plot

aaa_helpers.txt

@@ -1,19 +1,3 @@
-nohup python cvae.py --name="lhunor_abl_all_10p_curv_10_r2" > nohup/review/abl_all_10p_curv_10_r2.out 2>&1 &
-nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_10_10p_r2" > nohup/review/curv_1_orig_curv_10_10p_r2.out 2>&1 &
-nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_25_10p_r2" > nohup/review/curv_1_orig_curv_25_10p_r2.out 2>&1 &
-nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_100_10p_r2_2" > nohup/review/curv_1_orig_curv_100_10p_r2_2.out 2>&1 &
-nohup python cvae.py --name="lhunor_abl_all_10p_curv_10_r2" > nohup/review/abl_all_10p_curv_10_r2.out 2>&1 &
-nohup python cvae.py --name="lhunor_abl_all_10p_curv_25_r2" > nohup/review/abl_all_10p_curv_25_r2.out 2>&1 &
-nohup python cvae.py --name="lhunor_abl_all_10p_curv_100_r2" > nohup/review/abl_all_10p_curv_100_r2.out 2>&1 &
-nohup python evaluate_ae.py --name="lhunor_abl_all_10p_curv_100_r2" > nohup/evaluate/lhunor_abl_all_10p_curv_100_r2.out 2>&1 &
-nohup python evaluate_ae.py --name="lhunor_abl_all_10p_curv_10_r2" > nohup/evaluate/lhunor_abl_all_10p_curv_10_r2.out 2>&1 &
-nohup python evaluate_ae.py --name="lhunor_abl_all_10p_curv_25_r2" > nohup/evaluate/lhunor_abl_all_10p_curv_25_r2.out 2>&1 &
-nohup python calculate_error.py --name="lhunor_abl_all_10p_curv_100_r2" > nohup/error/abl_all_10p_curv_100_r2.out 2>&1 &
-nohup python calculate_error.py --name="lhunor_abl_all_10p_curv_10_r2" > nohup/error/abl_all_10p_curv_10_r2.out 2>&1 &
-nohup python calculate_error.py --name="lhunor_abl_all_10p_curv_25_r2" > nohup/error/abl_all_10p_curv_25_r2.out 2>&1 &
-
-
-
 one final convolutional layer for smoothing, on the final coordinates
 
 skirts!!!!

auto_encoder_curv.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_ae_curv_1_orig_curv_10_10p_r2_2 import config
+from configs.config_ae_curv_post import config
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 
 import tensorflow as tf
@@ -31,13 +31,13 @@ import plotly.express as ex
 from tf_utils import check_tf_version, allow_memory_growth
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.custom_utils import batch_to_dict
-from models.cond_encoder import AutoEncoder
+from models.cond_encoder import AutoEncoderNew as AutoEncoder
 
 
 testing = False
 def init_wandb():
     if not testing:
-        run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp/', tags=config['tags'])
+        run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp/')
         artifact = wandb.Artifact('code', type='code')
         artifact.add_file('auto_encoder_curv.py')
         artifact.add_file('losses.py')
@@ -47,9 +47,7 @@ def init_wandb():
         artifact.add_file('convnext/models/convnext_tf.py')
         config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs.')]
         for config_file_name in config_file_names:
-            file_name = '/'.join(config_file_name.split('.')) + '.py'
-            if os.path.exists(file_name):
-                artifact.add_file(file_name)
+            artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
         run.log_artifact(artifact)
 
 uv2mesh = UV2MeshLabel(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
@@ -98,73 +96,46 @@ def dice_dense(prediction, ground_truth, weight_map=None):
 
 
 @tf.function
-def train_step(batch):	
-    with tf.GradientTape() as tape:	
-        pred = model([batch['uv_curvature'], batch['uv_mask']], training=True)	
-        ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)	
-        ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))	
-        loss = ce_uv_loss	
-    pred_mesh = tf.math.argmax(uv2mesh(pred), axis=-1)	
-    mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))	
-    mean_iou_mesh = get_mean_iou(tf.cast(batch['mesh_curvature'], tf.float32) * batch['mesh_mask'], tf.cast(pred_mesh, tf.float32) * batch['mesh_mask'])	
-    dice = dice_dense(pred, batch['uv_curvature'])	
-    	
-    gradients = tape.gradient(loss, model.trainable_variables)	
-    optimizer.apply_gradients(zip(gradients, model.trainable_variables))	
-    return { 'train/loss': loss, 'train/ce_uv_loss': ce_uv_loss, 'train/ssim': ms_ssim, 'train/mean_iou': mean_iou, 'train/mean_iou_mesh': mean_iou_mesh, 'train/dice': dice }	
-        	
-@tf.function	
-def valid_step(batch):	
-    pred = model([batch['uv_curvature'], batch['uv_mask']], training=False)	
-    ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)	
-    ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))	
-    loss = ce_uv_loss	
-    pred_mesh = tf.math.argmax(uv2mesh(pred), axis=-1)	
-    mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))	
-    mean_iou_mesh = get_mean_iou(tf.cast(batch['mesh_curvature'], tf.float32) * batch['mesh_mask'], tf.cast(pred_mesh, tf.float32) * batch['mesh_mask'])	
-    dice = dice_dense(pred, batch['uv_curvature'])	
-    return { 'valid/loss': loss, 'valid/ce_uv_loss': ce_uv_loss, 'valid/ssim': ms_ssim, 'valid/mean_iou': mean_iou, 'valid/mean_iou_mesh': mean_iou_mesh, 'valid/dice': dice }
+def train_step(batch):
+    with tf.GradientTape() as tape:
+        pred = model([batch['uv_curvature'], batch['uv_mask']], training=True)
 
-# def train_step(batch):
-#     with tf.GradientTape() as tape:
-#         pred = model([batch['uv_curvature'], batch['uv_mask']], training=True)
+        ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)
+        ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))
 
