OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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142 lines
4.6 KiB
142 lines
4.6 KiB
_base_ = '../_base_/default_runtime.py' |
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# model settings |
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model = dict( |
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type='YOLOV3', |
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backbone=dict( |
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type='MobileNetV2', |
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out_indices=(2, 4, 6), |
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act_cfg=dict(type='LeakyReLU', negative_slope=0.1), |
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init_cfg=dict( |
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type='Pretrained', checkpoint='open-mmlab://mmdet/mobilenet_v2')), |
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neck=dict( |
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type='YOLOV3Neck', |
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num_scales=3, |
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in_channels=[320, 96, 32], |
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out_channels=[96, 96, 96]), |
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bbox_head=dict( |
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type='YOLOV3Head', |
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num_classes=80, |
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in_channels=[96, 96, 96], |
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out_channels=[96, 96, 96], |
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anchor_generator=dict( |
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type='YOLOAnchorGenerator', |
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base_sizes=[[(116, 90), (156, 198), (373, 326)], |
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[(30, 61), (62, 45), (59, 119)], |
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[(10, 13), (16, 30), (33, 23)]], |
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strides=[32, 16, 8]), |
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bbox_coder=dict(type='YOLOBBoxCoder'), |
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featmap_strides=[32, 16, 8], |
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loss_cls=dict( |
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type='CrossEntropyLoss', |
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use_sigmoid=True, |
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loss_weight=1.0, |
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reduction='sum'), |
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loss_conf=dict( |
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type='CrossEntropyLoss', |
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use_sigmoid=True, |
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loss_weight=1.0, |
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reduction='sum'), |
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loss_xy=dict( |
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type='CrossEntropyLoss', |
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use_sigmoid=True, |
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loss_weight=2.0, |
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reduction='sum'), |
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loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum')), |
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# training and testing settings |
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train_cfg=dict( |
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assigner=dict( |
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type='GridAssigner', |
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pos_iou_thr=0.5, |
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neg_iou_thr=0.5, |
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min_pos_iou=0)), |
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test_cfg=dict( |
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nms_pre=1000, |
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min_bbox_size=0, |
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score_thr=0.05, |
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conf_thr=0.005, |
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nms=dict(type='nms', iou_threshold=0.45), |
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max_per_img=100)) |
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# dataset settings |
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dataset_type = 'CocoDataset' |
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data_root = 'data/coco/' |
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img_norm_cfg = dict( |
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) |
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train_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict(type='LoadAnnotations', with_bbox=True), |
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dict( |
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type='Expand', |
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mean=img_norm_cfg['mean'], |
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to_rgb=img_norm_cfg['to_rgb'], |
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ratio_range=(1, 2)), |
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dict( |
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type='MinIoURandomCrop', |
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min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9), |
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min_crop_size=0.3), |
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dict( |
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type='Resize', |
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img_scale=[(320, 320), (416, 416)], |
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multiscale_mode='range', |
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keep_ratio=True), |
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dict(type='RandomFlip', flip_ratio=0.5), |
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dict(type='PhotoMetricDistortion'), |
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dict(type='Normalize', **img_norm_cfg), |
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dict(type='Pad', size_divisor=32), |
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dict(type='DefaultFormatBundle'), |
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dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) |
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] |
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test_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict( |
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type='MultiScaleFlipAug', |
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img_scale=(416, 416), |
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flip=False, |
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transforms=[ |
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dict(type='Resize', keep_ratio=True), |
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dict(type='RandomFlip'), |
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dict(type='Normalize', **img_norm_cfg), |
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dict(type='Pad', size_divisor=32), |
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dict(type='DefaultFormatBundle'), |
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dict(type='Collect', keys=['img']) |
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]) |
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] |
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data = dict( |
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samples_per_gpu=24, |
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workers_per_gpu=4, |
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train=dict( |
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type='RepeatDataset', # use RepeatDataset to speed up training |
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times=10, |
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dataset=dict( |
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type=dataset_type, |
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ann_file=data_root + 'annotations/instances_train2017.json', |
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img_prefix=data_root + 'train2017/', |
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pipeline=train_pipeline)), |
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val=dict( |
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type=dataset_type, |
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ann_file=data_root + 'annotations/instances_val2017.json', |
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img_prefix=data_root + 'val2017/', |
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pipeline=test_pipeline), |
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test=dict( |
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type=dataset_type, |
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ann_file=data_root + 'annotations/instances_val2017.json', |
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img_prefix=data_root + 'val2017/', |
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pipeline=test_pipeline)) |
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# optimizer |
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optimizer = dict(type='SGD', lr=0.003, momentum=0.9, weight_decay=0.0005) |
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optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) |
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# learning policy |
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lr_config = dict( |
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policy='step', |
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warmup='linear', |
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warmup_iters=4000, |
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warmup_ratio=0.0001, |
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step=[24, 28]) |
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# runtime settings |
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runner = dict(type='EpochBasedRunner', max_epochs=30) |
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evaluation = dict(interval=1, metric=['bbox']) |
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find_unused_parameters = True |
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# NOTE: `auto_scale_lr` is for automatically scaling LR, |
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# USER SHOULD NOT CHANGE ITS VALUES. |
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# base_batch_size = (8 GPUs) x (24 samples per GPU) |
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auto_scale_lr = dict(base_batch_size=192)
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