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iccv_review
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| Author | SHA1 | Date | |
|---|---|---|---|
 luni
|
3abe7554bc | ||
|
luni
|
e34e2143e7 |
@@ -1,73 +0,0 @@
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from utils.convnext_utils import CNBlockConfig
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config = dict()
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config['gpu'] = '0'
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config['name'] = 'norm_torch_2'
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config['notes'] = ' '
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config['tags'] = ['torch', 'norm']
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config['script'] = 'scripts/ae_norm_train.py'
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config['pretrained_model'] = ''
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config['batch_size'] = 4
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config['train_dir'] = '/data-local/127/train'
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config['valid_dir'] = '/data-local/127/valid'
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config['test_dir'] = '/data-net/dataset/test_ready'
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config['train_frames'] = None
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config['valid_frames'] = None
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||||
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config['resolution'] = 512
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config['is_skirt'] = False
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config['dataset_in_memory'] = False
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config['is_cape'] = False
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config['remove_global_orientation'] = True
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config['device'] = 'cuda'
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||||
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config['smpl_res'] = 14475
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if config['is_cape']:
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config['smpl_res'] = 3638
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config['latent_dim'] = 128
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config['bottleneck_scale'] = 2
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config['cond_ae'] = dict()
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config['cond_ae']['device'] = config['device']
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config['cond_ae']['is_label'] = False
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config['cond_ae']['latent_dim'] = config['latent_dim']
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config['cond_ae']['kernel_size'] = 7
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config['cond_ae']['bottleneck_size'] = 40 * config['bottleneck_scale']
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block_setting = [
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CNBlockConfig(16, 32, 3),
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CNBlockConfig(32, 64, 3),
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CNBlockConfig(64, 128, 9),
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CNBlockConfig(128, config['cond_ae']['bottleneck_size'], 9),
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CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
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]
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block_setting_reverse = [
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CNBlockConfig(config['cond_ae']['bottleneck_size'], 128, 3),
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CNBlockConfig(128, 64, 9),
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CNBlockConfig(64, 32, 9),
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CNBlockConfig(32, 16, 3),
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CNBlockConfig(16, 3, 3),
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# CNBlockConfig(3, 3, 3),
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]
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config['cond_ae']['encoder_block_settings'] = block_setting
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config['cond_ae']['decoder_block_settings'] = block_setting_reverse
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config['cond_ae']['activation'] = 'tanh'
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config['lr'] = 0.0001
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config['cond_ae']['upscale_output'] = False
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config['signature'] = { 'id',
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'frame_nr',
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'uv_mask',
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||||
'uv_normals',
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'mesh_mask',
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'mesh_normals',
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}
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@@ -1,82 +0,0 @@
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from utils.convnext_utils import CNBlockConfig
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config = dict()
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config['gpu'] = '0'
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config['name'] = 'direct_3'
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config['notes'] = 'with normals input'
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config['tags'] = ['torch', 'direct']
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config['script'] = 'scripts/direct_train.py'
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config['pretrained_model'] = ''
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config['use_curvature'] = False
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config['use_normals'] = True
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# assert that only use curvature or normals
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assert not (config['use_curvature'] and config['use_normals'])
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config['batch_size'] = 4
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config['train_dir'] = '/data-local/127/train'
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config['valid_dir'] = '/data-local/127/valid'
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config['test_dir'] = '/data-net/dataset/test_ready'
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config['train_frames'] = None
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config['valid_frames'] = None
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||||
|
||||
config['resolution'] = 512
|
||||
config['is_skirt'] = False
|
||||
config['dataset_in_memory'] = False
|
||||
config['is_cape'] = False
|
||||
config['remove_global_orientation'] = True
|
||||
config['device'] = 'cuda'
|
||||
config['input_features'] = 9
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||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
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config['smpl_res'] = 3638
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||||
|
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config['latent_dim'] = 128
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||||
|
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config['bottleneck_scale'] = 2
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||||
|
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config['is_label'] = False
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config['kernel_size'] = 7
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||||
config['bottleneck_size'] = 64 * config['bottleneck_scale']
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||||
|
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block_setting = [
|
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CNBlockConfig(16, 32, 3),
|
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CNBlockConfig(32, 128, 3),
|
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CNBlockConfig(128, 256, 9),
|