-#         ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)
-#         ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))
+        mesh_pred = uv2mesh(pred) * batch['mesh_mask'][:,:,tf.newaxis]
+        mesh_orig = tf.cast(uv2mesh(tf.math.argmax(batch['uv_curvature'], axis=-1, output_type=tf.int32)), tf.float32) * batch['mesh_mask']
+        ce_mesh_loss = scce(mesh_orig, mesh_pred)
 
-#         mesh_pred = uv2mesh(pred) * batch['mesh_mask'][:,:,tf.newaxis]
-#         mesh_orig = tf.cast(uv2mesh(tf.math.argmax(batch['uv_curvature'], axis=-1, output_type=tf.int32)), tf.float32) * batch['mesh_mask']
-#         ce_mesh_loss = scce(mesh_orig, mesh_pred)
+        loss = ce_uv_loss + ce_mesh_loss
 
-#         loss = ce_uv_loss + ce_mesh_loss
-
-#     mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))
-#     dice = dice_dense(pred, batch['uv_curvature'])
+    mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))
+    dice = dice_dense(pred, batch['uv_curvature'])
     
-#     gradients = tape.gradient(loss, model.trainable_variables)
-#     optimizer.apply_gradients(zip(gradients, model.trainable_variables))
-#     return { 'train/loss': loss, 'train/ce_uv_loss': ce_uv_loss, 'train/ssim': ms_ssim, 'train/mean_iou': mean_iou,
-#              'train/dice': dice, 'train/ce_mesh_loss': ce_mesh_loss }
+    gradients = tape.gradient(loss, model.trainable_variables)
+    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
+    return { 'train/loss': loss, 'train/ce_uv_loss': ce_uv_loss, 'train/ssim': ms_ssim, 'train/mean_iou': mean_iou,
+             'train/dice': dice, 'train/ce_mesh_loss': ce_mesh_loss }
         
 
 
-# @tf.function
-# def valid_step(batch):
-#     pred = model([batch['uv_curvature'], batch['uv_mask']], training=False)
+@tf.function
+def valid_step(batch):
+    pred = model([batch['uv_curvature'], batch['uv_mask']], training=False)
 
-#     ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)
-#     ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))
+    ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)
+    ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))
 
-#     mesh_pred = uv2mesh(pred) * batch['mesh_mask'][:,:,tf.newaxis]
-#     mesh_orig = tf.cast(uv2mesh(tf.math.argmax(batch['uv_curvature'], axis=-1, output_type=tf.int32)), tf.float32) * batch['mesh_mask']
-#     ce_mesh_loss = scce(mesh_orig, mesh_pred)
-#     loss = ce_uv_loss + ce_mesh_loss
+    mesh_pred = uv2mesh(pred) * batch['mesh_mask'][:,:,tf.newaxis]
+    mesh_orig = tf.cast(uv2mesh(tf.math.argmax(batch['uv_curvature'], axis=-1, output_type=tf.int32)), tf.float32) * batch['mesh_mask']
+    ce_mesh_loss = scce(mesh_orig, mesh_pred)
+    loss = ce_uv_loss + ce_mesh_loss
 
-#     mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))
-#     dice = dice_dense(pred, batch['uv_curvature'])
+    mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))
+    dice = dice_dense(pred, batch['uv_curvature'])
 
-#     return { 'valid/loss': loss, 'valid/ce_uv_loss': ce_uv_loss, 'valid/ssim': ms_ssim, 'valid/mean_iou': mean_iou,
-#              'valid/dice': dice, 'valid/ce_mesh_loss': ce_mesh_loss }
+    return { 'valid/loss': loss, 'valid/ce_uv_loss': ce_uv_loss, 'valid/ssim': ms_ssim, 'valid/mean_iou': mean_iou,
+             'valid/dice': dice, 'valid/ce_mesh_loss': ce_mesh_loss }
         
 
 def plot_everything(batch: dict, type: str):
@@ -235,7 +206,7 @@ def test():
 
 if __name__ == '__main__':
     init_wandb()
-    # allow_memory_growth()
+    allow_memory_growth()
     parser = argparse.ArgumentParser(description='')
     parser.add_argument('--name', type=str)
     args = vars(parser.parse_args())
@@ -255,8 +226,7 @@ if __name__ == '__main__':
                                         in_memory=config['dataset_in_memory'],
                                         use_no_orient=config['remove_global_orientation'],
                                         frames_file=config['frames_file'] if 'frames_file' in config.keys() else None, 
-                                        is_cape=config['is_cape'],
-                                        curv_neighbourhood=config['curv_neighbourhood'])
+                                        is_cape=config['is_cape'])
     static_dataset_train = StaticDataset(folder=config['train_dir'],
                                          signature=config['signature_static'], 
                                          resolutions=[config['resolution']], 
@@ -270,8 +240,7 @@ if __name__ == '__main__':
                                         in_memory=config['dataset_in_memory'], 
                                         use_no_orient=config['remove_global_orientation'], 
                                         frames_file=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else None, 
-                                        is_cape=config['is_cape'],
-                                        curv_neighbourhood=config['curv_neighbourhood'])
+                                        is_cape=config['is_cape'])
     static_dataset_valid = StaticDataset(folder=config['valid_dir'],
                                          signature=config['signature_static'], 
                                          resolutions=[config['resolution']], 

calculate_error.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_abl_all_10p_curv_25_r2 import config
+from configs.config_abl_all_post import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 # import tensorflow as tf
@@ -59,7 +59,7 @@ if __name__ == '__main__':
     folder = config['test_dir']
 
     checkpoint_name = config['name']
-    iteration = '70000'
+    iteration = ''
 
