add cvae training
This commit is contained in:
@@ -0,0 +1,194 @@
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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'] = 'abl_torch_all'
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config['notes'] = ''
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config['tags'] = ['iccv', 'abl_all', 'torch']
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config['script'] = 'scripts/cvae.py'
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config['use_skinning'] = True # and deshaping
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config['remove_global_orientation'] = True
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config['use_curvature'] = True
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config['use_extra_losses'] = True # edge, collision, 3d mesh
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config['use_offsets'] = True
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config['use_norm_loss'] = True
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ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
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assert ablation_count == 1 or ablation_count == 0
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config['avg_garment_path'] = 'custom_data/avg/train_no_shape_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
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if config['use_skinning']:
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assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
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config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
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config['batch_size'] = 4
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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['device'] = 'cuda'
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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['static_model'] = 'checkpoints/iccv/static_torch_orig_2'
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config['body_model'] = 'checkpoints/iccv/body_torch_1'
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config['curvature_model'] = 'checkpoints/iccv/curv_torch_1'
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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['pretrained_model'] = ''
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###############
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config['lr'] = 0.0001# 0.0001
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###############
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config['starting_balancing_coeff'] = 0.0001
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config['signature']: set = { 'id',
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'frame_nr',
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'pose_shape',
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'uv_body_posed',
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'mesh_body_posed',
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'mesh_posed',
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'uv_curvature',
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'uv_static',
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'mesh_mask',
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'uv_mask',
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}
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if config['use_skinning']:
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config['signature'].add('uv_unposed')
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config['signature'].add('mesh_unposed')
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else:
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config['signature'].add('uv_posed')
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config['upscale_enhancer'] = ''
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config['freeze_encoders'] = True
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config['use_one_hot'] = False
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config['bottleneck_scale'] = 2
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config['cvae'] = dict()
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config['cvae']['device'] = config['device']
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config['cvae']['is_label'] = False
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config['cvae']['input_features'] = 3+3+3+7 if config['use_curvature'] else 3+3+3
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config['cvae']['kernel_size'] = 7
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config['cvae']['bottleneck_size'] = 64 * config['bottleneck_scale']
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config['cvae']['latent_dim'] = config['cvae']['bottleneck_size']
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block_setting = [
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CNBlockConfig(32, 64, 3),
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CNBlockConfig(64, 128, 3),
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CNBlockConfig(128, 256, 3),
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CNBlockConfig(256, config['cvae']['bottleneck_size'], 9),
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CNBlockConfig(config['cvae']['bottleneck_size'], None, 3),
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]
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block_setting_reverse = [
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CNBlockConfig(config['cvae']['bottleneck_size'] * 2, 256, 3),
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CNBlockConfig(256, 128, 9),
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CNBlockConfig(128, 64, 9),
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CNBlockConfig(64, 32, 3),
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CNBlockConfig(32, 3, 3),
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]
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config['cvae']['encoder_block_settings'] = block_setting
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config['cvae']['decoder_block_settings'] = block_setting_reverse
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####################### cond_ae_curv ############################
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config['cond_ae_curv'] = dict()
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config['cond_ae_curv']['device'] = config['device']
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config['cond_ae_curv']['is_label'] = True
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config['cond_ae_curv']['latent_dim'] = config['latent_dim']
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config['cond_ae_curv']['kernel_size'] = 7
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config['cond_ae_curv']['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_curv']['bottleneck_size'], 9),
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CNBlockConfig(config['cond_ae_curv']['bottleneck_size'], None, 3),
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]
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block_setting_reverse = [
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CNBlockConfig(config['cond_ae_curv']['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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]
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config['cond_ae_curv']['encoder_block_settings'] = block_setting
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config['cond_ae_curv']['decoder_block_settings'] = block_setting_reverse
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config['cond_ae_curv']['activation'] = 'tanh'
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####################### cond_ae_body ############################
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config['cond_ae_body'] = dict()
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config['cond_ae_body']['device'] = config['device']
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config['cond_ae_body']['is_label'] = False
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config['cond_ae_body']['latent_dim'] = config['latent_dim']
