evaluate training

aaa_helpers.txt

@@ -1,18 +1,9 @@
-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 &
-
-
+nohup python auto_encoder_static.py --name="lhunor_ae_static_r7_3" > nohup/review/ae_static_r7_3.out 2>&1 &
+nohup python auto_encoder_body.py --name="lhunor_ae_body_r7_3" > nohup/review/ae_body_r7_3.out 2>&1 &
+nohup python auto_encoder_curv.py --name="lhunor_ae_curv_r7_3" > nohup/review/ae_curv_r7_3.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_all_resnet_r7" > nohup/review/abl_all_resnet_r7.out 2>&1 &
+nohup python calculate_error.py --name abl_all_resnet_r7_70k > nohup/error/abl_all_resnet_r7_70k.out 2>&1 &
+nohup python evaluate_ae.py --name abl_all_resnet_r7_70k > nohup/evaluate/abl_all_resnet_r7_70k.out 2>&1 &
 
 one final convolutional layer for smoothing, on the final coordinates
 

auto_encoder_body.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_ae_body_post import config
+from configs.review.config_ae_body import config
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 
 import tensorflow as tf
@@ -34,7 +34,7 @@ from models.cond_encoder import AutoEncoderNew as AutoEncoder
 
 
 def init_wandb():
-    run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp')
+    run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp', tags=config['tags'])
     artifact = wandb.Artifact('code', type='code')
     artifact.add_file('auto_encoder_body.py')
     artifact.add_file('losses.py')
@@ -42,7 +42,7 @@ def init_wandb():
     artifact.add_file('models/cond_encoder.py')
     artifact.add_file('dataset.py')
     artifact.add_file('convnext/models/convnext_tf.py')
-    config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs.')]
+    config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs.review.')]
     for config_file_name in config_file_names:
         artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
     run.log_artifact(artifact)
@@ -137,7 +137,10 @@ def train():
         if step == 0:
             to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
 
-        wandb.log(to_log, step=step)  
+        if step % 1000 == 0:    
+            wandb.log(to_log, step=step, commit=True)
+        else:
+            wandb.log(to_log, step=step, commit=False)
             
         if step % 10000 == 0:
             manager_enc.save(checkpoint_number=step)

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.review.config_ae_curv 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', tags=config['tags'])
         artifact = wandb.Artifact('code', type='code')
         artifact.add_file('auto_encoder_curv.py')
         artifact.add_file('losses.py')
@@ -45,11 +45,9 @@ def init_wandb():
         artifact.add_file('models/cond_encoder.py')
         artifact.add_file('dataset.py')
         artifact.add_file('convnext/models/convnext_tf.py')
-        config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs.')]
+        config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs.review.')]
         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):
@@ -206,8 +177,11 @@ def train():
         if step == 0:
             to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
  
-        wandb.log(to_log, step=step)  
-        print(step)
+        if step % 1000 == 0:    
+            wandb.log(to_log, step=step, commit=True)
+        else:
+            wandb.log(to_log, step=step, commit=False)  
+        # print(step)
 
         if step % 10000 == 0:
             manager_enc.save(checkpoint_number=step)
@@ -255,8 +229,7 @@ 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 +243,7 @@ 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']], 

auto_encoder_static.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_ae_static import config
+from configs.review.config_ae_static import config
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 
 import tensorflow as tf
@@ -37,15 +37,16 @@ from models.cond_encoder import AutoEncoderNew as AutoEncoder
 
 
 def init_wandb():
-    run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp')
+    run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp', tags=config['tags'])
     artifact = wandb.Artifact('code', type='code')
     artifact.add_file('auto_encoder_static.py')
     artifact.add_file('losses.py')
     artifact.add_file('models/base.py')
+    artifact.add_file('models/resnet.py')
     artifact.add_file('models/cond_encoder.py')
     artifact.add_file('dataset.py')
     artifact.add_file('convnext/models/convnext_tf.py')
-    config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs.')]
+    config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs.review.')]
     for config_file_name in config_file_names:
         artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
     run.log_artifact(artifact)
@@ -138,7 +139,10 @@ def train():
         if step == 0:
             to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
 
-            
+        # if step % 1000 == 0:    
+        #     wandb.log(to_log, step=step, commit=True)
+        # else:
+            # wandb.log(to_log, step=step, commit=False)
         wandb.log(to_log, step=step)
 
         if step % 10000 == 0:

calculate_error.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_abl_all_10p_curv_25_r2 import config
+from configs.review.config_abl_all 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_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_pyr_32.py