     F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
     garment_type_table = np.load('custom_data/garment_type_table_test.npy', allow_pickle=True).item()

configs/config_abl_all_10p_curv_100_r2.py

@@ -1,125 +0,0 @@
-config = dict()
-
-# DON'T FORGET TO RESET SSIM LOSS USAGE
-# DON'T FORGET TO CHECK MODEL LOADING
-# DON'T FORGET TO CHECK IF STORE IN MEMORY
-# DON'T FORGET TO CHECK REG LOSS COEFF
-# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
-# DON'T FORGET TO CHECK AVG GARMENT USED
-
-config['use_skinning'] =              True # and deshaping
-config['remove_global_orientation'] = True
-config['use_curvature'] =             True
-config['use_extra_losses'] =          True # edge, collision, 3d mesh
-config['use_offsets'] =               True
-
-config['use_norm_loss'] =             True
-config['only_curvatures'] =           False
-
-config['curv_neighbourhood'] = 100
-
-ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
-assert ablation_count == 1 or ablation_count == 0
-
-config['gpu'] = '1'
-config['name'] = 'abl_all_10p_curv_100_r2'
-config['notes'] = ''
-config['tags'] = ['review_2']
-
-config['avg_garment_path'] = 'custom_data/train_no_shape_skirt_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv  train_no_shape_one_hot_avg_uv
-if config['use_skinning']:
-  assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
-
-config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
-
-config['batch_size'] = 4
-config['resolution'] = 512
-config['is_skirt'] = True
-config['dataset_in_memory'] = False
-config['is_cape'] = False
-
-config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
-config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
-config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
-
-config['frames_file'] = 'frames_skirt_10p.txt'
-config['frames_file_valid'] = 'frames_skirt_10p.txt'
-# config['frames_file_test'] = 'frames_dress_30k.txt'
-
-config['static_model'] = 'checkpoints/static_1_skirt'
-config['body_model'] = 'checkpoints/body_1_skirt_2_2'
-config['curvature_model'] = 'checkpoints/post/curv_1_orig_curv_100_10p_r2_2'
-
-
-config['smpl_res'] = 14475
-if config['is_cape']:
-  config['smpl_res'] = 3638
-
-
-config['pretrained_model'] = ''
-config['latent_dim'] = 128
-
-###############    
-config['lr'] = 0.0001#                 0.0001
-###############
-config['starting_balancing_coeff'] = 0.001
-
-config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
-                          'pose_shape': (72 + 11,),
-                          'uv_body_posed': (512, 256, 3),
-                          'mesh_body_posed': (config['smpl_res'], 3,),
-                          'mesh_posed': (config['smpl_res'], 3,),
-                          'uv_curvature': (512, 256, 7),
-                        }
-config['signature_static'] = { 'uv_static': (512, 256, 3),
-                              'mesh_mask': (config['smpl_res'],),
-                              'uv_mask': (512, 256,),
-                              }
-
-if config['use_skinning']:
-  config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
-  config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
-else:
-  config['signature_dynamic']['uv_posed'] = (512, 256, 3)
-
-
-config['upscale_enhancer'] = ''
-config['freeze_encoders'] = True
-config['use_one_hot'] = False
-
-config['cvae'] = dict()
-config['cvae']['is_label'] = False
-config['cvae']['latent_dim'] = config['latent_dim']
-config['cvae']['add_noise'] = True
-config['cvae']['noise_amount'] = 0.025
-
-config['cvae']['kernel_size'] = 7
-config['cvae']['encoder_depths'] = [3, 3, 9, 3]
-config['cvae']['encoder_dims'] = [32, 64, 128, 256]
-config['cvae']['decoder_input'] = (16, 8, 80)
-config['cvae']['decoder_depths'] = [3, 3, 9, 3]
-config['cvae']['decoder_dims'] = [256, 128, 64, 16]
-
-
-config['cond_ae'] = dict()
-config['cond_ae']['is_label'] = False
-config['cond_ae']['upscale_output'] = False
-config['cond_ae']['latent_dim'] = config['latent_dim']
-config['cond_ae']['kernel_size'] = 7
-config['cond_ae']['bottleneck_size'] = 64
-config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
-config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
-config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
-config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
-config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
-
-config['cond_ae_body'] = dict()
-config['cond_ae_body']['latent_dim'] = config['latent_dim']
-config['cond_ae_body']['kernel_size'] = 7
-config['cond_ae_body']['upscale_output'] = True
-config['cond_ae_body']['bottleneck_size'] = 512
-config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
-config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
-config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
-config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
-config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]

