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update configs & demo & add bottom-up models
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configs/bottom_up/higherhrnet/coco/higher_hrnet32_coco_512x512.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,209 @@ | ||
| 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=299, metric='mAP') | ||
|
|
||
| optimizer = dict( | ||
| type='Adam', | ||
| lr=0.0015, | ||
| ) | ||
| optimizer_config = dict(grad_clip=None) | ||
| # learning policy | ||
| lr_config = dict( | ||
| policy='step', | ||
| warmup='linear', | ||
| warmup_iters=500, | ||
| warmup_ratio=0.001, | ||
| step=[200, 260]) | ||
| total_epochs = 300 | ||
| log_config = dict( | ||
| interval=50, | ||
| hooks=[ | ||
| dict(type='TextLoggerHook'), | ||
| # dict(type='TensorboardLoggerHook') | ||
| ]) | ||
|
|
||
| channel_cfg = dict( | ||
| dataset_joints=17, | ||
| 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 | ||
| ]) | ||
|
|
||
| data_cfg = dict( | ||
| image_size=512, | ||
| base_size=256, | ||
| base_sigma=2, | ||
| heatmap_size=[128, 256], | ||
| num_joints=channel_cfg['dataset_joints'], | ||
| dataset_channel=channel_cfg['dataset_channel'], | ||
| inference_channel=channel_cfg['inference_channel'], | ||
| num_scales=2, | ||
| scale_aware_sigma=False, | ||
| ) | ||
|
|
||
| # model settings | ||
| model = dict( | ||
| type='BottomUp', | ||
| pretrained='models/pytorch/imagenet/hrnet_w32-36af842e.pth', | ||
| backbone=dict( | ||
| type='HRNet', | ||
| in_channels=3, | ||
| extra=dict( | ||
| stage1=dict( | ||
| num_modules=1, | ||
| num_branches=1, | ||
| block='BOTTLENECK', | ||
| num_blocks=(4, ), | ||
| num_channels=(64, )), | ||
| stage2=dict( | ||
| num_modules=1, | ||
| num_branches=2, | ||
| block='BASIC', | ||
| num_blocks=(4, 4), | ||
| num_channels=(32, 64)), | ||
| stage3=dict( | ||
| num_modules=4, | ||
| num_branches=3, | ||
| block='BASIC', | ||
| num_blocks=(4, 4, 4), | ||
| num_channels=(32, 64, 128)), | ||
| stage4=dict( | ||
| num_modules=3, | ||
| num_branches=4, | ||
| block='BASIC', | ||
| num_blocks=(4, 4, 4, 4), | ||
| num_channels=(32, 64, 128, 256))), | ||
| ), | ||
| keypoint_head=dict( | ||
| type='BottomUpHigherResolutionHead', | ||
| in_channels=32, | ||
| num_joints=17, | ||
| tag_per_joint=True, | ||
| extra=dict(final_conv_kerne=1, ), | ||
| num_deconv_layers=1, | ||
| num_deconv_filters=[32], | ||
| num_deconv_kernels=[4], | ||
| num_basic_blocks=4, | ||
| cat_output=[True], | ||
| with_ae_loss=[True, False]), | ||
| train_cfg=dict( | ||
| num_joints=channel_cfg['dataset_joints'], | ||
| img_size=data_cfg['image_size']), | ||
| test_cfg=dict( | ||
| num_joints=channel_cfg['dataset_joints'], | ||
| max_num_people=30, | ||
| scale_factor=[1], | ||
| with_heatmaps=[True, True], | ||
| with_ae=[True, False], | ||
| project2image=True, | ||
| nms_kernel=5, | ||
| nms_padding=2, | ||
| tag_per_joint=True, | ||
| detection_threshold=0.1, | ||
| tag_threshold=1, | ||
| use_detection_val=True, | ||
| ignore_too_much=False, | ||
| adjust=True, | ||
| refine=True, | ||
| flip_test=True), | ||
| loss_pose=dict( | ||
| type='MultiLossFactory', | ||
| num_joints=17, | ||
| num_stages=2, | ||
| ae_loss_type='exp', | ||
| with_ae_loss=[True, False], | ||
| push_loss_factor=[0.001, 0.001], | ||
| pull_loss_factor=[0.001, 0.001], | ||
| with_heatmaps_loss=[True, True], | ||
| heatmaps_loss_factor=[1.0, 1.0], | ||
| ), | ||
| ) | ||
|
|
||
| train_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict( | ||
| type='BottomUpRandomAffine', | ||
| rot_factor=30, | ||
| scale_factor=[0.75, 1.5], | ||
| scale_type='short', | ||
| trans_factor=40), | ||
| dict(type='BottomUpRandomFlip', flip_prob=0.5), | ||
| dict(type='ToTensor'), | ||
| dict( | ||
| type='NormalizeTensor', | ||
| mean=[0.485, 0.456, 0.406], | ||
| std=[0.229, 0.224, 0.225]), | ||
| dict( | ||
| type='BottomUpGenerateTarget', | ||
| sigma=2, | ||
| max_num_people=30, | ||
| ), | ||
| dict( | ||
| type='Collect', | ||
| keys=['img', 'joints', 'targets', 'masks'], | ||
| meta_keys=[]), | ||
| ] | ||
|
|
||
| valid_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='BottomUpGetImgSize', test_scale_factor=[1]), | ||
| dict( | ||
| type='BottomUpResizeAlign', | ||
| transforms=[ | ||
| 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', 'aug_data', 'test_scale_factor', 'base_size', | ||
| 'center', 'scale', 'flip_index' | ||
| ]), | ||
| ] | ||
|
|
||
| data_root = 'data/coco' | ||
| data = dict( | ||
| samples_per_gpu=24, | ||
| workers_per_gpu=1, | ||
| train=dict( | ||
| type='BottomUpCocoDataset', | ||
| ann_file=f'{data_root}/annotations/person_keypoints_train2017.json', | ||
| img_prefix=f'{data_root}/train2017/', | ||
| data_cfg=data_cfg, | ||
| pipeline=train_pipeline), | ||
| val=dict( | ||
| type='BottomUpCocoDataset', | ||
| ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', | ||
| img_prefix=f'{data_root}/val2017/', | ||
| data_cfg=data_cfg, | ||
| pipeline=valid_pipeline), | ||
| test=dict( | ||
| type='BottomUpCocoDataset', | ||
| ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', | ||
| img_prefix=f'{data_root}/val2017/', | ||
| data_cfg=data_cfg, | ||
| pipeline=valid_pipeline), | ||
| ) | ||
|
|
||
| loss = dict( | ||
| type='MultiLossFactory', | ||
| num_stages=32, | ||
| ae_loss_type='exp', | ||
| with_ae_loss=[True, False], | ||
| push_loss_factor=[0.001, 0.001], | ||
| pull_loss_factor=[0.001, 0.001], | ||
| with_heatmaps_loss=[True, True], | ||
| heatmaps_loss_factor=[1.0, 1.0], | ||
| ) |
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