mirror of https://github.com/open-mmlab/mmpose
301 lines
10 KiB
Python
301 lines
10 KiB
Python
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=300, val_interval=10)
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val_cfg = dict(type='ValLoop')
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test_cfg = dict(type='TestLoop')
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param_scheduler = [
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dict(
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type='mmdet.QuadraticWarmupLR',
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by_epoch=True,
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begin=0,
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end=5,
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convert_to_iter_based=True),
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dict(
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type='CosineAnnealingLR',
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eta_min=0.0005,
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begin=5,
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T_max=285,
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end=285,
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by_epoch=True,
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convert_to_iter_based=True),
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dict(type='ConstantLR', by_epoch=True, factor=1, begin=285, end=300)
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]
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optim_wrapper = dict(
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type='OptimWrapper',
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optimizer=dict(
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type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005, nesterov=True),
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paramwise_cfg=dict(norm_decay_mult=0.0, bias_decay_mult=0.0))
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auto_scale_lr = dict(enable=False, base_batch_size=64)
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default_scope = 'mmdet'
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default_hooks = dict(
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timer=dict(type='IterTimerHook'),
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logger=dict(type='LoggerHook', interval=50),
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param_scheduler=dict(type='ParamSchedulerHook'),
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checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=3),
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sampler_seed=dict(type='DistSamplerSeedHook'),
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visualization=dict(type='DetVisualizationHook'))
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env_cfg = dict(
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cudnn_benchmark=False,
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mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
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dist_cfg=dict(backend='nccl'))
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vis_backends = [dict(type='LocalVisBackend')]
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visualizer = dict(
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type='DetLocalVisualizer',
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vis_backends=[dict(type='LocalVisBackend')],
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name='visualizer')
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log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
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log_level = 'INFO'
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load_from = 'https://download.openmmlab.com/mmdetection/' \
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'v2.0/yolox/yolox_s_8x8_300e_coco/' \
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'yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth'
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resume = False
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img_scale = (640, 640)
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model = dict(
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type='YOLOX',
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data_preprocessor=dict(
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type='DetDataPreprocessor',
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pad_size_divisor=32,
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batch_augments=[
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dict(
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type='BatchSyncRandomResize',
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random_size_range=(480, 800),
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size_divisor=32,
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interval=10)
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]),
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backbone=dict(
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type='CSPDarknet',
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deepen_factor=0.33,
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widen_factor=0.5,
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out_indices=(2, 3, 4),
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use_depthwise=False,
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spp_kernal_sizes=(5, 9, 13),
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norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
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act_cfg=dict(type='Swish')),
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neck=dict(
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type='YOLOXPAFPN',
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in_channels=[128, 256, 512],
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out_channels=128,
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num_csp_blocks=1,
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use_depthwise=False,
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upsample_cfg=dict(scale_factor=2, mode='nearest'),
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norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
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act_cfg=dict(type='Swish')),
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bbox_head=dict(
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type='YOLOXHead',
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num_classes=1,
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in_channels=128,
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feat_channels=128,
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stacked_convs=2,
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strides=(8, 16, 32),
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use_depthwise=False,
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norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
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act_cfg=dict(type='Swish'),
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loss_cls=dict(
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type='CrossEntropyLoss',
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use_sigmoid=True,
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reduction='sum',
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loss_weight=1.0),
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loss_bbox=dict(
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type='IoULoss',
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mode='square',
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eps=1e-16,
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reduction='sum',
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loss_weight=5.0),
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loss_obj=dict(
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type='CrossEntropyLoss',
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use_sigmoid=True,
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reduction='sum',
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loss_weight=1.0),
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loss_l1=dict(type='L1Loss', reduction='sum', loss_weight=1.0)),
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train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)),
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test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))
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data_root = 'data/coco/'
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dataset_type = 'CocoDataset'
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backend_args = dict(backend='local')
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train_pipeline = [
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dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0),
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dict(
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type='RandomAffine', scaling_ratio_range=(0.1, 2),
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border=(-320, -320)),
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dict(
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type='MixUp',
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img_scale=(640, 640),
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ratio_range=(0.8, 1.6),
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pad_val=114.0),
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dict(type='YOLOXHSVRandomAug'),
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dict(type='RandomFlip', prob=0.5),
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dict(type='Resize', scale=(640, 640), keep_ratio=True),
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dict(
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type='Pad',
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pad_to_square=True,
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pad_val=dict(img=(114.0, 114.0, 114.0))),
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dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
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dict(type='PackDetInputs')
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]
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train_dataset = dict(
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type='MultiImageMixDataset',
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dataset=dict(
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type='CocoDataset',
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data_root='data/coco/',
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ann_file='annotations/instances_train2017.json',
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data_prefix=dict(img='train2017/'),
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pipeline=[
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dict(type='LoadImageFromFile', backend_args=dict(backend='local')),
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dict(type='LoadAnnotations', with_bbox=True)
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],
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filter_cfg=dict(filter_empty_gt=False, min_size=32)),
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pipeline=[