@@ -1,7 +1,7 @@
 config = dict()
 
 config['gpu'] = '0'
-config['name'] = 'pyr_abl_32_r1'
+config['name'] = 'pyr_abl_32'
 config['notes'] = ''
 
 # DON'T FORGET TO RESET SSIM LOSS USAGE

configs/config_abl_pyr_32_skirt.py

@@ -1,8 +1,8 @@
 config = dict()
 
 config['gpu'] = '3'
-config['name'] = 'pyr_abl_32_skirt_r1'
-config['notes'] = ''
+config['name'] = 'pyr_abl_32_skirt_2'
+config['notes'] = 'continued from pyr_abl_32_skirt-180k'
 
 # DON'T FORGET TO RESET SSIM LOSS USAGE
 # DON'T FORGET TO CHECK MODEL LOADING
@@ -24,10 +24,7 @@ config['pyramid']['resolutions'] = [32]
 config['pyramid']['pretrained_pyramid'] = ''
 config['pyramid']['final_upscale_enhancer'] = 'checkpoints/32_512_skirt'
 
-config['pretrained_model'] = ''
-config['pretrained_model_iteration'] = ''
-
-config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
+config['train_dir'] = '/home/workspace/local-data/train_skirt'
 config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
 config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
 config['batch_size'] = 4
@@ -38,7 +35,7 @@ config['pyramid']['static_model'] = 'checkpoints/static_1_skirt'
 config['pyramid']['body_model'] = 'checkpoints/body_1_skirt_2_2'
 config['pyramid']['curvature_model'] = 'checkpoints/curv_1_skirt'
 
-config['dataset_in_memory'] = True
+config['dataset_in_memory'] = False
 config['resolution'] = None
 config['latent_dim'] = 128
 
@@ -68,7 +65,6 @@ config['signature_static'] = { 'uv_static': (512, 256, 3),
 config['pyramid']['is_label'] = False
 config['pyramid']['latent_dim'] = config['latent_dim']
 config['pyramid']['add_noise'] = True
-config['pyramid']['noise_amount'] = 0.001
 
 config['pyramid']['loss_weights'] = [1,1,1,1,1]
 

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,),
-                        }
-

configs/config_ae_static.py

@@ -31,7 +31,7 @@ config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
 config['cond_ae']['activation'] = 'linear'
 
 
-config['gpu'] = '5'
+config['gpu'] = '0'
 config['lr'] = 0.0005
 config['name'] = 'static_post_4'
 config['notes'] = 'convnext + convnext, | \

configs/review/config_abl_all.py

@@ -16,17 +16,14 @@ 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['gpu'] = '4'
+config['name'] = 'abl_all_resnet_r7'
 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
+config['avg_garment_path'] = 'custom_data/train_no_shape_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv  train_no_shape_one_hot_avg_uv
 if config['use_skinning']:
   assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
 
@@ -34,21 +31,21 @@ 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['is_skirt'] = False
 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['train_dir'] = '/home/workspace/local-data/train'
+config['valid_dir'] = '/home/workspace/local-data/valid'
+config['test_dir'] = '/home/workspace/data/dataset/test_ready'
 
-config['frames_file'] = 'frames_skirt_10p.txt'
-config['frames_file_valid'] = 'frames_skirt_10p.txt'
+# config['frames_file'] = 'frames_dress_30k.txt'
+# config['frames_file_valid'] = 'frames_dress_30k.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['static_model'] = 'checkpoints/post/static_r7_3'
+config['body_model'] = 'checkpoints/post/body_r7_3'
+config['curvature_model'] = 'checkpoints/post/curv_r7_3'
 
 
 config['smpl_res'] = 14475
@@ -57,7 +54,6 @@ if config['is_cape']:
 
 
 config['pretrained_model'] = ''
-config['latent_dim'] = 128
 
 ###############    
 config['lr'] = 0.0001#                 0.0001
@@ -89,37 +85,42 @@ 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']['noise_amount'] = 0.001
 
 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['cvae']['encoder_depths'] = [3, 3, 3, 9, 3]
+config['cvae']['encoder_dims'] = [32, 64, 128, 256, 64]
+config['cvae']['decoder_depths'] = config['cvae']['encoder_depths']
+config['cvae']['decoder_dims'] = config['cvae']['encoder_dims']
 