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config['cond_ae_body']['kernel_size'] = 7
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config['cond_ae_body']['bottleneck_size'] = 16 * 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_body']['bottleneck_size'], 9),
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CNBlockConfig(config['cond_ae_body']['bottleneck_size'], None, 3),
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]
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block_setting_reverse = [
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CNBlockConfig(config['cond_ae_body']['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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]
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config['cond_ae_body']['encoder_block_settings'] = block_setting
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config['cond_ae_body']['decoder_block_settings'] = block_setting_reverse
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config['cond_ae_body']['activation'] = 'tanh'
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####################### cond_ae_static ############################
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config['cond_ae_static'] = dict()
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config['cond_ae_static']['device'] = config['device']
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config['cond_ae_static']['is_label'] = False
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config['cond_ae_static']['latent_dim'] = config['latent_dim']
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config['cond_ae_static']['kernel_size'] = 7
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config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
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block_setting = [
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CNBlockConfig(8, 16, 3),
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CNBlockConfig(16, 32, 3),
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CNBlockConfig(32, 64, 3),
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CNBlockConfig(64, config['cond_ae_static']['bottleneck_size'], 9),
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CNBlockConfig(config['cond_ae_static']['bottleneck_size'], None, 3),
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]
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block_setting_reverse = [
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CNBlockConfig(config['cond_ae_static']['bottleneck_size'], 64, 3),
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CNBlockConfig(64, 32, 9),
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CNBlockConfig(32, 16, 3),
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CNBlockConfig(16, 8, 3),
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CNBlockConfig(8, 3, 3),
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]
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config['cond_ae_static']['encoder_block_settings'] = block_setting
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config['cond_ae_static']['decoder_block_settings'] = block_setting_reverse
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config['cond_ae_static']['activation'] = 'tanh'
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+8
-6
@@ -6,22 +6,24 @@ from typing import Any, Callable, List, Optional, Sequence
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class VariationalEncoder(nn.Module):
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def __init__(self, block_setting: CNBlockConfig, kernel_size: int = 7):
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def __init__(self, block_setting: List[CNBlockConfig], kernel_size: int = 7, input_features: int = 16, device: str = 'cuda'):
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super(VariationalEncoder, self).__init__()
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self.encoder = ConvNeXt(block_setting=block_setting, kernel_size=kernel_size)
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self.encoder = ConvNeXt(block_setting=block_setting, kernel_size=kernel_size, input_features=input_features, device=device)
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self.mean_branch = nn.Sequential([CNBlock(block_setting[-1].out_channels, kernel_size=kernel_size) for _ in range(5)])
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self.var_branch = nn.Sequential([CNBlock(block_setting[-1].out_channels, kernel_size=kernel_size) for _ in range(5)])
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layers = [CNBlock(block_setting[-1].input_channels, kernel_size=kernel_size) for _ in range(5)]
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self.mean_branch = nn.Sequential(*layers).to(device)
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layers = [CNBlock(block_setting[-1].input_channels, kernel_size=kernel_size) for _ in range(5)]
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self.var_branch = nn.Sequential(*layers).to(device)
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self.flatten_mean = nn.Flatten()
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self.flatten_var = nn.Flatten()
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def call(self, inputs, training):
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def forward(self, inputs):
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x = inputs
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x = self.encoder(x, training=training)
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x = self.encoder(x)
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mean = self.mean_branch(x)
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log_var = self.var_branch(x)
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@@ -66,6 +66,7 @@ class ConvNeXtBase(nn.Module):
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kernel_size: int = 7,
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is_label: bool = False,
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nr_of_labels: Optional[int] = None,
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input_features: int = 3,
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) -> None:
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super().__init__()
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@@ -85,7 +86,7 @@ class ConvNeXtBase(nn.Module):
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# Stem
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firstconv_output_channels = block_setting[0].input_channels
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self.stem = Conv2dNormActivation(
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3 if not is_label else nr_of_labels,
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input_features if not is_label else nr_of_labels,
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firstconv_output_channels,
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kernel_size=4,
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stride=4,
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@@ -229,6 +230,7 @@ def _convnext(
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is_label: bool,
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nr_of_labels: Optional[int],
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kernel_size: int,
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input_features: int,
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device='cuda',
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**kwargs: Any,
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) -> ConvNeXtBase:
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@@ -236,17 +238,17 @@ def _convnext(
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if reversed:
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model = ConvNeXtReverseBase(block_setting, stochastic_depth_prob=stochastic_depth_prob, is_label=is_label, nr_of_labels=nr_of_labels, kernel_size=kernel_size, device=device, **kwargs)
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else:
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model = ConvNeXtBase(block_setting, stochastic_depth_prob=stochastic_depth_prob, kernel_size=kernel_size, is_label=is_label, nr_of_labels=nr_of_labels, device=device, **kwargs)