configs/config_abl_all_10p_curv_10_r2.py

@@ -1,125 +0,0 @@
-config = dict()
-
-# DON'T FORGET TO RESET SSIM LOSS USAGE
-# DON'T FORGET TO CHECK MODEL LOADING
-# DON'T FORGET TO CHECK IF STORE IN MEMORY
-# DON'T FORGET TO CHECK REG LOSS COEFF
-# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
-# DON'T FORGET TO CHECK AVG GARMENT USED
-
-config['use_skinning'] =              True # and deshaping
-config['remove_global_orientation'] = True
-config['use_curvature'] =             True
-config['use_extra_losses'] =          True # edge, collision, 3d mesh
-config['use_offsets'] =               True
-
-config['use_norm_loss'] =             True
-config['only_curvatures'] =           False
-
-config['curv_neighbourhood'] = 10
-
-ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
-assert ablation_count == 1 or ablation_count == 0
-
-config['gpu'] = '6'
-config['name'] = 'abl_all_10p_curv_10_r2_2'
-config['notes'] = ''
-config['tags'] = ['review_2']
-
-config['avg_garment_path'] = 'custom_data/train_no_shape_skirt_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv  train_no_shape_one_hot_avg_uv
-if config['use_skinning']:
-  assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
-
-config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
-
-config['batch_size'] = 4
-config['resolution'] = 512
-config['is_skirt'] = True
-config['dataset_in_memory'] = True
-config['is_cape'] = False
-
-config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
-config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
-config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
-
-config['frames_file'] = 'frames_skirt_10p.txt'
-config['frames_file_valid'] = 'frames_skirt_10p.txt'
-# config['frames_file_test'] = 'frames_dress_30k.txt'
-
-config['static_model'] = 'checkpoints/static_1_skirt'
-config['body_model'] = 'checkpoints/body_1_skirt_2_2'
-config['curvature_model'] = 'checkpoints/post/curv_1_orig_curv_10_10p_r2_2'
-
-
-config['smpl_res'] = 14475
-if config['is_cape']:
-  config['smpl_res'] = 3638
-
-
-config['pretrained_model'] = ''
-config['latent_dim'] = 128
-
-###############    
-config['lr'] = 0.0001#                 0.0001
-###############
-config['starting_balancing_coeff'] = 0.001
-
-config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
-                          'pose_shape': (72 + 11,),
-                          'uv_body_posed': (512, 256, 3),
-                          'mesh_body_posed': (config['smpl_res'], 3,),
-                          'mesh_posed': (config['smpl_res'], 3,),
-                          'uv_curvature': (512, 256, 7),
-                        }
-config['signature_static'] = { 'uv_static': (512, 256, 3),
-                              'mesh_mask': (config['smpl_res'],),
-                              'uv_mask': (512, 256,),
-                              }
-
-if config['use_skinning']:
-  config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
-  config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
-else:
-  config['signature_dynamic']['uv_posed'] = (512, 256, 3)
-
-
-config['upscale_enhancer'] = ''
-config['freeze_encoders'] = True
-config['use_one_hot'] = False
-
-config['cvae'] = dict()
-config['cvae']['is_label'] = False
-config['cvae']['latent_dim'] = config['latent_dim']
-config['cvae']['add_noise'] = True
-config['cvae']['noise_amount'] = 0.025
-
-config['cvae']['kernel_size'] = 7
-config['cvae']['encoder_depths'] = [3, 3, 9, 3]
-config['cvae']['encoder_dims'] = [32, 64, 128, 256]
-config['cvae']['decoder_input'] = (16, 8, 80)
-config['cvae']['decoder_depths'] = [3, 3, 9, 3]
-config['cvae']['decoder_dims'] = [256, 128, 64, 16]
-
-
-config['cond_ae'] = dict()
-config['cond_ae']['is_label'] = False
-config['cond_ae']['upscale_output'] = False
-config['cond_ae']['latent_dim'] = config['latent_dim']
-config['cond_ae']['kernel_size'] = 7
-config['cond_ae']['bottleneck_size'] = 64
-config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
-config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
-config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
-config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
-config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
-
-config['cond_ae_body'] = dict()
-config['cond_ae_body']['latent_dim'] = config['latent_dim']
-config['cond_ae_body']['kernel_size'] = 7
-config['cond_ae_body']['upscale_output'] = True
-config['cond_ae_body']['bottleneck_size'] = 512
-config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
-config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
-config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
-config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
-config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]

configs/config_abl_all_10p_curv_25_r2.py

@@ -1,125 +0,0 @@
-config = dict()
-
-# DON'T FORGET TO RESET SSIM LOSS USAGE
-# DON'T FORGET TO CHECK MODEL LOADING
-# DON'T FORGET TO CHECK IF STORE IN MEMORY
-# DON'T FORGET TO CHECK REG LOSS COEFF
-# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
-# DON'T FORGET TO CHECK AVG GARMENT USED
-
-config['use_skinning'] =              True # and deshaping
-config['remove_global_orientation'] = True
-config['use_curvature'] =             True
-config['use_extra_losses'] =          True # edge, collision, 3d mesh
-config['use_offsets'] =               True
-
-config['use_norm_loss'] =             True
-config['only_curvatures'] =           False
-
-config['curv_neighbourhood'] = 25
-
-ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
-assert ablation_count == 1 or ablation_count == 0
-
-config['gpu'] = '0'
-config['name'] = 'abl_all_10p_curv_25_r2'
-config['notes'] = ''
-config['tags'] = ['review_2']
-
-config['avg_garment_path'] = 'custom_data/train_no_shape_skirt_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv  train_no_shape_one_hot_avg_uv
-if config['use_skinning']:
-  assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
-
-config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
-
-config['batch_size'] = 4
-config['resolution'] = 512
-config['is_skirt'] = True
-config['dataset_in_memory'] = True
-config['is_cape'] = False
-
-config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
-config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
-config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
-
-config['frames_file'] = 'frames_skirt_10p.txt'
-config['frames_file_valid'] = 'frames_skirt_10p.txt'
-# config['frames_file_test'] = 'frames_dress_30k.txt'
-
-config['static_model'] = 'checkpoints/static_1_skirt'
-config['body_model'] = 'checkpoints/body_1_skirt_2_2'
-config['curvature_model'] = 'checkpoints/post/curv_1_orig_curv_25_10p_r2'
-
-
-config['smpl_res'] = 14475
-if config['is_cape']:
-  config['smpl_res'] = 3638
-
-
-config['pretrained_model'] = ''
-config['latent_dim'] = 128
-
-###############    
-config['lr'] = 0.0001#                 0.0001
-###############
-config['starting_balancing_coeff'] = 0.001
-
-config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
-                          'pose_shape': (72 + 11,),
-                          'uv_body_posed': (512, 256, 3),
-                          'mesh_body_posed': (config['smpl_res'], 3,),
-                          'mesh_posed': (config['smpl_res'], 3,),
-                          'uv_curvature': (512, 256, 7),
-                        }
-config['signature_static'] = { 'uv_static': (512, 256, 3),
-                              'mesh_mask': (config['smpl_res'],),
-                              'uv_mask': (512, 256,),
-                              }
-
-if config['use_skinning']:
-  config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
-  config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
-else:
-  config['signature_dynamic']['uv_posed'] = (512, 256, 3)
-
-
-config['upscale_enhancer'] = ''
-config['freeze_encoders'] = True
-config['use_one_hot'] = False
-
-config['cvae'] = dict()
-config['cvae']['is_label'] = False
-config['cvae']['latent_dim'] = config['latent_dim']
-config['cvae']['add_noise'] = True
-config['cvae']['noise_amount'] = 0.025
-
-config['cvae']['kernel_size'] = 7
-config['cvae']['encoder_depths'] = [3, 3, 9, 3]
-config['cvae']['encoder_dims'] = [32, 64, 128, 256]
-config['cvae']['decoder_input'] = (16, 8, 80)
-config['cvae']['decoder_depths'] = [3, 3, 9, 3]
-config['cvae']['decoder_dims'] = [256, 128, 64, 16]
-
-
-config['cond_ae'] = dict()
-config['cond_ae']['is_label'] = False
-config['cond_ae']['upscale_output'] = False
-config['cond_ae']['latent_dim'] = config['latent_dim']
-config['cond_ae']['kernel_size'] = 7
-config['cond_ae']['bottleneck_size'] = 64
-config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
-config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
-config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
-config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
-config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
-
-config['cond_ae_body'] = dict()
-config['cond_ae_body']['latent_dim'] = config['latent_dim']
-config['cond_ae_body']['kernel_size'] = 7
-config['cond_ae_body']['upscale_output'] = True
-config['cond_ae_body']['bottleneck_size'] = 512
-config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
-config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
-config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
-config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
-config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]

