch": "MosquitoModel", + "pt": "my_models/classifiers_model_weights/classifier_512_tf_efficientnetv2_s_4.1.0/seed42/fold99/stage1/", + }, + + } + + for name, model_info in CLS_MODELS.items(): + model_path = model_info.get("pt") + # ckpt_file = os.path.join(model_path, "best.pt") + # ckpt_file = os.path.join(model_path, "swa_5_best.pt") + # ckpt_file = os.path.join(model_path, "last.pt") + ckpt_file = os.path.join(model_path, "last-EMA.pt") + print(name, "... loading:", ckpt_file) if debug is True else None + if os.path.exists(ckpt_file): + config = Config(json.load(open(os.path.join(model_path, "config.json"), "r"))) + print("Config loaded", config.__dict__) + cls_model = get_cls_model(model_info.get("arch"), ckpt_file, config) + cls_models.append(cls_model) + else: + raise Exception("Weights not found: %s" % ckpt_file) + + return bbx_models, cls_models + + +def predict_image_torch(model, img, imgsz, debug=False): + # Inference + + t0 = time.time() + + outputs = model.predict(source=img, imgsz=imgsz, max_det=1, conf=0.00001, iou=0.7, augment=BBX_TTA, + device=DEVICE, verbose=False) + # Extract BB + best_box = None + best_score = None + best_label = None + for r in outputs: + boxes = r.boxes.cpu().numpy() + for bbox in boxes: + box = bbox.xyxy[0] # get box coordinates in (top, left, bottom, right) format + score = bbox.conf[0] + label = bbox.cls[0] + best_box = box if best_box is None else best_box + best_score = score if best_score is None else best_score + best_label = label if best_label is None else best_label + if score > best_score: + best_score = score + best_box = box + best_label = label + + h, w = img.shape[0], img.shape[1] + xmin_, ymin_, xmax_, ymax_ = None, None, None, None + if best_box is not None: + xmin_, ymin_, xmax_, ymax_ = best_box[0], best_box[1], best_box[2], best_box[3] + else: + # Nothing found + xmin_, ymin_, xmax_, ymax_ = 0, 0, w - 1, h - 1 + best_label = 1 + + ret = (w, h, xmin_, ymin_, xmax_, ymax_, best_score, best_label) + print("BBX infer time: {:.4f}s".format(time.time() - t0)) if debug is True else None + print("BBX result:", ret) if debug is True else None + + return ret + + +def merge_predictions(dfs, fct=list): + # Merge all models + df = pd.concat(dfs, axis=0, ignore_index=True) + df = df.groupby(["uid", "img_w", "img_h"]).agg( + bbx_xtl=("bbx_xtl", fct), + bbx_ytl=("bbx_ytl", fct), + bbx_xbr=("bbx_xbr", fct), + bbx_ybr=("bbx_ybr", fct), + score=("score", fct), + label=("label", fct), + ).reset_index() + + return df + + +def run_wbf(x, iou_thr=0.5, skip_box_thr=0.000): + img_w = x["img_w"] + img_h = x["img_h"] + bbx_xtl = x["bbx_xtl"] + bbx_ytl = x["bbx_ytl"] + bbx_xbr = x["bbx_xbr"] + bbx_ybr = x["bbx_ybr"] + score = x["score"] + label = x["label"] + + boxes_list, scores_list, labels_list = [], [score], [label] + + for xtl, ytl, xbr, ybr in zip(bbx_xtl, bbx_ytl, bbx_xbr, bbx_ybr): + xtl, ytl, xbr, ybr = xtl / img_w, ytl / img_h, xbr / img_w, ybr / img_h + boxes_list.append([xtl, ytl, xbr, ybr]) + + boxes, scores, labels = weighted_boxes_fusion([boxes_list], scores_list, labels_list, iou_thr=iou_thr, + skip_box_thr=skip_box_thr) # weights=weights + + return boxes[0][0] * img_w, boxes[0][1] * img_h, boxes[0][2] * img_w, boxes[0][3] * img_h, scores[0], labels[0] + + +def predict_bbx_from_image(models, np_image, bbx_imgsz, debug=False, uid="no-uid"): + if len(models) > 1: + dfs = [] + + # Predict boxes for each model + for model in models: + results = [] + w, h, xmin, ymin, xmax, ymax, best_score_, best_cls_ = predict_image_torch(model, np_image, bbx_imgsz, debug=debug) + results.append((uid, w, h, xmin, ymin, xmax, ymax, best_score_, best_cls_)) + dfs.append(pd.DataFrame(results, + columns=["uid", "img_w", "img_h", "bbx_xtl", "bbx_ytl", "bbx_xbr", "bbx_ybr", + "score", "label"])) + + # Ensemble boxes predictions + boxes_pd = merge_predictions(dfs) + boxes_pd[ + ["wbf_bbx_xtl", "wbf_bbx_ytl", "wbf_bbx_xbr", "wbf_bbx_ybr", "wbf_score", "wbf_label"]] = boxes_pd.apply( + run_wbf, axis=1, result_type="expand") + + # Final boxes + w, h, xmin, ymin, xmax, ymax, best_score_, best_cls_ = boxes_pd[ + ["img_w", "img_h", "wbf_bbx_xtl", "wbf_bbx_ytl", "wbf_bbx_xbr", "wbf_bbx_ybr", "wbf_score", + "wbf_label"]].values[0] + + else: + w, h, xmin, ymin, xmax, ymax, best_score_, best_cls_ = predict_image_torch(models[0], np_image, bbx_imgsz, debug=debug) + + return (w, h, xmin, ymin, xmax, ymax, best_score_, best_cls_) + + +def resize(new_size, ar=None, p=1.0): + if ar is None: + return A.Compose([ + A.Resize(new_size, new_size, interpolation=cv2.INTER_LINEAR, p=1.0, always_apply=True), + ], p=p) + elif ar == 1.0: + return A.Compose([ + A.LongestMaxSize(max_size=new_size, interpolation=cv2.INTER_LINEAR, p=1.0, always_apply=True), + A.PadIfNeeded(min_height=new_size, min_width=new_size, border_mode=cv2.BORDER_CONSTANT, value=(114, 114, 114), p=1.0, always_apply=True), + ], p=p) + elif ar == 0.0: + return A.Compose([ + A.PadIfNeeded(min_height=new_size, min_width=new_size, border_mode=cv2.BORDER_CONSTANT, value=(114, 114, 114), p=1.0, always_apply=True), + A.LongestMaxSize(max_size=new_size, interpolation=cv2.INTER_LINEAR, p=1.0, always_apply=True), + A.PadIfNeeded(min_height=new_size, min_width=new_size, border_mode=cv2.BORDER_CONSTANT, value=(114, 114, 114), p=1.0, always_apply=True), + ], p=p) + + +def normalize(mean, std, max_pixel, p=1.0): + return A.Compose([ + + A.Normalize(mean=mean, std=std, max_pixel_value=max_pixel, p=1.0, always_apply=True), + ToTensorV2(p=1.0, always_apply=True) + + ], p=p) + + +# (*, C, H, W) +def hflip(data): + w = data.shape[-1] + return data[..., torch.arange(w - 1, -1, -1, device=data.device)] + + +# (*, C, H, W) +def vflip(data): + h = data.shape[-2] + return data[..., torch.arange(h - 1, -1, -1, device=data.device), :] + + +def predict_cls_from_image(models, bb_image, debug=False, uid="no-uid"): + + print("CLS box %s %s %s %s" % (bb_image.shape, bb_image.dtype, bb_image.min(), bb_image.max())) if debug is True else None + + t0 = time.time() + + if len(models) > 1: + probs = [] + # image_tensor = None + for model in models: + # if image_tensor is None: + preprocess_image = resize(model.config.imgsz, ar=getattr(model.config, "ar", None), p=1.0) if model.config.imgsz is not None else None + prepare_feed = normalize(model.config.IMG_MEAN, model.config.IMG_STD, model.config.max_pixel, p=1.0) + image = preprocess_image(image=bb_image)["image"] # (384, 384, 3) uint8 [0-255] + image_tensor = prepare_feed(image=image)["image"] # torch.Size([3, 384, 384]) + image_tensor = image_tensor.unsqueeze(dim=0) # torch.Size([1, 3, 384, 384]), torch.float32, -1, +1 + with torch.no_grad(): + logits_ = model(image_tensor) # torch.Size([1, 6]) + logits_ = logits_[:, 0:6] + + # if model.config.imgsz == 512: + # logits_tta_ = model(hflip(image_tensor)) # torch.Size([1, 6]) + # logits_tta_ = logits_tta_[:, 0:6] + # logits_ = torch.mean(torch.stack([logits_, logits_tta_]), dim=0) + + probs.append(logits_) + + logits = torch.mean(torch.stack(probs), dim=0) # (N, 1, 6) => (1, 6) + label = torch.argmax(logits, dim=1).numpy()[0] + + else: + model = models[0] + preprocess_image = resize(model.config.imgsz, ar=getattr(model.config, "ar", None), p=1.0) if model.config.imgsz is not None else None + prepare_feed = normalize(model.config.IMG_MEAN, model.config.IMG_STD, model.config.max_pixel, p=1.0) + + image = preprocess_image(image=bb_image)["image"] # (384, 384, 3) uint8 [0-255] + # print("image:", image.shape, image.dtype, image.min(), image.max()) + image_tensor = prepare_feed(image=image)["image"] # torch.Size([3, 384, 384]) + # print("image_tensor:", image_tensor.shape, image_tensor.dtype, image_tensor.min(), image_tensor.max()) + image_tensor = image_tensor.unsqueeze(dim=0) # torch.Size([1, 3, 384, 384]), torch.float32, -1, +1 + # print("image_tensor:", image_tensor.shape, image_tensor.dtype, image_tensor.min(), image_tensor.max()) + with torch.no_grad(): + logits = model(image_tensor) # torch.Size([1, 6]) + logits = logits[:, 0:6] + if CLS_TTA == True: + logits_tta = model(hflip(image_tensor)) # torch.Size([1, 6]) + logits_tta = logits_tta[:, 0:6] + # logits_ttav = model(vflip(image_tensor)) # torch.Size([1, 6]) + # logits_ttav = logits_ttav[:, 0:6] + logits = torch.mean(torch.stack([logits, logits_tta]), dim=0) + label = torch.argmax(logits, dim=1).numpy()[0] + # print("label", label) + + print("CLS infer time: {:.4f}s".format(time.time() - t0)) if debug is True else None + print("CLS result:", label) if debug is True else None + + + return label + + +def predict_image(bbx_models, cls_models, rgb_image, debug=False, uid="no-uid"): + + print("[%s] Image %s %s %s %s" % (prefix, rgb_image.shape, rgb_image.dtype, rgb_image.min(), rgb_image.max())) if debug is True else None + + # Predict boxes + # Yolo has been trained with CV2 loader which is BGR + bgr_image = cv2.cvtColor(rgb_image.copy(), cv2.COLOR_RGB2BGR) + w, h, xmin, ymin, xmax, ymax, best_score_, best_cls_ = predict_bbx_from_image(bbx_models, bgr_image, BBX_IMAGE_SIZE, debug=debug) + # print("Box:", xmin, xmax, ymin, ymax) + # best_label_ = best_cls_ + + # Predict label + # Sanity checks + xmin, xmax, ymin, ymax = int(xmin), int(xmax), int(ymin), int(ymax) + if xmin < 0: xmin = int(0) + if ymin < 0: ymin = int(0) + if xmax >= w: xmax = int(w - 1) + if ymax >= h: ymax = int(h - 1) + + # Crop from original RGB image + # print("Box", xmin, ymin, xmax, ymax, (xmax-xmin), (ymax-ymin)) + if MARGIN is not None: + wm = np.ceil((xmax-xmin) * MARGIN / 2.) + hm = np.ceil((ymax-ymin) * MARGIN / 2.) + xmin_ = int(xmin - wm) + xmax_ = int(xmax + wm) + ymin_ = int(ymin - hm) + ymax_ = int(ymax + hm) + if xmin_ < 0: xmin_ = int(0) + if ymin_ < 0: ymin_ = int(0) + if xmax_ >= w: xmax_ = int(w - 1) + if ymax_ >= h: ymax_ = int(h - 1) + # print("Box with margin", xmin, ymin, xmax, ymax, (xmax-xmin), (ymax-ymin)) + crop_image = rgb_image[ymin_:ymax_, xmin_:xmax_] + else: + crop_image = rgb_image[ymin:ymax, xmin:xmax] + + # w, h = np_image.shape[1], np_image.shape[0] + # xmin, ymin, xmax, ymax = 0, 0, w-1, h-1 + # best_score_ = 0 + # crop_image = np_image + + # print("Crop", crop_image.shape) + # Run classifier + best_label_ = predict_cls_from_image(cls_models, crop_image, debug=debug) + + return (w, h, xmin, ymin, xmax, ymax, best_score_, best_label_) + + +class ComboModel: + """ + Predicts random bounding boxes and classes for every image + """ + + def __init__(self): + """ + Initialize your model here + """ + + seed_everything(SEEDS[0]) + + print("Model:", type(self).__name__, "BBX_TTA:", BBX_TTA, "CLS_TTA:", CLS_TTA) + print("------") + + print("Python", sys.version) + print("Numpy", np.__version__) + print("Pandas", pd.__version__) + import torch + print("Pytorch", torch.__version__) + import ultralytics + print("Ultralytics", ultralytics.__version__) + print("Timm", timm.__version__) + print("Albumentations", A.__version__) + + if ONNX: + import onnxruntime + print("ONNX runtime", onnxruntime.__version__) + + if OPENVINO: + import openvino.inference_engine as ie + print("OPENVINO runtime", ie.__version__) + + print("------") + print() + + self.bbx_models, self.cls_models = load_model(debug=True) + + self.warmup(debug=True) + + def warmup(self, debug=False): + if debug: + print("Warmup start") + + for i in range(4): + image = np.random.randint(0, 255, size=(4000+(16*i), 3000+(16*i), 3), dtype=np.uint8) + _ = predict_image(self.bbx_models, self.cls_models, image, debug=debug) + del image + gc.collect() + + image = np.zeros((4000, 3000, 3), dtype=np.uint8) + _ = predict_image(self.bbx_models, self.cls_models, image, debug=debug) + del image + gc.collect() + + if debug: + print("Warmup completed") + + def predict(self, image): + """ + Implements the object detection and classification for every image + Inputs: + image: RGB Image read with np.array(Image.open( path )) + + Outputs: + class_label: text name of the class label + bbox: bounding box prediction for the image in the format + [bbx_xtl, bbx_ytl, bbx_xbr, bbx_ybr] + (Same format as the training dataset) + """ + + w, h, xtl, ytl, xbr, ybr, score, label = predict_image(self.bbx_models, self.cls_models, image, debug=DEBUG) + + # label = 3 + class_label = MAP_CLASSES_REVERSE.get(int(label)) + if class_label is None: + class_label = "albopictus" + bbox = [xtl, ytl, xbr, ybr] + + return class_label, bbox + diff --git a/my_models/yolo/utilz.py b/my_models/yolo/utilz.py new file mode 100644 index 0000000000000000000000000000000000000000..1e19fedfc564e4721545752d285efbf8a0c21f5e --- /dev/null +++ b/my_models/yolo/utilz.py @@ -0,0 +1,161 @@ +# from typing import Tuple +from ultralytics.utils import ops +import torch +import numpy as np +import cv2 + +try: + scale_segments = ops.scale_segments +except AttributeError: + scale_segments = ops.scale_coords + + +def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=False, scale_fill=False, scaleup=False, stride=32): + """ + Resize image and padding for detection. Takes image as input, + resizes image to fit into new shape with saving original aspect ratio and pads it to meet stride-multiple constraints + + Parameters: + img (np.ndarray): image for preprocessing + new_shape (Tuple(int, int)): image size after preprocessing in format [height, width] + color (Tuple(int, int, int)): color for filling padded area + auto (bool): use dynamic input size, only padding for stride constrins applied + scale_fill (bool): scale image to fill new_shape + scaleup (bool): allow scale image if it is lower then desired input size, can affect model accuracy + stride (int): input padding stride + Returns: + img (np.ndarray): image after preprocessing + ratio (Tuple(float, float)): hight and width scaling ratio + padding_size (Tuple(int, int)): height and width padding size + + + """ + # Resize and pad image while meeting stride-multiple constraints + shape = img.shape[:2] # current shape [height, width] + if isinstance(new_shape, int): + new_shape = (new_shape, new_shape) + + # Scale ratio (new / old) + r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) + if not scaleup: # only scale down, do not scale up (for better test mAP) + r = min(r, 1.0) + + # Compute padding + ratio = r, r # width, height ratios + new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) + dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding + if auto: # minimum rectangle + dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding + elif scale_fill: # stretch + dw, dh = 0.0, 0.0 + new_unpad = (new_shape[1], new_shape[0]) + ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios + + dw /= 2 # divide padding into 2 sides + dh /= 2 + + if shape[::-1] != new_unpad: # resize + img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border + return img, ratio, (dw, dh) + + +def preprocess_image_(img0, imgsz): + """ + Preprocess image according to YOLOv8 input requirements. + Takes image in np.array format, resizes it to specific size using letterbox resize and changes data layout from HWC to CHW. + + Parameters: + img0 (np.ndarray): image for preprocessing + Returns: + img (np.ndarray): image after preprocessing + """ + # resize + img = letterbox(img0, new_shape=(imgsz, imgsz))[0] + + # Convert HWC to CHW + img = img.transpose(2, 0, 1) + img = np.ascontiguousarray(img) + return img + + +def image_to_tensor_(image): + """ + Preprocess image according to YOLOv8 input requirements. + Takes image in np.array format, resizes it to specific size using letterbox resize and changes data layout from HWC to CHW. + + Parameters: + image (np.ndarray): image for preprocessing + Returns: + input_tensor (np.ndarray): input tensor in NCHW format with float32 values in [0, 1] range + """ + input_tensor = image.astype(np.float32) # uint8 to fp32 + input_tensor /= 255.0 # 0 - 255 to 0.0 - 1.0 + + # add batch dimension + if input_tensor.ndim == 3: + input_tensor = np.expand_dims(input_tensor, 0) + return input_tensor + + +def postprocess_( + pred_boxes, + input_hw, + orig_img, + min_conf_threshold=0.25, + nms_iou_threshold=0.7, + agnosting_nms=False, + max_detections=300, + pred_masks=None, + retina_mask=False, + nc=80, +): + """ + YOLOv8 model postprocessing function. Applied non maximum suppression algorithm to detections and rescale boxes to original image size + Parameters: + pred_boxes (np.ndarray): model output prediction boxes + input_hw (np.ndarray): preprocessed image + orig_image (np.ndarray): image before preprocessing + min_conf_threshold (float, *optional*, 0.25): minimal accepted confidence for object filtering + nms_iou_threshold (float, *optional*, 0.45): minimal overlap score for removing objects duplicates in NMS + agnostic_nms (bool, *optiona*, False): apply class agnostinc NMS approach or not + max_detections (int, *optional*, 300): maximum detections after NMS + pred_masks (np.ndarray, *optional*, None): model ooutput prediction masks, if not provided only boxes will be postprocessed + retina_mask (bool, *optional*, False): retina mask postprocessing instead of native decoding + Returns: + pred (List[Dict[str, np.ndarray]]): list of dictionary with det - detected boxes in format [x1, y1, x2, y2, score, label] and segment - segmentation polygons for each element in batch + """ + nms_kwargs = {"agnostic": agnosting_nms, "max_det": max_detections} + # if pred_masks is not None: + # nms_kwargs["nm"] = 32 + preds = ops.non_max_suppression( + torch.from_numpy(pred_boxes), + min_conf_threshold, + nms_iou_threshold, + nc=nc, + **nms_kwargs + ) + results = [] + proto = torch.from_numpy(pred_masks) if pred_masks is not None else None + + for i, pred in enumerate(preds): + shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape + if not len(pred): + results.append({"det": [], "segment": []}) + continue + if proto is None: + pred[:, :4] = ops.scale_boxes(input_hw, pred[:, :4], shape).round() + results.append({"det": pred}) + continue + if retina_mask: + pred[:, :4] = ops.scale_boxes(input_hw, pred[:, :4], shape).round() + masks = ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], shape[:2]) # HWC + segments = [scale_segments(input_hw, x, shape, normalize=False) for x in ops.masks2segments(masks)] + else: + masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], input_hw, upsample=True) + pred[:, :4] = ops.scale_boxes(input_hw, pred[:, :4], shape).round() + segments = [scale_segments(input_hw, x, shape, normalize=False) for x in ops.masks2segments(masks)] + results.append({"det": pred[:, :6].numpy(), "segment": segments}) + return results diff --git a/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold0_1.4/best.pt b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold0_1.4/best.pt new file mode 100644 index 0000000000000000000000000000000000000000..97acc2caac625a103225d567da923d16fa5b5209 --- /dev/null +++ b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold0_1.4/best.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:89dfba756e29e281acccfc4d61375d3ec5807100f646a5afa62770e5767da067 +size 6223534 diff --git a/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold0_1.4/best_openvino_model/best.bin b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold0_1.4/best_openvino_model/best.bin new file mode 100644 index 0000000000000000000000000000000000000000..4adb35bf97cc6b506bc394d09db2d44930f043cb --- /dev/null +++ b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold0_1.4/best_openvino_model/best.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7639fcd6febe1b1dde58977f51077a5a0527468b425abe382147226d11b23ccf +size 12168796 diff --git a/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold0_1.4/best_openvino_model/best.xml b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold0_1.4/best_openvino_model/best.xml new file mode 100644 index 0000000000000000000000000000000000000000..65d6a932c8aa934c5dc93d29f328ce133a551b5a --- /dev/null +++ b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold0_1.4/best_openvino_model/best.xml @@ -0,0 +1,7987 @@ +<?xml version="1.0"?> +<net name="torch_jit" version="11"> + <layers> + <layer id="0" name="images" type="Parameter" version="opset1"> + <data shape="1,3,768,768" element_type="f32" /> + <output> + <port id="0" precision="FP32" names="images"> + <dim>1</dim> + <dim>3</dim> + <dim>768</dim> + <dim>768</dim> + </port> + </output> + </layer> + <layer id="1" name="/model.22/Constant_9" type="Const" version="opset1"> + <data element_type="f32" shape="1, 2, 12096" offset="0" size="96768" /> + <output> + <port id="0" precision="FP32" names="/model.22/Constant_9_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="2" name="model.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="16, 3, 3, 3" offset="96768" size="1728" /> + <output> + <port id="0" precision="FP32" names="model.0.conv.weight"> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="3" name="/model.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>3</dim> + <dim>768</dim> + <dim>768</dim> + </port> + <port id="1" precision="FP32"> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + </output> + </layer> + <layer id="4" name="Reshape_140" type="Const" version="opset1"> + <data element_type="f32" shape="1, 16, 1, 1" offset="98496" size="64" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="5" name="/model.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + </output> + </layer> + <layer id="6" name="/model.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.0/act/Mul_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + </output> + </layer> + <layer id="7" name="model.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 16, 3, 3" offset="98560" size="18432" /> + <output> + <port id="0" precision="FP32" names="model.1.conv.weight"> + <dim>32</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="8" name="/model.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="9" name="Reshape_157" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="116992" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="10" name="/model.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="11" name="/model.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.1/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="12" name="model.2.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 1, 1" offset="117120" size="4096" /> + <output> + <port id="0" precision="FP32" names="model.2.cv1.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="13" name="/model.2/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="14" name="Reshape_174" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="121216" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="15" name="/model.2/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.2/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="16" name="/model.2/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.2/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="17" name="Constant_181" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="18" name="Constant_9" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="121352" size="16" /> + <output> + <port id="0" precision="I64" names="onnx::Split_137"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="19" name="/model.2/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.2/Split_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="4" precision="FP32" names="/model.2/Split_output_1"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="20" name="model.2.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="16, 16, 3, 3" offset="121368" size="9216" /> + <output> + <port id="0" precision="FP32" names="model.2.m.0.cv1.conv.weight"> + <dim>16</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="21" name="/model.2/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>16</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="22" name="Reshape_194" type="Const" version="opset1"> + <data element_type="f32" shape="1, 16, 1, 1" offset="130584" size="64" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="23" name="/model.2/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.2/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="24" name="/model.2/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.2/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="25" name="model.2.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="16, 16, 3, 3" offset="130648" size="9216" /> + <output> + <port id="0" precision="FP32" names="model.2.m.0.cv2.conv.weight"> + <dim>16</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="26" name="/model.2/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>16</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="27" name="Reshape_211" type="Const" version="opset1"> + <data element_type="f32" shape="1, 16, 1, 1" offset="139864" size="64" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="28" name="/model.2/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.2/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="29" name="/model.2/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.2/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="30" name="/model.2/m.0/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.2/m.0/Add_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="31" name="/model.2/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.2/Concat_output_0"> + <dim>1</dim> + <dim>48</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="32" name="model.2.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 48, 1, 1" offset="139928" size="6144" /> + <output> + <port id="0" precision="FP32" names="model.2.cv2.conv.weight"> + <dim>32</dim> + <dim>48</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="33" name="/model.2/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>48</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>48</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="34" name="Reshape_230" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="146072" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="35" name="/model.2/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.2/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="36" name="/model.2/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.2/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="37" name="model.3.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 32, 3, 3" offset="146200" size="73728" /> + <output> + <port id="0" precision="FP32" names="model.3.conv.weight"> + <dim>64</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="38" name="/model.3/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="39" name="Reshape_247" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="219928" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="40" name="/model.3/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.3/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="41" name="/model.3/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.3/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="42" name="model.4.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 1, 1" offset="220184" size="16384" /> + <output> + <port id="0" precision="FP32" names="model.4.cv1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="43" name="/model.4/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="44" name="Reshape_264" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="236568" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="45" name="/model.4/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="46" name="/model.4/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="47" name="Constant_271" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="48" name="Constant_28" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="236824" size="16" /> + <output> + <port id="0" precision="I64" names="onnx::Split_157"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="49" name="/model.4/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.4/Split_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="4" precision="FP32" names="/model.4/Split_output_1"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="50" name="model.4.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="236840" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.4.m.0.cv1.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="51" name="/model.4/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="52" name="Reshape_284" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="273704" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="53" name="/model.4/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="54" name="/model.4/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="55" name="model.4.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="273832" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.4.m.0.cv2.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="56" name="/model.4/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="57" name="Reshape_301" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="310696" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="58" name="/model.4/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="59" name="/model.4/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="60" name="/model.4/m.0/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.0/Add_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="61" name="model.4.m.1.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="310824" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.4.m.1.cv1.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="62" name="/model.4/m.1/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="63" name="Reshape_319" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="347688" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="64" name="/model.4/m.1/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.1/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="65" name="/model.4/m.1/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/m.1/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="66" name="model.4.m.1.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="347816" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.4.m.1.cv2.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="67" name="/model.4/m.1/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="68" name="Reshape_336" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="384680" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="69" name="/model.4/m.1/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.1/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="70" name="/model.4/m.1/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/m.1/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="71" name="/model.4/m.1/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.1/Add_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="72" name="/model.4/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="3" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="4" precision="FP32" names="/model.4/Concat_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="73" name="model.4.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 128, 1, 1" offset="384808" size="32768" /> + <output> + <port id="0" precision="FP32" names="model.4.cv2.conv.weight"> + <dim>64</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="74" name="/model.4/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="75" name="Reshape_355" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="417576" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="76" name="/model.4/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="77" name="/model.4/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="78" name="model.5.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 64, 3, 3" offset="417832" size="294912" /> + <output> + <port id="0" precision="FP32" names="model.5.conv.weight"> + <dim>128</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="79" name="/model.5/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="80" name="Reshape_372" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="712744" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="81" name="/model.5/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.5/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="82" name="/model.5/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.5/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="83" name="model.6.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 1, 1" offset="713256" size="65536" /> + <output> + <port id="0" precision="FP32" names="model.6.cv1.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="84" name="/model.6/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="85" name="Reshape_389" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="778792" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="86" name="/model.6/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="87" name="/model.6/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="88" name="Constant_396" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="89" name="Constant_54" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="779304" size="16" /> + <output> + <port id="0" precision="I64" names="onnx::Split_184"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="90" name="/model.6/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.6/Split_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="4" precision="FP32" names="/model.6/Split_output_1"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="91" name="model.6.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="779320" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.6.m.0.cv1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="92" name="/model.6/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="93" name="Reshape_409" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="926776" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="94" name="/model.6/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="95" name="/model.6/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="96" name="model.6.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="927032" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.6.m.0.cv2.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="97" name="/model.6/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="98" name="Reshape_426" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="1074488" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="99" name="/model.6/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="100" name="/model.6/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="101" name="/model.6/m.0/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.0/Add_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="102" name="model.6.m.1.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="1074744" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.6.m.1.cv1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="103" name="/model.6/m.1/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="104" name="Reshape_444" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="1222200" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="105" name="/model.6/m.1/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.1/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="106" name="/model.6/m.1/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/m.1/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="107" name="model.6.m.1.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="1222456" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.6.m.1.cv2.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="108" name="/model.6/m.1/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="109" name="Reshape_461" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="1369912" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="110" name="/model.6/m.1/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.1/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="111" name="/model.6/m.1/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/m.1/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="112" name="/model.6/m.1/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.1/Add_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="113" name="/model.6/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="3" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="4" precision="FP32" names="/model.6/Concat_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="114" name="model.6.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 256, 1, 1" offset="1370168" size="131072" /> + <output> + <port id="0" precision="FP32" names="model.6.cv2.conv.weight"> + <dim>128</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="115" name="/model.6/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="116" name="Reshape_480" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="1501240" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="117" name="/model.6/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="118" name="/model.6/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="119" name="model.7.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 128, 3, 3" offset="1501752" size="1179648" /> + <output> + <port id="0" precision="FP32" names="model.7.conv.weight"> + <dim>256</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="120" name="/model.7/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="121" name="Reshape_497" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="2681400" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="122" name="/model.7/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.7/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="123" name="/model.7/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.7/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="124" name="model.8.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 256, 1, 1" offset="2682424" size="262144" /> + <output> + <port id="0" precision="FP32" names="model.8.cv1.conv.weight"> + <dim>256</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="125" name="/model.8/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="126" name="Reshape_514" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="2944568" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="127" name="/model.8/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.8/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="128" name="/model.8/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.8/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="129" name="Constant_521" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="130" name="Constant_80" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="2945592" size="16" /> + <output> + <port id="0" precision="I64" names="onnx::Split_211"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="131" name="/model.8/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.8/Split_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="4" precision="FP32" names="/model.8/Split_output_1"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="132" name="model.8.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 3, 3" offset="2945608" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.8.m.0.cv1.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="133" name="/model.8/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="134" name="Reshape_534" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="3535432" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="135" name="/model.8/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.8/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="136" name="/model.8/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.8/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="137" name="model.8.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 3, 3" offset="3535944" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.8.m.0.cv2.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="138" name="/model.8/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="139" name="Reshape_551" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="4125768" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="140" name="/model.8/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.8/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="141" name="/model.8/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.8/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="142" name="/model.8/m.0/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.8/m.0/Add_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="143" name="/model.8/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.8/Concat_output_0"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="144" name="model.8.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 384, 1, 1" offset="4126280" size="393216" /> + <output> + <port id="0" precision="FP32" names="model.8.cv2.conv.weight"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="145" name="/model.8/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="146" name="Reshape_570" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="4519496" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="147" name="/model.8/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.8/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="148" name="/model.8/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.8/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="149" name="model.9.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 256, 1, 1" offset="4520520" size="131072" /> + <output> + <port id="0" precision="FP32" names="model.9.cv1.conv.weight"> + <dim>128</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="150" name="/model.9/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="151" name="Reshape_587" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="4651592" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="152" name="/model.9/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.9/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="153" name="/model.9/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.9/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="154" name="/model.9/m/MaxPool" type="MaxPool" version="opset8"> + <data strides="1, 1" dilations="1, 1" pads_begin="2, 2" pads_end="2, 2" kernel="5, 5" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.9/m/MaxPool_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="155" name="/model.9/m_1/MaxPool" type="MaxPool" version="opset8"> + <data strides="1, 1" dilations="1, 1" pads_begin="2, 2" pads_end="2, 2" kernel="5, 5" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.9/m_1/MaxPool_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="156" name="/model.9/m_2/MaxPool" type="MaxPool" version="opset8"> + <data strides="1, 1" dilations="1, 1" pads_begin="2, 2" pads_end="2, 2" kernel="5, 5" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.9/m_2/MaxPool_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="157" name="/model.9/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="3" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="4" precision="FP32" names="/model.9/Concat_output_0"> + <dim>1</dim> + <dim>512</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="158" name="model.9.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 512, 1, 1" offset="4652104" size="524288" /> + <output> + <port id="0" precision="FP32" names="model.9.cv2.conv.weight"> + <dim>256</dim> + <dim>512</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="159" name="/model.9/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>512</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>512</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="160" name="Reshape_608" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="5176392" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="161" name="/model.9/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.9/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="162" name="/model.9/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.9/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="163" name="/model.10/Constant" type="Const" version="opset1"> + <data element_type="f32" shape="4" offset="5177416" size="16" /> + <output> + <port id="0" precision="FP32" names="/model.10/Constant_output_0"> + <dim>4</dim> + </port> + </output> + </layer> + <layer id="164" name="/model.10/Resize" type="Interpolate" version="opset11"> + <data mode="nearest" shape_calculation_mode="scales" coordinate_transformation_mode="asymmetric" nearest_mode="floor" antialias="false" pads_begin="0, 0, 0, 0" pads_end="0, 0, 0, 0" cube_coeff="-0.75" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>4</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.10/Resize_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="165" name="/model.11/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.11/Concat_output_0"> + <dim>1</dim> + <dim>384</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="166" name="model.12.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 384, 1, 1" offset="5177432" size="196608" /> + <output> + <port id="0" precision="FP32" names="model.12.cv1.conv.weight"> + <dim>128</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="167" name="/model.12/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>384</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="168" name="Reshape_629" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="5374040" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="169" name="/model.12/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.12/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="170" name="/model.12/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.12/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="171" name="Constant_635" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="172" name="/model.12/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.12/Split_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="4" precision="FP32" names="/model.12/Split_output_1"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="173" name="model.12.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="5374552" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.12.m.0.cv1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="174" name="/model.12/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="175" name="Reshape_648" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="5522008" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="176" name="/model.12/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.12/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="177" name="/model.12/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.12/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="178" name="model.12.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="5522264" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.12.m.0.cv2.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="179" name="/model.12/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="180" name="Reshape_665" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="5669720" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="181" name="/model.12/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.12/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="182" name="/model.12/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.12/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="183" name="/model.12/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.12/Concat_output_0"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="184" name="model.12.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 192, 1, 1" offset="5669976" size="98304" /> + <output> + <port id="0" precision="FP32" names="model.12.cv2.conv.weight"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="185" name="/model.12/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="186" name="Reshape_683" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="5768280" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="187" name="/model.12/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.12/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="188" name="/model.12/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.12/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="189" name="/model.13/Constant" type="Const" version="opset1"> + <data element_type="f32" shape="4" offset="5177416" size="16" /> + <output> + <port id="0" precision="FP32" names="/model.13/Constant_output_0"> + <dim>4</dim> + </port> + </output> + </layer> + <layer id="190" name="/model.13/Resize" type="Interpolate" version="opset11"> + <data mode="nearest" shape_calculation_mode="scales" coordinate_transformation_mode="asymmetric" nearest_mode="floor" antialias="false" pads_begin="0, 0, 0, 0" pads_end="0, 0, 0, 0" cube_coeff="-0.75" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>4</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.13/Resize_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="191" name="/model.14/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.14/Concat_output_0"> + <dim>1</dim> + <dim>192</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="192" name="model.15.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 192, 1, 1" offset="5768792" size="49152" /> + <output> + <port id="0" precision="FP32" names="model.15.cv1.conv.weight"> + <dim>64</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="193" name="/model.15/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>192</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="194" name="Reshape_704" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="5817944" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="195" name="/model.15/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.15/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="196" name="/model.15/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.15/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="197" name="Constant_710" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="198" name="/model.15/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.15/Split_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="4" precision="FP32" names="/model.15/Split_output_1"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="199" name="model.15.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="5818200" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.15.m.0.cv1.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="200" name="/model.15/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="201" name="Reshape_723" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="5855064" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="202" name="/model.15/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.15/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="203" name="/model.15/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.15/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="204" name="model.15.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="5855192" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.15.m.0.cv2.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="205" name="/model.15/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="206" name="Reshape_740" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="5892056" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="207" name="/model.15/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.15/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="208" name="/model.15/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.15/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="209" name="/model.15/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.15/Concat_output_0"> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="210" name="model.15.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 96, 1, 1" offset="5892184" size="24576" /> + <output> + <port id="0" precision="FP32" names="model.15.cv2.conv.weight"> + <dim>64</dim> + <dim>96</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="211" name="/model.15/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>96</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="212" name="Reshape_758" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="5916760" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="213" name="/model.15/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.15/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="214" name="/model.15/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.15/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="215" name="model.22.cv2.0.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="5917016" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.0.0.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="216" name="/model.22/cv2.0/cv2.0.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="217" name="Reshape_953" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6064472" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="218" name="/model.22/cv2.0/cv2.0.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.0/cv2.0.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="219" name="/model.22/cv2.0/cv2.0.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.0/cv2.0.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="220" name="model.22.cv2.0.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6064728" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.0.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="221" name="/model.22/cv2.0/cv2.0.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="222" name="Reshape_970" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6212184" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="223" name="/model.22/cv2.0/cv2.0.