add new volume mapping

configs/cape_00032_SL.py

@@ -1,193 +0,0 @@
-from utils.convnext_utils import CNBlockConfig
-
-config = dict()
-
-config['gpu'] = '0'
-config['name'] = 'cape_00032_SL'
-config['notes'] = ''
-config['tags'] = ['iccv', 'cape_00032_SL', 'torch']
-config['script'] = 'scripts/cvae.py'
-
-config['pretrained_model'] = ''
-
-config['use_skinning'] =              True # and deshaping
-config['remove_global_orientation'] = True
-config['use_curvature'] =             True
-config['use_extra_losses'] =          True # edge, collision, 3d mesh
-config['use_offsets'] =               True
-
-config['use_norm_loss'] =             True
-
-ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
-assert ablation_count == 1 or ablation_count == 0
-
-config['avg_garment_path'] = 'custom_data/avg/train_cape_f_03223_SL_2_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
-if config['use_skinning']:
-  assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
-
-config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
-
-config['batch_size'] = 4
-config['resolution'] = 512
-config['is_skirt'] = False
-config['dataset_in_memory'] = True
-config['is_cape'] = True
-config['device'] = 'cuda'
-
-config['train_dir'] = '/data-local/127/cape_processed/train/male'
-config['valid_dir'] = '/data-local/127/cape_processed/train/male'
-config['test_dir'] = '/data-local/127/cape_processed/train/male'
-config['train_frames'] = 'frames_00032_SL_train_2.txt'
-config['valid_frames'] = 'frames_00032_SL_test_2.txt'
-config['test_frames'] = 'frames_00032_SL_test_2.txt'
-
-config['static_model'] = 'checkpoints/iccv/torch/static_torch_cape_m'
-config['body_model'] = 'checkpoints/iccv/torch/body_torch_cape_m'
-config['curvature_model'] = 'checkpoints/iccv/torch/curv_torch_cape_m'
-
-config['smpl_res'] = 14475
-if config['is_cape']:
-  config['smpl_res'] = 3638
-
-config['latent_dim'] = 128
-
-###############    
-config['lr'] = 0.0001#                 0.0001
-###############
-config['starting_balancing_coeff'] = 0.0001
-
-config['signature']: set = { 'id',
-                            'frame_nr',
-                            'pose_shape',
-                            'uv_body_posed',
-                            'mesh_body_posed',
-                            'mesh_posed',
-                            'uv_curvature',
-                            'uv_static',
-                            'mesh_mask',
-                            'uv_mask',
-                        }
-
-if config['use_skinning']:
-  config['signature'].add('uv_unposed')
-  config['signature'].add('mesh_unposed')
-else:
-  config['signature'].add('uv_posed')
-
-config['upscale_enhancer'] = ''
-config['freeze_encoders'] = True
-config['use_one_hot'] = False
-config['bottleneck_scale'] = 2
-
-config['cvae'] = dict()
-config['cvae']['device'] = config['device']
-config['cvae']['is_label'] = False
-config['cvae']['input_features'] = 3+3+3+7 if config['use_curvature'] else 3+3+3
-config['cvae']['kernel_size'] = 7
-config['cvae']['bottleneck_size'] = 64 * config['bottleneck_scale']
-config['cvae']['latent_dim'] = config['cvae']['bottleneck_size']
-
-block_setting = [
-    CNBlockConfig(32, 64, 3),
-    CNBlockConfig(64, 128, 3),
-    CNBlockConfig(128, 256, 3),
-    CNBlockConfig(256, config['cvae']['bottleneck_size'], 9),
-    CNBlockConfig(config['cvae']['bottleneck_size'], None, 3),
-]
-
-block_setting_reverse = [