configs/config_abl_overfit_jumpsuit_2.py

@@ -19,7 +19,7 @@ config['only_curvatures'] =           True
 ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
 assert ablation_count == 1 or ablation_count == 0
 
-config['gpu'] = '5'
+config['gpu'] = '3'
 config['name'] = 'abl_overfit_jumpsuit_2'
 config['notes'] = 'remove other conditions from input as well'
 

configs/config_ae_curv.py

@@ -1,23 +1,13 @@
 from configs.config_base import config
 
-config['gpu'] = '4'
+config['gpu'] = '5'
 config['lr'] = 0.001
-config['name'] = 'curv_1_orig_r2' #'ae_curv_9'
+config['name'] = 'curv_1' #'ae_curv_9'
 config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
 | \
   '
-config['tags'] = ['review_2']
-config['remove_global_orientation'] = True
 
 config['cond_ae']['is_label'] = True
-config['is_skirt'] = True
-
-config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
-config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
-config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
-config['frames_file'] = 'frames_skirt_10p.txt'
-config['frames_file_valid'] = 'frames_skirt_10p.txt'
-config['curv_neighbourhood'] = 10
 
 config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
                           'uv_curvature': (512, 256, 7),

configs/config_ae_curv_1_orig_curv_100_10p_r2.py

@@ -1,30 +0,0 @@
-from configs.config_base import config
-
-config['gpu'] = '1'
-config['lr'] = 0.001
-config['name'] = 'curv_1_orig_curv_100_10p_r2' #'ae_curv_9'
-config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
-| \
-  '
-config['tags'] = ['review_2']
-config['remove_global_orientation'] = True
-config['dataset_in_memory'] = True
-
-config['cond_ae']['is_label'] = True
-config['is_skirt'] = True
-
-config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
-config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
-config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
-config['frames_file'] = 'frames_skirt_10p.txt'
-config['frames_file_valid'] = 'frames_skirt_10p.txt'
-config['curv_neighbourhood'] = 100
-
-config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
-                          'uv_curvature': (512, 256, 7),
-                          'mesh_curvature':  (config['smpl_res'])
-                        }
-config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
-                         'uv_mask': (512, 256,),
-                        }
-

configs/config_ae_curv_1_orig_curv_100_10p_r2_2.py

@@ -1,30 +0,0 @@
-from configs.config_base import config
-
-config['gpu'] = '1'
-config['lr'] = 0.001
-config['name'] = 'curv_1_orig_curv_100_10p_r2_2' #'ae_curv_9'
-config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
-| \
-  '
-config['tags'] = ['review_2']
-config['remove_global_orientation'] = True
-config['dataset_in_memory'] = True
-
-config['cond_ae']['is_label'] = True
-config['is_skirt'] = True
-
-config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
-config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
-config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
-config['frames_file'] = 'frames_skirt_10p.txt'
-config['frames_file_valid'] = 'frames_skirt_10p.txt'
-config['curv_neighbourhood'] = 100
-
-config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
-                          'uv_curvature': (512, 256, 7),
-                          'mesh_curvature':  (config['smpl_res'])
-                        }
-config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
-                         'uv_mask': (512, 256,),
-                        }
-

configs/config_ae_curv_1_orig_curv_10_10p_r2.py

@@ -1,30 +0,0 @@
-from configs.config_base import config
-
-config['gpu'] = '4'
-config['lr'] = 0.001
-config['name'] = 'curv_1_orig_curv_10_10p_r2' #'ae_curv_9'
-config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
-| \
-  '
-config['tags'] = ['review_2']
-config['remove_global_orientation'] = True
-config['dataset_in_memory'] = True
-
-config['cond_ae']['is_label'] = True
-config['is_skirt'] = True
-
-config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
-config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
-config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
-config['frames_file'] = 'frames_skirt_10p.txt'
-config['frames_file_valid'] = 'frames_skirt_10p.txt'
-config['curv_neighbourhood'] = 10
-
-config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
-                          'uv_curvature': (512, 256, 7),
-                          'mesh_curvature':  (config['smpl_res'])
-                        }
-config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
-                         'uv_mask': (512, 256,),
-                        }
-

configs/config_ae_curv_1_orig_curv_10_10p_r2_2.py

@@ -1,30 +0,0 @@
-from configs.config_base import config
-
-config['gpu'] = '4'
-config['lr'] = 0.001
-config['name'] = 'curv_1_orig_curv_10_10p_r2_2' #'ae_curv_9'
-config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
-| \
-  '
-config['tags'] = ['review_2']
-config['remove_global_orientation'] = True
-config['dataset_in_memory'] = True
-
-config['cond_ae']['is_label'] = True
-config['is_skirt'] = True
-
-config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
-config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
-config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
-config['frames_file'] = 'frames_skirt_10p.txt'
-config['frames_file_valid'] = 'frames_skirt_10p.txt'
-config['curv_neighbourhood'] = 10
-
-config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
-                          'uv_curvature': (512, 256, 7),
-                          'mesh_curvature':  (config['smpl_res'])
-                        }
-config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
-                         'uv_mask': (512, 256,),
-                        }
-

