barely working decoder

aaa_helpers.txt

@@ -6,20 +6,11 @@ nohup python auto_encoder_static.py --name="lhunor_static_iccv_1_cape_f" > nohup
 nohup python auto_encoder_static.py --name="lhunor_static_iccv_1_cape_m" > nohup/iccv/encoders/static_iccv_1_cape_m.out 2>&1 &
 nohup python auto_encoder_body.py --name="lhunor_body_iccv_1" > nohup/iccv/encoders/body_iccv_1.out 2>&1 &
 nohup python auto_encoder_body.py --name="lhunor_body_iccv_1_skirt" > nohup/iccv/encoders/body_iccv_1_skirt.out 2>&1 &
-nohup python auto_encoder_body.py --name="lhunor_body_iccv_1_cape_m" > nohup/iccv/encoders/body_iccv_1_cape_m.out 2>&1 &
+nohup python auto_encoder_body.py --name="lhunor_body_iccv_1_skirt" > nohup/iccv/encoders/body_iccv_1_skirt.out 2>&1 &
-nohup python auto_encoder_curv.py --name="lhunor_curv_iccv_1" > nohup/iccv/encoders/curv_iccv_1.out 2>&1 &
+nohup python auto_encoder_curv.py --name="lhunor_curv_iccv_1 " > nohup/iccv/encoders/curv_iccv_1.out 2>&1 &
 nohup python auto_encoder_curv.py --name="lhunor_curv_iccv_1_skirt" > nohup/iccv/encoders/curv_iccv_1_skirt.out 2>&1 &
 nohup python auto_encoder_curv.py --name="lhunor_curv_iccv_1_cape_f" > nohup/iccv/encoders/curv_iccv_1_cape_f.out 2>&1 &
-nohup python auto_encoder_curv.py --name="lhunor_curv_iccv_1_cape_m" > nohup/iccv/encoders/curv_iccv_1_cape_m.out 2>&1 &
+nohup python cvae.py --name="lhunor_cvae_iccv_gnn_decoder_1" > nohup/iccv/cvae_iccv_gnn_decoder_1.out 2>&1 &
-
-nohup python cvae.py --name="lhunor_abl_all" > nohup/iccv/ablation/abl_all.out 2>&1 &
-
-nohup python upscale_test.py --name="iccv_32" > nohup/iccv/upscale/iccv_32.out 2>&1 &
-nohup python upscale_test.py --name="iccv_64" > nohup/iccv/upscale/iccv_64.out 2>&1 &
-nohup python upscale_test.py --name="iccv_128" > nohup/iccv/upscale/iccv_128.out 2>&1 &
-nohup python upscale_test.py --name="iccv_256" > nohup/iccv/upscale/iccv_256.out 2>&1 &
-
-nohup python upscale_test.py --name="iccv_32_skirt" > nohup/iccv/upscale/iccv_32_skirt.out 2>&1 &
 
 skirts!!!!
 pyramid next week

auto_encoder_body.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_ae_body_cape_m import config
+from configs.iccv.config_ae_body_cape_f import config
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 
 import tensorflow as tf

auto_encoder_curv.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_ae_curv_cape_m import config
+from configs.iccv.config_ae_curv_cape_f import config
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 
 import tensorflow as tf

auto_encoder_static.py

@@ -247,9 +247,9 @@ if __name__ == '__main__':
         load_model(model.decoder, config['pretrained_model'] + '_dec')
 
     ckpt_enc = tf.train.Checkpoint(model=model.encoder)
-    manager_enc = tf.train.CheckpointManager(ckpt_enc, 'checkpoints/iccv/' + config['name'] + '_enc', max_to_keep=20)
+    manager_enc = tf.train.CheckpointManager(ckpt_enc, 'checkpoints/post/' + config['name'] + '_enc', max_to_keep=20)
     ckpt_dec = tf.train.Checkpoint(model=model.decoder)
-    manager_dec = tf.train.CheckpointManager(ckpt_dec, 'checkpoints/iccv/' + config['name'] + '_dec', max_to_keep=20)
+    manager_dec = tf.train.CheckpointManager(ckpt_dec, 'checkpoints/post/' + config['name'] + '_dec', max_to_keep=20)
 
     # test()
     train()

configs/iccv/config_abl_all.py

@@ -14,18 +14,19 @@ config['use_extra_losses'] =          True # edge, collision, 3d mesh
 config['use_offsets'] =               True
 
 config['use_norm_loss'] =             True
+config['only_curvatures'] =           False
 