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CNBlockConfig(256, config['bottleneck_size'], 9),
|
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CNBlockConfig(config['bottleneck_size'], None, 3),
|
||||
]
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||||
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block_setting_reverse = [
|
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CNBlockConfig(config['bottleneck_size'], 256, 3),
|
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CNBlockConfig(256, 128, 9),
|
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CNBlockConfig(128, 32, 9),
|
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CNBlockConfig(32, 16, 3),
|
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CNBlockConfig(16, 3, 3),
|
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# CNBlockConfig(3, 3, 3),
|
||||
]
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||||
|
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config['encoder_block_settings'] = block_setting
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||||
config['decoder_block_settings'] = block_setting_reverse
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config['activation'] = 'tanh'
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||||
|
||||
|
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config['lr'] = 0.0001
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||||
config['reg_loss_coeff'] = 0.1
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||||
|
||||
config['signature'] = { 'id',
|
||||
'frame_nr',
|
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'pose_shape',
|
||||
'uv_normals',
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'mesh_mask',
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'mesh_body_posed',
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'mesh_posed',
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'uv_unposed',
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'mesh_unposed',
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'uv_static',
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'uv_mask',
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'uv_body_posed',
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||||
}
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@@ -1,82 +0,0 @@
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from utils.convnext_utils import CNBlockConfig
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config = dict()
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config['gpu'] = '5'
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config['name'] = 'direct_5'
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config['notes'] = 'with normals input but only single garment sequence'
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config['tags'] = ['torch', 'direct']
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config['script'] = 'scripts/direct_train.py'
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['use_curvature'] = False
|
||||
config['use_normals'] = True
|
||||
|
||||
# assert that only use curvature or normals
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||||
assert not (config['use_curvature'] and config['use_normals'])
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||||
|
||||
config['batch_size'] = 4
|
||||
config['train_dir'] = '/data-local/127/train'
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config['valid_dir'] = '/data-local/127/train'
|
||||
config['test_dir'] = '/data-net/dataset/test_ready'
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||||
config['train_frames'] = 'frames_00761.txt'
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||||
config['valid_frames'] = 'frames_00761.txt'
|
||||
|
||||
config['resolution'] = 512
|
||||
config['is_skirt'] = False
|
||||
config['dataset_in_memory'] = True
|
||||
config['is_cape'] = False
|
||||
config['remove_global_orientation'] = True
|
||||
config['device'] = 'cuda'
|
||||
config['input_features'] = 9
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['latent_dim'] = 128
|
||||
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['is_label'] = False
|
||||
config['kernel_size'] = 7
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||||
config['bottleneck_size'] = 64 * config['bottleneck_scale']
|
||||
|
||||
block_setting = [
|
||||
CNBlockConfig(16, 32, 3),
|
||||
CNBlockConfig(32, 128, 3),
|
||||
CNBlockConfig(128, 256, 9),
|
||||
CNBlockConfig(256, config['bottleneck_size'], 9),
|
||||
CNBlockConfig(config['bottleneck_size'], None, 3),
|
||||
]
|
||||
|
||||
block_setting_reverse = [
|
||||
CNBlockConfig(config['bottleneck_size'], 256, 3),
|
||||
CNBlockConfig(256, 128, 9),
|
||||
CNBlockConfig(128, 32, 9),
|
||||
CNBlockConfig(32, 16, 3),
|
||||
CNBlockConfig(16, 3, 3),
|
||||
# CNBlockConfig(3, 3, 3),
|
||||
]
|
||||
|
||||
config['encoder_block_settings'] = block_setting
|
||||
config['decoder_block_settings'] = block_setting_reverse
|
||||
config['activation'] = 'tanh'
|
||||
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['reg_loss_coeff'] = 0.1
|
||||
|
||||
config['signature'] = { 'id',
|
||||
'frame_nr',
|
||||
'pose_shape',
|
||||
'uv_normals',
|
||||
'mesh_mask',
|
||||
'mesh_body_posed',
|
||||
'mesh_posed',
|
||||
'uv_unposed',
|
||||
'mesh_unposed',
|
||||
'uv_static',
|
||||
'uv_mask',
|
||||
'uv_body_posed',
|
||||
}
|
||||
@@ -1,83 +0,0 @@
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from utils.convnext_utils import CNBlockConfig
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|
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config = dict()
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||||
|
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config['gpu'] = '4'
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config['name'] = 'direct_6'
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config['notes'] = 'with normals input but only single garment sequence with msssim and l2 mesh loss only'
|
||||
config['tags'] = ['torch', 'direct']
|
||||
config['script'] = 'scripts/direct_train.py'
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['use_curvature'] = False
|
||||
config['use_normals'] = True
|
||||
config['use_skinning'] = True
|
||||
|
||||
# assert that only use curvature or normals
|
||||
assert not (config['use_curvature'] and config['use_normals'])
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['train_dir'] = '/data-local/127/train'
|
||||
config['valid_dir'] = '/data-local/127/train'
|
||||
config['test_dir'] = '/data-net/dataset/test_ready'
|
||||
config['train_frames'] = 'frames_00761.txt'
|
||||
config['valid_frames'] = 'frames_00761.txt'
|
||||
|
||||
config['resolution'] = 512
|
||||
config['is_skirt'] = False
|
||||
config['dataset_in_memory'] = True
|
||||
config['is_cape'] = False
|
||||
config['remove_global_orientation'] = True
|
||||
config['device'] = 'cuda'
|
||||
config['input_features'] = 9
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['latent_dim'] = 128
|
||||
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['is_label'] = False
|
||||
config['kernel_size'] = 7
|
||||
config['bottleneck_size'] = 64 * config['bottleneck_scale']
|
||||
|
||||
block_setting = [
|
||||
CNBlockConfig(16, 32, 3),
|
||||
CNBlockConfig(32, 128, 3),
|
||||
CNBlockConfig(128, 256, 9),
|
||||
CNBlockConfig(256, config['bottleneck_size'], 9),
|
||||
CNBlockConfig(config['bottleneck_size'], None, 3),
|
||||
]
|
||||
|
||||
block_setting_reverse = [
|
||||
CNBlockConfig(config['bottleneck_size'], 256, 3),
|
||||
CNBlockConfig(256, 128, 9),
|
||||
CNBlockConfig(128, 32, 9),
|
||||
CNBlockConfig(32, 16, 3),
|
||||
CNBlockConfig(16, 3, 3),
|
||||
# CNBlockConfig(3, 3, 3),
|
||||
]
|
||||
|
||||
config['encoder_block_settings'] = block_setting
|
||||
config['decoder_block_settings'] = block_setting_reverse
|
||||
config['activation'] = 'tanh'
|
||||
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['reg_loss_coeff'] = 0.1
|
||||
|
||||
config['signature'] = { 'id',
|
||||
'frame_nr',
|
||||
'pose_shape',
|
||||
'uv_normals',
|
||||
'mesh_mask',
|
||||
'mesh_body_posed',
|
||||
'mesh_posed',
|
||||
'uv_unposed',
|
||||
'mesh_unposed',
|
||||
'uv_static',
|
||||
'uv_mask',
|
||||
'uv_body_posed',
|
||||
}
|
||||
@@ -1,87 +0,0 @@
|
||||
from utils.convnext_utils import CNBlockConfig
|
||||
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '0'
|
||||
config['name'] = 'direct_9'
|
||||
