_base_ = '../../../_base_/default_runtime.py' # runtime train_cfg = dict( _delete_=True, type='EpochBasedTrainLoop', max_epochs=300, val_interval=10, dynamic_intervals=[(280, 1)]) auto_scale_lr = dict(base_batch_size=256) default_hooks = dict( checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=3)) optim_wrapper = dict( type='OptimWrapper', optimizer=dict(type='AdamW', lr=0.004, weight_decay=0.05), paramwise_cfg=dict( norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True, ), clip_grad=dict(max_norm=0.1, norm_type=2)) param_scheduler = [ dict( type='QuadraticWarmupLR', by_epoch=True, begin=0, end=5, convert_to_iter_based=True), dict( type='CosineAnnealingLR', eta_min=0.0002, begin=5, T_max=280, end=280, by_epoch=True, convert_to_iter_based=True), dict(type='ConstantLR', by_epoch=True, factor=1, begin=280, end=300), ] # model widen_factor = 0.5 deepen_factor = 0.33 model = dict( type='BottomupPoseEstimator', init_cfg=dict( type='Kaiming', layer='Conv2d', a=2.23606797749979, distribution='uniform', mode='fan_in', nonlinearity='leaky_relu'), data_preprocessor=dict( type='PoseDataPreprocessor', pad_size_divisor=32, mean=[0, 0, 0], std=[1, 1, 1], batch_augments=[ dict( type='BatchSyncRandomResize', random_size_range=(480, 800), size_divisor=32, interval=1), ]), backbone=dict( type='CSPDarknet', deepen_factor=deepen_factor, widen_factor=widen_factor, out_indices=(2, 3, 4), spp_kernal_sizes=(5, 9, 13), norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), act_cfg=dict(type='Swish'), init_cfg=dict( type='Pretrained', checkpoint='https://download.openmmlab.com/mmdetection/v2.0/' 'yolox/yolox_s_8x8_300e_coco/yolox_s_8x8_300e_coco_' '20211121_095711-4592a793.pth', prefix='backbone.', )), neck=dict( type='YOLOXPAFPN', in_channels=[128, 256, 512], out_channels=128, num_csp_blocks=1, use_depthwise=False, upsample_cfg=dict(scale_factor=2, mode='nearest'), norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), act_cfg=dict(type='Swish')), head=dict( type='YOLOXPoseHead', num_keypoints=17, featmap_strides=(8, 16, 32), head_module_cfg=dict( num_classes=1, in_channels=256, feat_channels=256, widen_factor=widen_factor, stacked_convs=2, norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), act_cfg=dict(type='Swish')), prior_generator=dict( type='MlvlPointGenerator', offset=0, strides=[8, 16, 32]), assigner=dict(type='SimOTAAssigner', dynamic_k_indicator='oks'), overlaps_power=0.5, loss_cls=dict(type='BCELoss', reduction='sum', loss_weight=1.0), loss_bbox=dict( type='IoULoss', mode='square', eps=1e-16, reduction='sum', loss_weight=5.0), loss_obj=dict( type='BCELoss', use_target_weight=True, reduction='sum', loss_weight=1.0), loss_oks=dict( type='OKSLoss', reduction='none', metainfo='configs/_base_/datasets/coco.py', norm_target_weight=True, loss_weight=30.0), loss_vis=dict( type='BCELoss', use_target_weight=True, reduction='mean', loss_weight=1.0), loss_bbox_aux=dict(type='L1Loss', reduction='sum', loss_weight=1.0), ), test_cfg=dict( score_thr=0.01, nms_thr=0.65, )) # data input_size = (640, 640) codec = dict(type='YOLOXPoseAnnotationProcessor', input_size=input_size) train_pipeline_stage1 = [ dict(type='LoadImage', backend_args=None), dict( type='Mosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[dict(type='LoadImage', backend_args=None)]), dict( type='BottomupRandomAffine', input_size=(640, 640), shift_factor=0.1, rotate_factor=10, scale_factor=(0.75, 1.0), pad_val=114, distribution='uniform', transform_mode='perspective', bbox_keep_corner=False, clip_border=True, ), dict( type='YOLOXMixUp', img_scale=(640, 640), ratio_range=(0.8, 1.6), pad_val=114.0, pre_transform=[dict(type='LoadImage', backend_args=None)]), dict(type='YOLOXHSVRandomAug'), dict(type='RandomFlip'), dict(type='FilterAnnotations', by_kpt=True, by_box=True, keep_empty=False), dict(type='GenerateTarget', encoder=codec), dict(type='PackPoseInputs'), ] train_pipeline_stage2 = [ dict(type='LoadImage'), dict( type='BottomupRandomAffine', input_size=(640, 640), shift_prob=0, rotate_prob=0, scale_prob=0, scale_type='long', pad_val=(114, 114, 114), bbox_keep_corner=False, clip_border=True, ), dict(type='YOLOXHSVRandomAug'), dict(type='RandomFlip'), dict(type='FilterAnnotations', by_kpt=True, by_box=True, keep_empty=False), dict(type='GenerateTarget', encoder=codec), dict(type='PackPoseInputs'), ] data_mode = 'bottomup' data_root = 'data/' dataset_coco = dict( type='CocoDataset', data_root=data_root, data_mode=data_mode, filter_cfg=dict(filter_empty_gt=False, min_size=32), ann_file='coco/annotations/person_keypoints_train2017.json', data_prefix=dict(img='coco/train2017/'), pipeline=train_pipeline_stage1, ) train_dataloader = dict( batch_size=32, num_workers=8, persistent_workers=True, pin_memory=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dataset_coco) val_pipeline = [ dict(type='LoadImage'), dict( type='BottomupResize', input_size=input_size, pad_val=(114, 114, 114)), dict( type='PackPoseInputs', meta_keys=('id', 'img_id', 'img_path', 'ori_shape', 'img_shape', 'input_size', 'input_center', 'input_scale')) ] val_dataloader = dict( batch_size=1, num_workers=2, persistent_workers=True, pin_memory=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type='CocoDataset', data_root=data_root, data_mode=data_mode, ann_file='coco/annotations/person_keypoints_val2017.json', data_prefix=dict(img='coco/val2017/'), test_mode=True, pipeline=val_pipeline, )) test_dataloader = val_dataloader # evaluators val_evaluator = dict( type='CocoMetric', ann_file=data_root + 'coco/annotations/person_keypoints_val2017.json', score_mode='bbox', nms_mode='none', ) test_evaluator = val_evaluator custom_hooks = [ dict( type='YOLOXPoseModeSwitchHook', num_last_epochs=20, new_train_pipeline=train_pipeline_stage2, priority=48), dict(type='SyncNormHook', priority=48), dict( type='EMAHook', ema_type='ExpMomentumEMA', momentum=0.0002, update_buffers=True, strict_load=False, priority=49), ]