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model = ConvNeXtBase(block_setting, stochastic_depth_prob=stochastic_depth_prob, kernel_size=kernel_size, is_label=is_label, nr_of_labels=nr_of_labels, input_features=input_features, device=device, **kwargs)
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return model
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class ConvNeXt(nn.Module):
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def __init__(self, block_setting, stochastic_depth_prob=0.0, reversed=False, is_label=False, nr_of_labels=None, kernel_size=7, device='cuda', **kwargs) -> None:
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def __init__(self, block_setting, stochastic_depth_prob=0.0, reversed=False, is_label=False, nr_of_labels=None, kernel_size=7, input_features=3, device='cuda', **kwargs) -> None:
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super().__init__()
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self.model = _convnext(block_setting, stochastic_depth_prob, reversed, is_label, nr_of_labels, kernel_size, device=device, **kwargs)
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self.model: nn.Module = _convnext(block_setting, stochastic_depth_prob, reversed, is_label, nr_of_labels, kernel_size, input_features=input_features, device=device, **kwargs)
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def forward(self, x: Tensor) -> Tensor:
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x = self.model(x)
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+38
-193
@@ -1,63 +1,54 @@
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import os
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import time
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import random
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import tensorflow as tf
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import torch
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from torch import nn
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import numpy as np
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import scipy as sp
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# import plotly.graph_objects as go
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# import plotly.express as ex
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from custom_utils.custom_utils import load_model
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from utils.checkpoints import load_model
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from models.base import AEEncoder, VariationalEncoder, Decoder
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from models.base import AEEncoderNew, VariationalEncoderNew, DecoderNew
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class CVAE(tf.keras.Model):
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class CVAE(nn.Module):
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def __init__(self, config):
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super(CVAE, self).__init__()
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self.latent_dim = config['cvae']['latent_dim']
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self.size = config['resolution']
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if not('only_curvatures' in config.keys() and config['only_curvatures'] == True):
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self.static_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
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self.static_enc : AEEncoder = AEEncoder(config['cond_ae_static']['encoder_block_settings'], config['cond_ae_static']['kernel_size'], config['cond_ae_static']['activation'], device=config['device'])
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if config['static_model'] != '':
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load_model(self.static_enc, config['static_model'] + '_enc')
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self.body_enc : AEEncoder = AEEncoder(config['cond_ae_body']['latent_dim'], config['cond_ae_body']['encoder_depths'], config['cond_ae_body']['encoder_dims'], config['cond_ae_body']['bottleneck_size'], config['cond_ae_body']['kernel_size'])
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self.body_enc : AEEncoder = AEEncoder(config['cond_ae_body']['encoder_block_settings'], config['cond_ae_body']['kernel_size'], config['cond_ae_body']['activation'], device=config['device'])
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if config['body_model'] != '':
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load_model(self.body_enc, config['body_model'] + '_enc')
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if config['use_curvature']:
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self.curv_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
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self.curv_enc : AEEncoder = AEEncoder(config['cond_ae_curv']['encoder_block_settings'], config['cond_ae_curv']['kernel_size'], config['cond_ae_curv']['activation'], device=config['device'], is_label=True, nr_of_labels=7)
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if config['curvature_model'] != '':
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load_model(self.curv_enc, config['curvature_model'] + '_enc')
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if config['freeze_encoders']:
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if not('only_curvatures' in config.keys() and config['only_curvatures'] == True):
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self.static_enc.trainable = False
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self.body_enc.trainable = False
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for param in self.static_enc.parameters(): param.requires_grad = False
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for param in self.body_enc.parameters(): param.requires_grad = False
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if config['use_curvature']:
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self.curv_enc.trainable = False
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for param in self.curv_enc.parameters(): param.requires_grad = False
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self.cond_encoder = tf.keras.Sequential([tf.keras.layers.Dense(512, activation='relu'),
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tf.keras.layers.Dense(256, activation='relu'),
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tf.keras.layers.Dense(128, activation='relu'),
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# tf.keras.layers.Lambda(lambda x: x / 1000),
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tf.keras.layers.Dense(config['latent_dim'], activation='tanh')])
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self.encoder = VariationalEncoder(config['cvae']['encoder_block_settings'], config['cvae']['kernel_size'], config['cvae']['input_features'], device=config['device'])
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self.decoder : Decoder = Decoder(config['cvae']['decoder_block_settings'], kernel_size=config['cvae']['kernel_size'], device=config['device'])
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self.encoder = VariationalEncoder(config['cvae']['latent_dim'], config['cvae']['encoder_depths'], config['cvae']['encoder_dims'], kernel_size=config['cvae']['kernel_size'])
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self.decoder = Decoder(config['cvae']['decoder_input'], config['cvae']['decoder_depths'], config['cvae']['decoder_dims'], config['cvae']['kernel_size'])
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self.latent_shape = (config['cvae']['latent_dim'], 8, 4)
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if config['avg_garment_path'] != '':
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self.avg_garment = np.load(config['avg_garment_path'])
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self.avg_garment = torch.permute(torch.tensor(np.load(config['avg_garment_path']), device=config['device']), [2,0,1])
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else:
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self.avg_garment = None