config['notes'] = 'with normals input whole dataset with msssim and l2 mesh loss and reg losses and without skinning'
|
||||
config['tags'] = ['torch', 'direct']
|
||||
config['script'] = 'scripts/direct_train.py'
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['use_curvature'] = False
|
||||
config['use_normals'] = True
|
||||
config['use_skinning'] = False
|
||||
|
||||
# assert that only use curvature or normals
|
||||
assert not (config['use_curvature'] and config['use_normals'])
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['train_dir'] = '/data-local/127/train'
|
||||
config['valid_dir'] = '/data-local/127/train'
|
||||
config['test_dir'] = '/data-net/dataset/test_ready'
|
||||
config['train_frames'] = None
|
||||
config['valid_frames'] = None
|
||||
|
||||
config['resolution'] = 512
|
||||
config['is_skirt'] = False
|
||||
config['dataset_in_memory'] = False
|
||||
config['is_cape'] = False
|
||||
config['remove_global_orientation'] = True
|
||||
config['device'] = 'cuda'
|
||||
config['input_features'] = 9
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['latent_dim'] = 128
|
||||
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['is_label'] = False
|
||||
config['kernel_size'] = 7
|
||||
config['bottleneck_size'] = 64 * config['bottleneck_scale']
|
||||
|
||||
block_setting = [
|
||||
CNBlockConfig(16, 32, 3),
|
||||
CNBlockConfig(32, 128, 3),
|
||||
CNBlockConfig(128, 256, 9),
|
||||
CNBlockConfig(256, config['bottleneck_size'], 9),
|
||||
CNBlockConfig(config['bottleneck_size'], None, 3),
|
||||
]
|
||||
|
||||
block_setting_reverse = [
|
||||
CNBlockConfig(config['bottleneck_size'], 256, 3),
|
||||
CNBlockConfig(256, 128, 9),
|
||||
CNBlockConfig(128, 32, 9),
|
||||
CNBlockConfig(32, 16, 3),
|
||||
CNBlockConfig(16, 3, 3),
|
||||
# CNBlockConfig(3, 3, 3),
|
||||
]
|
||||
|
||||
config['encoder_block_settings'] = block_setting
|
||||
config['decoder_block_settings'] = block_setting_reverse
|
||||
config['activation'] = 'tanh'
|
||||
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['reg_loss_coeff'] = 0.1
|
||||
|
||||
config['signature'] = { 'id',
|
||||
'frame_nr',
|
||||
'pose_shape',
|
||||
'uv_normals',
|
||||
'mesh_mask',
|
||||
'mesh_body_posed',
|
||||
'mesh_posed',
|
||||
'uv_static',
|
||||
'uv_mask',
|
||||
'uv_body_posed',
|
||||
}
|
||||
|
||||
if config['use_skinning']:
|
||||
config['signature'].add('uv_unposed')
|
||||
config['signature'].add('mesh_unposed')
|
||||
else:
|
||||
config['signature'].add('uv_posed')
|
||||
@@ -1,87 +0,0 @@
|
||||
from utils.convnext_utils import CNBlockConfig
|
||||
|
||||
config = dict()
|
||||
|
||||
config['gpu'] = '6'
|
||||
config['name'] = 'direct_8'
|
||||
config['notes'] = 'with normals input but only single garment sequence with msssim and l2 mesh loss only and without skinning and remasked'
|
||||
config['tags'] = ['torch', 'direct']
|
||||
config['script'] = 'scripts/direct_train.py'
|
||||
|
||||
config['pretrained_model'] = ''
|
||||
config['use_curvature'] = False
|
||||
config['use_normals'] = True
|
||||
config['use_skinning'] = False
|
||||
|
||||
# assert that only use curvature or normals
|
||||
assert not (config['use_curvature'] and config['use_normals'])
|
||||
|
||||
config['batch_size'] = 4
|
||||
config['train_dir'] = '/data-net/dataset/00761'
|
||||
config['valid_dir'] = '/data-net/dataset/00761'
|
||||
config['test_dir'] = '/data-net/dataset/test_ready'
|
||||
config['train_frames'] = None
|
||||
config['valid_frames'] = None
|
||||
|
||||
config['resolution'] = 512
|
||||
config['is_skirt'] = False
|
||||
config['dataset_in_memory'] = True
|
||||
config['is_cape'] = False
|
||||
config['remove_global_orientation'] = True
|
||||
config['device'] = 'cuda'
|
||||
config['input_features'] = 9
|
||||
|
||||
config['smpl_res'] = 14475
|
||||
if config['is_cape']:
|
||||
config['smpl_res'] = 3638
|
||||
|
||||
config['latent_dim'] = 128
|
||||
|
||||
config['bottleneck_scale'] = 2
|
||||
|
||||
config['is_label'] = False
|
||||
config['kernel_size'] = 7
|
||||
config['bottleneck_size'] = 64 * config['bottleneck_scale']
|
||||
|
||||
block_setting = [
|
||||
CNBlockConfig(16, 32, 3),
|
||||
CNBlockConfig(32, 128, 3),
|
||||
CNBlockConfig(128, 256, 9),
|
||||
CNBlockConfig(256, config['bottleneck_size'], 9),
|
||||
CNBlockConfig(config['bottleneck_size'], None, 3),
|
||||
]
|
||||
|
||||
block_setting_reverse = [
|
||||
CNBlockConfig(config['bottleneck_size'], 256, 3),
|
||||
CNBlockConfig(256, 128, 9),
|
||||
CNBlockConfig(128, 32, 9),
|
||||
CNBlockConfig(32, 16, 3),
|
||||
CNBlockConfig(16, 3, 3),
|
||||
# CNBlockConfig(3, 3, 3),
|
||||
]
|
||||
|
||||
config['encoder_block_settings'] = block_setting
|
||||
config['decoder_block_settings'] = block_setting_reverse
|
||||
config['activation'] = 'tanh'
|
||||
|
||||
|
||||
config['lr'] = 0.0001
|
||||
config['reg_loss_coeff'] = 0.1
|
||||
|
||||
config['signature'] = { 'id',
|
||||
'frame_nr',
|
||||
'pose_shape',
|
||||
'uv_normals',
|
||||
'mesh_mask',
|
||||
'mesh_body_posed',
|
||||
'mesh_posed',
|
||||
'uv_static',
|
||||
'uv_mask',
|
||||
'uv_body_posed',
|
||||
}
|
||||
|
||||
if config['use_skinning']:
|
||||
config['signature'].add('uv_unposed')
|
||||
config['signature'].add('mesh_unposed')
|
||||
else:
|
||||
config['signature'].add('uv_posed')
|
||||
@@ -1,112 +0,0 @@
|
||||
import os
|
||||
import sys
|
||||
import numpy as np
|
||||
import trimesh as tm
|
||||
import scipy.io as sio
|
||||
import plotly.graph_objects as go
|
||||
import plotly.express as px
|
||||
from tqdm import tqdm
|
||||
import torch
|
||||
from scipy.spatial.transform import Rotation as R
|
||||
import argparse
|
||||
|
||||
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
|
||||
|
||||
from data.cloth3d_io import readOBJ, readPC2
|
||||
from data.utils import Frame
|
||||
from smpl.smpl_torch import SMPL
|
||||
|
||||
|
||||
def calculate_normals(vertices, mesh_file, poses):
|
||||
_, faces, _, _ = readOBJ(mesh_file)
|
||||
rodr = smpl.rodrigues(torch.tensor(poses.T.reshape(-1, 1, 3), dtype=torch.float32)).numpy().reshape(vertices.shape[0], -1, 3, 3)
|
||||
|
||||
normals = []
|
||||
for i in range(vertices.shape[0]):
|
||||
r = R.from_matrix(rodr[i, 0, :, :])
|
||||
# assert r.as_matrix() == rodr[frame, 0, :, :]
|
||||
out = r.apply(vertices[i], inverse=True)
|
||||
|
||||
# create a plotly mesh plot of out and vertices [i]
|
||||
# fig = go.Figure(data=[
|
||||
# go.Mesh3d(
|
||||
# x=out[:, 0], y=out[:, 2], z=out[:, 1],
|
||||
# # i, j and k give the vertices of triangles
|
||||
# i=faces[:, 0], j=faces[:, 1], k=faces[:, 2],
|
||||
# name='y',
|
||||
# showscale=False
|
||||
# ),
|
||||
# go.Mesh3d(
|
||||
# x=vertices[i][:, 0], y=vertices[i][:, 1], z=vertices[i][:, 2],
|
||||
# # i, j and k give the vertices of triangles
|
||||
# i=faces[:, 0], j=faces[:, 1], k=faces[:, 2],
|
||||
# name='x',
|
||||
# showscale=False
|
||||
# )
|
||||
# ])
|
||||
# fig.show()
|
||||
|
||||
mesh = tm.Trimesh(vertices=out, faces=faces, process=False, validate=False)
|
||||
normals.append(mesh.vertex_normals)
|
||||
return np.stack(normals)
|
||||
|
||||
|
||||
def get_frames(folder):
|
||||
uv_static_folder = os.path.join(folder, 'pose_shape')
|
||||
frames = sorted(os.listdir(uv_static_folder))
|
||||
|
||||
frames = [frame.split('.')[0] for frame in frames] # .split('_')[0]
|
||||
frames = [Frame(frame.split('_')[0], frame.split('_')[1]) for frame in frames]
|
||||
return frames
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("-f", "--folder", help = "end of range", default='')
|
||||
parser.add_argument("-f_obj", "--folder_object", help = "end of range", default='/')
|
||||
parser.add_argument("-s", "--start", help = "start of range", default=0)
|
||||
parser.add_argument("-e", "--end", help = "end of range", default=10)
|
||||
parser.add_argument("-n", "--name", help = "end of range", default='lhunor')
|
||||
args = parser.parse_args()
|
||||
|
||||
obj_folder = args.folder_object # '/data-net/dataset/cloth3d/train'
|
||||
folder = args.folder #'/data-local/127/train'
|
||||
|
||||
os.makedirs(os.path.join(folder, 'mesh_normals'), exist_ok=True)
|
||||
frames = get_frames(folder)
|
||||
smpl = SMPL('cpu', 'smpl')
|
||||
|
||||
ids = {frame.id for frame in frames}
|
||||
ids = sorted(list(ids))[int(args.start):int(args.end)]
|
||||
|
||||
for id in tqdm(ids):
|
||||
# find obj files in the folder
|
||||
garment_obj_folder = os.path.join(obj_folder, id[:5])
|
||||
garments = [file.split('.')[0] for file in os.listdir(garment_obj_folder) if file.endswith('.obj')]