 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'] = '3'
+config['gpu'] = '4'
-config['name'] = 'abl_all'
+config['name'] = 'iccv_gnn_decoder_1'
 config['notes'] = ''
-config['tags'] = ['iccv', 'abl_all']
+config['tags'] = ['iccv', 'gnn', 'ggn_decoder']
 
-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
+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']:
+# if config['use_skinning']:
-  assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
+  # 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
 
@@ -39,39 +40,42 @@ 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['static_model'] = 'checkpoints/iccv/static_iccv_1'
+# config['frames_file'] = 'frames_dress_30k.txt'
-config['body_model'] = 'checkpoints/iccv/body_iccv_1'
+# config['frames_file_valid'] = 'frames_dress_30k.txt'
-config['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'
+# config['frames_file_test'] = 'frames_dress_30k.txt'
+
+config['static_model'] = 'checkpoints/post/static_post_2'
+config['body_model'] = 'checkpoints/post/body_post_2'
+config['curvature_model'] = 'checkpoints/post/curv_post_1'
 
 
 config['smpl_res'] = 14475
 if config['is_cape']:
   config['smpl_res'] = 3638
 
-config['latent_dim'] = 128
 
 config['pretrained_model'] = ''
 
 ###############    
 config['lr'] = 0.0001#                 0.0001
 ###############
-config['starting_balancing_coeff'] = 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': (14475, 3,),
+                          'mesh_body_posed': (config['smpl_res'], 3,),
-                          'mesh_posed': (14475, 3,),
+                          'mesh_posed': (config['smpl_res'], 3,),
                           'uv_curvature': (512, 256, 7),
                         }
 config['signature_static'] = { 'uv_static': (512, 256, 3),
-                              'mesh_mask': (14475,),
+                              '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'] = (14475, 3,)
+  config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
 else:
   config['signature_dynamic']['uv_posed'] = (512, 256, 3)
 
@@ -79,17 +83,15 @@ else:
 config['upscale_enhancer'] = ''
 config['freeze_encoders'] = True
 config['use_one_hot'] = False
-config['bottleneck_scale'] = 2
 
 config['cvae'] = dict()
 config['cvae']['is_label'] = False
-config['cvae']['latent_dim'] = 64 * config['bottleneck_scale']
+config['cvae']['add_noise'] = True
-config['cvae']['add_noise'] = False
+config['cvae']['noise_amount'] = 0.001
-config['cvae']['noise_amount'] = 0.0
 
 config['cvae']['kernel_size'] = 7
 config['cvae']['encoder_depths'] = [3, 3, 3, 9, 3]
-config['cvae']['encoder_dims'] = [32, 64, 128, 256, config['cvae']['latent_dim']]
+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']
 
@@ -97,9 +99,9 @@ config['cvae']['decoder_dims'] = config['cvae']['encoder_dims']
 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['bottleneck_scale']
+config['cond_ae_static']['bottleneck_size'] = 8
-config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
+config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 9, 3]
-config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
+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'
@@ -107,7 +109,7 @@ 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['bottleneck_scale']
+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']
@@ -117,7 +119,7 @@ config['cond_ae_curv']['activation'] = 'tanh'
 config['cond_ae_body'] = dict()
 config['cond_ae_body']['is_label'] = False
 config['cond_ae_body']['kernel_size'] = 7
-config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
+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']

