From d568f7bfcfae242cdb2ee03bd130e8ce7d2fc27d Mon Sep 17 00:00:00 2001
From: Kai Chen <chenkaidev@gmail.com>
Date: Wed, 12 Dec 2018 14:24:42 +0800
Subject: [PATCH] add voc training configs and eval script
---
.../faster_rcnn_r50_fpn_1x_voc0712.py | 157 ++++++++++++++++++
tools/voc_eval.py | 62 +++++++
2 files changed, 219 insertions(+)
create mode 100644 configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py
create mode 100644 tools/voc_eval.py
diff --git a/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py b/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py
new file mode 100644
index 0000000..cae0a06
--- /dev/null
+++ b/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py
@@ -0,0 +1,157 @@
+# model settings
+model = dict(
+ type='FasterRCNN',
+ pretrained='modelzoo://resnet50',
+ backbone=dict(
+ type='ResNet',
+ depth=50,
+ num_stages=4,
+ out_indices=(0, 1, 2, 3),
+ frozen_stages=1,
+ style='pytorch'),
+ neck=dict(
+ type='FPN',
+ in_channels=[256, 512, 1024, 2048],
+ out_channels=256,
+ num_outs=5),
+ rpn_head=dict(
+ type='RPNHead',
+ in_channels=256,
+ feat_channels=256,
+ anchor_scales=[8],
+ anchor_ratios=[0.5, 1.0, 2.0],
+ anchor_strides=[4, 8, 16, 32, 64],
+ target_means=[.0, .0, .0, .0],
+ target_stds=[1.0, 1.0, 1.0, 1.0],
+ use_sigmoid_cls=True),
+ bbox_roi_extractor=dict(
+ type='SingleRoIExtractor',
+ roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
+ out_channels=256,
+ featmap_strides=[4, 8, 16, 32]),
+ bbox_head=dict(
+ type='SharedFCBBoxHead',
+ num_fcs=2,
+ in_channels=256,
+ fc_out_channels=1024,
+ roi_feat_size=7,
+ num_classes=21,
+ target_means=[0., 0., 0., 0.],
+ target_stds=[0.1, 0.1, 0.2, 0.2],
+ reg_class_agnostic=False))
+# model training and testing settings
+train_cfg = dict(
+ rpn=dict(
+ assigner=dict(
+ type='MaxIoUAssigner',
+ pos_iou_thr=0.7,
+ neg_iou_thr=0.3,
+ min_pos_iou=0.3,
+ ignore_iof_thr=-1),
+ sampler=dict(
+ type='RandomSampler',
+ num=256,
+ pos_fraction=0.5,
+ neg_pos_ub=-1,
+ add_gt_as_proposals=False),
+ allowed_border=0,
+ pos_weight=-1,
+ smoothl1_beta=1 / 9.0,
+ debug=False),
+ rcnn=dict(
+ assigner=dict(
+ type='MaxIoUAssigner',
+ pos_iou_thr=0.5,
+ neg_iou_thr=0.5,
+ min_pos_iou=0.5,
+ ignore_iof_thr=-1),
+ sampler=dict(
+ type='RandomSampler',
+ num=512,
+ pos_fraction=0.25,
+ neg_pos_ub=-1,
+ add_gt_as_proposals=True),
+ pos_weight=-1,
+ debug=False))
+test_cfg = dict(
+ rpn=dict(
+ nms_across_levels=False,
+ nms_pre=2000,
+ nms_post=2000,
+ max_num=2000,
+ nms_thr=0.7,
+ min_bbox_size=0),
+ rcnn=dict(
+ score_thr=0.05, nms=dict(type='nms', iou_thr=0.5), max_per_img=100)
+ # soft-nms is also supported for rcnn testing
+ # e.g., nms=dict(type='soft_nms', iou_thr=0.5, min_score=0.05)
+)
+# dataset settings
+dataset_type = 'VOCDataset'
+data_root = 'data/VOCdevkit/'
+img_norm_cfg = dict(
+ mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+data = dict(
+ imgs_per_gpu=2,
+ workers_per_gpu=2,
+ train=dict(
+ type='RepeatDataset', # to avoid reloading datasets frequently
+ times=3,
+ dataset=dict(
+ type=dataset_type,
+ ann_file=[
+ data_root + 'VOC2007/ImageSets/Main/trainval.txt',
+ data_root + 'VOC2012/ImageSets/Main/trainval.txt'