|
||||
|
||||
if len(id) == 5:
|
||||
garment = garments[0]
|
||||
elif len(id) == 6:
|
||||
garment = garments[1]
|
||||
else:
|
||||
raise ValueError('Invalid frame id')
|
||||
|
||||
mesh_file = os.path.join(garment_obj_folder, f'{garment}.obj')
|
||||
vertices =readPC2(os.path.join(garment_obj_folder, f'{garment}.pc16'), True)['V']
|
||||
poses = sio.loadmat(os.path.join(garment_obj_folder, f'info.mat'))['poses']
|
||||
normals = calculate_normals(vertices, mesh_file, poses)
|
||||
|
||||
map = sio.loadmat(os.path.join(garment_obj_folder, f'{garment}_map.mat'))['map'][0]
|
||||
mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy'))
|
||||
|
||||
mesh_normals = normals[:,map,:]
|
||||
mesh_normals = mesh_normals * mask[None,:, None]
|
||||
|
||||
for i in range(vertices.shape[0]):
|
||||
# np.savez(os.path.join(folder, 'mesh_normals', f'{id}_{i}.npz'), mesh_normals=mesh_normals)
|
||||
np.savez_compressed(os.path.join(folder, 'mesh_normals', f'{id}_{i}.npz'), mesh_normals=mesh_normals[i])
|
||||
|
||||
# pass
|
||||
|
||||
# print(frame.id, frame.frame_nr)
|
||||
|
||||
@@ -0,0 +1,259 @@
|
||||
import numpy as np
|
||||
import os
|
||||
import sys
|
||||
import pickle
|
||||
import plotly.express as ex
|
||||
import plotly.graph_objects as go
|
||||
import torch
|
||||
import open3d as o3d
|
||||
import trimesh
|
||||
# from vedo import trimesh as trimesh_plot
|
||||
from scipy.spatial import KDTree
|
||||
from scipy.sparse import lil_matrix
|
||||
from scipy.optimize import minimize
|
||||
|
||||
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
|
||||
|
||||
from smpl.smpl_torch import SMPL
|
||||
|
||||
# import mask faces
|
||||
from custom_utils.custom_utils import _maskFaces
|
||||
|
||||
smpl = SMPL(path='smpl')
|
||||
|
||||
def energy_function(params, source_points, target_points, indices):
|
||||
num_points = source_points.shape[0]
|
||||
num_dims = source_points.shape[1]
|
||||
|
||||
# Reshape the parameters
|
||||
weights = params[:num_points * num_dims].reshape((num_points, num_dims))
|
||||
translation = params[num_points * num_dims:num_points * num_dims + num_dims]
|
||||
control_points = params[num_points * num_dims + num_dims:].reshape((-1, num_dims))
|
||||
|
||||
# Compute the transformed source points
|
||||
transformed_points = source_points + np.dot(weights, control_points.T)
|
||||
|
||||
# Compute the TPS energy
|
||||
tps_energy = 0.5 * np.sum(np.square(target_points[indices] - transformed_points))
|
||||
|
||||
return tps_energy
|
||||
|
||||
def non_rigid(source_points, target_points):
|
||||
tree = KDTree(target_points)
|
||||
distances, indices = tree.query(source_points)
|
||||
|
||||
# Initialize the optimization parameters
|
||||
num_points = source_points.shape[0]
|
||||
num_dims = source_points.shape[1]
|
||||
initial_params = np.zeros(num_points * num_dims + num_dims + num_points)
|
||||
|
||||
# Define the optimization bounds
|
||||
bounds = [(None, None)] * (num_points * num_dims)
|
||||
bounds += [(None, None)] * num_dims
|
||||
bounds += [(None, None)] * (num_points * num_dims)
|
||||
|
||||
# Perform the optimization
|
||||
result = minimize(energy_function, initial_params, method='L-BFGS-B',
|
||||
args=(source_points, target_points, indices), bounds=bounds)
|
||||
|
||||
# Get the optimized parameters
|
||||
optimized_params = result.x
|
||||
|
||||
# Reshape the parameters
|
||||
optimized_weights = optimized_params[:num_points * num_dims].reshape((num_points, num_dims))
|
||||
optimized_translation = optimized_params[num_points * num_dims:num_points * num_dims + num_dims]
|
||||
optimized_control_points = optimized_params[num_points * num_dims + num_dims:].reshape((-1, num_dims))
|
||||
|
||||
transformed_points = source_points + np.dot(optimized_weights, optimized_control_points.T)
|
||||
return transformed_points
|
||||
|
||||
|
||||
def register_smpl():
|
||||
# generate body with zero pose and shape
|
||||
pose = torch.zeros((1, 72))
|
||||
shape = torch.zeros((1, 11))
|
||||
smpl_out = smpl(pose, shape)[0][0]
|
||||
|
||||
with open('/data-net/code/vto-dataset/tshirt/simulations/tshirt_shape00_55_27.pkl', "rb") as f:
|
||||
data = pickle.load(f)
|
||||
garment = data['vertices'][100]
|
||||
faces = data['faces']
|
||||
|
||||
# rotate garment x degrees along y axis
|
||||
# convert degrees to radians
|
||||
degrees = 107
|
||||
theta = degrees * np.pi / 180
|
||||
rot = np.array([[np.cos(theta), 0, np.sin(theta)],
|
||||
[0, 1, 0],
|
||||
[-np.sin(theta), 0, np.cos(theta)]])
|
||||
garment = np.dot(rot, garment.T).T
|
||||
|
||||
# rotate garment x degrees along x axis
|
||||
# convert degrees to radians
|
||||
degrees = 5
|
||||
theta = degrees * np.pi / 180
|
||||
rot = np.array([[1, 0, 0],
|
||||
[0, np.cos(theta), -np.sin(theta)],
|
||||
[0, np.sin(theta), np.cos(theta)]])
|
||||
garment = np.dot(rot, garment.T).T
|
||||
|
||||
# rotate garment x degrees along z axis
|
||||
# convert degrees to radians
|
||||
degrees = -5
|
||||
theta = degrees * np.pi / 180
|
||||
rot = np.array([[np.cos(theta), -np.sin(theta), 0],
|
||||
[np.sin(theta), np.cos(theta), 0],
|
||||
[0, 0, 1]])
|
||||
garment = np.dot(rot, garment.T).T
|
||||
|
||||
|
||||
garment[:,0] -= 0.06
|
||||
garment[:,1] -= 0.03
|
||||
garment[:,2] -= 0.015
|
||||
|
||||
# visualize the body and garment
|
||||
# fig = go.Figure(data=[go.Mesh3d(x=smpl_out[:,0], y=smpl_out[:,1], z=smpl_out[:,2], i=smpl.f[:,0], j=smpl.f[:,1], k=smpl.f[:,2])])
|
||||
# fig.add_trace(go.Mesh3d(x=garment[:,0], y=garment[:,1], z=garment[:,2], i=faces[:,0], j=faces[:,1], k=faces[:,2]))
|
||||
# fig.update_layout(scene=dict(xaxis=dict(range=[-1,1],), yaxis=dict(range=[-1,1],), zaxis=dict(range=[-1,1],),),)
|
||||
# fig.show()
|
||||
|
||||
# non_rigid(garment, smpl_out.numpy())
|
||||
|
||||
# Load the source and target meshes
|
||||
source_mesh = o3d.geometry.TriangleMesh(o3d.utility.Vector3dVector(garment), o3d.utility.Vector3iVector(faces))
|
||||
target_mesh = o3d.geometry.TriangleMesh(o3d.utility.Vector3dVector(smpl_out), o3d.utility.Vector3iVector(smpl.f))
|
||||
# flip source and target mesh
|
||||
source_mesh, target_mesh = target_mesh, source_mesh
|
||||
|
||||
source_mesh.compute_vertex_normals()
|
||||
source_mesh = source_mesh.filter_smooth_laplacian(number_of_iterations=10)
|
||||
# plot the smoothed mesh with plotly
|
||||
# fig = go.Figure(data=[go.Mesh3d(x=np.asarray(source_mesh.vertices)[:,0], y=np.asarray(source_mesh.vertices)[:,1], z=np.asarray(source_mesh.vertices)[:,2], i=np.asarray(source_mesh.triangles)[:,0], j=np.asarray(source_mesh.triangles)[:,1], k=np.asarray(source_mesh.triangles)[:,2])])
|
||||
# fig.show()
|
||||
|
||||
# Perform initial alignment (translation and/or scaling)
|
||||
# You can modify this step based on your specific needs
|
||||
# For example, you can manually set an initial transformation
|
||||
init_transformation = [[1.0, 0.0, 0.0, 0.0],
|
||||
[0.0, 1.0, 0.0, 0.0],
|
||||
[0.0, 0.0, 1.0, 0.0],
|
||||
[0.0, 0.0, 0.0, 1.0]]
|
||||
|
||||
# Create the ICP object and set the parameters
|
||||
icp = o3d.pipelines.registration.TransformationEstimationPointToPoint()
|
||||
criteria = o3d.pipelines.registration.ICPConvergenceCriteria(max_iteration=2000)
|
||||
|
||||
# Perform ICP registration
|
||||
reg_result = o3d.pipelines.registration.registration_icp(o3d.geometry.PointCloud(source_mesh.vertices),
|
||||
o3d.geometry.PointCloud(target_mesh.vertices),
|
||||
0.075,
|
||||
init_transformation, icp, criteria)
|
||||
|
||||
# Get the aligned source mesh after registration
|
||||
aligned_mesh = source_mesh.transform(reg_result.transformation)
|
||||
|
||||
# Visualize the aligned mesh and target mesh with plotly
|
||||
fig = go.Figure(data=[go.Mesh3d(x=np.asarray(aligned_mesh.vertices)[:,0], y=np.asarray(aligned_mesh.vertices)[:,1], z=np.asarray(aligned_mesh.vertices)[:,2], i=np.asarray(aligned_mesh.triangles)[:,0], j=np.asarray(aligned_mesh.triangles)[:,1], k=np.asarray(aligned_mesh.triangles)[:,2])])
|
||||
fig.add_trace(go.Mesh3d(x=np.asarray(target_mesh.vertices)[:,0], y=np.asarray(target_mesh.vertices)[:,1], z=np.asarray(target_mesh.vertices)[:,2], i=np.asarray(target_mesh.triangles)[:,0], j=np.asarray(target_mesh.triangles)[:,1], k=np.asarray(target_mesh.triangles)[:,2]))
|
||||
fig.update_layout(scene=dict(xaxis=dict(range=[-1,1],), yaxis=dict(range=[-1,1],), zaxis=dict(range=[-1,1],),),)
|
||||
fig.show()
|
||||
|
||||
# Get the vertices of the aligned source mesh and target mesh
|
||||