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self.add_noise = config['cvae']['add_noise']
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self.noise_amount = config['cvae']['noise_amount']
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self.use_curvature = config['use_curvature']
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self.only_curvatures = config['only_curvatures'] if 'only_curvatures' in config.keys() and config['only_curvatures'] == True else None
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self.use_offsets_over_body = not config['use_skinning']
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if self.use_offsets_over_body:
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@@ -68,7 +59,7 @@ class CVAE(tf.keras.Model):
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# @tf.function
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def sample(self, uv_template, uv_body_posed, uv_curv, uv_mask, noise=None, add_noise=False):
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if noise is None:
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eps = tf.random.normal(shape=(uv_template.shape[0], self.latent_dim))
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eps = tf.random.normal(shape=(uv_template.shape[0], 8, 4, self.latent_dim))
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else:
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eps = noise
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@@ -91,195 +82,49 @@ class CVAE(tf.keras.Model):
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return x
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def reparameterize(self, mean, logvar, training, fix_noise=False):
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def reparameterize(self, mean: torch.Tensor, logvar: torch.Tensor, training: bool = True, fix_noise: bool = False):
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if training == True:
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if not fix_noise:
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eps = tf.random.normal(shape=mean.shape)
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eps = torch.randn(size=mean.shape).to(mean.device)
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else:
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eps = tf.random.normal(shape=(1, mean.shape[1]))
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eps = tf.tile(eps, [mean.shape[0],1])
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out = eps * tf.exp(logvar * .5) + mean
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eps = torch.randn(size=(1, mean.shape[1]))
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eps = torch.tile(eps, [mean.shape[0],1])
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out = eps * torch.exp(logvar * 0.5) + mean
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else:
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out = tf.exp(logvar * .5) + mean
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||||
out = torch.exp(logvar * 0.5) + mean
|
||||
return out
|
||||
|
||||
|
||||
def encode_conds(self, uv_template, uv_body_posed, uv_curv, training):
|
||||
if self.only_curvatures:
|
||||
z_cond = self.curv_enc(uv_curv, training=training)
|
||||
def encode_conds(self, uv_template, uv_body_posed, uv_curv):
|
||||
z_body = self.body_enc(uv_body_posed)
|
||||
z_static = self.static_enc(uv_template)
|
||||
if self.use_curvature:
|
||||
z_curv = self.curv_enc(uv_curv)
|
||||
z_cond = torch.concat([z_static, z_body, z_curv], dim=1)
|
||||
else:
|
||||
z_body = self.body_enc(uv_body_posed, training=training)
|
||||
z_static = self.static_enc(uv_template, training=training)
|
||||
if self.use_curvature:
|
||||
z_curv = self.curv_enc(uv_curv, training=training)
|
||||
z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
|
||||
else:
|
||||
z_cond = tf.concat([z_static, z_body], axis=-1)
|
||||
|
||||
z_cond = self.cond_encoder(z_cond, training=training)
|
||||
z_cond = torch.concat([z_static, z_body], dim=1)
|
||||
|
||||
return z_cond
|
||||
|
||||
|
||||
def call(self, inputs, training):
|
||||
def forward(self, inputs):
|
||||
uv_unposed, uv_template, uv_body_posed, uv_curv, uv_mask = inputs
|
||||
if self.only_curvatures:
|
||||
x = tf.concat([uv_unposed, uv_curv], axis=-1)
|
||||
else:
|
||||
x = tf.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], axis=-1)
|
||||
x = torch.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], dim=1)
|
||||
|
||||
mean, logvar = self.encoder(x, training=training)
|
||||
z = self.reparameterize(mean, logvar, training)
|
||||
z_cond = self.encode_conds(uv_template, uv_body_posed, uv_curv, training=training)
|
||||
mean, logvar = self.encoder(x)
|
||||
z: torch.Tensor = self.reparameterize(mean, logvar)
|
||||
z = z.view((mean.shape[0], *self.latent_shape))
|
||||
z_cond = self.encode_conds(uv_template, uv_body_posed, uv_curv)
|
||||
|
||||
if self.add_noise and training == True:
|
||||
z_cond = z_cond + (tf.random.uniform(z_cond.shape, maxval=self.noise_amount * 2) - self.noise_amount)
|
||||
|
||||
|
||||
z = tf.concat([z, z_cond], axis=-1)
|
||||
x = self.decoder(z, training=training)
|
||||
z = torch.concat([z, z_cond], dim=1)
|
||||
x = self.decoder(z)
|
||||
|
||||
if self.avg_garment is not None:
|
||||
x = x + self.avg_garment[tf.newaxis,:,:,:]
|
||||
x = x + self.avg_garment[None,:,:,:]
|
||||
|
||||
if self.use_offsets_over_body:
|
||||
x = x + uv_body_posed
|
||||
|
||||
x = x * uv_mask[:,:,:,tf.newaxis]
|
||||
x = x * uv_mask[:,None,:,:]
|
||||
|
||||
return x, mean, logvar
|
||||
|
||||
|
||||
|
||||
|
||||
class CVAENew(tf.keras.Model):
|
||||
|
||||
def __init__(self, config):
|
||||
super(CVAENew, self).__init__()
|
||||
self.size = config['resolution']
|
||||
|
||||
if not('only_curvatures' in config.keys() and config['only_curvatures'] == True):
|
||||
self.static_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_static']['encoder_depths'], config['cond_ae_static']['encoder_dims'], config['cond_ae_static']['kernel_size'], config['cond_ae_static']['activation'])
|
||||
if config['static_model'] != '':
|
||||
load_model(self.static_enc, config['static_model'] + '_enc')
|
||||
|
||||
self.body_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_body']['encoder_depths'], config['cond_ae_body']['encoder_dims'], config['cond_ae_body']['kernel_size'])
|
||||
if config['body_model'] != '':
|
||||
load_model(self.body_enc, config['body_model'] + '_enc')
|
||||
|
||||
if config['use_curvature']:
|
||||
self.curv_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_curv']['encoder_depths'], config['cond_ae_curv']['encoder_dims'], config['cond_ae_curv']['kernel_size'], config['cond_ae_curv']['activation'])
|
||||
if config['curvature_model'] != '':
|
||||
load_model(self.curv_enc, config['curvature_model'] + '_enc')
|
||||
|
||||
if config['freeze_encoders']:
|
||||
if not('only_curvatures' in config.keys() and config['only_curvatures'] == True):
|
||||
self.static_enc.trainable = False
|
||||
self.body_enc.trainable = False
|
||||
if config['use_curvature']:
|
||||
self.curv_enc.trainable = False
|
||||
|
||||
self.encoder = VariationalEncoderNew(config['cvae']['encoder_depths'], config['cvae']['encoder_dims'], kernel_size=config['cvae']['kernel_size'])
|
||||
self.decoder = DecoderNew(config['cvae']['decoder_depths'], config['cvae']['decoder_dims'], config['cvae']['kernel_size'])
|
||||
|
||||
self.reshape = tf.keras.layers.Reshape((8, 4, 64))
|
||||
|
||||
if config['avg_garment_path'] != '':
|
||||
self.avg_garment = np.load(config['avg_garment_path'])
|
||||
else:
|
||||
self.avg_garment = None
|
||||
|
||||
self.add_noise = config['cvae']['add_noise']
|
||||
self.noise_amount = config['cvae']['noise_amount']
|
||||
self.use_curvature = config['use_curvature']
|
||||
self.only_curvatures = config['only_curvatures'] if 'only_curvatures' in config.keys() and config['only_curvatures'] == True else None
|
||||
self.use_offsets_over_body = not config['use_skinning']
|
||||
|
||||
if self.use_offsets_over_body:
|
||||
assert self.avg_garment is None, 'When we are not using skinning, we can\'t use avg_garment!'
|
||||
if self.avg_garment is not None:
|
||||
assert not self.use_offsets_over_body, 'When avg_garment is given, we must use skinning!'
|
||||
|
||||
# @tf.function
|
||||
def sample(self, uv_template, uv_body_posed, uv_curv, uv_mask, noise=None, add_noise=False):