configs/config_ae_curv_1_orig_curv_25_10p_r2.py

@@ -1,30 +0,0 @@
-from configs.config_base import config
-
-config['gpu'] = '6'
-config['lr'] = 0.001
-config['name'] = 'curv_1_orig_curv_25_10p_r2' #'ae_curv_9'
-config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
-| \
-  '
-config['tags'] = ['review_2']
-config['remove_global_orientation'] = True
-config['dataset_in_memory'] = True
-
-config['cond_ae']['is_label'] = True
-config['is_skirt'] = True
-
-config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
-config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
-config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
-config['frames_file'] = 'frames_skirt_10p.txt'
-config['frames_file_valid'] = 'frames_skirt_10p.txt'
-config['curv_neighbourhood'] = 25
-
-config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
-                          'uv_curvature': (512, 256, 7),
-                          'mesh_curvature':  (config['smpl_res'])
-                        }
-config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
-                         'uv_mask': (512, 256,),
-                        }
-

convnext/models/convnext_tf.py

@@ -366,8 +366,8 @@ class ConvNeXtReverseLabels(tf.keras.Model):
             self.stages.append(stage)
             cur += depths[i]
 
-        self.last = Dense(nr_of_classes, activation='softmax')
-        # self.last = Dense(nr_of_classes - 1)
+        # self.last = Dense(nr_of_classes, activation='softmax')
+        self.last = Dense(nr_of_classes - 1)
 
     def call(self, x, training):
         for i in reversed(range(len(self.downsample_layers))):