configs/iccv/config_abl_pyr_32.py

@@ -1,184 +0,0 @@
-config = dict()
-
-config['gpu'] = '2'
-config['name'] = 'iccv_pyr_32'
-config['notes'] = ''
-config['tags'] = ['pyr', 'iccv']
-
-# 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 IF SKIRT
-# DON'T FORGET TO CHECK IF CAPE
-# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
-# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
-
-config['use_skinning'] =              True # and deshaping
-config['remove_global_orientation'] = True
-config['use_extra_losses'] =          True # edge, collision, 3d mesh
-config['use_offsets'] =               False
-config['use_norm_loss'] =             True
-
-config['pretrained_model'] = ''
-config['pretrained_model_iteration'] = ''
-
-config['pyramid'] = dict()
-config['pyramid']['resolutions'] = [32]
-config['pyramid']['pretrained_pyramid'] = ''
-config['pyramid']['final_upscale_enhancer'] = ''
-
-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['batch_size'] = 4
-config['is_skirt'] = False
-config['is_cape'] = False
-
-config['dataset_in_memory'] = False
-config['resolution'] = None
-
-config['smpl_res'] = 14475
-if config['is_cape']:
-  config['smpl_res'] = 3638
-
-config['lr'] = 0.0001
-config['starting_balancing_coeff'] = 0.001
-config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
-config['avg_garment_path'] = ''
-
-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,),
-                          'mesh_unposed': (config['smpl_res'], 3,),
-                          'uv_unposed': (512, 256, 3),
-                          'uv_curvature': (512, 256, 7),
-                        }
-config['signature_static'] = { 'uv_static': (512, 256, 3),
-                              'mesh_mask': (config['smpl_res'],),
-                              'uv_mask': (512, 256,),
-                              }
-
-config['pyramid']['is_label'] = False
-config['pyramid']['add_noise'] = True
-config['pyramid']['noise_amount'] = 0.001
-
-config['pyramid']['loss_weights'] = [1,1,1,1,1]
-
-for res in config['pyramid']['resolutions']:
-  if res != 512:
-      res_modifier = '_' + str(res)
-      config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
-      config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
-      config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
-      config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
-      config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
-
-config['pyramid']['static_model'] = 'checkpoints/post/static_post_2'
-config['pyramid']['body_model'] = 'checkpoints/post/body_post_2'
-config['pyramid']['curvature_model'] = 'checkpoints/post/curv_post_1'
-config['pyramid']['freeze_encoders'] = True
-
-
-config['pyramid']['levels'] = []
-for res in config['pyramid']['resolutions']:
-  if res == 512:
-    level = dict()
-    level['resolution'] = 512
-    level['latent_dim'] = config['latent_dim']
-    level['kernel_size'] = 7
-    level['encoder_depths'] = [3, 3, 9, 3]
-    level['encoder_dims'] = [32, 64, 128, 64]
-    level['decoder_input'] = (16, 8, 80)
-    level['decoder_depths'] = [3, 3, 9, 3]
-    level['decoder_dims'] = [256, 128, 64, 16]
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = ''
-    config['pyramid']['levels'].append(level)
-  elif res == 256:
-    level = dict()
-    level['resolution'] = 256
-    level['latent_dim'] = config['latent_dim']
-    level['kernel_size'] = 7
-    level['encoder_depths'] = [9, 9, 9, 9]
-    level['encoder_dims'] = [32, 64, 128, 64]
-    level['decoder_input'] = (8, 4, 128)
-    level['decoder_depths'] = [9, 9, 9, 9]
-    level['decoder_dims'] = [256, 128, 64, 16]
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = ''#'checkpoints/up_3_d256_512_2'
-    level['upscale_enhancer_freeze'] = True
-    config['pyramid']['levels'].append(level)
-  elif res == 128:
-    level = dict()
-    level['resolution'] = 128
-    level['latent_dim'] = config['latent_dim']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [16, 9, 9, 9]
-    level['encoder_dims'] = [64, 128, 128, 64]
-    level['decoder_input'] = (4, 2, 128)
-    level['decoder_depths'] = [16, 9, 9, 9]
-    level['decoder_dims'] = [256, 128, 128, 64]
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = ''#'checkpoints/up_3_d128_256_2'
-    level['upscale_enhancer_freeze'] = True
-    config['pyramid']['levels'].append(level)
-  elif res == 64:
-    level = dict()
-    level['resolution'] = 64
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [9, 9]
-    level['encoder_dims'] = [64, 64]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/64_512_2'#checkpoints/up_3_d64_128_2'
-    level['upscale_enhancer_freeze'] = True
-    config['pyramid']['levels'].append(level)
-  elif res == 32:
-    level = dict()
-    level['resolution'] = 32
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [16]
-    level['encoder_dims'] = [64]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/32_512_4'#'checkpoints/up_3_d32_64_2'
-    level['upscale_enhancer_freeze'] = True
-    config['pyramid']['levels'].append(level)
-  else:
-    raise NotImplementedError
-
-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']['is_label'] = False
-config['cond_ae_body']['kernel_size'] = 7
-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/iccv/config_ae_body_cape_m.py

@@ -1,52 +0,0 @@
-config = dict()
-
-
-config['batch_size'] = 4
-config['train_dir'] = '/home/workspace/local-data/cape_processed/train/male'
-config['valid_dir'] = '/home/workspace/local-data/cape_processed/test/male'
-config['test_dir'] = '/home/workspace/local-data/cape_processed/test/male'
-config['use_filled'] = False
-config['resolution'] = 512
-config['is_skirt'] = False
-config['dataset_in_memory'] = False
-config['is_cape'] = True
-config['remove_global_orientation'] = True
-
-config['smpl_res'] = 14475
-if config['is_cape']:
-  config['smpl_res'] = 3638
-
-config['latent_dim'] = 128
-config['bottleneck_scale'] = 2
-
-config['cond_ae'] = dict()
-config['cond_ae']['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['bottleneck_scale']
-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_iccv_1_cape_m'
-config['notes'] = '' 
-config['tags'] = ['iccv', 'body', 'cape_m'] 
-
-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/iccv/config_ae_curv_cape_m.py