 
-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_static'] = dict()
+config['cond_ae_static']['is_label'] = False
+config['cond_ae_static']['kernel_size'] = 7
+config['cond_ae_static']['bottleneck_size'] = 8
+config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 9, 3]
+config['cond_ae_static']['encoder_dims'] = [16, 32, 64, 64, config['cond_ae_static']['bottleneck_size']]
+config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
+config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
+config['cond_ae_static']['activation'] = 'tanh'
+
+config['cond_ae_curv'] = dict()
+config['cond_ae_curv']['is_label'] = True
+config['cond_ae_curv']['kernel_size'] = 7
+config['cond_ae_curv']['bottleneck_size'] = 40
+config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
+config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
+config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
+config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
+config['cond_ae_curv']['activation'] = 'tanh'
 
 config['cond_ae_body'] = dict()
-config['cond_ae_body']['latent_dim'] = config['latent_dim']
+config['cond_ae_body']['is_label'] = False
 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]
+config['cond_ae_body']['bottleneck_size'] = 16
+config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
+config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
+config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
+config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
+config['cond_ae_body']['activation'] = 'tanh'

configs/review/config_ae_body.py

@@ -0,0 +1,51 @@
+config = dict()
+
+
+config['batch_size'] = 4
+config['train_dir'] = '/home/workspace/local-data/train'
+config['valid_dir'] = '/home/workspace/local-data/valid'
+config['test_dir'] = '/home/workspace/data/dataset/test_ready'
+config['use_filled'] = False
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = False
+config['is_cape'] = False
+config['remove_global_orientation'] = True
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+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'] = 16
+config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
+config['cond_ae']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae']['bottleneck_size']]
+config['cond_ae']['decoder_depths'] = config['cond_ae']['encoder_depths']
+config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['gpu'] = '4'
+config['lr'] = 0.0001
+config['name'] = 'body_r7_3'
+config['notes'] = 'resnet18' 
+config['tags'] = ['review_7']
+
+config['cond_ae']['upscale_output'] = False
+config['pretrained_model'] = ''
+
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'pose_shape': (72 + 11,),
+                          'uv_body_posed': (3, 512, 256,),
+                          'mesh_body_posed': (config['smpl_res'], 3,),
+                        }
+config['signature_static'] = { 
+                         'mesh_mask': (config['smpl_res'],),
+                        }

configs/review/config_ae_curv.py

@@ -0,0 +1,50 @@
+config = dict()
+
+
+config['batch_size'] = 4
+config['train_dir'] = '/home/workspace/local-data/train'
+config['valid_dir'] = '/home/workspace/local-data/valid'
+config['test_dir'] = '/home/workspace/data/dataset/test_ready'
+config['use_filled'] = False
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = False
+config['is_cape'] = False
+config['remove_global_orientation'] = True
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+config['cond_ae'] = dict()
+config['cond_ae']['is_label'] = True
+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'] = 40
+config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
+config['cond_ae']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae']['bottleneck_size']]
+config['cond_ae']['decoder_depths'] = config['cond_ae']['encoder_depths']
+config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['gpu'] = '3'
+config['lr'] = 0.001
+config['name'] = 'curv_r7_3'
+config['notes'] = 'resnet18' 
+config['tags'] = ['review_7']
+
+config['cond_ae']['upscale_output'] = False
+config['pretrained_model'] = ''
+
+
+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/review/config_ae_static.py

@@ -0,0 +1,51 @@
+config = dict()
+
+
+config['batch_size'] = 4
+config['train_dir'] = '/home/workspace/local-data/train'
+config['valid_dir'] = '/home/workspace/local-data/valid'
+config['test_dir'] = '/home/workspace/data/dataset/test_ready'
+config['use_filled'] = False
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = True
+config['is_cape'] = False
+config['remove_global_orientation'] = True
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+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'] = 8
+config['cond_ae']['encoder_depths'] = [3, 3, 3, 27, 3]
+config['cond_ae']['encoder_dims'] = [8, 16, 32, 32, config['cond_ae']['bottleneck_size']]
+config['cond_ae']['decoder_depths'] = config['cond_ae']['encoder_depths']
+config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
+config['cond_ae']['activation'] = 'linear'
+
+
+config['gpu'] = '6'
+config['lr'] = 0.0005
+config['name'] = 'static_r7_3'
+config['notes'] = 'resnet18 based, one more block'
+config['tags'] = ['review_7']
+
+    
+
+config['cond_ae']['upscale_output'] = False
+config['pretrained_model'] = ''
+
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (), }
+config['signature_static'] = { 'uv_static': (512, 256, 3),
+                         'mesh_static': (config['smpl_res'], 3,),
+                         '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))):