aligned_source_vertices = np.asarray(aligned_mesh.vertices)
|
||||
target_vertices = np.asarray(target_mesh.vertices)
|
||||
|
||||
# Perform nearest neighbor search to find corresponding vertex indices
|
||||
kd_tree = o3d.geometry.KDTreeFlann(target_mesh)
|
||||
correspondences = []
|
||||
matched = set()
|
||||
for i in range(len(aligned_source_vertices)):
|
||||
k = 1
|
||||
_, idx, _ = kd_tree.search_knn_vector_3d(aligned_source_vertices[i], k)
|
||||
# while idx[-1] in matched:
|
||||
# k += 1
|
||||
# _, idx, _ = kd_tree.search_knn_vector_3d(aligned_source_vertices[i], k)
|
||||
# matched.add(idx[-1])
|
||||
correspondences.append((i, idx[-1]))
|
||||
|
||||
# correspondences is a list of tuples where each tuple contains the corresponding vertex indices
|
||||
# For example, correspondences = [(0, 10), (1, 5), (2, 15), ...]
|
||||
|
||||
# Visualize the correspondences with plotly
|
||||
# fig = go.Figure(data=[go.Scatter3d(x=aligned_source_vertices[:,0], y=aligned_source_vertices[:,1], z=aligned_source_vertices[:,2], mode='markers', marker=dict(color='red', size=1))])
|
||||
# fig.add_trace(go.Scatter3d(x=target_vertices[:,0], y=target_vertices[:,1], z=target_vertices[:,2], mode='markers', marker=dict(color='blue', size=1)))
|
||||
# for corr in correspondences:
|
||||
# fig.add_trace(go.Scatter3d(x=[aligned_source_vertices[corr[0],0], target_vertices[corr[1],0]], y=[aligned_source_vertices[corr[0],1], target_vertices[corr[1],1]], z=[aligned_source_vertices[corr[0],2], target_vertices[corr[1],2]], mode='lines', line=dict(color='green', width=1)))
|
||||
# fig.update_layout(scene=dict(xaxis=dict(range=[-1,1],), yaxis=dict(range=[-1,1],), zaxis=dict(range=[-1,1],),),)
|
||||
# fig.show()
|
||||
|
||||
registered = np.zeros((14475, 3))
|
||||
mask = np.zeros((14475), dtype=bool)
|
||||
for i in range(len(correspondences)):
|
||||
registered[correspondences[i][1]] = aligned_source_vertices[correspondences[i][0]]
|
||||
mask[correspondences[i][1]] = True
|
||||
|
||||
# F = np.load(f'smpl/smpl_faces.npy').astype(np.int32)
|
||||
|
||||
# registered_masked, masked_faces = _maskFaces(registered, np.asarray(target_mesh.triangles), mask)
|
||||
registered_masked, masked_faces = registered, np.asarray(target_mesh.triangles)
|
||||
|
||||
# plot aligned source mesh with the target topology
|
||||
fig = go.Figure(data=[go.Mesh3d(x=registered_masked[:,0], y=registered_masked[:,1], z=registered_masked[:,2], i=masked_faces[:,0], j=masked_faces[:,1], k=masked_faces[:,2])])
|
||||
fig.update_layout(scene=dict(xaxis=dict(range=[-1,1],), yaxis=dict(range=[-1,1],), zaxis=dict(range=[-1,1],),),)
|
||||
fig.show()
|
||||
|
||||
return smpl
|
||||
|
||||
|
||||
def convert_vto(folder, out_folder):
|
||||
os.makedirs(out_folder, exist_ok=True)
|
||||
os.makedirs(os.path.join(out_folder, 'mesh_posed_no_orient'), exist_ok=True)
|
||||
os.makedirs(os.path.join(out_folder, 'mesh_posed_body_no_orient'), exist_ok=True)
|
||||
os.makedirs(os.path.join(out_folder, 'mesh_mask'), exist_ok=True)
|
||||
os.makedirs(os.path.join(out_folder, 'pose_shape'), exist_ok=True)
|
||||
F = np.load(f'smpl/smpl_faces.npy').astype(np.int32)
|
||||
|
||||
files = os.listdir(folder)
|
||||
# files = ['/data-net/code/vto-dataset/tshirt/simulations/tshirt_shape00_55_27.pkl']
|
||||
for file in files:
|
||||
# read pickle files and get vetices and faces from them
|
||||
with open(os.path.join(folder, file), "rb") as f:
|
||||
data = pickle.load(f)
|
||||
vertices = data['vertices']
|
||||
faces = data['faces']
|
||||
pose = data['pose']
|
||||
shape = data['shape']
|
||||
trans = data['translation']
|
||||
id = file.split('.')[0]
|
||||
# pose[:, :3] = 0
|
||||
id = id.replace('_', '-')
|
||||
subject = id.split('-')[2]
|
||||
sequence = '_'.join(id.split('-')[2:])
|
||||
# cmu_data = np.load(f'/data-net/code/vto-garment-collisions/assets/CMU/{subject}/{sequence}_poses.npz')
|
||||
# shape = np.tile(cmu_data['betas'], (shape.shape[0], 1))
|
||||
# pose = cmu_data['poses']
|
||||
|
||||
body = smpl(torch.tensor(pose), torch.tensor(np.concatenate((shape, np.zeros((shape.shape[0],1),dtype=np.float32)),axis=1)))[0].numpy()
|
||||
pose_shape = np.concatenate((pose, shape, np.zeros((shape.shape[0],1),dtype=np.float32)),axis=1)
|
||||
|
||||
# for i in range(vertices.shape[0]):
|
||||
# np.savez_compressed(os.path.join(out_folder, 'mesh_posed_body_no_orient', f'{id}_{i}.npz'), mesh_posed_body_no_orient=body[i])
|
||||
# np.save(os.path.join(out_folder, 'pose_shape', f'{id}_{i}.npy'), pose_shape[i])
|
||||
|
||||
|
||||
# plot mesh with plotly
|
||||
frame = 0
|
||||
fig = go.Figure(data=[go.Mesh3d(x=vertices[frame,:,0], y=vertices[frame,:,1], z=vertices[frame,:,2], i=faces[:,0], j=faces[:,1], k=faces[:,2])])
|
||||
fig.add_trace(go.Mesh3d(x=body[frame,:,0], y=body[frame,:,1], z=body[frame,:,2], i=F[:,0], j=F[:,1], k=F[:,2]))
|
||||
fig.update_layout(scene=dict(xaxis=dict(range=[-1,1],), yaxis=dict(range=[-1,1],), zaxis=dict(range=[-1,1],),),)
|
||||
fig.show()
|
||||
|
||||
|
||||
pass
|
||||
pass
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# register_smpl()
|
||||
convert_vto('/data-net/code/vto-dataset/dress/simulations', '/data-net/code/vto-dataset/dress_converted')
|
||||
pass
|
||||
@@ -175,9 +175,6 @@ class Dataset(Dataset):
|
||||
if 'mesh_curvature' in self.signature:
|
||||
mesh_curvature = np.load(os.path.join(self.folder, 'mesh_curvature', frame.id + separator + frame.frame_nr + '.npz')); mesh_curvature = mesh_curvature['mesh_curvature']
|
||||
item['mesh_curvature'] = mesh_curvature.astype(np.int32)
|
||||
if 'mesh_normals' in self.signature:
|
||||
mesh_normals = np.load(os.path.join(self.folder, 'mesh_normals', frame.id + separator + frame.frame_nr + '.npz')); mesh_normals = mesh_normals['mesh_normals']
|
||||
item['mesh_normals'] = mesh_normals
|
||||
|
||||
for res in self.resolutions:
|
||||
res_string = ''
|
||||
@@ -206,10 +203,6 @@ class Dataset(Dataset):
|
||||
uv_posed = np.load(os.path.join(self.folder, 'uv_posed_no_orient' + res_string, frame.id + separator + frame.frame_nr + '.npz')); uv_posed = uv_posed['uv_posed_no_orient']
|
||||
item['uv_posed' + res_string] = uv_posed
|
||||
|
||||
if 'uv_normals' + res_string in self.signature:
|
||||
uv_normals = np.load(os.path.join(self.folder, 'uv_normals' + res_string, frame.id + separator + frame.frame_nr + '.npz')); uv_normals = uv_normals['uv_normals']
|
||||
item['uv_normals' + res_string] = uv_normals
|
||||
|
||||
|
||||
data = { key: (torch.from_numpy(value) if not (type(value) == str or type(value) == torch.Tensor) else value) for key, value in item.items()}
|
||||
|
||||
|
||||
@@ -0,0 +1,40 @@
|
||||
import os
|
||||
import sys
|
||||
|
||||
import argparse
|
||||
parser = argparse.ArgumentParser(description='')
|
||||
parser.add_argument('--debug', action='store_true')
|
||||
args = vars(parser.parse_args())
|
||||
|
||||
if args['debug']:
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
|
||||
|
||||
import math
|
||||
import time
|
||||
import torch
|
||||
import random
|
||||
import pytorch3d
|
||||
import torchvision as tv
|
||||
import numpy as np
|
||||
import scipy.io as sio
|
||||
import plotly.express as ex
|
||||
from torch.utils.data import Dataset
|
||||
|
||||
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
|
||||
|
||||
from data.utils import Frame
|
||||
|
||||
class Dataset(Dataset):
|
||||
def __init__(self, folder):
|
||||
pass
|
||||
|
||||
def __len__(self):
|
||||
return len(self.frames)
|
||||
|
||||
|
||||
def __getitem__(self, idx):
|
||||
frame : Frame = self.frames[idx]
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
from torch.utils.data import DataLoader
|
||||
@@ -34,7 +34,6 @@ services:
|
||||
- '/data-net/guest/lhunor/data:/data-net'
|
||||
- '/data-fast/126-data1/lhunor/data:/data-local/126'
|
||||
- '/data-fast/127-data1/lhunor/data:/data-local/127'
|
||||
- '/data-fast/127-data2/lhunor/data:/data-local/127_2'
|
||||
- '/data-fast/128-data1/lhunor/data:/data-local/128'
|
||||
deploy:
|
||||
resources:
|
||||
|
||||
+5
-5
@@ -18,12 +18,12 @@ from torch_cluster import knn
|
||||
smpl_to_keep_orig = sio.loadmat('smpl/keep_remove.mat')['keep'].reshape(-1)
|
||||
smpl_to_keep_orig = smpl_to_keep_orig[smpl_to_keep_orig < 6890]
|
||||
|
||||
# to_keep = sio.loadmat('smpl/keep_remove.mat')['keep'].reshape(-1)
|
||||
F_coll = torch.tensor(np.load("smpl/smpl_faces.npy"), device='cuda')
|
||||
F_coll_skirt = torch.tensor(np.load("smpl/smpl_faces_skirt.npy"), device='cuda')
|
||||