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.0/cv2.0.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="224" name="/model.22/cv2.0/cv2.0.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.0/cv2.0.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="225" name="model.22.cv2.0.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 1, 1" offset="6212440" size="16384" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.0.2.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="226" name="/model.22/cv2.0/cv2.0.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="227" name="Reshape_987" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6228824" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="228" name="/model.22/cv2.0/cv2.0.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.0/cv2.0.2/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="229" name="model.22.cv3.0.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6229080" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.0.0.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="230" name="/model.22/cv3.0/cv3.0.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="231" name="Reshape_1002" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6376536" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="232" name="/model.22/cv3.0/cv3.0.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.0/cv3.0.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="233" name="/model.22/cv3.0/cv3.0.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.0/cv3.0.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="234" name="model.22.cv3.0.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6376792" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.0.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="235" name="/model.22/cv3.0/cv3.0.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="236" name="Reshape_1019" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6524248" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="237" name="/model.22/cv3.0/cv3.0.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.0/cv3.0.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="238" name="/model.22/cv3.0/cv3.0.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.0/cv3.0.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="239" name="model.22.cv3.0.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6524504" size="256" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.0.2.weight"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="240" name="/model.22/cv3.0/cv3.0.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="241" name="Reshape_1036" type="Const" version="opset1"> + <data element_type="f32" shape="1, 1, 1, 1" offset="6524760" size="4" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="242" name="/model.22/cv3.0/cv3.0.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.0/cv3.0.2/Conv_output_0"> + <dim>1</dim> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="243" name="/model.22/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Concat_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="244" name="/model.22/Constant" type="Const" version="opset1"> + <data element_type="i64" shape="3" offset="6524764" size="24" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_output_0"> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="245" name="/model.22/Reshape" type="Reshape" version="opset1"> + <data special_zero="true" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="I64"> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Reshape_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>9216</dim> + </port> + </output> + </layer> + <layer id="246" name="model.16.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6524788" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.16.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="247" name="/model.16/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="248" name="Reshape_775" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6672244" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="249" name="/model.16/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.16/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="250" name="/model.16/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.16/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="251" name="/model.17/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.17/Concat_output_0"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="252" name="model.18.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 192, 1, 1" offset="6672500" size="98304" /> + <output> + <port id="0" precision="FP32" names="model.18.cv1.conv.weight"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="253" name="/model.18/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="254" name="Reshape_793" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="6770804" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="255" name="/model.18/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.18/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="256" name="/model.18/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.18/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="257" name="Constant_799" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="258" name="/model.18/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.18/Split_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="4" precision="FP32" names="/model.18/Split_output_1"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="259" name="model.18.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6771316" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.18.m.0.cv1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="260" name="/model.18/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="261" name="Reshape_812" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6918772" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="262" name="/model.18/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.18/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="263" name="/model.18/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.18/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="264" name="model.18.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6919028" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.18.m.0.cv2.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="265" name="/model.18/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="266" name="Reshape_829" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="7066484" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="267" name="/model.18/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.18/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="268" name="/model.18/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.18/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="269" name="/model.18/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.18/Concat_output_0"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="270" name="model.18.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 192, 1, 1" offset="7066740" size="98304" /> + <output> + <port id="0" precision="FP32" names="model.18.cv2.conv.weight"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="271" name="/model.18/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="272" name="Reshape_847" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="7165044" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="273" name="/model.18/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.18/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="274" name="/model.18/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.18/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="275" name="model.22.cv2.1.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 128, 3, 3" offset="7165556" size="294912" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.1.0.conv.weight"> + <dim>64</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="276" name="/model.22/cv2.1/cv2.1.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="277" name="Reshape_1052" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="7460468" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="278" name="/model.22/cv2.1/cv2.1.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.1/cv2.1.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="279" name="/model.22/cv2.1/cv2.1.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.1/cv2.1.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="280" name="model.22.cv2.1.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="7460724" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.1.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="281" name="/model.22/cv2.1/cv2.1.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="282" name="Reshape_1069" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="7608180" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="283" name="/model.22/cv2.1/cv2.1.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.1/cv2.1.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="284" name="/model.22/cv2.1/cv2.1.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.1/cv2.1.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="285" name="model.22.cv2.1.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 1, 1" offset="7608436" size="16384" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.1.2.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="286" name="/model.22/cv2.1/cv2.1.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="287" name="Reshape_1086" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="7624820" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="288" name="/model.22/cv2.1/cv2.1.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.1/cv2.1.2/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="289" name="model.22.cv3.1.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 128, 3, 3" offset="7625076" size="294912" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.1.0.conv.weight"> + <dim>64</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="290" name="/model.22/cv3.1/cv3.1.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="291" name="Reshape_1101" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="7919988" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="292" name="/model.22/cv3.1/cv3.1.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.1/cv3.1.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="293" name="/model.22/cv3.1/cv3.1.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.1/cv3.1.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="294" name="model.22.cv3.1.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="7920244" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.1.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="295" name="/model.22/cv3.1/cv3.1.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="296" name="Reshape_1118" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="8067700" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="297" name="/model.22/cv3.1/cv3.1.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.1/cv3.1.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="298" name="/model.22/cv3.1/cv3.1.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.1/cv3.1.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="299" name="model.22.cv3.1.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="8067956" size="256" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.1.2.weight"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="300" name="/model.22/cv3.1/cv3.1.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="301" name="Reshape_1135" type="Const" version="opset1"> + <data element_type="f32" shape="1, 1, 1, 1" offset="8068212" size="4" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="302" name="/model.22/cv3.1/cv3.1.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.1/cv3.1.2/Conv_output_0"> + <dim>1</dim> + <dim>1</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="303" name="/model.22/Concat_1" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Concat_1_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="304" name="/model.22/Constant_1" type="Const" version="opset1"> + <data element_type="i64" shape="3" offset="6524764" size="24" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_1_output_0"> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="305" name="/model.22/Reshape_1" type="Reshape" version="opset1"> + <data special_zero="true" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="I64"> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Reshape_1_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>2304</dim> + </port> + </output> + </layer> + <layer id="306" name="model.19.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 3, 3" offset="8068216" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.19.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="307" name="/model.19/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="308" name="Reshape_864" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="8658040" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="309" name="/model.19/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.19/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="310" name="/model.19/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.19/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="311" name="/model.20/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.20/Concat_output_0"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="312" name="model.21.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 384, 1, 1" offset="8658552" size="393216" /> + <output> + <port id="0" precision="FP32" names="model.21.cv1.conv.weight"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="313" name="/model.21/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="314" name="Reshape_882" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="9051768" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="315" name="/model.21/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.21/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="316" name="/model.21/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.21/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="317" name="Constant_888" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="318" name="/model.21/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.21/Split_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="4" precision="FP32" names="/model.21/Split_output_1"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="319" name="model.21.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 3, 3" offset="9052792" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.21.m.0.cv1.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="320" name="/model.21/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="321" name="Reshape_901" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="9642616" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="322" name="/model.21/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.21/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="323" name="/model.21/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.21/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="324" name="model.21.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 3, 3" offset="9643128" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.21.m.0.cv2.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="325" name="/model.21/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="326" name="Reshape_918" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="10232952" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="327" name="/model.21/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.21/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="328" name="/model.21/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.21/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="329" name="/model.21/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.21/Concat_output_0"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="330" name="model.21.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 384, 1, 1" offset="10233464" size="393216" /> + <output> + <port id="0" precision="FP32" names="model.21.cv2.conv.weight"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="331" name="/model.21/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="332" name="Reshape_936" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="10626680" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="333" name="/model.21/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.21/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="334" name="/model.21/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.21/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="335" name="model.22.cv2.2.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 256, 3, 3" offset="10627704" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.2.0.conv.weight"> + <dim>64</dim> + <dim>256</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="336" name="/model.22/cv2.2/cv2.2.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>256</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="337" name="Reshape_1151" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="11217528" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="338" name="/model.22/cv2.2/cv2.2.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.2/cv2.2.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="339" name="/model.22/cv2.2/cv2.2.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.2/cv2.2.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="340" name="model.22.cv2.2.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="11217784" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.2.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="341" name="/model.22/cv2.2/cv2.2.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="342" name="Reshape_1168" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="11365240" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="343" name="/model.22/cv2.2/cv2.2.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.2/cv2.2.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="344" name="/model.22/cv2.2/cv2.2.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.2/cv2.2.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="345" name="model.22.cv2.2.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 1, 1" offset="11365496" size="16384" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.2.2.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="346" name="/model.22/cv2.2/cv2.2.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="347" name="Reshape_1185" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="11381880" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="348" name="/model.22/cv2.2/cv2.2.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.2/cv2.2.2/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="349" name="model.22.cv3.2.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 256, 3, 3" offset="11382136" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.2.0.conv.weight"> + <dim>64</dim> + <dim>256</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="350" name="/model.22/cv3.2/cv3.2.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>256</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="351" name="Reshape_1200" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="11971960" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="352" name="/model.22/cv3.2/cv3.2.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.2/cv3.2.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="353" name="/model.22/cv3.2/cv3.2.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.2/cv3.2.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="354" name="model.22.cv3.2.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="11972216" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.2.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="355" name="/model.22/cv3.2/cv3.2.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="356" name="Reshape_1217" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="12119672" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="357" name="/model.22/cv3.2/cv3.2.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.2/cv3.2.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="358" name="/model.22/cv3.2/cv3.2.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.2/cv3.2.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="359" name="model.22.cv3.2.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="12119928" size="256" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.2.2.weight"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="360" name="/model.22/cv3.2/cv3.2.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="361" name="Reshape_1234" type="Const" version="opset1"> + <data element_type="f32" shape="1, 1, 1, 1" offset="12120184" size="4" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="362" name="/model.22/cv3.2/cv3.2.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.2/cv3.2.2/Conv_output_0"> + <dim>1</dim> + <dim>1</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="363" name="/model.22/Concat_2" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Concat_2_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="364" name="/model.22/Constant_2" type="Const" version="opset1"> + <data element_type="i64" shape="3" offset="6524764" size="24" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_2_output_0"> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="365" name="/model.22/Reshape_2" type="Reshape" version="opset1"> + <data special_zero="true" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="I64"> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Reshape_2_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>576</dim> + </port> + </output> + </layer> + <layer id="366" name="/model.22/Concat_3" type="Concat" version="opset1"> + <data axis="2" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>9216</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>2304</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>576</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.22/Concat_3_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="367" name="Constant_1253" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="368" name="Constant_225" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120188" size="16" /> + <output> + <port id="0" precision="I64" names="onnx::Split_388"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="369" name="/model.22/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.22/Split_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>12096</dim> + </port> + <port id="4" precision="FP32" names="/model.22/Split_output_1"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="370" name="/model.22/dfl/Constant" type="Const" version="opset1"> + <data element_type="i64" shape="4" offset="12120204" size="32" /> + <output> + <port id="0" precision="I64" names="/model.22/dfl/Constant_output_0"> + <dim>4</dim> + </port> + </output> + </layer> + <layer id="371" name="/model.22/dfl/Reshape" type="Reshape" version="opset1"> + <data special_zero="true" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64"> + <dim>4</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/dfl/Reshape_output_0"> + <dim>1</dim> + <dim>4</dim> + <dim>16</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="372" name="Constant_1259" type="Const" version="opset1"> + <data element_type="i64" shape="4" offset="12120236" size="32" /> + <output> + <port id="0" precision="I64"> + <dim>4</dim> + </port> + </output> + </layer> + <layer id="373" name="/model.22/dfl/Transpose" type="Transpose" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>16</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64"> + <dim>4</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/dfl/Transpose_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="374" name="/model.22/dfl/Softmax" type="SoftMax" version="opset8"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/dfl/Softmax_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="375" name="model.22.dfl.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="1, 16, 1, 1" offset="12120268" size="64" /> + <output> + <port id="0" precision="FP32" names="model.22.dfl.conv.weight"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="376" name="/model.22/dfl/conv/Conv" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/dfl/conv/Conv_output_0"> + <dim>1</dim> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="377" name="/model.22/dfl/Constant_1" type="Const" version="opset1"> + <data element_type="i64" shape="3" offset="12120332" size="24" /> + <output> + <port id="0" precision="I64" names="/model.22/dfl/Constant_1_output_0"> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="378" name="/model.22/dfl/Reshape_1" type="Reshape" version="opset1"> + <data special_zero="true" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64"> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/dfl/Reshape_1_output_0"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="379" name="Constant_3556" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120356" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="380" name="Constant_3557" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120356" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="381" name="Constant_3553" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="121344" size="8" /> + <output> + <port id="0" precision="I64"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="382" name="/model.22/Shape" type="ShapeOf" version="opset3"> + <data output_type="i64" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="1" precision="I64" names="/model.22/Shape_output_0"> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="383" name="/model.22/Constant_3" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_3_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="384" name="Constant_1270" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="12120372" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="385" name="/model.22/Gather" type="Gather" version="opset8"> + <data batch_dims="0" /> + <input> + <port id="0" precision="I64"> + <dim>3</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + <port id="2" precision="I64" /> + </input> + <output> + <port id="3" precision="I64" names="/model.22/Gather_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="386" name="/model.22/Constant_5" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_5_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="387" name="/model.22/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="I64"> + <dim>1</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="I64" names="/model.22/Add_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="388" name="/model.22/Constant_6" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="12120380" size="8" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_6_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="389" name="/model.22/Div" type="Divide" version="opset1"> + <data auto_broadcast="numpy" m_pythondiv="true" /> + <input> + <port id="0" precision="I64"> + <dim>1</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="I64" names="/model.22/Div_output_0,/model.22/Mul_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="390" name="Constant_3552" type="Const" version="opset1"> + <data element_type="i32" shape="1" offset="12120388" size="4" /> + <output> + <port id="0" precision="I32"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="391" name="ScatterUpdate_3558" type="ScatterUpdate" version="opset3"> + <input> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + </port> + <port id="3" precision="I32"> + <dim>1</dim> + </port> + </input> + <output> + <port id="4" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="392" name="Constant_3561" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120392" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="393" name="/model.22/Slice" type="StridedSlice" version="opset1"> + <data begin_mask="1, 0" end_mask="1, 0" new_axis_mask="" shrink_axis_mask="" ellipsis_mask="" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64"> + <dim>2</dim> + </port> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + <port id="3" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="4" precision="FP32" names="/model.22/Slice_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="394" name="/model.22/Sub" type="Subtract" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Sub_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="395" name="/model.22/Constant_10" type="Const" version="opset1"> + <data element_type="f32" shape="1, 2, 12096" offset="0" size="96768" /> + <output> + <port id="0" precision="FP32" names="/model.22/Constant_10_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="396" name="Constant_3605" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120356" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="397" name="Constant_3604" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="121344" size="8" /> + <output> + <port id="0" precision="I64"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="398" name="Constant_3603" type="Const" version="opset1"> + <data element_type="i32" shape="1" offset="12120388" size="4" /> + <output> + <port id="0" precision="I32"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="399" name="ScatterUpdate_3606" type="ScatterUpdate" version="opset3"> + <input> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + </port> + <port id="3" precision="I32"> + <dim>1</dim> + </port> + </input> + <output> + <port id="4" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="400" name="Constant_3607" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120356" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="401" name="/model.22/Constant_8" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="12120380" size="8" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_8_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="402" name="/model.22/Mul_1" type="Multiply" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="I64"> + <dim>1</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="I64" names="/model.22/Mul_1_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="403" name="ScatterUpdate_3608" type="ScatterUpdate" version="opset3"> + <input> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + </port> + <port id="3" precision="I32"> + <dim>1</dim> + </port> + </input> + <output> + <port id="4" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="404" name="Constant_3611" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120392" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="405" name="/model.22/Slice_1" type="StridedSlice" version="opset1"> + <data begin_mask="1, 0" end_mask="1, 0" new_axis_mask="" shrink_axis_mask="" ellipsis_mask="" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64"> + <dim>2</dim> + </port> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + <port id="3" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="4" precision="FP32" names="/model.22/Slice_1_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="406" name="/model.22/Add_1" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Add_1_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="407" name="/model.22/Add_2" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Add_2_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="408" name="Constant_3954" type="Const" version="opset1"> + <data element_type="f32" shape="1, 1, 1" offset="12120408" size="4" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="409" name="/model.22/Div_1" type="Multiply" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Div_1_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="410" name="/model.22/Sub_1" type="Subtract" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Sub_1_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="411" name="/model.22/Concat_4" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Concat_4_output_0"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="412" name="Constant_3955" type="Const" version="opset1"> + <data element_type="f32" shape="1, 1, 12096" offset="12120412" size="48384" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="413" name="/model.22/Mul_2" type="Multiply" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Mul_2_output_0"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="414" name="/model.22/Sigmoid" type="Sigmoid" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/Sigmoid_output_0"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="415" name="output0" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="output0"> + <dim>1</dim> + <dim>5</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="416" name="output0/sink_port_0" type="Result" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>5</dim> + <dim>12096</dim> + </port> + </input> + </layer> + </layers> + <edges> + <edge from-layer="0" from-port="0" to-layer="3" to-port="0" /> + <edge from-layer="1" from-port="0" to-layer="394" to-port="0" /> + <edge from-layer="2" from-port="0" to-layer="3" to-port="1" /> + <edge from-layer="3" from-port="2" to-layer="5" to-port="0" /> + <edge from-layer="4" from-port="0" to-layer="5" to-port="1" /> + <edge from-layer="5" from-port="2" to-layer="6" to-port="0" /> + <edge from-layer="6" from-port="1" to-layer="8" to-port="0" /> + <edge from-layer="7" from-port="0" to-layer="8" to-port="1" /> + <edge from-layer="8" from-port="2" to-layer="10" to-port="0" /> + <edge from-layer="9" from-port="0" to-layer="10" to-port="1" /> + <edge from-layer="10" from-port="2" to-layer="11" to-port="0" /> + <edge from-layer="11" from-port="1" to-layer="13" to-port="0" /> + <edge from-layer="12" from-port="0" to-layer="13" to-port="1" /> + <edge from-layer="13" from-port="2" to-layer="15" to-port="0" /> + <edge from-layer="14" from-port="0" to-layer="15" to-port="1" /> + <edge from-layer="15" from-port="2" to-layer="16" to-port="0" /> + <edge from-layer="16" from-port="1" to-layer="19" to-port="0" /> + <edge from-layer="17" from-port="0" to-layer="19" to-port="1" /> + <edge from-layer="18" from-port="0" to-layer="19" to-port="2" /> + <edge from-layer="19" from-port="4" to-layer="21" to-port="0" /> + <edge from-layer="19" from-port="4" to-layer="31" to-port="1" /> + <edge from-layer="19" from-port="3" to-layer="31" to-port="0" /> + <edge from-layer="19" from-port="4" to-layer="30" to-port="0" /> + <edge from-layer="20" from-port="0" to-layer="21" to-port="1" /> + <edge from-layer="21" from-port="2" to-layer="23" to-port="0" /> + <edge from-layer="22" from-port="0" to-layer="23" to-port="1" /> + <edge from-layer="23" from-port="2" to-layer="24" to-port="0" /> + <edge from-layer="24" from-port="1" to-layer="26" to-port="0" /> + <edge from-layer="25" from-port="0" to-layer="26" to-port="1" /> + <edge from-layer="26" from-port="2" to-layer="28" to-port="0" /> + <edge from-layer="27" from-port="0" to-layer="28" to-port="1" /> + <edge from-layer="28" from-port="2" to-layer="29" to-port="0" /> + <edge from-layer="29" from-port="1" to-layer="30" to-port="1" /> + <edge from-layer="30" from-port="2" to-layer="31" to-port="2" /> + <edge from-layer="31" from-port="3" to-layer="33" to-port="0" /> + <edge from-layer="32" from-port="0" to-layer="33" to-port="1" /> + <edge from-layer="33" from-port="2" to-layer="35" to-port="0" /> + <edge from-layer="34" from-port="0" to-layer="35" to-port="1" /> + <edge from-layer="35" from-port="2" to-layer="36" to-port="0" /> + <edge from-layer="36" from-port="1" to-layer="38" to-port="0" /> + <edge from-layer="37" from-port="0" to-layer="38" to-port="1" /> + <edge from-layer="38" from-port="2" to-layer="40" to-port="0" /> + <edge from-layer="39" from-port="0" to-layer="40" to-port="1" /> + <edge from-layer="40" from-port="2" to-layer="41" to-port="0" /> + <edge from-layer="41" from-port="1" to-layer="43" to-port="0" /> + <edge from-layer="42" from-port="0" to-layer="43" to-port="1" /> + <edge from-layer="43" from-port="2" to-layer="45" to-port="0" /> + <edge from-layer="44" from-port="0" to-layer="45" to-port="1" /> + <edge from-layer="45" from-port="2" to-layer="46" to-port="0" /> + <edge from-layer="46" from-port="1" to-layer="49" to-port="0" /> + <edge from-layer="47" from-port="0" to-layer="49" to-port="1" /> + <edge from-layer="48" from-port="0" to-layer="49" to-port="2" /> + <edge from-layer="48" from-port="0" to-layer="198" to-port="2" /> + <edge from-layer="49" from-port="3" to-layer="72" to-port="0" /> + <edge from-layer="49" from-port="4" to-layer="72" to-port="1" /> + <edge from-layer="49" from-port="4" to-layer="51" to-port="0" /> + <edge from-layer="49" from-port="4" to-layer="60" to-port="0" /> + <edge from-layer="50" from-port="0" to-layer="51" to-port="1" /> + <edge from-layer="51" from-port="2" to-layer="53" to-port="0" /> + <edge from-layer="52" from-port="0" to-layer="53" to-port="1" /> + <edge from-layer="53" from-port="2" to-layer="54" to-port="0" /> + <edge from-layer="54" from-port="1" to-layer="56" to-port="0" /> + <edge from-layer="55" from-port="0" to-layer="56" to-port="1" /> + <edge from-layer="56" from-port="2" to-layer="58" to-port="0" /> + <edge from-layer="57" from-port="0" to-layer="58" to-port="1" /> + <edge from-layer="58" from-port="2" to-layer="59" to-port="0" /> + <edge from-layer="59" from-port="1" to-layer="60" to-port="1" /> + <edge from-layer="60" from-port="2" to-layer="62" to-port="0" /> + <edge from-layer="60" from-port="2" to-layer="72" to-port="2" /> + <edge from-layer="60" from-port="2" to-layer="71" to-port="0" /> + <edge from-layer="61" from-port="0" to-layer="62" to-port="1" /> + <edge from-layer="62" from-port="2" to-layer="64" to-port="0" /> + <edge from-layer="63" from-port="0" to-layer="64" to-port="1" /> + <edge from-layer="64" from-port="2" to-layer="65" to-port="0" /> + <edge from-layer="65" from-port="1" to-layer="67" to-port="0" /> + <edge from-layer="66" from-port="0" to-layer="67" to-port="1" /> + <edge from-layer="67" from-port="2" to-layer="69" to-port="0" /> + <edge from-layer="68" from-port="0" to-layer="69" to-port="1" /> + <edge from-layer="69" from-port="2" to-layer="70" to-port="0" /> + <edge from-layer="70" from-port="1" to-layer="71" to-port="1" /> + <edge from-layer="71" from-port="2" to-layer="72" to-port="3" /> + <edge from-layer="72" from-port="4" to-layer="74" to-port="0" /> + <edge from-layer="73" from-port="0" to-layer="74" to-port="1" /> + <edge from-layer="74" from-port="2" to-layer="76" to-port="0" /> + <edge from-layer="75" from-port="0" to-layer="76" to-port="1" /> + <edge from-layer="76" from-port="2" to-layer="77" to-port="0" /> + <edge from-layer="77" from-port="1" to-layer="79" to-port="0" /> + <edge from-layer="77" from-port="1" to-layer="191" to-port="1" /> + <edge from-layer="78" from-port="0" to-layer="79" to-port="1" /> + <edge from-layer="79" from-port="2" to-layer="81" to-port="0" /> + <edge from-layer="80" from-port="0" to-layer="81" to-port="1" /> + <edge from-layer="81" from-port="2" to-layer="82" to-port="0" /> + <edge from-layer="82" from-port="1" to-layer="84" to-port="0" /> + <edge from-layer="83" from-port="0" to-layer="84" to-port="1" /> + <edge from-layer="84" from-port="2" to-layer="86" to-port="0" /> + <edge from-layer="85" from-port="0" to-layer="86" to-port="1" /> + <edge from-layer="86" from-port="2" to-layer="87" to-port="0" /> + <edge from-layer="87" from-port="1" to-layer="90" to-port="0" /> + <edge from-layer="88" from-port="0" to-layer="90" to-port="1" /> + <edge from-layer="89" from-port="0" to-layer="258" to-port="2" /> + <edge from-layer="89" from-port="0" to-layer="172" to-port="2" /> + <edge from-layer="89" from-port="0" to-layer="90" to-port="2" /> + <edge from-layer="90" from-port="4" to-layer="92" to-port="0" /> + <edge from-layer="90" from-port="4" to-layer="113" to-port="1" /> + <edge from-layer="90" from-port="3" to-layer="113" to-port="0" /> + <edge from-layer="90" from-port="4" to-layer="101" to-port="0" /> + <edge from-layer="91" from-port="0" to-layer="92" to-port="1" /> + <edge from-layer="92" from-port="2" to-layer="94" to-port="0" /> + <edge from-layer="93" from-port="0" to-layer="94" to-port="1" /> + <edge from-layer="94" from-port="2" to-layer="95" to-port="0" /> + <edge from-layer="95" from-port="1" to-layer="97" to-port="0" /> + <edge from-layer="96" from-port="0" to-layer="97" to-port="1" /> + <edge from-layer="97" from-port="2" to-layer="99" to-port="0" /> + <edge from-layer="98" from-port="0" to-layer="99" to-port="1" /> + <edge from-layer="99" from-port="2" to-layer="100" to-port="0" /> + <edge from-layer="100" from-port="1" to-layer="101" to-port="1" /> + <edge from-layer="101" from-port="2" to-layer="103" to-port="0" /> + <edge from-layer="101" from-port="2" to-layer="112" to-port="0" /> + <edge from-layer="101" from-port="2" to-layer="113" to-port="2" /> + <edge from-layer="102" from-port="0" to-layer="103" to-port="1" /> + <edge from-layer="103" from-port="2" to-layer="105" to-port="0" /> + <edge from-layer="104" from-port="0" to-layer="105" to-port="1" /> + <edge from-layer="105" from-port="2" to-layer="106" to-port="0" /> + <edge from-layer="106" from-port="1" to-layer="108" to-port="0" /> + <edge from-layer="107" from-port="0" to-layer="108" to-port="1" /> + <edge from-layer="108" from-port="2" to-layer="110" to-port="0" /> + <edge from-layer="109" from-port="0" to-layer="110" to-port="1" /> + <edge from-layer="110" from-port="2" to-layer="111" to-port="0" /> + <edge from-layer="111" from-port="1" to-layer="112" to-port="1" /> + <edge from-layer="112" from-port="2" to-layer="113" to-port="3" /> + <edge from-layer="113" from-port="4" to-layer="115" to-port="0" /> + <edge from-layer="114" from-port="0" to-layer="115" to-port="1" /> + <edge from-layer="115" from-port="2" to-layer="117" to-port="0" /> + <edge from-layer="116" from-port="0" to-layer="117" to-port="1" /> + <edge from-layer="117" from-port="2" to-layer="118" to-port="0" /> + <edge from-layer="118" from-port="1" to-layer="120" to-port="0" /> + <edge from-layer="118" from-port="1" to-layer="165" to-port="1" /> + <edge from-layer="119" from-port="0" to-layer="120" to-port="1" /> + <edge from-layer="120" from-port="2" to-layer="122" to-port="0" /> + <edge from-layer="121" from-port="0" to-layer="122" to-port="1" /> + <edge from-layer="122" from-port="2" to-layer="123" to-port="0" /> + <edge from-layer="123" from-port="1" to-layer="125" to-port="0" /> + <edge from-layer="124" from-port="0" to-layer="125" to-port="1" /> + <edge from-layer="125" from-port="2" to-layer="127" to-port="0" /> + <edge from-layer="126" from-port="0" to-layer="127" to-port="1" /> + <edge from-layer="127" from-port="2" to-layer="128" to-port="0" /> + <edge from-layer="128" from-port="1" to-layer="131" to-port="0" /> + <edge from-layer="129" from-port="0" to-layer="131" to-port="1" /> + <edge from-layer="130" from-port="0" to-layer="131" to-port="2" /> + <edge from-layer="130" from-port="0" to-layer="318" to-port="2" /> + <edge from-layer="131" from-port="4" to-layer="142" to-port="0" /> + <edge from-layer="131" from-port="3" to-layer="143" to-port="0" /> + <edge from-layer="131" from-port="4" to-layer="143" to-port="1" /> + <edge from-layer="131" from-port="4" to-layer="133" to-port="0" /> + <edge from-layer="132" from-port="0" to-layer="133" to-port="1" /> + <edge from-layer="133" from-port="2" to-layer="135" to-port="0" /> + <edge from-layer="134" from-port="0" to-layer="135" to-port="1" /> + <edge from-layer="135" from-port="2" to-layer="136" to-port="0" /> + <edge from-layer="136" from-port="1" to-layer="138" to-port="0" /> + <edge from-layer="137" from-port="0" to-layer="138" to-port="1" /> + <edge from-layer="138" from-port="2" to-layer="140" to-port="0" /> + <edge from-layer="139" from-port="0" to-layer="140" to-port="1" /> + <edge from-layer="140" from-port="2" to-layer="141" to-port="0" /> + <edge from-layer="141" from-port="1" to-layer="142" to-port="1" /> + <edge from-layer="142" from-port="2" to-layer="143" to-port="2" /> + <edge from-layer="143" from-port="3" to-layer="145" to-port="0" /> + <edge from-layer="144" from-port="0" to-layer="145" to-port="1" /> + <edge from-layer="145" from-port="2" to-layer="147" to-port="0" /> + <edge from-layer="146" from-port="0" to-layer="147" to-port="1" /> + <edge from-layer="147" from-port="2" to-layer="148" to-port="0" /> + <edge from-layer="148" from-port="1" to-layer="150" to-port="0" /> + <edge from-layer="149" from-port="0" to-layer="150" to-port="1" /> + <edge from-layer="150" from-port="2" to-layer="152" to-port="0" /> + <edge from-layer="151" from-port="0" to-layer="152" to-port="1" /> + <edge from-layer="152" from-port="2" to-layer="153" to-port="0" /> + <edge from-layer="153" from-port="1" to-layer="154" to-port="0" /> + <edge from-layer="153" from-port="1" to-layer="157" to-port="0" /> + <edge from-layer="154" from-port="1" to-layer="157" to-port="1" /> + <edge from-layer="154" from-port="1" to-layer="155" to-port="0" /> + <edge from-layer="155" from-port="1" to-layer="157" to-port="2" /> + <edge from-layer="155" from-port="1" to-layer="156" to-port="0" /> + <edge from-layer="156" from-port="1" to-layer="157" to-port="3" /> + <edge from-layer="157" from-port="4" to-layer="159" to-port="0" /> + <edge from-layer="158" from-port="0" to-layer="159" to-port="1" /> + <edge from-layer="159" from-port="2" to-layer="161" to-port="0" /> + <edge from-layer="160" from-port="0" to-layer="161" to-port="1" /> + <edge from-layer="161" from-port="2" to-layer="162" to-port="0" /> + <edge from-layer="162" from-port="1" to-layer="164" to-port="0" /> + <edge from-layer="162" from-port="1" to-layer="311" to-port="1" /> + <edge from-layer="163" from-port="0" to-layer="164" to-port="1" /> + <edge from-layer="164" from-port="2" to-layer="165" to-port="0" /> + <edge from-layer="165" from-port="2" to-layer="167" to-port="0" /> + <edge from-layer="166" from-port="0" to-layer="167" to-port="1" /> + <edge from-layer="167" from-port="2" to-layer="169" to-port="0" /> + <edge from-layer="168" from-port="0" to-layer="169" to-port="1" /> + <edge from-layer="169" from-port="2" to-layer="170" to-port="0" /> + <edge from-layer="170" from-port="1" to-layer="172" to-port="0" /> + <edge from-layer="171" from-port="0" to-layer="172" to-port="1" /> + <edge from-layer="172" from-port="3" to-layer="183" to-port="0" /> + <edge from-layer="172" from-port="4" to-layer="174" to-port="0" /> + <edge from-layer="172" from-port="4" to-layer="183" to-port="1" /> + <edge from-layer="173" from-port="0" to-layer="174" to-port="1" /> + <edge from-layer="174" from-port="2" to-layer="176" to-port="0" /> + <edge from-layer="175" from-port="0" to-layer="176" to-port="1" /> + <edge from-layer="176" from-port="2" to-layer="177" to-port="0" /> + <edge from-layer="177" from-port="1" to-layer="179" to-port="0" /> + <edge from-layer="178" from-port="0" to-layer="179" to-port="1" /> + <edge from-layer="179" from-port="2" to-layer="181" to-port="0" /> + <edge from-layer="180" from-port="0" to-layer="181" to-port="1" /> + <edge from-layer="181" from-port="2" to-layer="182" to-port="0" /> + <edge from-layer="182" from-port="1" to-layer="183" to-port="2" /> + <edge from-layer="183" from-port="3" to-layer="185" to-port="0" /> + <edge from-layer="184" from-port="0" to-layer="185" to-port="1" /> + <edge from-layer="185" from-port="2" to-layer="187" to-port="0" /> + <edge from-layer="186" from-port="0" to-layer="187" to-port="1" /> + <edge from-layer="187" from-port="2" to-layer="188" to-port="0" /> + <edge from-layer="188" from-port="1" to-layer="190" to-port="0" /> + <edge from-layer="188" from-port="1" to-layer="251" to-port="1" /> + <edge from-layer="189" from-port="0" to-layer="190" to-port="1" /> + <edge from-layer="190" from-port="2" to-layer="191" to-port="0" /> + <edge from-layer="191" from-port="2" to-layer="193" to-port="0" /> + <edge from-layer="192" from-port="0" to-layer="193" to-port="1" /> + <edge from-layer="193" from-port="2" to-layer="195" to-port="0" /> + <edge from-layer="194" from-port="0" to-layer="195" to-port="1" /> + <edge from-layer="195" from-port="2" to-layer="196" to-port="0" /> + <edge from-layer="196" from-port="1" to-layer="198" to-port="0" /> + <edge from-layer="197" from-port="0" to-layer="198" to-port="1" /> + <edge from-layer="198" from-port="4" to-layer="209" to-port="1" /> + <edge from-layer="198" from-port="3" to-layer="209" to-port="0" /> + <edge from-layer="198" from-port="4" to-layer="200" to-port="0" /> + <edge from-layer="199" from-port="0" to-layer="200" to-port="1" /> + <edge from-layer="200" from-port="2" to-layer="202" to-port="0" /> + <edge from-layer="201" from-port="0" to-layer="202" to-port="1" /> + <edge from-layer="202" from-port="2" to-layer="203" to-port="0" /> + <edge from-layer="203" from-port="1" to-layer="205" to-port="0" /> + <edge from-layer="204" from-port="0" to-layer="205" to-port="1" /> + <edge from-layer="205" from-port="2" to-layer="207" to-port="0" /> + <edge from-layer="206" from-port="0" to-layer="207" to-port="1" /> + <edge from-layer="207" from-port="2" to-layer="208" to-port="0" /> + <edge from-layer="208" from-port="1" to-layer="209" to-port="2" /> + <edge from-layer="209" from-port="3" to-layer="211" to-port="0" /> + <edge from-layer="210" from-port="0" to-layer="211" to-port="1" /> + <edge from-layer="211" from-port="2" to-layer="213" to-port="0" /> + <edge from-layer="212" from-port="0" to-layer="213" to-port="1" /> + <edge from-layer="213" from-port="2" to-layer="214" to-port="0" /> + <edge from-layer="214" from-port="1" to-layer="216" to-port="0" /> + <edge from-layer="214" from-port="1" to-layer="247" to-port="0" /> + <edge from-layer="214" from-port="1" to-layer="230" to-port="0" /> + <edge from-layer="215" from-port="0" to-layer="216" to-port="1" /> + <edge from-layer="216" from-port="2" to-layer="218" to-port="0" /> + <edge from-layer="217" from-port="0" to-layer="218" to-port="1" /> + <edge from-layer="218" from-port="2" to-layer="219" to-port="0" /> + <edge from-layer="219" from-port="1" to-layer="221" to-port="0" /> + <edge from-layer="220" from-port="0" to-layer="221" to-port="1" /> + <edge from-layer="221" from-port="2" to-layer="223" to-port="0" /> + <edge from-layer="222" from-port="0" to-layer="223" to-port="1" /> + <edge from-layer="223" from-port="2" to-layer="224" to-port="0" /> + <edge from-layer="224" from-port="1" to-layer="226" to-port="0" /> + <edge from-layer="225" from-port="0" to-layer="226" to-port="1" /> + <edge from-layer="226" from-port="2" to-layer="228" to-port="0" /> + <edge from-layer="227" from-port="0" to-layer="228" to-port="1" /> + <edge from-layer="228" from-port="2" to-layer="243" to-port="0" /> + <edge from-layer="229" from-port="0" to-layer="230" to-port="1" /> + <edge from-layer="230" from-port="2" to-layer="232" to-port="0" /> + <edge from-layer="231" from-port="0" to-layer="232" to-port="1" /> + <edge from-layer="232" from-port="2" to-layer="233" to-port="0" /> + <edge from-layer="233" from-port="1" to-layer="235" to-port="0" /> + <edge from-layer="234" from-port="0" to-layer="235" to-port="1" /> + <edge from-layer="235" from-port="2" to-layer="237" to-port="0" /> + <edge from-layer="236" from-port="0" to-layer="237" to-port="1" /> + <edge from-layer="237" from-port="2" to-layer="238" to-port="0" /> + <edge from-layer="238" from-port="1" to-layer="240" to-port="0" /> + <edge from-layer="239" from-port="0" to-layer="240" to-port="1" /> + <edge from-layer="240" from-port="2" to-layer="242" to-port="0" /> + <edge from-layer="241" from-port="0" to-layer="242" to-port="1" /> + <edge from-layer="242" from-port="2" to-layer="243" to-port="1" /> + <edge from-layer="243" from-port="2" to-layer="245" to-port="0" /> + <edge from-layer="244" from-port="0" to-layer="245" to-port="1" /> + <edge from-layer="245" from-port="2" to-layer="366" to-port="0" /> + <edge from-layer="246" from-port="0" to-layer="247" to-port="1" /> + <edge from-layer="247" from-port="2" to-layer="249" to-port="0" /> + <edge from-layer="248" from-port="0" to-layer="249" to-port="1" /> + <edge from-layer="249" from-port="2" to-layer="250" to-port="0" /> + <edge from-layer="250" from-port="1" to-layer="251" to-port="0" /> + <edge from-layer="251" from-port="2" to-layer="253" to-port="0" /> + <edge from-layer="252" from-port="0" to-layer="253" to-port="1" /> + <edge from-layer="253" from-port="2" to-layer="255" to-port="0" /> + <edge from-layer="254" from-port="0" to-layer="255" to-port="1" /> + <edge from-layer="255" from-port="2" to-layer="256" to-port="0" /> + <edge from-layer="256" from-port="1" to-layer="258" to-port="0" /> + <edge from-layer="257" from-port="0" to-layer="258" to-port="1" /> + <edge from-layer="258" from-port="3" to-layer="269" to-port="0" /> + <edge from-layer="258" from-port="4" to-layer="269" to-port="1" /> + <edge from-layer="258" from-port="4" to-layer="260" to-port="0" /> + <edge from-layer="259" from-port="0" to-layer="260" to-port="1" /> + <edge from-layer="260" from-port="2" to-layer="262" to-port="0" /> + <edge from-layer="261" from-port="0" to-layer="262" to-port="1" /> + <edge from-layer="262" from-port="2" to-layer="263" to-port="0" /> + <edge from-layer="263" from-port="1" to-layer="265" to-port="0" /> + <edge from-layer="264" from-port="0" to-layer="265" to-port="1" /> + <edge from-layer="265" from-port="2" to-layer="267" to-port="0" /> + <edge