Merge remote-tracking branch 'origin/main' into main

configs/archive/ae_body_cape_m.py

@@ -2,7 +2,7 @@ from utils.convnext_utils import CNBlockConfig
 
 config = dict()
 
-config['gpu'] = '4'
+config['gpu'] = '0'
 config['name'] = 'body_torch_cape_m'
 config['notes'] = ' ' 
 config['tags'] = ['torch', 'body'] 
@@ -11,9 +11,9 @@ config['script'] = 'scripts/ae_body_train.py'
 config['pretrained_model'] = ''
 
 config['batch_size'] = 4
-config['train_dir'] = '/data-local/127/cape_processed/train/male'
+config['train_dir'] = '/data-local/128/cape_processed/train/male'
-config['valid_dir'] = '/data-local/127/cape_processed/test/male'
+config['valid_dir'] = '/data-local/128/cape_processed/test/male'
-config['test_dir'] = '/data-local/127/cape_processed/test/male'
+config['test_dir'] = '/data-local/128/cape_processed/test/male'
 config['train_frames'] = None
 config['valid_frames'] = None
 

configs/iccvw/ae_body_1.py

@@ -0,0 +1,78 @@
+from utils.convnext_utils import CNBlockConfig
+
+config = dict()
+
+config['gpu'] = '2'
+config['name'] = 'body_iccvw_1'
+config['notes'] = ' ' 
+config['tags'] = ['torch', 'body'] 
+config['script'] = 'scripts/ae_body_train.py'
+
+config['pretrained_model'] = ''
+
+config['batch_size'] = 4
+config['train_dir'] = '/data-local/127/cloth3d_processed'
+config['valid_dir'] = '/data-local/127/cloth3d_processed'
+config['test_dir'] = '/data-net/dataset/test_ready'
+
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = False
+config['is_cape'] = False
+config['remove_global_orientation'] = True
+config['device'] = 'cuda'
+
+if config['is_skirt']:
+  config['train_frames'] = 'frames_train_skirt.txt'
+  config['valid_frames'] = 'frames_valid_skirt.txt'
+else:
+  config['train_frames'] = 'frames_train.txt'
+  config['valid_frames'] = 'frames_valid.txt'
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['device'] = config['device']
+config['cond_ae']['is_label'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['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']['bottleneck_size'], 9),
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
+]
+
+block_setting_reverse = [
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], 128, 3),
+    CNBlockConfig(128, 64, 9),
+    CNBlockConfig(64, 32, 9),
+    CNBlockConfig(32, 16, 3),
+    CNBlockConfig(16, 3, 3),
+    # CNBlockConfig(3, 3, 3),
+]
+
+config['cond_ae']['encoder_block_settings'] = block_setting
+config['cond_ae']['decoder_block_settings'] = block_setting_reverse
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['lr'] = 0.0001
+config['cond_ae']['upscale_output'] = False
+
+config['signature'] = { 'id',
+                        'frame_nr',
+                        'pose_shape',
+                        'uv_body_posed',
+                        'mesh_mask',
+                        'mesh_body_posed',
+                        }

configs/iccvw/ae_body_1_10p.py

@@ -0,0 +1,78 @@
+from utils.convnext_utils import CNBlockConfig
+
+config = dict()
+
+config['gpu'] = '5'
+config['name'] = 'body_iccvw_1_10p'
+config['notes'] = ' ' 
+config['tags'] = ['torch', 'body'] 
+config['script'] = 'scripts/ae_body_train.py'
+
+config['pretrained_model'] = ''
+
+config['batch_size'] = 4
+config['train_dir'] = '/data-local/127/cloth3d_processed'
+config['valid_dir'] = '/data-local/127/cloth3d_processed'
+config['test_dir'] = '/data-net/dataset/test_ready'
+
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = False
+config['is_cape'] = False
+config['remove_global_orientation'] = True
+config['device'] = 'cuda'
+
+if config['is_skirt']:
+  config['train_frames'] = 'frames_train_skirt_10p.txt'
+  config['valid_frames'] = 'frames_valid_skirt.txt'
+else:
+  config['train_frames'] = 'frames_train_10p.txt'
+  config['valid_frames'] = 'frames_valid.txt'
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['device'] = config['device']
+config['cond_ae']['is_label'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['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']['bottleneck_size'], 9),
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
+]
+
+block_setting_reverse = [
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], 128, 3),
+    CNBlockConfig(128, 64, 9),
+    CNBlockConfig(64, 32, 9),
+    CNBlockConfig(32, 16, 3),
+    CNBlockConfig(16, 3, 3),
+    # CNBlockConfig(3, 3, 3),
+]
+
+config['cond_ae']['encoder_block_settings'] = block_setting
+config['cond_ae']['decoder_block_settings'] = block_setting_reverse
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['lr'] = 0.0001
+config['cond_ae']['upscale_output'] = False
+
+config['signature'] = { 'id',
+                        'frame_nr',
+                        'pose_shape',
+                        'uv_body_posed',
+                        'mesh_mask',
+                        'mesh_body_posed',
+                        }

configs/iccvw/ae_body_1_25p.py

@@ -0,0 +1,78 @@
+from utils.convnext_utils import CNBlockConfig
+
+config = dict()
+
+config['gpu'] = '5'
+config['name'] = 'body_iccvw_1_25p'
+config['notes'] = ' ' 
+config['tags'] = ['torch', 'body'] 
+config['script'] = 'scripts/ae_body_train.py'
+
+config['pretrained_model'] = ''
+
+config['batch_size'] = 4
+config['train_dir'] = '/data-local/127/cloth3d_processed'
+config['valid_dir'] = '/data-local/127/cloth3d_processed'
+config['test_dir'] = '/data-net/dataset/test_ready'
+
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = False
+config['is_cape'] = False
+config['remove_global_orientation'] = True
+config['device'] = 'cuda'
+
+if config['is_skirt']:
+  config['train_frames'] = 'frames_train_skirt_25p.txt'
+  config['valid_frames'] = 'frames_valid_skirt.txt'
+else:
+  config['train_frames'] = 'frames_train_25p.txt'
+  config['valid_frames'] = 'frames_valid.txt'
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['device'] = config['device']
+config['cond_ae']['is_label'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['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']['bottleneck_size'], 9),
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
+]
+
+block_setting_reverse = [
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], 128, 3),
+    CNBlockConfig(128, 64, 9),
+    CNBlockConfig(64, 32, 9),
+    CNBlockConfig(32, 16, 3),
+    CNBlockConfig(16, 3, 3),
+    # CNBlockConfig(3, 3, 3),
+]
+
+config['cond_ae']['encoder_block_settings'] = block_setting
+config['cond_ae']['decoder_block_settings'] = block_setting_reverse
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['lr'] = 0.0001
+config['cond_ae']['upscale_output'] = False
+
+config['signature'] = { 'id',
+                        'frame_nr',
+                        'pose_shape',
+                        'uv_body_posed',
+                        'mesh_mask',
+                        'mesh_body_posed',
+                        }

configs/iccvw/ae_body_1_50p.py

@@ -0,0 +1,78 @@
+from utils.convnext_utils import CNBlockConfig
+
+config = dict()
+
+config['gpu'] = '5'
+config['name'] = 'body_iccvw_1_50p'
+config['notes'] = ' ' 
+config['tags'] = ['torch', 'body'] 
+config['script'] = 'scripts/ae_body_train.py'
+
+config['pretrained_model'] = ''
+
+config['batch_size'] = 4
+config['train_dir'] = '/data-local/127/cloth3d_processed'
+config['valid_dir'] = '/data-local/127/cloth3d_processed'
+config['test_dir'] = '/data-net/dataset/test_ready'
+
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = False
+config['is_cape'] = False
+config['remove_global_orientation'] = True
+config['device'] = 'cuda'
+
+if config['is_skirt']:
+  config['train_frames'] = 'frames_train_skirt_50p.txt'
+  config['valid_frames'] = 'frames_valid_skirt.txt'
+else:
+  config['train_frames'] = 'frames_train_50p.txt'
+  config['valid_frames'] = 'frames_valid.txt'
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['device'] = config['device']
+config['cond_ae']['is_label'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['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']['bottleneck_size'], 9),
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
+]
+
+block_setting_reverse = [
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], 128, 3),
+    CNBlockConfig(128, 64, 9),
+    CNBlockConfig(64, 32, 9),
+    CNBlockConfig(32, 16, 3),
+    CNBlockConfig(16, 3, 3),
+    # CNBlockConfig(3, 3, 3),
+]
+
+config['cond_ae']['encoder_block_settings'] = block_setting
+config['cond_ae']['decoder_block_settings'] = block_setting_reverse
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['lr'] = 0.0001
+config['cond_ae']['upscale_output'] = False
+
+config['signature'] = { 'id',
+                        'frame_nr',
+                        'pose_shape',
+                        'uv_body_posed',
+                        'mesh_mask',
+                        'mesh_body_posed',
+                        }

configs/iccvw/ae_body_1_skirt.py

@@ -0,0 +1,78 @@
+from utils.convnext_utils import CNBlockConfig
+
+config = dict()
+
+config['gpu'] = '1'
+config['name'] = 'body_iccvw_1_skirt'
+config['notes'] = ' ' 
+config['tags'] = ['torch', 'body'] 
+config['script'] = 'scripts/ae_body_train.py'
+
+config['pretrained_model'] = ''
+
+config['batch_size'] = 4
+config['train_dir'] = '/data-local/127/cloth3d_processed'
+config['valid_dir'] = '/data-local/127/cloth3d_processed'
+config['test_dir'] = '/data-net/dataset/test_ready'
+
+config['resolution'] = 512
+config['is_skirt'] = True
+config['dataset_in_memory'] = False
+config['is_cape'] = False
+config['remove_global_orientation'] = True
+config['device'] = 'cuda'
+
+if config['is_skirt']:
+  config['train_frames'] = 'frames_train_skirt.txt'
+  config['valid_frames'] = 'frames_valid_skirt.txt'
+else:
+  config['train_frames'] = 'frames_train.txt'
+  config['valid_frames'] = 'frames_valid.txt'
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['device'] = config['device']
+config['cond_ae']['is_label'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['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']['bottleneck_size'], 9),
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
+]
+
+block_setting_reverse = [
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], 128, 3),
+    CNBlockConfig(128, 64, 9),
+    CNBlockConfig(64, 32, 9),
+    CNBlockConfig(32, 16, 3),
+    CNBlockConfig(16, 3, 3),
+    # CNBlockConfig(3, 3, 3),
+]
+
+config['cond_ae']['encoder_block_settings'] = block_setting
+config['cond_ae']['decoder_block_settings'] = block_setting_reverse
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['lr'] = 0.0001
+config['cond_ae']['upscale_output'] = False
+
+config['signature'] = { 'id',
+                        'frame_nr',
+                        'pose_shape',
+                        'uv_body_posed',
+                        'mesh_mask',
+                        'mesh_body_posed',
+                        }

configs/iccvw/ae_body_cape_f.py

@@ -0,0 +1,73 @@
+from utils.convnext_utils import CNBlockConfig
+
+config = dict()
+
+config['gpu'] = '0'
+config['name'] = 'body_iccvw_cape_f'
+config['notes'] = ' ' 
+config['tags'] = ['torch', 'body'] 
+config['script'] = 'scripts/ae_body_train.py'
+
+config['pretrained_model'] = ''
+
+config['batch_size'] = 4
+config['train_dir'] = '/data-local/128/cape_processed/train/female'
+config['valid_dir'] = '/data-local/128/cape_processed/test/female'
+config['test_dir'] = '/data-local/128/cape_processed/test/female'
+config['train_frames'] = None
+config['valid_frames'] = None
+
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = False
+config['is_cape'] = True
+config['remove_global_orientation'] = True
+config['device'] = 'cuda'
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['device'] = config['device']
+config['cond_ae']['is_label'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['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']['bottleneck_size'], 9),
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
+]
+
+block_setting_reverse = [
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], 128, 3),
+    CNBlockConfig(128, 64, 9),
+    CNBlockConfig(64, 32, 9),
+    CNBlockConfig(32, 16, 3),
+    CNBlockConfig(16, 3, 3),
+    # CNBlockConfig(3, 3, 3),
+]
+
+config['cond_ae']['encoder_block_settings'] = block_setting
+config['cond_ae']['decoder_block_settings'] = block_setting_reverse
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['lr'] = 0.0001
+config['cond_ae']['upscale_output'] = False
+
+config['signature'] = { 'id',
+                        'frame_nr',
+                        'pose_shape',
+                        'uv_body_posed',
+                        'mesh_mask',
+                        'mesh_body_posed',
+                        }

configs/iccvw/ae_body_cape_m.py

@@ -0,0 +1,73 @@
+from utils.convnext_utils import CNBlockConfig
+
+config = dict()
+
+config['gpu'] = '0'
+config['name'] = 'body_torch_cape_m'
+config['notes'] = ' ' 
+config['tags'] = ['torch', 'body'] 
+config['script'] = 'scripts/ae_body_train.py'
+
+config['pretrained_model'] = ''
+
+config['batch_size'] = 4
+config['train_dir'] = '/data-local/128/cape_processed/train/male'
+config['valid_dir'] = '/data-local/128/cape_processed/test/male'
+config['test_dir'] = '/data-local/128/cape_processed/test/male'
+config['train_frames'] = None
+config['valid_frames'] = None
+
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = False
+config['is_cape'] = True
+config['remove_global_orientation'] = True
+config['device'] = 'cuda'
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['device'] = config['device']
+config['cond_ae']['is_label'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['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']['bottleneck_size'], 9),
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
+]
+
+block_setting_reverse = [
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], 128, 3),
+    CNBlockConfig(128, 64, 9),
+    CNBlockConfig(64, 32, 9),
+    CNBlockConfig(32, 16, 3),
+    CNBlockConfig(16, 3, 3),
+    # CNBlockConfig(3, 3, 3),
+]
+
+config['cond_ae']['encoder_block_settings'] = block_setting
+config['cond_ae']['decoder_block_settings'] = block_setting_reverse
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['lr'] = 0.0001
+config['cond_ae']['upscale_output'] = False
+
+config['signature'] = { 'id',
+                        'frame_nr',
+                        'pose_shape',
+                        'uv_body_posed',
+                        'mesh_mask',
+                        'mesh_body_posed',
+                        }

configs/iccvw/ae_norm_1.py

@@ -0,0 +1,78 @@
+from utils.convnext_utils import CNBlockConfig
+
+config = dict()
+
+config['gpu'] = '1'
+config['name'] = 'norm_iccvw_1'
+config['notes'] = ' ' 
+config['tags'] = ['torch', 'norm'] 
+config['script'] = 'scripts/ae_norm_train.py'
+
+config['pretrained_model'] = ''
+
+config['batch_size'] = 4
+config['train_dir'] = '/data-local/127/cloth3d_processed'
+config['valid_dir'] = '/data-local/127/cloth3d_processed'
+config['test_dir'] = '/data-net/dataset/test_ready'
+
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = False
+config['is_cape'] = False
+config['remove_global_orientation'] = True
+config['device'] = 'cuda'
+
+if config['is_skirt']:
+  config['train_frames'] = 'frames_train_skirt.txt'
+  config['valid_frames'] = 'frames_valid_skirt.txt'
+else:
+  config['train_frames'] = 'frames_train.txt'
+  config['valid_frames'] = 'frames_valid.txt'
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['device'] = config['device']
+config['cond_ae']['is_label'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['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']['bottleneck_size'], 9),
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
+]
+
+block_setting_reverse = [
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], 128, 3),
+    CNBlockConfig(128, 64, 9),
+    CNBlockConfig(64, 32, 9),
+    CNBlockConfig(32, 16, 3),
+    CNBlockConfig(16, 3, 3),
+    # CNBlockConfig(3, 3, 3),
+]
+
+config['cond_ae']['encoder_block_settings'] = block_setting
+config['cond_ae']['decoder_block_settings'] = block_setting_reverse
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['lr'] = 0.0001
+config['cond_ae']['upscale_output'] = False
+
+config['signature'] = { 'id',
+                        'frame_nr',
+                        'uv_mask',
+                        'uv_normals',
+                        'mesh_mask',
+                        'mesh_normals',
+                        }

configs/iccvw/ae_norm_1_skirt.py

@@ -0,0 +1,78 @@
+from utils.convnext_utils import CNBlockConfig
+
+config = dict()
+
+config['gpu'] = '4'
+config['name'] = 'norm_iccvw_1_skirt'
+config['notes'] = ' ' 
+config['tags'] = ['torch', 'norm'] 
+config['script'] = 'scripts/ae_norm_train.py'
+
+config['pretrained_model'] = ''
+
+config['batch_size'] = 4
+config['train_dir'] = '/data-local/127/cloth3d_processed'
+config['valid_dir'] = '/data-local/127/cloth3d_processed'
+config['test_dir'] = '/data-net/dataset/test_ready'
+
+config['resolution'] = 512
+config['is_skirt'] = True
+config['dataset_in_memory'] = False
+config['is_cape'] = False
+config['remove_global_orientation'] = True
+config['device'] = 'cuda'
+
+if config['is_skirt']:
+  config['train_frames'] = 'frames_train_skirt.txt'
+  config['valid_frames'] = 'frames_valid_skirt.txt'
+else:
+  config['train_frames'] = 'frames_train.txt'
+  config['valid_frames'] = 'frames_valid.txt'
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['device'] = config['device']
+config['cond_ae']['is_label'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['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']['bottleneck_size'], 9),
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
+]
+
+block_setting_reverse = [
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], 128, 3),
+    CNBlockConfig(128, 64, 9),
+    CNBlockConfig(64, 32, 9),
+    CNBlockConfig(32, 16, 3),
+    CNBlockConfig(16, 3, 3),
+    # CNBlockConfig(3, 3, 3),
+]
+
+config['cond_ae']['encoder_block_settings'] = block_setting
+config['cond_ae']['decoder_block_settings'] = block_setting_reverse
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['lr'] = 0.0001
+config['cond_ae']['upscale_output'] = False
+
+config['signature'] = { 'id',
+                        'frame_nr',
+                        'uv_mask',
+                        'uv_normals',
+                        'mesh_mask',
+                        'mesh_normals',
+                        }

configs/iccvw/ae_norm_cape_f.py

@@ -0,0 +1,73 @@
+from utils.convnext_utils import CNBlockConfig
+
+config = dict()
+
+config['gpu'] = '6'
+config['name'] = 'norm_iccvw_cape_f'
+config['notes'] = ' ' 
+config['tags'] = ['torch', 'norm'] 
+config['script'] = 'scripts/ae_norm_train.py'
+
+config['pretrained_model'] = ''
+
+config['batch_size'] = 4
+config['train_dir'] = '/data-local/127_2/cape_processed/train/female'
+config['valid_dir'] = '/data-local/127_2/cape_processed/test/female'
+config['test_dir'] = '/data-local/127_2/cape_processed/test/female'
+config['train_frames'] = None
+config['valid_frames'] = None
+
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = False
+config['is_cape'] = True
+config['remove_global_orientation'] = True
+config['device'] = 'cuda'
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['device'] = config['device']
+config['cond_ae']['is_label'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['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']['bottleneck_size'], 9),
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
+]
+
+block_setting_reverse = [
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], 128, 3),
+    CNBlockConfig(128, 64, 9),
+    CNBlockConfig(64, 32, 9),
+    CNBlockConfig(32, 16, 3),
+    CNBlockConfig(16, 3, 3),
+    # CNBlockConfig(3, 3, 3),
+]
+
+config['cond_ae']['encoder_block_settings'] = block_setting
+config['cond_ae']['decoder_block_settings'] = block_setting_reverse
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['lr'] = 0.0001
+config['cond_ae']['upscale_output'] = False
+
+config['signature'] = { 'id',
+                        'frame_nr',
+                        'uv_mask',
+                        'uv_normals',
+                        'mesh_mask',
+                        'mesh_normals',
+                        }