to_keep = sio.loadmat('smpl/keep_remove.mat')['keep'].reshape(-1)
|
||||
F_coll = torch.tensor(np.load("smpl/smpl_faces.npy")[to_keep,:], device='cuda')
|
||||
F_coll_skirt = torch.tensor(np.load("smpl/smpl_faces_skirt.npy")[to_keep,:], device='cuda')
|
||||
F_coll_orig = torch.tensor(np.load("smpl/smpl_faces_orig.npy")[:6890,:], device='cuda')
|
||||
F_norm = torch.tensor(np.tile(np.load("smpl/smpl_faces.npy")[None,:,:], [4,1,1]), device='cuda')
|
||||
F_norm_skirt = torch.tensor(np.tile(np.load("smpl/smpl_faces_skirt.npy")[None,:,:], [4,1,1]), device='cuda')
|
||||
F_norm = torch.tensor(np.tile(np.load("smpl/smpl_faces.npy")[to_keep,:][None,:,:], [4,1,1]), device='cuda')
|
||||
F_norm_skirt = torch.tensor(np.tile(np.load("smpl/smpl_faces_skirt.npy")[to_keep,:][None,:,:], [4,1,1]), device='cuda')
|
||||
F_norm_orig = torch.tensor(np.tile(np.load("smpl/smpl_faces_orig.npy")[:6890,:][None,:,:], [4,1,1]), device='cuda')
|
||||
F_norm_orig = F_norm_orig[:,smpl_to_keep_orig,:]
|
||||
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import os
|
||||
import sys
|
||||
from configs.direct_6 import config
|
||||
from configs.direct_1 import config
|
||||
import argparse
|
||||
|
||||
# parser = argparse.ArgumentParser(description='')
|
||||
|
||||
+5
-8
@@ -4,27 +4,24 @@ import os
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# folder = '/home/workspace/data/dataset/cloth3d/train'
|
||||
folder = '/home/workspace/data/dataset/cloth3d/train'
|
||||
# folder = '/home/workspace/local-data/cape_processed/train/male'
|
||||
# folder = '/home/workspace/local-data/valid_skirt'
|
||||
# folder = '/home/workspace/local-data/valid'
|
||||
# ids = os.listdir(folder)
|
||||
# ids = os.listdir(folder + '/mesh_posed_no_orient')#[280000:]
|
||||
skirt = 0
|
||||
obj_folder = '/data-net/dataset/cloth3d/train'
|
||||
folder = '/data-local/127/train'
|
||||
|
||||
count = 0
|
||||
step = 100
|
||||
count = int(500 * 1e3)
|
||||
step = 10000
|
||||
|
||||
# !!!!!!! change skirt parameter in script
|
||||
|
||||
while count <1200:
|
||||
while count < 510 * 1e3:
|
||||
# os.system('nohup python convert_to_uv_cape.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_2_cape_train_male_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_2_cape_train_male_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
|
||||
# os.system('nohup python convert_to_uv.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_train_unposed_onehot_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_train_unposed_onehot_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
|
||||
# os.system('nohup python convert_to_uv_local.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_train_local_unposed_onehot_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_train_local_unposed_onehot_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
|
||||
# os.system('nohup python data/convert_to_uv.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_train_2_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv/train_2_' + str(count) + '_' + str(count+step) + '_again.out' + ' 2>&1 &')
|
||||
os.system(f'nohup python data/calculate_normals.py -f {folder} -f_obj {obj_folder} -s {count} -e {(count+step)} -n normals_train_calc_{count}_{(count+step)} > nohup/normals/normals_train_calc_{count}_{(count+step)}.out' + ' 2>&1 &')
|
||||
os.system('nohup python data/convert_to_uv.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_train_2_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv/train_2_' + str(count) + '_' + str(count+step) + '_again.out' + ' 2>&1 &')
|
||||
# os.system('nohup python convert_to_uv_local_single.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_train_skirt_local_unposed_onehot_3_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_train_skirt_local_unposed_onehot_3_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
|
||||
# os.system('nohup python convert_to_uv_local_single.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_valid_skirt_local_unposed_onehot_3_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_valid_skirt_local_unposed_onehot_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
|
||||
# os.system('nohup python downscale.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'downscale_valid_posed_no_orient_' + str(count) + '_' + str(count+step) + ' > nohup/downscale_valid_posed_no_orient_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
|
||||
|
||||
@@ -1,218 +0,0 @@
|
||||
import os
|
||||
import sys
|
||||
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
|
||||
|
||||
import argparse
|
||||
import importlib
|
||||
|
||||
parser = argparse.ArgumentParser(description='')
|
||||
parser.add_argument('--name', type=str)
|
||||
parser.add_argument('--config', type=str)
|
||||
parser.add_argument('--debug', action='store_true')
|
||||
args = vars(parser.parse_args())
|
||||
|
||||
config_module = importlib.import_module(args['config'])
|
||||
config = config_module.config
|
||||
if args['debug']:
|
||||
from configs.ae_norm_1 import config
|
||||
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
if args['debug']:
|
||||
os.environ["CUDA_LAUNCH_BLOCKING"] = '1'
|
||||
|
||||
import time
|
||||
import wandb
|
||||
import random
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch import nn
|
||||
# from torchmetrics import MultiScaleStructuralSimilarityIndexMeasure as MSSSIM
|
||||
from custom_utils.custom_utils import UV2Mesh, get_summary_html, maskFacesBatch, get_summary_str
|
||||
import plotly.graph_objects as go
|
||||
import plotly.express as ex
|
||||
import torch.optim as optim
|
||||
|
||||
if config['is_skirt']:
|
||||
from losses.losses import edge_loss_skirt as edge_loss
|
||||
else:
|
||||
from losses.losses import edge_loss
|
||||
|
||||
from data.dataset import Dataset
|
||||
# from torch.utils.data import DataLoader
|
||||
from custom_utils.custom_utils import plot_3d
|
||||
from models.cond_encoder import AutoEncoder
|
||||
from utils.checkpoints import CheckpointSaver, load_model
|
||||
from custom_utils.custom_utils import Accumulator
|
||||
from utils.utils import InfiniteDataLoader as DataLoader
|
||||
|
||||
from pytorch_msssim import MS_SSIM
|
||||
|
||||
|
||||
def init_wandb():
|
||||
run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/data-local/127_2/temp', tags=config['tags'])
|
||||
artifact = wandb.Artifact('code', type='code')
|
||||
artifact.add_file('scripts/ae_norm_train.py')
|
||||
artifact.add_file('losses/losses.py')
|
||||
artifact.add_file('models/base.py')
|
||||
artifact.add_file('models/cond_encoder.py')
|
||||
artifact.add_file('models/convnext.py')
|
||||
artifact.add_file('data/dataset.py')
|
||||
config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.')]
|
||||
for config_file_name in config_file_names:
|
||||
file_name = '/'.join(config_file_name.split('.')) + '.py'
|
||||
if os.path.exists(file_name):
|
||||
artifact.add_file(file_name)
|
||||
run.log_artifact(artifact)
|
||||
|
||||
uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
|
||||
|
||||
|
||||
|
||||
def train_step(batch):
|
||||
model.train()
|
||||
pred = model([batch['uv_normals'], batch['uv_mask']]) * 1.1
|
||||
|
||||
ms_ssim = torch.mean(_MSSSIM(pred + 1.1, batch['uv_normals'] + 1.1))
|
||||
loss = 1e3 * (1 - ms_ssim)
|
||||
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
model.eval()
|
||||
with torch.inference_mode():
|
||||
mesh_pred = uv2mesh(pred)
|
||||
mesh_orig = batch['mesh_normals']
|
||||
l1_loss = torch.mean(torch.sum((torch.abs(batch['uv_normals'] - pred)), axis=1))
|
||||
metric = torch.mean(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred) ** 2, axis=-1)))
|
||||
|
||||
return { 'train/loss': loss, 'train/l1_loss': l1_loss, 'train/euclid_error': metric, 'train/ms_ssim': ms_ssim, }
|
||||
|
||||
|
||||
def valid_step(batch):
|
||||
model.eval()
|
||||
|
||||
with torch.inference_mode():
|
||||
pred = model([batch['uv_normals'], batch['uv_mask']]) * 1.1
|
||||
|
||||
mesh_pred = uv2mesh(pred)
|
||||
mesh_orig = batch['mesh_normals']
|
||||
|
||||
l1_loss = torch.mean(torch.sum((torch.abs(batch['uv_normals'] - pred)), axis=1))
|
||||
ms_ssim = torch.mean(_MSSSIM(pred + 1.1, batch['uv_normals'] + 1.1))
|
||||
loss = 1e3 * (1 - ms_ssim)
|
||||
|
||||
metric = torch.mean(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred) ** 2, axis=-1)))
|
||||
|
||||
return { 'valid/loss': loss, 'valid/l1_loss': l1_loss, 'valid/euclid_error': metric, 'valid/ms_ssim': ms_ssim, }
|
||||
|