-    CNBlockConfig(config['cvae']['bottleneck_size'] * 2, 256, 3),
-    CNBlockConfig(256, 128, 9),
-    CNBlockConfig(128, 64, 9),
-    CNBlockConfig(64, 32, 3),
-    CNBlockConfig(32, 3, 3),
-]
-
-config['cvae']['encoder_block_settings'] = block_setting
-config['cvae']['decoder_block_settings'] = block_setting_reverse
-
-
-
-####################### cond_ae_curv ############################
-config['cond_ae_curv'] = dict()
-config['cond_ae_curv']['device'] = config['device']
-config['cond_ae_curv']['is_label'] = True
-config['cond_ae_curv']['latent_dim'] = config['latent_dim']
-config['cond_ae_curv']['kernel_size'] = 7
-config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
-
-block_setting = [
-    CNBlockConfig(16, 32, 3),
-    CNBlockConfig(32, 64, 3),
-    CNBlockConfig(64, 128, 9),
-    CNBlockConfig(128, config['cond_ae_curv']['bottleneck_size'], 9),
-    CNBlockConfig(config['cond_ae_curv']['bottleneck_size'], None, 3),
-]
-
-block_setting_reverse = [
-    CNBlockConfig(config['cond_ae_curv']['bottleneck_size'], 128, 3),
-    CNBlockConfig(128, 64, 9),
-    CNBlockConfig(64, 32, 9),
-    CNBlockConfig(32, 16, 3),
-    CNBlockConfig(16, 3, 3),
-]
-
-config['cond_ae_curv']['encoder_block_settings'] = block_setting
-config['cond_ae_curv']['decoder_block_settings'] = block_setting_reverse
-config['cond_ae_curv']['activation'] = 'tanh'
-
-####################### cond_ae_body ############################
-config['cond_ae_body'] = dict()
-config['cond_ae_body']['device'] = config['device']
-config['cond_ae_body']['is_label'] = False
-config['cond_ae_body']['latent_dim'] = config['latent_dim']
-config['cond_ae_body']['kernel_size'] = 7
-config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
-
-block_setting = [
-    CNBlockConfig(16, 32, 3),
-    CNBlockConfig(32, 64, 3),
-    CNBlockConfig(64, 128, 9),
-    CNBlockConfig(128, config['cond_ae_body']['bottleneck_size'], 9),
-    CNBlockConfig(config['cond_ae_body']['bottleneck_size'], None, 3),
-]
-
-block_setting_reverse = [
-    CNBlockConfig(config['cond_ae_body']['bottleneck_size'], 128, 3),
-    CNBlockConfig(128, 64, 9),
-    CNBlockConfig(64, 32, 9),
-    CNBlockConfig(32, 16, 3),
-    CNBlockConfig(16, 3, 3),
-]
-
-config['cond_ae_body']['encoder_block_settings'] = block_setting
-config['cond_ae_body']['decoder_block_settings'] = block_setting_reverse
-config['cond_ae_body']['activation'] = 'tanh'
-
-####################### cond_ae_static ############################
-config['cond_ae_static'] = dict()
-config['cond_ae_static']['device'] = config['device']
-config['cond_ae_static']['is_label'] = False
-config['cond_ae_static']['latent_dim'] = config['latent_dim']
-config['cond_ae_static']['kernel_size'] = 7
-config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
-
-block_setting = [
-    CNBlockConfig(8, 16, 3),
-    CNBlockConfig(16, 32, 3),
-    CNBlockConfig(32, 64, 3),
-    CNBlockConfig(64, config['cond_ae_static']['bottleneck_size'], 9),
-    CNBlockConfig(config['cond_ae_static']['bottleneck_size'], None, 3),
-]
-
-block_setting_reverse = [
-    CNBlockConfig(config['cond_ae_static']['bottleneck_size'], 64, 3),
-    CNBlockConfig(64, 32, 9),
-    CNBlockConfig(32, 16, 3),
-    CNBlockConfig(16, 8, 3),
-    CNBlockConfig(8, 3, 3),
-]
-
-config['cond_ae_static']['encoder_block_settings'] = block_setting
-config['cond_ae_static']['decoder_block_settings'] = block_setting_reverse
-config['cond_ae_static']['activation'] = 'tanh'