configs/iccvw/ae_norm_cape_m.py

@@ -0,0 +1,74 @@
+from utils.convnext_utils import CNBlockConfig
+
+config = dict()
+
+config['gpu'] = '6'
+config['name'] = 'norm_iccvw_cape_m'
+config['notes'] = ' ' 
+config['tags'] = ['torch', 'norm'] 
+config['script'] = 'scripts/ae_norm_train.py'
+
+config['pretrained_model'] = ''
+
+config['batch_size'] = 4
+config['train_dir'] = '/data-local/127_2/cape_processed/train/male'
+config['valid_dir'] = '/data-local/127_2/cape_processed/test/male'
+config['test_dir'] = '/data-local/127_2/cape_processed/test/male'
+config['train_frames'] = None
+config['valid_frames'] = None
+
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = False
+config['is_cape'] = True
+config['remove_global_orientation'] = True
+config['device'] = 'cuda'
+
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['device'] = config['device']
+config['cond_ae']['is_label'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['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']['bottleneck_size'], 9),
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
+]
+
+block_setting_reverse = [
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], 128, 3),
+    CNBlockConfig(128, 64, 9),
+    CNBlockConfig(64, 32, 9),
+    CNBlockConfig(32, 16, 3),
+    CNBlockConfig(16, 3, 3),
+    # CNBlockConfig(3, 3, 3),
+]
+
+config['cond_ae']['encoder_block_settings'] = block_setting
+config['cond_ae']['decoder_block_settings'] = block_setting_reverse
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['lr'] = 0.0001
+config['cond_ae']['upscale_output'] = False
+
+config['signature'] = { 'id',
+                        'frame_nr',
+                        'uv_mask',
+                        'uv_normals',
+                        'mesh_mask',
+                        'mesh_normals',
+                        }

configs/iccvw/ae_static_1.py

@@ -0,0 +1,78 @@
+from utils.convnext_utils import CNBlockConfig
+
+config = dict()
+
+config['gpu'] = '3'
+config['name'] = 'static_iccvw_4'
+config['notes'] = ' ' 
+config['tags'] = ['torch', 'static'] 
+config['script'] = 'scripts/ae_static_train.py'
+
+config['pretrained_model'] = 'checkpoints/iccvw/static_iccvw_3'
+
+config['batch_size'] = 4
+config['train_dir'] = '/data-local/127/cloth3d_processed'
+config['valid_dir'] = '/data-local/127/cloth3d_processed'
+config['test_dir'] = '/data-net/dataset/test_ready'
+
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = False
+config['is_cape'] = False
+config['remove_global_orientation'] = True
+config['device'] = 'cuda'
+
+if config['is_skirt']:
+  config['train_frames'] = 'frames_train_skirt.txt'
+  config['valid_frames'] = 'frames_valid_skirt.txt'
+else:
+  config['train_frames'] = 'frames_train.txt'
+  config['valid_frames'] = 'frames_valid.txt'
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['device'] = config['device']
+config['cond_ae']['is_label'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['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']['bottleneck_size'], 9),
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
+]
+
+block_setting_reverse = [
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], 64, 3),
+    CNBlockConfig(64, 32, 9),
+    CNBlockConfig(32, 16, 3),
+    CNBlockConfig(16, 8, 3),
+    CNBlockConfig(8, 3, 3),
+    # CNBlockConfig(3, 3, 3),
+]
+
+config['cond_ae']['encoder_block_settings'] = block_setting
+config['cond_ae']['decoder_block_settings'] = block_setting_reverse
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['lr'] = 0.0005
+config['cond_ae']['upscale_output'] = False
+
+config['signature'] = { 'id',
+                        'frame_nr',
+                        'uv_static',
+                        'mesh_static',
+                        'mesh_mask',
+                        'uv_mask',
+                        }

configs/iccvw/ae_static_1_skirt.py

@@ -0,0 +1,78 @@
+from utils.convnext_utils import CNBlockConfig
+
+config = dict()
+
+config['gpu'] = '3'
+config['name'] = 'static_iccvw_1_skirt'
+config['notes'] = ' ' 
+config['tags'] = ['torch', 'static'] 
+config['script'] = 'scripts/ae_static_train.py'
+
+config['pretrained_model'] = ''
+
+config['batch_size'] = 4
+config['train_dir'] = '/data-local/127/cloth3d_processed'
+config['valid_dir'] = '/data-local/127/cloth3d_processed'
+config['test_dir'] = '/data-net/dataset/test_ready'
+
+config['resolution'] = 512
+config['is_skirt'] = True
+config['dataset_in_memory'] = False
+config['is_cape'] = False
+config['remove_global_orientation'] = True
+config['device'] = 'cuda'
+
+if config['is_skirt']:
+  config['train_frames'] = 'frames_train_skirt.txt'
+  config['valid_frames'] = 'frames_valid_skirt.txt'
+else:
+  config['train_frames'] = 'frames_train.txt'
+  config['valid_frames'] = 'frames_valid.txt'
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['device'] = config['device']
+config['cond_ae']['is_label'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['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']['bottleneck_size'], 9),
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
+]
+
+block_setting_reverse = [
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], 64, 3),
+    CNBlockConfig(64, 32, 9),
+    CNBlockConfig(32, 16, 3),
+    CNBlockConfig(16, 8, 3),
+    CNBlockConfig(8, 3, 3),
+    # CNBlockConfig(3, 3, 3),
+]
+
+config['cond_ae']['encoder_block_settings'] = block_setting
+config['cond_ae']['decoder_block_settings'] = block_setting_reverse
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['lr'] = 0.0005
+config['cond_ae']['upscale_output'] = False
+
+config['signature'] = { 'id',
+                        'frame_nr',
+                        'uv_static',
+                        'mesh_static',
+                        'mesh_mask',
+                        'uv_mask',
+                        }

configs/iccvw/ae_static_5.py

@@ -0,0 +1,78 @@
+from utils.convnext_utils import CNBlockConfig
+
+config = dict()
+
+config['gpu'] = '5'
+config['name'] = 'static_iccvw_5'
+config['notes'] = 'lower learning rate' 
+config['tags'] = ['torch', 'static'] 
+config['script'] = 'scripts/ae_static_train.py'
+
+config['pretrained_model'] = 'checkpoints/iccvw/static_iccvw_3'
+
+config['batch_size'] = 4
+config['train_dir'] = '/data-local/127/cloth3d_processed'
+config['valid_dir'] = '/data-local/127/cloth3d_processed'
+config['test_dir'] = '/data-net/dataset/test_ready'
+
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = False
+config['is_cape'] = False
+config['remove_global_orientation'] = True
+config['device'] = 'cuda'
+
+if config['is_skirt']:
+  config['train_frames'] = 'frames_train_skirt.txt'
+  config['valid_frames'] = 'frames_valid_skirt.txt'
+else:
+  config['train_frames'] = 'frames_train.txt'
+  config['valid_frames'] = 'frames_valid.txt'
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['device'] = config['device']
+config['cond_ae']['is_label'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['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']['bottleneck_size'], 9),
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
+]
+
+block_setting_reverse = [
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], 64, 3),
+    CNBlockConfig(64, 32, 9),
+    CNBlockConfig(32, 16, 3),
+    CNBlockConfig(16, 8, 3),
+    CNBlockConfig(8, 3, 3),
+    # CNBlockConfig(3, 3, 3),
+]
+
+config['cond_ae']['encoder_block_settings'] = block_setting
+config['cond_ae']['decoder_block_settings'] = block_setting_reverse
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['lr'] = 0.00005
+config['cond_ae']['upscale_output'] = False
+
+config['signature'] = { 'id',
+                        'frame_nr',
+                        'uv_static',
+                        'mesh_static',
+                        'mesh_mask',
+                        'uv_mask',
+                        }

configs/iccvw/ae_static_cape_f.py

@@ -0,0 +1,73 @@
+from utils.convnext_utils import CNBlockConfig
+
+config = dict()
+
+config['gpu'] = '4'
+config['name'] = 'static_iccvw_cape_f'
+config['notes'] = ' ' 
+config['tags'] = ['torch', 'static'] 
+config['script'] = 'scripts/ae_static_train.py'
+
+config['pretrained_model'] = ''
+
+config['batch_size'] = 4
+config['train_dir'] = '/data-local/127_2/cape_processed/train/female'
+config['valid_dir'] = '/data-local/127_2/cape_processed/test/female'
+config['test_dir'] = '/data-local/127_2/cape_processed/test/female'
+config['train_frames'] = None
+config['valid_frames'] = None
+
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = False
+config['is_cape'] = True
+config['remove_global_orientation'] = True
+config['device'] = 'cuda'
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['device'] = config['device']
+config['cond_ae']['is_label'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['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']['bottleneck_size'], 9),
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
+]
+
+block_setting_reverse = [
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], 64, 3),
+    CNBlockConfig(64, 32, 9),
+    CNBlockConfig(32, 16, 3),
+    CNBlockConfig(16, 8, 3),
+    CNBlockConfig(8, 3, 3),
+    # CNBlockConfig(3, 3, 3),
+]
+
+config['cond_ae']['encoder_block_settings'] = block_setting
+config['cond_ae']['decoder_block_settings'] = block_setting_reverse
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['lr'] = 0.0005
+config['cond_ae']['upscale_output'] = False
+
+config['signature'] = { 'id',
+                        'frame_nr',
+                        'uv_static',
+                        'mesh_static',
+                        'mesh_mask',
+                        'uv_mask',
+                        }

configs/iccvw/ae_static_cape_m.py

@@ -0,0 +1,73 @@
+from utils.convnext_utils import CNBlockConfig
+
+config = dict()
+
+config['gpu'] = '5'
+config['name'] = 'static_iccvw_cape_m'
+config['notes'] = ' ' 
+config['tags'] = ['torch', 'static'] 
+config['script'] = 'scripts/ae_static_train.py'
+
+config['pretrained_model'] = ''
+
+config['batch_size'] = 4
+config['train_dir'] = '/data-local/127_2/cape_processed/train/male'
+config['valid_dir'] = '/data-local/127_2/cape_processed/test/male'
+config['test_dir'] = '/data-local/127_2/cape_processed/test/male'
+config['train_frames'] = None
+config['valid_frames'] = None
+
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = False
+config['is_cape'] = True
+config['remove_global_orientation'] = True
+config['device'] = 'cuda'
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['device'] = config['device']
+config['cond_ae']['is_label'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['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']['bottleneck_size'], 9),
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
+]
+
+block_setting_reverse = [
+    CNBlockConfig(config['cond_ae']['bottleneck_size'], 64, 3),
+    CNBlockConfig(64, 32, 9),
+    CNBlockConfig(32, 16, 3),
+    CNBlockConfig(16, 8, 3),
+    CNBlockConfig(8, 3, 3),
+    # CNBlockConfig(3, 3, 3),
+]
+
+config['cond_ae']['encoder_block_settings'] = block_setting
+config['cond_ae']['decoder_block_settings'] = block_setting_reverse
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['lr'] = 0.0005
+config['cond_ae']['upscale_output'] = False
+
+config['signature'] = { 'id',
+                        'frame_nr',
+                        'uv_static',
+                        'mesh_static',
+                        'mesh_mask',
+                        'uv_mask',
+                        }

custom_utils/custom_utils.py

@@ -9,9 +9,6 @@ from plotly.subplots import make_subplots
 from torchvision.transforms import Resize
 from torchvision.transforms.functional import InterpolationMode
 
-# from tensorflow_graphics.geometry.convolution.graph_pooling import pool, unpool
-# from tensorflow_graphics.geometry.convolution.graph_convolution import edge_convolution_template
-
 
 def m2uv_array(m2uv, is_smpl_orig=False):
     result = np.zeros((14475 if not is_smpl_orig else 3638, 2), dtype=np.int32)
@@ -74,35 +71,6 @@ class UV2Mesh(nn.Module):
         return x
 
 
-
-class UV2MeshLabel():
-    def __init__(self, res=256, is_skirt=False, is_smpl_orig=False):
-        super(UV2MeshLabel, self).__init__()
-        self.res = res
-        uv_map = np.load('custom_utils/stretched_uv_map_' + ('skirt' if is_skirt else '3') + ('_orig' if is_smpl_orig else '') + '.npy')
-        self.uv_map_discreet = np.floor(uv_map * self.res).astype(np.int32)
-        m2uv = np.load('custom_utils/m2uv' + ('_skirt' if is_skirt else '') + ('_orig' if is_smpl_orig else '') + '.npy', allow_pickle=True).item()
-        m2uv = m2uv_array(m2uv, is_smpl_orig=is_smpl_orig)
-        self.uv_pixels = self.uv_map_discreet[m2uv]
-
-    # input = channels_first
-    # @tf.function
-    def uv_to_mesh(self, uv):
-        batch_size = uv.shape[0]
-
-        x1_ind = tf.tile(self.uv_pixels[:, 0, :][tf.newaxis, :, :], [batch_size, 1, 1])
-        x2_ind = tf.tile(self.uv_pixels[:, 1, :][tf.newaxis, :, :], [batch_size, 1, 1])
-
-        x1 = tf.gather_nd(uv, x1_ind, batch_dims=1)
-        x2 = tf.gather_nd(uv, x2_ind, batch_dims=1)
-
-        mesh = (x1 + x2) / 2
-        return mesh
-
-    def __call__(self, uv):
-        return self.uv_to_mesh(uv)
-
-
 def _maskFaces(V, F, mask, return_unfixed=False):
     V_mask = mask
     F_mask = np.take(V_mask, F.reshape(-1), axis=0)
@@ -119,20 +87,6 @@ def _maskFaces(V, F, mask, return_unfixed=False):
         return V_new, F_new
 
 
-# def _maskFaces_tf(V, F, mask):
-#     V_mask = mask
-#     F_mask = tf.gather(V_mask, F.reshape(-1), axis=0)
-#     F_mask = F_mask.reshape((-1, 3)).sum(axis=1) == 3
-#     F_mask = tf.reduce_sum(tf.reshape(F_mask, (-1, 3)), axis=1) == 3
-
-#     V_ind = tf.zeros(V_mask.shape)
-#     V_ind[V_mask] = tf.range(V_mask.sum())
-#     F_new = tf.gather(V_ind, F[F_mask, :].reshape(-1), axis=0).reshape((-1, 3))
-#     V_new = V[V_mask, :]
-
-#     return V_new, F_new
-
-
 def maskFacesBatch(V, F, mask):
     Vs = []
     Fs = []
@@ -272,25 +226,6 @@ def plot_3d_collision(gt, collision_1, collision_2, collision_faces, collision_c
     return fig
 
 
-def batch_to_dict(batch, signature):
-    result = dict()
-    for i, key in enumerate(signature.keys()):
-            result[key] = batch[i]
-    return result
-
-conditions = ['pose_shape', 'uv_static', 'uv_mask', 'uv_shaped_body']
-def nest_conditions(batch):
-    batch['conditions'] = {d: batch.pop(d) for d in conditions}
-    return batch
-
-
-def sparse_to_tensor(L):
-    idx = np.mat([L.row, L.col]).transpose().astype(np.float32)
-    data = L.data.astype(dtype=np.float32)
-    shape = L.shape
-    return tf.SparseTensor(idx, data, shape)
-
-
 
 def get_summary_str(model):
     # from torchview import draw_graph
@@ -304,6 +239,7 @@ def get_summary_str(model):
 
     return [f'all_params: {total_params}, trainable: {trainable_params}']
 
+
 def get_summary_html(model):
     lines = get_summary_str(model)
     html = ''
@@ -312,88 +248,6 @@ def get_summary_html(model):
     return html
 
 
-def repeat_laplacian(L, times):
-    idx = np.repeat(np.mat([L.row, L.col]), repeats=times, axis=1)
-    idx_offset = np.linspace(0, L.shape[0]  * (times-1), times).astype(np.int32)
-    idx_offset = np.repeat(idx_offset, L.row.shape[0], axis=0)
-    idx += idx_offset[np.newaxis,:]
-    data = np.repeat(L.data, repeats=times, axis=0)
-    shape = (L.shape[0] * times, L.shape[1] * times)
-    return sp.sparse.coo_matrix((data, (np.squeeze(np.asarray(idx[0,:])), np.squeeze(np.asarray(idx[1,:])))), shape)
-
-
-def merge_verts(Vs, batchDim):
-    V = tf.concat(tf.unstack(Vs, num=batchDim), axis=0)
-    return V
-
-
-class GraphConvolution(nn.Module):
-    def __init__(self, shape, activation=None, name='gcn', L=None, batchSize=None, normalization=None):
-        super(GraphConvolution, self).__init__()
-        self.shape = shape
-        self.w = self.add_weight(name=name + '/weight', shape=(2, *shape), initializer=tf.initializers.glorot_normal, trainable=True)
-        self.b = self.add_weight(name=name + '/bias', shape=shape[-1], initializer=tf.initializers.zeros, trainable=True)
-        self.activation = activation or (lambda x: x)
-        self.normalization = normalization
-        self.batchSize = batchSize
-        self.indices = tf.cast(tf.linspace(0, self.batchSize * L.shape[1], self.batchSize+1), dtype=tf.int32)
-        self.L = repeat_laplacian(L, self.batchSize)
-
-    def gather(self):
-        return [self.w, self.b]
-
-    def _single(self, X):
-        X0 = tf.einsum('ab,bc->ac', X, self.w[0]) # Node
-        X1 = tf.einsum('ab,bc->ac', X, self.w[1]) # Neigh
-        # Neigh conv
-        X1 = edge_convolution_template(
-            X1, sparse_to_tensor(self.L), sizes=None,
-            edge_function=lambda x, y: y, 
-            reduction='weighted', 
-            edge_function_kwargs={}
-        ) 
-        X = X0 + X1 + self.b
-        return X
-
-    def call(self, X, training):
-        X = merge_verts(X, self.batchSize)
-        X = self._single(X)
-        X = tf.stack([X[self.indices[i-1]:self.indices[i]] for i in range(1, len(self.indices))])
-        X = self.activation(X)
-        if self.normalization:
-            X = self.normalization(X, training=training)
-        return X
-
-
-def replace_arrays(string):
-    out = string[:1]
-    i = 0
-    while i < len(string) and string[i+1:].find('[') != -1:
-        start = string[i+1:].find('[')
-        end = string[i+1+start:].find(']')
-        out += string[i+1:i+1+start]
-        array = string[i+1+start:i+1+start+end+1].replace('\n', '')
-        out += array
-        i = i + 1 + start + end
-
-    out += string[i+1:]
-
-    return out
-
-
-def group_inputs_for_levels(batch, config):
-    levels_input = []
-    for res in config['pyramid']['resolutions']:
-        res_modifier = '_' + str(res) if res != 512 else ''
-        if config['use_skinning']:
-            uv_orig = batch['uv_unposed' + res_modifier]
-        else:
-            uv_orig = batch['uv_posed' + res_modifier]
-        levels_input.append([uv_orig, batch['uv_static' + res_modifier], batch['uv_body_posed' + res_modifier], batch['uv_curvature' + res_modifier], batch['uv_mask' + res_modifier], batch['uv_mask']])
-    conds = [batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature']]
-    return levels_input, conds
-
-
 class Accumulator():
     def __init__(self):
         self.dict = dict()

