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| from config import cfg | |
| log_level = 'INFO' | |
| load_from = None | |
| resume_from = None | |
| dist_params = dict(backend='nccl') | |
| workflow = [('train', 1)] | |
| checkpoint_config = dict(interval=10) | |
| evaluation = dict(interval=25, metric='mAP', key_indicator='AP', rle_score=True) | |
| optimizer = dict( | |
| type='AdamW', | |
| lr=1e-3, | |
| weight_decay=1e-4, | |
| paramwise_cfg = dict( | |
| custom_keys={ | |
| # 'backbone': dict(lr_mult=0.1), | |
| 'sampling_offsets': dict(lr_mult=0.1), | |
| 'reference_points': dict(lr_mult=0.1), | |
| # 'query_embed': dict(lr_mult=0.5, decay_mult=1.0), | |
| }, | |
| ) | |
| ) | |
| optimizer_config = dict(grad_clip=None) | |
| lr_config = dict( | |
| policy='step', | |
| warmup='linear', | |
| warmup_iters=500, | |
| warmup_ratio=0.001, | |
| step=[255, 310]) | |
| total_epochs = 325 | |
| log_config = dict( | |
| interval=50, hooks=[ | |
| dict(type='TextLoggerHook'), | |
| dict(type='TensorboardLoggerHook'), | |
| ]) | |
| channel_cfg = dict( | |
| num_output_channels=20, | |
| dataset_joints=20, | |
| dataset_channel=[ | |
| [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], | |
| ], | |
| inference_channel=[ | |
| 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 | |
| ]) | |
| emb_dim = 256 | |
| if cfg.upscale==1: | |
| neck_in_channels = [cfg.feat_dim] | |
| num_levels = 1 | |
| elif cfg.upscale==2: | |
| neck_in_channels = [cfg.feat_dim//2, cfg.feat_dim] | |
| # neck_in_channels = [768, 768] | |
| num_levels = 2 | |
| elif cfg.upscale==4: | |
| neck_in_channels = [cfg.feat_dim//4, cfg.feat_dim//2, cfg.feat_dim] | |
| # neck_in_channels = [768, 768, 768] | |
| num_levels = 3 | |
| elif cfg.upscale==8: | |
| neck_in_channels = [cfg.feat_dim//8, cfg.feat_dim//4, cfg.feat_dim//2, cfg.feat_dim] | |
| # neck_in_channels = [768, 768, 768, 768] | |
| num_levels = 4 | |
| # model settings | |
| norm_cfg = dict(type='BN', requires_grad=True) | |
| # norm_cfg = dict(type='SyncBN', requires_grad=True) | |
| model = dict( | |
| type='Poseur', | |
| pretrained='torchvision://resnet50', | |
| backbone=dict(type='ResNet', norm_cfg = norm_cfg, depth=50, num_stages=4, out_indices=(0, 1, 2, 3)), | |
| neck=dict( | |
| type='ChannelMapper', | |
| in_channels=neck_in_channels, | |
| kernel_size=1, | |
| out_channels=emb_dim, | |
| act_cfg=None, | |
| norm_cfg=dict(type='GN', num_groups=32), | |
| ), | |
| keypoint_head=dict( | |
| type='Poseur_noise_sample', | |
| in_channels=512, | |
| num_queries=channel_cfg['num_output_channels'], | |
| num_reg_fcs=2, | |
| num_joints=channel_cfg['num_output_channels'], | |
| with_box_refine=True, | |
| loss_coord_enc=dict(type='RLELoss_poseur', use_target_weight=True), | |
| loss_coord_dec=dict(type='RLELoss_poseur', use_target_weight=True), | |
| # loss_coord_dec=dict(type='L1Loss', use_target_weight=True, loss_weight=5), | |
| loss_hp_keypoint=dict(type='JointsMSELoss', use_target_weight=True, loss_weight=10), | |
| # loss_coord_keypoint=dict(type='L1Loss', use_target_weight=True, loss_weight=1), | |
| positional_encoding=dict( | |
| type='SinePositionalEncoding', | |
| num_feats=emb_dim//2, | |
| normalize=True, | |
| offset=-0.5), | |
| transformer=dict( | |
| type='PoseurTransformer_v3', | |
| num_joints=channel_cfg['num_output_channels'], | |
| query_pose_emb = True, | |
| embed_dims = emb_dim, | |
| encoder=dict( | |
| type='DetrTransformerEncoder_zero_layer', | |
| num_layers=0, | |
| transformerlayers=dict( | |
| type='BaseTransformerLayer', | |
| ffn_cfgs = dict( | |
| embed_dims=emb_dim, | |
| ), | |
| attn_cfgs=dict( | |
| type='MultiScaleDeformableAttention', | |
| num_levels=num_levels, | |
| num_points=4, | |
| embed_dims=emb_dim), | |
| feedforward_channels=1024, | |
| ffn_dropout=0.1, | |
| operation_order=('self_attn', 'norm', 'ffn', 'norm'))), | |
| decoder=dict( | |
| type='DeformableDetrTransformerDecoder', | |
| num_layers=6, | |
| return_intermediate=True, | |
| transformerlayers=dict( | |