configs/cape_03223_SL.py

@@ -1,193 +0,0 @@
-from utils.convnext_utils import CNBlockConfig
-
-config = dict()
-
-config['gpu'] = '4'
-config['name'] = 'cape_03223_SL'
-config['notes'] = ''
-config['tags'] = ['iccv', 'cape_03223_SL', 'torch']
-config['script'] = 'scripts/cvae.py'
-
-config['pretrained_model'] = ''
-
-config['use_skinning'] =              True # and deshaping
-config['remove_global_orientation'] = True
-config['use_curvature'] =             True
-config['use_extra_losses'] =          True # edge, collision, 3d mesh
-config['use_offsets'] =               True
-
-config['use_norm_loss'] =             True
-
-ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
-assert ablation_count == 1 or ablation_count == 0
-
-config['avg_garment_path'] = 'custom_data/avg/train_cape_f_03223_SL_2_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
-if config['use_skinning']:
-  assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
-
-config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
-
-config['batch_size'] = 4
-config['resolution'] = 512
-config['is_skirt'] = False
-config['dataset_in_memory'] = True
-config['is_cape'] = True
-config['device'] = 'cuda'
-
-config['train_dir'] = '/data-local/127/cape_processed/train/female'
-config['valid_dir'] = '/data-local/127/cape_processed/train/female'
-config['test_dir'] = '/data-local/127/cape_processed/train/female'
-config['train_frames'] = 'frames_03223_SL_train_2.txt'
-config['valid_frames'] = 'frames_03223_SL_test_2.txt'
-config['test_frames'] = 'frames_03223_SL_test_2.txt'
-
-config['static_model'] = 'checkpoints/iccv/torch/static_torch_cape_f'
-config['body_model'] = 'checkpoints/iccv/torch/body_torch_cape_f'
-config['curvature_model'] = 'checkpoints/iccv/torch/curv_torch_cape_f'
-
-config['smpl_res'] = 14475
-if config['is_cape']:
-  config['smpl_res'] = 3638
-
-config['latent_dim'] = 128
-
-###############    
-config['lr'] = 0.0001#                 0.0001
-###############
-config['starting_balancing_coeff'] = 0.0001
-
-config['signature']: set = { 'id',
-                            'frame_nr',
-                            'pose_shape',
-                            'uv_body_posed',
-                            'mesh_body_posed',
-                            'mesh_posed',
-                            'uv_curvature',
-                            'uv_static',
-                            'mesh_mask',
-                            'uv_mask',
-                        }
-
-if config['use_skinning']:
-  config['signature'].add('uv_unposed')
-  config['signature'].add('mesh_unposed')
-else:
-  config['signature'].add('uv_posed')
-
-config['upscale_enhancer'] = ''
-config['freeze_encoders'] = True
-config['use_one_hot'] = False
-config['bottleneck_scale'] = 2
-
-config['cvae'] = dict()
-config['cvae']['device'] = config['device']
-config['cvae']['is_label'] = False
-config['cvae']['input_features'] = 3+3+3+7 if config['use_curvature'] else 3+3+3
-config['cvae']['kernel_size'] = 7
-config['cvae']['bottleneck_size'] = 64 * config['bottleneck_scale']
-config['cvae']['latent_dim'] = config['cvae']['bottleneck_size']
-
-block_setting = [
-    CNBlockConfig(32, 64, 3),
-    CNBlockConfig(64, 128, 3),
-    CNBlockConfig(128, 256, 3),
-    CNBlockConfig(256, config['cvae']['bottleneck_size'], 9),
-    CNBlockConfig(config['cvae']['bottleneck_size'], None, 3),
-]
-
-block_setting_reverse = [
-    CNBlockConfig(config['cvae']['bottleneck_size'] * 2, 256, 3),