|
||||
if noise is None:
|
||||
eps = tf.random.normal(shape=(uv_template.shape[0], self.latent_dim))
|
||||
else:
|
||||
eps = noise
|
||||
|
||||
z_cond = self.encode_conds(uv_template, uv_body_posed, uv_curv, training=False)
|
||||
if add_noise:
|
||||
z_cond = z_cond + (tf.random.uniform(z_cond.shape, maxval=self.noise_amount * 2) - self.noise_amount)
|
||||
|
||||
z = tf.concat([eps, z_cond], axis=-1)
|
||||
|
||||
x = self.decoder(z, training=False)
|
||||
|
||||
if self.avg_garment is not None:
|
||||
x = x + self.avg_garment[tf.newaxis,:,:,:]
|
||||
|
||||
if self.use_offsets_over_body:
|
||||
x = x + uv_body_posed
|
||||
|
||||
x = x * uv_mask[:,:,:,tf.newaxis]
|
||||
|
||||
return x
|
||||
|
||||
|
||||
def reparameterize(self, mean, logvar, training, fix_noise=False):
|
||||
if training == True:
|
||||
if not fix_noise:
|
||||
eps = tf.random.normal(shape=mean.shape)
|
||||
else:
|
||||
eps = tf.random.normal(shape=(1, mean.shape[1]))
|
||||
eps = tf.tile(eps, [mean.shape[0],1])
|
||||
out = eps * tf.exp(logvar * .5) + mean
|
||||
else:
|
||||
out = tf.exp(logvar * .5) + mean
|
||||
return out
|
||||
|
||||
|
||||
def encode_conds(self, uv_template, uv_body_posed, uv_curv, training):
|
||||
if self.only_curvatures:
|
||||
z_cond = self.curv_enc(uv_curv, training=training)
|
||||
else:
|
||||
z_body = self.body_enc(uv_body_posed, training=training)
|
||||
z_static = self.static_enc(uv_template, training=training)
|
||||
if self.use_curvature:
|
||||
z_curv = self.curv_enc(uv_curv, training=training)
|
||||
z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
|
||||
else:
|
||||
z_cond = tf.concat([z_static, z_body], axis=-1)
|
||||
|
||||
return z_cond
|
||||
|
||||
|
||||
def call(self, inputs, training):
|
||||
uv_unposed, uv_template, uv_body_posed, uv_curv, uv_mask = inputs
|
||||
if self.only_curvatures:
|
||||
x = tf.concat([uv_unposed, uv_curv], axis=-1)
|
||||
else:
|
||||
x = tf.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], axis=-1)
|
||||
|
||||
mean, logvar = self.encoder(x, training=training)
|
||||
z = self.reparameterize(mean, logvar, training)
|
||||
z = self.reshape(z)
|
||||
z_cond = self.encode_conds(uv_template, uv_body_posed, uv_curv, training=training)
|
||||
|
||||
if self.add_noise and training == True:
|
||||
z_cond = z_cond + (tf.random.uniform(z_cond.shape, maxval=self.noise_amount * 2) - self.noise_amount)
|
||||
|
||||
|
||||
z = tf.concat([z, z_cond], axis=-1)
|
||||
x = self.decoder(z, training=training)
|
||||
|
||||
if self.avg_garment is not None:
|
||||
x = x + self.avg_garment[tf.newaxis,:,:,:]
|
||||
|
||||
if self.use_offsets_over_body:
|
||||
x = x + uv_body_posed
|
||||
|
||||
x = x * uv_mask[:,:,:,tf.newaxis]
|
||||
|
||||
return x, mean, logvar
|
||||
|
||||
|
||||
+191
-279
@@ -1,21 +1,20 @@
|
||||
import os
|
||||
import sys
|
||||
from configs.config_abl_all_post import config
|
||||
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
|
||||
|
||||
import importlib
|
||||
import argparse
|
||||
parser = argparse.ArgumentParser(description='')
|
||||
parser.add_argument('--name', type=str)
|
||||
parser.add_argument('--config', type=str)
|
||||
args = vars(parser.parse_args())
|
||||
|
||||
config_module = importlib.import_module(args['config'])
|
||||
config = config_module.config
|
||||
|
||||
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
import tensorflow as tf
|
||||
|
||||
gpus = tf.config.experimental.list_physical_devices('GPU')
|
||||
if gpus:
|
||||
try:
|
||||
# Currently, memory growth needs to be the same across GPUs
|
||||
for gpu in gpus:
|
||||
tf.config.experimental.set_memory_growth(gpu, True)
|
||||
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
|
||||
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
|
||||
except RuntimeError as e:
|
||||
# Memory growth must be set before GPUs have been initialized
|
||||
print(e)
|
||||
# os.environ["CUDA_LAUNCH_BLOCKING"] = '1'
|
||||
|
||||
import time
|
||||
import argparse
|
||||
@@ -26,256 +25,205 @@ import numpy as np
|
||||
import plotly.graph_objects as go
|
||||
import plotly.express as ex
|
||||
from PIL import Image
|
||||
from utils.postprocess import fix_collision
|
||||
|
||||
if config['is_skirt']:
|
||||
from utils.losses import edge_loss_skirt as edge_loss
|
||||
from losses.losses import edge_loss_skirt as edge_loss
|
||||
else:
|
||||
from utils.losses import edge_loss
|
||||
from losses.losses import edge_loss
|
||||
|
||||
from utils.losses import collision_ratio, kl_loss, normal_loss, collision_loss
|
||||
from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
|
||||
from data.dataset import DatasetFactory, StaticDataset
|
||||
from custom_utils.smpl import SMPL
|
||||
from models.cvae_model import CVAENew as CVAE
|
||||
from utils.utils import Accumulator
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.optim as optim
|
||||
from data.dataset import Dataset
|
||||
from models.cond_encoder import AutoEncoder
|
||||
from utils.checkpoints import CheckpointSaver, load_model
|
||||
from utils.utils import InfiniteDataLoader as DataLoader
|
||||
from utils.postprocess import fix_collision
|
||||
from losses.losses import collision_loss, normal_loss
|
||||
from pytorch_msssim import MS_SSIM
|
||||
from models.cvae_model import CVAE
|
||||
from smpl.smpl_torch import SMPL
|
||||
from custom_utils.custom_utils import UV2Mesh, Accumulator, plot_3d, get_summary_html, maskFacesBatch, get_summary_str, plot_3d_collision
|
||||
|
||||
|
||||
def init_wandb():
|
||||
run = wandb.init(project='ablation', name=config['name'], notes=config['notes'], dir='/wandb', tags=config['tags'])
|
||||
run = wandb.init(project='iccv', name=config['name'], notes=config['notes'], dir='/tmp', tags=config['tags'])
|
||||
artifact = wandb.Artifact('code', type='code')
|
||||
config_file_names = [key for key in sys.modules.keys() if (key.startswith('configs.') or key.startswith('custom_utils.') or key.startswith('utils.') or key.startswith('data.') or key.startswith('models.') or key.startswith('scripts.'))]
|
||||
artifact.add_file('scripts/cvae.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('models/cvae_model.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_adaptive=False, is_smpl_orig=config['is_cape'])
|
||||
uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
|
||||
|
||||
|
||||
def kl_loss(mean, logvar):
|
||||
kl_loss = 1 + logvar - tf.square(mean) - tf.exp(logvar)
|
||||
kl_loss = tf.reduce_mean(kl_loss)
|
||||
def kl_loss(mean: torch.Tensor, logvar: torch.Tensor) -> torch.Tensor:
|
||||
kl_loss = 1 + logvar - torch.square(mean) - torch.exp(logvar)
|
||||
kl_loss = torch.mean(kl_loss)
|
||||
kl_loss *= -0.5
|
||||
return kl_loss
|
||||
|
||||
|
||||
# 3d loss on posed !!!!!!!!!!!!
|
||||
|
||||
# @tf.function
|
||||
def train_step(batch):
|
||||
if config['use_skinning']:
|
||||
mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
|
||||
else:
|
||||
mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
|
||||
|
||||
with tf.GradientTape() as tape:
|
||||
if config['use_skinning']:
|
||||
mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
|
||||
else:
|
||||
mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
|
||||
model.train()
|
||||
pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']])
|
||||
mesh_pred = uv2mesh(pred)
|
||||
|
||||
pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=True)
|
||||
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])])
|
||||
# shape_offsets = torch.stack([torch.matmul(shape[i, :10], smpl.shapedirs[gender[i]][:, smpl.to_keep]) for i in range(shape.shape[0])])
|
||||
|
||||
mesh_pred = uv2mesh(pred)
|
||||
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_pred_posed = mesh_pred
|
||||
|
||||
if config['use_skinning']:
|
||||
shape = batch['pose_shape'][:, 72:]
|
||||
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
|
||||
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
|
||||
|
||||
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
|
||||
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
|
||||
else:
|
||||
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'])
|
||||
c_loss, c_ratio = torch.mean(c_loss), torch.mean(c_ratio)
|
||||
l2_loss = torch.mean(1e3 * torch.mean(torch.sum(((uv_orig - 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'])
|
||||
kl = kl_loss(mean, logvar)