data/convert_to_uv_cape.py

@@ -0,0 +1,530 @@
+import os
+import sys
+import glob
+import tqdm
+import math
+import time
+import random
+import shutil
+import collections
+import argparse
+import pickle
+import numpy as np
+import scipy as sp
+import trimesh as tm
+# import cmapy as cm
+import tables as tb
+import scipy.io as sio
+import scipy.interpolate as spi
+import matplotlib.pyplot as plt
+import scipy.stats as stat
+import cv2
+import plotly.express as ex
+import plotly.graph_objects as go
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
+from data.cloth3d_io import read_garment_mask, read_garment_template, read_posed_garment_frame, loadInfo, read_posed_body_frame, read_posed_body, read_posed_garment, read_curvatures
+from data.cloth3d_io import readPC2
+
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "6"
+
+def convert_to_uv(res, m2uv, uv_map, verts, neighbours=10, mask=None, method='rbf', garment_uv_mask=None):
+    verts_uv = np.zeros((res * 2, res, 3))
+    uv_values = np.zeros((uv_map.shape[0], 3))
+    uv_values[m2uv[:, 0]] = verts
+    uv_values[m2uv[:, 1]] += verts
+    uv_values = uv_values[uv_map_mask[type], :] #   add garment mask
+    uv_values /= uv_counter[type][:, np.newaxis]
+    return_garment_uv_mask = False
+
+    if mask is not None:
+        garment_mask = np.zeros((uv_map.shape[0]), dtype=bool)
+        garment_mask[m2uv[:, 0]] = mask
+        garment_mask[m2uv[:, 1]] += mask
+        garment_mask = garment_mask[uv_map_mask[type]] #   add garment mask
+    else:
+        garment_mask = np.sum(uv_values, axis=-1) != 0
+
+    # test
+    verts_uv[uv_map_discreet_unique[type][:, 0], uv_map_discreet_unique[type][:, 1], :] = uv_values
+    # verts_uv_save = verts_uv.copy()
+
+    # garment_mask = np.sum(uv_values, axis=-1) != 0
+    unique_uv_values = uv_values[garment_mask, :]
+    uv_map_discreet_unique_unique = uv_map_discreet_unique[type][garment_mask, :]
+    rbf_dest_current = rbf_dest[type]
+
+    if garment_uv_mask is None and mask is not None:
+        garment_uv_mask = np.zeros((res * 2, res))
+        garment_uv_mask[uv_map_discreet_unique_unique[:, 0], uv_map_discreet_unique_unique[:, 1]] = 255
+        garment_uv_mask = cv2.morphologyEx(garment_uv_mask, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_RECT, (4 * 2 + 1, 4 * 2 + 1), (4, 4)), iterations=4)
+        garment_uv_mask_border = garment_uv_mask.astype(bool)
+        garment_uv_mask = cv2.dilate(garment_uv_mask, cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2).astype(bool)
+        garment_uv_mask_border = ~garment_uv_mask_border & garment_uv_mask
+        garment_uv_mask = garment_uv_mask * uv_mask[type][:,:,0]
+        rbf_dest_current = rbf_dest[type] * garment_uv_mask
+        return_garment_uv_mask = True
+
+
+    rbf_dest_current = np.nonzero(rbf_dest_current)
+    rbf_dest_current = np.array(rbf_dest_current).T
+
+    # uv_verts_uniq = np.array(np.nonzero(np.sum(verts_uv, axis=-1))).T
+    if method == 'rbf':
+        interpolator =  spi.RBFInterpolator(y=uv_map_discreet_unique_unique, d=unique_uv_values, neighbors=neighbours, kernel='linear')
+    elif method == 'nearest':
+        interpolator =  spi.NearestNDInterpolator(x=uv_map_discreet_unique_unique, y=unique_uv_values)
+    else:
+        raise NotImplementedError
+
+    final = interpolator(rbf_dest_current)
+
+    result = np.zeros((res * 2, res, 3))
+
+    # not sure, shouldn't be needed
+    # rbf_dest[:, [0,1]] = rbf_dest[:, [1, 0]]
+
+    result[rbf_dest_current[:, 0], rbf_dest_current[:, 1], :] = final
+    result[uv_map_discreet_unique[type][:, 0], uv_map_discreet_unique[type][:, 1], :] = uv_values
+    if garment_uv_mask is not None:
+        result = result * garment_uv_mask[:,:,np.newaxis]
+
+    if return_garment_uv_mask:
+        return result, garment_uv_mask, garment_uv_mask_border
+    else:
+        return result 
+
+
+def convert_to_uv_batch_body(res, m2uv, uv_map, verts, neighbours=15, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=None):
+    batch = verts.shape[0]
+    verts_uv = np.zeros((batch, res * 2, res, 3))
+    uv_values = np.zeros((batch, uv_map.shape[0], 3))
+    uv_values[:, m2uv[:, 0]] = verts
+    uv_values[:, m2uv[:, 1]] += verts
+    uv_values = uv_values[:, uv_map_mask['non-skirt'], :]
+    uv_values /= uv_counter['non-skirt'][np.newaxis, :, np.newaxis]
+    return_garment_uv_mask = False
+    # test
+    verts_uv[:, uv_map_discreet_unique['non-skirt'][:, 0], uv_map_discreet_unique['non-skirt'][:, 1], :] = uv_values
+
+    if mask is not None:
+        garment_mask = np.zeros((uv_map.shape[0]), dtype=bool)
+        garment_mask[m2uv[:, 0]] = mask
+        garment_mask[m2uv[:, 1]] += mask
+        garment_mask = garment_mask[uv_map_mask['non-skirt']] #   add garment mask
+    else:
+        garment_mask = np.sum(uv_values[0,:,:], axis=-1) != 0
+
+    unique_uv_values = uv_values[:, garment_mask, :]
+    uv_map_discreet_unique_unique = uv_map_discreet_unique['non-skirt'][garment_mask, :]
+    rbf_dest_current = rbf_dest['non-skirt']
+
+    # add back mesh vertices that are not part of the garment
+    uv_map_discreet_unique_unique_inv = uv_map_discreet_unique['non-skirt'][np.invert(garment_mask), :]
+    rbf_dest_fill = np.zeros((res * 2, res), dtype=bool)
+    rbf_dest_fill[uv_map_discreet_unique_unique_inv[:, 0], uv_map_discreet_unique_unique_inv[:, 1]] = True
+    
+    if garment_uv_mask is None and mask is not None:
+        garment_uv_mask = np.zeros((res * 2, res))
+        garment_uv_mask[uv_map_discreet_unique_unique[:, 0], uv_map_discreet_unique_unique[:, 1]] = 255
+        garment_uv_mask = cv2.morphologyEx(garment_uv_mask, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=4)
+        garment_uv_mask = cv2.dilate(garment_uv_mask, cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2).astype(bool)
+        return_garment_uv_mask = True
+
+    if garment_uv_mask is not None:
+        rbf_dest_current = (rbf_dest['non-skirt'] + rbf_dest_fill) * garment_uv_mask + garment_uv_mask_border
+
+    rbf_dest_current = np.nonzero(rbf_dest_current)
+    rbf_dest_current = np.array(rbf_dest_current).T
+
+    final = []
+    for i in range(batch):
+        print('processing frame: ' + str(i))
+        if method == 'rbf':
+            interpolator =  spi.RBFInterpolator(y=uv_map_discreet_unique_unique, d=unique_uv_values[i,:,:], neighbors=neighbours, kernel='linear')
+        elif method == 'nearest':
+            interpolator =  spi.NearestNDInterpolator(x=uv_map_discreet_unique_unique, y=unique_uv_values[i,:,:])
+        else:
+            raise NotImplementedError
+
+        result = np.zeros((res * 2, res, 3), np.float32)
+        result[rbf_dest_current[:, 0], rbf_dest_current[:, 1], :] = interpolator(rbf_dest_current)
+        result[uv_map_discreet_unique_unique[:, 0], uv_map_discreet_unique_unique[:, 1], :] = unique_uv_values[i,:,:]
+
+        if garment_uv_mask is not None:
+            result *= garment_uv_mask[:,:,np.newaxis]
+        
+        final.append(result)
+
+    if return_garment_uv_mask:
+        return final, garment_uv_mask
+    else:
+        return final 
+    
+
+def convert_to_uv_batch(res, m2uv, uv_map, verts, neighbours=15, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=None, id=None):
+    batch = len(verts)
+
+    assert method == 'rbf'
+
+    assert mask is not None
+    garment_mask = np.zeros((uv_map.shape[0]), dtype=bool)
+    garment_mask[m2uv[:, 0]] = mask
+    garment_mask[m2uv[:, 1]] += mask
+    garment_mask = garment_mask[uv_map_mask[type]] #   add garment mask
+
+    rbf_dest_current = rbf_dest[type]
+
+    # add back mesh vertices that are not part of the garment
+    uv_map_discreet_unique_unique_inv = uv_map_discreet_unique[type][np.invert(garment_mask), :]
+    rbf_dest_fill = np.zeros((res * 2, res), dtype=bool)
+    rbf_dest_fill[uv_map_discreet_unique_unique_inv[:, 0], uv_map_discreet_unique_unique_inv[:, 1]] = True
+
+    assert not (garment_uv_mask is None and mask is not None)
+    assert garment_uv_mask is not None
+    rbf_dest_current = (rbf_dest[type] + rbf_dest_fill) * garment_uv_mask + garment_uv_mask_border
+
+    rbf_dest_current = np.nonzero(rbf_dest_current)
+    rbf_dest_current = np.array(rbf_dest_current).T
+
+    out = []
+
+    for i in range(batch):
+        print('processing frame: ' + str(i))
+
+        uv_values = np.zeros((uv_map.shape[0], 3))
+        uv_values[m2uv[:, 0]] = verts[i]
+        uv_values[m2uv[:, 1]] += verts[i]
+        uv_values = uv_values[uv_map_mask[type], :]
+        uv_values /= uv_counter[type][:, np.newaxis]
+
+        unique_uv_values = uv_values[garment_mask, :]
+        uv_map_discreet_unique_unique = uv_map_discreet_unique[type][garment_mask, :]
+
+        interpolator =  spi.RBFInterpolator(y=uv_map_discreet_unique_unique, d=unique_uv_values[:,:], neighbors=neighbours, kernel='linear')
+        result = np.zeros((res * 2, res, 3), np.float32)
+        result[rbf_dest_current[:, 0], rbf_dest_current[:, 1], :] = interpolator(rbf_dest_current)
+        result[uv_map_discreet_unique_unique[:, 0], uv_map_discreet_unique_unique[:, 1], :] = unique_uv_values[:,:]
+
+        if garment_uv_mask is not None:
+            result *= garment_uv_mask[:,:,np.newaxis]
+        
+        out.append(result)
+    
+    return out
+
+
+def convert_to_uv_batch_label(res, m2uv, uv_map, values, neighbours=15, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=None):
+    batch = values.shape[0]
+    values = (values + 1) * mask
+    values_uv = np.zeros((batch, res * 2, res))
+    missing = np.zeros((res * 2, res))
+    uv_values = np.zeros((batch, uv_map.shape[0]))
+    uv_values[:, m2uv[:, 0]] = values
+    uv_values[:, m2uv[:, 1]] = values
+    uv_values = uv_values[:, uv_map_mask]
+    return_garment_uv_mask = False
+
+    if mask is not None:
+        garment_mask = np.zeros((uv_map.shape[0]), dtype=bool)
+        garment_mask[m2uv[:, 0]] = mask
+        garment_mask[m2uv[:, 1]] += mask
+        garment_mask = garment_mask[uv_map_mask] #   add garment mask
+    else:
+        raise Exception
+
+
+    # test 
+    values_uv[:, uv_map_discreet_unique[:, 0], uv_map_discreet_unique[:, 1]] = uv_values
+    missing[uv_map_discreet_unique[:, 0], uv_map_discreet_unique[:, 1]] = (uv_values[0,:] == 0)
+    
+    unique_uv_values = uv_values[:, garment_mask]
+    uv_map_discreet_unique_unique = uv_map_discreet_unique[garment_mask, :]
+    # rbf_dest_current = rbf_dest.astype(bool) + garment_uv_mask_border * uv_mask[:,:,0]
+
+    if garment_uv_mask is None and mask is not None:
+        garment_uv_mask = np.zeros((res * 2, res))
+        garment_uv_mask[uv_map_discreet_unique_unique[:, 0], uv_map_discreet_unique_unique[:, 1]] = 255
+        garment_uv_mask = cv2.morphologyEx(garment_uv_mask, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=4)
+        garment_uv_mask = cv2.dilate(garment_uv_mask, cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2).astype(bool)
+        return_garment_uv_mask = True
+
+    rbf_dest_current = rbf_dest * garment_uv_mask + missing * garment_uv_mask
+    rbf_dest_current = np.nonzero(rbf_dest_current)
+    rbf_dest_current = np.array(rbf_dest_current).T
+
+    # uv_verts_uniq = np.array(np.nonzero(np.sum(verts_uv, axis=-1))).T
+    final = []
+    for i in range(batch):
+        print('processing frame: ' + str(i))
+        interpolator =  spi.NearestNDInterpolator(x=uv_map_discreet_unique_unique, y=unique_uv_values[i,:])
+        final.append(interpolator(rbf_dest_current))
+    final = np.asarray(final)
+
+    result = np.zeros((batch, res * 2, res))
+    result[:, uv_map_discreet_unique[:, 0], uv_map_discreet_unique[:, 1]] = uv_values
+    result[:, rbf_dest_current[:, 0], rbf_dest_current[:, 1]] = final
+    if garment_uv_mask is not None:
+        result = result * garment_uv_mask[np.newaxis,:,:]
+
+    if return_garment_uv_mask:
+        return result, garment_uv_mask
+    else:
+        return result 
+
+
+def m2uv_array(m2uv):
+    if m2uv is None:
+        return None
+    result = np.zeros((3638, 2), dtype=np.int32)
+    for k, v in m2uv.items():
+        if len(v) == 2:
+            result[k] = list(v)
+        elif len(v) == 1:
+            x = v.pop()
+            result[k] = [x, x]
+        else:
+            assert False
+
+    return result
+
+
+def uv_to_mesh(uv, m2uv):
+    res = uv.shape[0]
+    mesh = np.zeros((14475, 3))
+    uv_pixels = uv_map_discreet[m2uv]
+    # counter = np.ones((14475))
+    # counter[m2uv[:, 0] == m2uv[:, 1]] = 2
+    mesh = np.mean(uv[uv_pixels[:, :, 0], uv_pixels[:, :, 1], :], axis=1) # / counter[:, np.newaxis]
+    return mesh
+    pass
+
+
+def verify_reconstruction(orig, reconstructed):
+    error = np.sqrt(np.sum((orig - reconstructed) ** 2, axis=-1))
+    colors = np.array([cm.color('jet', 1 - i) for i in error])
+    error_sum = np.sum(error)
+
+
+    verts_uv = np.zeros((uv_res * 2, uv_res, 3))
+    uv_values = np.zeros((uv_map.shape[0], 3))
+    uv_values[m2uv[:, 0]] = colors
+    uv_values[m2uv[:, 1]] += colors
+    uv_counter = np.zeros((uv_map.shape[0]))
+    uv_counter[m2uv[:, 0]] = 1
+    uv_counter[m2uv[:, 1]] += 1
+    uv_values /= uv_counter[:, np.newaxis]
+    verts_uv[uv_map_discreet[:, 0], uv_map_discreet[:, 1], :] = uv_values
+    fig = plt.figure(figsize=(15, 15))
+    ax = fig.add_subplot(111)
+    ax.scatter(uv_map[:, 0], uv_map[:, 1], s=5, c=uv_values/255)
+    plt.show()
+
+
+
+    fig = plt.figure(figsize=(15, 15))
+    ax = fig.add_subplot(121, projection='3d')
+    x = orig
+    ax.set_xlim3d(-1, 1)
+    ax.set_ylim3d(-1, 1)
+    ax.set_zlim3d(-1, 1)
+    ax.set_xlabel("X")
+    ax.set_ylabel("Y")
+    ax.set_zlabel("Z")
+    ax.scatter(x[:, 0], x[:, 1], x[:, 2], s=5)
+
+    ax = fig.add_subplot(122, projection='3d')
+    x = reconstructed#[error > 0.1, :]
+    ax.set_xlim3d(-1, 1)
+    ax.set_ylim3d(-1, 1)
+    ax.set_zlim3d(-1, 1)
+    ax.set_xlabel("X")
+    ax.set_ylabel("Y")
+    ax.set_zlabel("Z")
+    ax.scatter(x[:, 0], x[:, 1], x[:, 2], s=5, c=colors/255) #[error > 0.1, :]
+
+    plt.show()
+
+    pass
+
+# https://stackoverflow.com/questions/38187286/find-unique-pairs-in-list-of-pairs
+def unique_pairs(a):
+    a.sort(axis=1)
+    b = np.ascontiguousarray(a).view(
+        np.dtype((np.void, a.dtype.itemsize * a.shape[1]))
+    )
+    _, inv, ct = np.unique(b, return_inverse=True, return_counts=True)
+    return ct[inv] == 1
+
+
+def unique_uv_map_mask(uv_map_discreet):
+    mask = np.zeros((uv_map_discreet.shape[0]), dtype=bool)
+    temp = np.zeros((uv_res * 2, uv_res))
+    for p in uv_map_discreet:
+        temp[p[0], p[1]] += 1
+    # temp = temp == 1
+    for i, p in enumerate(uv_map_discreet):
+        if temp[p[0], p[1]] == 1:
+            mask[i] = True
+        elif temp[p[0], p[1]] == 2:
+            mask[i] = True
+            temp[p[0], p[1]] = 0
+    return mask
+
+
+def test(verts, faces, values):
+    uv_values = np.zeros((uv_map.shape[0], 3))
+    uv_values[m2uv[:, 0]] = values
+    uv_values[m2uv[:, 1]] += values
+    uv_counter = np.zeros((uv_map.shape[0]))
+    uv_counter[m2uv[:, 0]] = 1
+    uv_counter[m2uv[:, 1]] += 1
+    uv_values /= uv_counter[:, np.newaxis]
+    x = uv_values
+    verts = np.concatenate((verts, np.zeros((15068, 1))), axis=-1)
+    im = ((x - x.min()) / (x.max() - x.min())) * 255
+    tm.Trimesh(verts, faces, vertex_colors=im.astype(np.int32)).show()
+
+
+def get_shapedirs():
+    to_keep = sio.loadmat(os.path.join('smpl/keep_remove.mat'))['keep'].reshape(-1)
+    shapedirs = []
+    with open(os.path.join('smpl/super_model_f.pkl'), 'rb') as f:
+        female = pickle.load(f, encoding="latin1")
+        shapedirs.append(female['shapedirs'][to_keep, :])
+    with open(os.path.join('smpl/super_model_m.pkl'), 'rb') as f:
+        male = pickle.load(f, encoding="latin1")
+        shapedirs.append(male['shapedirs'][to_keep, :])
+    return shapedirs
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-f", "--folder", help = "end of range", default='/data-local/127_2/cape_processed/test/female')
+    parser.add_argument("-s", "--start", help = "start of range", default=0, type=int)
+    parser.add_argument("-e", "--end", help = "end of range", default=100, type=int)
+    parser.add_argument("-n", "--name", help = "end of range", default='lhunor')
+    parser.add_argument("-d", "--debug", help = "end of range", default=False, action='store_true')
+    args = parser.parse_args()
+
+    F = {'non-skirt': np.load("smpl/smpl_faces_orig.npy"), 'skirt': None}
+    uv_map = {'non-skirt': np.load('custom_utils/stretched_uv_map_3_orig.npy'), 'skirt': None}
+    m2uv = {'non-skirt': np.load('custom_utils/m2uv_orig.npy', allow_pickle=True).item(), 'skirt': None}
+    m2uv = {k: m2uv_array(v) for k, v in m2uv.items()}
+    uv_mask = {'non-skirt': np.load('custom_utils/mask_256x512.npy'), 'skirt': np.load('custom_utils/mask_256x512_skirt.npy')}
+
+
+    uv_res = 256
+    uv_map_discreet = dict()
+    uv_map_discreet_unique = dict()
+    uv_counter = dict()
+    rbf_dest = dict()
+    uv_map_mask = dict()
+    for k,v in uv_mask.items():
+        if k == 'skirt':
+            continue
+        uv_mask[k] = (cv2.resize(v * 255.0, (uv_res, uv_res * 2)) / 255).astype(bool)[:, :, np.newaxis]
+        # uv_mask_dilate = cv2.erode(uv_mask.astype(np.float32), cv2.getStructuringElement(cv2.MORPH_CROSS, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=1).astype(bool)[:, :, np.newaxis]
+        # uv_mask_border = uv_mask * np.invert(uv_mask_dilate)
+        uv_mask[k] = cv2.dilate(v.astype(np.float32), cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2).astype(bool)[:, :, np.newaxis]
+
+        uv_map_discreet[k] = np.floor(uv_map[k] * uv_res).astype(np.int32)
+        uv_map_mask[k] = unique_uv_map_mask(uv_map_discreet[k])
+
+        uv_counter[k] = np.zeros((uv_map[k].shape[0]))
+        uv_counter[k][m2uv[k][:, 0]] = 1
+        uv_counter[k][m2uv[k][:, 1]] += 1
+        uv_counter[k] = uv_counter[k][uv_map_mask[k]]
+
+        uv_map_discreet_unique[k] = uv_map_discreet[k][uv_map_mask[k], :]
+
+        rbf_dest[k] = np.ones((uv_res * 2, uv_res))
+        rbf_dest[k][uv_map_discreet_unique[k][:, 0], uv_map_discreet_unique[k][:, 1]] = 0
+        rbf_dest[k][np.invert(uv_mask[k][:,:,0])] = 0
+
+
+    #########################################################################################
+
+    folder = args.folder
+    # data_to_convert = ['mesh_posed', 'mesh_static', 'mesh_normals', 'body_posed', 'mesh_unposed'] # 'mesh_unposed',
+    data_to_convert = ['mesh_static', 'mesh_normals'] # 'mesh_unposed',
+
+    frames = sorted(os.listdir(folder + '/mesh_posed_body_no_orient'))#[args.start:args.end]
+    frames = [[frame.split('.')[0], frame.split('.')[1]] for frame in frames]
+    ids = list(set([frame[0] for frame in frames]))
+    frames_by_id = {id: [frame[1] for frame in frames if frame[0] == id] for id in ids}
+
+    shapedirs = get_shapedirs()
+
+    root = '/'.join(folder.split('/')[:-1])
+    type = 'non-skirt'
+
+    os.makedirs(folder, exist_ok=True)
+    os.makedirs(os.path.join(folder, 'uv_posed_no_orient'), exist_ok=True)
+    os.makedirs(os.path.join(folder, 'uv_static'), exist_ok=True)
+    os.makedirs(os.path.join(folder, 'uv_normals'), exist_ok=True)
+    os.makedirs(os.path.join(folder, 'uv_unposed_no_shape'), exist_ok=True)
+    os.makedirs(os.path.join(folder, 'uv_body_posed_no_orient'), exist_ok=True)
+    os.makedirs(os.path.join(folder, 'uv_mask'), exist_ok=True)
+
+    mask = np.ones((3638), dtype=bool)
+
+    for id in tqdm.tqdm(ids):
+
+
+        # x = readPC2(os.path.join(folder, 'mesh_posed_no_orient', f'{id}_{garment}.pc16'), True)['V']
+        # mesh_posed = np.zeros((x.shape[0], 14475, 3), dtype=np.float32)
+        # mesh_posed[:,mask, :] = x
+        if 'mesh_unposed' in data_to_convert:
+            mesh_unposed = np.zeros((x.shape[0], 14475, 3), dtype=np.float32)
+            mesh_unposed[:,mask, :] = readPC2(os.path.join(folder, 'mesh_unposed_no_shape' ,f'{id}_{garment}.pc16'), True)['V']
+        if 'mesh_static' in data_to_convert:
+            mesh_static = np.load(os.path.join(folder, 'mesh_static', f'{id}.npz'))['mesh_static']
+        if 'mesh_normals' in data_to_convert:
+            mesh_normals = np.stack([np.load(os.path.join(folder, 'mesh_normals', f'{id}.{int(i):06d}.npz'))['mesh_normals'] for i in frames_by_id[id]], axis=0)
+        if 'body_posed' in data_to_convert and k != 1:
+            body_posed = readPC2(os.path.join(folder, 'mesh_body_posed_no_orient', f'{id}.pc16'), True)['V']
+
+        start = time.perf_counter()
+
+        uv_static, uv_mask_local, garment_uv_mask_border = convert_to_uv(uv_res, m2uv[type], uv_map[type], mesh_static, neighbours=3, mask=mask, method='rbf', garment_uv_mask=None)
+        uv_static = convert_to_uv_batch(uv_res, m2uv[type], uv_map[type], mesh_static[None, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=uv_mask_local, garment_uv_mask_border=garment_uv_mask_border)
+        uv_static = uv_static[0].astype(np.float32)
+
+        if 'mesh_posed' in data_to_convert:
+            uv_posed = convert_to_uv_batch(uv_res, m2uv[type], uv_map[type], mesh_posed[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=uv_mask_local, garment_uv_mask_border=garment_uv_mask_border)
+        if 'mesh_unposed' in data_to_convert:
+            uv_unposed = convert_to_uv_batch(uv_res, m2uv[type], uv_map[type], mesh_unposed[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=uv_mask_local, garment_uv_mask_border=garment_uv_mask_border)
+        if 'mesh_normals' in data_to_convert:
+            uv_normals = convert_to_uv_batch(uv_res, m2uv[type], uv_map[type], mesh_normals[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=uv_mask_local, garment_uv_mask_border=garment_uv_mask_border)
+        if 'body_posed' in data_to_convert and k != 1:
+            uv_body_posed = convert_to_uv_batch_body(uv_res, m2uv['non-skirt'], uv_map['non-skirt'], body_posed[:, :, :], neighbours=3, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=garment_uv_mask_border)
+
+        print(time.perf_counter() - start)
+
+        # ex.imshow(uv_mask_local).show()
+        # ex.imshow((uv_static+1)/2).show()
+        # ex.imshow((uv_posed[0]+1)/2).show()
+        # ex.imshow((uv_unposed[0]+1)/2).show()
+        # ex.imshow((uv_normals[0]+1.5)/3).show()
+        # ex.imshow((uv_body_posed[0]+1)/2).show()
+
+        # pass
+        # np.savez_compressed(os.path.join(folder, 'uv_static',  f'{id}_{garment}.npz'), uv_static=uv_static)
+        # np.savez_compressed(os.path.join(folder, 'uv_mask',  f'{id}_{garment}.npz'), uv_mask=uv_mask_local)
+
+        if 'mesh_posed' in data_to_convert:
+            for i in range(len(uv_posed)):
+                np.savez_compressed(os.path.join(folder, 'uv_posed_no_orient',  f'{id}_{i}_{garment}.npz'), uv_posed=uv_posed[i])
+        if 'mesh_unposed' in data_to_convert:
+            for i in range(len(uv_unposed)):
+                np.savez_compressed(os.path.join(folder, 'uv_unposed_no_shape',  f'{id}_{i}_{garment}.npz'), uv_unposed=uv_unposed[i])
+        if 'mesh_normals' in data_to_convert:
+            for i, frame_nr in enumerate(frames_by_id[id]):
+                np.savez_compressed(os.path.join(folder, 'uv_normals',  f'{id}.{frame_nr}.npz'), uv_normals=uv_normals[i])
+        if 'body_posed' in data_to_convert and k != 1:
+            for i in range(len(uv_body_posed)):
+                np.savez_compressed(os.path.join(folder, 'uv_body_posed_no_orient',  f'{id}_{i}.npz'), uv_body_posed=uv_body_posed[i])
+        
+        # pass

