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
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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
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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
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+{"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
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+++ 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>
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+ <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