@@ -1,51 +0,0 @@
-config = dict()
-
-
-config['batch_size'] = 4
-config['train_dir'] = '/home/workspace/local-data/cape_processed/train/male'
-config['valid_dir'] = '/home/workspace/local-data/cape_processed/test/male'
-config['test_dir'] = '/home/workspace/local-data/cape_processed/test/male'
-config['use_filled'] = False
-config['resolution'] = 512
-config['is_skirt'] = False
-config['dataset_in_memory'] = False
-config['is_cape'] = True
-config['remove_global_orientation'] = True
-
-config['smpl_res'] = 14475
-if config['is_cape']:
-  config['smpl_res'] = 3638
-
-config['latent_dim'] = 128
-config['bottleneck_scale'] = 2
-
-config['cond_ae'] = dict()
-config['cond_ae']['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['bottleneck_scale']
-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'] = '5'
-config['lr'] = 0.001
-config['name'] = 'curv_iccv_1_cape_m'
-config['notes'] = '' 
-config['tags'] = ['iccv', 'curv', 'cape_m'] 
-
-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/iccv/config_ae_static_cape_m.py

@@ -33,14 +33,14 @@ config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
 config['cond_ae']['activation'] = 'tanh'
 
 
-config['gpu'] = '3'
+config['gpu'] = '5'
 config['lr'] = 0.0005
 config['name'] = 'static_iccv_1_cape_m'
 config['notes'] = ' ' 
 config['tags'] = ['iccv', 'static', 'cape_m'] 
 
 config['cond_ae']['upscale_output'] = False
-config['pretrained_model'] = 'checkpoints/iccv/static_iccv_1_cape_m_old'
+config['pretrained_model'] = ''
 
 
 config['signature_dynamic'] = { 'id': (), 'frame_nr': (), }

cvae.py

@@ -42,10 +42,11 @@ from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mes
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.smpl import SMPL
 from models.cvae_model import CVAENew as CVAE
+from models.gnn import Smoother
 
 
 def init_wandb():
-    run = wandb.init(project='iccv', name=config['name'], notes=config['notes'], dir='/tmp', tags=config['tags'])
+    run = wandb.init(project='ablation', name=config['name'], notes=config['notes'], dir='/tmp', tags=config['tags'])
     artifact = wandb.Artifact('code', type='code')
     artifact.add_file('cvae.py')
     artifact.add_file('models/cvae_model.py')
@@ -53,6 +54,7 @@ def init_wandb():
     # artifact.add_file('configs/config_base.py')
     # artifact.add_file('configs/config_cvae.py')
     artifact.add_file('models/base.py')
+    artifact.add_file('models/gnn.py')
     artifact.add_file('models/cond_encoder.py')
     artifact.add_file('dataset.py')
     artifact.add_file('convnext/models/convnext_tf.py')
@@ -86,7 +88,7 @@ def train_step(batch):
 
         pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=True)
 
-        mesh_pred = uv2mesh(pred)
+        mesh_pred = pred
 
         if config['use_skinning']:
             shape = batch['pose_shape'][:, 72:]
@@ -103,18 +105,18 @@ def train_step(batch):
         e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
         c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
         c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
-        l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
+        # l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
         l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
         n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
         # conc_loss = concept_loss(pred, batch['uv_unposed'])
         kl = kl_loss(mean, logvar)
 
-        reconstr_loss = l2_loss
+        reconstr_loss = l2_mesh_loss
         if config['use_extra_losses']:
             if 'block_collision_loss' in config.keys() and config['block_collision_loss'] == True:
-                reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss) * config['reg_loss_coeff']
+                reconstr_loss = reconstr_loss + (e_loss) * config['reg_loss_coeff']
             else:
-                reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss) * config['reg_loss_coeff']
+                reconstr_loss = reconstr_loss + (e_loss + c_loss) * config['reg_loss_coeff']
         if config['use_norm_loss'] and step > 2000:
             reconstr_loss = reconstr_loss + n_loss * config['reg_loss_coeff']
 