cvae.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_abl_all_10p_curv_100_r2 import config
+from configs.review.config_abl_all import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -41,7 +41,7 @@ from losses import collision_loss as collision_penalty
 from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.smpl import SMPL
-from models.cvae_model import CVAE
+from models.cvae_model import CVAENew as CVAE
 
 
 def init_wandb():
@@ -58,7 +58,9 @@ 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:
-        artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
+        file_name = '/'.join(config_file_name.split('.')) + '.py'
+        if os.path.exists(file_name):
+            artifact.add_file(file_name)
     run.log_artifact(artifact)
 
 uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_adaptive=False, is_smpl_orig=config['is_cape'])
@@ -425,8 +427,6 @@ def sample(folder):
 
 
 
-
-
 def test():
     str_split = config['name'].split('_')
     test_idx = str_split.index('test')
@@ -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']], 

cvae_pyramid.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_abl_pyr_32_skirt import config
+from configs.post.config_abl_pyr_64_128_5 import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -41,7 +41,7 @@ from losses import collision_loss as collision_penalty
 from custom_utils.custom_utils import batch_to_dict, load_model
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.smpl import SMPL
-from models.pyramid_model_optimized import Pyramid
+from models.pyramid_model_optimized import PyramidNew as Pyramid
 from postprocessing import fix_collision
 
 
@@ -59,11 +59,9 @@ def init_wandb():
     artifact.add_file('models/cond_encoder.py')
     artifact.add_file('dataset.py')
     artifact.add_file('convnext/models/convnext_tf.py')
-    config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs')]
+    config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs.post.')]
     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 = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
@@ -210,7 +208,7 @@ def plot_3d(mesh_gt, mesh_pred_unposed_levels, faces, mesh_posed, collision, col
     return fig
 
 
-# @tf.function
+@tf.function
 def train_step(batch):
     levels_input, conds = group_inputs_for_levels(batch, config)
 
@@ -263,8 +261,8 @@ def train_step(batch):
         else:
             reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
 
-        # if config['use_norm_loss'] and step > 2000:
-        #     reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
+        if config['use_norm_loss'] and step > 2000:
+            reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
         
         kl_total += kl
 
@@ -336,8 +334,8 @@ def valid_step(batch):
     else:
         reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
     
-    # if config['use_norm_loss'] and step > 2000:
-    #     reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
+    if config['use_norm_loss'] and step > 2000:
+        reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
         
     kl_total += kl
 
@@ -733,7 +731,7 @@ def speed_step(levels_input, conds):
 
 
 if __name__ == '__main__':
-    # init_wandb()
+    init_wandb()
     parser = argparse.ArgumentParser(description='')
     parser.add_argument('--name', type=str, default='lhunor')
     args = vars(parser.parse_args())
@@ -792,7 +790,7 @@ if __name__ == '__main__':
     # generate_results()
 
     ckpt = tf.train.Checkpoint(model=model)
-    manager = tf.train.CheckpointManager(ckpt, 'checkpoints/' + config['name'], max_to_keep=20)
+    manager = tf.train.CheckpointManager(ckpt, 'checkpoints/post/' + config['name'], max_to_keep=20)
 
     # load_model(model, 'checkpoints/pyr_abl_32_skirt', iteration='180000')
 
@@ -809,18 +807,18 @@ if __name__ == '__main__':
         model.static_enc.trainable = False
         model.body_enc.trainable = False
         model.curv_enc.trainable = False
-        model.cond_encoder.trainable = False
+
 
     # load_model(model.levels[0].upscale_enhancer, config['pyramid']['levels'][0]['upscale_enhancer'])
     # model.levels[0].upscale_enhancer.trainable = False
 
 
-    for i in range(len(model.levels)-1):
+    for i in range(len(model.levels)):
         load_model(model.levels[i].upscale_enhancer, config['pyramid']['levels'][i]['upscale_enhancer'])
         model.levels[i].upscale_enhancer.trainable = False
-    if config['pyramid']['resolutions'][-1] != 512:
-        load_model(model.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
-        model.upscale_enhancer.trainable = False
+    # if config['pyramid']['resolutions'][-1] != 512:
+    #     load_model(model.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
+    #     model.upscale_enhancer.trainable = False
 
     for i in range(len(model.levels)-1):
         model.levels[i].trainable = False

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.review.config_abl_all import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -42,7 +42,7 @@ from losses import collision_loss as collision_penalty
 from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.smpl import SMPL
-from models.cvae_model import CVAE
+from models.cvae_model import CVAENew as CVAE
 from custom_utils.supersmpl_io import writeOBJ
 # from unif_metric import chamfer_and_score
 
