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hrnet_hmr_expose_body.py
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hrnet_hmr_expose_body.py
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_base_ = ['../_base_/default_runtime.py']
use_adversarial_train = True
img_res = 256
# evaluate
evaluation = dict(interval=10, metric=['pa-mpjpe', 'mpjpe'])
optimizer = dict(
backbone=dict(type='Adam', lr=1.0e-4, weight_decay=1.0e-4),
head=dict(type='Adam', lr=1.0e-4, weight_decay=1.0e-4),
)
optimizer_config = dict(grad_clip=None)
lr_config = dict(policy='step', step=[60, 100], gamma=0.1)
runner = dict(type='EpochBasedRunner', max_epochs=100)
log_config = dict(
interval=50, hooks=[
dict(type='TextLoggerHook'),
])
checkpoint_config = dict(interval=10)
# model settings
hrnet_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=(48, 96)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(48, 96, 192)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(48, 96, 192, 384)),
downsample=True,
use_conv=True,
pretrained_layers=[
'conv1',
'bn1',
'conv2',
'bn2',
'layer1',
'transition1',
'stage2',
'transition2',
'stage3',
'transition3',
'stage4',
],
final_conv_kernel=1,
return_list=False)
find_unused_parameters = True
model = dict(
type='SMPLXImageBodyModelEstimator',
backbone=dict(
type='PoseHighResolutionNetExpose',
extra=hrnet_extra,
init_cfg=dict(
type='Pretrained',
checkpoint='data/pretrained_models/hrnet_pretrain.pth')),
head=dict(
type='ExPoseBodyHead',
num_betas=10,
num_expression_coeffs=10,
mean_pose_path='data/body_models/smplx/all_means.pkl',
shape_mean_path='data/body_models/smplx/shape_mean.npy',
pose_param_conf=[
dict(
name='global_orient',
num_angles=1,
use_mean=False,
rotate_axis_x=True),
dict(
name='body_pose',
num_angles=21,
use_mean=True,
rotate_axis_x=False),
dict(
name='left_hand_pose',
num_angles=15,
use_mean=True,
rotate_axis_x=False),
dict(
name='right_hand_pose',
num_angles=15,
use_mean=True,
rotate_axis_x=False),
dict(
name='jaw_pose',
num_angles=1,
use_mean=False,
rotate_axis_x=False),
],
input_feat_dim=2048,
regressor_cfg=dict(
layers=[1024, 1024], activ_type='none', dropout=0.5, gain=0.01),
camera_cfg=dict(pos_func='softplus', mean_scale=0.9)),
body_model_train=dict(
type='SMPLXLayer',
num_expression_coeffs=10,
num_betas=10,
use_face_contour=True,
use_pca=False,
flat_hand_mean=True,
model_path='data/body_models/smplx',
keypoint_src='smplx',
keypoint_dst='smplx',
),
body_model_test=dict(
type='SMPLXLayer',
num_expression_coeffs=10,
num_betas=10,
use_face_contour=True,
use_pca=False,
flat_hand_mean=True,
model_path='data/body_models/smplx',
keypoint_src='lsp',
keypoint_dst='lsp',
joints_regressor='data/body_models/smplx/SMPLX_to_J14.npy'),
loss_keypoints3d=dict(type='L1Loss', reduction='sum', loss_weight=1),
loss_keypoints2d=dict(type='L1Loss', reduction='sum', loss_weight=1),
loss_smplx_global_orient=dict(
type='RotationDistance', reduction='sum', loss_weight=1),
loss_smplx_body_pose=dict(
type='RotationDistance', reduction='sum', loss_weight=1),
loss_smplx_jaw_pose=dict(
type='RotationDistance', reduction='sum', loss_weight=1),
loss_smplx_hand_pose=dict(
type='RotationDistance', reduction='sum', loss_weight=1),
loss_smplx_betas=dict(type='MSELoss', reduction='sum', loss_weight=0.001),
loss_smplx_expression=dict(type='MSELoss', reduction='sum', loss_weight=1),
loss_smplx_betas_prior=dict(
type='ShapeThresholdPriorLoss', margin=3.0, norm='l2', loss_weight=1),
convention='smplx')