data/dataset.py

@@ -1,7 +1,7 @@
 import os
 import sys
 
-# os.environ["CUDA_VISIBLE_DEVICES"] = '6'
+# os.environ["CUDA_VISIBLE_DEVICES"] = '0,1,2,3'
 
 import math
 import time
@@ -14,13 +14,14 @@ import scipy.io as sio
 import plotly.express as ex
 import torchvision
 
-from torch.utils.data import Dataset
 from torchvision.transforms import Resize
 from torchvision.transforms.functional import InterpolationMode
 
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
 
 from data.utils import Frame
+from data.cloth3d_io import readPC2, readPC2Frame
+from data.dataset_cape import DatasetCape
 
 
 def read_frames(folder, shuffle=True, is_cape=False, testing=False, frames_file=None):
@@ -60,7 +61,6 @@ class StaticCache():
         self.folder = folder
         assert set(resolutions).issubset(set([32, 64, 128, 256, 512]))
         self.resolutions = resolutions
-        self.filled = ''
         self.is_cape = is_cape
         self.device = device
         
@@ -74,50 +74,65 @@ class StaticCache():
                 self.uv_masks[res] = down.cpu().numpy()
 
 
-    def read(self, id):
+    def read(self, id, garment):
         data = dict()
 
+        mesh_mask = np.load(os.path.join(self.folder, 'mesh_mask', f'{id}_{garment}.npy')); mesh_mask = mesh_mask.astype(bool)
+        data['mesh_mask'] = mesh_mask
+
         for res in self.resolutions:
             res_string = ''
             if res != 512:
                 res_string = '_' + str(res)
 
             if 'uv_static' + res_string in self.signature:
-                uv_static = np.load(os.path.join(self.folder, 'uv_static' + self.filled + res_string, id +  '.npz')); uv_static = uv_static['uv_static' + self.filled]; data['uv_static' + res_string] = uv_static
+                uv_static = np.load(os.path.join(self.folder, f'uv_static{res_string}', f'{id}_{garment}.npz')); uv_static = uv_static['uv_static']; data['uv_static' + res_string] = uv_static
             if 'uv_mask' + res_string in self.signature:
                 if self.is_cape:
                     data['uv_mask' + res_string] = self.uv_masks[res]
                 else:
-                    uv_mask = np.load(os.path.join(self.folder, 'uv_mask' + res_string, id + '.npz')); uv_mask=uv_mask['uv_mask']; uv_mask = uv_mask.astype(np.float32); data['uv_mask' + res_string] = uv_mask
+                    uv_mask = np.load(os.path.join(self.folder, f'uv_mask{res_string}', f'{id}_{garment}.npz')); uv_mask=uv_mask['uv_mask']; uv_mask = uv_mask.astype(np.float32); data['uv_mask' + res_string] = uv_mask
 
         if 'mesh_static' in self.signature:
-            mesh_static = np.load(os.path.join(self.folder, 'mesh_static', id +  '.npz')); mesh_static = mesh_static['mesh_static']; data['mesh_static'] = mesh_static.astype(np.float32)
+            mesh_static = np.load(os.path.join(self.folder, 'mesh_static', f'{id}_{garment}.npy'))
-        if 'mesh_mask' in self.signature:
+            data['mesh_static'] = np.zeros((14475, 3), dtype=np.float32)
-            if self.is_cape:
+            data['mesh_static'][mesh_mask] = mesh_static.astype(np.float32)
-                data['mesh_mask'] = np.ones((3638), np.float32)
+
-            else:
-                mesh_mask = np.load(os.path.join(self.folder, 'mesh_mask', id + '.npy')); mesh_mask = mesh_mask.astype(np.float32); data['mesh_mask'] = mesh_mask
         # if 'uv_shaped_body' in self.signature:
         #     uv_shaped_body = np.load(os.path.join(self.folder, 'uv_shaped_body', id + '.npz')); uv_shaped_body = uv_shaped_body['uv_shaped_body']; data['uv_shaped_body'] = uv_shaped_body
 
         return data
 
 
-    def __getitem__(self, id) -> dict:
+    def __getitem__(self, frame: Frame) -> dict:
-        if id not in self.store:
+        if (frame.id, frame.garment) not in self.store:
-            item = self.read(id)
+            item = self.read(frame.id, frame.garment)
             stored_data = { key: torch.from_numpy(value).to('cpu') for key, value in item.items()}
             for key, value in stored_data.items():
                 if key.startswith('uv_') and value.shape[-1] == 3:
                     stored_data[key] = torch.permute(value, [2,0,1])
-            self.store[id] = stored_data
+            self.store[(frame.id, frame.garment)] = stored_data
         
-        data = { key: value.to(self.device) for key, value in self.store[id].items()}
+        data = { key: value.to(self.device) for key, value in self.store[(frame.id, frame.garment)].items()}
         return data
 
 
-class Dataset(Dataset):
+
-    def __init__(self, folder, signature, resolutions=[512], frames_file='frames_all.txt', device='cuda', use_cache=False, is_cape=False):
+def read_frames_file(frames_file, shuffle=False):
+    frames = []
+    with open(frames_file, 'r') as f:
+        lines = f.readlines()
+        lines = [line.strip() for line in lines]
+    frames = [Frame(line.split()[0], int(line.split()[1]), line.split()[2]) for line in lines]
+
+    if shuffle:
+        random.shuffle(frames)
+
+    return frames
+
+
+class DatasetCloth3D(torch.utils.data.Dataset):
+    def __init__(self, folder, signature, resolutions=[512], frames_file='', device='cuda', use_cache=False, is_cape=False):
         self.folder = folder
         self.device = device
         self.is_cape = is_cape
@@ -125,13 +140,20 @@ class Dataset(Dataset):
         self.resolutions = resolutions
 
         self.transforms = tv.transforms.ToTensor()
-        self.frames = read_frames(folder, frames_file=frames_file, is_cape=is_cape, shuffle=True)
+        self.frames = read_frames_file(os.path.join(folder, frames_file), shuffle=True)
 
-        self.body_faces = np.load('smpl/smpl_faces.npy')
         self.static_cache = StaticCache(folder, signature, is_cape=is_cape, resolutions=resolutions, device=device)
 
         self.cache = dict() if use_cache else None
 
+        ids = set([frame.id for frame in self.frames])
+        self.pose_cache = dict()
+        for id in ids:
+            self.pose_cache[id] = np.load(os.path.join(self.folder, 'pose', f'{id}.npy'))
+        self.shape_cache = dict()
+        for id in ids:
+            self.shape_cache[id] = np.load(os.path.join(self.folder, 'shape', f'{id}.npy'))
+
 
     def __len__(self):
         return len(self.frames)
@@ -142,76 +164,59 @@ class Dataset(Dataset):
         if self.cache is not None:
             if frame in self.cache.keys():
                 data = { key: (value.to(self.device) if type(value) == torch.Tensor else value) for key, value in self.cache[frame].items()}
-                data = { **data, **self.static_cache[frame.id] }
+                data = { **data, **self.static_cache[frame] }
                 return data
 
-        separator = '_'
-        if self.is_cape:
-            separator = '.'
-
-        uv_posed_body_no_orient_folder_name = 'uv_posed_body_no_orient_2'
-        if self.is_cape:
-            uv_posed_body_no_orient_folder_name = 'uv_posed_body_no_orient'
-
         item = dict()
+        mesh_mask = self.static_cache[frame]['mesh_mask'].cpu().numpy().astype(bool)
+
+        item['id'] = frame.id
+        item['frame_nr'] = frame.frame_nr
+        item['garment'] = frame.garment
 
-        if 'id' in self.signature:
-            item['id'] = frame.id
-        if 'frame_nr' in self.signature:
-            item['frame_nr'] = frame.frame_nr
         if 'pose_shape' in self.signature:
-            pose_shape = np.load(os.path.join(self.folder, 'pose_shape', frame.id + separator + frame.frame_nr + '.npy')).astype(np.float32)
+            pose = self.pose_cache[frame.id][frame.frame_nr]
-            pose_shape[:3] = 0
+            pose[:3] = 0
-            item['pose_shape'] = pose_shape
+            item['pose_shape'] = np.concatenate((pose, self.shape_cache[frame.id]), axis=0)
         if 'mesh_body_posed' in self.signature:
-            mesh_posed_body_no_orient = np.load(os.path.join(self.folder, 'mesh_posed_body_no_orient', frame.id + separator + frame.frame_nr + '.npz')); mesh_posed_body_no_orient = mesh_posed_body_no_orient['mesh_posed_body_no_orient']
+            mesh_body_posed_no_orient = readPC2Frame(os.path.join(self.folder, 'mesh_body_posed_no_orient', f'{frame.id}.pc16'), frame.frame_nr, True)
-            item['mesh_body_posed'] = mesh_posed_body_no_orient
+            item['mesh_body_posed'] = mesh_body_posed_no_orient
         if 'mesh_posed' in self.signature:
-            mesh_posed = np.load(os.path.join(self.folder, 'mesh_posed_no_orient', frame.id + separator + frame.frame_nr + '.npz')); mesh_posed = mesh_posed['mesh_posed_no_orient'].astype(np.float32)
+            mesh_posed = readPC2Frame(os.path.join(self.folder, 'mesh_posed_no_orient', f'{frame.id}_{frame.garment}.pc16'), frame.frame_nr, True)
-            item['mesh_posed'] = mesh_posed
+            item['mesh_posed'] = np.zeros((14475, 3), dtype=np.float32)
+            item['mesh_posed'][mesh_mask] = mesh_posed
         if 'mesh_unposed' in self.signature:
-            mesh_unposed_no_shape = np.load(os.path.join(self.folder, 'mesh_unposed_no_shape', frame.id + separator + frame.frame_nr + '.npz')); mesh_unposed_no_shape = mesh_unposed_no_shape['mesh_unposed_no_shape']
+            mesh_unposed_no_shape = readPC2Frame(os.path.join(self.folder, 'mesh_unposed_no_shape', f'{frame.id}_{frame.garment}.pc16'), frame.frame_nr, True)
-            item['mesh_unposed'] = mesh_unposed_no_shape
+            item['mesh_unposed'] = np.zeros((14475, 3), dtype=np.float32)
-        if 'mesh_curvature' in self.signature:
+            item['mesh_unposed'][mesh_mask] = mesh_unposed_no_shape
-            mesh_curvature = np.load(os.path.join(self.folder, 'mesh_curvature', frame.id + separator + frame.frame_nr + '.npz')); mesh_curvature = mesh_curvature['mesh_curvature']
-            item['mesh_curvature'] = mesh_curvature.astype(np.int32)
         if 'mesh_normals' in self.signature:
-            mesh_normals = np.load(os.path.join(self.folder, 'mesh_normals', frame.id + separator + frame.frame_nr + '.npz')); mesh_normals = mesh_normals['mesh_normals']
+            mesh_normals = readPC2Frame(os.path.join(self.folder, 'mesh_normals', f'{frame.id}_{frame.garment}.pc16'), frame.frame_nr, True)
-            item['mesh_normals'] = mesh_normals
+            item['mesh_normals'] = np.zeros((14475, 3), dtype=np.float32)
+            item['mesh_normals'][mesh_mask] = mesh_normals
 
         for res in self.resolutions:
             res_string = ''
             if res != 512:
                 res_string = '_' + str(res)
 