|
||||
|
||||
# is the output already masked?
|
||||
reconstr_loss = l2_loss
|
||||
if config['use_extra_losses']:
|
||||
reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss + n_loss) * config['reg_loss_coeff']
|
||||
|
||||
e_loss = tf.reduce_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 = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
|
||||
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
|
||||
l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (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'])
|
||||
# conc_loss = concept_loss(pred, batch['uv_unposed'])
|
||||
kl = kl_loss(mean, logvar)
|
||||
loss = reconstr_loss + g_balancing_coeff * kl
|
||||
|
||||
reconstr_loss = l2_loss
|
||||
if config['use_extra_losses']:
|
||||
if 'block_collision_loss' in config.keys() and config['block_collision_loss'] == True:
|
||||
reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss) * config['reg_loss_coeff']
|
||||
else:
|
||||
reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss) * config['reg_loss_coeff']
|
||||
if config['use_norm_loss'] and step > 2000:
|
||||
reconstr_loss = reconstr_loss + n_loss * config['reg_loss_coeff']
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
loss = reconstr_loss + g_balancing_coeff * kl
|
||||
model.eval()
|
||||
with torch.inference_mode():
|
||||
mean = torch.mean(mean)
|
||||
logvar = torch.mean(logvar)
|
||||
ms_ssim = torch.mean(_MSSSIM(pred + 1, batch['uv_static'] + 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'])
|
||||
|
||||
mean = tf.reduce_mean(mean)
|
||||
logvar = tf.reduce_mean(logvar)
|
||||
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
|
||||
metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
|
||||
gradients = tape.gradient(loss, model.trainable_variables)
|
||||
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
|
||||
return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/collision_loss': c_loss, 'train/euclid_error': metric,
|
||||
'train/reconstr_loss': reconstr_loss, 'train/collision_ratio': c_ratio,
|
||||
'train/mean': mean, 'train/logvar': logvar, 'train/kl_loss': kl, 'train/var': np.e ** logvar,
|
||||
'train/l2_loss': l2_loss, 'train/l2_mesh_loss': l2_mesh_loss, 'train/normal_loss': n_loss,
|
||||
# 'train/ms_ssim': ms_ssim,
|
||||
'train/ms_ssim': ms_ssim,
|
||||
}
|
||||
|
||||
|
||||
@tf.function
|
||||
|
||||
|
||||
def valid_step(batch):
|
||||
if config['use_skinning']:
|
||||
mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
|
||||
else:
|
||||
mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
|
||||
|
||||
pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
|
||||
model.eval()
|
||||
with torch.inference_mode():
|
||||
pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']])
|
||||
mesh_pred = uv2mesh(pred)
|
||||
|
||||
mesh_pred = uv2mesh(pred)
|
||||
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])])
|
||||
# shape_offsets = torch.stack([torch.matmul(shape[i, :10], smpl.shapedirs[gender[i]][:, smpl.to_keep]) for i in range(shape.shape[0])])
|
||||
|
||||
if config['use_skinning']:
|
||||
shape = batch['pose_shape'][:, 72:]
|
||||
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
|
||||
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) 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_pred_posed = mesh_pred
|
||||
|
||||
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
|
||||
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
|
||||
else:
|
||||
mesh_pred_posed = mesh_pred
|
||||
|
||||
# is the output already masked?
|
||||
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(((uv_orig - 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'])
|
||||
kl = kl_loss(mean, logvar)
|
||||
|
||||
e_loss = tf.reduce_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 = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
|
||||
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
|
||||
l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (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'])
|
||||
# conc_loss = concept_loss(pred, batch['uv_unposed'])
|
||||
kl = kl_loss(mean, logvar)
|
||||
reconstr_loss = l2_loss
|
||||
if config['use_extra_losses']:
|
||||
reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss + n_loss) * config['reg_loss_coeff']
|
||||
|
||||
reconstr_loss = l2_loss
|
||||
if config['use_extra_losses']:
|
||||
reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss) * config['reg_loss_coeff']
|
||||
if config['use_norm_loss']:
|
||||
reconstr_loss = reconstr_loss + n_loss * config['reg_loss_coeff']
|
||||
loss = reconstr_loss + g_balancing_coeff * kl
|
||||
|
||||
loss = reconstr_loss + g_balancing_coeff * kl
|
||||
mean = torch.mean(mean)
|
||||
logvar = torch.mean(logvar)
|
||||
ms_ssim = torch.mean(_MSSSIM(pred + 1, batch['uv_static'] + 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'])
|
||||
|
||||
mean = tf.reduce_mean(mean)
|
||||
logvar = tf.reduce_mean(logvar)
|
||||
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
|
||||
metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
|
||||
# c_ratio = tf.reduce_mean(collision_ratio(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']))
|
||||
|
||||
return { 'valid/loss': loss, 'valid/edge_loss': e_loss, 'valid/collision_loss': c_loss, 'valid/euclid_error': metric,
|
||||
'valid/reconstr_loss': reconstr_loss, 'valid/l2_mesh_loss': l2_mesh_loss, 'valid/collision_ratio': c_ratio,
|
||||
'valid/mean': mean, 'valid/logvar': logvar, 'valid/kl_loss': kl, 'valid/var': np.e ** logvar,
|
||||
'valid/normal_loss': n_loss,# 'valid/ms_ssim': ms_ssim,
|
||||
'valid/normal_loss': n_loss, 'valid/ms_ssim': ms_ssim,
|
||||
}
|
||||
|
||||
|
||||
@tf.function
|
||||
def test_step(batch):
|
||||
if config['use_skinning']:
|
||||
mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
|
||||
else:
|
||||
mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
|
||||
|
||||
pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
|
||||
|
||||
mesh_pred = uv2mesh(pred)
|
||||
|
||||
if config['use_skinning']:
|
||||
shape = batch['pose_shape'][:, 72:]
|
||||
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
|
||||
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
|
||||
|
||||
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
|
||||
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
|
||||
mesh_orig_shaped = mesh_orig + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
|
||||
mesh_orig_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_orig_shaped)
|
||||
else:
|
||||
mesh_pred_posed = mesh_pred
|
||||
mesh_orig_posed = mesh_orig
|
||||
|
||||
mesh_pred_posed_fixed = fix_collision(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