@@ -130,7 +132,7 @@ def train_step(batch):
     return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/collision_loss': c_loss, 'train/euclid_error': metric,
              'train/reconstr_loss': reconstr_loss, 'train/collision_ratio': c_ratio,
              'train/mean': mean, 'train/logvar': logvar, 'train/kl_loss': kl, 'train/var': np.e ** logvar,
-             'train/l2_loss': l2_loss, 'train/l2_mesh_loss': l2_mesh_loss, 'train/normal_loss': n_loss,
+              'train/l2_mesh_loss': l2_mesh_loss, 'train/normal_loss': n_loss,
              # 'train/ms_ssim': ms_ssim, 
             }
 
@@ -144,7 +146,7 @@ def valid_step(batch):
 
     pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
 
-    mesh_pred = uv2mesh(pred)
+    mesh_pred = pred
 
     if config['use_skinning']:
         shape = batch['pose_shape'][:, 72:]
@@ -161,15 +163,15 @@ def valid_step(batch):
     e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
     c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
     c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
-    l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
+    # l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
     l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
     n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
     # conc_loss = concept_loss(pred, batch['uv_unposed'])
     kl = kl_loss(mean, logvar)
 
-    reconstr_loss = l2_loss
+    reconstr_loss = l2_mesh_loss
     if config['use_extra_losses']:
-        reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss) * config['reg_loss_coeff']
+        reconstr_loss = reconstr_loss + (e_loss + c_loss) * config['reg_loss_coeff']
     if config['use_norm_loss']:
         reconstr_loss = reconstr_loss + n_loss * config['reg_loss_coeff']
 
@@ -254,13 +256,12 @@ def plot_everything(batch: dict, type: str):
 
     pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
     
-    mesh_pred = uv2mesh(pred).numpy()
+    mesh_pred = pred.numpy()
     
     orig_img = uv_orig
     uv_static_img = batch['uv_static']
     uv_body_posed_img = batch['uv_body_posed']
-    pred_img = pred
+    img = tf.concat((uv_static_img, uv_body_posed_img, orig_img), axis=2)
-    img = tf.concat((uv_static_img, uv_body_posed_img, orig_img, pred_img), axis=2)
     imgs = [wandb.Image((img[i] + 1) / 2.0) for i in range(4)]
     
     F = np.load("custom_utils/smpl_faces" + skirt_modifier + ("_orig" if config['is_cape'] else "") + ".npy")
@@ -427,8 +428,6 @@ def sample(folder):
 
 
 
-
-
 def test():
     str_split = config['name'].split('_')
     test_idx = str_split.index('test')
@@ -528,7 +527,7 @@ if __name__ == '__main__':
     # test()
 
     ckpt = tf.train.Checkpoint(model=model)
-    manager = tf.train.CheckpointManager(ckpt, 'checkpoints/iccv/' + config['name'], max_to_keep=20)
+    manager = tf.train.CheckpointManager(ckpt, 'checkpoints/post/' + config['name'], max_to_keep=20)
 
 
     if config['pretrained_model'] != '':
@@ -543,7 +542,6 @@ if __name__ == '__main__':
 
         train_loss = train_step(batch)
 
-        if step % 10 == 0:
         data_valid = valid_iterator.get_next()
         data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
         valid_loss = valid_step(data_valid)

models/base.py

@@ -111,18 +111,18 @@ class Decoder(tf.keras.Model):
 
 class VariationalEncoderNew(tf.keras.Model):
     
-    def __init__(self, depths, dims, kernel_size=7, latent_dim=64):
+    def __init__(self, depths, dims, kernel_size=7):
         super(VariationalEncoderNew, self).__init__()
 
         self.encoder = ConvNeXt(depths=depths, dims=dims, include_top=False, kernel_size=kernel_size)
 
         self.mean_branch = tf.keras.Sequential(
-                [Block(dim=latent_dim, drop_path=0, kernel_size=kernel_size,
+                [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(
-                [Block(dim=latent_dim, drop_path=0, kernel_size=kernel_size,
+                [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)]
             )
 

models/cvae_model.py

@@ -10,7 +10,7 @@ import scipy as sp
 from custom_utils.custom_utils import load_model
 from models.base import AEEncoder, VariationalEncoder, Decoder
 from models.base import AEEncoderNew, VariationalEncoderNew, DecoderNew
-
+from models.gnn import GNNDecoder
 
 class CVAE(tf.keras.Model):
     
@@ -150,8 +150,6 @@ class CVAE(tf.keras.Model):
         return x, mean, logvar
 
 
-
-
 class CVAENew(tf.keras.Model):
     
     def __init__(self, config):
@@ -179,10 +177,11 @@ 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'], latent_dim=config['cvae']['latent_dim'])
+        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.decoder = GNNDecoder()
 