-            if 'uv_curvature' + res_string in self.signature:
+            if 'uv_normals' + res_string in self.signature:
-                uv_curvature = np.load(os.path.join(self.folder, 'uv_curvature' + res_string, frame.id + separator + frame.frame_nr + '.npz')); 
+                uv_normals = np.load(os.path.join(self.folder, 'uv_normals' + res_string, f'{frame.id}_{frame.frame_nr}_{frame.garment}.npz')); uv_normals = uv_normals['uv_normals']
-                if res_string != '':
+                item['uv_normals' + res_string] = uv_normals
-                    uv_curvature = uv_curvature['uv_curvature'] 
-                else:
-                    uv_curvature = uv_curvature['curvature_uv']
-                uv_curvature = torch.nn.functional.one_hot(torch.tensor(uv_curvature, device=self.device, dtype=torch.long), 7).float().to(self.device)
-                item['uv_curvature' + res_string] = uv_curvature
 
             if 'uv_unposed' + res_string in self.signature:
-                uv_unposed_no_shape = np.load(os.path.join(self.folder, 'uv_unposed_no_shape' + res_string, frame.id + separator + frame.frame_nr + '.npz')); uv_unposed_no_shape = uv_unposed_no_shape['uv_unposed_no_shape']
+                uv_unposed_no_shape = np.load(os.path.join(self.folder, 'uv_unposed_no_shape' + res_string, f'{frame.id}_{frame.frame_nr}_{frame.garment}.npz')); uv_unposed_no_shape = uv_unposed_no_shape['uv_unposed']
                 item['uv_unposed' + res_string] = uv_unposed_no_shape
 
             if 'uv_body_posed' + res_string in self.signature:
-                uv_posed_body_no_orient = np.load(os.path.join(self.folder, uv_posed_body_no_orient_folder_name + res_string, frame.id + separator + frame.frame_nr + '.npz'))
+                uv_body_posed_no_orient = np.load(os.path.join(self.folder, 'uv_body_posed_no_orient' + res_string, f'{frame.id}_{frame.frame_nr}.npz')); uv_body_posed_no_orient = uv_body_posed_no_orient['uv_body_posed']
-                uv_posed_body_no_orient = uv_posed_body_no_orient['uv_posed_body_no_orient']
+                item['uv_body_posed' + res_string] = uv_body_posed_no_orient
-                item['uv_body_posed' + res_string] = uv_posed_body_no_orient
 
             if 'uv_posed' + res_string in self.signature:
-                uv_posed = np.load(os.path.join(self.folder, 'uv_posed_no_orient' + res_string, frame.id + separator + frame.frame_nr + '.npz')); uv_posed = uv_posed['uv_posed_no_orient']
+                uv_posed = np.load(os.path.join(self.folder, 'uv_posed_no_orient' + res_string, f'{frame.id}_{frame.frame_nr}_{frame.garment}.npz')); uv_posed = uv_posed['uv_posed']
                 item['uv_posed' + res_string] = uv_posed
 
-            if 'uv_normals' + res_string in self.signature:
-                uv_normals = np.load(os.path.join(self.folder, 'uv_normals' + res_string, frame.id + separator + frame.frame_nr + '.npz')); uv_normals = uv_normals['uv_normals']
-                item['uv_normals' + res_string] = uv_normals
 
-
+        data = { key: (torch.from_numpy(value) if not (type(value) == str or type(value) == torch.Tensor or type(value) == int) else value) for key, value in item.items()}
-        data = { key: (torch.from_numpy(value) if not (type(value) == str or type(value) == torch.Tensor) else value) for key, value in item.items()}
 
         for key, value in data.items():
             if key.startswith('uv_') and (value.shape[-1] == 3 or value.shape[-1] == 7):
@@ -221,38 +226,101 @@ class Dataset(Dataset):
             self.cache[frame] = data
         
         data = { key: (value.to(self.device) if type(value) == torch.Tensor else value) for key, value in data.items()}
-        data = { **data, **self.static_cache[frame.id] }
+        data = { **data, **self.static_cache[frame] }
         return data
 
+def Dataset(folder, signature, resolutions=[512], frames_file='', device='cuda', use_cache=False, is_cape=False):
+    if not is_cape:
+        return DatasetCloth3D(folder, signature, resolutions=resolutions, frames_file=frames_file, device=device, use_cache=use_cache, is_cape=False)
+    else:
+        return DatasetCape(folder, signature, resolutions=resolutions, frames_file=frames_file, device=device, use_cache=use_cache, is_cape=True)
+
+
+
 
 if __name__ == '__main__':
     from torch.utils.data import DataLoader
-    
+    import plotly.graph_objects as go
+    import plotly.express as ex
+    from plotly.subplots import make_subplots
+    from custom_utils.custom_utils import _maskFaces
+
+    F = np.load('smpl/smpl_faces.npy')
+    F_skirt = np.load('smpl/smpl_faces_skirt.npy')
+
     device = "cuda" if torch.cuda.is_available() else "cpu"
 
-    signature = { 'id', 
+    signature = { 'pose_shape',
-                  'frame_nr',
-                  'pose_shape',
                   'uv_posed',
                   'uv_body_posed',
                   'mesh_body_posed',
                   'mesh_posed',
                   'mesh_unposed',
                   'uv_unposed',
-                  'uv_curvature',
+                  'uv_normals',
-                  'mesh_curvature',
+                  'mesh_normals',
                   'uv_static',
                   'mesh_static',
                   'mesh_mask',
                   'uv_mask',
-                  'uv_shaped_body',
                   }
     
-    dataset = Dataset('/data-local/127/train', signature, resolutions=[512], device=device, frames_file='frames_jumpsuit.txt')
+    dataset = Dataset('/data-local/127/cloth3d_processed', signature, resolutions=[512], device=device, frames_file='frames_train.txt')
     train_dataloader = DataLoader(dataset, batch_size=4, drop_last=True, shuffle=False)
 
     for batch in train_dataloader:
         batch
+        b = 0
+        mesh_mask = batch['mesh_mask'][b].cpu().numpy()
+        faces = F_skirt if batch['garment'][b] in ['Skirt', 'Dress'] else F
+        _, faces = _maskFaces(np.zeros((14475, 3)), faces, mesh_mask)
+
+        mesh_static = batch['mesh_static'][b].cpu().numpy()[mesh_mask]
+        mesh_posed = batch['mesh_posed'][b].cpu().numpy()[mesh_mask]
+        mesh_unposed = batch['mesh_unposed'][b].cpu().numpy()[mesh_mask]
+        mesh_body_posed = batch['mesh_body_posed'][b].cpu().numpy()
+
+        fig = make_subplots(rows=2, cols=2,
+                            specs=[[{'type': 'mesh3d'}, {'type': 'mesh3d'}],
+                                [{'type': 'mesh3d'}, {'type': 'mesh3d'}]])
+
+        fig.add_trace(go.Mesh3d(x=mesh_static[:,0], y=mesh_static[:,2], z=mesh_static[:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], opacity=1), row=1, col=1)
+        fig.add_trace(go.Mesh3d(x=mesh_posed[:,0], y=mesh_posed[:,2], z=mesh_posed[:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], opacity=1), row=1, col=2)
+        fig.add_trace(go.Mesh3d(x=mesh_unposed[:,0], y=mesh_unposed[:,2], z=mesh_unposed[:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], opacity=1), row=2, col=1)
+        fig.add_trace(go.Mesh3d(x=mesh_body_posed[:,0], y=mesh_body_posed[:,2], z=mesh_body_posed[:,1], i=F[:,0], j=F[:,1], k=F[:,2], opacity=1), row=2, col=2)
+
+        fig.update_layout(scene=dict(xaxis=dict(range=[-1, 1], autorange=False),
+                                    yaxis=dict(range=[-1, 1], autorange=False),
+                                    zaxis=dict(range=[-1, 1], autorange=False),
+                                    aspectmode='cube'),
+                        scene2=dict(xaxis=dict(range=[-1, 1], autorange=False),
+                                    yaxis=dict(range=[-1, 1], autorange=False),
+                                    zaxis=dict(range=[-1, 1], autorange=False),
+                                    aspectmode='cube'),
+                        scene3=dict(xaxis=dict(range=[-1, 1], autorange=False),
+                                    yaxis=dict(range=[-1, 1], autorange=False),
+                                    zaxis=dict(range=[-1, 1], autorange=False),
+                                    aspectmode='cube'),
+                        scene4=dict(xaxis=dict(range=[-1, 1], autorange=False),
+                                    yaxis=dict(range=[-1, 1], autorange=False),
+                                    zaxis=dict(range=[-1, 1], autorange=False),
+                                    aspectmode='cube'))
+
+        fig.show()
+
+
+        uv_mask = batch['uv_mask'][b].cpu().numpy()
+        uv_mask = np.repeat(uv_mask[np.newaxis, :, :], 3, axis=0)
+        uv_static = (batch['uv_static'][b].cpu().numpy() + 1) / 2
+        uv_posed = (batch['uv_posed'][b].cpu().numpy() + 1) / 2
+        uv_unposed = (batch['uv_unposed'][b].cpu().numpy() + 1) / 2
+        uv_body_posed = (batch['uv_body_posed'][b].cpu().numpy() + 1) / 2
+        uv_normals = (batch['uv_normals'][b].cpu().numpy() + 1.5) / 3
+
+        img = np.concatenate([uv_mask, uv_static, uv_posed, uv_unposed, uv_body_posed, uv_normals], axis=2)
+        img = np.transpose(img, (1, 2, 0))
+        ex.imshow(img).show()
+
         pass
 
     pass 
@@ -281,7 +349,7 @@ if __name__ == '__main__':
 #     for i, batch in enumerate(dataset):
 #         print(i)
 #         batch = static_dataset.expand_batch(batch_to_dict(batch, signature_dynamic))
-#         ex.imshow(batch['uv_curvature'][0].numpy().argmax(-1) / 6.0).show()
+#         ex.imshow(batch['uv_normals'][0].numpy().argmax(-1) / 6.0).show()
 #         sum_uv += batch['uv_unposed'].numpy().sum(axis=0)
 #         sum_mask += batch['uv_mask'].numpy().sum(axis=0)
 

data/dataset_cape.py

@@ -0,0 +1,306 @@
+import os
+import sys
+
+# os.environ["CUDA_VISIBLE_DEVICES"] = '6'
+
+import math
+import time
+import torch
+import random
+import pytorch3d
+import torchvision as tv
+import numpy as np
+import scipy.io as sio
+import plotly.express as ex
+import torchvision
+
+from torchvision.transforms import Resize
+from torchvision.transforms.functional import InterpolationMode
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
+
+from data.utils import Frame
+
+
+def read_frames(folder, shuffle=True, is_cape=False, testing=False, frames_file=None):
+    if frames_file is None:
+        uv_static_folder = os.path.join(folder, 'pose_shape') 
+        frames = sorted(os.listdir(uv_static_folder))
+
+        if is_cape:
+            frames = ['.'.join(frame.split('.')[:-1]) for frame in frames] # .split('_')[0]
+            frames = [Frame(frame.split('.')[0], frame.split('.')[1]) for frame in frames]
+        else:
+            frames = [frame.split('.')[0] for frame in frames] # .split('_')[0]
+            frames = [Frame(frame.split('_')[0], frame.split('_')[1]) for frame in frames]
+
+        # if testing:
+        #     frames = sorted(os.listdir(os.path.join(folder, 'mesh_static') ))
+        #     frames = [frame.split('.')[0] + '_4' for frame in frames]
+
+
+    if frames_file is not None:
+        frames = []
+        with open(os.path.join(folder, frames_file), 'r') as f:
+            lines = f.readlines()
+            lines = [line.strip() for line in lines]
+        frames = [Frame(line.split('_')[0], line.split('_')[1]) for line in lines]
+
+    if shuffle:
+        random.shuffle(frames)
+
+    return frames
+
+
+class StaticCache():
+    def __init__(self, folder, signature, resolutions=[512], is_cape=False, device='cuda') -> None:
+        self.store = dict()
+        self.signature = signature
+        self.folder = folder
+        assert set(resolutions).issubset(set([32, 64, 128, 256, 512]))
+        self.resolutions = resolutions
+        self.filled = ''
+        self.is_cape = is_cape
+        self.device = device
+        
+        if is_cape:
+            self.uv_masks = dict()
+            uv_mask = torch.tensor(np.load('custom_data/garment_uv_mask_full.npy').astype(np.int32), device=device)
+            self.uv_masks[512] = uv_mask.to(torch.float32).cpu().numpy()
+            for res in [32, 64, 128, 256]:
+                down = Resize(size=(res, int(res/2)), interpolation=InterpolationMode.NEAREST_EXACT)(uv_mask[None,:,:])
+                down = torch.squeeze(down).to(torch.float32)
+                self.uv_masks[res] = down.cpu().numpy()
+
+
+    def read(self, id):
+        data = dict()
+
+        for res in self.resolutions:
+            res_string = ''
+            if res != 512:
+                res_string = '_' + str(res)
+
+            if 'uv_static' + res_string in self.signature:
+                uv_static = np.load(os.path.join(self.folder, 'uv_static' + self.filled + res_string, id +  '.npz')); uv_static = uv_static['uv_static' + self.filled]; data['uv_static' + res_string] = uv_static
+            if 'uv_mask' + res_string in self.signature:
+                if self.is_cape:
+                    data['uv_mask' + res_string] = self.uv_masks[res]
+                else:
+                    uv_mask = np.load(os.path.join(self.folder, 'uv_mask' + res_string, id + '.npz')); uv_mask=uv_mask['uv_mask']; uv_mask = uv_mask.astype(np.float32); data['uv_mask' + res_string] = uv_mask
+
+        if 'mesh_static' in self.signature:
+            mesh_static = np.load(os.path.join(self.folder, 'mesh_static', id +  '.npz')); mesh_static = mesh_static['mesh_static']; data['mesh_static'] = mesh_static.astype(np.float32)
+        if 'mesh_mask' in self.signature:
+            if self.is_cape:
+                data['mesh_mask'] = np.ones((3638), np.float32)
+            else:
+                mesh_mask = np.load(os.path.join(self.folder, 'mesh_mask', id + '.npy')); mesh_mask = mesh_mask.astype(np.float32); data['mesh_mask'] = mesh_mask
+        # if 'uv_shaped_body' in self.signature:
+        #     uv_shaped_body = np.load(os.path.join(self.folder, 'uv_shaped_body', id + '.npz')); uv_shaped_body = uv_shaped_body['uv_shaped_body']; data['uv_shaped_body'] = uv_shaped_body
+
+        return data
+
+
+    def __getitem__(self, id) -> dict:
+        if id not in self.store:
+            item = self.read(id)
+            stored_data = { key: torch.from_numpy(value).to('cpu') for key, value in item.items()}
+            for key, value in stored_data.items():
+                if key.startswith('uv_') and value.shape[-1] == 3:
+                    stored_data[key] = torch.permute(value, [2,0,1])
+            self.store[id] = stored_data
+        
+        data = { key: value.to(self.device) for key, value in self.store[id].items()}
+        return data
+
+
+class DatasetCape(torch.utils.data.Dataset):
+    def __init__(self, folder, signature, resolutions=[512], frames_file='frames_all.txt', device='cuda', use_cache=False, is_cape=False):
+        self.folder = folder
+        self.device = device
+        self.is_cape = is_cape
+        self.signature = signature
+        self.resolutions = resolutions
+
+        self.transforms = tv.transforms.ToTensor()
+        self.frames = read_frames(folder, frames_file=frames_file, is_cape=is_cape, shuffle=True)
+
+        self.body_faces = np.load('smpl/smpl_faces.npy')
+        self.static_cache = StaticCache(folder, signature, is_cape=is_cape, resolutions=resolutions, device=device)
+
+        self.cache = dict() if use_cache else None
+
+
+    def __len__(self):
+        return len(self.frames)
+
+
+    def __getitem__(self, idx):
+        frame : Frame = self.frames[idx]
+        if self.cache is not None:
+            if frame in self.cache.keys():
+                data = { key: (value.to(self.device) if type(value) == torch.Tensor else value) for key, value in self.cache[frame].items()}
+                data = { **data, **self.static_cache[frame.id] }
+                return data
+
+        separator = '_'
+        if self.is_cape:
+            separator = '.'
+
+        uv_posed_body_no_orient_folder_name = 'uv_posed_body_no_orient_2'
+        if self.is_cape:
+            uv_posed_body_no_orient_folder_name = 'uv_posed_body_no_orient'
+
+        item = dict()
+
+        if 'id' in self.signature:
+            item['id'] = frame.id
+        if 'frame_nr' in self.signature:
+            item['frame_nr'] = frame.frame_nr
+        if 'pose_shape' in self.signature:
+            pose_shape = np.load(os.path.join(self.folder, 'pose_shape', frame.id + separator + frame.frame_nr + '.npy')).astype(np.float32)
+            pose_shape[:3] = 0
+            item['pose_shape'] = pose_shape
+        if 'mesh_body_posed' in self.signature:
+            mesh_posed_body_no_orient = np.load(os.path.join(self.folder, 'mesh_posed_body_no_orient', frame.id + separator + frame.frame_nr + '.npz')); mesh_posed_body_no_orient = mesh_posed_body_no_orient['mesh_posed_body_no_orient']
+            item['mesh_body_posed'] = mesh_posed_body_no_orient
+        if 'mesh_posed' in self.signature:
+            mesh_posed = np.load(os.path.join(self.folder, 'mesh_posed_no_orient', frame.id + separator + frame.frame_nr + '.npz')); mesh_posed = mesh_posed['mesh_posed_no_orient'].astype(np.float32)
+            item['mesh_posed'] = mesh_posed
+        if 'mesh_unposed' in self.signature:
+            mesh_unposed_no_shape = np.load(os.path.join(self.folder, 'mesh_unposed_no_shape', frame.id + separator + frame.frame_nr + '.npz')); mesh_unposed_no_shape = mesh_unposed_no_shape['mesh_unposed_no_shape']
+            item['mesh_unposed'] = mesh_unposed_no_shape
+        if 'mesh_curvature' in self.signature:
+            mesh_curvature = np.load(os.path.join(self.folder, 'mesh_curvature', frame.id + separator + frame.frame_nr + '.npz')); mesh_curvature = mesh_curvature['mesh_curvature']
+            item['mesh_curvature'] = mesh_curvature.astype(np.int32)
+        if 'mesh_normals' in self.signature:
+            mesh_normals = np.load(os.path.join(self.folder, 'mesh_normals', frame.id + separator + frame.frame_nr + '.npz')); mesh_normals = mesh_normals['mesh_normals']
+            item['mesh_normals'] = mesh_normals
+
+        for res in self.resolutions:
+            res_string = ''
+            if res != 512:
+                res_string = '_' + str(res)
+
+            if 'uv_curvature' + res_string in self.signature:
+                uv_curvature = np.load(os.path.join(self.folder, 'uv_curvature' + res_string, frame.id + separator + frame.frame_nr + '.npz')); 
+                if res_string != '':
+                    uv_curvature = uv_curvature['uv_curvature'] 
+                else:
+                    uv_curvature = uv_curvature['curvature_uv']
+                uv_curvature = torch.nn.functional.one_hot(torch.tensor(uv_curvature, device=self.device, dtype=torch.long), 7).float().to(self.device)
+                item['uv_curvature' + res_string] = uv_curvature
+
+            if 'uv_unposed' + res_string in self.signature:
+                uv_unposed_no_shape = np.load(os.path.join(self.folder, 'uv_unposed_no_shape' + res_string, frame.id + separator + frame.frame_nr + '.npz')); uv_unposed_no_shape = uv_unposed_no_shape['uv_unposed_no_shape']
+                item['uv_unposed' + res_string] = uv_unposed_no_shape
+
+            if 'uv_body_posed' + res_string in self.signature:
+                uv_posed_body_no_orient = np.load(os.path.join(self.folder, uv_posed_body_no_orient_folder_name + res_string, frame.id + separator + frame.frame_nr + '.npz'))
+                uv_posed_body_no_orient = uv_posed_body_no_orient['uv_posed_body_no_orient']
+                item['uv_body_posed' + res_string] = uv_posed_body_no_orient
+
+            if 'uv_posed' + res_string in self.signature:
+                uv_posed = np.load(os.path.join(self.folder, 'uv_posed_no_orient' + res_string, frame.id + separator + frame.frame_nr + '.npz')); uv_posed = uv_posed['uv_posed_no_orient']
+                item['uv_posed' + res_string] = uv_posed
+
+            if 'uv_normals' + res_string in self.signature:
+                uv_normals = np.load(os.path.join(self.folder, 'uv_normals' + res_string, frame.id + separator + frame.frame_nr + '.npz')); uv_normals = uv_normals['uv_normals']
+                item['uv_normals' + res_string] = uv_normals
+
+
+        data = { key: (torch.from_numpy(value) if not (type(value) == str or type(value) == torch.Tensor) else value) for key, value in item.items()}
+
+        for key, value in data.items():
+            if key.startswith('uv_') and (value.shape[-1] == 3 or value.shape[-1] == 7):
+                data[key] = torch.permute(value, [2,0,1])
+
+        if self.cache is not None:
+            self.cache[frame] = data
+        
+        data = { key: (value.to(self.device) if type(value) == torch.Tensor else value) for key, value in data.items()}
+        data = { **data, **self.static_cache[frame.id] }
+        return data
+
+
+if __name__ == '__main__':
+    from torch.utils.data import DataLoader
+    
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    signature = { 'id', 
+                  'frame_nr',
+                  'pose_shape',
+                  'uv_posed',
+                  'uv_body_posed',
+                  'mesh_body_posed',
+                  'mesh_posed',
+                  'mesh_unposed',
+                  'uv_unposed',
+                  'uv_curvature',
+                  'mesh_curvature',
+                  'uv_static',
+                  'mesh_static',
+                  'mesh_mask',
+                  'uv_mask',
+                  'uv_shaped_body',
+                  }
+    
+    dataset = Dataset('/data-local/127/train', signature, resolutions=[512], device=device, frames_file='frames_jumpsuit.txt')
+    train_dataloader = DataLoader(dataset, batch_size=4, drop_last=True, shuffle=False)
+
+    for batch in train_dataloader:
+        batch
+        pass
+
+    pass 
+
+
+
+
+
+
+
+
+
+
+# if __name__ == '__main__':
+#     os.environ["CUDA_VISIBLE_DEVICES"] = "6"
+#     from custom_utils.custom_utils import batch_to_dict, UV2Mesh, _maskFaces
+#     import plotly.graph_objects as go
+#     import plotly.express as ex
+#     # import cmapy as cm
+#     uv2mesh = UV2Mesh(is_skirt=True)
+
+#     F = np.load("custom_utils/smpl_faces.npy")
+
+#     sum_uv = np.zeros((512, 256, 3), dtype=np.float32)
+#     sum_mask = np.zeros((512, 256), dtype=np.float32)
+#     for i, batch in enumerate(dataset):
+#         print(i)
+#         batch = static_dataset.expand_batch(batch_to_dict(batch, signature_dynamic))
+#         ex.imshow(batch['uv_curvature'][0].numpy().argmax(-1) / 6.0).show()
+#         sum_uv += batch['uv_unposed'].numpy().sum(axis=0)
+#         sum_mask += batch['uv_mask'].numpy().sum(axis=0)
+
+#     sum_mask[sum_mask == 0] = 1
+#     avg_garment = sum_uv / sum_mask[:, :, np.newaxis]
+
+#     # x = np.transpose(avg_garment, [1,2,0])
+#     x = uv2mesh(avg_garment[tf.newaxis,:,:,:])[0]
+#     go.Figure(data=[
+#             go.Mesh3d(
+#                 # 8 vertices of a cube
+#                 x=x[:,0],
+#                 y=x[:,1],
+#                 z=x[:,2],
+#                 # i, j and k give the vertices of triangles
+#                 i = F[:,0],
+#                 j = F[:,1],
+#                 k = F[:,2]
+#             )]).show()
+
+#     np.save('custom_data/train_dress_30k_no_shape_avg_uv.npy', avg_garment)
+
+#     print()

