mirror of https://github.com/open-mmlab/mmpose
141 lines
4.4 KiB
Python
141 lines
4.4 KiB
Python
# model settings
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model = dict(
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type='YOLOV3',
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pretrained='open-mmlab://darknet53',
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backbone=dict(type='Darknet', depth=53, out_indices=(3, 4, 5)),
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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=[1024, 512, 256],
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out_channels=[512, 256, 128]),
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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=[512, 256, 128],
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out_channels=[1024, 512, 256],
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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(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile', to_float32=True),
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dict(type='LoadAnnotations', with_bbox=True),
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dict(type='PhotoMetricDistortion'),
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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(type='Resize', img_scale=(320, 320), keep_ratio=True),
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dict(type='RandomFlip', flip_ratio=0.5),
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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=(320, 320),
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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=8,
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workers_per_gpu=4,
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train=dict(
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type=dataset_type,
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ann_file=f'{data_root}/annotations/instances_train2017.json',
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img_prefix=f'{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=f'{data_root}/annotations/instances_val2017.json',
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img_prefix=f'{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=f'{data_root}/annotations/instances_val2017.json',
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img_prefix=f'{data_root}/val2017/',
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pipeline=test_pipeline))
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# optimizer
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optimizer = dict(type='SGD', lr=0.001, 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=2000, # same as burn-in in darknet
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warmup_ratio=0.1,
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step=[218, 246])
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# runtime settings
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runner = dict(type='EpochBasedRunner', max_epochs=273)
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evaluation = dict(interval=1, metric=['bbox'])
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checkpoint_config = dict(interval=1)
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# yapf:disable
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log_config = dict(
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interval=50,
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hooks=[
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dict(type='TextLoggerHook'),
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# dict(type='TensorboardLoggerHook')
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])
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# yapf:enable
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custom_hooks = [dict(type='NumClassCheckHook')]
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dist_params = dict(backend='nccl')
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log_level = 'INFO'
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load_from = None
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resume_from = None
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workflow = [('train', 1)]
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