from-layer="266" from-port="0" to-layer="267" to-port="1" /> + <edge from-layer="267" from-port="2" to-layer="268" to-port="0" /> + <edge from-layer="268" from-port="1" to-layer="269" to-port="2" /> + <edge from-layer="269" from-port="3" to-layer="271" to-port="0" /> + <edge from-layer="270" from-port="0" to-layer="271" to-port="1" /> + <edge from-layer="271" from-port="2" to-layer="273" to-port="0" /> + <edge from-layer="272" from-port="0" to-layer="273" to-port="1" /> + <edge from-layer="273" from-port="2" to-layer="274" to-port="0" /> + <edge from-layer="274" from-port="1" to-layer="307" to-port="0" /> + <edge from-layer="274" from-port="1" to-layer="290" to-port="0" /> + <edge from-layer="274" from-port="1" to-layer="276" to-port="0" /> + <edge from-layer="275" from-port="0" to-layer="276" to-port="1" /> + <edge from-layer="276" from-port="2" to-layer="278" to-port="0" /> + <edge from-layer="277" from-port="0" to-layer="278" to-port="1" /> + <edge from-layer="278" from-port="2" to-layer="279" to-port="0" /> + <edge from-layer="279" from-port="1" to-layer="281" to-port="0" /> + <edge from-layer="280" from-port="0" to-layer="281" to-port="1" /> + <edge from-layer="281" from-port="2" to-layer="283" to-port="0" /> + <edge from-layer="282" from-port="0" to-layer="283" to-port="1" /> + <edge from-layer="283" from-port="2" to-layer="284" to-port="0" /> + <edge from-layer="284" from-port="1" to-layer="286" to-port="0" /> + <edge from-layer="285" from-port="0" to-layer="286" to-port="1" /> + <edge from-layer="286" from-port="2" to-layer="288" to-port="0" /> + <edge from-layer="287" from-port="0" to-layer="288" to-port="1" /> + <edge from-layer="288" from-port="2" to-layer="303" to-port="0" /> + <edge from-layer="289" from-port="0" to-layer="290" to-port="1" /> + <edge from-layer="290" from-port="2" to-layer="292" to-port="0" /> + <edge from-layer="291" from-port="0" to-layer="292" to-port="1" /> + <edge from-layer="292" from-port="2" to-layer="293" to-port="0" /> + <edge from-layer="293" from-port="1" to-layer="295" to-port="0" /> + <edge from-layer="294" from-port="0" to-layer="295" to-port="1" /> + <edge from-layer="295" from-port="2" to-layer="297" to-port="0" /> + <edge from-layer="296" from-port="0" to-layer="297" to-port="1" /> + <edge from-layer="297" from-port="2" to-layer="298" to-port="0" /> + <edge from-layer="298" from-port="1" to-layer="300" to-port="0" /> + <edge from-layer="299" from-port="0" to-layer="300" to-port="1" /> + <edge from-layer="300" from-port="2" to-layer="302" to-port="0" /> + <edge from-layer="301" from-port="0" to-layer="302" to-port="1" /> + <edge from-layer="302" from-port="2" to-layer="303" to-port="1" /> + <edge from-layer="303" from-port="2" to-layer="305" to-port="0" /> + <edge from-layer="304" from-port="0" to-layer="305" to-port="1" /> + <edge from-layer="305" from-port="2" to-layer="366" to-port="1" /> + <edge from-layer="306" from-port="0" to-layer="307" to-port="1" /> + <edge from-layer="307" from-port="2" to-layer="309" to-port="0" /> + <edge from-layer="308" from-port="0" to-layer="309" to-port="1" /> + <edge from-layer="309" from-port="2" to-layer="310" to-port="0" /> + <edge from-layer="310" from-port="1" to-layer="311" to-port="0" /> + <edge from-layer="311" from-port="2" to-layer="313" to-port="0" /> + <edge from-layer="312" from-port="0" to-layer="313" to-port="1" /> + <edge from-layer="313" from-port="2" to-layer="315" to-port="0" /> + <edge from-layer="314" from-port="0" to-layer="315" to-port="1" /> + <edge from-layer="315" from-port="2" to-layer="316" to-port="0" /> + <edge from-layer="316" from-port="1" to-layer="318" to-port="0" /> + <edge from-layer="317" from-port="0" to-layer="318" to-port="1" /> + <edge from-layer="318" from-port="4" to-layer="320" to-port="0" /> + <edge from-layer="318" from-port="4" to-layer="329" to-port="1" /> + <edge from-layer="318" from-port="3" to-layer="329" to-port="0" /> + <edge from-layer="319" from-port="0" to-layer="320" to-port="1" /> + <edge from-layer="320" from-port="2" to-layer="322" to-port="0" /> + <edge from-layer="321" from-port="0" to-layer="322" to-port="1" /> + <edge from-layer="322" from-port="2" to-layer="323" to-port="0" /> + <edge from-layer="323" from-port="1" to-layer="325" to-port="0" /> + <edge from-layer="324" from-port="0" to-layer="325" to-port="1" /> + <edge from-layer="325" from-port="2" to-layer="327" to-port="0" /> + <edge from-layer="326" from-port="0" to-layer="327" to-port="1" /> + <edge from-layer="327" from-port="2" to-layer="328" to-port="0" /> + <edge from-layer="328" from-port="1" to-layer="329" to-port="2" /> + <edge from-layer="329" from-port="3" to-layer="331" to-port="0" /> + <edge from-layer="330" from-port="0" to-layer="331" to-port="1" /> + <edge from-layer="331" from-port="2" to-layer="333" to-port="0" /> + <edge from-layer="332" from-port="0" to-layer="333" to-port="1" /> + <edge from-layer="333" from-port="2" to-layer="334" to-port="0" /> + <edge from-layer="334" from-port="1" to-layer="336" to-port="0" /> + <edge from-layer="334" from-port="1" to-layer="350" to-port="0" /> + <edge from-layer="335" from-port="0" to-layer="336" to-port="1" /> + <edge from-layer="336" from-port="2" to-layer="338" to-port="0" /> + <edge from-layer="337" from-port="0" to-layer="338" to-port="1" /> + <edge from-layer="338" from-port="2" to-layer="339" to-port="0" /> + <edge from-layer="339" from-port="1" to-layer="341" to-port="0" /> + <edge from-layer="340" from-port="0" to-layer="341" to-port="1" /> + <edge from-layer="341" from-port="2" to-layer="343" to-port="0" /> + <edge from-layer="342" from-port="0" to-layer="343" to-port="1" /> + <edge from-layer="343" from-port="2" to-layer="344" to-port="0" /> + <edge from-layer="344" from-port="1" to-layer="346" to-port="0" /> + <edge from-layer="345" from-port="0" to-layer="346" to-port="1" /> + <edge from-layer="346" from-port="2" to-layer="348" to-port="0" /> + <edge from-layer="347" from-port="0" to-layer="348" to-port="1" /> + <edge from-layer="348" from-port="2" to-layer="363" to-port="0" /> + <edge from-layer="349" from-port="0" to-layer="350" to-port="1" /> + <edge from-layer="350" from-port="2" to-layer="352" to-port="0" /> + <edge from-layer="351" from-port="0" to-layer="352" to-port="1" /> + <edge from-layer="352" from-port="2" to-layer="353" to-port="0" /> + <edge from-layer="353" from-port="1" to-layer="355" to-port="0" /> + <edge from-layer="354" from-port="0" to-layer="355" to-port="1" /> + <edge from-layer="355" from-port="2" to-layer="357" to-port="0" /> + <edge from-layer="356" from-port="0" to-layer="357" to-port="1" /> + <edge from-layer="357" from-port="2" to-layer="358" to-port="0" /> + <edge from-layer="358" from-port="1" to-layer="360" to-port="0" /> + <edge from-layer="359" from-port="0" to-layer="360" to-port="1" /> + <edge from-layer="360" from-port="2" to-layer="362" to-port="0" /> + <edge from-layer="361" from-port="0" to-layer="362" to-port="1" /> + <edge from-layer="362" from-port="2" to-layer="363" to-port="1" /> + <edge from-layer="363" from-port="2" to-layer="365" to-port="0" /> + <edge from-layer="364" from-port="0" to-layer="365" to-port="1" /> + <edge from-layer="365" from-port="2" to-layer="366" to-port="2" /> + <edge from-layer="366" from-port="3" to-layer="369" to-port="0" /> + <edge from-layer="367" from-port="0" to-layer="369" to-port="1" /> + <edge from-layer="368" from-port="0" to-layer="369" to-port="2" /> + <edge from-layer="369" from-port="4" to-layer="414" to-port="0" /> + <edge from-layer="369" from-port="3" to-layer="371" to-port="0" /> + <edge from-layer="370" from-port="0" to-layer="371" to-port="1" /> + <edge from-layer="371" from-port="2" to-layer="373" to-port="0" /> + <edge from-layer="372" from-port="0" to-layer="373" to-port="1" /> + <edge from-layer="373" from-port="2" to-layer="374" to-port="0" /> + <edge from-layer="374" from-port="1" to-layer="376" to-port="0" /> + <edge from-layer="375" from-port="0" to-layer="376" to-port="1" /> + <edge from-layer="376" from-port="2" to-layer="378" to-port="0" /> + <edge from-layer="377" from-port="0" to-layer="378" to-port="1" /> + <edge from-layer="378" from-port="2" to-layer="405" to-port="0" /> + <edge from-layer="378" from-port="2" to-layer="393" to-port="0" /> + <edge from-layer="378" from-port="2" to-layer="382" to-port="0" /> + <edge from-layer="379" from-port="0" to-layer="393" to-port="1" /> + <edge from-layer="380" from-port="0" to-layer="391" to-port="0" /> + <edge from-layer="381" from-port="0" to-layer="391" to-port="1" /> + <edge from-layer="382" from-port="1" to-layer="385" to-port="0" /> + <edge from-layer="383" from-port="0" to-layer="385" to-port="1" /> + <edge from-layer="384" from-port="0" to-layer="385" to-port="2" /> + <edge from-layer="385" from-port="3" to-layer="387" to-port="0" /> + <edge from-layer="386" from-port="0" to-layer="387" to-port="1" /> + <edge from-layer="387" from-port="2" to-layer="389" to-port="0" /> + <edge from-layer="388" from-port="0" to-layer="389" to-port="1" /> + <edge from-layer="389" from-port="2" to-layer="391" to-port="2" /> + <edge from-layer="389" from-port="2" to-layer="399" to-port="2" /> + <edge from-layer="389" from-port="2" to-layer="402" to-port="0" /> + <edge from-layer="390" from-port="0" to-layer="391" to-port="3" /> + <edge from-layer="391" from-port="4" to-layer="393" to-port="2" /> + <edge from-layer="392" from-port="0" to-layer="393" to-port="3" /> + <edge from-layer="393" from-port="4" to-layer="394" to-port="1" /> + <edge from-layer="394" from-port="2" to-layer="410" to-port="1" /> + <edge from-layer="394" from-port="2" to-layer="407" to-port="0" /> + <edge from-layer="395" from-port="0" to-layer="406" to-port="0" /> + <edge from-layer="396" from-port="0" to-layer="399" to-port="0" /> + <edge from-layer="397" from-port="0" to-layer="399" to-port="1" /> + <edge from-layer="397" from-port="0" to-layer="403" to-port="1" /> + <edge from-layer="398" from-port="0" to-layer="399" to-port="3" /> + <edge from-layer="398" from-port="0" to-layer="403" to-port="3" /> + <edge from-layer="399" from-port="4" to-layer="405" to-port="1" /> + <edge from-layer="400" from-port="0" to-layer="403" to-port="0" /> + <edge from-layer="401" from-port="0" to-layer="402" to-port="1" /> + <edge from-layer="402" from-port="2" to-layer="403" to-port="2" /> + <edge from-layer="403" from-port="4" to-layer="405" to-port="2" /> + <edge from-layer="404" from-port="0" to-layer="405" to-port="3" /> + <edge from-layer="405" from-port="4" to-layer="406" to-port="1" /> + <edge from-layer="406" from-port="2" to-layer="410" to-port="0" /> + <edge from-layer="406" from-port="2" to-layer="407" to-port="1" /> + <edge from-layer="407" from-port="2" to-layer="409" to-port="0" /> + <edge from-layer="408" from-port="0" to-layer="409" to-port="1" /> + <edge from-layer="409" from-port="2" to-layer="411" to-port="0" /> + <edge from-layer="410" from-port="2" to-layer="411" to-port="1" /> + <edge from-layer="411" from-port="2" to-layer="413" to-port="0" /> + <edge from-layer="412" from-port="0" to-layer="413" to-port="1" /> + <edge from-layer="413" from-port="2" to-layer="415" to-port="0" /> + <edge from-layer="414" from-port="1" to-layer="415" to-port="1" /> + <edge from-layer="415" from-port="2" to-layer="416" to-port="0" /> + </edges> + <rt_info> + <MO_version value="2023.0.1-11005-fa1c41994f3-releases/2023/0" /> + <Runtime_version value="2023.0.1-11005-fa1c41994f3-releases/2023/0" /> + <conversion_parameters> + <framework value="onnx" /> + <input_model value="DIR/best.onnx" /> + <is_python_api_used value="True" /> + <model_name value="best" /> + </conversion_parameters> + <framework> + <author value="Ultralytics" /> + <batch value="1" /> + <date value="2023-08-31T04:32:45.876273" /> + <description value="Ultralytics best model trained on mqt_v3_42_0.yaml" /> + <imgsz value="[768, 768]" /> + <license value="AGPL-3.0 https://ultralytics.com/license" /> + <names value="{0: 'mosquito'}" /> + <stride value="32" /> + <task value="detect" /> + <version value="8.0.165" /> + </framework> + <legacy_frontend value="False" /> + <model_info> + <iou_threshold value="0.7" /> + <labels value="mosquito" /> + <model_type value="YOLOv8" /> + <pad_value value="114" /> + <resize_type value="fit_to_window_letterbox" /> + <reverse_input_channels value="YES" /> + <scale_values value="255" /> + </model_info> + </rt_info> +</net> diff --git a/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold0_1.4/best_openvino_model/metadata.yaml b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold0_1.4/best_openvino_model/metadata.yaml new file mode 100644 index 0000000000000000000000000000000000000000..eb6383546205654211676cad34700f2a7452c699 --- /dev/null +++ b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold0_1.4/best_openvino_model/metadata.yaml @@ -0,0 +1,13 @@ +description: Ultralytics best model trained on mqt_v3_42_0.yaml +author: Ultralytics +license: AGPL-3.0 https://ultralytics.com/license +date: '2023-08-31T04:32:45.876273' +version: 8.0.165 +stride: 32 +task: detect +batch: 1 +imgsz: +- 768 +- 768 +names: + 0: mosquito diff --git a/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold1_1.4/best.pt b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold1_1.4/best.pt new file mode 100644 index 0000000000000000000000000000000000000000..771906f7ffe8e37b9d47e3a58658ccdd39738af6 --- /dev/null +++ b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold1_1.4/best.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f9e33b93ab19f9e26d827d2f95f3fa275bb158b7ae83a946663112a350427c40 +size 6223534 diff --git a/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold1_1.4/best_openvino_model/best.bin b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold1_1.4/best_openvino_model/best.bin new file mode 100644 index 0000000000000000000000000000000000000000..bdd26280aaf7781e081d6dbd1382fc7835b8d0e0 --- /dev/null +++ b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold1_1.4/best_openvino_model/best.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:599eeb18755f0452ba7398554c98199aebf69d019bdc22ec5140aaf9fdd6c167 +size 12168796 diff --git a/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold1_1.4/best_openvino_model/best.xml b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold1_1.4/best_openvino_model/best.xml new file mode 100644 index 0000000000000000000000000000000000000000..e250b63787a92ff34971733ccddda54d4418aae6 --- /dev/null +++ b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold1_1.4/best_openvino_model/best.xml @@ -0,0 +1,7987 @@ +<?xml version="1.0"?> +<net name="torch_jit" version="11"> + <layers> + <layer id="0" name="images" type="Parameter" version="opset1"> + <data shape="1,3,768,768" element_type="f32" /> + <output> + <port id="0" precision="FP32" names="images"> + <dim>1</dim> + <dim>3</dim> + <dim>768</dim> + <dim>768</dim> + </port> + </output> + </layer> + <layer id="1" name="/model.22/Constant_9" type="Const" version="opset1"> + <data element_type="f32" shape="1, 2, 12096" offset="0" size="96768" /> + <output> + <port id="0" precision="FP32" names="/model.22/Constant_9_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="2" name="model.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="16, 3, 3, 3" offset="96768" size="1728" /> + <output> + <port id="0" precision="FP32" names="model.0.conv.weight"> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="3" name="/model.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>3</dim> + <dim>768</dim> + <dim>768</dim> + </port> + <port id="1" precision="FP32"> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + </output> + </layer> + <layer id="4" name="Reshape_25688" type="Const" version="opset1"> + <data element_type="f32" shape="1, 16, 1, 1" offset="98496" size="64" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="5" name="/model.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + </output> + </layer> + <layer id="6" name="/model.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.0/act/Mul_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + </output> + </layer> + <layer id="7" name="model.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 16, 3, 3" offset="98560" size="18432" /> + <output> + <port id="0" precision="FP32" names="model.1.conv.weight"> + <dim>32</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="8" name="/model.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="9" name="Reshape_25705" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="116992" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="10" name="/model.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="11" name="/model.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.1/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="12" name="model.2.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 1, 1" offset="117120" size="4096" /> + <output> + <port id="0" precision="FP32" names="model.2.cv1.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="13" name="/model.2/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="14" name="Reshape_25722" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="121216" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="15" name="/model.2/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.2/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="16" name="/model.2/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.2/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="17" name="Constant_25729" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="18" name="Constant_9" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="121352" size="16" /> + <output> + <port id="0" precision="I64" names="onnx::Split_137"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="19" name="/model.2/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.2/Split_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="4" precision="FP32" names="/model.2/Split_output_1"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="20" name="model.2.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="16, 16, 3, 3" offset="121368" size="9216" /> + <output> + <port id="0" precision="FP32" names="model.2.m.0.cv1.conv.weight"> + <dim>16</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="21" name="/model.2/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>16</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="22" name="Reshape_25742" type="Const" version="opset1"> + <data element_type="f32" shape="1, 16, 1, 1" offset="130584" size="64" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="23" name="/model.2/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.2/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="24" name="/model.2/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.2/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="25" name="model.2.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="16, 16, 3, 3" offset="130648" size="9216" /> + <output> + <port id="0" precision="FP32" names="model.2.m.0.cv2.conv.weight"> + <dim>16</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="26" name="/model.2/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>16</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="27" name="Reshape_25759" type="Const" version="opset1"> + <data element_type="f32" shape="1, 16, 1, 1" offset="139864" size="64" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="28" name="/model.2/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.2/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="29" name="/model.2/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.2/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="30" name="/model.2/m.0/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.2/m.0/Add_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="31" name="/model.2/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.2/Concat_output_0"> + <dim>1</dim> + <dim>48</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="32" name="model.2.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 48, 1, 1" offset="139928" size="6144" /> + <output> + <port id="0" precision="FP32" names="model.2.cv2.conv.weight"> + <dim>32</dim> + <dim>48</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="33" name="/model.2/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>48</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>48</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="34" name="Reshape_25778" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="146072" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="35" name="/model.2/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.2/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="36" name="/model.2/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.2/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="37" name="model.3.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 32, 3, 3" offset="146200" size="73728" /> + <output> + <port id="0" precision="FP32" names="model.3.conv.weight"> + <dim>64</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="38" name="/model.3/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="39" name="Reshape_25795" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="219928" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="40" name="/model.3/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.3/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="41" name="/model.3/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.3/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="42" name="model.4.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 1, 1" offset="220184" size="16384" /> + <output> + <port id="0" precision="FP32" names="model.4.cv1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="43" name="/model.4/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="44" name="Reshape_25812" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="236568" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="45" name="/model.4/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="46" name="/model.4/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="47" name="Constant_25819" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="48" name="Constant_28" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="236824" size="16" /> + <output> + <port id="0" precision="I64" names="onnx::Split_157"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="49" name="/model.4/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.4/Split_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="4" precision="FP32" names="/model.4/Split_output_1"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="50" name="model.4.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="236840" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.4.m.0.cv1.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="51" name="/model.4/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="52" name="Reshape_25832" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="273704" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="53" name="/model.4/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="54" name="/model.4/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="55" name="model.4.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="273832" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.4.m.0.cv2.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="56" name="/model.4/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="57" name="Reshape_25849" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="310696" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="58" name="/model.4/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="59" name="/model.4/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="60" name="/model.4/m.0/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.0/Add_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="61" name="model.4.m.1.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="310824" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.4.m.1.cv1.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="62" name="/model.4/m.1/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="63" name="Reshape_25867" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="347688" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="64" name="/model.4/m.1/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.1/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="65" name="/model.4/m.1/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/m.1/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="66" name="model.4.m.1.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="347816" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.4.m.1.cv2.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="67" name="/model.4/m.1/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="68" name="Reshape_25884" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="384680" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="69" name="/model.4/m.1/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.1/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="70" name="/model.4/m.1/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/m.1/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="71" name="/model.4/m.1/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.1/Add_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="72" name="/model.4/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="3" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="4" precision="FP32" names="/model.4/Concat_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="73" name="model.4.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 128, 1, 1" offset="384808" size="32768" /> + <output> + <port id="0" precision="FP32" names="model.4.cv2.conv.weight"> + <dim>64</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="74" name="/model.4/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="75" name="Reshape_25903" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="417576" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="76" name="/model.4/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="77" name="/model.4/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="78" name="model.5.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 64, 3, 3" offset="417832" size="294912" /> + <output> + <port id="0" precision="FP32" names="model.5.conv.weight"> + <dim>128</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="79" name="/model.5/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="80" name="Reshape_25920" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="712744" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="81" name="/model.5/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.5/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="82" name="/model.5/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.5/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="83" name="model.6.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 1, 1" offset="713256" size="65536" /> + <output> + <port id="0" precision="FP32" names="model.6.cv1.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="84" name="/model.6/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="85" name="Reshape_25937" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="778792" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="86" name="/model.6/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="87" name="/model.6/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="88" name="Constant_25944" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="89" name="Constant_54" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="779304" size="16" /> + <output> + <port id="0" precision="I64" names="onnx::Split_184"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="90" name="/model.6/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.6/Split_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="4" precision="FP32" names="/model.6/Split_output_1"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="91" name="model.6.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="779320" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.6.m.0.cv1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="92" name="/model.6/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="93" name="Reshape_25957" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="926776" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="94" name="/model.6/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="95" name="/model.6/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="96" name="model.6.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="927032" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.6.m.0.cv2.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="97" name="/model.6/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="98" name="Reshape_25974" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="1074488" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="99" name="/model.6/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="100" name="/model.6/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="101" name="/model.6/m.0/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.0/Add_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="102" name="model.6.m.1.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="1074744" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.6.m.1.cv1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="103" name="/model.6/m.1/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="104" name="Reshape_25992" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="1222200" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="105" name="/model.6/m.1/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.1/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="106" name="/model.6/m.1/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/m.1/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="107" name="model.6.m.1.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="1222456" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.6.m.1.cv2.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="108" name="/model.6/m.1/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="109" name="Reshape_26009" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="1369912" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="110" name="/model.6/m.1/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.1/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="111" name="/model.6/m.1/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/m.1/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="112" name="/model.6/m.1/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.1/Add_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="113" name="/model.6/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="3" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="4" precision="FP32" names="/model.6/Concat_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="114" name="model.6.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 256, 1, 1" offset="1370168" size="131072" /> + <output> + <port id="0" precision="FP32" names="model.6.cv2.conv.weight"> + <dim>128</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="115" name="/model.6/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="116" name="Reshape_26028" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="1501240" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="117" name="/model.6/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="118" name="/model.6/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="119" name="model.7.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 128, 3, 3" offset="1501752" size="1179648" /> + <output> + <port id="0" precision="FP32" names="model.7.conv.weight"> + <dim>256</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="120" name="/model.7/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="121" name="Reshape_26045" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="2681400" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="122" name="/model.7/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.7/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="123" name="/model.7/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.7/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="124" name="model.8.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 256, 1, 1" offset="2682424" size="262144" /> + <output> + <port id="0" precision="FP32" names="model.8.cv1.conv.weight"> + <dim>256</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="125" name="/model.8/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="126" name="Reshape_26062" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="2944568" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="127" name="/model.8/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.8/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="128" name="/model.8/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.8/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="129" name="Constant_26069" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="130" name="Constant_80" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="2945592" size="16" /> + <output> + <port id="0" precision="I64" names="onnx::Split_211"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="131" name="/model.8/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.8/Split_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="4" precision="FP32" names="/model.8/Split_output_1"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="132" name="model.8.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 3, 3" offset="2945608" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.8.m.0.cv1.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="133" name="/model.8/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="134" name="Reshape_26082" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="3535432" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="135" name="/model.8/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.8/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="136" name="/model.8/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.8/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="137" name="model.8.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 3, 3" offset="3535944" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.8.m.0.cv2.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="138" name="/model.8/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="139" name="Reshape_26099" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="4125768" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="140" name="/model.8/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.8/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="141" name="/model.8/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.8/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="142" name="/model.8/m.0/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.8/m.0/Add_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="143" name="/model.8/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.8/Concat_output_0"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="144" name="model.8.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 384, 1, 1" offset="4126280" size="393216" /> + <output> + <port id="0" precision="FP32" names="model.8.cv2.conv.weight"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="145" name="/model.8/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="146" name="Reshape_26118" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="4519496" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="147" name="/model.8/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.8/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="148" name="/model.8/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.8/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="149" name="model.9.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 256, 1, 1" offset="4520520" size="131072" /> + <output> + <port id="0" precision="FP32" names="model.9.cv1.conv.weight"> + <dim>128</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="150" name="/model.9/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="151" name="Reshape_26135" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="4651592" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="152" name="/model.9/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.9/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="153" name="/model.9/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.9/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="154" name="/model.9/m/MaxPool" type="MaxPool" version="opset8"> + <data strides="1, 1" dilations="1, 1" pads_begin="2, 2" pads_end="2, 2" kernel="5, 5" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.9/m/MaxPool_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="155" name="/model.9/m_1/MaxPool" type="MaxPool" version="opset8"> + <data strides="1, 1" dilations="1, 1" pads_begin="2, 2" pads_end="2, 2" kernel="5, 5" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.9/m_1/MaxPool_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="156" name="/model.9/m_2/MaxPool" type="MaxPool" version="opset8"> + <data strides="1, 1" dilations="1, 1" pads_begin="2, 2" pads_end="2, 2" kernel="5, 5" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.9/m_2/MaxPool_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="157" name="/model.9/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="3" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="4" precision="FP32" names="/model.9/Concat_output_0"> + <dim>1</dim> + <dim>512</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="158" name="model.9.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 512, 1, 1" offset="4652104" size="524288" /> + <output> + <port id="0" precision="FP32" names="model.9.cv2.conv.weight"> + <dim>256</dim> + <dim>512</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="159" name="/model.9/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>512</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>512</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="160" name="Reshape_26156" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="5176392" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="161" name="/model.9/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.9/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="162" name="/model.9/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.9/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="163" name="/model.10/Constant" type="Const" version="opset1"> + <data element_type="f32" shape="4" offset="5177416" size="16" /> + <output> + <port id="0" precision="FP32" names="/model.10/Constant_output_0"> + <dim>4</dim> + </port> + </output> + </layer> + <layer id="164" name="/model.10/Resize" type="Interpolate" version="opset11"> + <data mode="nearest" shape_calculation_mode="scales" coordinate_transformation_mode="asymmetric" nearest_mode="floor" antialias="false" pads_begin="0, 0, 0, 0" pads_end="0, 0, 0, 0" cube_coeff="-0.75" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>4</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.10/Resize_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="165" name="/model.11/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.11/Concat_output_0"> + <dim>1</dim> + <dim>384</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="166" name="model.12.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 384, 1, 1" offset="5177432" size="196608" /> + <output> + <port id="0" precision="FP32" names="model.12.cv1.conv.weight"> + <dim>128</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="167" name="/model.12/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>384</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="168" name="Reshape_26177" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="5374040" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="169" name="/model.12/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.12/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="170" name="/model.12/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.12/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="171" name="Constant_26183" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="172" name="/model.12/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.12/Split_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="4" precision="FP32" names="/model.12/Split_output_1"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="173" name="model.12.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="5374552" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.12.m.0.cv1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="174" name="/model.12/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="175" name="Reshape_26196" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="5522008" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="176" name="/model.12/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.12/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="177" name="/model.12/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.12/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="178" name="model.12.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="5522264" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.12.m.0.cv2.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="179" name="/model.12/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="180" name="Reshape_26213" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="5669720" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="181" name="/model.12/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.12/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="182" name="/model.12/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.12/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="183" name="/model.12/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.12/Concat_output_0"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="184" name="model.12.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 192, 1, 1" offset="5669976" size="98304" /> + <output> + <port id="0" precision="FP32" names="model.12.cv2.conv.weight"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="185" name="/model.12/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="186" name="Reshape_26231" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="5768280" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="187" name="/model.12/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.12/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="188" name="/model.12/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.12/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="189" name="/model.13/Constant" type="Const" version="opset1"> + <data element_type="f32" shape="4" offset="5177416" size="16" /> + <output> + <port id="0" precision="FP32" names="/model.13/Constant_output_0"> + <dim>4</dim> + </port> + </output> + </layer> + <layer id="190" name="/model.13/Resize" type="Interpolate" version="opset11"> + <data mode="nearest" shape_calculation_mode="scales" coordinate_transformation_mode="asymmetric" nearest_mode="floor" antialias="false" pads_begin="0, 0, 0, 0" pads_end="0, 0, 0, 0" cube_coeff="-0.75" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>4</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.13/Resize_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="191" name="/model.14/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.14/Concat_output_0"> + <dim>1</dim> + <dim>192</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="192" name="model.15.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 192, 1, 1" offset="5768792" size="49152" /> + <output> + <port id="0" precision="FP32" names="model.15.cv1.conv.weight"> + <dim>64</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="193" name="/model.15/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>192</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="194" name="Reshape_26252" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="5817944" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="195" name="/model.15/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.15/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="196" name="/model.15/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.15/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="197" name="Constant_26258" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="198" name="/model.15/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.15/Split_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="4" precision="FP32" names="/model.15/Split_output_1"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="199" name="model.15.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="5818200" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.15.m.0.cv1.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="200" name="/model.15/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="201" name="Reshape_26271" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="5855064" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="202" name="/model.15/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.15/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="203" name="/model.15/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.15/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="204" name="model.15.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="5855192" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.15.m.0.cv2.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="205" name="/model.15/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="206" name="Reshape_26288" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="5892056" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="207" name="/model.15/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.15/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="208" name="/model.15/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.15/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="209" name="/model.15/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.15/Concat_output_0"> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="210" name="model.15.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 96, 1, 1" offset="5892184" size="24576" /> + <output> + <port id="0" precision="FP32" names="model.15.cv2.conv.weight"> + <dim>64</dim> + <dim>96</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="211" name="/model.15/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>96</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="212" name="Reshape_26306" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="5916760" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="213" name="/model.15/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.15/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="214" name="/model.15/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.15/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="215" name="model.22.cv2.0.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="5917016" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.0.0.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="216" name="/model.22/cv2.0/cv2.0.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="217" name="Reshape_26501" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6064472" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="218" name="/model.22/cv2.0/cv2.0.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.0/cv2.0.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="219" name="/model.22/cv2.0/cv2.0.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.0/cv2.0.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="220" name="model.22.cv2.0.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6064728" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.0.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="221" name="/model.22/cv2.0/cv2.0.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="222" name="Reshape_26518" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6212184" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="223" name="/model.22/cv2.0/cv2.0.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.0/cv2.0.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="224" name="/model.22/cv2.0/cv2.0.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.0/cv2.0.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="225" name="model.22.cv2.0.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 1, 1" offset="6212440" size="16384" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.0.2.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="226" name="/model.22/cv2.0/cv2.0.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="227" name="Reshape_26535" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6228824" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="228" name="/model.22/cv2.0/cv2.0.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.0/cv2.0.2/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="229" name="model.22.cv3.0.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6229080" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.0.0.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="230" name="/model.22/cv3.0/cv3.0.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="231" name="Reshape_26550" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6376536" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="232" name="/model.22/cv3.0/cv3.0.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.0/cv3.0.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="233" name="/model.22/cv3.0/cv3.0.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.0/cv3.0.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="234" name="model.22.cv3.0.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6376792" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.0.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="235" name="/model.22/cv3.0/cv3.0.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="236" name="Reshape_26567" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6524248" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="237" name="/model.22/cv3.0/cv3.0.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.0/cv3.0.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="238" name="/model.22/cv3.0/cv3.0.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.0/cv3.0.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="239" name="model.22.cv3.0.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6524504" size="256" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.0.2.weight"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="240" name="/model.22/cv3.0/cv3.0.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="241" name="Reshape_26584" type="Const" version="opset1"> + <data element_type="f32" shape="1, 1, 1, 1" offset="6524760" size="4" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="242" name="/model.22/cv3.0/cv3.0.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.0/cv3.0.2/Conv_output_0"> + <dim>1</dim> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="243" name="/model.22/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Concat_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="244" name="/model.22/Constant" type="Const" version="opset1"> + <data element_type="i64" shape="3" offset="6524764" size="24" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_output_0"> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="245" name="/model.22/Reshape" type="Reshape" version="opset1"> + <data special_zero="true" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="I64"> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Reshape_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>9216</dim> + </port> + </output> + </layer> + <layer id="246" name="model.16.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6524788" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.16.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="247" name="/model.16/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="248" name="Reshape_26323" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6672244" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="249" name="/model.16/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.16/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="250" name="/model.16/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.16/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="251" name="/model.17/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.17/Concat_output_0"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="252" name="model.18.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 192, 1, 1" offset="6672500" size="98304" /> + <output> + <port id="0" precision="FP32" names="model.18.cv1.conv.weight"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="253" name="/model.18/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="254" name="Reshape_26341" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="6770804" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="255" name="/model.18/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.18/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="256" name="/model.18/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.18/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="257" name="Constant_26347" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="258" name="/model.18/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.18/Split_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="4" precision="FP32" names="/model.18/Split_output_1"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="259" name="model.18.