data/downscale.py

@@ -0,0 +1,223 @@
+import argparse
+import os
+import sys
+import tqdm
+import cv2
+import torch
+import scipy.interpolate as spi
+import numpy as np
+import plotly.graph_objects as go
+import plotly.express as ex
+from torchvision.transforms import Resize
+from torchvision.transforms.functional import InterpolationMode
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "4"
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
+from data.utils import Frame
+
+
+def fill(img, mask):
+    coords = np.transpose(np.stack(np.nonzero((1 - mask))), (1,0))
+    img_idx = np.transpose(np.stack(np.nonzero((mask))), (1,0))
+    img_data = img[img_idx[:,0], img_idx[:,1], :]
+
+    interpolator =  spi.NearestNDInterpolator(x=img_idx, y=img_data)
+    # interpolator =  spi.RBFInterpolator(y=img_idx, d=img_data, neighbors=5, kernel='linear')
+
+    img_2 = interpolator(coords)
+    res = np.zeros((512,256,3), dtype=np.float32)
+    res[coords[:,0], coords[:,1], :] = img_2
+    res[img_idx[:,0], img_idx[:,1], :] = img_data
+
+    return res
+
+
+def fill_label(img, mask):
+    coords = np.transpose(np.stack(np.nonzero((1 - mask))), (1,0))
+    img_idx = np.transpose(np.stack(np.nonzero((mask))), (1,0))
+    img_data = img[img_idx[:,0], img_idx[:,1]]
+
+    interpolator =  spi.NearestNDInterpolator(x=img_idx, y=img_data)
+    img_2 = interpolator(coords)
+    res = np.zeros((512,256), dtype=np.float32)
+    res[coords[:,0], coords[:,1]] = img_2
+    res[img_idx[:,0], img_idx[:,1]] = img_data
+
+    return res
+
+
+def generate_downscaled_uv_map(folder, frame: Frame, size):
+    img = np.load(os.path.join(folder, 'uv_mask', f'{frame.id}_{frame.garment}.npz')); img = img['uv_mask'].astype(np.float32)
+    down = resizers_nearest[size](torch.from_numpy(img[None,:,:]).to(device))
+    down = np.squeeze(down.cpu().numpy().astype(bool))
+    if not os.path.exists(os.path.join(folder, 'uv_mask' + '_' + str(size))):
+        os.makedirs(os.path.join(folder, 'uv_mask' + '_' + str(size)), exist_ok=True)
+    np.savez_compressed(os.path.join(folder, 'uv_mask' + '_' + str(size), f'{frame.id}_{frame.garment}.npz'), uv_mask = down)
+
+
+class UVMaskStore():
+    def __init__(self, folder, size):
+        self.store = dict()
+        self.folder = folder
+        self.size = size
+
+    def read(self, frame: Frame):
+        path = os.path.join(self.folder, 'uv_mask' + ('_' + str(self.size) if self.size != 512 else ''), f'{frame.id}_{frame.garment}.npz')
+        if not os.path.exists(path):
+            generate_downscaled_uv_map(self.folder, frame, self.size)
+
+        mask = np.load(path)
+        mask = mask['uv_mask']
+        return mask  
+
+    def __getitem__(self, frame: Frame) -> dict:
+        if (frame.id, frame.garment) not in self.store:
+            self.store[(frame.id, frame.garment)] = self.read(frame)
+
+        return self.store[(frame.id, frame.garment)]
+
+
+def downscale_uv_maps_posed_body(folder, type, sizes):
+    for size in sizes:
+        os.makedirs(os.path.join(folder, type + str(size)), exist_ok=True)
+
+    frames = sorted(os.listdir(os.path.join(folder, type)))[int(args.start):int(args.end)]
+    frames = [Frame(frame.split('_')[0], frame.split('_')[1].split('.')[0], '') for frame in frames]
+
+    for frame in tqdm.tqdm(frames):
+        img = np.load(os.path.join(folder, type, f'{frame.id}_{frame.frame_nr}.npz'))['uv_body_posed']
+        img = fill(img, uv_masks_body[512])
+        for size in sizes:
+            mask = uv_masks_body[size]
+            down = resizers[size](torch.from_numpy(img.transpose((2,0,1))).to(device)).cpu().numpy()
+            down = down * mask[None,:,:]
+            np.savez_compressed(os.path.join(folder, type + str(size), f'{frame.id}_{frame.frame_nr}.npz'), uv_body_posed = down)
+
+
+def downscale_uv_maps_posed_no_orient(folder, type, sizes):
+    for size in sizes:
+        os.makedirs(os.path.join(folder, type + '_' + str(size)), exist_ok=True)
+
+    frames = sorted(os.listdir(os.path.join(folder, type)))[int(args.start):int(args.end)]
+    frames = [Frame(frame.split('_')[0], frame.split('_')[1], frame.split('_')[2].split('.')[0]) for frame in frames]
+
+    for frame in tqdm.tqdm(frames):
+        img = np.load(os.path.join(folder, type, f'{frame.id}_{frame.frame_nr}_{frame.garment}.npz'))['uv_posed']
+        img = fill(img, uv_mask_store[512][frame])
+        for size in sizes:
+            mask = uv_mask_store[size][frame]
+            down = resizers[size](torch.from_numpy(img.transpose((2,0,1))).to(device)).cpu().numpy()
+            down = down * mask[None,:,:]
+            np.savez_compressed(os.path.join(folder, type + '_' + str(size), f'{frame.id}_{frame.frame_nr}_{frame.garment}.npz'), uv_posed = down)
+
+
+def downscale_uv_maps_normals(folder, type, sizes):
+    for size in sizes:
+        os.makedirs(os.path.join(folder, type + '_' + str(size)), exist_ok=True)
+
+    frames = sorted(os.listdir(os.path.join(folder, type)))[int(args.start):int(args.end)]
+    frames = [Frame(frame.split('_')[0], frame.split('_')[1], frame.split('_')[2].split('.')[0]) for frame in frames]
+
+    for frame in tqdm.tqdm(frames):
+        img = np.load(os.path.join(folder, type, f'{frame.id}_{frame.frame_nr}_{frame.garment}.npz'))['uv_normals']
+        img = fill(img, uv_mask_store[512][frame])
+        for size in sizes:
+            mask = uv_mask_store[size][frame]
+            down = resizers[size](torch.from_numpy(img.transpose((2,0,1))).to(device)).cpu().numpy()
+            down = down * mask[None,:,:]
+            np.savez_compressed(os.path.join(folder, type + '_' + str(size), f'{frame.id}_{frame.frame_nr}_{frame.garment}.npz'), uv_normals = down)
+
+
+def downscale_uv_maps_unposed(folder, type, sizes):
+    for size in sizes:
+        os.makedirs(os.path.join(folder, type + '_' + str(size)), exist_ok=True)
+
+    frames = sorted(os.listdir(os.path.join(folder, type)))[int(args.start):int(args.end)]
+    frames = [Frame(frame.split('_')[0], frame.split('_')[1], frame.split('_')[2].split('.')[0]) for frame in frames]
+
+    for frame in tqdm.tqdm(frames):
+        img = np.load(os.path.join(folder, type, f'{frame.id}_{frame.frame_nr}_{frame.garment}.npz'))['uv_unposed']
+        img = fill(img, uv_mask_store[512][frame])
+        for size in sizes:
+            mask = uv_mask_store[size][frame]
+            down = resizers[size](torch.from_numpy(img.transpose((2,0,1))).to(device)).cpu().numpy()
+            down = down * mask[None,:,:]
+            np.savez_compressed(os.path.join(folder, type + '_' + str(size), f'{frame.id}_{frame.frame_nr}_{frame.garment}.npz'), uv_unposed = down)
+
+
+def downscale_uv_maps_static(folder, type, sizes):
+    for size in sizes:
+        os.makedirs(os.path.join(folder, type + '_' + str(size)), exist_ok=True)
+
+    frames = sorted(os.listdir(os.path.join(folder, type)))
+    import random
+    random.shuffle(frames)
+    frames = [(frame.split('_')[0], frame.split('_')[1].split('.')[0]) for frame in frames]
+
+    for frame in tqdm.tqdm(frames):
+        img = np.load(os.path.join(folder, type, f'{frame[0]}_{frame[1]}.npz'))['uv_static']
+        img = fill(img, uv_mask_store[512][Frame(frame[0], 0, frame[1])])
+        for size in sizes:
+            mask = uv_mask_store[size][Frame(frame[0], 0, frame[1])]
+            down = resizers[size](torch.from_numpy(img.transpose((2,0,1))).to(device)).cpu().numpy()
+            down = down * mask[None,:,:]
+            np.savez_compressed(os.path.join(folder, type + '_' + str(size), f'{frame[0]}_{frame[1]}.npz'), uv_static = down)
+
+
+def downscale_uv_maps_mask(folder, type, size):
+    os.makedirs(os.path.join(folder, type + '_' + str(size)), exist_ok=True)
+
+    frames = sorted(os.listdir(os.path.join(folder, type)))[int(args.start):int(args.end)]
+
+    for frame in tqdm.tqdm(frames):
+        id, _ = frame.split('.')
+        img = np.load(os.path.join(folder, type, frame)); img = img['uv_mask'].astype(np.float32)
+        down = tf.image.resize(img[:,:,tf.newaxis], size=(size, int(size/2)), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
+        down = np.squeeze(down.numpy().astype(bool))
+        np.savez_compressed(os.path.join(folder, type + '_' + str(size), frame), uv_mask = down)
+
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-f", "--folder", help = "end of range", default='/data-local/127/cloth3d_processed')
+    parser.add_argument("-s", "--start", help = "start of range", default=0)
+    parser.add_argument("-e", "--end", help = "end of range", default=25000)
+    parser.add_argument("-n", "--name", help = "end of range", default='lhunor')
+    parser.add_argument("-d", "--debug", help = "end of range", default=False, action='store_true')
+    args = parser.parse_args()
+
+    # sizes = [32, 64, 128, 256]
+    device = 'cuda'
+    sizes = [32, 64, 128, 256]
+    folder = args.folder
+    resizers = { res: Resize(size=(res, int(res/2)), interpolation=InterpolationMode.BICUBIC).to(device) for res in sizes }
+    resizers_nearest = { res: Resize(size=(res, int(res/2)), interpolation=InterpolationMode.NEAREST_EXACT).to(device) for res in sizes }
+
+    uv_masks_body = dict()
+    uv_mask_body = np.load('custom_utils/mask_256x512.npy')
+    uv_mask_body = cv2.dilate(uv_mask_body.astype(np.float32), cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2)
+    uv_masks_body[512] = uv_mask_body.astype(bool)
+    for res in [32, 64, 128, 256]:
+        down = resizers_nearest[res](torch.from_numpy(uv_mask_body[None,:,:].astype(np.int32)).to(device))
+        down = np.squeeze(down.cpu().numpy().astype(bool))
+        uv_masks_body[res] = down
+        
+
+    uv_mask_store = { size: UVMaskStore(folder, size=size) for size in sizes }
+    uv_mask_store[512] = UVMaskStore(folder, size=512)
+
+    # downscale_uv_maps_mask(folder=folder, type='uv_mask', size=32)
+    # downscale_uv_maps_mask(folder=folder, type='uv_mask', size=64)
+    # downscale_uv_maps_mask(folder=folder, type='uv_mask', size=128)
+    # downscale_uv_maps_mask(folder=folder, type='uv_mask', size=256)
+    # downscale_uv_maps_static(folder=folder, type='uv_static', sizes=sizes)
+
+    downscale_uv_maps_unposed(folder=folder, type='uv_unposed_no_shape', sizes=sizes)
+    downscale_uv_maps_posed_no_orient(folder=folder, type='uv_posed_no_orient', sizes=sizes)
+    downscale_uv_maps_normals(folder=folder, type='uv_normals', sizes=sizes)
+    downscale_uv_maps_posed_body(folder=folder, type='uv_body_posed_no_orient', sizes=sizes)
+
+
+    print()

data/preprocessing.py

@@ -6,8 +6,8 @@ parser = argparse.ArgumentParser()
 # parser.add_argument("-f", "--folder", help = "end of range")
 parser.add_argument("-s", "--start", help = "start of range", type=int, default=0)
 parser.add_argument("-e", "--end", help = "end of range", type=int, default=1)
-parser.add_argument("-g", "--gpu", help = "gpu id")
+parser.add_argument("-g", "--gpu", help = "gpu id", default='0')
-# parser.add_argument("-n", "--name", help = "end of range")
+parser.add_argument("-d", "--debug", help = "end of range", default=False, action='store_true')
 args = parser.parse_args()
 
 os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
@@ -30,7 +30,7 @@ import torch
 from scipy.spatial.transform import Rotation as R
 
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
-from data.cloth3d_io import readPC2, loadInfo, writePC2, readOBJ, quads2tris
+from data.cloth3d_io import readPC2, loadInfo, writePC2, readOBJ, quads2tris, readPC2Frame
 from data.utils import Frame
 from smpl.smpl_torch import SMPL
 from custom_utils.custom_utils import _maskFaces
@@ -160,7 +160,7 @@ def deorient(verts, pose):
 
 
 def create_mesh_posed_no_orient_and_mask(folder, folder_reg, out_folder):
-    ids = os.listdir(folder)
+    ids = sorted(os.listdir(folder))
     os.makedirs(os.path.join(out_folder, 'mesh_posed_no_orient'), exist_ok=True)
     os.makedirs(os.path.join(out_folder, 'mesh_mask'), exist_ok=True)
 
@@ -179,12 +179,18 @@ def create_mesh_posed_no_orient_and_mask(folder, folder_reg, out_folder):
                 continue
         
             verts = readPC2(os.path.join(garment_dir, garment + '.pc16'), True)['V']
+            # if info['poses'].shape[1] == verts.shape[0]:
+            #     continue
             reg_data = sio.loadmat(os.path.join(folder_reg, id, garment + '_weights_fixed.mat'))
             verts = (verts[:, reg_data['F'], :] * reg_data['W'][None, :, :, None]).sum(axis=2)
             mask_idx = sio.loadmat(os.path.join(folder_reg, id, garment + '_mask.mat'))['mask_idx'].reshape((-1)) - 1
             mask = np.zeros((14475), dtype=bool)
             mask[mask_idx] = True
 
+            if info['poses'].shape[1] != verts.shape[0]:
+                assert False, 'Somethign wrong here'
+                info['poses'] = info['poses'][:, :verts.shape[0]]
+
             verts = deorient(verts, info['poses'])
 
             # # plot as scatter plot with plotly
@@ -193,13 +199,12 @@ def create_mesh_posed_no_orient_and_mask(folder, folder_reg, out_folder):
             # fig = go.Figure(data=[go.Scatter3d(x=verts[frame,:,0], y=verts[frame,:,2], z=verts[frame,:,1], mode='markers', marker=dict(size=1))])
             # fig.show()
 