|
||||
|
||||
|
||||
# is the output already masked?
|
||||
|
||||
e_loss = tf.reduce_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 = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
|
||||
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
|
||||
l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1), axis=-1))
|
||||
# conc_loss = concept_loss(pred, batch['uv_unposed'])
|
||||
kl = kl_loss(mean, logvar)
|
||||
|
||||
reconstr_loss = l2_loss
|
||||
if config['use_extra_losses']:
|
||||
reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss) * config['reg_loss_coeff']
|
||||
|
||||
loss = reconstr_loss + g_balancing_coeff * kl
|
||||
|
||||
mean = tf.reduce_mean(mean)
|
||||
logvar = tf.reduce_mean(logvar)
|
||||
|
||||
ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, uv_orig + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
|
||||
metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
|
||||
euclid_posed = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig_posed - mesh_pred_posed) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
|
||||
euclid_posed_fixed = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig_posed - mesh_pred_posed_fixed) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
|
||||
|
||||
return { 'test/loss': loss, 'test/edge_loss': e_loss, 'test/collision_loss': c_loss, 'test/euclid_error': metric, 'test/ms_ssim': ms_ssim,
|
||||
'test/reconstr_loss': reconstr_loss, 'test/collision_ratio': c_ratio, 'test/l2_mesh_loss': l2_mesh_loss,
|
||||
'test/mean': mean, 'test/logvar': logvar, 'test/kl_loss': kl, 'test/var': np.e ** logvar,
|
||||
'test/euclid_posed': euclid_posed, 'test/euclid_posed_fixed': euclid_posed_fixed }
|
||||
|
||||
|
||||
def plot_everything(batch: dict, type: str):
|
||||
if config['use_skinning']:
|
||||
mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
|
||||
mesh_orig, uv_orig = batch['mesh_unposed'].cpu().numpy(), batch['uv_unposed']
|
||||
else:
|
||||
mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
|
||||
mesh_orig, uv_orig = batch['mesh_posed'].cpu().numpy(), batch['uv_posed']
|
||||
|
||||
pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
|
||||
model.eval()
|
||||
with torch.inference_mode():
|
||||
pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']])
|
||||
|
||||
mesh_pred = uv2mesh(pred).numpy()
|
||||
mesh_pred = uv2mesh(pred).detach().cpu().numpy()
|
||||
|
||||
orig_img = uv_orig
|
||||
uv_static_img = batch['uv_static']
|
||||
uv_body_posed_img = batch['uv_body_posed']
|
||||
pred_img = pred
|
||||
img = tf.concat((uv_static_img, uv_body_posed_img, orig_img, pred_img), axis=2)
|
||||
imgs = [wandb.Image((img[i] + 1) / 2.0) for i in range(4)]
|
||||
orig_img = torch.permute(uv_orig, [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])
|
||||
pred_img = torch.permute(pred, [0,2,3,1])
|
||||
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("custom_utils/smpl_faces" + skirt_modifier + ("_orig" if config['is_cape'] else "") + ".npy")
|
||||
mask = batch['mesh_mask'].numpy().astype(bool)
|
||||
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 = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
|
||||
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
|
||||
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()
|
||||
|
||||
garment_unposed, faces = maskFacesBatch(mesh_pred, F, mask)
|
||||
garment_unposed = [garment_unposed[i] + shape_offsets.numpy()[i, mask[i], :] for i in range(mask.shape[0])]
|
||||
gt = [mesh_orig[i].numpy()[mask[i], :] + shape_offsets.numpy()[i, mask[i], :] for i in range(pred.shape[0])]
|
||||
garment_unposed = [garment_unposed[i] + shape_offsets[i, mask[i], :] for i in range(mask.shape[0])]
|
||||
gt = [mesh_orig[i][mask[i], :] + shape_offsets[i, mask[i], :] for i in range(pred.shape[0])]
|
||||
|
||||
for i in range(mesh_orig.shape[0]):
|
||||
mesh_pred[i, mask[i], :] += shape_offsets.numpy()[i, mask[i], :]
|
||||
mesh_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred).numpy()
|
||||
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:
|
||||
mesh_posed = mesh_pred
|
||||
gt = [mesh_orig[i].numpy()[mask[i], :] for i in range(pred.shape[0])]
|
||||
garment_unposed, faces = maskFacesBatch(mesh_pred, F, mask)
|
||||
mesh_posed = mesh_pred.cpu().numpy()
|
||||
gt = [mesh_orig[i][mask[i], :] for i in range(pred.shape[0])]
|
||||
garment_unposed, faces = maskFacesBatch(mesh_pred.cpu().numpy(), F, mask)
|
||||
|
||||
gt_posed = [batch['mesh_posed'].numpy()[i, mask[i], :] for i in range(mask.shape[0])]
|
||||
gt_posed = [batch['mesh_posed'].cpu().numpy()[i, mask[i], :] for i in range(mask.shape[0])]
|
||||
|
||||
|
||||
body = batch['mesh_body_posed'].numpy()
|
||||
mesh_posed_fixed = fix_collision(mesh_posed, body, mask).numpy()
|
||||
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))]
|
||||
@@ -289,8 +237,8 @@ def plot_everything(batch: dict, type: str):
|
||||
figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces[i], collision[i], collision_faces[i], collision_color[i], flip_xy=(not config['use_skinning'] and config['remove_global_orientation'])) 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], flip_xy=(not config['use_skinning'] and config['remove_global_orientation'])) for i in range(4) }
|
||||
|
||||
mean_hist = wandb.Histogram(sequence=mean.numpy().mean(axis=0))
|
||||
logvar_hist = wandb.Histogram(sequence=logvar.numpy().mean(axis=0))
|
||||
mean_hist = wandb.Histogram(sequence=mean.cpu().numpy().mean(axis=0))
|
||||
logvar_hist = wandb.Histogram(sequence=logvar.cpu().numpy().mean(axis=0))
|
||||
|
||||