||||
|
||||
def plot_everything(batch: dict, type: str):
|
||||
model.eval()
|
||||
pred = model([batch['uv_normals'], batch['uv_mask']]).detach() * 1.1
|
||||
|
||||
# mesh_pred = uv2mesh(pred)
|
||||
# mesh_orig = batch['mesh_normals']
|
||||
|
||||
orig_img = torch.permute(batch['uv_normals'], [0,2,3,1])
|
||||
pred_img = torch.permute(pred, [0,2,3,1])
|
||||
img = torch.concat((orig_img, pred_img), axis=2)
|
||||
imgs = [wandb.Image((img[i].cpu().numpy() + 1.1) / 2.2) for i in range(4)]
|
||||
|
||||
# F = np.load("smpl/smpl_faces" + skirt_modifier + ("_orig" if config['is_cape'] else "") + ".npy")
|
||||
# garment_unposed, faces = mesh_pred.cpu().numpy(), F
|
||||
# gt = [mesh_orig[i].cpu().numpy() for i in range(pred.shape[0])]
|
||||
|
||||
# body = uv2mesh(batch['uv_body_posed']).cpu().numpy()
|
||||
# garment_posed = garment_unposed
|
||||
# collision = [np.concatenate((body[i], garment_posed[i]), axis=0) for i in range(len(garment_posed))]
|
||||
# collision_faces = [np.concatenate((F, faces + (3638 if config['is_cape'] else 14475)), axis=0) for i in range(len(garment_posed))]
|
||||
# collision_color = [np.concatenate((np.ones_like(body[i], dtype=np.int32)*100, np.ones_like(garment_posed[i], dtype=np.int32)*230), axis=0) for i in range(len(garment_posed))]
|
||||
|
||||
# figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces, collision[i], collision_faces[i], collision_color[i]) for i in range(4) }
|
||||
figs = {}
|
||||
|
||||
return { **figs, **{ type + '/images': imgs } }
|
||||
|
||||
|
||||
def train():
|
||||
valid_iterator = iter(valid_dataloader)
|
||||
|
||||
step = 0
|
||||
for batch in train_dataloader:
|
||||
train_loss = train_step(batch)
|
||||
train_accumulator.update(train_loss)
|
||||
|
||||
if step % 25 == 0:
|
||||
data_valid = next(valid_iterator)
|
||||
valid_loss = valid_step(data_valid)
|
||||
valid_accumulator.update(valid_loss)
|
||||
|
||||
if step % 1000 == 0 or (step < 1000 and step % 100 == 0):
|
||||
to_log = dict({ **train_loss, **valid_loss, **{ 'current_step': step } })
|
||||
if step % 5000 == 0 or (step < 5000 and step % 1000 == 0):
|
||||
train_figs = plot_everything(batch, 'train')
|
||||
valid_figs = plot_everything(data_valid, 'valid')
|
||||
to_log = { **to_log, **train_figs, **valid_figs }
|
||||
if step == 0:
|
||||
to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
|
||||
|
||||
if not args['debug']:
|
||||
wandb.log(to_log, step=step)
|
||||
train_accumulator.reset()
|
||||
valid_accumulator.reset()
|
||||
print(step)
|
||||
|
||||
if step % 10000 == 0:
|
||||
ckpt_saver_enc(model.encoder, step, optimizer, scaler=None)
|
||||
ckpt_saver_dec(model.decoder, step, optimizer, scaler=None)
|
||||
|
||||
step += 1
|
||||
|
||||
|
||||
class Scale(torch.nn.Module):
|
||||
def __init__(self, scale_factor):
|
||||
super().__init__()
|
||||
self.scale_factor = scale_factor
|
||||
|
||||
def forward(self, x):
|
||||
return x * self.scale_factor
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if not args['debug']:
|
||||
init_wandb()
|
||||
|
||||
skirt_modifier = '_skirt' if config['is_skirt'] else ''
|
||||
|
||||
dataset_train = Dataset(config['train_dir'], config['signature'], device=config['device'], frames_file=config['train_frames'], use_cache=config['dataset_in_memory'], is_cape=config['is_cape'])
|
||||
dataset_valid = Dataset(config['valid_dir'], config['signature'], device=config['device'], frames_file=config['valid_frames'], use_cache=config['dataset_in_memory'], is_cape=config['is_cape'])
|
||||
train_dataloader = DataLoader(dataset_train, batch_size=4, drop_last=True, shuffle=True)
|
||||
valid_dataloader = DataLoader(dataset_valid, batch_size=4, drop_last=True, shuffle=True)
|
||||
train_accumulator = Accumulator()
|
||||
valid_accumulator = Accumulator()
|
||||
|
||||
model : AutoEncoder = AutoEncoder(config['cond_ae'])
|
||||
optimizer = optim.Adam(model.parameters(), lr=config['lr'])
|
||||
|
||||
# _MSSSIM = MSSSIM(data_range=2).to(config['device'])
|
||||
_MSSSIM = MS_SSIM(data_range=2.2, size_average=True, channel=3)
|
||||
|
||||
if config['pretrained_model'] != '':
|
||||
load_model(model.encoder, config['pretrained_model'] + '_enc')
|
||||
load_model(model.decoder, config['pretrained_model'] + '_dec')
|
||||
|
||||
ckpt_saver_enc = CheckpointSaver('checkpoints/iccv/torch/' + config['name'] + '_enc', top_n=5)
|
||||
ckpt_saver_dec = CheckpointSaver('checkpoints/iccv/torch/' + config['name'] + '_dec', top_n=5)
|
||||
|
||||
run_name = config['name']
|
||||
print(f'started training: {run_name}')
|
||||
|
||||
if not args['debug']:
|
||||
# model = torch.compile(model)
|
||||
wandb.watch(model, log='all', log_freq=10000, log_graph=True)
|
||||
|
||||
train()
|
||||
|
||||
|
||||
|
||||
+12
-46
@@ -13,7 +13,7 @@ args = vars(parser.parse_args())
|
||||
config_module = importlib.import_module(args['config'])
|
||||
config = config_module.config
|
||||
if args['debug']:
|
||||
from configs.direct_7 import config
|
||||
from configs.direct_1 import config
|
||||
# from configs.ae_static_orig_2 import config
|
||||
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
@@ -52,7 +52,7 @@ from pytorch_msssim import MS_SSIM
|
||||
|
||||
|
||||
def init_wandb():
|
||||
run = wandb.init(project='iccv', name=config['name'], notes=config['notes'], dir='/data-local/127_2/temp', tags=config['tags'])
|
||||
run = wandb.init(project='iccv', name=config['name'], notes=config['notes'], dir='/data-local/127/temp', tags=config['tags'])
|
||||
artifact = wandb.Artifact('code', type='code')
|
||||
artifact.add_file('scripts/direct_train.py')
|
||||
artifact.add_file('losses/losses.py')
|
||||
@@ -76,37 +76,26 @@ def train_step(batch):
|
||||
# with torch.autocast(device_type='cuda', dtype=torch.float16, enabled=use_amp):
|
||||
if config['use_curvature']:
|
||||
input = torch.cat([batch['uv_curvature'], batch['uv_static']], axis=1)
|
||||
elif config['use_normals']:
|
||||
input = torch.cat([batch['uv_normals'], batch['uv_body_posed'], batch['uv_static']], axis=1)
|
||||
else:
|
||||
input = torch.cat([batch['uv_body_posed'], batch['uv_static']], axis=1)
|
||||
pred = model([input, batch['uv_mask']])
|
||||
|
||||
mesh_pred = uv2mesh(pred)
|
||||
if config['use_skinning']:
|
||||
mesh_orig = batch['mesh_unposed']
|
||||
uv_orig = batch['uv_unposed']
|
||||
shape = batch['pose_shape'][:, 72:]
|
||||
gender = torch.greater_equal(shape[:, 10] , 0.5).to(torch.long)
|
||||
shape_offsets = torch.stack([torch.tensordot(shape[i,:10][None, :], smpl.shapedirs[gender[i]], dims=[[1], [2]])[0,smpl.to_keep,:] for i in range(shape.shape[0])])
|
||||
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,None]
|
||||
mesh_pred_posed = smpl(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)[0]
|
||||
else:
|
||||
mesh_orig = batch['mesh_posed']
|
||||
uv_orig = batch['uv_posed']
|
||||
mesh_pred_posed = mesh_pred
|
||||
|
||||
l2_mesh_loss = torch.mean(1e3 * torch.mean(torch.sum(batch['mesh_mask'][:,:,None] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
|
||||
|
||||
e_loss = torch.mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
|
||||
c_loss, c_ratio = collision_loss(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
|
||||
c_loss, c_ratio = torch.mean(c_loss), torch.mean(c_ratio)
|
||||
# l2_loss = torch.mean(1e3 * torch.mean(torch.sum(((batch['uv_unposed'] - pred) ** 2), axis=-1), axis=[1, 2]))
|
||||
l2_mesh_loss = torch.mean(1e3 * torch.mean(torch.sum(batch['mesh_mask'][:,:,None] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
|
||||
n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
|
||||
|
||||
|
||||
ms_ssim = torch.mean(_MSSSIM(pred + 1, uv_orig + 1))
|
||||
loss = l2_mesh_loss + (1 - ms_ssim) * 1e3 + (e_loss + c_loss + n_loss) * config['reg_loss_coeff']
|
||||
loss = l2_mesh_loss + (e_loss + c_loss + n_loss) * config['reg_loss_coeff']
|
||||
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
@@ -118,6 +107,7 @@ def train_step(batch):
|
||||
|
||||
model.eval()
|
||||
with torch.inference_mode():
|
||||
ms_ssim = torch.mean(_MSSSIM(pred + 1, batch['uv_unposed'] + 1))
|
||||
metric = torch.sum(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred.detach()) ** 2, axis=-1))) / torch.sum(batch['mesh_mask'])
|
||||
|
||||
return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/euclid_error': metric, 'train/collision_ratio': c_ratio,
|
||||
@@ -131,25 +121,16 @@ def valid_step(batch):
|
||||
with torch.inference_mode():
|
||||
if config['use_curvature']:
|
||||