# dataset settings
dataset_type = 'HumanImageSMPLXDataset'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
data_keys = [
'has_smplx', 'has_keypoints3d', 'has_keypoints2d',
'has_smplx_global_orient', 'has_smplx_body_pose', 'has_smplx_jaw_pose',
'has_smplx_right_hand_pose', 'has_smplx_left_hand_pose', 'has_smplx_betas',
'has_smplx_expression', 'smplx_jaw_pose', 'smplx_body_pose',
'smplx_right_hand_pose', 'smplx_left_hand_pose', 'smplx_global_orient',
'smplx_betas', 'keypoints2d', 'keypoints3d', 'sample_idx',
'smplx_expression'
]
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='BBoxCenterJitter', factor=0.0, dist='normal'),
dict(type='RandomHorizontalFlip', flip_prob=0.5,
convention='smplx'), # hand = 0,head = body = 0.5
dict(
type='GetRandomScaleRotation',
rot_factor=30.0,
scale_factor=0.25,
rot_prob=0.6),
dict(type='MeshAffine', img_res=img_res), # hand = 224, body = head = 256
dict(type='RandomChannelNoise', noise_factor=0.4),
dict(
type='SimulateLowRes',
dist='categorical',
cat_factors=(1.0, ),
# head = (1.0, 1.2, 1.5, 2.0, 3.0, 4.0, 8.0)
# hand = (1.0, 1.2, 1.5, 2.0, 3.0, 4.0)
# body = (1.0,)
factor_min=1.0,
factor_max=1.0),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img', 'ori_img']),
dict(type='ToTensor', keys=data_keys),
dict(
type='Collect',
keys=['img', *data_keys],
meta_keys=['image_path', 'center', 'scale', 'rotation', 'ori_img'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='GetRandomScaleRotation', rot_factor=0, scale_factor=0),
dict(type='MeshAffine', img_res=img_res),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='ToTensor', keys=data_keys),
dict(
type='Collect',
keys=['img', *data_keys],
meta_keys=['image_path', 'center', 'scale', 'rotation'])
]
inference_pipeline = [
dict(type='GetRandomScaleRotation', rot_factor=0, scale_factor=0),
dict(type='MeshAffine', img_res=img_res),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(
type='Collect',
keys=['img', 'sample_idx'],
meta_keys=['image_path', 'center', 'scale', 'rotation'])
]
cache_files = {
'curated_fits': 'data/cache/curated_fits_train_smplx.npz',
'spin_smplx': 'data/cache/spin_smplx_train.npz',
'h36m': 'data/cache/h36m_train_smplx.npz'
}
data = dict(
samples_per_gpu=48, # body 48, head = hand = 64
workers_per_gpu=8,
train=dict(
type='MixedDataset',
configs=[
dict(
type=dataset_type,
pipeline=train_pipeline,
dataset_name='',
data_prefix='data',
ann_file='curated_fits_train.npz',
convention='smplx',
num_betas=10,
num_expression=10,
cache_data_path=cache_files['curated_fits'],
),
dict(
type=dataset_type,
pipeline=train_pipeline,
dataset_name='',
data_prefix='data',
ann_file='spin_smplx_train.npz',
convention='smplx',
num_betas=10,
num_expression=10,
cache_data_path=cache_files['spin_smplx'],
),
dict(
type=dataset_type,
pipeline=train_pipeline,
dataset_name='h36m',
data_prefix='data',
ann_file='h36m_train.npz',
convention='smplx',
num_betas=10,
num_expression=10,
cache_data_path=cache_files['h36m'],
),
],
partition=[0.08, 0.12, 0.8],
),
val=dict(
type=dataset_type,
body_model=dict(
type='SMPL',
keypoint_src='h36m',
keypoint_dst='h36m',
model_path='data/body_models/smpl',
joints_regressor='data/body_models/J_regressor_h36m.npy'),
dataset_name='3DPW',
data_prefix='data',
pipeline=test_pipeline,
ann_file='pw3d_test.npz'),
test=dict(
type=dataset_type,
body_model=dict(
type='SMPL',
keypoint_src='h36m',
keypoint_dst='h36m',
model_path='data/body_models/smpl',
joints_regressor='data/body_models/J_regressor_h36m.npy'),
dataset_name='3DPW',
data_prefix='data',
pipeline=test_pipeline,
ann_file='pw3d_test.npz'),
)