curvature_analysis.py

@@ -0,0 +1,283 @@
+import os
+import sys
+os.environ["CUDA_VISIBLE_DEVICES"] = '4'
+
+
+import tqdm
+import argparse
+import random
+import string
+import time
+import numpy as np
+import trimesh as tm
+import cmapy as cm
+import scipy as sp
+import scipy.io as sio
+import plotly.graph_objects as go
+from pyntcloud import PyntCloud
+from scipy.sparse import csr_matrix, coo_matrix
+from scipy.sparse.csgraph import floyd_warshall, dijkstra
+from plotly.subplots import make_subplots
+from custom_utils.custom_utils import _maskFaces
+from postprocessing import fix_collision
+
+from custom_utils.supersmpl_io import readPC2, readPC2Frame, writePC2, readOBJ
+from custom_utils.custom_utils import _maskFaces
+from auto_encoder_curv import dice_dense
+
+def postprocess(mesh, faces, mask=None, body=None, do_smoothing=True):
+    if body is not None:
+        mesh_fixed = fix_collision(mesh, body[np.newaxis,:,:], mask[np.newaxis,:])[0].numpy()[mask, :]
+    else:
+        mesh_fixed = mesh
+
+    if do_smoothing:
+        m = tm.Trimesh(mesh_fixed, faces, process=False, validate=False)
+        outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
+        outliers = np.nonzero(outliers)[0]
+        outliers_n_neighbours = get_closest_n_points(mesh_fixed, outliers, 2)
+        outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
+        faces_fixed = np.concatenate((faces, outliers_faces), axis=0)
+        m = tm.Trimesh(mesh_fixed, faces_fixed, process=False, validate=False)
+        tm.smoothing.filter_humphrey(m, iterations=1, alpha=0.6, beta=0.4)
+
+        mesh = m.vertices
+    else:
+        mesh = mesh_fixed
+
+    return mesh
+
+def export_to_ply(mesh, file):
+    ply = tm.exchange.ply.export_ply(mesh, encoding='binary', vertex_normal=True, include_attributes=True)
+    output_file = open(file, "wb+")
+    output_file.write(ply)
+    output_file.close()
+
+
+def get_closest_n_points(verts, idx, n):
+    dist = sp.spatial.distance.cdist(verts[idx, :], verts)
+    dist[dist == 0] = 1e9
+    idx_sort = np.argsort(dist, axis=-1)
+    closest = idx_sort[:, :n]
+    return closest
+
+
+def calculate_dist(folder, neighbourhood):
+    out = dict()
+    frames = sorted(os.listdir(os.path.join(folder, 'mesh_posed')))[int(args.start):int(args.end)]
+
+    for frame in tqdm.tqdm(frames):
+        id = frame.split('_')[0]
+        if id in out.keys():
+            continue
+
+        mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
+        k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
+        out[id] = k_neighbors
+
+    return out
+
+
+def histedges_equalN(x, nbin):
+    npt = len(x)
+    return np.interp(np.linspace(0, npt, nbin + 1),
+                     np.arange(npt),
+                     np.sort(x))
+
+
+def get_bins(values, nr_of_bins):
+    flattened = []
+    for c in values:
+        flattened.extend(c.flatten())
+    flattened = np.array(flattened)
+    bins = histedges_equalN(flattened, nr_of_bins)
+    return bins
+
+
+def generate_curvatures(v, faces, mask, neighbourhood=25, nr_of_labels=6):
+    # id = frame.split('_')[0]
+    # frame_nr = int(frame.split('_')[1].split('.')[0])
+
+    # mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
+    # _, faces = _maskFaces(np.zeros((14475,3)), F, mask)
+    # v = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
+    
+    m = tm.Trimesh(v, faces, process=False, validate=False)
+
+    outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
+    outliers = np.nonzero(outliers)[0]
+    outliers_n_neighbours = get_closest_n_points(v, outliers, 2)
+    outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
+    faces = np.concatenate((faces, outliers_faces), axis=0)
+
+    verts = v
+
+    m = tm.Trimesh(verts, faces, process=False, validate=False)
+    export_to_ply(m, temp_file)
+
+    cloud = PyntCloud.from_file(temp_file)
+
+    k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
+    #k_neighbors = k_neighbors_cache[id] # np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
+    ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
+    cloud.add_scalar_field("curvature", ev=ev)
+    curvature = np.array(cloud.points["curvature(" + str(neighbourhood+1) + ")"])
+    
+    bins = np.load(f'custom_data/train_bins.npy') # get_bins(curvature, nr_of_labels)
+    labels = np.linspace(0, bins.shape[0] - 2, bins.shape[0] - 1)
+
+    frame_labels = np.zeros((curvature.shape[0]), dtype=np.float32)
+    for j in range(bins.shape[0] - 2):
+        frame_labels[(curvature >= bins[j]) * (curvature < bins[j+1])] = labels[j]
+    frame_labels[curvature >= bins[-2]] = labels[-1]
+       
+    return frame_labels
+
+import tensorflow as tf
+
+def visualize_curvature_labels(folder, results_folder, frame):
+    id = frame.split('_')[0]
+    frame_nr = frame.split('_')[1].split('.')[0]
+
+    mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
+    _, faces = _maskFaces(np.zeros((14475,3)), F, mask)
+    verts = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
+    pred = np.load(os.path.join(results_folder, frame + '.npy'))
+    pred = postprocess(pred, faces)
+
+    curvature_labels_gt = generate_curvatures(verts, faces, mask, 25)
+    curvature_labels_pred = generate_curvatures(pred, faces, mask, 25)
+    # dice = dice_dense(curvature_labels_pred[None,:], curvature_labels_gt[None,:])
+    dice = dice_dense(tf.one_hot(curvature_labels_pred, 7)[None,:], tf.one_hot(curvature_labels_gt, 7)[None,:]).numpy()
+    colors_binned_all = []
+
+    for curvature_labels in [curvature_labels_gt, curvature_labels_pred]:
+        colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, int(curvature_labels.max()+1))])
+        colors_binned = np.zeros((curvature_labels.shape[0], 3))
+        for i in range(int(curvature_labels.max())+1):
+            # print(np.count_nonzero(curvature_labels == i))
+            colors_binned[curvature_labels == i, :] = colors[i]
+        colors_binned_all.append(colors_binned)
+
+    fig = make_subplots(rows=1, cols=3, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(3)] ])
+
+    fig.add_trace(go.Mesh3d(
+                # 8 vertices of a cube
+                x=-pred[:,0],
+                y=pred[:,2],
+                z=pred[:,1],
+                # i, j and k give the vertices of triangles
+                i = faces[:,0],
+                j = faces[:,1],
+                k = faces[:,2],
+                vertexcolor=np.ones((verts.shape[0],3))*np.array([255,200,200])[None,:]
+            ),
+            row=1, col=2)
+
+    fig.add_trace(go.Mesh3d(
+                # 8 vertices of a cube
+                x=verts[:,0],
+                y=verts[:,1],
+                z=verts[:,2],
+                # i, j and k give the vertices of triangles
+                i = faces[:,0],
+                j = faces[:,1],
+                k = faces[:,2],
+                vertexcolor=colors_binned_all[0]
+            ),
+            row=1, col=1)
+    
+    fig.add_trace(go.Mesh3d(
+                # 8 vertices of a cube
+                x=-pred[:,0],
+                y=pred[:,2],
+                z=pred[:,1],
+                # i, j and k give the vertices of triangles
+                i = faces[:,0],
+                j = faces[:,1],
+                k = faces[:,2],
+                vertexcolor=colors_binned_all[1],
+                text=f'dice: {dice}'
+            ),
+            row=1, col=3)
+
+    fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),
+                    scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1),
+                    scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1),
+                    # scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1),
+                    # scene5_aspectmode='manual', scene5_aspectratio=dict(x=1, y=1, z=1),
+                    )
+    helper = dict(xaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
+                yaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
+                zaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),)
+        
+    fig.update_layout(scene = helper,
+                        scene2 = helper,
+                        scene3 = helper,
+                        # scene4 = helper,
+                        # scene5 = helper,
+                        )
+
+
+    # meshes = [go.Mesh3d(
+    #             x=x[:,0],
+    #             y=x[:,1],
+    #             z=x[:,2],
+    #             i = faces[:,0],
+    #             j = faces[:,1],
+    #             k = faces[:,2],
+    #             vertexcolor=colors_binned
+    #             )]
+
+    # fig = go.Figure(data=meshes)
+    # fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1))
+    # fig.update_layout(
+    #     scene = dict(
+    #         xaxis = dict(nticks=4, range=[-1,1],),
+    #                     yaxis = dict(nticks=4, range=[-1,1],),
+    #                     zaxis = dict(nticks=4, range=[-1,1],),)
+    #                     )
+
+    return fig, dice
+
+import wandb
+
+def init_wandb():
+    run = wandb.init(project='plots', name='review_4', dir='/home/workspace/local-data/temp/')
+    artifact = wandb.Artifact('code', type='code')
+    artifact.add_file('curvature_analysis.py')
+    run.log_artifact(artifact)
+
+if __name__ == '__main__':
+    # init_wandb()
+
+    is_skirt = False
+    skirt_modifier = '_skirt' if is_skirt else ''
+    F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
+    # k_neighbours(k=25)
+
+    folder = '/home/workspace/local-data/train'
+    results_folder = '/home/workspace/data/results/post/abl_overfit_jumpsuit_2-'
+
+    frames_file = 'frames_jumpsuit_30k.txt'
+    frames = []
+    with open(os.path.join(folder, frames_file), 'r') as f:
+        lines = f.readlines()
+        lines = [line.strip() for line in lines]
+    frames = lines
+
+    temp_file = '/home/workspace/local-data/temp/' + ''.join(random.choice(string.ascii_lowercase) for i in range(10)) + '.ply'
+    if not is_skirt:
+        dist = np.load('custom_data/dijkstra.npy')
+    else:
+        dist = np.load('custom_data/dijkstra_skirt.npy')
+
+    i = 0
+    while True:
+        frame = np.random.choice(frames)
+        # frame = '03265_193'
+        frame = '03102_49'
+        fig, dice = visualize_curvature_labels(folder, results_folder, frame)
+        wandb.log({f'{frame}-dice:{dice}': fig}, step=i)
+        i += 1
+        print(frame)

cvae.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_abl_all_10p_curv_100_r2 import config
+from configs.config_abl_overfit_jumpsuit_2 import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -73,7 +73,7 @@ def kl_loss(mean, logvar):
 