-    CNBlockConfig(256, 128, 9),
-    CNBlockConfig(128, 64, 9),
-    CNBlockConfig(64, 32, 3),
-    CNBlockConfig(32, 3, 3),
-]
-
-config['cvae']['encoder_block_settings'] = block_setting
-config['cvae']['decoder_block_settings'] = block_setting_reverse
-
-
-
-####################### cond_ae_curv ############################
-config['cond_ae_curv'] = dict()
-config['cond_ae_curv']['device'] = config['device']
-config['cond_ae_curv']['is_label'] = True
-config['cond_ae_curv']['latent_dim'] = config['latent_dim']
-config['cond_ae_curv']['kernel_size'] = 7
-config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
-
-block_setting = [
-    CNBlockConfig(16, 32, 3),
-    CNBlockConfig(32, 64, 3),
-    CNBlockConfig(64, 128, 9),
-    CNBlockConfig(128, config['cond_ae_curv']['bottleneck_size'], 9),
-    CNBlockConfig(config['cond_ae_curv']['bottleneck_size'], None, 3),
-]
-
-block_setting_reverse = [
-    CNBlockConfig(config['cond_ae_curv']['bottleneck_size'], 128, 3),
-    CNBlockConfig(128, 64, 9),
-    CNBlockConfig(64, 32, 9),
-    CNBlockConfig(32, 16, 3),
-    CNBlockConfig(16, 3, 3),
-]
-
-config['cond_ae_curv']['encoder_block_settings'] = block_setting
-config['cond_ae_curv']['decoder_block_settings'] = block_setting_reverse
-config['cond_ae_curv']['activation'] = 'tanh'
-
-####################### cond_ae_body ############################
-config['cond_ae_body'] = dict()
-config['cond_ae_body']['device'] = config['device']
-config['cond_ae_body']['is_label'] = False
-config['cond_ae_body']['latent_dim'] = config['latent_dim']
-config['cond_ae_body']['kernel_size'] = 7
-config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
-
-block_setting = [
-    CNBlockConfig(16, 32, 3),
-    CNBlockConfig(32, 64, 3),
-    CNBlockConfig(64, 128, 9),
-    CNBlockConfig(128, config['cond_ae_body']['bottleneck_size'], 9),
-    CNBlockConfig(config['cond_ae_body']['bottleneck_size'], None, 3),
-]
-
-block_setting_reverse = [
-    CNBlockConfig(config['cond_ae_body']['bottleneck_size'], 128, 3),
-    CNBlockConfig(128, 64, 9),
-    CNBlockConfig(64, 32, 9),
-    CNBlockConfig(32, 16, 3),
-    CNBlockConfig(16, 3, 3),
-]
-
-config['cond_ae_body']['encoder_block_settings'] = block_setting
-config['cond_ae_body']['decoder_block_settings'] = block_setting_reverse
-config['cond_ae_body']['activation'] = 'tanh'
-
-####################### cond_ae_static ############################
-config['cond_ae_static'] = dict()
-config['cond_ae_static']['device'] = config['device']
-config['cond_ae_static']['is_label'] = False
-config['cond_ae_static']['latent_dim'] = config['latent_dim']
-config['cond_ae_static']['kernel_size'] = 7
-config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
-
-block_setting = [
-    CNBlockConfig(8, 16, 3),
-    CNBlockConfig(16, 32, 3),
-    CNBlockConfig(32, 64, 3),
-    CNBlockConfig(64, config['cond_ae_static']['bottleneck_size'], 9),
-    CNBlockConfig(config['cond_ae_static']['bottleneck_size'], None, 3),
-]
-
-block_setting_reverse = [
-    CNBlockConfig(config['cond_ae_static']['bottleneck_size'], 64, 3),
-    CNBlockConfig(64, 32, 9),
-    CNBlockConfig(32, 16, 3),
-    CNBlockConfig(16, 8, 3),
-    CNBlockConfig(8, 3, 3),
-]
-
-config['cond_ae_static']['encoder_block_settings'] = block_setting
-config['cond_ae_static']['decoder_block_settings'] = block_setting_reverse
-config['cond_ae_static']['activation'] = 'tanh'