@@ -102,7 +102,7 @@ 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/' + 'abl_all_120k')

models/base.py

@@ -1,5 +1,7 @@
 import tensorflow as tf
 from convnext.models.convnext_tf import ConvNeXt, ConvNeXtReverse, Block, ConvNeXtReverseLabels
+from models.resnet import resnet_18, make_basic_block_layer
+
 
 class Bottleneck(tf.keras.Model):
     
@@ -111,20 +113,34 @@ class Decoder(tf.keras.Model):
 
 class VariationalEncoderNew(tf.keras.Model):
     
-    def __init__(self, depths, dims, kernel_size=7):
+    def __init__(self, features):
         super(VariationalEncoderNew, self).__init__()
 
-        self.encoder = ConvNeXt(depths=depths, dims=dims, include_top=False, kernel_size=kernel_size)
+        self.encoder = resnet_18(features)
 
-        self.mean_branch = tf.keras.Sequential(
-                [Block(dim=64, drop_path=0, kernel_size=kernel_size,
-                       layer_scale_init_value=1e-6, prefix=f'bottleneck{j}') for j in range(5)]
-            )
+        self.mean_branch = tf.keras.Sequential([
+            tf.keras.layers.Conv2D(filters=features,
+                                            kernel_size=(3, 3),
+                                            strides=1,
+                                            padding="same"),
+            tf.keras.layers.BatchNormalization(),
+            tf.keras.layers.Conv2D(filters=features,
+                                            kernel_size=(3, 3),
+                                            strides=1,
+                                            padding="same")
+        ])
 
-        self.var_branch = tf.keras.Sequential(
-                [Block(dim=64, drop_path=0, kernel_size=kernel_size,
-                       layer_scale_init_value=1e-6, prefix=f'bottleneck{j}') for j in range(5)]
-            )
+        self.var_branch = tf.keras.Sequential([
+            tf.keras.layers.Conv2D(filters=features,
+                                            kernel_size=(3, 3),
+                                            strides=1,
+                                            padding="same"),
+            tf.keras.layers.BatchNormalization(),
+            tf.keras.layers.Conv2D(filters=features,
+                                            kernel_size=(3, 3),
+                                            strides=1,
+                                            padding="same")
+        ])
 
         self.flatten_mean = tf.keras.layers.Flatten()
         self.flatten_var = tf.keras.layers.Flatten()
@@ -147,10 +163,10 @@ class VariationalEncoderNew(tf.keras.Model):
 
 class AEEncoderNew(tf.keras.Model):
     
-    def __init__(self, depths, dims, kernel_size=7, activation='tanh'):
+    def __init__(self, features, activation='tanh'):
         super(AEEncoderNew, self).__init__()
 
-        self.encoder = ConvNeXt(depths=depths, dims=dims, include_top=False, kernel_size=kernel_size)
+        self.encoder = resnet_18(features)
         self.act = tf.keras.layers.Activation(activation)
 
 
@@ -171,15 +187,15 @@ class AEEncoderNew(tf.keras.Model):
 
 class DecoderNew(tf.keras.Model):
     
-    def __init__(self, depths, dims, kernel_size=7, is_label=False):
+    def __init__(self, features, is_label=False, is_cvae=False):
         super(DecoderNew, self).__init__()
 
         self.is_label = is_label
 
         if not is_label:
-            self.decoder = ConvNeXtReverse(depths=depths, dims=dims, kernel_size=kernel_size)
+            self.decoder = resnet_18(features, reversed=True, is_cvae=is_cvae)
         else:
-            self.decoder = ConvNeXtReverseLabels(depths=depths, dims=dims, nr_of_classes=7)
+            self.decoder = resnet_18(features, reversed=True, labels=6, is_cvae=is_cvae)
             
 
     def call(self, inputs, training):

models/cond_encoder.py

@@ -25,8 +25,8 @@ class AutoEncoderNew(tf.keras.Model):
     def __init__(self, config):
         super(AutoEncoderNew, self).__init__()
 
