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mvp_panoptic.py
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mvp_panoptic.py
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# finetune with the weight pretrained from panoptic dataset
__dataset__ = 'panoptic'
__train_dataset__ = __dataset__
__test_dataset__ = __dataset__
__resnet_n_layer__ = 50
__n_kps__ = 15
__n_instance__ = 10
__n_cameras__ = 5
__image_size__ = [960, 512]
__space_size__ = [8000.0, 8000.0, 2000.0]
__space_center__ = [0.0, -500.0, 800.0]
__decoder_size__ = 256
__projattn_pos_embed_mode__ = 'use_rayconv'
__pred_conf_threshold__ = 0.5
__n_heads__ = 8
model = 'multi_view_pose_transformer'
output_dir = 'output'
dataset = __dataset__
backbone_layers = __resnet_n_layer__
trainer_setup = dict(
type='MVPTrainer',
workers=4,
train_dataset=__train_dataset__,
test_dataset=__test_dataset__,
lr=0.0001,
lr_linear_proj_mult=0.1,
weight_decay=1e-4,
optimizer='adam',
end_epoch=200,
pretrained_backbone='xrmocap_pose_resnet50_panoptic-5a2e53c9_20220831.pth',
model_root='./weight/mvp',
finetune_model=None,
resume=False,
lr_decay_epoch=[40],
inference_conf_thr=[0.0],
train_batch_size=1,
test_batch_size=1,
test_model_file='model_best.pth.tar',
clip_max_norm=0.1,
print_freq=100,
cudnn_setup=dict(
benchmark=True,
deterministic=False,
enable=True,
),
dataset_setup=dict(
train_dataset_setup=dict(
type='MVPDataset',
test_mode=False,
meta_path= # noqa E251
'./xrmocap_data/panoptic/xrmocap_meta_trainset_5cam',
),
test_dataset_setup=dict(
type='MVPDataset',
test_mode=True,
meta_path='./xrmocap_data/panoptic/xrmocap_meta_testset_5cam',
),
base_dataset_setup=dict(
dataset=__dataset__,
data_root='./xrmocap_data/panoptic',
img_pipeline=[
dict(type='LoadImageCV2'),
dict(type='BGR2RGB'),
dict(
type='WarpAffine',
image_size=__image_size__,
flag='inter_linear'),
dict(type='ToTensor'),
dict(
type='Normalize',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
],
image_size=__image_size__,
heatmap_size=[240, 128],
metric_unit='millimeter',
root_kp='pelvis_openpose',
shuffled=False,
gt_kps3d_convention='panoptic',
cam_world2cam=True,
n_max_person=__n_instance__,
n_views=__n_cameras__,
n_kps=__n_kps__),
),
mvp_setup=dict(
type='MviewPoseTransformer',
n_kps=__n_kps__,
n_instance=__n_instance__,
image_size=__image_size__,
space_size=__space_size__,
space_center=__space_center__,
d_model=__decoder_size__,
use_feat_level=[0, 1, 2],
n_cameras=__n_cameras__,
query_embed_type='person_kp',
with_pose_refine=True,
loss_weight_loss_ce=0.0,
loss_per_kp=5.,
aux_loss=True,
pred_conf_threshold=__pred_conf_threshold__,
pred_class_fuse='mean',
projattn_pos_embed_mode=__projattn_pos_embed_mode__,
query_adaptation=True,
convert_kp_format_indexes=None,
backbone_setup=dict(
type='PoseResNet',
n_layers=__resnet_n_layer__,
n_kps=__n_kps__,
deconv_with_bias=False,
n_deconv_layers=3,
n_deconv_filters=[256, 256, 256],
n_deconv_kernels=[4, 4, 4],
final_conv_kernel=1,
),
proj_attn_setup=dict(
type='ProjAttn',
d_model=__decoder_size__,
n_levels=1,
n_heads=__n_heads__,
n_points=4,
projattn_pos_embed_mode=__projattn_pos_embed_mode__,
),
decoder_layer_setup=dict(
type='MvPDecoderLayer',
space_size=__space_size__,
space_center=__space_center__,
image_size=__image_size__,
d_model=__decoder_size__,
dim_feedforward=1024,
dropout=0.1,
activation='relu',
n_heads=__n_heads__,
detach_refpoints_cameraprj=True,
fuse_view_feats='cat_proj',
n_views=__n_cameras__,
),
decoder_setup=dict(
type='MvPDecoder',
n_decoder_layer=6,
return_intermediate=True,
),
pos_encoding_setup=dict(
type='PositionEmbeddingSine',
normalize=True,
temperature=10000,
),
pose_embed_setup=dict(
type='MLP', d_model=__decoder_size__, pose_embed_layer=3),
matcher_setup=dict(
type='HungarianMatcher',
match_coord='norm',
),
criterion_setup=dict(
type='SetCriterion',
image_size=__image_size__,
n_person=__n_instance__,
loss_kp_type='l1',
focal_alpha=0.25,
space_size=__space_size__,
space_center=__space_center__,
use_loss_pose_perprojection=True,
loss_pose_normalize=False,
pred_conf_threshold=__pred_conf_threshold__,
),
),
evaluation_setup=dict(
type='MVPEvaluation',
dataset_name=__test_dataset__,
pred_kps3d_convention='panoptic',
n_max_person=__n_instance__,
),
)