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6771316" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.18.m.0.cv1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="260" name="/model.18/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="261" name="Reshape_26360" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6918772" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="262" name="/model.18/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.18/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="263" name="/model.18/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.18/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="264" name="model.18.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6919028" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.18.m.0.cv2.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="265" name="/model.18/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="266" name="Reshape_26377" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="7066484" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="267" name="/model.18/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.18/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="268" name="/model.18/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.18/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="269" name="/model.18/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.18/Concat_output_0"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="270" name="model.18.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 192, 1, 1" offset="7066740" size="98304" /> + <output> + <port id="0" precision="FP32" names="model.18.cv2.conv.weight"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="271" name="/model.18/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="272" name="Reshape_26395" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="7165044" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="273" name="/model.18/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.18/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="274" name="/model.18/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.18/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="275" name="model.22.cv2.1.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 128, 3, 3" offset="7165556" size="294912" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.1.0.conv.weight"> + <dim>64</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="276" name="/model.22/cv2.1/cv2.1.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="277" name="Reshape_26600" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="7460468" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="278" name="/model.22/cv2.1/cv2.1.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.1/cv2.1.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="279" name="/model.22/cv2.1/cv2.1.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.1/cv2.1.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="280" name="model.22.cv2.1.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="7460724" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.1.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="281" name="/model.22/cv2.1/cv2.1.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="282" name="Reshape_26617" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="7608180" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="283" name="/model.22/cv2.1/cv2.1.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.1/cv2.1.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="284" name="/model.22/cv2.1/cv2.1.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.1/cv2.1.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="285" name="model.22.cv2.1.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 1, 1" offset="7608436" size="16384" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.1.2.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="286" name="/model.22/cv2.1/cv2.1.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="287" name="Reshape_26634" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="7624820" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="288" name="/model.22/cv2.1/cv2.1.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.1/cv2.1.2/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="289" name="model.22.cv3.1.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 128, 3, 3" offset="7625076" size="294912" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.1.0.conv.weight"> + <dim>64</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="290" name="/model.22/cv3.1/cv3.1.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="291" name="Reshape_26649" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="7919988" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="292" name="/model.22/cv3.1/cv3.1.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.1/cv3.1.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="293" name="/model.22/cv3.1/cv3.1.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.1/cv3.1.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="294" name="model.22.cv3.1.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="7920244" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.1.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="295" name="/model.22/cv3.1/cv3.1.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="296" name="Reshape_26666" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="8067700" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="297" name="/model.22/cv3.1/cv3.1.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.1/cv3.1.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="298" name="/model.22/cv3.1/cv3.1.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.1/cv3.1.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="299" name="model.22.cv3.1.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="8067956" size="256" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.1.2.weight"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="300" name="/model.22/cv3.1/cv3.1.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="301" name="Reshape_26683" type="Const" version="opset1"> + <data element_type="f32" shape="1, 1, 1, 1" offset="8068212" size="4" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="302" name="/model.22/cv3.1/cv3.1.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.1/cv3.1.2/Conv_output_0"> + <dim>1</dim> + <dim>1</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="303" name="/model.22/Concat_1" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Concat_1_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="304" name="/model.22/Constant_1" type="Const" version="opset1"> + <data element_type="i64" shape="3" offset="6524764" size="24" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_1_output_0"> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="305" name="/model.22/Reshape_1" type="Reshape" version="opset1"> + <data special_zero="true" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="I64"> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Reshape_1_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>2304</dim> + </port> + </output> + </layer> + <layer id="306" name="model.19.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 3, 3" offset="8068216" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.19.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="307" name="/model.19/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="308" name="Reshape_26412" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="8658040" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="309" name="/model.19/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.19/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="310" name="/model.19/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.19/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="311" name="/model.20/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.20/Concat_output_0"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="312" name="model.21.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 384, 1, 1" offset="8658552" size="393216" /> + <output> + <port id="0" precision="FP32" names="model.21.cv1.conv.weight"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="313" name="/model.21/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="314" name="Reshape_26430" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="9051768" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="315" name="/model.21/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.21/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="316" name="/model.21/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.21/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="317" name="Constant_26436" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="318" name="/model.21/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.21/Split_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="4" precision="FP32" names="/model.21/Split_output_1"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="319" name="model.21.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 3, 3" offset="9052792" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.21.m.0.cv1.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="320" name="/model.21/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="321" name="Reshape_26449" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="9642616" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="322" name="/model.21/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.21/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="323" name="/model.21/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.21/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="324" name="model.21.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 3, 3" offset="9643128" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.21.m.0.cv2.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="325" name="/model.21/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="326" name="Reshape_26466" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="10232952" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="327" name="/model.21/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.21/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="328" name="/model.21/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.21/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="329" name="/model.21/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.21/Concat_output_0"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="330" name="model.21.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 384, 1, 1" offset="10233464" size="393216" /> + <output> + <port id="0" precision="FP32" names="model.21.cv2.conv.weight"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="331" name="/model.21/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="332" name="Reshape_26484" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="10626680" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="333" name="/model.21/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.21/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="334" name="/model.21/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.21/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="335" name="model.22.cv2.2.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 256, 3, 3" offset="10627704" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.2.0.conv.weight"> + <dim>64</dim> + <dim>256</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="336" name="/model.22/cv2.2/cv2.2.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>256</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="337" name="Reshape_26699" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="11217528" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="338" name="/model.22/cv2.2/cv2.2.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.2/cv2.2.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="339" name="/model.22/cv2.2/cv2.2.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.2/cv2.2.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="340" name="model.22.cv2.2.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="11217784" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.2.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="341" name="/model.22/cv2.2/cv2.2.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="342" name="Reshape_26716" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="11365240" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="343" name="/model.22/cv2.2/cv2.2.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.2/cv2.2.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="344" name="/model.22/cv2.2/cv2.2.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.2/cv2.2.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="345" name="model.22.cv2.2.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 1, 1" offset="11365496" size="16384" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.2.2.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="346" name="/model.22/cv2.2/cv2.2.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="347" name="Reshape_26733" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="11381880" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="348" name="/model.22/cv2.2/cv2.2.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.2/cv2.2.2/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="349" name="model.22.cv3.2.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 256, 3, 3" offset="11382136" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.2.0.conv.weight"> + <dim>64</dim> + <dim>256</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="350" name="/model.22/cv3.2/cv3.2.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>256</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="351" name="Reshape_26748" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="11971960" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="352" name="/model.22/cv3.2/cv3.2.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.2/cv3.2.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="353" name="/model.22/cv3.2/cv3.2.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.2/cv3.2.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="354" name="model.22.cv3.2.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="11972216" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.2.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="355" name="/model.22/cv3.2/cv3.2.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="356" name="Reshape_26765" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="12119672" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="357" name="/model.22/cv3.2/cv3.2.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.2/cv3.2.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="358" name="/model.22/cv3.2/cv3.2.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.2/cv3.2.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="359" name="model.22.cv3.2.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="12119928" size="256" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.2.2.weight"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="360" name="/model.22/cv3.2/cv3.2.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="361" name="Reshape_26782" type="Const" version="opset1"> + <data element_type="f32" shape="1, 1, 1, 1" offset="12120184" size="4" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="362" name="/model.22/cv3.2/cv3.2.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.2/cv3.2.2/Conv_output_0"> + <dim>1</dim> + <dim>1</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="363" name="/model.22/Concat_2" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Concat_2_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="364" name="/model.22/Constant_2" type="Const" version="opset1"> + <data element_type="i64" shape="3" offset="6524764" size="24" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_2_output_0"> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="365" name="/model.22/Reshape_2" type="Reshape" version="opset1"> + <data special_zero="true" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="I64"> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Reshape_2_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>576</dim> + </port> + </output> + </layer> + <layer id="366" name="/model.22/Concat_3" type="Concat" version="opset1"> + <data axis="2" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>9216</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>2304</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>576</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.22/Concat_3_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="367" name="Constant_26801" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="368" name="Constant_225" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120188" size="16" /> + <output> + <port id="0" precision="I64" names="onnx::Split_388"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="369" name="/model.22/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.22/Split_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>12096</dim> + </port> + <port id="4" precision="FP32" names="/model.22/Split_output_1"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="370" name="/model.22/dfl/Constant" type="Const" version="opset1"> + <data element_type="i64" shape="4" offset="12120204" size="32" /> + <output> + <port id="0" precision="I64" names="/model.22/dfl/Constant_output_0"> + <dim>4</dim> + </port> + </output> + </layer> + <layer id="371" name="/model.22/dfl/Reshape" type="Reshape" version="opset1"> + <data special_zero="true" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64"> + <dim>4</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/dfl/Reshape_output_0"> + <dim>1</dim> + <dim>4</dim> + <dim>16</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="372" name="Constant_26807" type="Const" version="opset1"> + <data element_type="i64" shape="4" offset="12120236" size="32" /> + <output> + <port id="0" precision="I64"> + <dim>4</dim> + </port> + </output> + </layer> + <layer id="373" name="/model.22/dfl/Transpose" type="Transpose" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>16</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64"> + <dim>4</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/dfl/Transpose_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="374" name="/model.22/dfl/Softmax" type="SoftMax" version="opset8"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/dfl/Softmax_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="375" name="model.22.dfl.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="1, 16, 1, 1" offset="12120268" size="64" /> + <output> + <port id="0" precision="FP32" names="model.22.dfl.conv.weight"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="376" name="/model.22/dfl/conv/Conv" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/dfl/conv/Conv_output_0"> + <dim>1</dim> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="377" name="/model.22/dfl/Constant_1" type="Const" version="opset1"> + <data element_type="i64" shape="3" offset="12120332" size="24" /> + <output> + <port id="0" precision="I64" names="/model.22/dfl/Constant_1_output_0"> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="378" name="/model.22/dfl/Reshape_1" type="Reshape" version="opset1"> + <data special_zero="true" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64"> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/dfl/Reshape_1_output_0"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="379" name="Constant_29104" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120356" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="380" name="Constant_29105" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120356" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="381" name="Constant_29101" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="121344" size="8" /> + <output> + <port id="0" precision="I64"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="382" name="/model.22/Shape" type="ShapeOf" version="opset3"> + <data output_type="i64" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="1" precision="I64" names="/model.22/Shape_output_0"> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="383" name="/model.22/Constant_3" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_3_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="384" name="Constant_26818" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="12120372" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="385" name="/model.22/Gather" type="Gather" version="opset8"> + <data batch_dims="0" /> + <input> + <port id="0" precision="I64"> + <dim>3</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + <port id="2" precision="I64" /> + </input> + <output> + <port id="3" precision="I64" names="/model.22/Gather_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="386" name="/model.22/Constant_5" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_5_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="387" name="/model.22/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="I64"> + <dim>1</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="I64" names="/model.22/Add_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="388" name="/model.22/Constant_6" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="12120380" size="8" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_6_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="389" name="/model.22/Div" type="Divide" version="opset1"> + <data auto_broadcast="numpy" m_pythondiv="true" /> + <input> + <port id="0" precision="I64"> + <dim>1</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="I64" names="/model.22/Div_output_0,/model.22/Mul_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="390" name="Constant_29100" type="Const" version="opset1"> + <data element_type="i32" shape="1" offset="12120388" size="4" /> + <output> + <port id="0" precision="I32"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="391" name="ScatterUpdate_29106" type="ScatterUpdate" version="opset3"> + <input> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + </port> + <port id="3" precision="I32"> + <dim>1</dim> + </port> + </input> + <output> + <port id="4" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="392" name="Constant_29109" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120392" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="393" name="/model.22/Slice" type="StridedSlice" version="opset1"> + <data begin_mask="1, 0" end_mask="1, 0" new_axis_mask="" shrink_axis_mask="" ellipsis_mask="" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64"> + <dim>2</dim> + </port> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + <port id="3" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="4" precision="FP32" names="/model.22/Slice_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="394" name="/model.22/Sub" type="Subtract" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Sub_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="395" name="/model.22/Constant_10" type="Const" version="opset1"> + <data element_type="f32" shape="1, 2, 12096" offset="0" size="96768" /> + <output> + <port id="0" precision="FP32" names="/model.22/Constant_10_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="396" name="Constant_29153" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120356" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="397" name="Constant_29152" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="121344" size="8" /> + <output> + <port id="0" precision="I64"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="398" name="Constant_29151" type="Const" version="opset1"> + <data element_type="i32" shape="1" offset="12120388" size="4" /> + <output> + <port id="0" precision="I32"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="399" name="ScatterUpdate_29154" type="ScatterUpdate" version="opset3"> + <input> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + </port> + <port id="3" precision="I32"> + <dim>1</dim> + </port> + </input> + <output> + <port id="4" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="400" name="Constant_29155" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120356" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="401" name="/model.22/Constant_8" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="12120380" size="8" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_8_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="402" name="/model.22/Mul_1" type="Multiply" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="I64"> + <dim>1</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="I64" names="/model.22/Mul_1_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="403" name="ScatterUpdate_29156" type="ScatterUpdate" version="opset3"> + <input> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + </port> + <port id="3" precision="I32"> + <dim>1</dim> + </port> + </input> + <output> + <port id="4" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="404" name="Constant_29159" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120392" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="405" name="/model.22/Slice_1" type="StridedSlice" version="opset1"> + <data begin_mask="1, 0" end_mask="1, 0" new_axis_mask="" shrink_axis_mask="" ellipsis_mask="" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64"> + <dim>2</dim> + </port> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + <port id="3" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="4" precision="FP32" names="/model.22/Slice_1_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="406" name="/model.22/Add_1" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Add_1_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="407" name="/model.22/Add_2" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Add_2_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="408" name="Constant_29502" type="Const" version="opset1"> + <data element_type="f32" shape="1, 1, 1" offset="12120408" size="4" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="409" name="/model.22/Div_1" type="Multiply" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Div_1_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="410" name="/model.22/Sub_1" type="Subtract" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Sub_1_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="411" name="/model.22/Concat_4" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Concat_4_output_0"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="412" name="Constant_29503" type="Const" version="opset1"> + <data element_type="f32" shape="1, 1, 12096" offset="12120412" size="48384" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="413" name="/model.22/Mul_2" type="Multiply" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Mul_2_output_0"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="414" name="/model.22/Sigmoid" type="Sigmoid" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/Sigmoid_output_0"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="415" name="output0" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="output0"> + <dim>1</dim> + <dim>5</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="416" name="output0/sink_port_0" type="Result" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>5</dim> + <dim>12096</dim> + </port> + </input> + </layer> + </layers> + <edges> + <edge from-layer="0" from-port="0" to-layer="3" to-port="0" /> + <edge from-layer="1" from-port="0" to-layer="394" to-port="0" /> + <edge from-layer="2" from-port="0" to-layer="3" to-port="1" /> + <edge from-layer="3" from-port="2" to-layer="5" to-port="0" /> + <edge from-layer="4" from-port="0" to-layer="5" to-port="1" /> + <edge from-layer="5" from-port="2" to-layer="6" to-port="0" /> + <edge from-layer="6" from-port="1" to-layer="8" to-port="0" /> + <edge from-layer="7" from-port="0" to-layer="8" to-port="1" /> + <edge from-layer="8" from-port="2" to-layer="10" to-port="0" /> + <edge from-layer="9" from-port="0" to-layer="10" to-port="1" /> + <edge from-layer="10" from-port="2" to-layer="11" to-port="0" /> + <edge from-layer="11" from-port="1" to-layer="13" to-port="0" /> + <edge from-layer="12" from-port="0" to-layer="13" to-port="1" /> + <edge from-layer="13" from-port="2" to-layer="15" to-port="0" /> + <edge from-layer="14" from-port="0" to-layer="15" to-port="1" /> + <edge from-layer="15" from-port="2" to-layer="16" to-port="0" /> + <edge from-layer="16" from-port="1" to-layer="19" to-port="0" /> + <edge from-layer="17" from-port="0" to-layer="19" to-port="1" /> + <edge from-layer="18" from-port="0" to-layer="19" to-port="2" /> + <edge from-layer="19" from-port="4" to-layer="21" to-port="0" /> + <edge from-layer="19" from-port="4" to-layer="31" to-port="1" /> + <edge from-layer="19" from-port="3" to-layer="31" to-port="0" /> + <edge from-layer="19" from-port="4" to-layer="30" to-port="0" /> + <edge from-layer="20" from-port="0" to-layer="21" to-port="1" /> + <edge from-layer="21" from-port="2" to-layer="23" to-port="0" /> + <edge from-layer="22" from-port="0" to-layer="23" to-port="1" /> + <edge from-layer="23" from-port="2" to-layer="24" to-port="0" /> + <edge from-layer="24" from-port="1" to-layer="26" to-port="0" /> + <edge from-layer="25" from-port="0" to-layer="26" to-port="1" /> + <edge from-layer="26" from-port="2" to-layer="28" to-port="0" /> + <edge from-layer="27" from-port="0" to-layer="28" to-port="1" /> + <edge from-layer="28" from-port="2" to-layer="29" to-port="0" /> + <edge from-layer="29" from-port="1" to-layer="30" to-port="1" /> + <edge from-layer="30" from-port="2" to-layer="31" to-port="2" /> + <edge from-layer="31" from-port="3" to-layer="33" to-port="0" /> + <edge from-layer="32" from-port="0" to-layer="33" to-port="1" /> + <edge from-layer="33" from-port="2" to-layer="35" to-port="0" /> + <edge from-layer="34" from-port="0" to-layer="35" to-port="1" /> + <edge from-layer="35" from-port="2" to-layer="36" to-port="0" /> + <edge from-layer="36" from-port="1" to-layer="38" to-port="0" /> + <edge from-layer="37" from-port="0" to-layer="38" to-port="1" /> + <edge from-layer="38" from-port="2" to-layer="40" to-port="0" /> + <edge from-layer="39" from-port="0" to-layer="40" to-port="1" /> + <edge from-layer="40" from-port="2" to-layer="41" to-port="0" /> + <edge from-layer="41" from-port="1" to-layer="43" to-port="0" /> + <edge from-layer="42" from-port="0" to-layer="43" to-port="1" /> + <edge from-layer="43" from-port="2" to-layer="45" to-port="0" /> + <edge from-layer="44" from-port="0" to-layer="45" to-port="1" /> + <edge from-layer="45" from-port="2" to-layer="46" to-port="0" /> + <edge from-layer="46" from-port="1" to-layer="49" to-port="0" /> + <edge from-layer="47" from-port="0" to-layer="49" to-port="1" /> + <edge from-layer="48" from-port="0" to-layer="49" to-port="2" /> + <edge from-layer="48" from-port="0" to-layer="198" to-port="2" /> + <edge from-layer="49" from-port="3" to-layer="72" to-port="0" /> + <edge from-layer="49" from-port="4" to-layer="72" to-port="1" /> + <edge from-layer="49" from-port="4" to-layer="51" to-port="0" /> + <edge from-layer="49" from-port="4" to-layer="60" to-port="0" /> + <edge from-layer="50" from-port="0" to-layer="51" to-port="1" /> + <edge from-layer="51" from-port="2" to-layer="53" to-port="0" /> + <edge from-layer="52" from-port="0" to-layer="53" to-port="1" /> + <edge from-layer="53" from-port="2" to-layer="54" to-port="0" /> + <edge from-layer="54" from-port="1" to-layer="56" to-port="0" /> + <edge from-layer="55" from-port="0" to-layer="56" to-port="1" /> + <edge from-layer="56" from-port="2" to-layer="58" to-port="0" /> + <edge from-layer="57" from-port="0" to-layer="58" to-port="1" /> + <edge from-layer="58" from-port="2" to-layer="59" to-port="0" /> + <edge from-layer="59" from-port="1" to-layer="60" to-port="1" /> + <edge from-layer="60" from-port="2" to-layer="62" to-port="0" /> + <edge from-layer="60" from-port="2" to-layer="72" to-port="2" /> + <edge from-layer="60" from-port="2" to-layer="71" to-port="0" /> + <edge from-layer="61" from-port="0" to-layer="62" to-port="1" /> + <edge from-layer="62" from-port="2" to-layer="64" to-port="0" /> + <edge from-layer="63" from-port="0" to-layer="64" to-port="1" /> + <edge from-layer="64" from-port="2" to-layer="65" to-port="0" /> + <edge from-layer="65" from-port="1" to-layer="67" to-port="0" /> + <edge from-layer="66" from-port="0" to-layer="67" to-port="1" /> + <edge from-layer="67" from-port="2" to-layer="69" to-port="0" /> + <edge from-layer="68" from-port="0" to-layer="69" to-port="1" /> + <edge from-layer="69" from-port="2" to-layer="70" to-port="0" /> + <edge from-layer="70" from-port="1" to-layer="71" to-port="1" /> + <edge from-layer="71" from-port="2" to-layer="72" to-port="3" /> + <edge from-layer="72" from-port="4" to-layer="74" to-port="0" /> + <edge from-layer="73" from-port="0" to-layer="74" to-port="1" /> + <edge from-layer="74" from-port="2" to-layer="76" to-port="0" /> + <edge from-layer="75" from-port="0" to-layer="76" to-port="1" /> + <edge from-layer="76" from-port="2" to-layer="77" to-port="0" /> + <edge from-layer="77" from-port="1" to-layer="79" to-port="0" /> + <edge from-layer="77" from-port="1" to-layer="191" to-port="1" /> + <edge from-layer="78" from-port="0" to-layer="79" to-port="1" /> + <edge from-layer="79" from-port="2" to-layer="81" to-port="0" /> + <edge from-layer="80" from-port="0" to-layer="81" to-port="1" /> + <edge from-layer="81" from-port="2" to-layer="82" to-port="0" /> + <edge from-layer="82" from-port="1" to-layer="84" to-port="0" /> + <edge from-layer="83" from-port="0" to-layer="84" to-port="1" /> + <edge from-layer="84" from-port="2" to-layer="86" to-port="0" /> + <edge from-layer="85" from-port="0" to-layer="86" to-port="1" /> + <edge from-layer="86" from-port="2" to-layer="87" to-port="0" /> + <edge from-layer="87" from-port="1" to-layer="90" to-port="0" /> + <edge from-layer="88" from-port="0" to-layer="90" to-port="1" /> + <edge from-layer="89" from-port="0" to-layer="258" to-port="2" /> + <edge from-layer="89" from-port="0" to-layer="172" to-port="2" /> + <edge from-layer="89" from-port="0" to-layer="90" to-port="2" /> + <edge from-layer="90" from-port="4" to-layer="92" to-port="0" /> + <edge from-layer="90" from-port="4" to-layer="113" to-port="1" /> + <edge from-layer="90" from-port="3" to-layer="113" to-port="0" /> + <edge from-layer="90" from-port="4" to-layer="101" to-port="0" /> + <edge from-layer="91" from-port="0" to-layer="92" to-port="1" /> + <edge from-layer="92" from-port="2" to-layer="94" to-port="0" /> + <edge from-layer="93" from-port="0" to-layer="94" to-port="1" /> + <edge from-layer="94" from-port="2" to-layer="95" to-port="0" /> + <edge from-layer="95" from-port="1" to-layer="97" to-port="0" /> + <edge from-layer="96" from-port="0" to-layer="97" to-port="1" /> + <edge from-layer="97" from-port="2" to-layer="99" to-port="0" /> + <edge from-layer="98" from-port="0" to-layer="99" to-port="1" /> + <edge from-layer="99" from-port="2" to-layer="100" to-port="0" /> + <edge from-layer="100" from-port="1" to-layer="101" to-port="1" /> + <edge from-layer="101" from-port="2" to-layer="103" to-port="0" /> + <edge from-layer="101" from-port="2" to-layer="112" to-port="0" /> + <edge from-layer="101" from-port="2" to-layer="113" to-port="2" /> + <edge from-layer="102" from-port="0" to-layer="103" to-port="1" /> + <edge from-layer="103" from-port="2" to-layer="105" to-port="0" /> + <edge from-layer="104" from-port="0" to-layer="105" to-port="1" /> + <edge from-layer="105" from-port="2" to-layer="106" to-port="0" /> + <edge from-layer="106" from-port="1" to-layer="108" to-port="0" /> + <edge from-layer="107" from-port="0" to-layer="108" to-port="1" /> + <edge from-layer="108" from-port="2" to-layer="110" to-port="0" /> + <edge from-layer="109" from-port="0" to-layer="110" to-port="1" /> + <edge from-layer="110" from-port="2" to-layer="111" to-port="0" /> + <edge from-layer="111" from-port="1" to-layer="112" to-port="1" /> + <edge from-layer="112" from-port="2" to-layer="113" to-port="3" /> + <edge from-layer="113" from-port="4" to-layer="115" to-port="0" /> + <edge from-layer="114" from-port="0" to-layer="115" to-port="1" /> + <edge from-layer="115" from-port="2" to-layer="117" to-port="0" /> + <edge from-layer="116" from-port="0" to-layer="117" to-port="1" /> + <edge from-layer="117" from-port="2" to-layer="118" to-port="0" /> + <edge from-layer="118" from-port="1" to-layer="120" to-port="0" /> + <edge from-layer="118" from-port="1" to-layer="165" to-port="1" /> + <edge from-layer="119" from-port="0" to-layer="120" to-port="1" /> + <edge from-layer="120" from-port="2" to-layer="122" to-port="0" /> + <edge from-layer="121" from-port="0" to-layer="122" to-port="1" /> + <edge from-layer="122" from-port="2" to-layer="123" to-port="0" /> + <edge from-layer="123" from-port="1" to-layer="125" to-port="0" /> + <edge from-layer="124" from-port="0" to-layer="125" to-port="1" /> + <edge from-layer="125" from-port="2" to-layer="127" to-port="0" /> + <edge from-layer="126" from-port="0" to-layer="127" to-port="1" /> + <edge from-layer="127" from-port="2" to-layer="128" to-port="0" /> + <edge from-layer="128" from-port="1" to-layer="131" to-port="0" /> + <edge from-layer="129" from-port="0" to-layer="131" to-port="1" /> + <edge from-layer="130" from-port="0" to-layer="131" to-port="2" /> + <edge from-layer="130" from-port="0" to-layer="318" to-port="2" /> + <edge from-layer="131" from-port="4" to-layer="142" to-port="0" /> + <edge from-layer="131" from-port="3" to-layer="143" to-port="0" /> + <edge from-layer="131" from-port="4" to-layer="143" to-port="1" /> + <edge from-layer="131" from-port="4" to-layer="133" to-port="0" /> + <edge from-layer="132" from-port="0" to-layer="133" to-port="1" /> + <edge from-layer="133" from-port="2" to-layer="135" to-port="0" /> + <edge from-layer="134" from-port="0" to-layer="135" to-port="1" /> + <edge from-layer="135" from-port="2" to-layer="136" to-port="0" /> + <edge from-layer="136" from-port="1" to-layer="138" to-port="0" /> + <edge from-layer="137" from-port="0" to-layer="138" to-port="1" /> + <edge from-layer="138" from-port="2" to-layer="140" to-port="0" /> + <edge from-layer="139" from-port="0" to-layer="140" to-port="1" /> + <edge from-layer="140" from-port="2" to-layer="141" to-port="0" /> + <edge from-layer="141" from-port="1" to-layer="142" to-port="1" /> + <edge from-layer="142" from-port="2" to-layer="143" to-port="2" /> + <edge from-layer="143" from-port="3" to-layer="145" to-port="0" /> + <edge from-layer="144" from-port="0" to-layer="145" to-port="1" /> + <edge from-layer="145" from-port="2" to-layer="147" to-port="0" /> + <edge from-layer="146" from-port="0" to-layer="147" to-port="1" /> + <edge from-layer="147" from-port="2" to-layer="148" to-port="0" /> + <edge from-layer="148" from-port="1" to-layer="150" to-port="0" /> + <edge from-layer="149" from-port="0" to-layer="150" to-port="1" /> + <edge from-layer="150" from-port="2" to-layer="152" to-port="0" /> + <edge from-layer="151" from-port="0" to-layer="152" to-port="1" /> + <edge from-layer="152" from-port="2" to-layer="153" to-port="0" /> + <edge from-layer="153" from-port="1" to-layer="154" to-port="0" /> + <edge from-layer="153" from-port="1" to-layer="157" to-port="0" /> + <edge from-layer="154" from-port="1" to-layer="157" to-port="1" /> + <edge from-layer="154" from-port="1" to-layer="155" to-port="0" /> + <edge from-layer="155" from-port="1" to-layer="157" to-port="2" /> + <edge from-layer="155" from-port="1" to-layer="156" to-port="0" /> + <edge from-layer="156" from-port="1" to-layer="157" to-port="3" /> + <edge from-layer="157" from-port="4" to-layer="159" to-port="0" /> + <edge from-layer="158" from-port="0" to-layer="159" to-port="1" /> + <edge from-layer="159" from-port="2" to-layer="161" to-port="0" /> + <edge from-layer="160" from-port="0" to-layer="161" to-port="1" /> + <edge from-layer="161" from-port="2" to-layer="162" to-port="0" /> + <edge from-layer="162" from-port="1" to-layer="164" to-port="0" /> + <edge from-layer="162" from-port="1" to-layer="311" to-port="1" /> + <edge from-layer="163" from-port="0" to-layer="164" to-port="1" /> + <edge from-layer="164" from-port="2" to-layer="165" to-port="0" /> + <edge from-layer="165" from-port="2" to-layer="167" to-port="0" /> + <edge from-layer="166" from-port="0" to-layer="167" to-port="1" /> + <edge from-layer="167" from-port="2" to-layer="169" to-port="0" /> + <edge from-layer="168" from-port="0" to-layer="169" to-port="1" /> + <edge from-layer="169" from-port="2" to-layer="170" to-port="0" /> + <edge from-layer="170" from-port="1" to-layer="172" to-port="0" /> + <edge from-layer="171" from-port="0" to-layer="172" to-port="1" /> + <edge from-layer="172" from-port="3" to-layer="183" to-port="0" /> + <edge from-layer="172" from-port="4" to-layer="174" to-port="0" /> + <edge from-layer="172" from-port="4" to-layer="183" to-port="1" /> + <edge from-layer="173" from-port="0" to-layer="174" to-port="1" /> + <edge from-layer="174" from-port="2" to-layer="176" to-port="0" /> + <edge from-layer="175" from-port="0" to-layer="176" to-port="1" /> + <edge from-layer="176" from-port="2" to-layer="177" to-port="0" /> + <edge from-layer="177" from-port="1" to-layer="179" to-port="0" /> + <edge from-layer="178" from-port="0" to-layer="179" to-port="1" /> + <edge from-layer="179" from-port="2" to-layer="181" to-port="0" /> + <edge from-layer="180" from-port="0" to-layer="181" to-port="1" /> + <edge from-layer="181" from-port="2" to-layer="182" to-port="0" /> + <edge from-layer="182" from-port="1" to-layer="183" to-port="2" /> + <edge from-layer="183" from-port="3" to-layer="185" to-port="0" /> + <edge from-layer="184" from-port="0" to-layer="185" to-port="1" /> + <edge from-layer="185" from-port="2" to-layer="187" to-port="0" /> + <edge from-layer="186" from-port="0" to-layer="187" to-port="1" /> + <edge from-layer="187" from-port="2" to-layer="188" to-port="0" /> + <edge from-layer="188" from-port="1" to-layer="190" to-port="0" /> + <edge from-layer="188" from-port="1" to-layer="251" to-port="1" /> + <edge from-layer="189" from-port="0" to-layer="190" to-port="1" /> + <edge from-layer="190" from-port="2" to-layer="191" to-port="0" /> + <edge from-layer="191" from-port="2" to-layer="193" to-port="0" /> + <edge from-layer="192" from-port="0" to-layer="193" to-port="1" /> + <edge from-layer="193" from-port="2" to-layer="195" to-port="0" /> + <edge from-layer="194" from-port="0" to-layer="195" to-port="1" /> + <edge from-layer="195" from-port="2" to-layer="196" to-port="0" /> + <edge from-layer="196" from-port="1" to-layer="198" to-port="0" /> + <edge from-layer="197" from-port="0" to-layer="198" to-port="1" /> + <edge from-layer="198" from-port="4" to-layer="209" to-port="1" /> + <edge from-layer="198" from-port="3" to-layer="209" to-port="0" /> + <edge from-layer="198" from-port="4" to-layer="200" to-port="0" /> + <edge from-layer="199" from-port="0" to-layer="200" to-port="1" /> + <edge from-layer="200" from-port="2" to-layer="202" to-port="0" /> + <edge from-layer="201" from-port="0" to-layer="202" to-port="1" /> + <edge from-layer="202" from-port="2" to-layer="203" to-port="0" /> + <edge from-layer="203" from-port="1" to-layer="205" to-port="0" /> + <edge from-layer="204" from-port="0" to-layer="205" to-port="1" /> + <edge from-layer="205" from-port="2" to-layer="207" to-port="0" /> + <edge from-layer="206" from-port="0" to-layer="207" to-port="1" /> + <edge from-layer="207" from-port="2" to-layer="208" to-port="0" /> + <edge from-layer="208" from-port="1" to-layer="209" to-port="2" /> + <edge from-layer="209" from-port="3" to-layer="211" to-port="0" /> + <edge from-layer="210" from-port="0" to-layer="211" to-port="1" /> + <edge from-layer="211" from-port="2" to-layer="213" to-port="0" /> + <edge from-layer="212" from-port="0" to-layer="213" to-port="1" /> + <edge from-layer="213" from-port="2" to-layer="214" to-port="0" /> + <edge from-layer="214" from-port="1" to-layer="216" to-port="0" /> + <edge from-layer="214" from-port="1" to-layer="247" to-port="0" /> + <edge from-layer="214" from-port="1" to-layer="230" to-port="0" /> + <edge from-layer="215" from-port="0" to-layer="216" to-port="1" /> + <edge from-layer="216" from-port="2" to-layer="218" to-port="0" /> + <edge from-layer="217" from-port="0" to-layer="218" to-port="1" /> + <edge from-layer="218" from-port="2" to-layer="219" to-port="0" /> + <edge from-layer="219" from-port="1" to-layer="221" to-port="0" /> + <edge from-layer="220" from-port="0" to-layer="221" to-port="1" /> + <edge from-layer="221" from-port="2" to-layer="223" to-port="0" /> + <edge from-layer="222" from-port="0" to-layer="223" to-port="1" /> + <edge from-layer="223" from-port="2" to-layer="224" to-port="0" /> + <edge from-layer="224" from-port="1" to-layer="226" to-port="0" /> + <edge from-layer="225" from-port="0" to-layer="226" to-port="1" /> + <edge from-layer="226" from-port="2" to-layer="228" to-port="0" /> + <edge from-layer="227" from-port="0" to-layer="228" to-port="1" /> + <edge from-layer="228" from-port="2" to-layer="243" to-port="0" /> + <edge from-layer="229" from-port="0" to-layer="230" to-port="1" /> + <edge from-layer="230" from-port="2" to-layer="232" to-port="0" /> + <edge from-layer="231" from-port="0" to-layer="232" to-port="1" /> + <edge from-layer="232" from-port="2" to-layer="233" to-port="0" /> + <edge from-layer="233" from-port="1" to-layer="235" to-port="0" /> + <edge from-layer="234" from-port="0" to-layer="235" to-port="1" /> + <edge from-layer="235" from-port="2" to-layer="237" to-port="0" /> + <edge from-layer="236" from-port="0" to-layer="237" to-port="1" /> + <edge from-layer="237" from-port="2" to-layer="238" to-port="0" /> + <edge from-layer="238" from-port="1" to-layer="240" to-port="0" /> + <edge from-layer="239" from-port="0" to-layer="240" to-port="1" /> + <edge from-layer="240" from-port="2" to-layer="242" to-port="0" /> + <edge from-layer="241" from-port="0" to-layer="242" to-port="1" /> + <edge from-layer="242" from-port="2" to-layer="243" to-port="1" /> + <edge from-layer="243" from-port="2" to-layer="245" to-port="0" /> + <edge from-layer="244" from-port="0" to-layer="245" to-port="1" /> + <edge from-layer="245" from-port="2" to-layer="366" to-port="0" /> + <edge from-layer="246" from-port="0" to-layer="247" to-port="1" /> + <edge from-layer="247" from-port="2" to-layer="249" to-port="0" /> + <edge from-layer="248" from-port="0" to-layer="249" to-port="1" /> + <edge from-layer="249" from-port="2" to-layer="250" to-port="0" /> + <edge from-layer="250" from-port="1" to-layer="251" to-port="0" /> + <edge from-layer="251" from-port="2" to-layer="253" to-port="0" /> + <edge from-layer="252" from-port="0" to-layer="253" to-port="1" /> + <edge from-layer="253" from-port="2" to-layer="255" to-port="0" /> + <edge from-layer="254" from-port="0" to-layer="255" to-port="1" /> + <edge from-layer="255" from-port="2" to-layer="256" to-port="0" /> + <edge from-layer="256" from-port="1" to-layer="258" to-port="0" /> + <edge from-layer="257" from-port="0" to-layer="258" to-port="1" /> + <edge from-layer="258" from-port="3" to-layer="269" to-port="0" /> + <edge from-layer="258" from-port="4" to-layer="269" to-port="1" /> + <edge from-layer="258" from-port="4" to-layer="260" to-port="0" /> + <edge from-layer="259" from-port="0" to-layer="260" to-port="1" /> + <edge from-layer="260" from-port="2" to-layer="262" to-port="0" /> + <edge from-layer="261" from-port="0" to-layer="262" to-port="1" /> + <edge from-layer="262" from-port="2" to-layer="263" to-port="0" /> + <edge from-layer="263" from-port="1" to-layer="265" to-port="0" /> + <edge from-layer="264" from-port="0" to-layer="265" to-port="1" /> + <edge from-layer="265" from-port="2" to-layer="267" to-port="0" /> + <edge from-layer="266" from-port="0" to-layer="267" to-port="1" /> + <edge from-layer="267" from-port="2" to-layer="268" to-port="0" /> + <edge from-layer="268" from-port="1" to-layer="269" to-port="2" /> + <edge from-layer="269" from-port="3" to-layer="271" to-port="0" /> + <edge from-layer="270" from-port="0" to-layer="271" to-port="1" /> + <edge from-layer="271" from-port="2" to-layer="273" to-port="0" /> + <edge from-layer="272" from-port="0" to-layer="273" to-port="1" /> + <edge from-layer="273" from-port="2" to-layer="274" to-port="0" /> + <edge from-layer="274" from-port="1" to-layer="307" to-port="0" /> + <edge from-layer="274" from-port="1" to-layer="290" to-port="0" /> + <edge from-layer="274" from-port="1" to-layer="276" to-port="0" /> + <edge from-layer="275" from-port="0" to-layer="276" to-port="1" /> + <edge from-layer="276" from-port="2" to-layer="278" to-port="0" /> + <edge from-layer="277" from-port="0" to-layer="278" to-port="1" /> + <edge from-layer="278" from-port="2" to-layer="279" to-port="0" /> + <edge from-layer="279" from-port="1" to-layer="281" to-port="0" /> + <edge from-layer="280" from-port="0" to-layer="281" to-port="1" /> + <edge from-layer="281" from-port="2" to-layer="283" to-port="0" /> + <edge from-layer="282" from-port="0" to-layer="283" to-port="1" /> + <edge from-layer="283" from-port="2" to-layer="284" to-port="0" /> + <edge from-layer="284" from-port="1" to-layer="286" to-port="0" /> + <edge from-layer="285" from-port="0" to-layer="286" to-port="1" /> + <edge from-layer="286" from-port="2" to-layer="288" to-port="0" /> + <edge from-layer="287" from-port="0" to-layer="288" to-port="1" /> + <edge from-layer="288" from-port="2" to-layer="303" to-port="0" /> + <edge from-layer="289" from-port="0" to-layer="290" to-port="1" /> + <edge from-layer="290" from-port="2" to-layer="292" to-port="0" /> + <edge from-layer="291" from-port="0" to-layer="292" to-port="1" /> + <edge from-layer="292" from-port="2" to-layer="293" to-port="0" /> + <edge from-layer="293" from-port="1" to-layer="295" to-port="0" /> + <edge from-layer="294" from-port="0" to-layer="295" to-port="1" /> + <edge from-layer="295" from-port="2" to-layer="297" to-port="0" /> + <edge from-layer="296" from-port="0" to-layer="297" to-port="1" /> + <edge from-layer="297" from-port="2" to-layer="298" to-port="0" /> + <edge from-layer="298" from-port="1" to-layer="300" to-port="0" /> + <edge from-layer="299" from-port="0" to-layer="300" to-port="1" /> + <edge from-layer="300" from-port="2" to-layer="302" to-port="0" /> + <edge from-layer="301" from-port="0" to-layer="302" to-port="1" /> + <edge from-layer="302" from-port="2" to-layer="303" to-port="1" /> + <edge from-layer="303" from-port="2" to-layer="305" to-port="0" /> + <edge from-layer="304" from-port="0" to-layer="305" to-port="1" /> + <edge from-layer="305" from-port="2" to-layer="366" to-port="1" /> + <edge from-layer="306" from-port="0" to-layer="307" to-port="1" /> + <edge from-layer="307" from-port="2" to-layer="309" to-port="0" /> + <edge from-layer="308" from-port="0" to-layer="309" to-port="1" /> + <edge from-layer="309" from-port="2" to-layer="310" to-port="0" /> + <edge from-layer="310" from-port="1" to-layer="311" to-port="0" /> + <edge from-layer="311" from-port="2" to-layer="313" to-port="0" /> + <edge from-layer="312" from-port="0" to-layer="313" to-port="1" /> + <edge from-layer="313" from-port="2" to-layer="315" to-port="0" /> + <edge from-layer="314" from-port="0" to-layer="315" to-port="1" /> + <edge from-layer="315" from-port="2" to-layer="316" to-port="0" /> + <edge from-layer="316" from-port="1" to-layer="318" to-port="0" /> + <edge from-layer="317" from-port="0" to-layer="318" to-port="1" /> + <edge from-layer="318" from-port="4" to-layer="320" to-port="0" /> + <edge from-layer="318" from-port="4" to-layer="329" to-port="1" /> + <edge from-layer="318" from-port="3" to-layer="329" to-port="0" /> + <edge from-layer="319" from-port="0" to-layer="320" to-port="1" /> + <edge from-layer="320" from-port="2" to-layer="322" to-port="0" /> + <edge from-layer="321" from-port="0" to-layer="322" to-port="1" /> + <edge from-layer="322" from-port="2" to-layer="323" to-port="0" /> + <edge from-layer="323" from-port="1" to-layer="325" to-port="0" /> + <edge from-layer="324" from-port="0" to-layer="325" to-port="1" /> + <edge from-layer="325" from-port="2" to-layer="327" to-port="0" /> + <edge from-layer="326" from-port="0" to-layer="327" to-port="1" /> + <edge from-layer="327" from-port="2" to-layer="328" to-port="0" /> + <edge from-layer="328" from-port="1" to-layer="329" to-port="2" /> + <edge from-layer="329" from-port="3" to-layer="331" to-port="0" /> + <edge from-layer="330" from-port="0" to-layer="331" to-port="1" /> + <edge from-layer="331" from-port="2" to-layer="333" to-port="0" /> + <edge from-layer="332" from-port="0" to-layer="333" to-port="1" /> + <edge from-layer="333" from-port="2" to-layer="334" to-port="0" /> + <edge from-layer="334" from-port="1" to-layer="336" to-port="0" /> + <edge from-layer="334" from-port="1" to-layer="350" to-port="0" /> + <edge from-layer="335" from-port="0" to-layer="336" to-port="1" /> + <edge from-layer="336" from-port="2" to-layer="338" to-port="0" /> + <edge from-layer="337" from-port="0" to-layer="338" to-port="1" /> + <edge from-layer="338" from-port="2" to-layer="339" to-port="0" /> + <edge from-layer="339" from-port="1" to-layer="341" to-port="0" /> + <edge from-layer="340" from-port="0" to-layer="341" to-port="1" /> + <edge from-layer="341" from-port="2" to-layer="343" to-port="0" /> + <edge from-layer="342" from-port="0" to-layer="343" to-port="1" /> + <edge from-layer="343" from-port="2" to-layer="344" to-port="0" /> + <edge from-layer="344" from-port="1" to-layer="346" to-port="0" /> + <edge from-layer="345" from-port="0" to-layer="346" to-port="1" /> + <edge from-layer="346" from-port="2" to-layer="348" to-port="0" /> + <edge from-layer="347" from-port="0" to-layer="348" to-port="1" /> + <edge from-layer="348" from-port="2" to-layer="363" to-port="0" /> + <edge from-layer="349" from-port="0" to-layer="350" to-port="1" /> + <edge from-layer="350" from-port="2" to-layer="352" to-port="0" /> + <edge from-layer="351" from-port="0" to-layer="352" to-port="1" /> + <edge from-layer="352" from-port="2" to-layer="353" to-port="0" /> + <edge from-layer="353" from-port="1" to-layer="355" to-port="0" /> + <edge from-layer="354" from-port="0" to-layer="355" to-port="1" /> + <edge from-layer="355" from-port="2" to-layer="357" to-port="0" /> + <edge from-layer="356" from-port="0" to-layer="357" to-port="1" /> + <edge from-layer="357" from-port="2" to-layer="358" to-port="0" /> + <edge from-layer="358" from-port="1" to-layer="360" to-port="0" /> + <edge from-layer="359" from-port="0" to-layer="360" to-port="1" /> + <edge from-layer="360" from-port="2" to-layer="362" to-port="0" /> + <edge from-layer="361" from-port="0" to-layer="362" to-port="1" /> + <edge from-layer="362" from-port="2" to-layer="363" to-port="1" /> + <edge from-layer="363" from-port="2" to-layer="365" to-port="0" /> + <edge from-layer="364" from-port="0" to-layer="365" to-port="1" /> + <edge from-layer="365" from-port="2" to-layer="366" to-port="2" /> + <edge from-layer="366" from-port="3" to-layer="369" to-port="0" /> + <edge from-layer="367" from-port="0" to-layer="369" to-port="1" /> + <edge from-layer="368" from-port="0" to-layer="369" to-port="2" /> + <edge from-layer="369" from-port="4" to-layer="414" to-port="0" /> + <edge from-layer="369" from-port="3" to-layer="371" to-port="0" /> + <edge from-layer="370" from-port="0" to-layer="371" to-port="1" /> + <edge from-layer="371" from-port="2" to-layer="373" to-port="0" /> + <edge from-layer="372" from-port="0" to-layer="373" to-port="1" /> + <edge from-layer="373" from-port="2" to-layer="374" to-port="0" /> + <edge from-layer="374" from-port="1" to-layer="376" to-port="0" /> + <edge from-layer="375" from-port="0" to-layer="376" to-port="1" /> + <edge from-layer="376" from-port="2" to-layer="378" to-port="0" /> + <edge from-layer="377" from-port="0" to-layer="378" to-port="1" /> + <edge from-layer="378" from-port="2" to-layer="405" to-port="0" /> + <edge from-layer="378" from-port="2" to-layer="393" to-port="0" /> + <edge from-layer="378" from-port="2" to-layer="382" to-port="0" /> + <edge from-layer="379" from-port="0" to-layer="393" to-port="1" /> + <edge from-layer="380" from-port="0" to-layer="391" to-port="0" /> + <edge from-layer="381" from-port="0" to-layer="391" to-port="1" /> + <edge from-layer="382" from-port="1" to-layer="385" to-port="0" /> + <edge from-layer="383" from-port="0" to-layer="385" to-port="1" /> + <edge from-layer="384" from-port="0" to-layer="385" to-port="2" /> + <edge from-layer="385" from-port="3" to-layer="387" to-port="0" /> + <edge from-layer="386" from-port="0" to-layer="387" to-port="1" /> + <edge from-layer="387" from-port="2" to-layer="389" to-port="0" /> + <edge from-layer="388" from-port="0" to-layer="389" to-port="1" /> + <edge from-layer="389" from-port="2" to-layer="391" to-port="2" /> + <edge from-layer="389" from-port="2" to-layer="399" to-port="2" /> + <edge from-layer="389" from-port="2" to-layer="402" to-port="0" /> + <edge from-layer="390" from-port="0" to-layer="391" to-port="3" /> + <edge from-layer="391" from-port="4" to-layer="393" to-port="2" /> + <edge from-layer="392" from-port="0" to-layer="393" to-port="3" /> + <edge from-layer="393" from-port="4" to-layer="394" to-port="1" /> + <edge from-layer="394" from-port="2" to-layer="410" to-port="1" /> + <edge from-layer="394" from-port="2" to-layer="407" to-port="0" /> + <edge from-layer="395" from-port="0" to-layer="406" to-port="0" /> + <edge from-layer="396" from-port="0" to-layer="399" to-port="0" /> + <edge from-layer="397" from-port="0" to-layer="399" to-port="1" /> + <edge from-layer="397" from-port="0" to-layer="403" to-port="1" /> + <edge from-layer="398" from-port="0" to-layer="399" to-port="3" /> + <edge from-layer="398" from-port="0" to-layer="403" to-port="3" /> + <edge from-layer="399" from-port="4" to-layer="405" to-port="1" /> + <edge from-layer="400" from-port="0" to-layer="403" to-port="0" /> + <edge from-layer="401" from-port="0" to-layer="402" to-port="1" /> + <edge from-layer="402" from-port="2" to-layer="403" to-port="2" /> + <edge from-layer="403" from-port="4" to-layer="405" to-port="2" /> + <edge from-layer="404" from-port="0" to-layer="405" to-port="3" /> + <edge from-layer="405" from-port="4" to-layer="406" to-port="1" /> + <edge from-layer="406" from-port="2" to-layer="410" to-port="0" /> + <edge from-layer="406" from-port="2" to-layer="407" to-port="1" /> + <edge from-layer="407" from-port="2" to-layer="409" to-port="0" /> + <edge from-layer="408" from-port="0" to-layer="409" to-port="1" /> + <edge from-layer="409" from-port="2" to-layer="411" to-port="0" /> + <edge from-layer="410" from-port="2" to-layer="411" to-port="1" /> + <edge from-layer="411" from-port="2" to-layer="413" to-port="0" /> + <edge from-layer="412" from-port="0" to-layer="413" to-port="1" /> + <edge from-layer="413" from-port="2" to-layer="415" to-port="0" /> + <edge from-layer="414" from-port="1" to-layer="415" to-port="1" /> + <edge from-layer="415" from-port="2" to-layer="416" to-port="0" /> + </edges> + <rt_info> + <MO_version value="2023.0.1-11005-fa1c41994f3-releases/2023/0" /> + <Runtime_version value="2023.0.1-11005-fa1c41994f3-releases/2023/0" /> + <conversion_parameters> + <framework value="onnx" /> + <input_model value="DIR/best.onnx" /> + <is_python_api_used value="True" /> + <model_name value="best" /> + </conversion_parameters> + <framework> + <author value="Ultralytics" /> + <batch value="1" /> + <date value="2023-09-01T09:31:13.628211" /> + <description value="Ultralytics best model trained on mqt_v3_42_1.yaml" /> + <imgsz value="[768, 768]" /> + <license value="AGPL-3.0 https://ultralytics.com/license" /> + <names value="{0: 'mosquito'}" /> + <stride value="32" /> + <task value="detect" /> + <version value="8.0.165" /> + </framework> + <legacy_frontend value="False" /> + <model_info> + <iou_threshold value="0.7" /> + <labels value="mosquito" /> + <model_type value="YOLOv8" /> + <pad_value value="114" /> + <resize_type value="fit_to_window_letterbox" /> + <reverse_input_channels value="YES" /> + <scale_values value="255" /> + </model_info> + </rt_info> +</net> diff --git a/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold1_1.4/best_openvino_model/metadata.yaml b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold1_1.4/best_openvino_model/metadata.