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dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0),
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dict(
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type='RandomAffine',
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scaling_ratio_range=(0.1, 2),
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border=(-320, -320)),
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dict(
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type='MixUp',
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img_scale=(640, 640),
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ratio_range=(0.8, 1.6),
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pad_val=114.0),
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dict(type='YOLOXHSVRandomAug'),
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dict(type='RandomFlip', prob=0.5),
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dict(type='Resize', scale=(640, 640), keep_ratio=True),
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dict(
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type='Pad',
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pad_to_square=True,
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pad_val=dict(img=(114.0, 114.0, 114.0))),
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dict(
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type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
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dict(type='PackDetInputs')
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])
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test_pipeline = [
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dict(type='LoadImageFromFile', backend_args=dict(backend='local')),
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dict(type='Resize', scale=(640, 640), keep_ratio=True),
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dict(
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type='Pad',
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pad_to_square=True,
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pad_val=dict(img=(114.0, 114.0, 114.0))),
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dict(type='LoadAnnotations', with_bbox=True),
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dict(
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type='PackDetInputs',
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meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
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'scale_factor'))
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]
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train_dataloader = dict(
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batch_size=8,
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num_workers=4,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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dataset=dict(
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type='MultiImageMixDataset',
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dataset=dict(
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type='CocoDataset',
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data_root='data/coco/',
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ann_file='annotations/coco_face_train.json',
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data_prefix=dict(img='train2017/'),
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pipeline=[
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dict(
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type='LoadImageFromFile',
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backend_args=dict(backend='local')),
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dict(type='LoadAnnotations', with_bbox=True)
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],
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filter_cfg=dict(filter_empty_gt=False, min_size=32),
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metainfo=dict(CLASSES=('person', ), PALETTE=(220, 20, 60))),
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pipeline=[
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dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0),
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dict(
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type='RandomAffine',
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scaling_ratio_range=(0.1, 2),
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border=(-320, -320)),
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dict(
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type='MixUp',
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img_scale=(640, 640),
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ratio_range=(0.8, 1.6),
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pad_val=114.0),
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dict(type='YOLOXHSVRandomAug'),
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dict(type='RandomFlip', prob=0.5),
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dict(type='Resize', scale=(640, 640), keep_ratio=True),
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dict(
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type='Pad',
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pad_to_square=True,
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pad_val=dict(img=(114.0, 114.0, 114.0))),
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dict(
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type='FilterAnnotations',
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min_gt_bbox_wh=(1, 1),
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keep_empty=False),
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dict(type='PackDetInputs')
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]))
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val_dataloader = dict(
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batch_size=8,
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num_workers=4,
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persistent_workers=True,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type='CocoDataset',
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data_root='data/coco/',
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ann_file='annotations/coco_face_val.json',
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data_prefix=dict(img='val2017/'),
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test_mode=True,
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pipeline=[
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dict(type='LoadImageFromFile', backend_args=dict(backend='local')),
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dict(type='Resize', scale=(640, 640), keep_ratio=True),
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dict(
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type='Pad',
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pad_to_square=True,
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pad_val=dict(img=(114.0, 114.0, 114.0))),
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dict(type='LoadAnnotations', with_bbox=True),
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dict(
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type='PackDetInputs',
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meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
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'scale_factor'))
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],
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metainfo=dict(CLASSES=('person', ), PALETTE=(220, 20, 60))))
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test_dataloader = dict(
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batch_size=8,
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num_workers=4,
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persistent_workers=True,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type='CocoDataset',
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data_root='data/coco/',
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ann_file='annotations/coco_face_val.json',
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data_prefix=dict(img='val2017/'),
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test_mode=True,
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pipeline=[
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dict(type='LoadImageFromFile', backend_args=dict(backend='local')),
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dict(type='Resize', scale=(640, 640), keep_ratio=True),
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dict(
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type='Pad',
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pad_to_square=True,
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pad_val=dict(img=(114.0, 114.0, 114.0))),
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dict(type='LoadAnnotations', with_bbox=True),
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dict(
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type='PackDetInputs',
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meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
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'scale_factor'))
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],
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metainfo=dict(CLASSES=('person', ), PALETTE=(220, 20, 60))))
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val_evaluator = dict(
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type='CocoMetric',
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ann_file='data/coco/annotations/coco_face_val.json',
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metric='bbox')
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test_evaluator = dict(
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type='CocoMetric',
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ann_file='data/coco/annotations/instances_val2017.json',
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metric='bbox')
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max_epochs = 300
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num_last_epochs = 15
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interval = 10
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base_lr = 0.01
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custom_hooks = [
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dict(type='YOLOXModeSwitchHook', num_last_epochs=15, priority=48),
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dict(type='SyncNormHook', priority=48),
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dict(
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type='EMAHook',
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ema_type='ExpMomentumEMA',
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momentum=0.0001,
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strict_load=False,
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update_buffers=True,
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priority=49)
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]
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metainfo = dict(CLASSES=('person', ), PALETTE=(220, 20, 60))
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launcher = 'pytorch'
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