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|
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evaluation = dict(interval=10, metric='mAP', save_best='AP') |
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|
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optimizer = dict( |
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type='Adam', |
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lr=5e-4, |
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) |
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optimizer_config = dict(grad_clip=None) |
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|
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lr_config = dict( |
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policy='step', |
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warmup='linear', |
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warmup_iters=500, |
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warmup_ratio=0.001, |
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step=[170, 200]) |
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total_epochs = 210 |
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channel_cfg = dict( |
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num_output_channels=17, |
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dataset_joints=17, |
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dataset_channel=[ |
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], |
|
], |
|
inference_channel=[ |
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 |
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]) |
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|
|
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model = dict( |
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type='TopDown', |
|
pretrained='https://download.openmmlab.com/mmpose/' |
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'pretrain_models/hrnet_w48-8ef0771d.pth', |
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backbone=dict( |
|
type='HRNet', |
|
in_channels=3, |
|
extra=dict( |
|
stage1=dict( |
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num_modules=1, |
|
num_branches=1, |
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block='BOTTLENECK', |
|
num_blocks=(4, ), |
|
num_channels=(64, )), |
|
stage2=dict( |
|
num_modules=1, |
|
num_branches=2, |
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block='BASIC', |
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num_blocks=(4, 4), |
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num_channels=(48, 96)), |
|
stage3=dict( |
|
num_modules=4, |
|
num_branches=3, |
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block='BASIC', |
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num_blocks=(4, 4, 4), |
|
num_channels=(48, 96, 192)), |
|
stage4=dict( |
|
num_modules=3, |
|
num_branches=4, |
|
block='BASIC', |
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num_blocks=(4, 4, 4, 4), |
|
num_channels=(48, 96, 192, 384))), |
|
), |
|
keypoint_head=dict( |
|
type='TopdownHeatmapSimpleHead', |
|
in_channels=48, |
|
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_cfg = dict( |
|
image_size=[192, 256], |
|
heatmap_size=[48, 64], |
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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'], |
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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='data/coco/person_detection_results/' |
|
'COCO_val2017_detections_AP_H_56_person.json', |
|
) |
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|
|
train_pipeline = [ |
|
dict(type='LoadImageFromFile'), |
|
dict(type='TopDownGetBboxCenterScale', padding=1.25), |
|
dict(type='TopDownRandomShiftBboxCenter', shift_factor=0.16, prob=0.3), |
|
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' |
|
]), |
|
] |
|
|
|
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, |
|
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, |
|
dataset_info={{_base_.dataset_info}}), |
|
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, |
|
dataset_info={{_base_.dataset_info}}), |
|
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=test_pipeline, |
|
dataset_info={{_base_.dataset_info}}), |
|
) |
|
|
