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[Feature] Add hrformer backbone (open-mmlab#1203)
* hrformer * modify cfg * update url and readme for hrformer. * add readme for hrformer papar * modify reaadme * fix publish year Co-authored-by: ly015 <[email protected]>
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configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrformer_base_coco_256x192.py
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log_level = 'INFO' | ||
load_from = None | ||
resume_from = None | ||
dist_params = dict(backend='nccl') | ||
workflow = [('train', 1)] | ||
checkpoint_config = dict(interval=5, create_symlink=False) | ||
evaluation = dict(interval=10, metric='mAP', key_indicator='AP') | ||
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optimizer = dict( | ||
type='AdamW', | ||
lr=5e-4, | ||
betas=(0.9, 0.999), | ||
weight_decay=0.01, | ||
paramwise_cfg=dict( | ||
custom_keys={'relative_position_bias_table': dict(decay_mult=0.)})) | ||
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optimizer_config = dict(grad_clip=None) | ||
# learning policy | ||
lr_config = dict( | ||
policy='step', | ||
warmup='linear', | ||
warmup_iters=500, | ||
warmup_ratio=0.001, | ||
step=[170, 200]) | ||
total_epochs = 210 | ||
log_config = dict( | ||
interval=50, hooks=[ | ||
dict(type='TextLoggerHook'), | ||
]) | ||
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channel_cfg = dict( | ||
num_output_channels=17, | ||
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 | ||
]) | ||
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# model settings | ||
norm_cfg = dict(type='SyncBN', requires_grad=True) | ||
model = dict( | ||
type='TopDown', | ||
pretrained='https://download.openmmlab.com/mmpose/' | ||
'pretrain_models/hrformer_base-32815020_20220226.pth', | ||
backbone=dict( | ||
type='HRFormer', | ||
in_channels=3, | ||
norm_cfg=norm_cfg, | ||
extra=dict( | ||
drop_path_rate=0.2, | ||
with_rpe=False, | ||
stage1=dict( | ||
num_modules=1, | ||
num_branches=1, | ||
block='BOTTLENECK', | ||
num_blocks=(2, ), | ||
num_channels=(64, ), | ||
num_heads=[2], | ||
mlp_ratios=[4]), | ||
stage2=dict( | ||
num_modules=1, | ||
num_branches=2, | ||
block='HRFORMERBLOCK', | ||
num_blocks=(2, 2), | ||
num_channels=(78, 156), | ||
num_heads=[2, 4], | ||
mlp_ratios=[4, 4], | ||
window_sizes=[7, 7]), | ||
stage3=dict( | ||
num_modules=4, | ||
num_branches=3, | ||
block='HRFORMERBLOCK', | ||
num_blocks=(2, 2, 2), | ||
num_channels=(78, 156, 312), | ||
num_heads=[2, 4, 8], | ||
mlp_ratios=[4, 4, 4], | ||
window_sizes=[7, 7, 7]), | ||
stage4=dict( | ||
num_modules=2, | ||
num_branches=4, | ||
block='HRFORMERBLOCK', | ||
num_blocks=(2, 2, 2, 2), | ||
num_channels=(78, 156, 312, 624), | ||
num_heads=[2, 4, 8, 16], | ||
mlp_ratios=[4, 4, 4, 4], | ||
window_sizes=[7, 7, 7, 7]))), | ||
keypoint_head=dict( | ||
type='TopdownHeatmapSimpleHead', | ||
in_channels=78, | ||
out_channels=channel_cfg['num_output_channels'], | ||
num_deconv_layers=0, | ||
extra=dict(final_conv_kernel=1, ), | ||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), | ||
train_cfg=dict(), | ||
test_cfg=dict( | ||
flip_test=True, | ||
post_process='default', | ||
shift_heatmap=True, | ||
modulate_kernel=11)) | ||
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data_root = 'data/coco' | ||
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, | ||
nms_thr=1.0, | ||
oks_thr=0.9, | ||
vis_thr=0.2, | ||
use_gt_bbox=False, | ||
det_bbox_thr=0.0, | ||
bbox_file=f'{data_root}/person_detection_results/' | ||
'COCO_val2017_detections_AP_H_56_person.json', | ||
) | ||
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train_pipeline = [ | ||
dict(type='LoadImageFromFile'), | ||
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', sigma=2), | ||
dict( | ||
type='Collect', | ||
keys=['img', 'target', 'target_weight'], | ||
meta_keys=[ | ||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', | ||
'rotation', 'bbox_score', 'flip_pairs' | ||
]), | ||
] | ||
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val_pipeline = [ | ||
dict(type='LoadImageFromFile'), | ||
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' | ||
]), | ||
] | ||
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test_pipeline = val_pipeline | ||
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data = dict( | ||
samples_per_gpu=32, | ||
workers_per_gpu=2, | ||
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/', | ||
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), | ||
) | ||
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# fp16 settings | ||
fp16 = dict(loss_scale='dynamic') |
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