input = torch.cat([batch['uv_curvature'], batch['uv_static']], axis=1)
|
||||
elif config['use_normals']:
|
||||
input = torch.cat([batch['uv_normals'], batch['uv_body_posed'], batch['uv_static']], axis=1)
|
||||
else:
|
||||
input = torch.cat([batch['uv_body_posed'], batch['uv_static']], axis=1)
|
||||
|
||||
pred = model([input, batch['uv_mask']])
|
||||
mesh_pred = uv2mesh(pred)
|
||||
if config['use_skinning']:
|
||||
mesh_orig = batch['mesh_unposed']
|
||||
uv_orig = batch['uv_unposed']
|
||||
shape = batch['pose_shape'][:, 72:]
|
||||
gender = torch.greater_equal(shape[:, 10] , 0.5).to(torch.long)
|
||||
shape_offsets = torch.stack([torch.tensordot(shape[i,:10][None, :], smpl.shapedirs[gender[i]], dims=[[1], [2]])[0,smpl.to_keep,:] for i in range(shape.shape[0])])
|
||||
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,None]
|
||||
mesh_pred_posed = smpl(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)[0]
|
||||
else:
|
||||
mesh_orig = batch['mesh_posed']
|
||||
uv_orig = batch['uv_posed']
|
||||
mesh_pred_posed = mesh_pred
|
||||
|
||||
e_loss = torch.mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
|
||||
c_loss, c_ratio = collision_loss(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
|
||||
@@ -158,8 +139,8 @@ def valid_step(batch):
|
||||
l2_mesh_loss = torch.mean(1e3 * torch.mean(torch.sum(batch['mesh_mask'][:,:,None] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
|
||||
n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
|
||||
|
||||
ms_ssim = torch.mean(_MSSSIM(pred + 1, uv_orig + 1))
|
||||
loss = l2_mesh_loss + (1 - ms_ssim) * 1e3 + (e_loss + c_loss + n_loss) * config['reg_loss_coeff']
|
||||
ms_ssim = torch.mean(_MSSSIM(pred + 1, batch['uv_unposed'] + 1))
|
||||
loss = l2_mesh_loss + (e_loss + c_loss + n_loss) * config['reg_loss_coeff']
|
||||
|
||||
metric = torch.sum(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / torch.sum(batch['mesh_mask'])
|
||||
|
||||
@@ -173,32 +154,23 @@ def plot_everything(batch: dict, type: str):
|
||||
with torch.inference_mode():
|
||||
if config['use_curvature']:
|
||||
input = torch.cat([batch['uv_curvature'], batch['uv_static']], axis=1)
|
||||
elif config['use_normals']:
|
||||
input = torch.cat([batch['uv_normals'], batch['uv_body_posed'], batch['uv_static']], axis=1)
|
||||
else:
|
||||
input = torch.cat([batch['uv_body_posed'], batch['uv_static']], axis=1)
|
||||
pred = model([input, batch['uv_mask']]).detach()
|
||||
|
||||
mesh_pred = uv2mesh(pred).cpu().numpy()
|
||||
if config['use_skinning']:
|
||||
mesh_orig = batch['mesh_unposed'].cpu().numpy()
|
||||
uv_orig = batch['uv_unposed']
|
||||
else:
|
||||
mesh_orig = batch['mesh_posed'].cpu().numpy()
|
||||
uv_orig = batch['uv_posed']
|
||||
|
||||
orig_img = torch.permute(uv_orig, [0,2,3,1])
|
||||
orig_img = torch.permute(batch['uv_unposed'], [0,2,3,1])
|
||||
uv_static_img = torch.permute(batch['uv_static'], [0,2,3,1])
|
||||
uv_body_posed_img = torch.permute(batch['uv_body_posed'], [0,2,3,1])
|
||||
uv_normals_img = (torch.permute(batch['uv_normals'], [0,2,3,1]) + 1.1) / 2.2
|
||||
pred_img = torch.permute(pred, [0,2,3,1])
|
||||
img = torch.concat((uv_static_img, uv_body_posed_img, uv_normals_img, orig_img, pred_img), axis=2)
|
||||
img = torch.concat((uv_static_img, uv_body_posed_img, orig_img, pred_img), axis=2)
|
||||
imgs = [wandb.Image((img[i].cpu().numpy() + 1) / 2.0) for i in range(4)]
|
||||
|
||||
F = np.load("smpl/smpl_faces" + skirt_modifier + ("_orig" if config['is_cape'] else "") + ".npy")
|
||||
mask = batch['mesh_mask'].cpu().numpy().astype(bool)
|
||||
|
||||
if config['use_skinning']:
|
||||
shape = batch['pose_shape'][:, 72:]
|
||||
gender = torch.greater_equal(shape[:, 10] , 0.5).to(torch.long)
|
||||
shape_offsets = torch.stack([torch.tensordot(shape[i,:10][None, :], smpl.shapedirs[gender[i]], dims=[[1], [2]])[0,smpl.to_keep,:] for i in range(shape.shape[0])]).cpu().numpy()
|
||||
@@ -210,27 +182,21 @@ def plot_everything(batch: dict, type: str):
|
||||
for i in range(mesh_orig.shape[0]):
|
||||
mesh_pred[i, mask[i], :] += shape_offsets[i, mask[i], :]
|
||||
mesh_posed = smpl(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], torch.tensor(mesh_pred, device=config['device']))[0].cpu().numpy()
|
||||
else:
|
||||
garment_unposed, faces = maskFacesBatch(mesh_pred, F, mask)
|
||||
mesh_posed = mesh_pred
|
||||
gt = [mesh_orig[i][mask[i], :] for i in range(pred.shape[0])]
|
||||
|
||||
gt_posed = [batch['mesh_posed'].cpu().numpy()[i, mask[i], :] for i in range(mask.shape[0])]
|
||||
|
||||
mesh_posed_fixed = fix_collision(torch.tensor(mesh_posed, device=config['device']), batch['mesh_body_posed'], torch.tensor(mask, device=config['device'])).cpu().numpy()
|
||||
body = batch['mesh_body_posed'].cpu().numpy()
|
||||
|
||||
garment_posed = [mesh_posed[i, mask[i], :] for i in range(body.shape[0])]
|
||||
collision = [np.concatenate((body[i], garment_posed[i]), axis=0) for i in range(len(garment_posed))]
|
||||
collision_faces = [np.concatenate((F, faces[i] + config['smpl_res']), axis=0) for i in range(len(garment_posed))]
|
||||
collision_color = [np.concatenate((np.ones_like(body[i], dtype=np.int32)*100, np.ones_like(garment_posed[i], dtype=np.int32)*230), axis=0) for i in range(len(garment_posed))]
|
||||
|
||||
garment_posed_fixed = [mesh_posed_fixed[i, mask[i], :] for i in range(body.shape[0])]
|
||||
collision_fixed = [np.concatenate((body[i], garment_posed_fixed[i]), axis=0) for i in range(len(garment_posed))]
|
||||
collision_gt = [np.concatenate((body[i], gt_posed[i]), axis=0) for i in range(len(garment_posed))]
|
||||
|
||||
# faces_ = np.load('/data-net/dataset/00761/faces/00761.npy')
|
||||
# faces = [faces_ for i in range(len(garment_posed))]
|
||||
collision_faces = [np.concatenate((F, faces[i] + config['smpl_res']), axis=0) for i in range(len(garment_posed))]
|
||||
|
||||
figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces[i], collision[i], collision_faces[i], collision_color[i]) for i in range(4) }
|
||||
figs_coll = { type + "/garment_" + str(i) + '_collision': plot_3d_collision(collision_gt[i], collision[i], collision_fixed[i], collision_faces[i], collision_color[i]) for i in range(4) }
|
||||
|
||||
@@ -307,7 +273,7 @@ if __name__ == '__main__':
|
||||
|
||||
if not args['debug']:
|
||||
# model = torch.compile(model)
|
||||
wandb.watch(model, log='all', log_freq=10000, log_graph=True)
|
||||
wandb.watch(model, log='all', log_freq=1000, log_graph=True)
|
||||
|
||||
train()
|
||||
|
||||
|
||||
+3
-3
@@ -206,9 +206,9 @@ class SMPL(Module):
|
||||
)
|
||||
)
|
||||
|
||||
joints = torch.matmul(results, torch.transpose(torch.concat([J, torch.ones((J.shape[0], 24, 1), dtype=torch.float32, device=J.get_device())], axis=-1)[:,:,None,:],2,3))[:,:,:3,0]
|
||||
joints = torch.matmul(results, torch.transpose(torch.concat([J, torch.ones((J.shape[0], 24, 1), dtype=torch.float32, device=J.device)], axis=-1)[:,:,None,:],2,3))[:,:,:3,0]
|
||||
|
||||
T = torch.stack([torch.tensordot(results[i], self.weights[torch.greater(g, 0.5).to(torch.long)], dims=([0], [1])).permute(2, 0, 1) for i, g in enumerate(betas[:,-1])])
|
||||
T = torch.stack([torch.tensordot(results[i], self.weights_all[torch.greater(g, 0.5).to(torch.long)], dims=([0], [1])).permute(2, 0, 1) for i, g in enumerate(betas[:,-1])])
|
||||
rest_shape_h = torch.cat(
|
||||
(v_posed, torch.ones((batch_num, v_posed.shape[1], 1), dtype=torch.float32).to(self.device)), dim=2
|
||||
)
|
||||
@@ -230,4 +230,4 @@ class SMPL(Module):
|
||||
if remove and verts is None:
|
||||
result = result[:,self.to_keep,:]
|
||||
|
||||
return result, joints, v_shaped, v_posed
|
||||
return result, joints
|
||||
|
||||
@@ -22,7 +22,7 @@ from losses.losses import _nearest_neighbour
|
||||
""" COLLISION FUNCTIONS START """
|
||||
# F = np.load("smpl/smpl_faces.npy")
|
||||
to_keep = sio.loadmat('smpl/keep_remove.mat')['keep'].reshape(-1)
|
||||
F_fix_coll = torch.tensor(np.tile(np.load("smpl/smpl_faces.npy")[None,:,:], [4,1,1]), device='cuda')
|
||||
F_fix_coll = torch.tensor(np.tile(np.load("smpl/smpl_faces.npy")[to_keep,:][None,:,:], [4,1,1]), device='cuda')
|
||||
smpl_to_keep_orig_post = sio.loadmat('smpl/keep_remove.mat')['keep'].reshape(-1)
|
||||
smpl_to_keep_orig_post = smpl_to_keep_orig_post[smpl_to_keep_orig_post < 6890]
|
||||
F_fix_coll_orig = torch.tensor(np.tile(np.load("smpl/smpl_faces_orig.npy")[:6890,:][None,:,:], [4,1,1]), device='cuda')[:,smpl_to_keep_orig_post,:]
|
||||
|
||||
Reference in New Issue
Block a user