-        self.encoder : AEEncoderNew = AEEncoderNew(config['encoder_depths'], config['encoder_dims'], config['kernel_size'], config['activation'])
-        self.decoder : DecoderNew = DecoderNew(config['decoder_depths'], config['decoder_dims'], kernel_size=config['kernel_size'], is_label=config['is_label'])
+        self.encoder : AEEncoderNew = AEEncoderNew(config['bottleneck_size'], config['activation'])
+        self.decoder : DecoderNew = DecoderNew(config['bottleneck_size'], is_label=config['is_label'])
 
 
     def call(self, inputs, training):

models/cvae_model.py

@@ -43,7 +43,7 @@ class CVAE(tf.keras.Model):
         self.cond_encoder = tf.keras.Sequential([tf.keras.layers.Dense(512, activation='relu'),
                                                  tf.keras.layers.Dense(256, activation='relu'),
                                                  tf.keras.layers.Dense(128, activation='relu'),
-                                                #  tf.keras.layers.Lambda(lambda x: x / 1000),
+                                                 tf.keras.layers.Lambda(lambda x: x / 1000),
                                                  tf.keras.layers.Dense(config['latent_dim'], activation='tanh')])
 
         self.encoder = VariationalEncoder(config['cvae']['latent_dim'], config['cvae']['encoder_depths'], config['cvae']['encoder_dims'], kernel_size=config['cvae']['kernel_size'])
@@ -159,16 +159,16 @@ class CVAENew(tf.keras.Model):
         self.size = config['resolution']
 
         if not('only_curvatures' in config.keys() and config['only_curvatures'] == True):
-            self.static_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_static']['encoder_depths'], config['cond_ae_static']['encoder_dims'], config['cond_ae_static']['kernel_size'], config['cond_ae_static']['activation'])
+            self.static_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_static']['bottleneck_size'], config['cond_ae_static']['activation'])
             if config['static_model'] != '':
                 load_model(self.static_enc, config['static_model'] + '_enc')
 
-            self.body_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_body']['encoder_depths'], config['cond_ae_body']['encoder_dims'], config['cond_ae_body']['kernel_size'])
+            self.body_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_body']['bottleneck_size'])
             if config['body_model'] != '':
                 load_model(self.body_enc, config['body_model'] + '_enc')
 
         if config['use_curvature']:
-            self.curv_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_curv']['encoder_depths'], config['cond_ae_curv']['encoder_dims'], config['cond_ae_curv']['kernel_size'], config['cond_ae_curv']['activation'])
+            self.curv_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_curv']['bottleneck_size'], config['cond_ae_curv']['activation'])
             if config['curvature_model'] != '':
                 load_model(self.curv_enc, config['curvature_model'] + '_enc')
 
@@ -179,10 +179,10 @@ class CVAENew(tf.keras.Model):
             if config['use_curvature']:
                 self.curv_enc.trainable = False
 
-        self.encoder = VariationalEncoderNew(config['cvae']['encoder_depths'], config['cvae']['encoder_dims'], kernel_size=config['cvae']['kernel_size'])
-        self.decoder = DecoderNew(config['cvae']['decoder_depths'], config['cvae']['decoder_dims'], config['cvae']['kernel_size'])
+        self.encoder = VariationalEncoderNew(config['cvae']['encoder_dims'][-1])
+        self.decoder = DecoderNew(config['cvae']['decoder_dims'][-1], is_cvae=True)
 
-        self.reshape = tf.keras.layers.Reshape((8, 4, 64))
+        self.reshape = tf.keras.layers.Reshape((8, 4, config['cvae']['decoder_dims'][-1]))
 
         if config['avg_garment_path'] != '':
             self.avg_garment = np.load(config['avg_garment_path'])