-        self.reshape = tf.keras.layers.Reshape((8, 4, config['cvae']['latent_dim']))
+
+        self.reshape = tf.keras.layers.Reshape((8, 4, 64))
 
         if config['avg_garment_path'] != '':
             self.avg_garment = np.load(config['avg_garment_path'])
@@ -273,13 +272,13 @@ class CVAENew(tf.keras.Model):
         z = tf.concat([z, z_cond], axis=-1)
         x = self.decoder(z, training=training)
 
-        if self.avg_garment is not None:
+        # if self.avg_garment is not None:
-            x = x + self.avg_garment[tf.newaxis,:,:,:]
+        #     x = x + self.avg_garment[tf.newaxis,:,:,:]
     
-        if self.use_offsets_over_body:
+        # if self.use_offsets_over_body:
-            x = x + uv_body_posed
+        #     x = x + uv_body_posed
 
-        x = x * uv_mask[:,:,:,tf.newaxis]
+        # x = x * uv_mask[:,:,:,tf.newaxis]
 
         return x, mean, logvar
 

models/gnn.py

@@ -0,0 +1,76 @@
+import os
+import sys
+
+import scipy.io as sio
+import tensorflow as tf
+from spektral.layers import GCNConv, GeneralConv, GATConv
+from spektral.utils import sp_matrix_to_sp_tensor
+from tensorflow.keras.layers import (Conv2D, Conv2DTranspose, Dense, DepthwiseConv2D,
+                                     GlobalAveragePooling2D, Layer,
+                                     LayerNormalization, Flatten, Reshape, UpSampling2D)
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+
+class Smoother(tf.keras.Model):
+    def __init__(self, ):
+        super().__init__()
+        self.a = sp_matrix_to_sp_tensor(sio.loadmat("custom_utils/ssmpl0.mat")['L'].tocoo())
+        self.conv1 = GeneralConv(channels=32, batch_norm=True, dropout=0.1, aggregate='mean', activation='prelu', use_bias=True)
+        self.conv2 = GeneralConv(channels=128, batch_norm=True, dropout=0.1, aggregate='mean', activation='prelu', use_bias=True)
+        self.conv3 = GeneralConv(channels=64, batch_norm=True, dropout=0.0, aggregate='mean', activation='prelu', use_bias=True)
+        self.conv4 = GeneralConv(channels=16, batch_norm=True, dropout=0.0, aggregate='mean', activation='prelu', use_bias=True)
+
+        self.conv5 = GCNConv(3, activation='tanh', use_bias=False)
+        self.conv6 = GCNConv(3, activation='tanh', use_bias=False)
+
+    def call(self, inputs, training=None):
+        x = inputs
+        x = self.conv1([x, self.a], training=training)
+        x = self.conv2([x, self.a], training=training)
+        x = self.conv3([x, self.a], training=training)
+        x = self.conv4([x, self.a], training=training)
+        x = self.conv5([x, self.a], training=training)
+        x = self.conv6([x, self.a], training=training)
+        return x
+
+import numpy as np
+import scipy.sparse as sp
+
+class GNNDecoder(tf.keras.Model):
+    def __init__(self, ):
+        super().__init__()
+        self.a = sp_matrix_to_sp_tensor(sio.loadmat("custom_utils/ssmpl0.mat")['L'].tocoo())
+        # self.a_dense = sio.loadmat("custom_utils/ssmpl0.mat")['L'].todense()
+        # self.a_dense = np.ones((100,100), dtype=np.float32)
+        # self.a = sp_matrix_to_sp_tensor(sp.coo_matrix(self.a_dense))
+        
+        self.flatten = Flatten()
+        # self.conv1 = GATConv(channels=4, attn_heads=1, concat_heads=True, dropout_rate=0.0, return_attn_coef=False, add_self_loops=True, activation=None, use_bias=True)
+        # self.conv2 = GATConv(channels=4, attn_heads=1, concat_heads=True, dropout_rate=0.1, return_attn_coef=False, add_self_loops=True, activation=None, use_bias=True)
+        # self.conv3 = GATConv(channels=4, attn_heads=1, concat_heads=True, dropout_rate=0.1, return_attn_coef=False, add_self_loops=True, activation=None, use_bias=True)
+        self.dense1 = Dense(512)
+        self.dense2 = Dense(256)
+        self.conv1 = GCNConv(256, use_bias=True)
+        self.conv2 = GCNConv(256, use_bias=True)
+        self.conv3 = GeneralConv(channels=128, batch_norm=True, dropout=0.1, aggregate='sum', activation='prelu', use_bias=True)
+        self.conv4 = GeneralConv(channels=128, batch_norm=True, dropout=0.1, aggregate='sum', activation='prelu', use_bias=True)
+        self.conv5 = GeneralConv(channels=64, batch_norm=True, dropout=0.0, aggregate='sum', activation='prelu', use_bias=True)
+        self.conv6 = GCNConv(3, activation='tanh', use_bias=False)
+
+    def call(self, inputs, training=None):
+        x = inputs
+        x = self.flatten(x)
+        x = tf.tile(x[:,None,:], [1,14475,1])
+        x = self.dense1(x)
+        x = self.dense2(x)
+        x = self.conv1([x, self.a], training=training)
+        x = self.conv2([x, self.a], training=training)
+        x = self.conv3([x, self.a], training=training)
+        x = self.conv4([x, self.a], training=training)
+        x = self.conv5([x, self.a], training=training)
+        x = self.conv6([x, self.a], training=training)
+        return x
+
+
+