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5e740fe71ee1c0de390569bd1e35769075dcfe79 --- /dev/null +++ b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold1_1.4/best_openvino_model/metadata.yaml @@ -0,0 +1,13 @@ +description: Ultralytics best model trained on mqt_v3_42_1.yaml +author: Ultralytics +license: AGPL-3.0 https://ultralytics.com/license +date: '2023-09-01T09:31:13.628211' +version: 8.0.165 +stride: 32 +task: detect +batch: 1 +imgsz: +- 768 +- 768 +names: + 0: mosquito diff --git a/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold2_1.4/best.pt b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold2_1.4/best.pt new file mode 100644 index 0000000000000000000000000000000000000000..e25db69ebbae15518299c9b14dc85bbe12b06a96 --- /dev/null +++ b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold2_1.4/best.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d304e27ee7963316a985de9999e2d64f16a2a71739cedd29d1c0f4666a0d8197 +size 6223534 diff --git a/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold2_1.4/best_openvino_model/best.bin b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold2_1.4/best_openvino_model/best.bin new file mode 100644 index 0000000000000000000000000000000000000000..c9e12de907ec87b961ac2f1c50ab7732e0cc9787 --- /dev/null +++ b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold2_1.4/best_openvino_model/best.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a4462f8352a40a87b37c5246d19ca16a50280954ee9924728e0a301e9b0240f3 +size 12168796 diff --git a/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold2_1.4/best_openvino_model/best.xml b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold2_1.4/best_openvino_model/best.xml new file mode 100644 index 0000000000000000000000000000000000000000..52b653f9ec7e03d5232148b75b53e0d8ba204061 --- /dev/null +++ b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold2_1.4/best_openvino_model/best.xml @@ -0,0 +1,7987 @@ +<?xml version="1.0"?> +<net name="torch_jit" version="11"> + <layers> + <layer id="0" name="images" type="Parameter" version="opset1"> + <data shape="1,3,768,768" element_type="f32" /> + <output> + <port id="0" precision="FP32" names="images"> + <dim>1</dim> + <dim>3</dim> + <dim>768</dim> + <dim>768</dim> + </port> + </output> + </layer> + <layer id="1" name="/model.22/Constant_9" type="Const" version="opset1"> + <data element_type="f32" shape="1, 2, 12096" offset="0" size="96768" /> + <output> + <port id="0" precision="FP32" names="/model.22/Constant_9_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="2" name="model.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="16, 3, 3, 3" offset="96768" size="1728" /> + <output> + <port id="0" precision="FP32" names="model.0.conv.weight"> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="3" name="/model.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>3</dim> + <dim>768</dim> + <dim>768</dim> + </port> + <port id="1" precision="FP32"> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + </output> + </layer> + <layer id="4" name="Reshape_34204" type="Const" version="opset1"> + <data element_type="f32" shape="1, 16, 1, 1" offset="98496" size="64" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="5" name="/model.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + </output> + </layer> + <layer id="6" name="/model.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.0/act/Mul_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + </output> + </layer> + <layer id="7" name="model.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 16, 3, 3" offset="98560" size="18432" /> + <output> + <port id="0" precision="FP32" names="model.1.conv.weight"> + <dim>32</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="8" name="/model.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="9" name="Reshape_34221" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="116992" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="10" name="/model.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="11" name="/model.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.1/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="12" name="model.2.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 1, 1" offset="117120" size="4096" /> + <output> + <port id="0" precision="FP32" names="model.2.cv1.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="13" name="/model.2/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="14" name="Reshape_34238" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="121216" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="15" name="/model.2/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.2/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="16" name="/model.2/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.2/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="17" name="Constant_34245" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="18" name="Constant_9" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="121352" size="16" /> + <output> + <port id="0" precision="I64" names="onnx::Split_137"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="19" name="/model.2/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.2/Split_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="4" precision="FP32" names="/model.2/Split_output_1"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="20" name="model.2.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="16, 16, 3, 3" offset="121368" size="9216" /> + <output> + <port id="0" precision="FP32" names="model.2.m.0.cv1.conv.weight"> + <dim>16</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="21" name="/model.2/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>16</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="22" name="Reshape_34258" type="Const" version="opset1"> + <data element_type="f32" shape="1, 16, 1, 1" offset="130584" size="64" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="23" name="/model.2/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.2/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="24" name="/model.2/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.2/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="25" name="model.2.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="16, 16, 3, 3" offset="130648" size="9216" /> + <output> + <port id="0" precision="FP32" names="model.2.m.0.cv2.conv.weight"> + <dim>16</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="26" name="/model.2/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>16</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="27" name="Reshape_34275" type="Const" version="opset1"> + <data element_type="f32" shape="1, 16, 1, 1" offset="139864" size="64" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="28" name="/model.2/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.2/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="29" name="/model.2/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.2/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="30" name="/model.2/m.0/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.2/m.0/Add_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="31" name="/model.2/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.2/Concat_output_0"> + <dim>1</dim> + <dim>48</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="32" name="model.2.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 48, 1, 1" offset="139928" size="6144" /> + <output> + <port id="0" precision="FP32" names="model.2.cv2.conv.weight"> + <dim>32</dim> + <dim>48</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="33" name="/model.2/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>48</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>48</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="34" name="Reshape_34294" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="146072" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="35" name="/model.2/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.2/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="36" name="/model.2/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.2/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="37" name="model.3.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 32, 3, 3" offset="146200" size="73728" /> + <output> + <port id="0" precision="FP32" names="model.3.conv.weight"> + <dim>64</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="38" name="/model.3/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="39" name="Reshape_34311" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="219928" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="40" name="/model.3/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.3/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="41" name="/model.3/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.3/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="42" name="model.4.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 1, 1" offset="220184" size="16384" /> + <output> + <port id="0" precision="FP32" names="model.4.cv1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="43" name="/model.4/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="44" name="Reshape_34328" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="236568" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="45" name="/model.4/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="46" name="/model.4/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="47" name="Constant_34335" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="48" name="Constant_28" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="236824" size="16" /> + <output> + <port id="0" precision="I64" names="onnx::Split_157"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="49" name="/model.4/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.4/Split_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="4" precision="FP32" names="/model.4/Split_output_1"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="50" name="model.4.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="236840" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.4.m.0.cv1.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="51" name="/model.4/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="52" name="Reshape_34348" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="273704" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="53" name="/model.4/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="54" name="/model.4/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="55" name="model.4.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="273832" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.4.m.0.cv2.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="56" name="/model.4/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="57" name="Reshape_34365" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="310696" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="58" name="/model.4/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="59" name="/model.4/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="60" name="/model.4/m.0/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.0/Add_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="61" name="model.4.m.1.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="310824" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.4.m.1.cv1.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="62" name="/model.4/m.1/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="63" name="Reshape_34383" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="347688" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="64" name="/model.4/m.1/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.1/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="65" name="/model.4/m.1/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/m.1/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="66" name="model.4.m.1.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="347816" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.4.m.1.cv2.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="67" name="/model.4/m.1/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="68" name="Reshape_34400" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="384680" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="69" name="/model.4/m.1/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.1/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="70" name="/model.4/m.1/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/m.1/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="71" name="/model.4/m.1/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.1/Add_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="72" name="/model.4/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="3" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="4" precision="FP32" names="/model.4/Concat_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="73" name="model.4.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 128, 1, 1" offset="384808" size="32768" /> + <output> + <port id="0" precision="FP32" names="model.4.cv2.conv.weight"> + <dim>64</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="74" name="/model.4/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="75" name="Reshape_34419" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="417576" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="76" name="/model.4/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="77" name="/model.4/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="78" name="model.5.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 64, 3, 3" offset="417832" size="294912" /> + <output> + <port id="0" precision="FP32" names="model.5.conv.weight"> + <dim>128</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="79" name="/model.5/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="80" name="Reshape_34436" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="712744" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="81" name="/model.5/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.5/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="82" name="/model.5/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.5/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="83" name="model.6.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 1, 1" offset="713256" size="65536" /> + <output> + <port id="0" precision="FP32" names="model.6.cv1.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="84" name="/model.6/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="85" name="Reshape_34453" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="778792" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="86" name="/model.6/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="87" name="/model.6/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="88" name="Constant_34460" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="89" name="Constant_54" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="779304" size="16" /> + <output> + <port id="0" precision="I64" names="onnx::Split_184"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="90" name="/model.6/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.6/Split_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="4" precision="FP32" names="/model.6/Split_output_1"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="91" name="model.6.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="779320" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.6.m.0.cv1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="92" name="/model.6/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="93" name="Reshape_34473" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="926776" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="94" name="/model.6/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="95" name="/model.6/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="96" name="model.6.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="927032" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.6.m.0.cv2.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="97" name="/model.6/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="98" name="Reshape_34490" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="1074488" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="99" name="/model.6/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="100" name="/model.6/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="101" name="/model.6/m.0/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.0/Add_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="102" name="model.6.m.1.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="1074744" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.6.m.1.cv1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="103" name="/model.6/m.1/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="104" name="Reshape_34508" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="1222200" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="105" name="/model.6/m.1/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.1/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="106" name="/model.6/m.1/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/m.1/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="107" name="model.6.m.1.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="1222456" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.6.m.1.cv2.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="108" name="/model.6/m.1/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="109" name="Reshape_34525" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="1369912" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="110" name="/model.6/m.1/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.1/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="111" name="/model.6/m.1/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/m.1/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="112" name="/model.6/m.1/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.1/Add_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="113" name="/model.6/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="3" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="4" precision="FP32" names="/model.6/Concat_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="114" name="model.6.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 256, 1, 1" offset="1370168" size="131072" /> + <output> + <port id="0" precision="FP32" names="model.6.cv2.conv.weight"> + <dim>128</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="115" name="/model.6/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="116" name="Reshape_34544" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="1501240" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="117" name="/model.6/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="118" name="/model.6/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="119" name="model.7.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 128, 3, 3" offset="1501752" size="1179648" /> + <output> + <port id="0" precision="FP32" names="model.7.conv.weight"> + <dim>256</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="120" name="/model.7/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="121" name="Reshape_34561" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="2681400" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="122" name="/model.7/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.7/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="123" name="/model.7/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.7/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="124" name="model.8.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 256, 1, 1" offset="2682424" size="262144" /> + <output> + <port id="0" precision="FP32" names="model.8.cv1.conv.weight"> + <dim>256</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="125" name="/model.8/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="126" name="Reshape_34578" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="2944568" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="127" name="/model.8/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.8/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="128" name="/model.8/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.8/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="129" name="Constant_34585" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="130" name="Constant_80" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="2945592" size="16" /> + <output> + <port id="0" precision="I64" names="onnx::Split_211"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="131" name="/model.8/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.8/Split_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="4" precision="FP32" names="/model.8/Split_output_1"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="132" name="model.8.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 3, 3" offset="2945608" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.8.m.0.cv1.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="133" name="/model.8/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="134" name="Reshape_34598" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="3535432" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="135" name="/model.8/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.8/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="136" name="/model.8/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.8/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="137" name="model.8.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 3, 3" offset="3535944" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.8.m.0.cv2.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="138" name="/model.8/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="139" name="Reshape_34615" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="4125768" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="140" name="/model.8/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.8/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="141" name="/model.8/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.8/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="142" name="/model.8/m.0/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.8/m.0/Add_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="143" name="/model.8/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.8/Concat_output_0"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="144" name="model.8.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 384, 1, 1" offset="4126280" size="393216" /> + <output> + <port id="0" precision="FP32" names="model.8.cv2.conv.weight"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="145" name="/model.8/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="146" name="Reshape_34634" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="4519496" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="147" name="/model.8/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.8/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="148" name="/model.8/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.8/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="149" name="model.9.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 256, 1, 1" offset="4520520" size="131072" /> + <output> + <port id="0" precision="FP32" names="model.9.cv1.conv.weight"> + <dim>128</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="150" name="/model.9/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="151" name="Reshape_34651" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="4651592" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="152" name="/model.9/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.9/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="153" name="/model.9/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.9/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="154" name="/model.9/m/MaxPool" type="MaxPool" version="opset8"> + <data strides="1, 1" dilations="1, 1" pads_begin="2, 2" pads_end="2, 2" kernel="5, 5" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.9/m/MaxPool_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="155" name="/model.9/m_1/MaxPool" type="MaxPool" version="opset8"> + <data strides="1, 1" dilations="1, 1" pads_begin="2, 2" pads_end="2, 2" kernel="5, 5" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.9/m_1/MaxPool_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="156" name="/model.9/m_2/MaxPool" type="MaxPool" version="opset8"> + <data strides="1, 1" dilations="1, 1" pads_begin="2, 2" pads_end="2, 2" kernel="5, 5" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.9/m_2/MaxPool_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="157" name="/model.9/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="3" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="4" precision="FP32" names="/model.9/Concat_output_0"> + <dim>1</dim> + <dim>512</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="158" name="model.9.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 512, 1, 1" offset="4652104" size="524288" /> + <output> + <port id="0" precision="FP32" names="model.9.cv2.conv.weight"> + <dim>256</dim> + <dim>512</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="159" name="/model.9/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>512</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>512</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="160" name="Reshape_34672" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="5176392" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="161" name="/model.9/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.9/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="162" name="/model.9/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.9/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="163" name="/model.10/Constant" type="Const" version="opset1"> + <data element_type="f32" shape="4" offset="5177416" size="16" /> + <output> + <port id="0" precision="FP32" names="/model.10/Constant_output_0"> + <dim>4</dim> + </port> + </output> + </layer> + <layer id="164" name="/model.10/Resize" type="Interpolate" version="opset11"> + <data mode="nearest" shape_calculation_mode="scales" coordinate_transformation_mode="asymmetric" nearest_mode="floor" antialias="false" pads_begin="0, 0, 0, 0" pads_end="0, 0, 0, 0" cube_coeff="-0.75" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>4</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.10/Resize_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="165" name="/model.11/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.11/Concat_output_0"> + <dim>1</dim> + <dim>384</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="166" name="model.12.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 384, 1, 1" offset="5177432" size="196608" /> + <output> + <port id="0" precision="FP32" names="model.12.cv1.conv.weight"> + <dim>128</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="167" name="/model.12/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>384</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="168" name="Reshape_34693" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="5374040" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="169" name="/model.12/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.12/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="170" name="/model.12/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.12/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="171" name="Constant_34699" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="172" name="/model.12/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.12/Split_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="4" precision="FP32" names="/model.12/Split_output_1"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="173" name="model.12.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="5374552" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.12.m.0.cv1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="174" name="/model.12/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="175" name="Reshape_34712" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="5522008" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="176" name="/model.12/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.12/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="177" name="/model.12/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.12/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="178" name="model.12.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="5522264" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.12.m.0.cv2.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="179" name="/model.12/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="180" name="Reshape_34729" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="5669720" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="181" name="/model.12/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.12/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="182" name="/model.12/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.12/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="183" name="/model.12/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.12/Concat_output_0"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="184" name="model.12.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 192, 1, 1" offset="5669976" size="98304" /> + <output> + <port id="0" precision="FP32" names="model.12.cv2.conv.weight"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="185" name="/model.12/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="186" name="Reshape_34747" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="5768280" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="187" name="/model.12/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.12/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="188" name="/model.12/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.12/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="189" name="/model.13/Constant" type="Const" version="opset1"> + <data element_type="f32" shape="4" offset="5177416" size="16" /> + <output> + <port id="0" precision="FP32" names="/model.13/Constant_output_0"> + <dim>4</dim> + </port> + </output> + </layer> + <layer id="190" name="/model.13/Resize" type="Interpolate" version="opset11"> + <data mode="nearest" shape_calculation_mode="scales" coordinate_transformation_mode="asymmetric" nearest_mode="floor" antialias="false" pads_begin="0, 0, 0, 0" pads_end="0, 0, 0, 0" cube_coeff="-0.75" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>4</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.13/Resize_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="191" name="/model.14/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.14/Concat_output_0"> + <dim>1</dim> + <dim>192</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="192" name="model.15.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 192, 1, 1" offset="5768792" size="49152" /> + <output> + <port id="0" precision="FP32" names="model.15.cv1.conv.weight"> + <dim>64</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="193" name="/model.15/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>192</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="194" name="Reshape_34768" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="5817944" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="195" name="/model.15/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.15/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="196" name="/model.15/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.15/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="197" name="Constant_34774" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="198" name="/model.15/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.15/Split_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="4" precision="FP32" names="/model.15/Split_output_1"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="199" name="model.15.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="5818200" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.15.m.0.cv1.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="200" name="/model.15/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="201" name="Reshape_34787" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="5855064" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="202" name="/model.15/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.15/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="203" name="/model.15/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.15/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="204" name="model.15.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="5855192" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.15.m.0.cv2.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="205" name="/model.15/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="206" name="Reshape_34804" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="5892056" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="207" name="/model.15/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.15/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="208" name="/model.15/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.15/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="209" name="/model.15/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.15/Concat_output_0"> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="210" name="model.15.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 96, 1, 1" offset="5892184" size="24576" /> + <output> + <port id="0" precision="FP32" names="model.15.cv2.conv.weight"> + <dim>64</dim> + <dim>96</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="211" name="/model.15/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>96</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="212" name="Reshape_34822" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="5916760" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="213" name="/model.15/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.15/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="214" name="/model.15/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.15/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="215" name="model.22.cv2.0.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="5917016" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.0.0.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="216" name="/model.22/cv2.0/cv2.0.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="217" name="Reshape_35017" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6064472" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="218" name="/model.22/cv2.0/cv2.0.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.0/cv2.0.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="219" name="/model.22/cv2.0/cv2.0.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.0/cv2.0.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="220" name="model.22.cv2.0.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6064728" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.0.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="221" name="/model.22/cv2.0/cv2.0.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="222" name="Reshape_35034" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6212184" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="223" name="/model.22/cv2.0/cv2.0.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.0/cv2.0.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="224" name="/model.22/cv2.0/cv2.0.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.0/cv2.0.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="225" name="model.22.cv2.0.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 1, 1" offset="6212440" size="16384" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.0.2.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="226" name="/model.22/cv2.0/cv2.0.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="227" name="Reshape_35051" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6228824" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="228" name="/model.22/cv2.0/cv2.0.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.0/cv2.0.2/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="229" name="model.22.cv3.0.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6229080" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.0.0.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="230" name="/model.22/cv3.0/cv3.0.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="231" name="Reshape_35066" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6376536" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="232" name="/model.22/cv3.0/cv3.0.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.0/cv3.0.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="233" name="/model.22/cv3.0/cv3.0.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.0/cv3.0.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="234" name="model.22.cv3.0.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6376792" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.0.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="235" name="/model.22/cv3.0/cv3.0.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="236" name="Reshape_35083" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6524248" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="237" name="/model.22/cv3.0/cv3.0.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.0/cv3.0.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="238" name="/model.22/cv3.0/cv3.0.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.0/cv3.0.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="239" name="model.22.cv3.0.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6524504" size="256" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.0.2.weight"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="240" name="/model.22/cv3.0/cv3.0.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="241" name="Reshape_35100" type="Const" version="opset1"> + <data element_type="f32" shape="1, 1, 1, 1" offset="6524760" size="4" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="242" name="/model.22/cv3.0/cv3.0.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.0/cv3.0.2/Conv_output_0"> + <dim>1</dim> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="243" name="/model.22/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Concat_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="244" name="/model.22/Constant" type="Const" version="opset1"> + <data element_type="i64" shape="3" offset="6524764" size="24" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_output_0"> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="245" name="/model.22/Reshape" type="Reshape" version="opset1"> + <data special_zero="true" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="I64"> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Reshape_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>9216</dim> + </port> + </output> + </layer> + <layer id="246" name="model.16.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6524788" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.16.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="247" name="/model.16/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="248" name="Reshape_34839" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6672244" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="249" name="/model.16/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.16/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="250" name="/model.16/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.16/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="251" name="/model.17/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.17/Concat_output_0"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="252" name="model.18.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 192, 1, 1" offset="6672500" size="98304" /> + <output> + <port id="0" precision="FP32" names="model.18.cv1.conv.weight"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="253" name="/model.18/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="254" name="Reshape_34857" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="6770804" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="255" name="/model.18/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.18/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="256" name="/model.18/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.18/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="257" name="Constant_34863" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="258" name="/model.18/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.18/Split_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="4" precision="FP32" names="/model.18/Split_output_1"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="259" name="model.18.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6771316" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.18.m.0.cv1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="260" name="/model.18/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="261" name="Reshape_34876" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6918772" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="262" name="/model.18/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.18/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="263" name="/model.18/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.18/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="264" name="model.18.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6919028" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.18.m.0.cv2.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="265" name="/model.18/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="266" name="Reshape_34893" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="7066484" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="267" name="/model.18/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.18/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="268" name="/model.18/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.18/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="269" name="/model.18/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.18/Concat_output_0"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="270" name="model.18.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 192, 1, 1" offset="7066740" size="98304" /> + <output> + <port id="0" precision="FP32" names="model.18.cv2.conv.weight"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="271" name="/model.18/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="272" name="Reshape_34911" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="7165044" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="273" name="/model.18/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.18/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="274" name="/model.18/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.18/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="275" name="model.22.cv2.1.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 128, 3, 3" offset="7165556" size="294912" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.1.0.conv.weight"> + <dim>64</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="276" name="/model.22/cv2.1/cv2.1.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="277" name="Reshape_35116" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="7460468" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="278" name="/model.22/cv2.1/cv2.1.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.1/cv2.1.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="279" name="/model.22/cv2.1/cv2.1.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.1/cv2.1.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="280" name="model.22.cv2.1.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="7460724" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.1.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="281" name="/model.22/cv2.1/cv2.1.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="282" name="Reshape_35133" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="7608180" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="283" name="/model.22/cv2.1/cv2.1.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.1/cv2.1.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="284" name="/model.22/cv2.1/cv2.1.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.1/cv2.1.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="285" name="model.22.cv2.1.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 1, 1" offset="7608436" size="16384" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.1.2.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="286" name="/model.22/cv2.1/cv2.1.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="287" name="Reshape_35150" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="7624820" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="288" name="/model.22/cv2.1/cv2.1.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.1/cv2.1.2/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="289" name="model.22.cv3.1.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 128, 3, 3" offset="7625076" size="294912" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.1.0.conv.weight"> + <dim>64</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="290" name="/model.22/cv3.1/cv3.1.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="291" name="Reshape_35165" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="7919988" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="292" name="/model.22/cv3.1/cv3.1.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.1/cv3.1.