 # 3d loss on posed !!!!!!!!!!!!
 
-@tf.function
+# @tf.function
 def train_step(batch):
 
     with tf.GradientTape() as tape:
@@ -476,7 +476,7 @@ def test():
 
 
 if __name__ == '__main__':
-    init_wandb()
+    # init_wandb()
     parser = argparse.ArgumentParser(description='')
     parser.add_argument('--name', type=str)
     args = vars(parser.parse_args())
@@ -494,8 +494,7 @@ if __name__ == '__main__':
                                         use_one_hot=config['use_one_hot'],
                                         use_no_orient=config['remove_global_orientation'],
                                         frames_file=config['frames_file'] if 'frames_file' in config.keys() else None, 
-                                        is_cape=config['is_cape'],
-                                        curv_neighbourhood=config['curv_neighbourhood'])
+                                        is_cape=config['is_cape'])
     static_dataset_train = StaticDataset(folder=config['train_dir'],
                                          signature=config['signature_static'], 
                                          resolutions=[config['resolution']], 
@@ -510,8 +509,7 @@ if __name__ == '__main__':
                                         use_one_hot=config['use_one_hot'],
                                         use_no_orient=config['remove_global_orientation'], 
                                         frames_file=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else None, 
-                                        is_cape=config['is_cape'],
-                                        curv_neighbourhood=config['curv_neighbourhood'])
+                                        is_cape=config['is_cape'])
     static_dataset_valid = StaticDataset(folder=config['valid_dir'],
                                          signature=config['signature_static'], 
                                          resolutions=[config['resolution']], 

dataset.py

@@ -194,8 +194,7 @@ class DatasetFactory():
                     use_one_hot=False,
                     use_no_orient=True,
                     testing=False,
-                    frames_file=None,
-                    curv_neighbourhood=25):
+                    frames_file=None):
         assert set(resolutions).issubset(set([32, 64, 128, 256, 512]))
 
         dataset_idx = DatasetIdx(folder, shuffle, is_cape=is_cape, testing=testing, frames_file=frames_file)
@@ -261,7 +260,7 @@ class DatasetFactory():
 
 
             if 'mesh_curvature' in signature.keys():
-                mesh_curvature = np.load(os.path.join(folder, f'mesh_curvature_{curv_neighbourhood}_2' if curv_neighbourhood != 25 else 'mesh_curvature', id + separator + frame_nr + '.npz')); mesh_curvature = mesh_curvature['mesh_curvature']
+                mesh_curvature = np.load(os.path.join(folder, 'mesh_curvature', id + separator + frame_nr + '.npz')); mesh_curvature = mesh_curvature['mesh_curvature']
                 item['mesh_curvature'] = mesh_curvature.astype(np.int32)
 
 
@@ -271,8 +270,7 @@ class DatasetFactory():
                     res_string = '_' + str(res)
 
                 if 'uv_curvature' + res_string in signature.keys():
-                    # print(os.path.join(folder, (f'uv_curvature_{curv_neighbourhood}_2' if curv_neighbourhood != 25 else 'uv_curvature')+ res_string, id + separator + frame_nr + '.npz'))
-                    uv_curvature = np.load(os.path.join(folder, (f'uv_curvature_{curv_neighbourhood}_2' if curv_neighbourhood != 25 else 'uv_curvature')+ res_string, id + separator + frame_nr + '.npz')); 
+                    uv_curvature = np.load(os.path.join(folder, 'uv_curvature' + res_string, id + separator + frame_nr + '.npz')); 
                     if res_string != '':
                         uv_curvature = uv_curvature['uv_curvature'] 
                     else:
@@ -383,16 +381,15 @@ if __name__ == '__main__':
                         #  'uv_shaped_body': (3, 512, 256,),
                           }
 
-    dataset = factory.get_dataset(folder='/home/workspace/data/dataset/local-cache/train_skirt',
+    dataset = factory.get_dataset(folder='/home/workspace/local-data/train_skirt',
                                   batch_size=1,
                                   signature=signature_dynamic,
                                   resolutions=[512],
                                   do_repeat=False,
                                   use_one_hot=False,
                                   is_cape=False,
-                                  frames_file='frames_skirt_10p.txt',
-                                  curv_neighbourhood=25)
-    static_dataset = StaticDataset(folder='/home/workspace/data/dataset/local-cache/train_skirt',
+                                  frames_file='frames_dress_30k.txt')
+    static_dataset = StaticDataset(folder='/home/workspace/local-data/train_skirt',
                                    signature=signature_static,
                                    resolutions=[512],
                                   is_cape=False)

evaluate_ae.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_abl_all_10p_curv_25_r2 import config
+from configs.config_abl_overfit_jumpsuit_2 import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -102,9 +102,9 @@ if __name__ == '__main__':
     ids = [id.split('_')[0] for id in ids]
     ids = set(ids)
 
-    iteration = '70000'
+    iteration = ''
 
-    load_model(model, 'checkpoints/post/' + checkpoint_name, iteration=iteration)
+    load_model(model, 'checkpoints/' + checkpoint_name, iteration=iteration)
     # load_model(model, 'checkpoints/' + 'abl_all_120k')
 
     output_folder = '/home/workspace/data/results/post/' + checkpoint_name + '-' + iteration