models/resnet.py

@@ -0,0 +1,254 @@
+# taken from https://github.com/calmisential/TensorFlow2.0_ResNet/blob/master/models/resnet.py
+
+import tensorflow as tf
+
+
+class BasicBlock(tf.keras.layers.Layer):
+
+    def __init__(self, filter_num, stride=1, upsample=False):
+        super(BasicBlock, self).__init__()
+        if not upsample:
+            self.conv1 = tf.keras.layers.Conv2D(filters=filter_num,
+                                                kernel_size=(3, 3),
+                                                strides=stride,
+                                                padding="same")
+        else:
+            self.conv1 = tf.keras.layers.Conv2DTranspose(filters=filter_num,
+                                                kernel_size=(3, 3),
+                                                strides=stride,
+                                                padding="same")
+        self.bn1 = tf.keras.layers.BatchNormalization()
+        self.conv2 = tf.keras.layers.Conv2D(filters=filter_num,
+                                            kernel_size=(3, 3),
+                                            strides=1,
+                                            padding="same")
+        self.bn2 = tf.keras.layers.BatchNormalization()
+        if stride != 1:
+            if not upsample:
+                self.updownsample = tf.keras.Sequential()
+                self.updownsample.add(tf.keras.layers.Conv2D(filters=filter_num,
+                                                        kernel_size=(1, 1),
+                                                        strides=stride))
+                self.updownsample.add(tf.keras.layers.BatchNormalization())
+            else:
+                self.updownsample = tf.keras.Sequential()
+                self.updownsample.add(tf.keras.layers.Conv2DTranspose(filters=filter_num,
+                                                        kernel_size=(1, 1),
+                                                        strides=stride))
+                self.updownsample.add(tf.keras.layers.BatchNormalization())
+        else:
+            self.updownsample = lambda x: x
+
+    def call(self, inputs, training=None, **kwargs):
+        residual = self.updownsample(inputs)
+
+        x = self.conv1(inputs)
+        x = self.bn1(x, training=training)
+        x = tf.nn.relu(x)
+        x = self.conv2(x)
+        x = self.bn2(x, training=training)
+
+        output = tf.nn.relu(tf.keras.layers.add([residual, x]))
+
+        return output
+
+
+class BottleNeck(tf.keras.layers.Layer):
+    def __init__(self, filter_num, stride=1):
+        super(BottleNeck, self).__init__()
+        self.conv1 = tf.keras.layers.Conv2D(filters=filter_num,
+                                            kernel_size=(1, 1),
+                                            strides=1,
+                                            padding='same')
+        self.bn1 = tf.keras.layers.BatchNormalization()
+        self.conv2 = tf.keras.layers.Conv2D(filters=filter_num,
+                                            kernel_size=(3, 3),
+                                            strides=stride,
+                                            padding='same')
+        self.bn2 = tf.keras.layers.BatchNormalization()
+        self.conv3 = tf.keras.layers.Conv2D(filters=filter_num * 4,
+                                            kernel_size=(1, 1),
+                                            strides=1,
+                                            padding='same')
+        self.bn3 = tf.keras.layers.BatchNormalization()
+
+        self.downsample = tf.keras.Sequential()
+        self.downsample.add(tf.keras.layers.Conv2D(filters=filter_num * 4,
+                                                   kernel_size=(1, 1),
+                                                   strides=stride))
+        self.downsample.add(tf.keras.layers.BatchNormalization())
+
+    def call(self, inputs, training=None, **kwargs):
+        residual = self.downsample(inputs)
+
+        x = self.conv1(inputs)
+        x = self.bn1(x, training=training)
+        x = tf.nn.relu(x)
+        x = self.conv2(x)
+        x = self.bn2(x, training=training)
+        x = tf.nn.relu(x)
+        x = self.conv3(x)
+        x = self.bn3(x, training=training)
+
+        output = tf.nn.relu(tf.keras.layers.add([residual, x]))
+
+        return output
+
+
+def make_basic_block_layer(filter_num, blocks, stride=1, upsample=False):
+    res_block = tf.keras.Sequential()
+    res_block.add(BasicBlock(filter_num, stride=stride, upsample=upsample))
+
+    for _ in range(1, blocks):
+        res_block.add(BasicBlock(filter_num, stride=1))
+
+    return res_block
+
+
+def make_bottleneck_layer(filter_num, blocks, stride=1):
+    res_block = tf.keras.Sequential()
+    res_block.add(BottleNeck(filter_num, stride=stride))
+
+    for _ in range(1, blocks):
+        res_block.add(BottleNeck(filter_num, stride=1))
+
+    return res_block
+
+
+class ResNetTypeI(tf.keras.Model):
+    def __init__(self, layer_params, features, reversed=False, labels=None, is_cvae=False):
+        super(ResNetTypeI, self).__init__()
+        self.reversed = reversed
+        self.labels = labels
+
+        if not reversed:
+            self.conv1 = tf.keras.layers.Conv2D(filters=64,
+                                                kernel_size=(7, 7),
+                                                strides=2,
+                                                padding="same")
+            self.bn1 = tf.keras.layers.BatchNormalization()
+            self.pool1 = tf.keras.layers.MaxPool2D(pool_size=(3, 3),