configs/cape_03223_SS.py

@@ -1,193 +0,0 @@
-from utils.convnext_utils import CNBlockConfig
-
-config = dict()
-
-config['gpu'] = '4'
-config['name'] = 'cape_03223_SS'
-config['notes'] = ''
-config['tags'] = ['iccv', 'cape_03223_SS', 'torch']
-config['script'] = 'scripts/cvae.py'
-
-config['pretrained_model'] = ''
-
-config['use_skinning'] =              True # and deshaping
-config['remove_global_orientation'] = True
-config['use_curvature'] =             True
-config['use_extra_losses'] =          True # edge, collision, 3d mesh
-config['use_offsets'] =               True
-
-config['use_norm_loss'] =             True
-
-ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
-assert ablation_count == 1 or ablation_count == 0
-
-config['avg_garment_path'] = 'custom_data/avg/train_cape_f_03223_SL_2_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
-if config['use_skinning']:
-  assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
-
-config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
-
-config['batch_size'] = 4
-config['resolution'] = 512
-config['is_skirt'] = False
-config['dataset_in_memory'] = True
-config['is_cape'] = True
-config['device'] = 'cuda'
-
-config['train_dir'] = '/data-local/127/cape_processed/train/female'
-config['valid_dir'] = '/data-local/127/cape_processed/train/female'
-config['test_dir'] = '/data-local/127/cape_processed/train/female'
-config['train_frames'] = 'frames_03223_SS_train_2.txt'
-config['valid_frames'] = 'frames_03223_SS_test_2.txt'
-config['test_frames'] = 'frames_03223_SS_test_2.txt'
-
-config['static_model'] = 'checkpoints/iccv/torch/static_torch_cape_f'
-config['body_model'] = 'checkpoints/iccv/torch/body_torch_cape_f'
-config['curvature_model'] = 'checkpoints/iccv/torch/curv_torch_cape_f'
-
-config['smpl_res'] = 14475
-if config['is_cape']:
-  config['smpl_res'] = 3638
-
-config['latent_dim'] = 128
-
-###############    
-config['lr'] = 0.0001#                 0.0001
-###############
-config['starting_balancing_coeff'] = 0.0001
-
-config['signature']: set = { 'id',
-                            'frame_nr',
-                            'pose_shape',
-                            'uv_body_posed',
-                            'mesh_body_posed',
-                            'mesh_posed',
-                            'uv_curvature',
-                            'uv_static',
-                            'mesh_mask',
-                            'uv_mask',
-                        }
-
-if config['use_skinning']:
-  config['signature'].add('uv_unposed')
-  config['signature'].add('mesh_unposed')
-else:
-  config['signature'].add('uv_posed')
-
-config['upscale_enhancer'] = ''
-config['freeze_encoders'] = True
-config['use_one_hot'] = False
-config['bottleneck_scale'] = 2
-
-config['cvae'] = dict()
-config['cvae']['device'] = config['device']
-config['cvae']['is_label'] = False
-config['cvae']['input_features'] = 3+3+3+7 if config['use_curvature'] else 3+3+3
-config['cvae']['kernel_size'] = 7
-config['cvae']['bottleneck_size'] = 64 * config['bottleneck_scale']
-config['cvae']['latent_dim'] = config['cvae']['bottleneck_size']
-
-block_setting = [
-    CNBlockConfig(32, 64, 3),
-    CNBlockConfig(64, 128, 3),
-    CNBlockConfig(128, 256, 3),
-    CNBlockConfig(256, config['cvae']['bottleneck_size'], 9),
-    CNBlockConfig(config['cvae']['bottleneck_size'], None, 3),
-]
-
-block_setting_reverse = [
-    CNBlockConfig(config['cvae']['bottleneck_size'] * 2, 256, 3),