return { **figs, **figs_coll, **{ type + '/images': imgs, type + '/mean_hist': mean_hist, type + '/logvar_hist': logvar_hist } }
|
||||
|
||||
@@ -414,6 +362,7 @@ def sample(folder):
|
||||
print()
|
||||
|
||||
|
||||
|
||||
def test():
|
||||
str_split = config['name'].split('_')
|
||||
test_idx = str_split.index('test')
|
||||
@@ -462,108 +411,71 @@ def test():
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# init_wandb()
|
||||
parser = argparse.ArgumentParser(description='')
|
||||
parser.add_argument('--name', type=str)
|
||||
args = vars(parser.parse_args())
|
||||
def train():
|
||||
valid_iterator = iter(valid_dataloader)
|
||||
|
||||
smpl = SMPL(orig_smpl=config['is_cape'])
|
||||
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)) } }
|
||||
|
||||
wandb.log(to_log, step=step)
|
||||
train_accumulator.reset()
|
||||
valid_accumulator.reset()
|
||||
print(step)
|
||||
|
||||
if train_loss['train/euclid_error'] < (config['balancing_activation_limit'] if 'balancing_activation_limit' in config.keys() else 0.017):
|
||||
if balancing_activated_step == 0:
|
||||
balancing_activated_step = step
|
||||
g_balancing_coeff = g_balancing_coeff + 1 / 10000
|
||||
|
||||
if step % 10000 == 0:
|
||||
ckpt_saver(model, step, optimizer)
|
||||
|
||||
step += 1
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
init_wandb()
|
||||
# parser = argparse.ArgumentParser(description='')
|
||||
# parser.add_argument('--name', type=str)
|
||||
# parser.add_argument('--debug', action='store_true')
|
||||
# args = vars(parser.parse_args())
|
||||
|
||||
smpl = SMPL(orig_smpl=config['is_cape'], path='smpl', device=config['device'])
|
||||
skirt_modifier = '_skirt' if config['is_skirt'] else ''
|
||||
|
||||
factory = DatasetFactory()
|
||||
|
||||
dataset_train = factory.get_dataset(folder=config['train_dir'],
|
||||
batch_size=config['batch_size'],
|
||||
signature=config['signature_dynamic'],
|
||||
resolutions=[config['resolution']],
|
||||
in_memory=config['dataset_in_memory'],
|
||||
use_one_hot=config['use_one_hot'],
|
||||
use_no_orient=config['remove_global_orientation'],
|
||||
frames_file=config['frames_file'] if 'frames_file' in config.keys() else None,
|
||||
is_cape=config['is_cape'])
|
||||
static_dataset_train = StaticDataset(folder=config['train_dir'],
|
||||
signature=config['signature_static'],
|
||||
resolutions=[config['resolution']],
|
||||
in_memory=True,
|
||||
is_cape=config['is_cape'])
|
||||
|
||||
dataset_valid = factory.get_dataset(folder=config['valid_dir'],
|
||||
batch_size=config['batch_size'],
|
||||
signature=config['signature_dynamic'],
|
||||
resolutions=[config['resolution']],
|
||||
in_memory=config['dataset_in_memory'],
|
||||
use_one_hot=config['use_one_hot'],
|
||||
use_no_orient=config['remove_global_orientation'],
|
||||
frames_file=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else None,
|
||||
is_cape=config['is_cape'])
|
||||
static_dataset_valid = StaticDataset(folder=config['valid_dir'],
|
||||
signature=config['signature_static'],
|
||||
resolutions=[config['resolution']],
|
||||
in_memory=True,
|
||||
is_cape=config['is_cape'])
|
||||
|
||||
dataset_train = Dataset(config['train_dir'], config['signature'], device=config['device'], frames_file=config['train_frames'], use_cache=config['dataset_in_memory'])
|
||||
dataset_valid = Dataset(config['valid_dir'], config['signature'], device=config['device'], frames_file=config['valid_frames'], use_cache=config['dataset_in_memory'])
|
||||
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 = CVAE(config)
|
||||
|
||||
optimizer = tf.optimizers.Adam(learning_rate=config['lr'])
|
||||
optimizer = optim.Adam(model.parameters(), lr=config['lr'])
|
||||
_MSSSIM = MS_SSIM(data_range=2, size_average=True, channel=3)
|
||||
balancing_activated_step = 0
|
||||
g_balancing_coeff = config['starting_balancing_coeff']
|
||||
|
||||
# sample('checkpoints/cvae_13')
|
||||
# test()
|
||||
|
||||
ckpt = tf.train.Checkpoint(model=model)
|
||||
manager = tf.train.CheckpointManager(ckpt, 'checkpoints/post/' + config['name'], max_to_keep=20)
|
||||
ckpt_saver = CheckpointSaver('checkpoints/iccv/' + config['name'], top_n=5)
|
||||
|
||||
if config['pretrained_model'] != '':
|
||||
load_model(model, config['pretrained_model'])
|
||||
|
||||
# load_model(model, 'checkpoints/' + config['name'])
|
||||
|
||||
valid_iterator = iter(dataset_valid)
|
||||
step = 0
|
||||
for batch in dataset_train:
|
||||
batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
|
||||
|
||||
train_loss = train_step(batch)
|
||||
train_accumulator.update(train_loss)
|
||||
|
||||
data_valid = valid_iterator.get_next()
|
||||
data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
|
||||
valid_loss = valid_step(data_valid)
|
||||
valid_accumulator.update(valid_loss)
|
||||
|
||||
|
||||
if train_accumulator.get()['train/euclid_error'] < (config['balancing_activation_limit'] if 'balancing_activation_limit' in config.keys() else 0.017):
|
||||
if balancing_activated_step == 0:
|
||||
balancing_activated_step = step
|
||||
g_balancing_coeff = g_balancing_coeff + 1 / 10000
|
||||
|
||||
|
||||
if step % 1000 == 0 or (step < 1000 and step % 100 == 0):
|
||||
to_log = { **train_accumulator.get(), **valid_accumulator.get(), **{ '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)) } }
|
||||
|
||||
to_log = { **to_log, **{ 'train/balancing_coeff': g_balancing_coeff } }
|
||||
|
||||
wandb.log(to_log, step = step)
|
||||
train_accumulator.reset()
|
||||
valid_accumulator.reset()
|
||||
|
||||
if step % 5000 == 0:
|
||||
manager.save(checkpoint_number=step)
|
||||
|
||||
print(step)
|
||||
|
||||
step += 1
|
||||
|
||||
train()
|
||||
Reference in New Issue
Block a user