+                                                strides=2,
+                                                padding="same")
+        else:
+            self.conv1 = tf.keras.layers.Conv2DTranspose(filters=3 if labels is None else labels,
+                                                kernel_size=(7, 7),
+                                                strides=2,
+                                                padding="same")
+            self.bn1 = tf.keras.layers.BatchNormalization()
+            # self.pool1 = tf.keras.layers.MaxPool2D(pool_size=(3, 3),
+            #                                     strides=2,
+            #                                     padding="same")
+            self.pool1 = tf.keras.layers.UpSampling2D()
+
+        self.layer1 = make_basic_block_layer(filter_num=64,
+                                             blocks=layer_params[0],
+                                             stride=1 if not reversed else 2,
+                                             upsample=reversed)
+        self.layer2 = make_basic_block_layer(filter_num=64,
+                                             blocks=layer_params[1],
+                                             stride=2,
+                                             upsample=reversed)
+        self.layer3 = make_basic_block_layer(filter_num=64,
+                                             blocks=layer_params[2],
+                                             stride=2,
+                                             upsample=reversed)
+        self.layer4 = make_basic_block_layer(filter_num=128,
+                                             blocks=layer_params[3],
+                                             stride=2,
+                                             upsample=reversed)
+        self.layer5 = make_basic_block_layer(filter_num=features if not is_cvae else features*2,
+                                             blocks=layer_params[4],
+                                             stride=2 if not reversed else 1,
+                                             upsample=reversed)
+
+    def call(self, inputs, training=None, mask=None):
+        if not self.reversed:
+            x = self.conv1(inputs)
+            x = self.bn1(x, training=training)
+            x = tf.nn.relu(x)
+            x = self.pool1(x)
+            x = self.layer1(x, training=training)
+            x = self.layer2(x, training=training)
+            x = self.layer3(x, training=training)
+            x = self.layer4(x, training=training)
+            x = self.layer5(x, training=training)
+        else:
+            x = self.layer5(inputs, training=training)
+            x = self.layer4(x, training=training)
+            x = self.layer3(x, training=training)
+            x = self.layer2(x, training=training)
+            x = self.layer1(x, training=training)
+
+            x = self.pool1(x)
+            x = self.bn1(x, training=training)
+            # x = tf.nn.relu(x)
+            x = self.conv1(x)
+            if self.labels is None:
+                x = tf.nn.tanh(x)
+            # else:
+            #     x = tf.nn.softmax(x)
+
+        return x
+
+
+class ResNetTypeII(tf.keras.Model):
+    def __init__(self, layer_params):
+        super(ResNetTypeII, self).__init__()
+        self.conv1 = tf.keras.layers.Conv2D(filters=64,
+                                            kernel_size=(7, 7),
+                                            strides=2,
+                                            padding="same")
+        self.bn1 = tf.keras.layers.BatchNormalization()
+        self.pool1 = tf.keras.layers.MaxPool2D(pool_size=(3, 3),
+                                               strides=2,
+                                               padding="same")
+
+        self.layer1 = make_bottleneck_layer(filter_num=64,
+                                            blocks=layer_params[0])
+        self.layer2 = make_bottleneck_layer(filter_num=128,
+                                            blocks=layer_params[1],
+                                            stride=2)
+        self.layer3 = make_bottleneck_layer(filter_num=256,
+                                            blocks=layer_params[2],
+                                            stride=2)
+        self.layer4 = make_bottleneck_layer(filter_num=512,
+                                            blocks=layer_params[3],
+                                            stride=2)
+
+        self.avgpool = tf.keras.layers.GlobalAveragePooling2D()
+        self.fc = tf.keras.layers.Dense(units=NUM_CLASSES, activation=tf.keras.activations.softmax)
+
+    def call(self, inputs, training=None, mask=None):
+        x = self.conv1(inputs)
+        x = self.bn1(x, training=training)
+        x = tf.nn.relu(x)
+        x = self.pool1(x)
+        x = self.layer1(x, training=training)
+        x = self.layer2(x, training=training)
+        x = self.layer3(x, training=training)
+        x = self.layer4(x, training=training)
+        x = self.avgpool(x)
+        output = self.fc(x)
+
+        return output
+
+
+def resnet_18(features, reversed=False, labels=None, is_cvae=False):
+    return ResNetTypeI(layer_params=[2, 2, 2, 2, 2], features=features, reversed=reversed, labels=labels, is_cvae=is_cvae)
+
+
+def resnet_34():
+    return ResNetTypeI(layer_params=[3, 4, 6, 3])
+
+
+def resnet_50():
+    return ResNetTypeII(layer_params=[3, 4, 6, 3])
+
+
+def resnet_101():
+    return ResNetTypeII(layer_params=[3, 4, 23, 3])
+
+
+def resnet_152():
+    return ResNetTypeII(layer_params=[3, 8, 36, 3])