-    CNBlockConfig(256, 128, 9),
-    CNBlockConfig(128, 64, 9),
-    CNBlockConfig(64, 32, 3),
-    CNBlockConfig(32, 3, 3),
-]
-
-config['cvae']['encoder_block_settings'] = block_setting
-config['cvae']['decoder_block_settings'] = block_setting_reverse
-
-
-
-####################### cond_ae_curv ############################
-config['cond_ae_curv'] = dict()
-config['cond_ae_curv']['device'] = config['device']
-config['cond_ae_curv']['is_label'] = True
-config['cond_ae_curv']['latent_dim'] = config['latent_dim']
-config['cond_ae_curv']['kernel_size'] = 7
-config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
-
-block_setting = [
-    CNBlockConfig(16, 32, 3),
-    CNBlockConfig(32, 64, 3),
-    CNBlockConfig(64, 128, 9),
-    CNBlockConfig(128, config['cond_ae_curv']['bottleneck_size'], 9),
-    CNBlockConfig(config['cond_ae_curv']['bottleneck_size'], None, 3),
-]
-
-block_setting_reverse = [
-    CNBlockConfig(config['cond_ae_curv']['bottleneck_size'], 128, 3),
-    CNBlockConfig(128, 64, 9),
-    CNBlockConfig(64, 32, 9),
-    CNBlockConfig(32, 16, 3),
-    CNBlockConfig(16, 3, 3),
-]
-
-config['cond_ae_curv']['encoder_block_settings'] = block_setting
-config['cond_ae_curv']['decoder_block_settings'] = block_setting_reverse
-config['cond_ae_curv']['activation'] = 'tanh'
-
-####################### cond_ae_body ############################
-config['cond_ae_body'] = dict()
-config['cond_ae_body']['device'] = config['device']
-config['cond_ae_body']['is_label'] = False
-config['cond_ae_body']['latent_dim'] = config['latent_dim']
-config['cond_ae_body']['kernel_size'] = 7
-config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
-
-block_setting = [
-    CNBlockConfig(16, 32, 3),
-    CNBlockConfig(32, 64, 3),
-    CNBlockConfig(64, 128, 9),
-    CNBlockConfig(128, config['cond_ae_body']['bottleneck_size'], 9),
-    CNBlockConfig(config['cond_ae_body']['bottleneck_size'], None, 3),
-]
-
-block_setting_reverse = [
-    CNBlockConfig(config['cond_ae_body']['bottleneck_size'], 128, 3),
-    CNBlockConfig(128, 64, 9),
-    CNBlockConfig(64, 32, 9),
-    CNBlockConfig(32, 16, 3),
-    CNBlockConfig(16, 3, 3),
-]
-
-config['cond_ae_body']['encoder_block_settings'] = block_setting
-config['cond_ae_body']['decoder_block_settings'] = block_setting_reverse
-config['cond_ae_body']['activation'] = 'tanh'
-
-####################### cond_ae_static ############################
-config['cond_ae_static'] = dict()
-config['cond_ae_static']['device'] = config['device']
-config['cond_ae_static']['is_label'] = False
-config['cond_ae_static']['latent_dim'] = config['latent_dim']
-config['cond_ae_static']['kernel_size'] = 7
-config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
-
-block_setting = [
-    CNBlockConfig(8, 16, 3),
-    CNBlockConfig(16, 32, 3),
-    CNBlockConfig(32, 64, 3),
-    CNBlockConfig(64, config['cond_ae_static']['bottleneck_size'], 9),
-    CNBlockConfig(config['cond_ae_static']['bottleneck_size'], None, 3),
-]
-
-block_setting_reverse = [
-    CNBlockConfig(config['cond_ae_static']['bottleneck_size'], 64, 3),
-    CNBlockConfig(64, 32, 9),
-    CNBlockConfig(32, 16, 3),
-    CNBlockConfig(16, 8, 3),
-    CNBlockConfig(8, 3, 3),
-]
-
-config['cond_ae_static']['encoder_block_settings'] = block_setting
-config['cond_ae_static']['decoder_block_settings'] = block_setting_reverse
-config['cond_ae_static']['activation'] = 'tanh'