-
             writePC2(os.path.join(out_folder, 'mesh_posed_no_orient', f'{id}_{garment}.pc16'), verts, True)
             np.save(os.path.join(out_folder, 'mesh_mask', f'{id}_{garment}.npy'), mask)
 
 
 def create_pose_shape(folder, out_folder):
-    ids = os.listdir(folder)
+    ids = sorted(os.listdir(folder))
     os.makedirs(os.path.join(out_folder, 'pose'), exist_ok=True)
     os.makedirs(os.path.join(out_folder, 'shape'), exist_ok=True)
 
@@ -216,7 +221,7 @@ def create_pose_shape(folder, out_folder):
 
 
 def create_mesh_static(folder, out_folder):
-    ids = os.listdir(folder)
+    ids = sorted(os.listdir(folder))
     os.makedirs(os.path.join(out_folder, 'mesh_static'), exist_ok=True)
 
     for id in tqdm(ids):
@@ -247,7 +252,7 @@ def create_mesh_static(folder, out_folder):
 
 
 def create_mesh_body_posed_no_orient(folder, out_folder):
-    ids = os.listdir(folder)
+    ids = sorted(os.listdir(folder))
     os.makedirs(os.path.join(out_folder, 'mesh_body_posed_no_orient'), exist_ok=True)
 
     for id in tqdm(ids):
@@ -288,10 +293,11 @@ def calculate_normals(vertices, faces):
 
 
 def create_mesh_normals(folder, out_folder):
-    ids = os.listdir(folder)
+    ids = sorted(os.listdir(folder))
     os.makedirs(os.path.join(out_folder, 'mesh_normals'), exist_ok=True)
     # F = np.load('smpl/smpl_faces.npy')
     # F_skirt = np.load('smpl/smpl_faces_skirt.npy')
+    # ids = ['00438']
 
     for id in tqdm(ids):
         garment_dir = os.path.join(folder, id)
@@ -306,7 +312,7 @@ def create_mesh_normals(folder, out_folder):
                 continue
 
             verts = readPC2(os.path.join(garment_dir, garment + '.pc16'), True)['V']
-            verts = deorient(verts, info['poses'])
+            verts = deorient(verts, info['poses'][:, :verts.shape[0]])
             _, faces, _, _ = readOBJ(os.path.join(garment_dir, garment + '.obj'))
             faces = quads2tris(faces)
             normals = calculate_normals(verts, faces.astype(np.int32))
@@ -356,9 +362,64 @@ def create_mesh_normals(folder, out_folder):
 
             pass
 
+def create_mesh_normals_cape(folder, out_folder):
+    frames = os.listdir(os.path.join(folder, 'mesh_posed'))
+    os.makedirs(os.path.join(folder, 'mesh_normals'), exist_ok=True)
+    F = np.load('smpl/smpl_faces_orig.npy')
+    # F_skirt = np.load('smpl/smpl_faces_skirt.npy')
+
+    for frame in tqdm(frames):
+        id, frame_nr = frame.split('.')[:2]
+
+
+        verts = np.load(os.path.join(folder, 'mesh_posed', f'{id}.{frame_nr}.npz'))['mesh_posed']
+        normals = calculate_normals(verts[None,:,:], F)[0]
+
+        np.savez_compressed(os.path.join(folder, 'mesh_normals', f'{id}.{frame_nr}.npz'), mesh_normals=normals)
+
+        # normals = readPC2(os.path.join(out_folder, 'mesh_normals', f'{id}_{garment}.pc16'), True)['V']
+        # colors = np.clip(np.abs(normals), 0, 1)
+        # verts = (verts[:, reg_data['F'], :] * reg_data['W'][None, :, :, None]).sum(axis=2)
+        # plot as scatter plot with plotly
+        # import plotly.graph_objects as go
+        # frame = 5
+        # fig = go.Figure(data=[go.Scatter3d(
+        #     x=verts[:,0],
+        #     y=verts[:,2],
+        #     z=verts[:,1],
+        #     mode='markers',
+        #     marker=dict(
+        #         size=1,
+        #         color=normals[:,:],
+        #         colorscale='Viridis',
+        #         opacity=0.8
+        #     )
+        # )])
+        # moved_verts = verts + 0.05 * normals
+
+        # # Create the scatter plot for the moved vertices
+        # fig.add_trace(go.Scatter3d(
+        #     x=moved_verts[:,0],
+        #     y=moved_verts[:,2],
+        #     z=moved_verts[:,1],
+        #     mode='markers',
+        #     marker=dict(
+        #         size=1,
+        #         color='red',
+        #         opacity=0.8
+        #     )
+        # ))
+        # fig.show()
+
+        # # create a plotly mesh plot for verts and faces
+        # fig = go.Figure(data=[go.Mesh3d(x=verts[frame,:,0], y=verts[frame,:,2], z=verts[frame,:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightpink', opacity=1)])
+        # fig.show()
+
+        pass
+
 
 def verify_registration_ans_mask(folder, out_folder):
-    ids = os.listdir(folder)
+    ids = sorted(os.listdir(folder))
 
     for id in ids:
         garment_dir = os.path.join(folder, id)
@@ -510,9 +571,136 @@ def create_mesh_unposed_no_shape(folder, folder_reg, out_folder, start=0, end=1)
             # fig.show()
             pass
 
+
+def create_frames_file(folder, out_folder, split=0.9):
+    ids = os.listdir(folder)
+
+    frames_non_skirt = []
+    frames_skirt = []
+    ids_non_skirt = set()
+    ids_skirt = set()
+
+    for id in tqdm(ids):
+        garment_dir = os.path.join(folder, id)
+        if not os.path.isdir(garment_dir):
+            continue
+
+        info = sio.loadmat(os.path.join(folder, id, 'info.mat'))
+        garments = [f.replace('.obj','') for f in os.listdir(garment_dir) if f.endswith('.obj')]
+        frames = info['poses'].shape[1]
+
+        assert info['poses'].shape[0] == 72
+
+        for garment in garments:
+            for i in range(frames):
+                if garment == 'Skirt' or garment == 'Dress':
+                    frames_skirt.append(Frame(id, i, garment))
+                    ids_skirt.add(id)
+                else:
+                    frames_non_skirt.append(Frame(id, i, garment))
+                    ids_non_skirt.add(id)
+
+    nr_train_non_skirt = int(len(ids_non_skirt) * split)
+    nr_train_skirt = int(len(ids_skirt) * split)
+
+    ids_skirt = list(ids_skirt)
+    ids_non_skirt = list(ids_non_skirt)
+
+    with open(os.path.join(out_folder, 'frames_train.txt'), 'w') as f:
+        for frame in frames_non_skirt:
+            if frame.id in ids_non_skirt[:nr_train_non_skirt]:
+                f.write(f'{frame.id} {frame.frame_nr} {frame.garment}\n')
+
+    with open(os.path.join(out_folder, 'frames_train_skirt.txt'), 'w') as f:
+        for frame in frames_skirt:
+            if frame.id in ids_skirt[:nr_train_skirt]:
+                f.write(f'{frame.id} {frame.frame_nr} {frame.garment}\n')
+
+    with open(os.path.join(out_folder, 'frames_valid.txt'), 'w') as f:
+        for frame in frames_non_skirt:
+            if frame.id in ids_non_skirt[nr_train_non_skirt:]:
+                f.write(f'{frame.id} {frame.frame_nr} {frame.garment}\n')
+
+    with open(os.path.join(out_folder, 'frames_valid_skirt.txt'), 'w') as f:
+        for frame in frames_skirt:
+            if frame.id in ids_skirt[nr_train_skirt:]:
+                f.write(f'{frame.id} {frame.frame_nr} {frame.garment}\n')
+
+
+def create_frames_file_partial(folder, out_folder, split=0.9, part=0.1):
+    ids = os.listdir(folder)
+
+    frames_non_skirt = []
+    frames_skirt = []
+    ids_non_skirt = set()
+    ids_skirt = set()
+
+    for id in tqdm(ids):
+        garment_dir = os.path.join(folder, id)
+        if not os.path.isdir(garment_dir):
+            continue
+
+        info = sio.loadmat(os.path.join(folder, id, 'info.mat'))
+        garments = [f.replace('.obj','') for f in os.listdir(garment_dir) if f.endswith('.obj')]
+        frames = info['poses'].shape[1]
+
+        assert info['poses'].shape[0] == 72
+
+        for garment in garments:
+            for i in range(frames):
+                if garment == 'Skirt' or garment == 'Dress':
+                    frames_skirt.append(Frame(id, i, garment))
+                    ids_skirt.add(id)
+                else:
+                    frames_non_skirt.append(Frame(id, i, garment))
+                    ids_non_skirt.add(id)
+
+    nr_train_non_skirt = int(len(ids_non_skirt) * split * part)
+    nr_train_skirt = int(len(ids_skirt) * split * part)
+
+    ids_skirt = list(ids_skirt)
+    ids_non_skirt = list(ids_non_skirt)
+
+    with open(os.path.join(out_folder, f'frames_train_{int(part*100)}p.txt'), 'w') as f:
+        for frame in frames_non_skirt:
+            if frame.id in ids_non_skirt[:nr_train_non_skirt]:
+                f.write(f'{frame.id} {frame.frame_nr} {frame.garment}\n')
+
+    with open(os.path.join(out_folder, f'frames_train_skirt_{int(part*100)}p.txt'), 'w') as f:
+        for frame in frames_skirt:
+            if frame.id in ids_skirt[:nr_train_skirt]:
+                f.write(f'{frame.id} {frame.frame_nr} {frame.garment}\n')
+
+
+
+def check_irregular_poses(folder, folder_reg, out_folder):
+    frame: Frame = Frame('01512', 50, 'Dress')
+    mesh_posed = readPC2Frame(os.path.join(out_folder, 'mesh_posed_no_orient', f'{frame.id}_{frame.garment}.pc16'), frame.frame_nr, True)
+    # mesh_static = np.load(f'{out_folder}/mesh_static/{frame.id}_{frame.garment}.npy')
+
+    mesh_mask = np.load(f'{out_folder}/mesh_mask/{frame.id}_{frame.garment}.npy')
+    mesh_body_posed = readPC2Frame(os.path.join(out_folder, 'mesh_body_posed_no_orient', f'{frame.id}.pc16'), frame.frame_nr, True)
+
+    F = np.load('smpl/smpl_faces_skirt.npy')
+    _, faces = _maskFaces(np.zeros((14475, 3)), F, mesh_mask)
+
+    fig = go.Figure(data=[go.Mesh3d(x=mesh_posed[:,0], y=mesh_posed[:,2], z=mesh_posed[:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightpink', opacity=1)])
+    fig.add_trace(go.Mesh3d(x=mesh_body_posed[:,0], y=mesh_body_posed[:,2], z=mesh_body_posed[:,1], i=F[:,0], j=F[:,1], k=F[:,2], color='lightblue', opacity=1))
+    fig.update_layout(scene = dict(
+        xaxis = dict(range=[-1,1],),
+                    yaxis = dict(range=[-1,1],),
+                    zaxis = dict(range=[-1,1],),),)
+    fig.update_layout(scene_aspectmode='cube')
+    fig.show()
+    pass
+
 if __name__ == '__main__':
 
 
+    # folder = '/data-net/dataset/cloth3d/test'
+    # folder_reg = '/data-local/127/cloth3d_registration/test'
+    # out_folder = '/data-local/127/cloth3d_processed_test'
+
     folder = '/data-net/dataset/cloth3d/train'
     folder_reg = '/data-local/127/cloth3d_registration/train'
     out_folder = '/data-local/127/cloth3d_processed'
@@ -524,10 +712,18 @@ if __name__ == '__main__':
     # split_data(folder, 0.9)
     # create_extras_file(folder)
 
+    # check_irregular_poses(folder, folder_reg, out_folder)
+
     # create_mesh_posed_no_orient_and_mask(folder, folder_reg, out_folder)
     # verify_registration_ans_mask(folder, out_folder)
     # create_pose_shape(folder, out_folder)
     # create_mesh_static(folder, out_folder)
     # create_mesh_body_posed_no_orient(folder, out_folder)
     # create_mesh_normals(folder, out_folder)
-    create_mesh_unposed_no_shape(folder, folder_reg, out_folder, start=args.start, end=args.end)
+    # create_mesh_unposed_no_shape(folder, folder_reg, out_folder, start=args.start, end=args.end)
+
+    # create_frames_file(folder, out_folder)
+    create_frames_file_partial(folder, out_folder, part=0.5)
+    
+    # folder = '/data-local/127_2/cape_processed/test/female'
+    # create_mesh_normals_cape(folder, None)

data/utils.py

@@ -1,10 +1,10 @@
 import tables as tb
 
 class Frame:
-    def __init__(self, id, frame_nr, garment=''):
+    def __init__(self, id: str, frame_nr: int, garment: str=''):
-        self.id = id
+        self.id: str = id
-        self.frame_nr = frame_nr
+        self.frame_nr: int = frame_nr
-        self.garment = garment
+        self.garment: str = garment
 
     def __hash__(self):
         return hash((self.id, self.frame_nr, self.garment))

docker/Dockerfile2

@@ -1,7 +1,7 @@
 # Use an existing image as the base
-FROM nvcr.io/nvidia/pytorch:23.04-py3
+# FROM nvcr.io/nvidia/pytorch:23.04-py3
 # FROM pytorch/pytorch:1.13.1-cuda11.6-cudnn8-devel
-# FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel
+FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-devel
 # 
 # Add the current user to the image
 ARG USER_NAME=lhunor
@@ -10,7 +10,7 @@ ARG GROUP_NAME=guest
 ARG GROUP_ID=11000
 
 # Update the system and install required packages
-RUN apt-get update && apt-get upgrade -y && apt-get install -y sudo git apt-utils htop
+RUN apt-get update && apt-get install -y sudo git apt-utils htop
 
 # Create a new group with the same GID as the host
 RUN groupadd --gid $GROUP_ID $GROUP_NAME
@@ -25,6 +25,12 @@ RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
 # Add the new user to the sudo group
 RUN usermod -aG sudo $USER_NAME
 
+# install conda stuff still as root
+RUN conda update -n base conda
+RUN conda install -c fvcore -c iopath -c conda-forge fvcore iopath -y
+RUN conda install -c bottler nvidiacub -y
+RUN conda install pytorch3d -c pytorch3d -y
+
 # Set the default user to the new user
 USER $USER_NAME
 
@@ -33,8 +39,8 @@ WORKDIR /workspace
 ENV PATH="$PATH:/home/$USER_NAME/.local/bin"
 RUN pip install --upgrade pip
 COPY requirements.txt /tmp/
-RUN pip install --requirement /tmp/requirements.txt
 RUN pip install torch-cluster -f https://data.pyg.org/whl/torch-2.0.0+cu117.html
+RUN pip install --requirement /tmp/requirements.txt
 
 
 # defining the volumes that will be mounted

docker/docker-compose.yml

@@ -26,6 +26,28 @@ services:
     container_name: lhunor_torch_nv23.04
     ipc: host
     tty: true
+    ulimits:
+      memlock: -1
+      stack: 67108864
+    volumes:
+      - '/home-net/lhunor:/home-net'
+      - '/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:
+        reservations:
+          devices:
+            - capabilities: [gpu]
+
+
+  lhunor_iccvw:
+    image: lhunor_iccvw:pt2.0.1
+    container_name: lhunor_iccvw_pt2.0.1
+    ipc: host
+    tty: true
     ulimits:
       memlock: -1
       stack: 67108864

docker/requirements.txt

@@ -1,13 +1,10 @@
-torch==2.0.0    
 numpy
-matplotlib
 opencv-python
 wandb
 scipy
 plotly
 pandas
 cmapy
-pytorch3d
 tables
 numpy-stl
 torchmetrics

scripts/ae_body_train.py

@@ -11,10 +11,12 @@ parser.add_argument('--config', type=str)
 parser.add_argument('--debug', action='store_true')
 args = vars(parser.parse_args())
 
-config_module = importlib.import_module(args['config'])
-config = config_module.config
 if args['debug']:
-    from configs.ae_body_cape_m import config
+    from configs.iccvw.ae_body_cape_m import config
+else:
+    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']
 if args['debug']:
@@ -32,10 +34,7 @@ import plotly.graph_objects as go
 import plotly.express as ex
 import torch.optim as optim
 
-if config['is_skirt']:
+from losses.losses import edge_loss
-    from losses.losses import edge_loss_skirt as edge_loss
-else:
-    from losses.losses import edge_loss
 
 from data.dataset import Dataset
 # from torch.utils.data import DataLoader
@@ -49,6 +48,7 @@ from pytorch_msssim import MS_SSIM
 
 
 def init_wandb():
+    wandb.login(host='https://wandb2.lnrweb.com', key='local-2a78b434e9bc51e9f12625f8321a9e3ee34af16d', relogin=True, force=True)
     run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/data-local/127/temp', tags=config['tags'])
     artifact = wandb.Artifact('code', type='code')
     artifact.add_file('scripts/ae_body_train.py')
@@ -64,7 +64,7 @@ def init_wandb():
             artifact.add_file(file_name)
     run.log_artifact(artifact)
 
-uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
+uv2mesh = UV2Mesh(res=256, is_skirt=False, is_smpl_orig=config['is_cape'])
 
 
 
@@ -121,7 +121,7 @@ def plot_everything(batch: dict, type: str):
     img = torch.concat((orig_img, pred_img), axis=2)
     imgs = [wandb.Image((img[i].cpu().numpy() + 1) / 2.0) for i in range(4)]
 
-    F = np.load("smpl/smpl_faces" + skirt_modifier + ("_orig" if config['is_cape'] else "") + ".npy")
+    F = np.load("smpl/smpl_faces.npy")
     garment_unposed, faces = mesh_pred.cpu().numpy(), F
     gt = [mesh_orig[i].cpu().numpy() for i in range(pred.shape[0])]
     
@@ -175,9 +175,7 @@ if __name__ == '__main__':
     if not args['debug']:
         init_wandb()
 
-    skirt_modifier = '_skirt' if config['is_skirt'] else ''
+    uv_mask_body = np.load('custom_utils/mask_256x512.npy')
-
-    uv_mask_body = np.load('custom_utils/mask_256x512' + ('_skirt' if config['is_skirt'] else '') + '.npy')
     uv_mask_body = cv2.dilate(uv_mask_body.astype(np.float32), cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2)
     uv_mask_body = torch.from_numpy(uv_mask_body).float().to(config['device'])
 
@@ -198,15 +196,15 @@ if __name__ == '__main__':
         load_model(model.encoder, config['pretrained_model'] + '_enc')
         load_model(model.decoder, config['pretrained_model'] + '_dec')
 
-    ckpt_saver_enc = CheckpointSaver('checkpoints/iccv/torch/' + config['name'] + '_enc', top_n=5)
+    ckpt_saver_enc = CheckpointSaver('checkpoints/iccvw/' + config['name'] + '_enc', top_n=5)
-    ckpt_saver_dec = CheckpointSaver('checkpoints/iccv/torch/' + config['name'] + '_dec', top_n=5)
+    ckpt_saver_dec = CheckpointSaver('checkpoints/iccvw/' + config['name'] + '_dec', top_n=5)
 
     run_name = config['name']
     print(f'started training: {run_name}')
 
     if not args['debug']:
         # model = torch.compile(model)
-        wandb.watch(model, log='all', log_freq=1000, log_graph=True)
+        wandb.watch(model, log='all', log_freq=10000, log_graph=True)
 
     train()
 

scripts/ae_norm_train.py

@@ -11,10 +11,12 @@ parser.add_argument('--config', type=str)
 parser.add_argument('--debug', action='store_true')
 args = vars(parser.parse_args())
 