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="293" name="/model.22/cv3.1/cv3.1.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.1/cv3.1.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="294" name="model.22.cv3.1.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="7920244" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.1.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="295" name="/model.22/cv3.1/cv3.1.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="296" name="Reshape_35182" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="8067700" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="297" name="/model.22/cv3.1/cv3.1.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.1/cv3.1.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="298" name="/model.22/cv3.1/cv3.1.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.1/cv3.1.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="299" name="model.22.cv3.1.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="8067956" size="256" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.1.2.weight"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="300" name="/model.22/cv3.1/cv3.1.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="301" name="Reshape_35199" type="Const" version="opset1"> + <data element_type="f32" shape="1, 1, 1, 1" offset="8068212" size="4" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="302" name="/model.22/cv3.1/cv3.1.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.1/cv3.1.2/Conv_output_0"> + <dim>1</dim> + <dim>1</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="303" name="/model.22/Concat_1" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Concat_1_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="304" name="/model.22/Constant_1" type="Const" version="opset1"> + <data element_type="i64" shape="3" offset="6524764" size="24" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_1_output_0"> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="305" name="/model.22/Reshape_1" type="Reshape" version="opset1"> + <data special_zero="true" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="I64"> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Reshape_1_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>2304</dim> + </port> + </output> + </layer> + <layer id="306" name="model.19.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 3, 3" offset="8068216" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.19.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="307" name="/model.19/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="308" name="Reshape_34928" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="8658040" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="309" name="/model.19/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.19/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="310" name="/model.19/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.19/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="311" name="/model.20/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.20/Concat_output_0"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="312" name="model.21.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 384, 1, 1" offset="8658552" size="393216" /> + <output> + <port id="0" precision="FP32" names="model.21.cv1.conv.weight"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="313" name="/model.21/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="314" name="Reshape_34946" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="9051768" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="315" name="/model.21/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.21/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="316" name="/model.21/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.21/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="317" name="Constant_34952" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="318" name="/model.21/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.21/Split_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="4" precision="FP32" names="/model.21/Split_output_1"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="319" name="model.21.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 3, 3" offset="9052792" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.21.m.0.cv1.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="320" name="/model.21/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="321" name="Reshape_34965" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="9642616" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="322" name="/model.21/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.21/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="323" name="/model.21/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.21/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="324" name="model.21.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 3, 3" offset="9643128" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.21.m.0.cv2.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="325" name="/model.21/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="326" name="Reshape_34982" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="10232952" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="327" name="/model.21/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.21/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="328" name="/model.21/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.21/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="329" name="/model.21/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.21/Concat_output_0"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="330" name="model.21.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 384, 1, 1" offset="10233464" size="393216" /> + <output> + <port id="0" precision="FP32" names="model.21.cv2.conv.weight"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="331" name="/model.21/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="332" name="Reshape_35000" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="10626680" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="333" name="/model.21/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.21/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="334" name="/model.21/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.21/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="335" name="model.22.cv2.2.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 256, 3, 3" offset="10627704" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.2.0.conv.weight"> + <dim>64</dim> + <dim>256</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="336" name="/model.22/cv2.2/cv2.2.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>256</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="337" name="Reshape_35215" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="11217528" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="338" name="/model.22/cv2.2/cv2.2.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.2/cv2.2.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="339" name="/model.22/cv2.2/cv2.2.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.2/cv2.2.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="340" name="model.22.cv2.2.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="11217784" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.2.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="341" name="/model.22/cv2.2/cv2.2.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="342" name="Reshape_35232" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="11365240" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="343" name="/model.22/cv2.2/cv2.2.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.2/cv2.2.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="344" name="/model.22/cv2.2/cv2.2.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.2/cv2.2.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="345" name="model.22.cv2.2.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 1, 1" offset="11365496" size="16384" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.2.2.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="346" name="/model.22/cv2.2/cv2.2.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="347" name="Reshape_35249" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="11381880" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="348" name="/model.22/cv2.2/cv2.2.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.2/cv2.2.2/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="349" name="model.22.cv3.2.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 256, 3, 3" offset="11382136" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.2.0.conv.weight"> + <dim>64</dim> + <dim>256</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="350" name="/model.22/cv3.2/cv3.2.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>256</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="351" name="Reshape_35264" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="11971960" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="352" name="/model.22/cv3.2/cv3.2.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.2/cv3.2.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="353" name="/model.22/cv3.2/cv3.2.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.2/cv3.2.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="354" name="model.22.cv3.2.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="11972216" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.2.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="355" name="/model.22/cv3.2/cv3.2.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="356" name="Reshape_35281" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="12119672" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="357" name="/model.22/cv3.2/cv3.2.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.2/cv3.2.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="358" name="/model.22/cv3.2/cv3.2.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.2/cv3.2.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="359" name="model.22.cv3.2.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="12119928" size="256" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.2.2.weight"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="360" name="/model.22/cv3.2/cv3.2.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="361" name="Reshape_35298" type="Const" version="opset1"> + <data element_type="f32" shape="1, 1, 1, 1" offset="12120184" size="4" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="362" name="/model.22/cv3.2/cv3.2.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.2/cv3.2.2/Conv_output_0"> + <dim>1</dim> + <dim>1</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="363" name="/model.22/Concat_2" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Concat_2_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="364" name="/model.22/Constant_2" type="Const" version="opset1"> + <data element_type="i64" shape="3" offset="6524764" size="24" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_2_output_0"> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="365" name="/model.22/Reshape_2" type="Reshape" version="opset1"> + <data special_zero="true" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="I64"> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Reshape_2_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>576</dim> + </port> + </output> + </layer> + <layer id="366" name="/model.22/Concat_3" type="Concat" version="opset1"> + <data axis="2" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>9216</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>2304</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>576</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.22/Concat_3_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="367" name="Constant_35317" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="368" name="Constant_225" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120188" size="16" /> + <output> + <port id="0" precision="I64" names="onnx::Split_388"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="369" name="/model.22/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.22/Split_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>12096</dim> + </port> + <port id="4" precision="FP32" names="/model.22/Split_output_1"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="370" name="/model.22/dfl/Constant" type="Const" version="opset1"> + <data element_type="i64" shape="4" offset="12120204" size="32" /> + <output> + <port id="0" precision="I64" names="/model.22/dfl/Constant_output_0"> + <dim>4</dim> + </port> + </output> + </layer> + <layer id="371" name="/model.22/dfl/Reshape" type="Reshape" version="opset1"> + <data special_zero="true" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64"> + <dim>4</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/dfl/Reshape_output_0"> + <dim>1</dim> + <dim>4</dim> + <dim>16</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="372" name="Constant_35323" type="Const" version="opset1"> + <data element_type="i64" shape="4" offset="12120236" size="32" /> + <output> + <port id="0" precision="I64"> + <dim>4</dim> + </port> + </output> + </layer> + <layer id="373" name="/model.22/dfl/Transpose" type="Transpose" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>16</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64"> + <dim>4</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/dfl/Transpose_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="374" name="/model.22/dfl/Softmax" type="SoftMax" version="opset8"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/dfl/Softmax_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="375" name="model.22.dfl.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="1, 16, 1, 1" offset="12120268" size="64" /> + <output> + <port id="0" precision="FP32" names="model.22.dfl.conv.weight"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="376" name="/model.22/dfl/conv/Conv" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/dfl/conv/Conv_output_0"> + <dim>1</dim> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="377" name="/model.22/dfl/Constant_1" type="Const" version="opset1"> + <data element_type="i64" shape="3" offset="12120332" size="24" /> + <output> + <port id="0" precision="I64" names="/model.22/dfl/Constant_1_output_0"> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="378" name="/model.22/dfl/Reshape_1" type="Reshape" version="opset1"> + <data special_zero="true" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64"> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/dfl/Reshape_1_output_0"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="379" name="Constant_37620" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120356" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="380" name="Constant_37621" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120356" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="381" name="Constant_37617" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="121344" size="8" /> + <output> + <port id="0" precision="I64"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="382" name="/model.22/Shape" type="ShapeOf" version="opset3"> + <data output_type="i64" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="1" precision="I64" names="/model.22/Shape_output_0"> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="383" name="/model.22/Constant_3" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_3_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="384" name="Constant_35334" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="12120372" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="385" name="/model.22/Gather" type="Gather" version="opset8"> + <data batch_dims="0" /> + <input> + <port id="0" precision="I64"> + <dim>3</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + <port id="2" precision="I64" /> + </input> + <output> + <port id="3" precision="I64" names="/model.22/Gather_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="386" name="/model.22/Constant_5" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_5_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="387" name="/model.22/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="I64"> + <dim>1</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="I64" names="/model.22/Add_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="388" name="/model.22/Constant_6" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="12120380" size="8" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_6_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="389" name="/model.22/Div" type="Divide" version="opset1"> + <data auto_broadcast="numpy" m_pythondiv="true" /> + <input> + <port id="0" precision="I64"> + <dim>1</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="I64" names="/model.22/Div_output_0,/model.22/Mul_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="390" name="Constant_37616" type="Const" version="opset1"> + <data element_type="i32" shape="1" offset="12120388" size="4" /> + <output> + <port id="0" precision="I32"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="391" name="ScatterUpdate_37622" type="ScatterUpdate" version="opset3"> + <input> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + </port> + <port id="3" precision="I32"> + <dim>1</dim> + </port> + </input> + <output> + <port id="4" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="392" name="Constant_37625" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120392" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="393" name="/model.22/Slice" type="StridedSlice" version="opset1"> + <data begin_mask="1, 0" end_mask="1, 0" new_axis_mask="" shrink_axis_mask="" ellipsis_mask="" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64"> + <dim>2</dim> + </port> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + <port id="3" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="4" precision="FP32" names="/model.22/Slice_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="394" name="/model.22/Sub" type="Subtract" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Sub_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="395" name="/model.22/Constant_10" type="Const" version="opset1"> + <data element_type="f32" shape="1, 2, 12096" offset="0" size="96768" /> + <output> + <port id="0" precision="FP32" names="/model.22/Constant_10_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="396" name="Constant_37669" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120356" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="397" name="Constant_37668" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="121344" size="8" /> + <output> + <port id="0" precision="I64"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="398" name="Constant_37667" type="Const" version="opset1"> + <data element_type="i32" shape="1" offset="12120388" size="4" /> + <output> + <port id="0" precision="I32"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="399" name="ScatterUpdate_37670" type="ScatterUpdate" version="opset3"> + <input> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + </port> + <port id="3" precision="I32"> + <dim>1</dim> + </port> + </input> + <output> + <port id="4" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="400" name="Constant_37671" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120356" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="401" name="/model.22/Constant_8" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="12120380" size="8" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_8_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="402" name="/model.22/Mul_1" type="Multiply" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="I64"> + <dim>1</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="I64" names="/model.22/Mul_1_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="403" name="ScatterUpdate_37672" type="ScatterUpdate" version="opset3"> + <input> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + </port> + <port id="3" precision="I32"> + <dim>1</dim> + </port> + </input> + <output> + <port id="4" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="404" name="Constant_37675" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120392" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="405" name="/model.22/Slice_1" type="StridedSlice" version="opset1"> + <data begin_mask="1, 0" end_mask="1, 0" new_axis_mask="" shrink_axis_mask="" ellipsis_mask="" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64"> + <dim>2</dim> + </port> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + <port id="3" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="4" precision="FP32" names="/model.22/Slice_1_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="406" name="/model.22/Add_1" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Add_1_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="407" name="/model.22/Add_2" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Add_2_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="408" name="Constant_38018" type="Const" version="opset1"> + <data element_type="f32" shape="1, 1, 1" offset="12120408" size="4" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="409" name="/model.22/Div_1" type="Multiply" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Div_1_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="410" name="/model.22/Sub_1" type="Subtract" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Sub_1_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="411" name="/model.22/Concat_4" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Concat_4_output_0"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="412" name="Constant_38019" type="Const" version="opset1"> + <data element_type="f32" shape="1, 1, 12096" offset="12120412" size="48384" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="413" name="/model.22/Mul_2" type="Multiply" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Mul_2_output_0"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="414" name="/model.22/Sigmoid" type="Sigmoid" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/Sigmoid_output_0"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="415" name="output0" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="output0"> + <dim>1</dim> + <dim>5</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="416" name="output0/sink_port_0" type="Result" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>5</dim> + <dim>12096</dim> + </port> + </input> + </layer> + </layers> + <edges> + <edge from-layer="0" from-port="0" to-layer="3" to-port="0" /> + <edge from-layer="1" from-port="0" to-layer="394" to-port="0" /> + <edge from-layer="2" from-port="0" to-layer="3" to-port="1" /> + <edge from-layer="3" from-port="2" to-layer="5" to-port="0" /> + <edge from-layer="4" from-port="0" to-layer="5" to-port="1" /> + <edge from-layer="5" from-port="2" to-layer="6" to-port="0" /> + <edge from-layer="6" from-port="1" to-layer="8" to-port="0" /> + <edge from-layer="7" from-port="0" to-layer="8" to-port="1" /> + <edge from-layer="8" from-port="2" to-layer="10" to-port="0" /> + <edge from-layer="9" from-port="0" to-layer="10" to-port="1" /> + <edge from-layer="10" from-port="2" to-layer="11" to-port="0" /> + <edge from-layer="11" from-port="1" to-layer="13" to-port="0" /> + <edge from-layer="12" from-port="0" to-layer="13" to-port="1" /> + <edge from-layer="13" from-port="2" to-layer="15" to-port="0" /> + <edge from-layer="14" from-port="0" to-layer="15" to-port="1" /> + <edge from-layer="15" from-port="2" to-layer="16" to-port="0" /> + <edge from-layer="16" from-port="1" to-layer="19" to-port="0" /> + <edge from-layer="17" from-port="0" to-layer="19" to-port="1" /> + <edge from-layer="18" from-port="0" to-layer="19" to-port="2" /> + <edge from-layer="19" from-port="4" to-layer="21" to-port="0" /> + <edge from-layer="19" from-port="4" to-layer="31" to-port="1" /> + <edge from-layer="19" from-port="3" to-layer="31" to-port="0" /> + <edge from-layer="19" from-port="4" to-layer="30" to-port="0" /> + <edge from-layer="20" from-port="0" to-layer="21" to-port="1" /> + <edge from-layer="21" from-port="2" to-layer="23" to-port="0" /> + <edge from-layer="22" from-port="0" to-layer="23" to-port="1" /> + <edge from-layer="23" from-port="2" to-layer="24" to-port="0" /> + <edge from-layer="24" from-port="1" to-layer="26" to-port="0" /> + <edge from-layer="25" from-port="0" to-layer="26" to-port="1" /> + <edge from-layer="26" from-port="2" to-layer="28" to-port="0" /> + <edge from-layer="27" from-port="0" to-layer="28" to-port="1" /> + <edge from-layer="28" from-port="2" to-layer="29" to-port="0" /> + <edge from-layer="29" from-port="1" to-layer="30" to-port="1" /> + <edge from-layer="30" from-port="2" to-layer="31" to-port="2" /> + <edge from-layer="31" from-port="3" to-layer="33" to-port="0" /> + <edge from-layer="32" from-port="0" to-layer="33" to-port="1" /> + <edge from-layer="33" from-port="2" to-layer="35" to-port="0" /> + <edge from-layer="34" from-port="0" to-layer="35" to-port="1" /> + <edge from-layer="35" from-port="2" to-layer="36" to-port="0" /> + <edge from-layer="36" from-port="1" to-layer="38" to-port="0" /> + <edge from-layer="37" from-port="0" to-layer="38" to-port="1" /> + <edge from-layer="38" from-port="2" to-layer="40" to-port="0" /> + <edge from-layer="39" from-port="0" to-layer="40" to-port="1" /> + <edge from-layer="40" from-port="2" to-layer="41" to-port="0" /> + <edge from-layer="41" from-port="1" to-layer="43" to-port="0" /> + <edge from-layer="42" from-port="0" to-layer="43" to-port="1" /> + <edge from-layer="43" from-port="2" to-layer="45" to-port="0" /> + <edge from-layer="44" from-port="0" to-layer="45" to-port="1" /> + <edge from-layer="45" from-port="2" to-layer="46" to-port="0" /> + <edge from-layer="46" from-port="1" to-layer="49" to-port="0" /> + <edge from-layer="47" from-port="0" to-layer="49" to-port="1" /> + <edge from-layer="48" from-port="0" to-layer="49" to-port="2" /> + <edge from-layer="48" from-port="0" to-layer="198" to-port="2" /> + <edge from-layer="49" from-port="3" to-layer="72" to-port="0" /> + <edge from-layer="49" from-port="4" to-layer="72" to-port="1" /> + <edge from-layer="49" from-port="4" to-layer="51" to-port="0" /> + <edge from-layer="49" from-port="4" to-layer="60" to-port="0" /> + <edge from-layer="50" from-port="0" to-layer="51" to-port="1" /> + <edge from-layer="51" from-port="2" to-layer="53" to-port="0" /> + <edge from-layer="52" from-port="0" to-layer="53" to-port="1" /> + <edge from-layer="53" from-port="2" to-layer="54" to-port="0" /> + <edge from-layer="54" from-port="1" to-layer="56" to-port="0" /> + <edge from-layer="55" from-port="0" to-layer="56" to-port="1" /> + <edge from-layer="56" from-port="2" to-layer="58" to-port="0" /> + <edge from-layer="57" from-port="0" to-layer="58" to-port="1" /> + <edge from-layer="58" from-port="2" to-layer="59" to-port="0" /> + <edge from-layer="59" from-port="1" to-layer="60" to-port="1" /> + <edge from-layer="60" from-port="2" to-layer="62" to-port="0" /> + <edge from-layer="60" from-port="2" to-layer="72" to-port="2" /> + <edge from-layer="60" from-port="2" to-layer="71" to-port="0" /> + <edge from-layer="61" from-port="0" to-layer="62" to-port="1" /> + <edge from-layer="62" from-port="2" to-layer="64" to-port="0" /> + <edge from-layer="63" from-port="0" to-layer="64" to-port="1" /> + <edge from-layer="64" from-port="2" to-layer="65" to-port="0" /> + <edge from-layer="65" from-port="1" to-layer="67" to-port="0" /> + <edge from-layer="66" from-port="0" to-layer="67" to-port="1" /> + <edge from-layer="67" from-port="2" to-layer="69" to-port="0" /> + <edge from-layer="68" from-port="0" to-layer="69" to-port="1" /> + <edge from-layer="69" from-port="2" to-layer="70" to-port="0" /> + <edge from-layer="70" from-port="1" to-layer="71" to-port="1" /> + <edge from-layer="71" from-port="2" to-layer="72" to-port="3" /> + <edge from-layer="72" from-port="4" to-layer="74" to-port="0" /> + <edge from-layer="73" from-port="0" to-layer="74" to-port="1" /> + <edge from-layer="74" from-port="2" to-layer="76" to-port="0" /> + <edge from-layer="75" from-port="0" to-layer="76" to-port="1" /> + <edge from-layer="76" from-port="2" to-layer="77" to-port="0" /> + <edge from-layer="77" from-port="1" to-layer="79" to-port="0" /> + <edge from-layer="77" from-port="1" to-layer="191" to-port="1" /> + <edge from-layer="78" from-port="0" to-layer="79" to-port="1" /> + <edge from-layer="79" from-port="2" to-layer="81" to-port="0" /> + <edge from-layer="80" from-port="0" to-layer="81" to-port="1" /> + <edge from-layer="81" from-port="2" to-layer="82" to-port="0" /> + <edge from-layer="82" from-port="1" to-layer="84" to-port="0" /> + <edge from-layer="83" from-port="0" to-layer="84" to-port="1" /> + <edge from-layer="84" from-port="2" to-layer="86" to-port="0" /> + <edge from-layer="85" from-port="0" to-layer="86" to-port="1" /> + <edge from-layer="86" from-port="2" to-layer="87" to-port="0" /> + <edge from-layer="87" from-port="1" to-layer="90" to-port="0" /> + <edge from-layer="88" from-port="0" to-layer="90" to-port="1" /> + <edge from-layer="89" from-port="0" to-layer="258" to-port="2" /> + <edge from-layer="89" from-port="0" to-layer="172" to-port="2" /> + <edge from-layer="89" from-port="0" to-layer="90" to-port="2" /> + <edge from-layer="90" from-port="4" to-layer="92" to-port="0" /> + <edge from-layer="90" from-port="4" to-layer="113" to-port="1" /> + <edge from-layer="90" from-port="3" to-layer="113" to-port="0" /> + <edge from-layer="90" from-port="4" to-layer="101" to-port="0" /> + <edge from-layer="91" from-port="0" to-layer="92" to-port="1" /> + <edge from-layer="92" from-port="2" to-layer="94" to-port="0" /> + <edge from-layer="93" from-port="0" to-layer="94" to-port="1" /> + <edge from-layer="94" from-port="2" to-layer="95" to-port="0" /> + <edge from-layer="95" from-port="1" to-layer="97" to-port="0" /> + <edge from-layer="96" from-port="0" to-layer="97" to-port="1" /> + <edge from-layer="97" from-port="2" to-layer="99" to-port="0" /> + <edge from-layer="98" from-port="0" to-layer="99" to-port="1" /> + <edge from-layer="99" from-port="2" to-layer="100" to-port="0" /> + <edge from-layer="100" from-port="1" to-layer="101" to-port="1" /> + <edge from-layer="101" from-port="2" to-layer="103" to-port="0" /> + <edge from-layer="101" from-port="2" to-layer="112" to-port="0" /> + <edge from-layer="101" from-port="2" to-layer="113" to-port="2" /> + <edge from-layer="102" from-port="0" to-layer="103" to-port="1" /> + <edge from-layer="103" from-port="2" to-layer="105" to-port="0" /> + <edge from-layer="104" from-port="0" to-layer="105" to-port="1" /> + <edge from-layer="105" from-port="2" to-layer="106" to-port="0" /> + <edge from-layer="106" from-port="1" to-layer="108" to-port="0" /> + <edge from-layer="107" from-port="0" to-layer="108" to-port="1" /> + <edge from-layer="108" from-port="2" to-layer="110" to-port="0" /> + <edge from-layer="109" from-port="0" to-layer="110" to-port="1" /> + <edge from-layer="110" from-port="2" to-layer="111" to-port="0" /> + <edge from-layer="111" from-port="1" to-layer="112" to-port="1" /> + <edge from-layer="112" from-port="2" to-layer="113" to-port="3" /> + <edge from-layer="113" from-port="4" to-layer="115" to-port="0" /> + <edge from-layer="114" from-port="0" to-layer="115" to-port="1" /> + <edge from-layer="115" from-port="2" to-layer="117" to-port="0" /> + <edge from-layer="116" from-port="0" to-layer="117" to-port="1" /> + <edge from-layer="117" from-port="2" to-layer="118" to-port="0" /> + <edge from-layer="118" from-port="1" to-layer="120" to-port="0" /> + <edge from-layer="118" from-port="1" to-layer="165" to-port="1" /> + <edge from-layer="119" from-port="0" to-layer="120" to-port="1" /> + <edge from-layer="120" from-port="2" to-layer="122" to-port="0" /> + <edge from-layer="121" from-port="0" to-layer="122" to-port="1" /> + <edge from-layer="122" from-port="2" to-layer="123" to-port="0" /> + <edge from-layer="123" from-port="1" to-layer="125" to-port="0" /> + <edge from-layer="124" from-port="0" to-layer="125" to-port="1" /> + <edge from-layer="125" from-port="2" to-layer="127" to-port="0" /> + <edge from-layer="126" from-port="0" to-layer="127" to-port="1" /> + <edge from-layer="127" from-port="2" to-layer="128" to-port="0" /> + <edge from-layer="128" from-port="1" to-layer="131" to-port="0" /> + <edge from-layer="129" from-port="0" to-layer="131" to-port="1" /> + <edge from-layer="130" from-port="0" to-layer="131" to-port="2" /> + <edge from-layer="130" from-port="0" to-layer="318" to-port="2" /> + <edge from-layer="131" from-port="4" to-layer="142" to-port="0" /> + <edge from-layer="131" from-port="3" to-layer="143" to-port="0" /> + <edge from-layer="131" from-port="4" to-layer="143" to-port="1" /> + <edge from-layer="131" from-port="4" to-layer="133" to-port="0" /> + <edge from-layer="132" from-port="0" to-layer="133" to-port="1" /> + <edge from-layer="133" from-port="2" to-layer="135" to-port="0" /> + <edge from-layer="134" from-port="0" to-layer="135" to-port="1" /> + <edge from-layer="135" from-port="2" to-layer="136" to-port="0" /> + <edge from-layer="136" from-port="1" to-layer="138" to-port="0" /> + <edge from-layer="137" from-port="0" to-layer="138" to-port="1" /> + <edge from-layer="138" from-port="2" to-layer="140" to-port="0" /> + <edge from-layer="139" from-port="0" to-layer="140" to-port="1" /> + <edge from-layer="140" from-port="2" to-layer="141" to-port="0" /> + <edge from-layer="141" from-port="1" to-layer="142" to-port="1" /> + <edge from-layer="142" from-port="2" to-layer="143" to-port="2" /> + <edge from-layer="143" from-port="3" to-layer="145" to-port="0" /> + <edge from-layer="144" from-port="0" to-layer="145" to-port="1" /> + <edge from-layer="145" from-port="2" to-layer="147" to-port="0" /> + <edge from-layer="146" from-port="0" to-layer="147" to-port="1" /> + <edge from-layer="147" from-port="2" to-layer="148" to-port="0" /> + <edge from-layer="148" from-port="1" to-layer="150" to-port="0" /> + <edge from-layer="149" from-port="0" to-layer="150" to-port="1" /> + <edge from-layer="150" from-port="2" to-layer="152" to-port="0" /> + <edge from-layer="151" from-port="0" to-layer="152" to-port="1" /> + <edge from-layer="152" from-port="2" to-layer="153" to-port="0" /> + <edge from-layer="153" from-port="1" to-layer="154" to-port="0" /> + <edge from-layer="153" from-port="1" to-layer="157" to-port="0" /> + <edge from-layer="154" from-port="1" to-layer="157" to-port="1" /> + <edge from-layer="154" from-port="1" to-layer="155" to-port="0" /> + <edge from-layer="155" from-port="1" to-layer="157" to-port="2" /> + <edge from-layer="155" from-port="1" to-layer="156" to-port="0" /> + <edge from-layer="156" from-port="1" to-layer="157" to-port="3" /> + <edge from-layer="157" from-port="4" to-layer="159" to-port="0" /> + <edge from-layer="158" from-port="0" to-layer="159" to-port="1" /> + <edge from-layer="159" from-port="2" to-layer="161" to-port="0" /> + <edge from-layer="160" from-port="0" to-layer="161" to-port="1" /> + <edge from-layer="161" from-port="2" to-layer="162" to-port="0" /> + <edge from-layer="162" from-port="1" to-layer="164" to-port="0" /> + <edge from-layer="162" from-port="1" to-layer="311" to-port="1" /> + <edge from-layer="163" from-port="0" to-layer="164" to-port="1" /> + <edge from-layer="164" from-port="2" to-layer="165" to-port="0" /> + <edge from-layer="165" from-port="2" to-layer="167" to-port="0" /> + <edge from-layer="166" from-port="0" to-layer="167" to-port="1" /> + <edge from-layer="167" from-port="2" to-layer="169" to-port="0" /> + <edge from-layer="168" from-port="0" to-layer="169" to-port="1" /> + <edge from-layer="169" from-port="2" to-layer="170" to-port="0" /> + <edge from-layer="170" from-port="1" to-layer="172" to-port="0" /> + <edge from-layer="171" from-port="0" to-layer="172" to-port="1" /> + <edge from-layer="172" from-port="3" to-layer="183" to-port="0" /> + <edge from-layer="172" from-port="4" to-layer="174" to-port="0" /> + <edge from-layer="172" from-port="4" to-layer="183" to-port="1" /> + <edge from-layer="173" from-port="0" to-layer="174" to-port="1" /> + <edge from-layer="174" from-port="2" to-layer="176" to-port="0" /> + <edge from-layer="175" from-port="0" to-layer="176" to-port="1" /> + <edge from-layer="176" from-port="2" to-layer="177" to-port="0" /> + <edge from-layer="177" from-port="1" to-layer="179" to-port="0" /> + <edge from-layer="178" from-port="0" to-layer="179" to-port="1" /> + <edge from-layer="179" from-port="2" to-layer="181" to-port="0" /> + <edge from-layer="180" from-port="0" to-layer="181" to-port="1" /> + <edge from-layer="181" from-port="2" to-layer="182" to-port="0" /> + <edge from-layer="182" from-port="1" to-layer="183" to-port="2" /> + <edge from-layer="183" from-port="3" to-layer="185" to-port="0" /> + <edge from-layer="184" from-port="0" to-layer="185" to-port="1" /> + <edge from-layer="185" from-port="2" to-layer="187" to-port="0" /> + <edge from-layer="186" from-port="0" to-layer="187" to-port="1" /> + <edge from-layer="187" from-port="2" to-layer="188" to-port="0" /> + <edge from-layer="188" from-port="1" to-layer="190" to-port="0" /> + <edge from-layer="188" from-port="1" to-layer="251" to-port="1" /> + <edge from-layer="189" from-port="0" to-layer="190" to-port="1" /> + <edge from-layer="190" from-port="2" to-layer="191" to-port="0" /> + <edge from-layer="191" from-port="2" to-layer="193" to-port="0" /> + <edge from-layer="192" from-port="0" to-layer="193" to-port="1" /> + <edge from-layer="193" from-port="2" to-layer="195" to-port="0" /> + <edge from-layer="194" from-port="0" to-layer="195" to-port="1" /> + <edge from-layer="195" from-port="2" to-layer="196" to-port="0" /> + <edge from-layer="196" from-port="1" to-layer="198" to-port="0" /> + <edge from-layer="197" from-port="0" to-layer="198" to-port="1" /> + <edge from-layer="198" from-port="4" to-layer="209" to-port="1" /> + <edge from-layer="198" from-port="3" to-layer="209" to-port="0" /> + <edge from-layer="198" from-port="4" to-layer="200" to-port="0" /> + <edge from-layer="199" from-port="0" to-layer="200" to-port="1" /> + <edge from-layer="200" from-port="2" to-layer="202" to-port="0" /> + <edge from-layer="201" from-port="0" to-layer="202" to-port="1" /> + <edge from-layer="202" from-port="2" to-layer="203" to-port="0" /> + <edge from-layer="203" from-port="1" to-layer="205" to-port="0" /> + <edge from-layer="204" from-port="0" to-layer="205" to-port="1" /> + <edge from-layer="205" from-port="2" to-layer="207" to-port="0" /> + <edge from-layer="206" from-port="0" to-layer="207" to-port="1" /> + <edge from-layer="207" from-port="2" to-layer="208" to-port="0" /> + <edge from-layer="208" from-port="1" to-layer="209" to-port="2" /> + <edge from-layer="209" from-port="3" to-layer="211" to-port="0" /> + <edge from-layer="210" from-port="0" to-layer="211" to-port="1" /> + <edge from-layer="211" from-port="2" to-layer="213" to-port="0" /> + <edge from-layer="212" from-port="0" to-layer="213" to-port="1" /> + <edge from-layer="213" from-port="2" to-layer="214" to-port="0" /> + <edge from-layer="214" from-port="1" to-layer="216" to-port="0" /> + <edge from-layer="214" from-port="1" to-layer="247" to-port="0" /> + <edge from-layer="214" from-port="1" to-layer="230" to-port="0" /> + <edge from-layer="215" from-port="0" to-layer="216" to-port="1" /> + <edge from-layer="216" from-port="2" to-layer="218" to-port="0" /> + <edge from-layer="217" from-port="0" to-layer="218" to-port="1" /> + <edge from-layer="218" from-port="2" to-layer="219" to-port="0" /> + <edge from-layer="219" from-port="1" to-layer="221" to-port="0" /> + <edge from-layer="220" from-port="0" to-layer="221" to-port="1" /> + <edge from-layer="221" from-port="2" to-layer="223" to-port="0" /> + <edge from-layer="222" from-port="0" to-layer="223" to-port="1" /> + <edge from-layer="223" from-port="2" to-layer="224" to-port="0" /> + <edge from-layer="224" from-port="1" to-layer="226" to-port="0" /> + <edge from-layer="225" from-port="0" to-layer="226" to-port="1" /> + <edge from-layer="226" from-port="2" to-layer="228" to-port="0" /> + <edge from-layer="227" from-port="0" to-layer="228" to-port="1" /> + <edge from-layer="228" from-port="2" to-layer="243" to-port="0" /> + <edge from-layer="229" from-port="0" to-layer="230" to-port="1" /> + <edge from-layer="230" from-port="2" to-layer="232" to-port="0" /> + <edge from-layer="231" from-port="0" to-layer="232" to-port="1" /> + <edge from-layer="232" from-port="2" to-layer="233" to-port="0" /> + <edge from-layer="233" from-port="1" to-layer="235" to-port="0" /> + <edge from-layer="234" from-port="0" to-layer="235" to-port="1" /> + <edge from-layer="235" from-port="2" to-layer="237" to-port="0" /> + <edge from-layer="236" from-port="0" to-layer="237" to-port="1" /> + <edge from-layer="237" from-port="2" to-layer="238" to-port="0" /> + <edge from-layer="238" from-port="1" to-layer="240" to-port="0" /> + <edge from-layer="239" from-port="0" to-layer="240" to-port="1" /> + <edge from-layer="240" from-port="2" to-layer="242" to-port="0" /> + <edge from-layer="241" from-port="0" to-layer="242" to-port="1" /> + <edge from-layer="242" from-port="2" to-layer="243" to-port="1" /> + <edge from-layer="243" from-port="2" to-layer="245" to-port="0" /> + <edge from-layer="244" from-port="0" to-layer="245" to-port="1" /> + <edge from-layer="245" from-port="2" to-layer="366" to-port="0" /> + <edge from-layer="246" from-port="0" to-layer="247" to-port="1" /> + <edge from-layer="247" from-port="2" to-layer="249" to-port="0" /> + <edge from-layer="248" from-port="0" to-layer="249" to-port="1" /> + <edge from-layer="249" from-port="2" to-layer="250" to-port="0" /> + <edge from-layer="250" from-port="1" to-layer="251" to-port="0" /> + <edge from-layer="251" from-port="2" to-layer="253" to-port="0" /> + <edge from-layer="252" from-port="0" to-layer="253" to-port="1" /> + <edge from-layer="253" from-port="2" to-layer="255" to-port="0" /> + <edge from-layer="254" from-port="0" to-layer="255" to-port="1" /> + <edge from-layer="255" from-port="2" to-layer="256" to-port="0" /> + <edge from-layer="256" from-port="1" to-layer="258" to-port="0" /> + <edge from-layer="257" from-port="0" to-layer="258" to-port="1" /> + <edge from-layer="258" from-port="3" to-layer="269" to-port="0" /> + <edge from-layer="258" from-port="4" to-layer="269" to-port="1" /> + <edge from-layer="258" from-port="4" to-layer="260" to-port="0" /> + <edge from-layer="259" from-port="0" to-layer="260" to-port="1" /> + <edge from-layer="260" from-port="2" to-layer="262" to-port="0" /> + <edge from-layer="261" from-port="0" to-layer="262" to-port="1" /> + <edge from-layer="262" from-port="2" to-layer="263" to-port="0" /> + <edge from-layer="263" from-port="1" to-layer="265" to-port="0" /> + <edge from-layer="264" from-port="0" to-layer="265" to-port="1" /> + <edge from-layer="265" from-port="2" to-layer="267" to-port="0" /> + <edge from-layer="266" from-port="0" to-layer="267" to-port="1" /> + <edge from-layer="267" from-port="2" to-layer="268" to-port="0" /> + <edge from-layer="268" from-port="1" to-layer="269" to-port="2" /> + <edge from-layer="269" from-port="3" to-layer="271" to-port="0" /> + <edge from-layer="270" from-port="0" to-layer="271" to-port="1" /> + <edge from-layer="271" from-port="2" to-layer="273" to-port="0" /> + <edge from-layer="272" from-port="0" to-layer="273" to-port="1" /> + <edge from-layer="273" from-port="2" to-layer="274" to-port="0" /> + <edge from-layer="274" from-port="1" to-layer="307" to-port="0" /> + <edge from-layer="274" from-port="1" to-layer="290" to-port="0" /> + <edge from-layer="274" from-port="1" to-layer="276" to-port="0" /> + <edge from-layer="275" from-port="0" to-layer="276" to-port="1" /> + <edge from-layer="276" from-port="2" to-layer="278" to-port="0" /> + <edge from-layer="277" from-port="0" to-layer="278" to-port="1" /> + <edge from-layer="278" from-port="2" to-layer="279" to-port="0" /> + <edge from-layer="279" from-port="1" to-layer="281" to-port="0" /> + <edge from-layer="280" from-port="0" to-layer="281" to-port="1" /> + <edge from-layer="281" from-port="2" to-layer="283" to-port="0" /> + <edge from-layer="282" from-port="0" to-layer="283" to-port="1" /> + <edge from-layer="283" from-port="2" to-layer="284" to-port="0" /> + <edge from-layer="284" from-port="1" to-layer="286" to-port="0" /> + <edge from-layer="285" from-port="0" to-layer="286" to-port="1" /> + <edge from-layer="286" from-port="2" to-layer="288" to-port="0" /> + <edge from-layer="287" from-port="0" to-layer="288" to-port="1" /> + <edge from-layer="288" from-port="2" to-layer="303" to-port="0" /> + <edge from-layer="289" from-port="0" to-layer="290" to-port="1" /> + <edge from-layer="290" from-port="2" to-layer="292" to-port="0" /> + <edge from-layer="291" from-port="0" to-layer="292" to-port="1" /> + <edge from-layer="292" from-port="2" to-layer="293" to-port="0" /> + <edge from-layer="293" from-port="1" to-layer="295" to-port="0" /> + <edge from-layer="294" from-port="0" to-layer="295" to-port="1" /> + <edge from-layer="295" from-port="2" to-layer="297" to-port="0" /> + <edge from-layer="296" from-port="0" to-layer="297" to-port="1" /> + <edge from-layer="297" from-port="2" to-layer="298" to-port="0" /> + <edge from-layer="298" from-port="1" to-layer="300" to-port="0" /> + <edge from-layer="299" from-port="0" to-layer="300" to-port="1" /> + <edge from-layer="300" from-port="2" to-layer="302" to-port="0" /> + <edge from-layer="301" from-port="0" to-layer="302" to-port="1" /> + <edge from-layer="302" from-port="2" to-layer="303" to-port="1" /> + <edge from-layer="303" from-port="2" to-layer="305" to-port="0" /> + <edge from-layer="304" from-port="0" to-layer="305" to-port="1" /> + <edge from-layer="305" from-port="2" to-layer="366" to-port="1" /> + <edge from-layer="306" from-port="0" to-layer="307" to-port="1" /> + <edge from-layer="307" from-port="2" to-layer="309" to-port="0" /> + <edge from-layer="308" from-port="0" to-layer="309" to-port="1" /> + <edge from-layer="309" from-port="2" to-layer="310" to-port="0" /> + <edge from-layer="310" from-port="1" to-layer="311" to-port="0" /> + <edge from-layer="311" from-port="2" to-layer="313" to-port="0" /> + <edge from-layer="312" from-port="0" to-layer="313" to-port="1" /> + <edge from-layer="313" from-port="2" to-layer="315" to-port="0" /> + <edge from-layer="314" from-port="0" to-layer="315" to-port="1" /> + <edge from-layer="315" from-port="2" to-layer="316" to-port="0" /> + <edge from-layer="316" from-port="1" to-layer="318" to-port="0" /> + <edge from-layer="317" from-port="0" to-layer="318" to-port="1" /> + <edge from-layer="318" from-port="4" to-layer="320" to-port="0" /> + <edge from-layer="318" from-port="4" to-layer="329" to-port="1" /> + <edge from-layer="318" from-port="3" to-layer="329" to-port="0" /> + <edge from-layer="319" from-port="0" to-layer="320" to-port="1" /> + <edge from-layer="320" from-port="2" to-layer="322" to-port="0" /> + <edge from-layer="321" from-port="0" to-layer="322" to-port="1" /> + <edge from-layer="322" from-port="2" to-layer="323" to-port="0" /> + <edge from-layer="323" from-port="1" to-layer="325" to-port="0" /> + <edge from-layer="324" from-port="0" to-layer="325" to-port="1" /> + <edge from-layer="325" from-port="2" to-layer="327" to-port="0" /> + <edge from-layer="326" from-port="0" to-layer="327" to-port="1" /> + <edge from-layer="327" from-port="2" to-layer="328" to-port="0" /> + <edge from-layer="328" from-port="1" to-layer="329" to-port="2" /> + <edge from-layer="329" from-port="3" to-layer="331" to-port="0" /> + <edge from-layer="330" from-port="0" to-layer="331" to-port="1" /> + <edge from-layer="331" from-port="2" to-layer="333" to-port="0" /> + <edge from-layer="332" from-port="0" to-layer="333" to-port="1" /> + <edge from-layer="333" from-port="2" to-layer="334" to-port="0" /> + <edge from-layer="334" from-port="1" to-layer="336" to-port="0" /> + <edge from-layer="334" from-port="1" to-layer="350" to-port="0" /> + <edge from-layer="335" from-port="0" to-layer="336" to-port="1" /> + <edge from-layer="336" from-port="2" to-layer="338" to-port="0" /> + <edge from-layer="337" from-port="0" to-layer="338" to-port="1" /> + <edge from-layer="338" from-port="2" to-layer="339" to-port="0" /> + <edge from-layer="339" from-port="1" to-layer="341" to-port="0" /> + <edge from-layer="340" from-port="0" to-layer="341" to-port="1" /> + <edge from-layer="341" from-port="2" to-layer="343" to-port="0" /> + <edge from-layer="342" from-port="0" to-layer="343" to-port="1" /> + <edge from-layer="343" from-port="2" to-layer="344" to-port="0" /> + <edge from-layer="344" from-port="1" to-layer="346" to-port="0" /> + <edge from-layer="345" from-port="0" to-layer="346" to-port="1" /> + <edge from-layer="346" from-port="2" to-layer="348" to-port="0" /> + <edge from-layer="347" from-port="0" to-layer="348" to-port="1" /> + <edge from-layer="348" from-port="2" to-layer="363" to-port="0" /> + <edge from-layer="349" from-port="0" to-layer="350" to-port="1" /> + <edge from-layer="350" from-port="2" to-layer="352" to-port="0" /> + <edge from-layer="351" from-port="0" to-layer="352" to-port="1" /> + <edge from-layer="352" from-port="2" to-layer="353" to-port="0" /> + <edge from-layer="353" from-port="1" to-layer="355" to-port="0" /> + <edge from-layer="354" from-port="0" to-layer="355" to-port="1" /> + <edge from-layer="355" from-port="2" to-layer="357" to-port="0" /> + <edge from-layer="356" from-port="0" to-layer="357" to-port="1" /> + <edge from-layer="357" from-port="2" to-layer="358" to-port="0" /> + <edge from-layer="358" from-port="1" to-layer="360" to-port="0" /> + <edge from-layer="359" from-port="0" to-layer="360" to-port="1" /> + <edge from-layer="360" from-port="2" to-layer="362" to-port="0" /> + <edge from-layer="361" from-port="0" to-layer="362" to-port="1" /> + <edge from-layer="362" from-port="2" to-layer="363" to-port="1" /> + <edge from-layer="363" from-port="2" to-layer="365" to-port="0" /> + <edge from-layer="364" from-port="0" to-layer="365" to-port="1" /> + <edge from-layer="365" from-port="2" to-layer="366" to-port="2" /> + <edge from-layer="366" from-port="3" to-layer="369" to-port="0" /> + <edge from-layer="367" from-port="0" to-layer="369" to-port="1" /> + <edge from-layer="368" from-port="0" to-layer="369" to-port="2" /> + <edge from-layer="369" from-port="4" to-layer="414" to-port="0" /> + <edge from-layer="369" from-port="3" to-layer="371" to-port="0" /> + <edge from-layer="370" from-port="0" to-layer="371" to-port="1" /> + <edge from-layer="371" from-port="2" to-layer="373" to-port="0" /> + <edge from-layer="372" from-port="0" to-layer="373" to-port="1" /> + <edge from-layer="373" from-port="2" to-layer="374" to-port="0" /> + <edge from-layer="374" from-port="1" to-layer="376" to-port="0" /> + <edge from-layer="375" from-port="0" to-layer="376" to-port="1" /> + <edge from-layer="376" from-port="2" to-layer="378" to-port="0" /> + <edge from-layer="377" from-port="0" to-layer="378" to-port="1" /> + <edge from-layer="378" from-port="2" to-layer="405" to-port="0" /> + <edge from-layer="378" from-port="2" to-layer="393" to-port="0" /> + <edge from-layer="378" from-port="2" to-layer="382" to-port="0" /> + <edge from-layer="379" from-port="0" to-layer="393" to-port="1" /> + <edge from-layer="380" from-port="0" to-layer="391" to-port="0" /> + <edge from-layer="381" from-port="0" to-layer="391" to-port="1" /> + <edge from-layer="382" from-port="1" to-layer="385" to-port="0" /> + <edge from-layer="383" from-port="0" to-layer="385" to-port="1" /> + <edge from-layer="384" from-port="0" to-layer="385" to-port="2" /> + <edge from-layer="385" from-port="3" to-layer="387" to-port="0" /> + <edge from-layer="386" from-port="0" to-layer="387" to-port="1" /> + <edge from-layer="387" from-port="2" to-layer="389" to-port="0" /> + <edge from-layer="388" from-port="0" to-layer="389" to-port="1" /> + <edge from-layer="389" from-port="2" to-layer="391" to-port="2" /> + <edge from-layer="389" from-port="2" to-layer="399" to-port="2" /> + <edge from-layer="389" from-port="2" to-layer="402" to-port="0" /> + <edge from-layer="390" from-port="0" to-layer="391" to-port="3" /> + <edge from-layer="391" from-port="4" to-layer="393" to-port="2" /> + <edge from-layer="392" from-port="0" to-layer="393" to-port="3" /> + <edge from-layer="393" from-port="4" to-layer="394" to-port="1" /> + <edge from-layer="394" from-port="2" to-layer="410" to-port="1" /> + <edge from-layer="394" from-port="2" to-layer="407" to-port="0" /> + <edge from-layer="395" from-port="0" to-layer="406" to-port="0" /> + <edge from-layer="396" from-port="0" to-layer="399" to-port="0" /> + <edge from-layer="397" from-port="0" to-layer="399" to-port="1" /> + <edge from-layer="397" from-port="0" to-layer="403" to-port="1" /> + <edge from-layer="398" from-port="0" to-layer="399" to-port="3" /> + <edge from-layer="398" from-port="0" to-layer="403" to-port="3" /> + <edge from-layer="399" from-port="4" to-layer="405" to-port="1" /> + <edge from-layer="400" from-port="0" to-layer="403" to-port="0" /> + <edge from-layer="401" from-port="0" to-layer="402" to-port="1" /> + <edge from-layer="402" from-port="2" to-layer="403" to-port="2" /> + <edge from-layer="403" from-port="4" to-layer="405" to-port="2" /> + <edge from-layer="404" from-port="0" to-layer="405" to-port="3" /> + <edge from-layer="405" from-port="4" to-layer="406" to-port="1" /> + <edge from-layer="406" from-port="2" to-layer="410" to-port="0" /> + <edge from-layer="406" from-port="2" to-layer="407" to-port="1" /> + <edge from-layer="407" from-port="2" to-layer="409" to-port="0" /> + <edge from-layer="408" from-port="0" to-layer="409" to-port="1" /> + <edge from-layer="409" from-port="2" to-layer="411" to-port="0" /> + <edge from-layer="410" from-port="2" to-layer="411" to-port="1" /> + <edge from-layer="411" from-port="2" to-layer="413" to-port="0" /> + <edge from-layer="412" from-port="0" to-layer="413" to-port="1" /> + <edge from-layer="413" from-port="2" to-layer="415" to-port="0" /> + <edge from-layer="414" from-port="1" to-layer="415" to-port="1" /> + <edge from-layer="415" from-port="2" to-layer="416" to-port="0" /> + </edges> + <rt_info> + <MO_version value="2023.0.1-11005-fa1c41994f3-releases/2023/0" /> + <Runtime_version value="2023.0.1-11005-fa1c41994f3-releases/2023/0" /> + <conversion_parameters> + <framework value="onnx" /> + <input_model value="DIR/best.onnx" /> + <is_python_api_used value="True" /> + <model_name value="best" /> + </conversion_parameters> + <framework> + <author value="Ultralytics" /> + <batch value="1" /> + <date value="2023-09-01T09:31:19.467029" /> + <description value="Ultralytics best model trained on mqt_v3_42_2.yaml" /> + <imgsz value="[768, 768]" /> + <license value="AGPL-3.0 https://ultralytics.com/license" /> + <names value="{0: 'mosquito'}" /> + <stride value="32" /> + <task value="detect" /> + <version value="8.0.165" /> + </framework> + <legacy_frontend value="False" /> + <model_info> + <iou_threshold value="0.7" /> + <labels value="mosquito" /> + <model_type value="YOLOv8" /> + <pad_value value="114" /> + <resize_type value="fit_to_window_letterbox" /> + <reverse_input_channels value="YES" /> + <scale_values value="255" /> + </model_info> + </rt_info> +</net> diff --git a/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold2_1.4/best_openvino_model/metadata.yaml b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold2_1.4/best_openvino_model/metadata.