data/calculate_normals.py

@@ -0,0 +1,112 @@
+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)
+

data/dataset.py

@@ -45,10 +45,7 @@ def read_frames(folder, shuffle=True, is_cape=False, testing=False, frames_file=
         with open(os.path.join(folder, frames_file), 'r') as f:
             lines = f.readlines()
             lines = [line.strip() for line in lines]
-        if is_cape:
-            frames = [Frame(line.split('.')[0], line.split('.')[1]) for line in lines]
-        else:
-            frames = [Frame(line.split('_')[0], line.split('_')[1]) for line in lines]
+        frames = [Frame(line.split('_')[0], line.split('_')[1]) for line in lines]
 
     if shuffle:
         random.shuffle(frames)

docker/docker-compose.yml

@@ -34,6 +34,7 @@ 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:

run.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.cape_03223_SS import config
+from configs.pyr_64_cape_m import config
 import argparse
 
 # parser = argparse.ArgumentParser(description='')
@@ -10,9 +10,9 @@ import argparse
 
 
 if __name__ == '__main__':
-    # import wandb
+    import wandb
     # wandb.login(key='******************************************')
-    # wandb.login(host='https://wandb.lnrweb.com', key='local-40a577c9eccab708f1aa81027f21a995596a5838')
+    wandb.login(host='https://wandb.lnrweb.com', key='local-40a577c9eccab708f1aa81027f21a995596a5838')
     name = config['name']
     script = config['script']
     config = [key for key in sys.modules.keys()  if key.startswith('configs.')][-1]

run_parallel.py

@@ -4,24 +4,27 @@ 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 = int(500 * 1e3)
-    step = 10000
+    count = 0
+    step = 100
 
     # !!!!!!! change skirt parameter in script
 
-    while count < 510 * 1e3:
+    while count <1200:
         # 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('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 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 &')

scripts/cvae.py

@@ -10,12 +10,10 @@ 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.cape_00032_SL import config
-else:
-    config_module = importlib.import_module(args['config'])
-    config = config_module.config
-
+    pass
 os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 if args['debug']:
@@ -52,7 +50,7 @@ from custom_utils.custom_utils import UV2Mesh, Accumulator, plot_3d, get_summary
 
 
 def init_wandb():
-    run = wandb.init(project='iccv', name=config['name'], notes=config['notes'], dir='/data-local/127/temp', 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')
     artifact.add_file('scripts/cvae.py')
     artifact.add_file('losses/losses.py')
@@ -459,7 +457,7 @@ def train():
             g_balancing_coeff += 1 / 10000 
 
         if step % 10000 == 0:
-            ckpt_saver(model, step, optimizer, None)
+            ckpt_saver(model, step, optimizer)
 
         step += 1
 
@@ -471,8 +469,8 @@ if __name__ == '__main__':
     smpl = SMPL(orig_smpl=config['is_cape'], path='smpl', device=config['device'])
     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'])
+    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()
@@ -480,11 +478,12 @@ if __name__ == '__main__':
 
     model = CVAE(config)
 
+
     optimizer = optim.Adam(model.parameters(), lr=config['lr'])
     _MSSSIM = MS_SSIM(data_range=2, size_average=True, channel=3)
     g_balancing_coeff = config['starting_balancing_coeff']
 
-    ckpt_saver = CheckpointSaver('checkpoints/iccv/torch/' + config['name'], top_n=10)
+    ckpt_saver = CheckpointSaver('checkpoints/iccv/' + config['name'], top_n=5)
 
     step = 0
     if config['pretrained_model'] != '':