models/upscaler.py

@@ -131,13 +131,13 @@ class UpscalerBasicConv(tf.keras.Model):
         self.low_res = low_res
 
         self.seq = tf.keras.Sequential([
-                    tf.keras.layers.Conv2D(64, kernel_size=kernel_size, padding='same', activation='relu', name='upscale_enhancer_conv_1'),
+                    tf.keras.layers.Conv2D(256, kernel_size=kernel_size, padding='same', activation='relu', name='upscale_enhancer_conv_1'),
                     tf.keras.layers.BatchNormalization(name='upscale_enhancer_batch_norm_1'),
-                    tf.keras.layers.Conv2D(64, kernel_size=kernel_size, padding='same', activation='relu', name='upscale_enhancer_conv_2'),
+                    tf.keras.layers.Conv2D(128, kernel_size=kernel_size, padding='same', activation='relu', name='upscale_enhancer_conv_2'),
                     tf.keras.layers.BatchNormalization(name='upscale_enhancer_batch_norm_2'),
-                    tf.keras.layers.Conv2D(16, kernel_size=kernel_size, padding='same', activation='relu', name='upscale_enhancer_conv_3'),
+                    tf.keras.layers.Conv2D(64, kernel_size=kernel_size, padding='same', activation='relu', name='upscale_enhancer_conv_3'),
                     tf.keras.layers.BatchNormalization(name='upscale_enhancer_batch_norm_3'),
-                    tf.keras.layers.Conv2D(16, kernel_size=kernel_size, padding='same', activation='relu', name='upscale_enhancer_conv_4'),
+                    tf.keras.layers.Conv2D(32, kernel_size=kernel_size, padding='same', activation='relu', name='upscale_enhancer_conv_4'),
                     tf.keras.layers.BatchNormalization(name='upscale_enhancer_batch_norm_4'),
                     tf.keras.layers.Dense(3, activation='tanh', name='upscale_enhancer_dense_final'),
         ])

upscale_test.py

@@ -1,5 +1,5 @@
 import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "2"
+os.environ["CUDA_VISIBLE_DEVICES"] = "6"
 import tensorflow as tf
 
 gpus = tf.config.experimental.list_physical_devices('GPU')
@@ -35,15 +35,7 @@ from models.upscaler import UpscalerBasicConv as Upscaler
 # !!!!!!!!!! CHANGE SSIM BASED ON RES
 # !!!!!!!!!! CHANGE SKIRT OR NOT
 
-
+name='256_512_skirt'
-low_res = 128
-is_skirt = True
-is_cape = False
-skirt_modifier = '_skirt' if is_skirt else ''
-smpl_res = 3638 if is_cape else 14475
-
-
-name=f'iccv_{low_res}_2' + skirt_modifier
 def init_wandb():
     run = wandb.init(project='upscale', name=name,
             notes='\
@@ -67,10 +59,15 @@ smaller features  | \
     artifact.add_file('convnext/models/convnext_tf.py')
     run.log_artifact(artifact)
 
+is_skirt = True
+is_cape = False
+skirt_modifier = '_skirt' if is_skirt else ''
+smpl_res = 3638 if is_cape else 14475
 
 uv2mesh = UV2Mesh(res=256, is_skirt=is_skirt, is_smpl_orig=is_cape)
 
 
+
 def plot_3d(mesh_gt, mesh_pred_unposed, faces):
     mesh_pred_unposed[:,2] += 1
     fig = go.Figure(data=[
@@ -108,22 +105,18 @@ def plot_3d(mesh_gt, mesh_pred_unposed, faces):
     return fig
 