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ea09221b89b483eeb7485ad0e8a391a805cc5cff --- /dev/null +++ b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold2_1.4/best_openvino_model/metadata.yaml @@ -0,0 +1,13 @@ +description: Ultralytics best model trained on mqt_v3_42_2.yaml +author: Ultralytics +license: AGPL-3.0 https://ultralytics.com/license +date: '2023-09-01T09:31:19.467029' +version: 8.0.165 +stride: 32 +task: detect +batch: 1 +imgsz: +- 768 +- 768 +names: + 0: mosquito diff --git a/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold3_1.4/best.pt b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold3_1.4/best.pt new file mode 100644 index 0000000000000000000000000000000000000000..a15727583f6e8522370a4bcff23d6dac97e63ece --- /dev/null +++ b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold3_1.4/best.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e8ec9e91ff0518976d11e4db76fec7f1e4ad7d9d0b0e12f22d8d77105b22164d +size 6223534 diff --git a/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold3_1.4/best_openvino_model/best.bin b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold3_1.4/best_openvino_model/best.bin new file mode 100644 index 0000000000000000000000000000000000000000..ee57eaaf67f98835ebe91cd57217d35a81b6a827 --- /dev/null +++ b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold3_1.4/best_openvino_model/best.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0b6babbf7c8f1e944c5138bc4f596f49c64d072e1559661f781d460c1a526959 +size 12168796 diff --git a/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold3_1.4/best_openvino_model/best.xml b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold3_1.4/best_openvino_model/best.xml new file mode 100644 index 0000000000000000000000000000000000000000..b1a9062d0cc1fe7a74720b23c53586708c1db111 --- /dev/null +++ b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold3_1.4/best_openvino_model/best.xml @@ -0,0 +1,7987 @@ +<?xml version="1.0"?> +<net name="torch_jit" version="11"> + <layers> + <layer id="0" name="images" type="Parameter" version="opset1"> + <data shape="1,3,768,768" element_type="f32" /> + <output> + <port id="0" precision="FP32" names="images"> + <dim>1</dim> + <dim>3</dim> + <dim>768</dim> + <dim>768</dim> + </port> + </output> + </layer> + <layer id="1" name="/model.22/Constant_9" type="Const" version="opset1"> + <data element_type="f32" shape="1, 2, 12096" offset="0" size="96768" /> + <output> + <port id="0" precision="FP32" names="/model.22/Constant_9_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="2" name="model.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="16, 3, 3, 3" offset="96768" size="1728" /> + <output> + <port id="0" precision="FP32" names="model.0.conv.weight"> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="3" name="/model.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>3</dim> + <dim>768</dim> + <dim>768</dim> + </port> + <port id="1" precision="FP32"> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + </output> + </layer> + <layer id="4" name="Reshape_42720" type="Const" version="opset1"> + <data element_type="f32" shape="1, 16, 1, 1" offset="98496" size="64" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="5" name="/model.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + </output> + </layer> + <layer id="6" name="/model.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.0/act/Mul_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + </output> + </layer> + <layer id="7" name="model.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 16, 3, 3" offset="98560" size="18432" /> + <output> + <port id="0" precision="FP32" names="model.1.conv.weight"> + <dim>32</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="8" name="/model.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>384</dim> + <dim>384</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="9" name="Reshape_42737" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="116992" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="10" name="/model.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="11" name="/model.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.1/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="12" name="model.2.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 1, 1" offset="117120" size="4096" /> + <output> + <port id="0" precision="FP32" names="model.2.cv1.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="13" name="/model.2/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="14" name="Reshape_42754" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="121216" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="15" name="/model.2/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.2/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="16" name="/model.2/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.2/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="17" name="Constant_42761" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="18" name="Constant_9" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="121352" size="16" /> + <output> + <port id="0" precision="I64" names="onnx::Split_137"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="19" name="/model.2/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.2/Split_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="4" precision="FP32" names="/model.2/Split_output_1"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="20" name="model.2.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="16, 16, 3, 3" offset="121368" size="9216" /> + <output> + <port id="0" precision="FP32" names="model.2.m.0.cv1.conv.weight"> + <dim>16</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="21" name="/model.2/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>16</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="22" name="Reshape_42774" type="Const" version="opset1"> + <data element_type="f32" shape="1, 16, 1, 1" offset="130584" size="64" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="23" name="/model.2/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.2/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="24" name="/model.2/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.2/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="25" name="model.2.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="16, 16, 3, 3" offset="130648" size="9216" /> + <output> + <port id="0" precision="FP32" names="model.2.m.0.cv2.conv.weight"> + <dim>16</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="26" name="/model.2/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>16</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="27" name="Reshape_42791" type="Const" version="opset1"> + <data element_type="f32" shape="1, 16, 1, 1" offset="139864" size="64" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="28" name="/model.2/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.2/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="29" name="/model.2/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.2/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="30" name="/model.2/m.0/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.2/m.0/Add_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="31" name="/model.2/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.2/Concat_output_0"> + <dim>1</dim> + <dim>48</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="32" name="model.2.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 48, 1, 1" offset="139928" size="6144" /> + <output> + <port id="0" precision="FP32" names="model.2.cv2.conv.weight"> + <dim>32</dim> + <dim>48</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="33" name="/model.2/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>48</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>48</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="34" name="Reshape_42810" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="146072" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="35" name="/model.2/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.2/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="36" name="/model.2/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.2/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + </output> + </layer> + <layer id="37" name="model.3.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 32, 3, 3" offset="146200" size="73728" /> + <output> + <port id="0" precision="FP32" names="model.3.conv.weight"> + <dim>64</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="38" name="/model.3/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>192</dim> + <dim>192</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="39" name="Reshape_42827" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="219928" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="40" name="/model.3/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.3/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="41" name="/model.3/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.3/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="42" name="model.4.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 1, 1" offset="220184" size="16384" /> + <output> + <port id="0" precision="FP32" names="model.4.cv1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="43" name="/model.4/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="44" name="Reshape_42844" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="236568" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="45" name="/model.4/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="46" name="/model.4/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="47" name="Constant_42851" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="48" name="Constant_28" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="236824" size="16" /> + <output> + <port id="0" precision="I64" names="onnx::Split_157"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="49" name="/model.4/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.4/Split_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="4" precision="FP32" names="/model.4/Split_output_1"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="50" name="model.4.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="236840" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.4.m.0.cv1.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="51" name="/model.4/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="52" name="Reshape_42864" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="273704" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="53" name="/model.4/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="54" name="/model.4/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="55" name="model.4.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="273832" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.4.m.0.cv2.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="56" name="/model.4/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="57" name="Reshape_42881" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="310696" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="58" name="/model.4/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="59" name="/model.4/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="60" name="/model.4/m.0/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.0/Add_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="61" name="model.4.m.1.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="310824" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.4.m.1.cv1.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="62" name="/model.4/m.1/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="63" name="Reshape_42899" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="347688" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="64" name="/model.4/m.1/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.1/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="65" name="/model.4/m.1/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/m.1/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="66" name="model.4.m.1.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="347816" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.4.m.1.cv2.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="67" name="/model.4/m.1/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="68" name="Reshape_42916" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="384680" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="69" name="/model.4/m.1/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.1/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="70" name="/model.4/m.1/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/m.1/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="71" name="/model.4/m.1/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/m.1/Add_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="72" name="/model.4/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="3" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="4" precision="FP32" names="/model.4/Concat_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="73" name="model.4.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 128, 1, 1" offset="384808" size="32768" /> + <output> + <port id="0" precision="FP32" names="model.4.cv2.conv.weight"> + <dim>64</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="74" name="/model.4/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="75" name="Reshape_42935" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="417576" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="76" name="/model.4/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.4/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="77" name="/model.4/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.4/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="78" name="model.5.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 64, 3, 3" offset="417832" size="294912" /> + <output> + <port id="0" precision="FP32" names="model.5.conv.weight"> + <dim>128</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="79" name="/model.5/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="80" name="Reshape_42952" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="712744" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="81" name="/model.5/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.5/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="82" name="/model.5/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.5/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="83" name="model.6.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 1, 1" offset="713256" size="65536" /> + <output> + <port id="0" precision="FP32" names="model.6.cv1.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="84" name="/model.6/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="85" name="Reshape_42969" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="778792" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="86" name="/model.6/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="87" name="/model.6/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="88" name="Constant_42976" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="89" name="Constant_54" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="779304" size="16" /> + <output> + <port id="0" precision="I64" names="onnx::Split_184"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="90" name="/model.6/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.6/Split_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="4" precision="FP32" names="/model.6/Split_output_1"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="91" name="model.6.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="779320" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.6.m.0.cv1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="92" name="/model.6/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="93" name="Reshape_42989" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="926776" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="94" name="/model.6/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="95" name="/model.6/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="96" name="model.6.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="927032" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.6.m.0.cv2.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="97" name="/model.6/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="98" name="Reshape_43006" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="1074488" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="99" name="/model.6/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="100" name="/model.6/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="101" name="/model.6/m.0/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.0/Add_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="102" name="model.6.m.1.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="1074744" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.6.m.1.cv1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="103" name="/model.6/m.1/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="104" name="Reshape_43024" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="1222200" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="105" name="/model.6/m.1/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.1/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="106" name="/model.6/m.1/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/m.1/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="107" name="model.6.m.1.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="1222456" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.6.m.1.cv2.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="108" name="/model.6/m.1/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="109" name="Reshape_43041" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="1369912" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="110" name="/model.6/m.1/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.1/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="111" name="/model.6/m.1/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/m.1/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="112" name="/model.6/m.1/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/m.1/Add_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="113" name="/model.6/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="3" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="4" precision="FP32" names="/model.6/Concat_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="114" name="model.6.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 256, 1, 1" offset="1370168" size="131072" /> + <output> + <port id="0" precision="FP32" names="model.6.cv2.conv.weight"> + <dim>128</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="115" name="/model.6/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="116" name="Reshape_43060" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="1501240" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="117" name="/model.6/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.6/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="118" name="/model.6/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.6/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="119" name="model.7.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 128, 3, 3" offset="1501752" size="1179648" /> + <output> + <port id="0" precision="FP32" names="model.7.conv.weight"> + <dim>256</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="120" name="/model.7/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="121" name="Reshape_43077" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="2681400" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="122" name="/model.7/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.7/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="123" name="/model.7/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.7/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="124" name="model.8.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 256, 1, 1" offset="2682424" size="262144" /> + <output> + <port id="0" precision="FP32" names="model.8.cv1.conv.weight"> + <dim>256</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="125" name="/model.8/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="126" name="Reshape_43094" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="2944568" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="127" name="/model.8/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.8/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="128" name="/model.8/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.8/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="129" name="Constant_43101" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="130" name="Constant_80" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="2945592" size="16" /> + <output> + <port id="0" precision="I64" names="onnx::Split_211"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="131" name="/model.8/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.8/Split_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="4" precision="FP32" names="/model.8/Split_output_1"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="132" name="model.8.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 3, 3" offset="2945608" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.8.m.0.cv1.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="133" name="/model.8/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="134" name="Reshape_43114" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="3535432" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="135" name="/model.8/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.8/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="136" name="/model.8/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.8/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="137" name="model.8.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 3, 3" offset="3535944" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.8.m.0.cv2.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="138" name="/model.8/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="139" name="Reshape_43131" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="4125768" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="140" name="/model.8/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.8/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="141" name="/model.8/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.8/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="142" name="/model.8/m.0/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.8/m.0/Add_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="143" name="/model.8/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.8/Concat_output_0"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="144" name="model.8.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 384, 1, 1" offset="4126280" size="393216" /> + <output> + <port id="0" precision="FP32" names="model.8.cv2.conv.weight"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="145" name="/model.8/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="146" name="Reshape_43150" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="4519496" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="147" name="/model.8/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.8/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="148" name="/model.8/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.8/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="149" name="model.9.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 256, 1, 1" offset="4520520" size="131072" /> + <output> + <port id="0" precision="FP32" names="model.9.cv1.conv.weight"> + <dim>128</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="150" name="/model.9/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="151" name="Reshape_43167" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="4651592" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="152" name="/model.9/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.9/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="153" name="/model.9/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.9/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="154" name="/model.9/m/MaxPool" type="MaxPool" version="opset8"> + <data strides="1, 1" dilations="1, 1" pads_begin="2, 2" pads_end="2, 2" kernel="5, 5" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.9/m/MaxPool_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="155" name="/model.9/m_1/MaxPool" type="MaxPool" version="opset8"> + <data strides="1, 1" dilations="1, 1" pads_begin="2, 2" pads_end="2, 2" kernel="5, 5" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.9/m_1/MaxPool_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="156" name="/model.9/m_2/MaxPool" type="MaxPool" version="opset8"> + <data strides="1, 1" dilations="1, 1" pads_begin="2, 2" pads_end="2, 2" kernel="5, 5" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.9/m_2/MaxPool_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="157" name="/model.9/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="3" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="4" precision="FP32" names="/model.9/Concat_output_0"> + <dim>1</dim> + <dim>512</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="158" name="model.9.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 512, 1, 1" offset="4652104" size="524288" /> + <output> + <port id="0" precision="FP32" names="model.9.cv2.conv.weight"> + <dim>256</dim> + <dim>512</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="159" name="/model.9/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>512</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>512</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="160" name="Reshape_43188" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="5176392" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="161" name="/model.9/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.9/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="162" name="/model.9/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.9/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="163" name="/model.10/Constant" type="Const" version="opset1"> + <data element_type="f32" shape="4" offset="5177416" size="16" /> + <output> + <port id="0" precision="FP32" names="/model.10/Constant_output_0"> + <dim>4</dim> + </port> + </output> + </layer> + <layer id="164" name="/model.10/Resize" type="Interpolate" version="opset11"> + <data mode="nearest" shape_calculation_mode="scales" coordinate_transformation_mode="asymmetric" nearest_mode="floor" antialias="false" pads_begin="0, 0, 0, 0" pads_end="0, 0, 0, 0" cube_coeff="-0.75" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>4</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.10/Resize_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="165" name="/model.11/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.11/Concat_output_0"> + <dim>1</dim> + <dim>384</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="166" name="model.12.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 384, 1, 1" offset="5177432" size="196608" /> + <output> + <port id="0" precision="FP32" names="model.12.cv1.conv.weight"> + <dim>128</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="167" name="/model.12/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>384</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="168" name="Reshape_43209" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="5374040" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="169" name="/model.12/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.12/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="170" name="/model.12/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.12/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="171" name="Constant_43215" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="172" name="/model.12/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.12/Split_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="4" precision="FP32" names="/model.12/Split_output_1"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="173" name="model.12.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="5374552" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.12.m.0.cv1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="174" name="/model.12/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="175" name="Reshape_43228" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="5522008" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="176" name="/model.12/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.12/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="177" name="/model.12/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.12/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="178" name="model.12.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="5522264" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.12.m.0.cv2.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="179" name="/model.12/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="180" name="Reshape_43245" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="5669720" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="181" name="/model.12/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.12/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="182" name="/model.12/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.12/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="183" name="/model.12/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.12/Concat_output_0"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="184" name="model.12.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 192, 1, 1" offset="5669976" size="98304" /> + <output> + <port id="0" precision="FP32" names="model.12.cv2.conv.weight"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="185" name="/model.12/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="186" name="Reshape_43263" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="5768280" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="187" name="/model.12/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.12/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="188" name="/model.12/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.12/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="189" name="/model.13/Constant" type="Const" version="opset1"> + <data element_type="f32" shape="4" offset="5177416" size="16" /> + <output> + <port id="0" precision="FP32" names="/model.13/Constant_output_0"> + <dim>4</dim> + </port> + </output> + </layer> + <layer id="190" name="/model.13/Resize" type="Interpolate" version="opset11"> + <data mode="nearest" shape_calculation_mode="scales" coordinate_transformation_mode="asymmetric" nearest_mode="floor" antialias="false" pads_begin="0, 0, 0, 0" pads_end="0, 0, 0, 0" cube_coeff="-0.75" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>4</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.13/Resize_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="191" name="/model.14/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.14/Concat_output_0"> + <dim>1</dim> + <dim>192</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="192" name="model.15.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 192, 1, 1" offset="5768792" size="49152" /> + <output> + <port id="0" precision="FP32" names="model.15.cv1.conv.weight"> + <dim>64</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="193" name="/model.15/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>192</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="194" name="Reshape_43284" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="5817944" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="195" name="/model.15/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.15/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="196" name="/model.15/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.15/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="197" name="Constant_43290" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="198" name="/model.15/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.15/Split_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="4" precision="FP32" names="/model.15/Split_output_1"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="199" name="model.15.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="5818200" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.15.m.0.cv1.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="200" name="/model.15/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="201" name="Reshape_43303" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="5855064" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="202" name="/model.15/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.15/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="203" name="/model.15/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.15/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="204" name="model.15.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="5855192" size="36864" /> + <output> + <port id="0" precision="FP32" names="model.15.m.0.cv2.conv.weight"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="205" name="/model.15/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="206" name="Reshape_43320" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="5892056" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="207" name="/model.15/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.15/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="208" name="/model.15/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.15/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="209" name="/model.15/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.15/Concat_output_0"> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="210" name="model.15.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 96, 1, 1" offset="5892184" size="24576" /> + <output> + <port id="0" precision="FP32" names="model.15.cv2.conv.weight"> + <dim>64</dim> + <dim>96</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="211" name="/model.15/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>96</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="212" name="Reshape_43338" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="5916760" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="213" name="/model.15/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.15/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="214" name="/model.15/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.15/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="215" name="model.22.cv2.0.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="5917016" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.0.0.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="216" name="/model.22/cv2.0/cv2.0.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="217" name="Reshape_43533" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6064472" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="218" name="/model.22/cv2.0/cv2.0.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.0/cv2.0.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="219" name="/model.22/cv2.0/cv2.0.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.0/cv2.0.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="220" name="model.22.cv2.0.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6064728" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.0.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="221" name="/model.22/cv2.0/cv2.0.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="222" name="Reshape_43550" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6212184" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="223" name="/model.22/cv2.0/cv2.0.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.0/cv2.0.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="224" name="/model.22/cv2.0/cv2.0.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.0/cv2.0.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="225" name="model.22.cv2.0.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 1, 1" offset="6212440" size="16384" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.0.2.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="226" name="/model.22/cv2.0/cv2.0.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="227" name="Reshape_43567" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6228824" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="228" name="/model.22/cv2.0/cv2.0.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.0/cv2.0.2/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="229" name="model.22.cv3.0.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6229080" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.0.0.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="230" name="/model.22/cv3.0/cv3.0.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="231" name="Reshape_43582" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6376536" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="232" name="/model.22/cv3.0/cv3.0.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.0/cv3.0.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="233" name="/model.22/cv3.0/cv3.0.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.0/cv3.0.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="234" name="model.22.cv3.0.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6376792" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.0.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="235" name="/model.22/cv3.0/cv3.0.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="236" name="Reshape_43599" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6524248" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="237" name="/model.22/cv3.0/cv3.0.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.0/cv3.0.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="238" name="/model.22/cv3.0/cv3.0.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.0/cv3.0.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="239" name="model.22.cv3.0.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6524504" size="256" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.0.2.weight"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="240" name="/model.22/cv3.0/cv3.0.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="241" name="Reshape_43616" type="Const" version="opset1"> + <data element_type="f32" shape="1, 1, 1, 1" offset="6524760" size="4" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="242" name="/model.22/cv3.0/cv3.0.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.0/cv3.0.2/Conv_output_0"> + <dim>1</dim> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="243" name="/model.22/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Concat_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>96</dim> + <dim>96</dim> + </port> + </output> + </layer> + <layer id="244" name="/model.22/Constant" type="Const" version="opset1"> + <data element_type="i64" shape="3" offset="6524764" size="24" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_output_0"> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="245" name="/model.22/Reshape" type="Reshape" version="opset1"> + <data special_zero="true" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="I64"> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Reshape_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>9216</dim> + </port> + </output> + </layer> + <layer id="246" name="model.16.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6524788" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.16.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="247" name="/model.16/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>96</dim> + <dim>96</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="248" name="Reshape_43355" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6672244" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="249" name="/model.16/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.16/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="250" name="/model.16/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.16/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="251" name="/model.17/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.17/Concat_output_0"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="252" name="model.18.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 192, 1, 1" offset="6672500" size="98304" /> + <output> + <port id="0" precision="FP32" names="model.18.cv1.conv.weight"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="253" name="/model.18/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="254" name="Reshape_43373" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="6770804" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="255" name="/model.18/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.18/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="256" name="/model.18/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.18/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="257" name="Constant_43379" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="258" name="/model.18/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.18/Split_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="4" precision="FP32" names="/model.18/Split_output_1"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="259" name="model.18.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6771316" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.18.m.0.cv1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="260" name="/model.18/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="261" name="Reshape_43392" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="6918772" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="262" name="/model.18/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.18/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="263" name="/model.18/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.18/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="264" name="model.18.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="6919028" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.18.m.0.cv2.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="265" name="/model.18/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="266" name="Reshape_43409" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="7066484" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="267" name="/model.18/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.18/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="268" name="/model.18/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.18/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="269" name="/model.18/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.18/Concat_output_0"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="270" name="model.18.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 192, 1, 1" offset="7066740" size="98304" /> + <output> + <port id="0" precision="FP32" names="model.18.cv2.conv.weight"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="271" name="/model.18/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>192</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>192</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="272" name="Reshape_43427" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="7165044" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="273" name="/model.18/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.18/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="274" name="/model.18/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.18/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="275" name="model.22.cv2.1.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 128, 3, 3" offset="7165556" size="294912" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.1.0.conv.weight"> + <dim>64</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="276" name="/model.22/cv2.1/cv2.1.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="277" name="Reshape_43632" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="7460468" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="278" name="/model.22/cv2.1/cv2.1.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.1/cv2.1.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="279" name="/model.22/cv2.1/cv2.1.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.1/cv2.1.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="280" name="model.22.cv2.1.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="7460724" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.1.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="281" name="/model.22/cv2.1/cv2.1.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="282" name="Reshape_43649" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="7608180" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="283" name="/model.22/cv2.1/cv2.1.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.1/cv2.1.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="284" name="/model.22/cv2.1/cv2.1.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.1/cv2.1.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="285" name="model.22.cv2.1.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 1, 1" offset="7608436" size="16384" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.1.2.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="286" name="/model.22/cv2.1/cv2.1.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="287" name="Reshape_43666" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="7624820" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="288" name="/model.22/cv2.1/cv2.1.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.1/cv2.1.2/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="289" name="model.22.cv3.1.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 128, 3, 3" offset="7625076" size="294912" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.1.0.conv.weight"> + <dim>64</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="290" name="/model.22/cv3.1/cv3.1.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="291" name="Reshape_43681" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="7919988" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="292" name="/model.22/cv3.1/cv3.1.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.1/cv3.1.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="293" name="/model.22/cv3.1/cv3.1.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.1/cv3.1.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="294" name="model.22.cv3.1.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="7920244" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.1.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="295" name="/model.22/cv3.1/cv3.1.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="296" name="Reshape_43698" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="8067700" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="297" name="/model.22/cv3.1/cv3.1.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.1/cv3.1.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="298" name="/model.22/cv3.1/cv3.1.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.1/cv3.1.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="299" name="model.22.cv3.1.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="8067956" size="256" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.1.2.weight"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="300" name="/model.22/cv3.1/cv3.1.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="301" name="Reshape_43715" type="Const" version="opset1"> + <data element_type="f32" shape="1, 1, 1, 1" offset="8068212" size="4" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="302" name="/model.22/cv3.1/cv3.1.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.1/cv3.1.2/Conv_output_0"> + <dim>1</dim> + <dim>1</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="303" name="/model.22/Concat_1" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Concat_1_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>48</dim> + <dim>48</dim> + </port> + </output> + </layer> + <layer id="304" name="/model.22/Constant_1" type="Const" version="opset1"> + <data element_type="i64" shape="3" offset="6524764" size="24" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_1_output_0"> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="305" name="/model.22/Reshape_1" type="Reshape" version="opset1"> + <data special_zero="true" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="I64"> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Reshape_1_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>2304</dim> + </port> + </output> + </layer> + <layer id="306" name="model.19.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 3, 3" offset="8068216" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.19.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="307" name="/model.19/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>48</dim> + <dim>48</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="308" name="Reshape_43444" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="8658040" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="309" name="/model.19/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.19/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="310" name="/model.19/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.19/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="311" name="/model.20/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.20/Concat_output_0"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="312" name="model.21.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 384, 1, 1" offset="8658552" size="393216" /> + <output> + <port id="0" precision="FP32" names="model.21.cv1.conv.weight"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="313" name="/model.21/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="314" name="Reshape_43462" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="9051768" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="315" name="/model.21/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.21/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="316" name="/model.21/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.21/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="317" name="Constant_43468" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="318" name="/model.21/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.21/Split_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="4" precision="FP32" names="/model.21/Split_output_1"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="319" name="model.21.m.0.cv1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 3, 3" offset="9052792" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.21.m.0.cv1.