| type='DetrTransformerDecoderLayer_grouped', | |
| ffn_cfgs = dict( | |
| embed_dims=emb_dim, | |
| ), | |
| attn_cfgs=[ | |
| dict( | |
| type='MultiheadAttention', | |
| embed_dims=emb_dim, | |
| num_heads=8, | |
| dropout=0.1), | |
| dict( | |
| type='MultiScaleDeformableAttention_post_value', | |
| num_levels=num_levels, | |
| num_points=4, | |
| embed_dims=emb_dim) | |
| ], | |
| feedforward_channels=1024, | |
| num_joints=channel_cfg['num_output_channels'], | |
| ffn_dropout=0.1, | |
| operation_order=('self_attn', 'norm', 'cross_attn', 'norm', | |
| 'ffn', 'norm')))), | |
| as_two_stage=True, | |
| use_heatmap_loss=False, | |
| ), | |
| train_cfg=dict(image_size=[192, 256]), | |
| test_cfg = dict( | |
| image_size=[192, 256], | |
| flip_test=True, | |
| post_process='default', | |
| shift_heatmap=True, | |
| modulate_kernel=11) | |
| ) | |
| data_cfg = dict( | |
| image_size=[192, 256], | |
| heatmap_size=[48, 64], | |
| num_output_channels=channel_cfg['num_output_channels'], | |
| num_joints=channel_cfg['dataset_joints'], | |
| dataset_channel=channel_cfg['dataset_channel'], | |
| inference_channel=channel_cfg['inference_channel'], | |
| soft_nms=False, | |
| # use_nms=False, | |
| nms_thr=1.0, | |
| oks_thr=0.9, | |
| vis_thr=0.2, | |
| det_bbox_thr=0.0, | |
| # use_gt_bbox=True, | |
| # bbox_file='', | |
| use_gt_bbox=False, | |
| bbox_file='data/coco/person_detection_results/' | |
| 'COCO_val2017_detections_AP_H_56_person.json', | |
| ) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='TopDownGetBboxCenterScale', padding=1.25), | |
| dict(type='TopDownRandomFlip', flip_prob=0.5), | |
| dict( | |
| type='TopDownHalfBodyTransform', | |
| num_joints_half_body=8, | |
| prob_half_body=0.3), | |
| dict( | |
| type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), | |
| dict(type='TopDownAffine'), | |
| dict(type='ToTensor'), | |
| dict( | |
| type='NormalizeTensor', | |
| mean=[0.485, 0.456, 0.406], | |
| std=[0.229, 0.224, 0.225]), | |
| # dict( | |
| # type='TopDownGenerateTarget', | |
| # kernel=[(11, 11), (9, 9), (7, 7), (5, 5)], | |
| # encoding='Megvii'), | |
| dict( | |
| target_type='wo_mask', | |
| type='TopDownGenerateCoordAndHeatMapTarget', | |
| encoding='MSRA', | |
| sigma=2), | |
| dict( | |
| type='Collect', | |
| keys=['img', 'coord_target', 'coord_target_weight', 'hp_target', 'hp_target_weight'], | |
| meta_keys=[ | |
| 'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', | |
| 'rotation', 'bbox_score', 'flip_pairs' | |
| ]), | |
| ] | |
| val_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='TopDownGetBboxCenterScale', padding=1.25), | |
| dict(type='TopDownAffine'), | |
| dict(type='ToTensor'), | |
| dict( | |
| type='NormalizeTensor', | |
| mean=[0.485, 0.456, 0.406], | |
| std=[0.229, 0.224, 0.225]), | |
| dict( | |
| type='Collect', | |
| keys=[ | |
| 'img', | |
| ], | |
| meta_keys=[ | |
| 'image_file', 'center', 'scale', 'rotation', 'bbox_score', | |
| 'flip_pairs' | |
| ]), | |
| ] | |
| test_pipeline = val_pipeline | |
| data_root = 'data/coco' | |
| data = dict( | |
| samples_per_gpu=32, | |
| # samples_per_gpu=64, | |
| workers_per_gpu=8, | |
| val_dataloader=dict(samples_per_gpu=32), | |
| test_dataloader=dict(samples_per_gpu=32), | |
| train=dict( | |
| type='TopDownCocoDataset', | |
| ann_file=f'{data_root}/annotations/person_keypoints_train2017.json', | |
| img_prefix=f'{data_root}/train2017/', | |
| # ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', | |
| # img_prefix=f'{data_root}/val2017/', | |
| data_cfg=data_cfg, | |
| pipeline=train_pipeline), | |
| val=dict( | |
| type='TopDownCocoDataset', | |
| ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', | |
| img_prefix=f'{data_root}/val2017/', | |
| data_cfg=data_cfg, | |
| pipeline=val_pipeline), | |
| test=dict( | |
| type='TopDownCocoDataset', | |
| ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', | |
| img_prefix=f'{data_root}/val2017/', | |
| data_cfg=data_cfg, | |
| pipeline=val_pipeline), | |
| ) | |
| fp16 = dict(loss_scale='dynamic') | |