+ ],
+ img_prefix=[data_root + 'VOC2007/', data_root + 'VOC2012/'],
+ img_scale=(1000, 600),
+ img_norm_cfg=img_norm_cfg,
+ size_divisor=32,
+ flip_ratio=0.5,
+ with_mask=False,
+ with_crowd=True,
+ with_label=True)),
+ val=dict(
+ type=dataset_type,
+ ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt',
+ img_prefix=data_root + 'VOC2007/',
+ img_scale=(1000, 600),
+ img_norm_cfg=img_norm_cfg,
+ size_divisor=32,
+ flip_ratio=0,
+ with_mask=False,
+ with_crowd=True,
+ with_label=True),
+ test=dict(
+ type=dataset_type,
+ ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt',
+ img_prefix=data_root + 'VOC2007/',
+ img_scale=(1000, 600),
+ img_norm_cfg=img_norm_cfg,
+ size_divisor=32,
+ flip_ratio=0,
+ with_mask=False,
+ with_label=False,
+ test_mode=True))
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+# learning policy
+lr_config = dict(policy='step', step=[3]) # actual epoch = 3 * 3 = 9
+checkpoint_config = dict(interval=1)
+# yapf:disable
+log_config = dict(
+ interval=50,
+ hooks=[
+ dict(type='TextLoggerHook'),
+ # dict(type='TensorboardLoggerHook')
+ ])
+# yapf:enable
+# runtime settings
+total_epochs = 4 # actual epoch = 4 * 3 = 12
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = './work_dirs/faster_rcnn_r50_fpn_1x_voc0712'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]
diff --git a/tools/voc_eval.py b/tools/voc_eval.py
new file mode 100644
index 0000000..478ec3c
--- /dev/null
+++ b/tools/voc_eval.py
@@ -0,0 +1,62 @@
+from argparse import ArgumentParser
+
+import mmcv
+import numpy as np
+
+from mmdet import datasets
+from mmdet.core import eval_map
+
+
+def voc_eval(result_file, dataset, iou_thr=0.5):
+ det_results = mmcv.load(result_file)
+ gt_bboxes = []
+ gt_labels = []
+ gt_ignore = []
+ for i in range(len(dataset)):
+ ann = dataset.get_ann_info(i)
+ bboxes = ann['bboxes']
+ labels = ann['labels']
+ if 'bboxes_ignore' in ann:
+ ignore = np.concatenate([
+ np.zeros(bboxes.shape[0], dtype=np.bool),
+ np.ones(ann['bboxes_ignore'].shape[0], dtype=np.bool)
+ ])
+ gt_ignore.append(ignore)
+ bboxes = np.vstack([bboxes, ann['bboxes_ignore']])
+ labels = np.concatenate([labels, ann['labels_ignore']])
+ gt_bboxes.append(bboxes)
+ gt_labels.append(labels)
+ if not gt_ignore:
+ gt_ignore = gt_ignore
+ if hasattr(dataset, 'year') and dataset.year == 2007:
+ dataset_name = 'voc07'
+ else:
+ dataset_name = dataset.CLASSES
+ eval_map(
+ det_results,
+ gt_bboxes,
+ gt_labels,
+ gt_ignore=gt_ignore,
+ scale_ranges=None,
+ iou_thr=iou_thr,
+ dataset=dataset_name,
+ print_summary=True)
+
+
+def main():
+ parser = ArgumentParser(description='VOC Evaluation')
+ parser.add_argument('result', help='result file path')
+ parser.add_argument('config', help='config file path')
+ parser.add_argument(
+ '--iou-thr',
+ type=float,
+ default=0.5,
+ help='IoU threshold for evaluation')
+ args = parser.parse_args()
+ cfg = mmcv.Config.fromfile(args.config)
+ test_dataset = mmcv.runner.obj_from_dict(cfg.data.test, datasets)
+ voc_eval(args.result, test_dataset, args.iou_thr)
+
+
+if __name__ == '__main__':
+ main()
--
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