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="320" name="/model.21/m.0/cv1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="321" name="Reshape_43481" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="9642616" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="322" name="/model.21/m.0/cv1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.21/m.0/cv1/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="323" name="/model.21/m.0/cv1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.21/m.0/cv1/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="324" name="model.21.m.0.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="128, 128, 3, 3" offset="9643128" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.21.m.0.cv2.conv.weight"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="325" name="/model.21/m.0/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>128</dim> + <dim>128</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="326" name="Reshape_43498" type="Const" version="opset1"> + <data element_type="f32" shape="1, 128, 1, 1" offset="10232952" size="512" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="327" name="/model.21/m.0/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.21/m.0/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="328" name="/model.21/m.0/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.21/m.0/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="329" name="/model.21/Concat" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>128</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.21/Concat_output_0"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="330" name="model.21.cv2.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="256, 384, 1, 1" offset="10233464" size="393216" /> + <output> + <port id="0" precision="FP32" names="model.21.cv2.conv.weight"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="331" name="/model.21/cv2/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>384</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>256</dim> + <dim>384</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="332" name="Reshape_43516" type="Const" version="opset1"> + <data element_type="f32" shape="1, 256, 1, 1" offset="10626680" size="1024" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="333" name="/model.21/cv2/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.21/cv2/conv/Conv_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="334" name="/model.21/cv2/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.21/cv2/act/Mul_output_0"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="335" name="model.22.cv2.2.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 256, 3, 3" offset="10627704" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.2.0.conv.weight"> + <dim>64</dim> + <dim>256</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="336" name="/model.22/cv2.2/cv2.2.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>256</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="337" name="Reshape_43731" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="11217528" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="338" name="/model.22/cv2.2/cv2.2.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.2/cv2.2.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="339" name="/model.22/cv2.2/cv2.2.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.2/cv2.2.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="340" name="model.22.cv2.2.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="11217784" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.2.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="341" name="/model.22/cv2.2/cv2.2.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="342" name="Reshape_43748" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="11365240" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="343" name="/model.22/cv2.2/cv2.2.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.2/cv2.2.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="344" name="/model.22/cv2.2/cv2.2.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv2.2/cv2.2.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="345" name="model.22.cv2.2.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 1, 1" offset="11365496" size="16384" /> + <output> + <port id="0" precision="FP32" names="model.22.cv2.2.2.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="346" name="/model.22/cv2.2/cv2.2.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="347" name="Reshape_43765" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="11381880" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="348" name="/model.22/cv2.2/cv2.2.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv2.2/cv2.2.2/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="349" name="model.22.cv3.2.0.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 256, 3, 3" offset="11382136" size="589824" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.2.0.conv.weight"> + <dim>64</dim> + <dim>256</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="350" name="/model.22/cv3.2/cv3.2.0/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>256</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>256</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="351" name="Reshape_43780" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="11971960" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="352" name="/model.22/cv3.2/cv3.2.0/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.2/cv3.2.0/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="353" name="/model.22/cv3.2/cv3.2.0/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.2/cv3.2.0/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="354" name="model.22.cv3.2.1.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="11972216" size="147456" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.2.1.conv.weight"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="355" name="/model.22/cv3.2/cv3.2.1/conv/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="356" name="Reshape_43797" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="12119672" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="357" name="/model.22/cv3.2/cv3.2.1/conv/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.2/cv3.2.1/conv/Conv_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="358" name="/model.22/cv3.2/cv3.2.1/act/Mul" type="Swish" version="opset4"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/cv3.2/cv3.2.1/act/Mul_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="359" name="model.22.cv3.2.2.weight" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="12119928" size="256" /> + <output> + <port id="0" precision="FP32" names="model.22.cv3.2.2.weight"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="360" name="/model.22/cv3.2/cv3.2.2/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="361" name="Reshape_43814" type="Const" version="opset1"> + <data element_type="f32" shape="1, 1, 1, 1" offset="12120184" size="4" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="362" name="/model.22/cv3.2/cv3.2.2/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/cv3.2/cv3.2.2/Conv_output_0"> + <dim>1</dim> + <dim>1</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="363" name="/model.22/Concat_2" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Concat_2_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>24</dim> + <dim>24</dim> + </port> + </output> + </layer> + <layer id="364" name="/model.22/Constant_2" type="Const" version="opset1"> + <data element_type="i64" shape="3" offset="6524764" size="24" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_2_output_0"> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="365" name="/model.22/Reshape_2" type="Reshape" version="opset1"> + <data special_zero="true" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>24</dim> + <dim>24</dim> + </port> + <port id="1" precision="I64"> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Reshape_2_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>576</dim> + </port> + </output> + </layer> + <layer id="366" name="/model.22/Concat_3" type="Concat" version="opset1"> + <data axis="2" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>9216</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>2304</dim> + </port> + <port id="2" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>576</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.22/Concat_3_output_0"> + <dim>1</dim> + <dim>65</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="367" name="Constant_43833" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="368" name="Constant_225" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120188" size="16" /> + <output> + <port id="0" precision="I64" names="onnx::Split_388"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="369" name="/model.22/Split" type="VariadicSplit" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>65</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64" /> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="3" precision="FP32" names="/model.22/Split_output_0"> + <dim>1</dim> + <dim>64</dim> + <dim>12096</dim> + </port> + <port id="4" precision="FP32" names="/model.22/Split_output_1"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="370" name="/model.22/dfl/Constant" type="Const" version="opset1"> + <data element_type="i64" shape="4" offset="12120204" size="32" /> + <output> + <port id="0" precision="I64" names="/model.22/dfl/Constant_output_0"> + <dim>4</dim> + </port> + </output> + </layer> + <layer id="371" name="/model.22/dfl/Reshape" type="Reshape" version="opset1"> + <data special_zero="true" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64"> + <dim>4</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/dfl/Reshape_output_0"> + <dim>1</dim> + <dim>4</dim> + <dim>16</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="372" name="Constant_43839" type="Const" version="opset1"> + <data element_type="i64" shape="4" offset="12120236" size="32" /> + <output> + <port id="0" precision="I64"> + <dim>4</dim> + </port> + </output> + </layer> + <layer id="373" name="/model.22/dfl/Transpose" type="Transpose" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>16</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64"> + <dim>4</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/dfl/Transpose_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="374" name="/model.22/dfl/Softmax" type="SoftMax" version="opset8"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/dfl/Softmax_output_0"> + <dim>1</dim> + <dim>16</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="375" name="model.22.dfl.conv.weight" type="Const" version="opset1"> + <data element_type="f32" shape="1, 16, 1, 1" offset="12120268" size="64" /> + <output> + <port id="0" precision="FP32" names="model.22.dfl.conv.weight"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="376" name="/model.22/dfl/conv/Conv" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/dfl/conv/Conv_output_0"> + <dim>1</dim> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="377" name="/model.22/dfl/Constant_1" type="Const" version="opset1"> + <data element_type="i64" shape="3" offset="12120332" size="24" /> + <output> + <port id="0" precision="I64" names="/model.22/dfl/Constant_1_output_0"> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="378" name="/model.22/dfl/Reshape_1" type="Reshape" version="opset1"> + <data special_zero="true" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64"> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/dfl/Reshape_1_output_0"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="379" name="Constant_46136" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120356" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="380" name="Constant_46137" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120356" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="381" name="Constant_46133" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="121344" size="8" /> + <output> + <port id="0" precision="I64"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="382" name="/model.22/Shape" type="ShapeOf" version="opset3"> + <data output_type="i64" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="1" precision="I64" names="/model.22/Shape_output_0"> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="383" name="/model.22/Constant_3" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_3_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="384" name="Constant_43850" type="Const" version="opset1"> + <data element_type="i64" shape="" offset="12120372" size="8" /> + <output> + <port id="0" precision="I64" /> + </output> + </layer> + <layer id="385" name="/model.22/Gather" type="Gather" version="opset8"> + <data batch_dims="0" /> + <input> + <port id="0" precision="I64"> + <dim>3</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + <port id="2" precision="I64" /> + </input> + <output> + <port id="3" precision="I64" names="/model.22/Gather_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="386" name="/model.22/Constant_5" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="121344" size="8" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_5_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="387" name="/model.22/Add" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="I64"> + <dim>1</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="I64" names="/model.22/Add_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="388" name="/model.22/Constant_6" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="12120380" size="8" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_6_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="389" name="/model.22/Div" type="Divide" version="opset1"> + <data auto_broadcast="numpy" m_pythondiv="true" /> + <input> + <port id="0" precision="I64"> + <dim>1</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="I64" names="/model.22/Div_output_0,/model.22/Mul_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="390" name="Constant_46132" type="Const" version="opset1"> + <data element_type="i32" shape="1" offset="12120388" size="4" /> + <output> + <port id="0" precision="I32"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="391" name="ScatterUpdate_46138" type="ScatterUpdate" version="opset3"> + <input> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + </port> + <port id="3" precision="I32"> + <dim>1</dim> + </port> + </input> + <output> + <port id="4" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="392" name="Constant_46141" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120392" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="393" name="/model.22/Slice" type="StridedSlice" version="opset1"> + <data begin_mask="1, 0" end_mask="1, 0" new_axis_mask="" shrink_axis_mask="" ellipsis_mask="" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64"> + <dim>2</dim> + </port> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + <port id="3" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="4" precision="FP32" names="/model.22/Slice_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="394" name="/model.22/Sub" type="Subtract" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Sub_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="395" name="/model.22/Constant_10" type="Const" version="opset1"> + <data element_type="f32" shape="1, 2, 12096" offset="0" size="96768" /> + <output> + <port id="0" precision="FP32" names="/model.22/Constant_10_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="396" name="Constant_46185" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120356" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="397" name="Constant_46184" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="121344" size="8" /> + <output> + <port id="0" precision="I64"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="398" name="Constant_46183" type="Const" version="opset1"> + <data element_type="i32" shape="1" offset="12120388" size="4" /> + <output> + <port id="0" precision="I32"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="399" name="ScatterUpdate_46186" type="ScatterUpdate" version="opset3"> + <input> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + </port> + <port id="3" precision="I32"> + <dim>1</dim> + </port> + </input> + <output> + <port id="4" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="400" name="Constant_46187" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120356" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="401" name="/model.22/Constant_8" type="Const" version="opset1"> + <data element_type="i64" shape="1" offset="12120380" size="8" /> + <output> + <port id="0" precision="I64" names="/model.22/Constant_8_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="402" name="/model.22/Mul_1" type="Multiply" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="I64"> + <dim>1</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="I64" names="/model.22/Mul_1_output_0"> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="403" name="ScatterUpdate_46188" type="ScatterUpdate" version="opset3"> + <input> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + <port id="1" precision="I64"> + <dim>1</dim> + </port> + <port id="2" precision="I64"> + <dim>1</dim> + </port> + <port id="3" precision="I32"> + <dim>1</dim> + </port> + </input> + <output> + <port id="4" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="404" name="Constant_46191" type="Const" version="opset1"> + <data element_type="i64" shape="2" offset="12120392" size="16" /> + <output> + <port id="0" precision="I64"> + <dim>2</dim> + </port> + </output> + </layer> + <layer id="405" name="/model.22/Slice_1" type="StridedSlice" version="opset1"> + <data begin_mask="1, 0" end_mask="1, 0" new_axis_mask="" shrink_axis_mask="" ellipsis_mask="" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="I64"> + <dim>2</dim> + </port> + <port id="2" precision="I64"> + <dim>2</dim> + </port> + <port id="3" precision="I64"> + <dim>2</dim> + </port> + </input> + <output> + <port id="4" precision="FP32" names="/model.22/Slice_1_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="406" name="/model.22/Add_1" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Add_1_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="407" name="/model.22/Add_2" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Add_2_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="408" name="Constant_46534" type="Const" version="opset1"> + <data element_type="f32" shape="1, 1, 1" offset="12120408" size="4" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="409" name="/model.22/Div_1" type="Multiply" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Div_1_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="410" name="/model.22/Sub_1" type="Subtract" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Sub_1_output_0"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="411" name="/model.22/Concat_4" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>2</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Concat_4_output_0"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="412" name="Constant_46535" type="Const" version="opset1"> + <data element_type="f32" shape="1, 1, 12096" offset="12120412" size="48384" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="413" name="/model.22/Mul_2" type="Multiply" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/model.22/Mul_2_output_0"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="414" name="/model.22/Sigmoid" type="Sigmoid" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/model.22/Sigmoid_output_0"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="415" name="output0" type="Concat" version="opset1"> + <data axis="1" /> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>4</dim> + <dim>12096</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>1</dim> + <dim>12096</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="output0"> + <dim>1</dim> + <dim>5</dim> + <dim>12096</dim> + </port> + </output> + </layer> + <layer id="416" name="output0/sink_port_0" type="Result" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>5</dim> + <dim>12096</dim> + </port> + </input> + </layer> + </layers> + <edges> + <edge from-layer="0" from-port="0" to-layer="3" to-port="0" /> + <edge from-layer="1" from-port="0" to-layer="394" to-port="0" /> + <edge from-layer="2" from-port="0" to-layer="3" to-port="1" /> + <edge from-layer="3" from-port="2" to-layer="5" to-port="0" /> + <edge from-layer="4" from-port="0" to-layer="5" to-port="1" /> + <edge from-layer="5" from-port="2" to-layer="6" to-port="0" /> + <edge from-layer="6" from-port="1" to-layer="8" to-port="0" /> + <edge from-layer="7" from-port="0" to-layer="8" to-port="1" /> + <edge from-layer="8" from-port="2" to-layer="10" to-port="0" /> + <edge from-layer="9" from-port="0" to-layer="10" to-port="1" /> + <edge from-layer="10" from-port="2" to-layer="11" to-port="0" /> + <edge from-layer="11" from-port="1" to-layer="13" to-port="0" /> + <edge from-layer="12" from-port="0" to-layer="13" to-port="1" /> + <edge from-layer="13" from-port="2" to-layer="15" to-port="0" /> + <edge from-layer="14" from-port="0" to-layer="15" to-port="1" /> + <edge from-layer="15" from-port="2" to-layer="16" to-port="0" /> + <edge from-layer="16" from-port="1" to-layer="19" to-port="0" /> + <edge from-layer="17" from-port="0" to-layer="19" to-port="1" /> + <edge from-layer="18" from-port="0" to-layer="19" to-port="2" /> + <edge from-layer="19" from-port="4" to-layer="21" to-port="0" /> + <edge from-layer="19" from-port="4" to-layer="31" to-port="1" /> + <edge from-layer="19" from-port="3" to-layer="31" to-port="0" /> + <edge from-layer="19" from-port="4" to-layer="30" to-port="0" /> + <edge from-layer="20" from-port="0" to-layer="21" to-port="1" /> + <edge from-layer="21" from-port="2" to-layer="23" to-port="0" /> + <edge from-layer="22" from-port="0" to-layer="23" to-port="1" /> + <edge from-layer="23" from-port="2" to-layer="24" to-port="0" /> + <edge from-layer="24" from-port="1" to-layer="26" to-port="0" /> + <edge from-layer="25" from-port="0" to-layer="26" to-port="1" /> + <edge from-layer="26" from-port="2" to-layer="28" to-port="0" /> + <edge from-layer="27" from-port="0" to-layer="28" to-port="1" /> + <edge from-layer="28" from-port="2" to-layer="29" to-port="0" /> + <edge from-layer="29" from-port="1" to-layer="30" to-port="1" /> + <edge from-layer="30" from-port="2" to-layer="31" to-port="2" /> + <edge from-layer="31" from-port="3" to-layer="33" to-port="0" /> + <edge from-layer="32" from-port="0" to-layer="33" to-port="1" /> + <edge from-layer="33" from-port="2" to-layer="35" to-port="0" /> + <edge from-layer="34" from-port="0" to-layer="35" to-port="1" /> + <edge from-layer="35" from-port="2" to-layer="36" to-port="0" /> + <edge from-layer="36" from-port="1" to-layer="38" to-port="0" /> + <edge from-layer="37" from-port="0" to-layer="38" to-port="1" /> + <edge from-layer="38" from-port="2" to-layer="40" to-port="0" /> + <edge from-layer="39" from-port="0" to-layer="40" to-port="1" /> + <edge from-layer="40" from-port="2" to-layer="41" to-port="0" /> + <edge from-layer="41" from-port="1" to-layer="43" to-port="0" /> + <edge from-layer="42" from-port="0" to-layer="43" to-port="1" /> + <edge from-layer="43" from-port="2" to-layer="45" to-port="0" /> + <edge from-layer="44" from-port="0" to-layer="45" to-port="1" /> + <edge from-layer="45" from-port="2" to-layer="46" to-port="0" /> + <edge from-layer="46" from-port="1" to-layer="49" to-port="0" /> + <edge from-layer="47" from-port="0" to-layer="49" to-port="1" /> + <edge from-layer="48" from-port="0" to-layer="49" to-port="2" /> + <edge from-layer="48" from-port="0" to-layer="198" to-port="2" /> + <edge from-layer="49" from-port="3" to-layer="72" to-port="0" /> + <edge from-layer="49" from-port="4" to-layer="72" to-port="1" /> + <edge from-layer="49" from-port="4" to-layer="51" to-port="0" /> + <edge from-layer="49" from-port="4" to-layer="60" to-port="0" /> + <edge from-layer="50" from-port="0" to-layer="51" to-port="1" /> + <edge from-layer="51" from-port="2" to-layer="53" to-port="0" /> + <edge from-layer="52" from-port="0" to-layer="53" to-port="1" /> + <edge from-layer="53" from-port="2" to-layer="54" to-port="0" /> + <edge from-layer="54" from-port="1" to-layer="56" to-port="0" /> + <edge from-layer="55" from-port="0" to-layer="56" to-port="1" /> + <edge from-layer="56" from-port="2" to-layer="58" to-port="0" /> + <edge from-layer="57" from-port="0" to-layer="58" to-port="1" /> + <edge from-layer="58" from-port="2" to-layer="59" to-port="0" /> + <edge from-layer="59" from-port="1" to-layer="60" to-port="1" /> + <edge from-layer="60" from-port="2" to-layer="62" to-port="0" /> + <edge from-layer="60" from-port="2" to-layer="72" to-port="2" /> + <edge from-layer="60" from-port="2" to-layer="71" to-port="0" /> + <edge from-layer="61" from-port="0" to-layer="62" to-port="1" /> + <edge from-layer="62" from-port="2" to-layer="64" to-port="0" /> + <edge from-layer="63" from-port="0" to-layer="64" to-port="1" /> + <edge from-layer="64" from-port="2" to-layer="65" to-port="0" /> + <edge from-layer="65" from-port="1" to-layer="67" to-port="0" /> + <edge from-layer="66" from-port="0" to-layer="67" to-port="1" /> + <edge from-layer="67" from-port="2" to-layer="69" to-port="0" /> + <edge from-layer="68" from-port="0" to-layer="69" to-port="1" /> + <edge from-layer="69" from-port="2" to-layer="70" to-port="0" /> + <edge from-layer="70" from-port="1" to-layer="71" to-port="1" /> + <edge from-layer="71" from-port="2" to-layer="72" to-port="3" /> + <edge from-layer="72" from-port="4" to-layer="74" to-port="0" /> + <edge from-layer="73" from-port="0" to-layer="74" to-port="1" /> + <edge from-layer="74" from-port="2" to-layer="76" to-port="0" /> + <edge from-layer="75" from-port="0" to-layer="76" to-port="1" /> + <edge from-layer="76" from-port="2" to-layer="77" to-port="0" /> + <edge from-layer="77" from-port="1" to-layer="79" to-port="0" /> + <edge from-layer="77" from-port="1" to-layer="191" to-port="1" /> + <edge from-layer="78" from-port="0" to-layer="79" to-port="1" /> + <edge from-layer="79" from-port="2" to-layer="81" to-port="0" /> + <edge from-layer="80" from-port="0" to-layer="81" to-port="1" /> + <edge from-layer="81" from-port="2" to-layer="82" to-port="0" /> + <edge from-layer="82" from-port="1" to-layer="84" to-port="0" /> + <edge from-layer="83" from-port="0" to-layer="84" to-port="1" /> + <edge from-layer="84" from-port="2" to-layer="86" to-port="0" /> + <edge from-layer="85" from-port="0" to-layer="86" to-port="1" /> + <edge from-layer="86" from-port="2" to-layer="87" to-port="0" /> + <edge from-layer="87" from-port="1" to-layer="90" to-port="0" /> + <edge from-layer="88" from-port="0" to-layer="90" to-port="1" /> + <edge from-layer="89" from-port="0" to-layer="258" to-port="2" /> + <edge from-layer="89" from-port="0" to-layer="172" to-port="2" /> + <edge from-layer="89" from-port="0" to-layer="90" to-port="2" /> + <edge from-layer="90" from-port="4" to-layer="92" to-port="0" /> + <edge from-layer="90" from-port="4" to-layer="113" to-port="1" /> + <edge from-layer="90" from-port="3" to-layer="113" to-port="0" /> + <edge from-layer="90" from-port="4" to-layer="101" to-port="0" /> + <edge from-layer="91" from-port="0" to-layer="92" to-port="1" /> + <edge from-layer="92" from-port="2" to-layer="94" to-port="0" /> + <edge from-layer="93" from-port="0" to-layer="94" to-port="1" /> + <edge from-layer="94" from-port="2" to-layer="95" to-port="0" /> + <edge from-layer="95" from-port="1" to-layer="97" to-port="0" /> + <edge from-layer="96" from-port="0" to-layer="97" to-port="1" /> + <edge from-layer="97" from-port="2" to-layer="99" to-port="0" /> + <edge from-layer="98" from-port="0" to-layer="99" to-port="1" /> + <edge from-layer="99" from-port="2" to-layer="100" to-port="0" /> + <edge from-layer="100" from-port="1" to-layer="101" to-port="1" /> + <edge from-layer="101" from-port="2" to-layer="103" to-port="0" /> + <edge from-layer="101" from-port="2" to-layer="112" to-port="0" /> + <edge from-layer="101" from-port="2" to-layer="113" to-port="2" /> + <edge from-layer="102" from-port="0" to-layer="103" to-port="1" /> + <edge from-layer="103" from-port="2" to-layer="105" to-port="0" /> + <edge from-layer="104" from-port="0" to-layer="105" to-port="1" /> + <edge from-layer="105" from-port="2" to-layer="106" to-port="0" /> + <edge from-layer="106" from-port="1" to-layer="108" to-port="0" /> + <edge from-layer="107" from-port="0" to-layer="108" to-port="1" /> + <edge from-layer="108" from-port="2" to-layer="110" to-port="0" /> + <edge from-layer="109" from-port="0" to-layer="110" to-port="1" /> + <edge from-layer="110" from-port="2" to-layer="111" to-port="0" /> + <edge from-layer="111" from-port="1" to-layer="112" to-port="1" /> + <edge from-layer="112" from-port="2" to-layer="113" to-port="3" /> + <edge from-layer="113" from-port="4" to-layer="115" to-port="0" /> + <edge from-layer="114" from-port="0" to-layer="115" to-port="1" /> + <edge from-layer="115" from-port="2" to-layer="117" to-port="0" /> + <edge from-layer="116" from-port="0" to-layer="117" to-port="1" /> + <edge from-layer="117" from-port="2" to-layer="118" to-port="0" /> + <edge from-layer="118" from-port="1" to-layer="120" to-port="0" /> + <edge from-layer="118" from-port="1" to-layer="165" to-port="1" /> + <edge from-layer="119" from-port="0" to-layer="120" to-port="1" /> + <edge from-layer="120" from-port="2" to-layer="122" to-port="0" /> + <edge from-layer="121" from-port="0" to-layer="122" to-port="1" /> + <edge from-layer="122" from-port="2" to-layer="123" to-port="0" /> + <edge from-layer="123" from-port="1" to-layer="125" to-port="0" /> + <edge from-layer="124" from-port="0" to-layer="125" to-port="1" /> + <edge from-layer="125" from-port="2" to-layer="127" to-port="0" /> + <edge from-layer="126" from-port="0" to-layer="127" to-port="1" /> + <edge from-layer="127" from-port="2" to-layer="128" to-port="0" /> + <edge from-layer="128" from-port="1" to-layer="131" to-port="0" /> + <edge from-layer="129" from-port="0" to-layer="131" to-port="1" /> + <edge from-layer="130" from-port="0" to-layer="131" to-port="2" /> + <edge from-layer="130" from-port="0" to-layer="318" to-port="2" /> + <edge from-layer="131" from-port="4" to-layer="142" to-port="0" /> + <edge from-layer="131" from-port="3" to-layer="143" to-port="0" /> + <edge from-layer="131" from-port="4" to-layer="143" to-port="1" /> + <edge from-layer="131" from-port="4" to-layer="133" to-port="0" /> + <edge from-layer="132" from-port="0" to-layer="133" to-port="1" /> + <edge from-layer="133" from-port="2" to-layer="135" to-port="0" /> + <edge from-layer="134" from-port="0" to-layer="135" to-port="1" /> + <edge from-layer="135" from-port="2" to-layer="136" to-port="0" /> + <edge from-layer="136" from-port="1" to-layer="138" to-port="0" /> + <edge from-layer="137" from-port="0" to-layer="138" to-port="1" /> + <edge from-layer="138" from-port="2" to-layer="140" to-port="0" /> + <edge from-layer="139" from-port="0" to-layer="140" to-port="1" /> + <edge from-layer="140" from-port="2" to-layer="141" to-port="0" /> + <edge from-layer="141" from-port="1" to-layer="142" to-port="1" /> + <edge from-layer="142" from-port="2" to-layer="143" to-port="2" /> + <edge from-layer="143" from-port="3" to-layer="145" to-port="0" /> + <edge from-layer="144" from-port="0" to-layer="145" to-port="1" /> + <edge from-layer="145" from-port="2" to-layer="147" to-port="0" /> + <edge from-layer="146" from-port="0" to-layer="147" to-port="1" /> + <edge from-layer="147" from-port="2" to-layer="148" to-port="0" /> + <edge from-layer="148" from-port="1" to-layer="150" to-port="0" /> + <edge from-layer="149" from-port="0" to-layer="150" to-port="1" /> + <edge from-layer="150" from-port="2" to-layer="152" to-port="0" /> + <edge from-layer="151" from-port="0" to-layer="152" to-port="1" /> + <edge from-layer="152" from-port="2" to-layer="153" to-port="0" /> + <edge from-layer="153" from-port="1" to-layer="154" to-port="0" /> + <edge from-layer="153" from-port="1" to-layer="157" to-port="0" /> + <edge from-layer="154" from-port="1" to-layer="157" to-port="1" /> + <edge from-layer="154" from-port="1" to-layer="155" to-port="0" /> + <edge from-layer="155" from-port="1" to-layer="157" to-port="2" /> + <edge from-layer="155" from-port="1" to-layer="156" to-port="0" /> + <edge from-layer="156" from-port="1" to-layer="157" to-port="3" /> + <edge from-layer="157" from-port="4" to-layer="159" to-port="0" /> + <edge from-layer="158" from-port="0" to-layer="159" to-port="1" /> + <edge from-layer="159" from-port="2" to-layer="161" to-port="0" /> + <edge from-layer="160" from-port="0" to-layer="161" to-port="1" /> + <edge from-layer="161" from-port="2" to-layer="162" to-port="0" /> + <edge from-layer="162" from-port="1" to-layer="164" to-port="0" /> + <edge from-layer="162" from-port="1" to-layer="311" to-port="1" /> + <edge from-layer="163" from-port="0" to-layer="164" to-port="1" /> + <edge from-layer="164" from-port="2" to-layer="165" to-port="0" /> + <edge from-layer="165" from-port="2" to-layer="167" to-port="0" /> + <edge from-layer="166" from-port="0" to-layer="167" to-port="1" /> + <edge from-layer="167" from-port="2" to-layer="169" to-port="0" /> + <edge from-layer="168" from-port="0" to-layer="169" to-port="1" /> + <edge from-layer="169" from-port="2" to-layer="170" to-port="0" /> + <edge from-layer="170" from-port="1" to-layer="172" to-port="0" /> + <edge from-layer="171" from-port="0" to-layer="172" to-port="1" /> + <edge from-layer="172" from-port="3" to-layer="183" to-port="0" /> + <edge from-layer="172" from-port="4" to-layer="174" to-port="0" /> + <edge from-layer="172" from-port="4" to-layer="183" to-port="1" /> + <edge from-layer="173" from-port="0" to-layer="174" to-port="1" /> + <edge from-layer="174" from-port="2" to-layer="176" to-port="0" /> + <edge from-layer="175" from-port="0" to-layer="176" to-port="1" /> + <edge from-layer="176" from-port="2" to-layer="177" to-port="0" /> + <edge from-layer="177" from-port="1" to-layer="179" to-port="0" /> + <edge from-layer="178" from-port="0" to-layer="179" to-port="1" /> + <edge from-layer="179" from-port="2" to-layer="181" to-port="0" /> + <edge from-layer="180" from-port="0" to-layer="181" to-port="1" /> + <edge from-layer="181" from-port="2" to-layer="182" to-port="0" /> + <edge from-layer="182" from-port="1" to-layer="183" to-port="2" /> + <edge from-layer="183" from-port="3" to-layer="185" to-port="0" /> + <edge from-layer="184" from-port="0" to-layer="185" to-port="1" /> + <edge from-layer="185" from-port="2" to-layer="187" to-port="0" /> + <edge from-layer="186" from-port="0" to-layer="187" to-port="1" /> + <edge from-layer="187" from-port="2" to-layer="188" to-port="0" /> + <edge from-layer="188" from-port="1" to-layer="190" to-port="0" /> + <edge from-layer="188" from-port="1" to-layer="251" to-port="1" /> + <edge from-layer="189" from-port="0" to-layer="190" to-port="1" /> + <edge from-layer="190" from-port="2" to-layer="191" to-port="0" /> + <edge from-layer="191" from-port="2" to-layer="193" to-port="0" /> + <edge from-layer="192" from-port="0" to-layer="193" to-port="1" /> + <edge from-layer="193" from-port="2" to-layer="195" to-port="0" /> + <edge from-layer="194" from-port="0" to-layer="195" to-port="1" /> + <edge from-layer="195" from-port="2" to-layer="196" to-port="0" /> + <edge from-layer="196" from-port="1" to-layer="198" to-port="0" /> + <edge from-layer="197" from-port="0" to-layer="198" to-port="1" /> + <edge from-layer="198" from-port="4" to-layer="209" to-port="1" /> + <edge from-layer="198" from-port="3" to-layer="209" to-port="0" /> + <edge from-layer="198" from-port="4" to-layer="200" to-port="0" /> + <edge from-layer="199" from-port="0" to-layer="200" to-port="1" /> + <edge from-layer="200" from-port="2" to-layer="202" to-port="0" /> + <edge from-layer="201" from-port="0" to-layer="202" to-port="1" /> + <edge from-layer="202" from-port="2" to-layer="203" to-port="0" /> + <edge from-layer="203" from-port="1" to-layer="205" to-port="0" /> + <edge from-layer="204" from-port="0" to-layer="205" to-port="1" /> + <edge from-layer="205" from-port="2" to-layer="207" to-port="0" /> + <edge from-layer="206" from-port="0" to-layer="207" to-port="1" /> + <edge from-layer="207" from-port="2" to-layer="208" to-port="0" /> + <edge from-layer="208" from-port="1" to-layer="209" to-port="2" /> + <edge from-layer="209" from-port="3" to-layer="211" to-port="0" /> + <edge from-layer="210" from-port="0" to-layer="211" to-port="1" /> + <edge from-layer="211" from-port="2" to-layer="213" to-port="0" /> + <edge from-layer="212" from-port="0" to-layer="213" to-port="1" /> + <edge from-layer="213" from-port="2" to-layer="214" to-port="0" /> + <edge from-layer="214" from-port="1" to-layer="216" to-port="0" /> + <edge from-layer="214" from-port="1" to-layer="247" to-port="0" /> + <edge from-layer="214" from-port="1" to-layer="230" to-port="0" /> + <edge from-layer="215" from-port="0" to-layer="216" to-port="1" /> + <edge from-layer="216" from-port="2" to-layer="218" to-port="0" /> + <edge from-layer="217" from-port="0" to-layer="218" to-port="1" /> + <edge from-layer="218" from-port="2" to-layer="219" to-port="0" /> + <edge from-layer="219" from-port="1" to-layer="221" to-port="0" /> + <edge from-layer="220" from-port="0" to-layer="221" to-port="1" /> + <edge from-layer="221" from-port="2" to-layer="223" to-port="0" /> + <edge from-layer="222" from-port="0" to-layer="223" to-port="1" /> + <edge from-layer="223" from-port="2" to-layer="224" to-port="0" /> + <edge from-layer="224" from-port="1" to-layer="226" to-port="0" /> + <edge from-layer="225" from-port="0" to-layer="226" to-port="1" /> + <edge from-layer="226" from-port="2" to-layer="228" to-port="0" /> + <edge from-layer="227" from-port="0" to-layer="228" to-port="1" /> + <edge from-layer="228" from-port="2" to-layer="243" to-port="0" /> + <edge from-layer="229" from-port="0" to-layer="230" to-port="1" /> + <edge from-layer="230" from-port="2" to-layer="232" to-port="0" /> + <edge from-layer="231" from-port="0" to-layer="232" to-port="1" /> + <edge from-layer="232" from-port="2" to-layer="233" to-port="0" /> + <edge from-layer="233" from-port="1" to-layer="235" to-port="0" /> + <edge from-layer="234" from-port="0" to-layer="235" to-port="1" /> + <edge from-layer="235" from-port="2" to-layer="237" to-port="0" /> + <edge from-layer="236" from-port="0" to-layer="237" to-port="1" /> + <edge from-layer="237" from-port="2" to-layer="238" to-port="0" /> + <edge from-layer="238" from-port="1" to-layer="240" to-port="0" /> + <edge from-layer="239" from-port="0" to-layer="240" to-port="1" /> + <edge from-layer="240" from-port="2" to-layer="242" to-port="0" /> + <edge from-layer="241" from-port="0" to-layer="242" to-port="1" /> + <edge from-layer="242" from-port="2" to-layer="243" to-port="1" /> + <edge from-layer="243" from-port="2" to-layer="245" to-port="0" /> + <edge from-layer="244" from-port="0" to-layer="245" to-port="1" /> + <edge from-layer="245" from-port="2" to-layer="366" to-port="0" /> + <edge from-layer="246" from-port="0" to-layer="247" to-port="1" /> + <edge from-layer="247" from-port="2" to-layer="249" to-port="0" /> + <edge from-layer="248" from-port="0" to-layer="249" to-port="1" /> + <edge from-layer="249" from-port="2" to-layer="250" to-port="0" /> + <edge from-layer="250" from-port="1" to-layer="251" to-port="0" /> + <edge from-layer="251" from-port="2" to-layer="253" to-port="0" /> + <edge from-layer="252" from-port="0" to-layer="253" to-port="1" /> + <edge from-layer="253" from-port="2" to-layer="255" to-port="0" /> + <edge from-layer="254" from-port="0" to-layer="255" to-port="1" /> + <edge from-layer="255" from-port="2" to-layer="256" to-port="0" /> + <edge from-layer="256" from-port="1" to-layer="258" to-port="0" /> + <edge from-layer="257" from-port="0" to-layer="258" to-port="1" /> + <edge from-layer="258" from-port="3" to-layer="269" to-port="0" /> + <edge from-layer="258" from-port="4" to-layer="269" to-port="1" /> + <edge from-layer="258" from-port="4" to-layer="260" to-port="0" /> + <edge from-layer="259" from-port="0" to-layer="260" to-port="1" /> + <edge from-layer="260" from-port="2" to-layer="262" to-port="0" /> + <edge from-layer="261" from-port="0" to-layer="262" to-port="1" /> + <edge from-layer="262" from-port="2" to-layer="263" to-port="0" /> + <edge from-layer="263" from-port="1" to-layer="265" to-port="0" /> + <edge from-layer="264" from-port="0" to-layer="265" to-port="1" /> + <edge from-layer="265" from-port="2" to-layer="267" to-port="0" /> + <edge from-layer="266" from-port="0" to-layer="267" to-port="1" /> + <edge from-layer="267" from-port="2" to-layer="268" to-port="0" /> + <edge from-layer="268" from-port="1" to-layer="269" to-port="2" /> + <edge from-layer="269" from-port="3" to-layer="271" to-port="0" /> + <edge from-layer="270" from-port="0" to-layer="271" to-port="1" /> + <edge from-layer="271" from-port="2" to-layer="273" to-port="0" /> + <edge from-layer="272" from-port="0" to-layer="273" to-port="1" /> + <edge from-layer="273" from-port="2" to-layer="274" to-port="0" /> + <edge from-layer="274" from-port="1" to-layer="307" to-port="0" /> + <edge from-layer="274" from-port="1" to-layer="290" to-port="0" /> + <edge from-layer="274" from-port="1" to-layer="276" to-port="0" /> + <edge from-layer="275" from-port="0" to-layer="276" to-port="1" /> + <edge from-layer="276" from-port="2" to-layer="278" to-port="0" /> + <edge from-layer="277" from-port="0" to-layer="278" to-port="1" /> + <edge from-layer="278" from-port="2" to-layer="279" to-port="0" /> + <edge from-layer="279" from-port="1" to-layer="281" to-port="0" /> + <edge from-layer="280" from-port="0" to-layer="281" to-port="1" /> + <edge from-layer="281" from-port="2" to-layer="283" to-port="0" /> + <edge from-layer="282" from-port="0" to-layer="283" to-port="1" /> + <edge from-layer="283" from-port="2" to-layer="284" to-port="0" /> + <edge from-layer="284" from-port="1" to-layer="286" to-port="0" /> + <edge from-layer="285" from-port="0" to-layer="286" to-port="1" /> + <edge from-layer="286" from-port="2" to-layer="288" to-port="0" /> + <edge from-layer="287" from-port="0" to-layer="288" to-port="1" /> + <edge from-layer="288" from-port="2" to-layer="303" to-port="0" /> + <edge from-layer="289" from-port="0" to-layer="290" to-port="1" /> + <edge from-layer="290" from-port="2" to-layer="292" to-port="0" /> + <edge from-layer="291" from-port="0" to-layer="292" to-port="1" /> + <edge from-layer="292" from-port="2" to-layer="293" to-port="0" /> + <edge from-layer="293" from-port="1" to-layer="295" to-port="0" /> + <edge from-layer="294" from-port="0" to-layer="295" to-port="1" /> + <edge from-layer="295" from-port="2" to-layer="297" to-port="0" /> + <edge from-layer="296" from-port="0" to-layer="297" to-port="1" /> + <edge from-layer="297" from-port="2" to-layer="298" to-port="0" /> + <edge from-layer="298" from-port="1" to-layer="300" to-port="0" /> + <edge from-layer="299" from-port="0" to-layer="300" to-port="1" /> + <edge from-layer="300" from-port="2" to-layer="302" to-port="0" /> + <edge from-layer="301" from-port="0" to-layer="302" to-port="1" /> + <edge from-layer="302" from-port="2" to-layer="303" to-port="1" /> + <edge from-layer="303" from-port="2" to-layer="305" to-port="0" /> + <edge from-layer="304" from-port="0" to-layer="305" to-port="1" /> + <edge from-layer="305" from-port="2" to-layer="366" to-port="1" /> + <edge from-layer="306" from-port="0" to-layer="307" to-port="1" /> + <edge from-layer="307" from-port="2" to-layer="309" to-port="0" /> + <edge from-layer="308" from-port="0" to-layer="309" to-port="1" /> + <edge from-layer="309" from-port="2" to-layer="310" to-port="0" /> + <edge from-layer="310" from-port="1" to-layer="311" to-port="0" /> + <edge from-layer="311" from-port="2" to-layer="313" to-port="0" /> + <edge from-layer="312" from-port="0" to-layer="313" to-port="1" /> + <edge from-layer="313" from-port="2" to-layer="315" to-port="0" /> + <edge from-layer="314" from-port="0" to-layer="315" to-port="1" /> + <edge from-layer="315" from-port="2" to-layer="316" to-port="0" /> + <edge from-layer="316" from-port="1" to-layer="318" to-port="0" /> + <edge from-layer="317" from-port="0" to-layer="318" to-port="1" /> + <edge from-layer="318" from-port="4" to-layer="320" to-port="0" /> + <edge from-layer="318" from-port="4" to-layer="329" to-port="1" /> + <edge from-layer="318" from-port="3" to-layer="329" to-port="0" /> + <edge from-layer="319" from-port="0" to-layer="320" to-port="1" /> + <edge from-layer="320" from-port="2" to-layer="322" to-port="0" /> + <edge from-layer="321" from-port="0" to-layer="322" to-port="1" /> + <edge from-layer="322" from-port="2" to-layer="323" to-port="0" /> + <edge from-layer="323" from-port="1" to-layer="325" to-port="0" /> + <edge from-layer="324" from-port="0" to-layer="325" to-port="1" /> + <edge from-layer="325" from-port="2" to-layer="327" to-port="0" /> + <edge from-layer="326" from-port="0" to-layer="327" to-port="1" /> + <edge from-layer="327" from-port="2" to-layer="328" to-port="0" /> + <edge from-layer="328" from-port="1" to-layer="329" to-port="2" /> + <edge from-layer="329" from-port="3" to-layer="331" to-port="0" /> + <edge from-layer="330" from-port="0" to-layer="331" to-port="1" /> + <edge from-layer="331" from-port="2" to-layer="333" to-port="0" /> + <edge from-layer="332" from-port="0" to-layer="333" to-port="1" /> + <edge from-layer="333" from-port="2" to-layer="334" to-port="0" /> + <edge from-layer="334" from-port="1" to-layer="336" to-port="0" /> + <edge from-layer="334" from-port="1" to-layer="350" to-port="0" /> + <edge from-layer="335" from-port="0" to-layer="336" to-port="1" /> + <edge from-layer="336" from-port="2" to-layer="338" to-port="0" /> + <edge from-layer="337" from-port="0" to-layer="338" to-port="1" /> + <edge from-layer="338" from-port="2" to-layer="339" to-port="0" /> + <edge from-layer="339" from-port="1" to-layer="341" to-port="0" /> + <edge from-layer="340" from-port="0" to-layer="341" to-port="1" /> + <edge from-layer="341" from-port="2" to-layer="343" to-port="0" /> + <edge from-layer="342" from-port="0" to-layer="343" to-port="1" /> + <edge from-layer="343" from-port="2" to-layer="344" to-port="0" /> + <edge from-layer="344" from-port="1" to-layer="346" to-port="0" /> + <edge from-layer="345" from-port="0" to-layer="346" to-port="1" /> + <edge from-layer="346" from-port="2" to-layer="348" to-port="0" /> + <edge from-layer="347" from-port="0" to-layer="348" to-port="1" /> + <edge from-layer="348" from-port="2" to-layer="363" to-port="0" /> + <edge from-layer="349" from-port="0" to-layer="350" to-port="1" /> + <edge from-layer="350" from-port="2" to-layer="352" to-port="0" /> + <edge from-layer="351" from-port="0" to-layer="352" to-port="1" /> + <edge from-layer="352" from-port="2" to-layer="353" to-port="0" /> + <edge from-layer="353" from-port="1" to-layer="355" to-port="0" /> + <edge from-layer="354" from-port="0" to-layer="355" to-port="1" /> + <edge from-layer="355" from-port="2" to-layer="357" to-port="0" /> + <edge from-layer="356" from-port="0" to-layer="357" to-port="1" /> + <edge from-layer="357" from-port="2" to-layer="358" to-port="0" /> + <edge from-layer="358" from-port="1" to-layer="360" to-port="0" /> + <edge from-layer="359" from-port="0" to-layer="360" to-port="1" /> + <edge from-layer="360" from-port="2" to-layer="362" to-port="0" /> + <edge from-layer="361" from-port="0" to-layer="362" to-port="1" /> + <edge from-layer="362" from-port="2" to-layer="363" to-port="1" /> + <edge from-layer="363" from-port="2" to-layer="365" to-port="0" /> + <edge from-layer="364" from-port="0" to-layer="365" to-port="1" /> + <edge from-layer="365" from-port="2" to-layer="366" to-port="2" /> + <edge from-layer="366" from-port="3" to-layer="369" to-port="0" /> + <edge from-layer="367" from-port="0" to-layer="369" to-port="1" /> + <edge from-layer="368" from-port="0" to-layer="369" to-port="2" /> + <edge from-layer="369" from-port="4" to-layer="414" to-port="0" /> + <edge from-layer="369" from-port="3" to-layer="371" to-port="0" /> + <edge from-layer="370" from-port="0" to-layer="371" to-port="1" /> + <edge from-layer="371" from-port="2" to-layer="373" to-port="0" /> + <edge from-layer="372" from-port="0" to-layer="373" to-port="1" /> + <edge from-layer="373" from-port="2" to-layer="374" to-port="0" /> + <edge from-layer="374" from-port="1" to-layer="376" to-port="0" /> + <edge from-layer="375" from-port="0" to-layer="376" to-port="1" /> + <edge from-layer="376" from-port="2" to-layer="378" to-port="0" /> + <edge from-layer="377" from-port="0" to-layer="378" to-port="1" /> + <edge from-layer="378" from-port="2" to-layer="405" to-port="0" /> + <edge from-layer="378" from-port="2" to-layer="393" to-port="0" /> + <edge from-layer="378" from-port="2" to-layer="382" to-port="0" /> + <edge from-layer="379" from-port="0" to-layer="393" to-port="1" /> + <edge from-layer="380" from-port="0" to-layer="391" to-port="0" /> + <edge from-layer="381" from-port="0" to-layer="391" to-port="1" /> + <edge from-layer="382" from-port="1" to-layer="385" to-port="0" /> + <edge from-layer="383" from-port="0" to-layer="385" to-port="1" /> + <edge from-layer="384" from-port="0" to-layer="385" to-port="2" /> + <edge from-layer="385" from-port="3" to-layer="387" to-port="0" /> + <edge from-layer="386" from-port="0" to-layer="387" to-port="1" /> + <edge from-layer="387" from-port="2" to-layer="389" to-port="0" /> + <edge from-layer="388" from-port="0" to-layer="389" to-port="1" /> + <edge from-layer="389" from-port="2" to-layer="391" to-port="2" /> + <edge from-layer="389" from-port="2" to-layer="399" to-port="2" /> + <edge from-layer="389" from-port="2" to-layer="402" to-port="0" /> + <edge from-layer="390" from-port="0" to-layer="391" to-port="3" /> + <edge from-layer="391" from-port="4" to-layer="393" to-port="2" /> + <edge from-layer="392" from-port="0" to-layer="393" to-port="3" /> + <edge from-layer="393" from-port="4" to-layer="394" to-port="1" /> + <edge from-layer="394" from-port="2" to-layer="410" to-port="1" /> + <edge from-layer="394" from-port="2" to-layer="407" to-port="0" /> + <edge from-layer="395" from-port="0" to-layer="406" to-port="0" /> + <edge from-layer="396" from-port="0" to-layer="399" to-port="0" /> + <edge from-layer="397" from-port="0" to-layer="399" to-port="1" /> + <edge from-layer="397" from-port="0" to-layer="403" to-port="1" /> + <edge from-layer="398" from-port="0" to-layer="399" to-port="3" /> + <edge from-layer="398" from-port="0" to-layer="403" to-port="3" /> + <edge from-layer="399" from-port="4" to-layer="405" to-port="1" /> + <edge from-layer="400" from-port="0" to-layer="403" to-port="0" /> + <edge from-layer="401" from-port="0" to-layer="402" to-port="1" /> + <edge from-layer="402" from-port="2" to-layer="403" to-port="2" /> + <edge from-layer="403" from-port="4" to-layer="405" to-port="2" /> + <edge from-layer="404" from-port="0" to-layer="405" to-port="3" /> + <edge from-layer="405" from-port="4" to-layer="406" to-port="1" /> + <edge from-layer="406" from-port="2" to-layer="410" to-port="0" /> + <edge from-layer="406" from-port="2" to-layer="407" to-port="1" /> + <edge from-layer="407" from-port="2" to-layer="409" to-port="0" /> + <edge from-layer="408" from-port="0" to-layer="409" to-port="1" /> + <edge from-layer="409" from-port="2" to-layer="411" to-port="0" /> + <edge from-layer="410" from-port="2" to-layer="411" to-port="1" /> + <edge from-layer="411" from-port="2" to-layer="413" to-port="0" /> + <edge from-layer="412" from-port="0" to-layer="413" to-port="1" /> + <edge from-layer="413" from-port="2" to-layer="415" to-port="0" /> + <edge from-layer="414" from-port="1" to-layer="415" to-port="1" /> + <edge from-layer="415" from-port="2" to-layer="416" to-port="0" /> + </edges> + <rt_info> + <MO_version value="2023.0.1-11005-fa1c41994f3-releases/2023/0" /> + <Runtime_version value="2023.0.1-11005-fa1c41994f3-releases/2023/0" /> + <conversion_parameters> + <framework value="onnx" /> + <input_model value="DIR/best.onnx" /> + <is_python_api_used value="True" /> + <model_name value="best" /> + </conversion_parameters> + <framework> + <author value="Ultralytics" /> + <batch value="1" /> + <date value="2023-09-01T09:31:25.195899" /> + <description value="Ultralytics best model trained on mqt_v3_42_3.yaml" /> + <imgsz value="[768, 768]" /> + <license value="AGPL-3.0 https://ultralytics.com/license" /> + <names value="{0: 'mosquito'}" /> + <stride value="32" /> + <task value="detect" /> + <version value="8.0.165" /> + </framework> + <legacy_frontend value="False" /> + <model_info> + <iou_threshold value="0.7" /> + <labels value="mosquito" /> + <model_type value="YOLOv8" /> + <pad_value value="114" /> + <resize_type value="fit_to_window_letterbox" /> + <reverse_input_channels value="YES" /> + <scale_values value="255" /> + </model_info> + </rt_info> +</net> diff --git a/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold3_1.4/best_openvino_model/metadata.yaml b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold3_1.4/best_openvino_model/metadata.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4feec3861c9511d797db2f61888647f89108bb03 --- /dev/null +++ b/my_models/yolov8_model_weights/yolov8n_v3_768_seed_42_fold3_1.4/best_openvino_model/metadata.yaml @@ -0,0 +1,13 @@ +description: Ultralytics best model trained on mqt_v3_42_3.yaml +author: Ultralytics +license: AGPL-3.0 https://ultralytics.com/license +date: '2023-09-01T09:31:25.195899' +version: 8.0.165 +stride: 32 +task: detect +batch: 1 +imgsz: +- 768 +- 768 +names: + 0: mosquito