-config_module = importlib.import_module(args['config'])
-config = config_module.config
 if args['debug']:
-    from configs.ae_norm_1 import config
+    from configs.iccvw.ae_norm_cape_m import config
+else:
+    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']
 if args['debug']:
@@ -49,7 +51,8 @@ from pytorch_msssim import MS_SSIM
 
 
 def init_wandb():
-    run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/data-local/127_2/temp', tags=config['tags'])
+    wandb.login(host='https://wandb2.lnrweb.com', key='local-2a78b434e9bc51e9f12625f8321a9e3ee34af16d', relogin=True, force=True)
+    run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/data-local/127/temp', tags=config['tags'])
     artifact = wandb.Artifact('code', type='code')
     artifact.add_file('scripts/ae_norm_train.py')
     artifact.add_file('losses/losses.py')
@@ -70,9 +73,9 @@ uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_
 
 def train_step(batch):
     model.train()
-    pred = model([batch['uv_normals'], batch['uv_mask']]) * 1.1
+    pred = model([batch['uv_normals'], batch['uv_mask']]) * 1.4
 
-    ms_ssim = torch.mean(_MSSSIM(pred + 1.1, batch['uv_normals'] + 1.1))
+    ms_ssim = torch.mean(_MSSSIM(pred + 1.4, batch['uv_normals'] + 1.4))
     loss = 1e3 * (1 - ms_ssim)
 
     optimizer.zero_grad()
@@ -93,13 +96,13 @@ def valid_step(batch):
     model.eval()
 
     with torch.inference_mode():
-        pred = model([batch['uv_normals'], batch['uv_mask']]) * 1.1
+        pred = model([batch['uv_normals'], batch['uv_mask']]) * 1.4
 
         mesh_pred = uv2mesh(pred)
         mesh_orig = batch['mesh_normals']
 
         l1_loss = torch.mean(torch.sum((torch.abs(batch['uv_normals'] - pred)), axis=1))
-        ms_ssim = torch.mean(_MSSSIM(pred + 1.1, batch['uv_normals'] + 1.1))
+        ms_ssim = torch.mean(_MSSSIM(pred + 1.4, batch['uv_normals'] + 1.4))
         loss = 1e3 * (1 - ms_ssim)
 
         metric = torch.mean(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred) ** 2, axis=-1)))
@@ -109,7 +112,7 @@ def valid_step(batch):
 
 def plot_everything(batch: dict, type: str):
     model.eval()
-    pred = model([batch['uv_normals'], batch['uv_mask']]).detach() * 1.1
+    pred = model([batch['uv_normals'], batch['uv_mask']]).detach() * 1.4
     
     # mesh_pred = uv2mesh(pred)
     # mesh_orig = batch['mesh_normals']
@@ -117,7 +120,7 @@ def plot_everything(batch: dict, type: str):
     orig_img = torch.permute(batch['uv_normals'], [0,2,3,1])
     pred_img = torch.permute(pred, [0,2,3,1])
     img = torch.concat((orig_img, pred_img), axis=2)
-    imgs = [wandb.Image((img[i].cpu().numpy() + 1.1) / 2.2) for i in range(4)]
+    imgs = [wandb.Image((img[i].cpu().numpy() + 1.4) / 2.8) for i in range(4)]
 
     # F = np.load("smpl/smpl_faces" + skirt_modifier + ("_orig" if config['is_cape'] else "") + ".npy")
     # garment_unposed, faces = mesh_pred.cpu().numpy(), F
@@ -196,7 +199,7 @@ if __name__ == '__main__':
     optimizer = optim.Adam(model.parameters(), lr=config['lr'])
 
     # _MSSSIM = MSSSIM(data_range=2).to(config['device'])
-    _MSSSIM = MS_SSIM(data_range=2.2, size_average=True, channel=3)
+    _MSSSIM = MS_SSIM(data_range=2.8, size_average=True, channel=3)
 
     if config['pretrained_model'] != '':
         load_model(model.encoder, config['pretrained_model'] + '_enc')

scripts/ae_static_train.py

@@ -10,11 +10,13 @@ parser.add_argument('--config', type=str)
 parser.add_argument('--debug', action='store_true')
 args = vars(parser.parse_args())
 
-config_module = importlib.import_module(args['config'])
-config = config_module.config
 if args['debug']:
-    from configs.ae_static_cape_m import config
+    # from configs.ae_static_cape_m import config
-    # from configs.ae_static_orig_2 import config
+    from configs.iccvw.ae_static_1 import config
+else:
+    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']
 if args['debug']:
@@ -49,6 +51,7 @@ from pytorch_msssim import MS_SSIM
 
 
 def init_wandb():
+    wandb.login(host='https://wandb2.lnrweb.com', key='local-2a78b434e9bc51e9f12625f8321a9e3ee34af16d', relogin=True, force=True)
     run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/data-local/127/temp', tags=config['tags'])
     artifact = wandb.Artifact('code', type='code')
     artifact.add_file('scripts/ae_static_train.py')
@@ -91,7 +94,7 @@ def train_step(batch):
         mesh_orig = batch['mesh_static']
         l1_loss = torch.mean(torch.sum((torch.abs(batch['uv_static'] - pred)), axis=1))
         e_loss = 1e-2 * torch.mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
-        metric = torch.sum(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred.detach()) ** 2, axis=-1))) / torch.sum(batch['mesh_mask'])
+        metric = torch.sum(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / torch.sum(batch['mesh_mask'])
     
     return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/l1_loss': l1_loss, 'train/euclid_error': metric, 'train/ms_ssim': ms_ssim, }
         
@@ -205,18 +208,20 @@ if __name__ == '__main__':
     _MSSSIM = MS_SSIM(data_range=2, size_average=True, channel=3)
 
     if config['pretrained_model'] != '':
-        load_model(model.encoder, config['pretrained_model'] + '_enc')
+        it = load_model(model.encoder, config['pretrained_model'] + '_enc', optimizer=optimizer)
-        load_model(model.decoder, config['pretrained_model'] + '_dec')
+        load_model(model.decoder, config['pretrained_model'] + '_dec', optimizer=optimizer)
+        pre_name = config['pretrained_model']
+        print(f'continued training from: {pre_name} at iteration: {it}')
 
-    ckpt_saver_enc = CheckpointSaver('checkpoints/iccv/torch/' + config['name'] + '_enc', top_n=5)
+    ckpt_saver_enc = CheckpointSaver('checkpoints/iccvw/' + config['name'] + '_enc', top_n=5)
-    ckpt_saver_dec = CheckpointSaver('checkpoints/iccv/torch/' + config['name'] + '_dec', top_n=5)
+    ckpt_saver_dec = CheckpointSaver('checkpoints/iccvw/' + config['name'] + '_dec', top_n=5)
 
     run_name = config['name']
     print(f'started training: {run_name}')
 
     if not args['debug']:
         # model = torch.compile(model)
-        wandb.watch(model, log='all', log_freq=1000, log_graph=True)
+        wandb.watch(model, log='all', log_freq=10000, log_graph=True)
 
     train()
 

scripts/upscaler_train.py

@@ -11,7 +11,7 @@ parser.add_argument('--debug', action='store_true')
 args = vars(parser.parse_args())
 
 os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
-os.environ["CUDA_VISIBLE_DEVICES"] = '6'
+os.environ["CUDA_VISIBLE_DEVICES"] = '2'
 
 
 import time
@@ -49,26 +49,36 @@ gender_modifier = '' if not is_cape else ('_m' if is_male else '_f')
 smpl_res = 3638 if is_cape else 14475
 use_cache = False
 name=f'iccv_{low_res}' + skirt_modifier + cape_modifier + gender_modifier
+train_dir = '/data-local/127/cloth3d_processed'
+valid_dir = '/data-local/127/cloth3d_processed'
 
-if not is_skirt:
+if is_skirt:
-    train_dir = '/data-local/127/train'
+  train_frames = 'frames_train_skirt.txt'
-    valid_dir = '/data-local/127/valid'
+  valid_frames = 'frames_valid_skirt.txt'
 else:
-    train_dir = '/data-local/128/train_skirt'
+  train_frames = 'frames_train.txt'
-    valid_dir = '/data-local/127/valid_skirt'
+  valid_frames = 'frames_valid.txt'
+
+if is_cape:
+    train_frames = None
+    valid_frames = None
 
 if is_cape:
     if is_male:
-        train_dir = '/data-local/127/cape_processed/train/male'
+        train_dir = '/data-local/127_2/cape_processed/train/male'
-        valid_dir = '/data-local/127/cape_processed/test/male'
+        valid_dir = '/data-local/127_2/cape_processed/test/male'
     else:
-        train_dir = '/data-local/127/cape_processed/train/female'
+        train_dir = '/data-local/127_2/cape_processed/train/female'
-        valid_dir = '/data-local/127/cape_processed/test/female'   
+        valid_dir = '/data-local/127_2/cape_processed/test/female'   
 
+print(f'nohup python scripts/upscaler_train.py --name {name} > nohup/up/up_{name}.out 2>&1 &')
+if args['debug']:
+    exit()
 
 
 def init_wandb():
-    run = wandb.init(project='upscale', name=name, notes='', dir='/data-local/127/temp', tags=['upscaler', 'iccv'])
+    wandb.login(host='https://wandb2.lnrweb.com', key='local-2a78b434e9bc51e9f12625f8321a9e3ee34af16d', relogin=True, force=True)
+    run = wandb.init(project='upscale', name=name, notes='', dir='/data-local/127/temp', tags=['upscaler', 'iccvw'])
     artifact = wandb.Artifact('code', type='code')
     artifact.add_file('scripts/upscaler_train.py')
     artifact.add_file('losses/losses.py')
@@ -255,8 +265,8 @@ if __name__ == '__main__':
                         }
     
     # dataset = Dataset('/data-local/127/train', config['signature'], resolutions=[512], device=device, frames_file='frames_jumpsuit.txt')
-    dataset_train = Dataset(train_dir, device='cuda', use_cache=use_cache, signature=signature, frames_file=None, resolutions=[512, low_res], is_cape=is_cape)
+    dataset_train = Dataset(train_dir, device='cuda', use_cache=use_cache, signature=signature, frames_file=train_frames, resolutions=[512, low_res], is_cape=is_cape)
-    dataset_valid = Dataset(valid_dir, device='cuda', use_cache=use_cache, signature=signature, frames_file=None, resolutions=[512, low_res], is_cape=is_cape)
+    dataset_valid = Dataset(valid_dir, device='cuda', use_cache=use_cache, signature=signature, frames_file=valid_frames, resolutions=[512, low_res], is_cape=is_cape)
     train_dataloader = DataLoader(dataset_train, batch_size=4, drop_last=True, shuffle=True)
     valid_dataloader = DataLoader(dataset_valid, batch_size=4, drop_last=True, shuffle=True)
     train_accumulator = Accumulator()
@@ -279,13 +289,13 @@ if __name__ == '__main__':
     _MSSSIM_small = MS_SSIM(data_range=2, size_average=True, channel=3, weights=[1.0])
 
     step = 0
-    ckpt_saver = CheckpointSaver('checkpoints/iccv/torch/' + name, top_n=5)
+    ckpt_saver = CheckpointSaver('checkpoints/iccvw/' + name, top_n=5)
 
     print(f'started training: {name}')
 
     if not args['debug']:
         # model = torch.compile(model)
-        wandb.watch(model, log='all', log_freq=1000, log_graph=True)
+        wandb.watch(model, log='all', log_freq=10000, log_graph=True)
 
     train()
 

utils/generate_figures.py

@@ -0,0 +1,126 @@
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
+
+import numpy as np
+
+from custom_utils.custom_utils import save_stl, write_obj
+from data.cloth3d_io import readPC2Frame
+from data.utils import Frame
+from custom_utils.custom_utils import _maskFaces
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+
+
+def remove_long_edges(vertices, faces):
+    # Compute edge lengths
+    edges = np.vstack((faces[:,[0,1]], faces[:,[1,2]], faces[:,[2,0]]))
+    edge_lengths = np.linalg.norm(vertices[edges[:,0]] - vertices[edges[:,1]], axis=1)
+
+    # Find edges longer than 0.1
+    long_edges = edges[edge_lengths > 0.1]
+
+    # Find vertices that are part of long edges
+    vertices_to_remove = np.unique(long_edges)
+
+    # Remove vertices and update faces
+    mask = np.ones(vertices.shape[0], dtype=bool)
+    mask[vertices_to_remove] = False
+    new_vertices = vertices[mask]
+
+
+    return new_vertices, mask
+
+
+def plot_unposing_example():
+    frame = Frame('00438', 146, 'Jumpsuit')
+    mesh_static = np.load(f'{folder_test}/mesh_static/{frame.id}_{frame.garment}.npy')
+
+    mesh_mask = np.load(f'{folder_test}/mesh_mask/{frame.id}_{frame.garment}.npy')
+    mesh_posed = readPC2Frame(os.path.join(folder_test, 'mesh_posed_no_orient', f'{frame.id}_{frame.garment}.pc16'), frame.frame_nr, True)
+    mesh_unposed = readPC2Frame(os.path.join(folder_test, 'mesh_unposed_no_shape', f'{frame.id}_{frame.garment}.pc16'), frame.frame_nr, True)
+    mesh_unposed_shaped = readPC2Frame(os.path.join('temp', f'{frame.id}_{frame.garment}_unposed_shaped.pc16'), frame.frame_nr, True)[mesh_mask]
+
+
+    F = np.load('smpl/smpl_faces.npy')
+    _, faces = _maskFaces(np.zeros((14475, 3)), F, mesh_mask)
+
+    mesh_static, v_mask = remove_long_edges(mesh_static, faces.astype(np.int32))
+
+    mesh_posed = mesh_posed[v_mask]
+    mesh_unposed = mesh_unposed[v_mask]
+    mesh_unposed_shaped = mesh_unposed_shaped[v_mask]
+    new_mask = np.zeros_like(mesh_mask)
+    new_mask[mesh_mask] = v_mask
+    _, faces = _maskFaces(np.zeros((14475, 3)), F, new_mask)
+
+    fig = make_subplots(rows=1, cols=4, specs=[[{'type': 'mesh3d'}, {'type': 'mesh3d'}, {'type': 'mesh3d'}, {'type': 'mesh3d'}]])
+
+    fig.add_trace(go.Mesh3d(x=mesh_static[:,0], y=mesh_static[:,2], z=mesh_static[:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightpink', opacity=1), row=1, col=1)
+    fig.add_trace(go.Mesh3d(x=mesh_posed[:,0], y=mesh_posed[:,2], z=mesh_posed[:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightblue', opacity=1), row=1, col=2)
+    fig.add_trace(go.Mesh3d(x=mesh_unposed_shaped[:,0], y=mesh_unposed_shaped[:,2], z=mesh_unposed_shaped[:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightgreen', opacity=1), row=1, col=3)
+    fig.add_trace(go.Mesh3d(x=mesh_unposed[:,0], y=mesh_unposed[:,2], z=mesh_unposed[:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightyellow', opacity=1), row=1, col=4)
+
+    fig.update_layout(scene=dict(
+        xaxis=dict(range=[-1, 1]),
+        yaxis=dict(range=[-1, 1]),
+        zaxis=dict(range=[-1, 1]),
+    ), scene_aspectmode='cube')
+
+    # fig.show()
+    faces = faces.astype(np.uint32)
+
+    save_stl(f'temp/unposing_plots/{frame.id}_{frame.frame_nr}_{frame.garment}_mesh_static.stl', mesh_static, faces)
+    save_stl(f'temp/unposing_plots/{frame.id}_{frame.frame_nr}_{frame.garment}_mesh_posed.stl', mesh_posed, faces)
+    save_stl(f'temp/unposing_plots/{frame.id}_{frame.frame_nr}_{frame.garment}_mesh_unposed.stl', mesh_unposed, faces)
+    save_stl(f'temp/unposing_plots/{frame.id}_{frame.frame_nr}_{frame.garment}_mesh_unposed_shaped.stl', mesh_unposed_shaped, faces)
+
+    pass
+
+
+def plot_normals_example():
+    frame = Frame('00438', 146, 'Jumpsuit')
+    mesh_static = np.load(f'{folder_test}/mesh_static/{frame.id}_{frame.garment}.npy')
+
+    mesh_mask = np.load(f'{folder_test}/mesh_mask/{frame.id}_{frame.garment}.npy')
+    mesh_posed = readPC2Frame(os.path.join(folder_test, 'mesh_posed_no_orient', f'{frame.id}_{frame.garment}.pc16'), frame.frame_nr, True)
+    # mesh_unposed = readPC2Frame(os.path.join(folder_test, 'mesh_unposed_no_shape', f'{frame.id}_{frame.garment}.pc16'), frame.frame_nr, True)
+    # mesh_unposed_shaped = readPC2Frame(os.path.join('temp', f'{frame.id}_{frame.garment}_unposed_shaped.pc16'), frame.frame_nr, True)[mesh_mask]
+    mesh_normals = readPC2Frame(os.path.join(folder_test, 'mesh_normals', f'{frame.id}_{frame.garment}.pc16'), frame.frame_nr, True)
+    uv_normals = np.load(os.path.join(folder_test, 'uv_normals', f'{frame.id}_{frame.frame_nr}_{frame.garment}.npz'))
+
+    F = np.load('smpl/smpl_faces.npy')
+    _, faces = _maskFaces(np.zeros((14475, 3)), F, mesh_mask)
+
+    mesh_static, v_mask = remove_long_edges(mesh_static, faces.astype(np.int32))
+
+    mesh_posed = mesh_posed[v_mask]
+    # mesh_unposed = mesh_unposed[v_mask]
+    # mesh_unposed_shaped = mesh_unposed_shaped[v_mask]
+    new_mask = np.zeros_like(mesh_mask)
+    new_mask[mesh_mask] = v_mask
+    _, faces = _maskFaces(np.zeros((14475, 3)), F, new_mask)
+
+    fig = make_subplots(rows=1, cols=4, specs=[[{'type': 'mesh3d'}, {'type': 'mesh3d'}, {'type': 'mesh3d'}, {'type': 'mesh3d'}]])
+
+    fig.add_trace(go.Mesh3d(x=mesh_static[:,0], y=mesh_static[:,2], z=mesh_static[:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightpink', opacity=1), row=1, col=1)
+    fig.add_trace(go.Mesh3d(x=mesh_posed[:,0], y=mesh_posed[:,2], z=mesh_posed[:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightblue', opacity=1), row=1, col=2)
+    fig.add_trace(go.Mesh3d(x=mesh_unposed_shaped[:,0], y=mesh_unposed_shaped[:,2], z=mesh_unposed_shaped[:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightgreen', opacity=1), row=1, col=3)
+    fig.add_trace(go.Mesh3d(x=mesh_unposed[:,0], y=mesh_unposed[:,2], z=mesh_unposed[:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightyellow', opacity=1), row=1, col=4)
+
+    fig.update_layout(scene=dict(
+        xaxis=dict(range=[-1, 1]),
+        yaxis=dict(range=[-1, 1]),
+        zaxis=dict(range=[-1, 1]),
+    ), scene_aspectmode='cube')
+
+
+
+if __name__ == '__main__':
+    folder_train = '/data-local/127/cloth3d_processed'
+    folder_test = '/data-local/127/cloth3d_processed_test'
+    
+    # plot_unposing_example()
+    plot_normals_example()
+
+    pass