 
-@tf.function
+# @tf.function
 def train_step(batch):
     with tf.GradientTape() as tape:
         pred = model([batch['uv_unposed' + low_resolution_modifier], batch['uv_mask' + low_resolution_modifier]], training=True)
-        pred = tf.image.resize(pred, size=(high_res, high_res//2), method=tf.image.ResizeMethod.BILINEAR)
+        pred = tf.image.resize(pred, size=(high_res, high_res//2), method=tf.image.ResizeMethod.LANCZOS5)
 
         mesh_pred = uv2mesh(pred)
         mesh_orig = batch['mesh_unposed']
 
         l1_loss = tf.reduce_mean(tf.reduce_sum(((tf.math.abs(batch['uv_unposed' + high_resolution_modifier] - pred * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis]) )), axis=-1))
-        
-        if low_res == 32:
-            ms_ssim = tf.reduce_mean(tf.image.ssim(pred * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, batch['uv_unposed' + high_resolution_modifier] * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, max_val=2))
-        else:
         ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, batch['uv_unposed' + high_resolution_modifier] * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, max_val=2, power_factors=(0.5, 0.2, 0.2, 0.1)))
-            
+        # ms_ssim = tf.reduce_mean(tf.image.ssim(pred * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, batch['uv_unposed' + high_resolution_modifier] * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, max_val=2))
         loss = 1e3 * (1 - ms_ssim)
 
     metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
@@ -132,31 +125,29 @@ def train_step(batch):
     return { 'train/loss': loss, 'train/l1_loss': l1_loss, 'train/euclid_error': metric, 'train/ms_ssim': ms_ssim }
         
 
+
 @tf.function
 def valid_step(batch):
     pred = model([batch['uv_unposed' + low_resolution_modifier], batch['uv_mask' + low_resolution_modifier]], training=False)
-    pred = tf.image.resize(pred, size=(high_res, high_res//2), method=tf.image.ResizeMethod.BILINEAR)
+    pred = tf.image.resize(pred, size=(high_res, high_res//2), method=tf.image.ResizeMethod.LANCZOS5)
 
     mesh_pred = uv2mesh(pred)
     mesh_orig = batch['mesh_unposed']
 
     l1_loss = tf.reduce_mean(tf.reduce_sum(((tf.math.abs(batch['uv_unposed' + high_resolution_modifier] - pred * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis]) )), axis=-1))
-    
-    if low_res == 32:
-        ms_ssim = tf.reduce_mean(tf.image.ssim(pred * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, batch['uv_unposed' + high_resolution_modifier] * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, max_val=2))
-    else:
     ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, batch['uv_unposed' + high_resolution_modifier] * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, max_val=2, power_factors=(0.5, 0.2, 0.2, 0.1)))
-    
+    # ms_ssim = tf.reduce_mean(tf.image.ssim(pred * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, batch['uv_unposed' + high_resolution_modifier] * batch['uv_mask' + high_resolution_modifier][:,:,:,tf.newaxis] + 1, max_val=2))
     loss = 1e3 * (1 - ms_ssim)
 
     metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
     return { 'valid/loss': loss, 'valid/l1_loss': l1_loss, 'valid/euclid_error': metric, 'valid/ms_ssim': ms_ssim }
    
 
+
 def plot_everything(batch: dict, type: str):
 
     pred = model([batch['uv_unposed' + low_resolution_modifier], batch['uv_mask' + low_resolution_modifier]], training=False)
-    pred = tf.image.resize(pred, size=(high_res, high_res//2), method=tf.image.ResizeMethod.BILINEAR)
+    pred = tf.image.resize(pred, size=(high_res, high_res//2), method=tf.image.ResizeMethod.LANCZOS5)
     
     mesh_pred = uv2mesh(pred)
     mesh_orig = batch['mesh_unposed']
@@ -177,6 +168,7 @@ def plot_everything(batch: dict, type: str):
     return { **figs, **{ type + '/images': imgs} }
 
 
+
 def train():
     valid_iterator = iter(dataset_valid)
 
@@ -185,7 +177,6 @@ def train():
         batch = static_dataset_train.expand_batch(batch_to_dict(batch, signature_dynamic))
         train_loss = train_step(batch)
 
-        if step % 100 == 0:
         data_valid = valid_iterator.get_next()
         data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, signature_dynamic))
         valid_loss = valid_step(data_valid)
@@ -213,8 +204,8 @@ def train():
         step += 1
 
 if __name__ == '__main__':
-    init_wandb()
+    # init_wandb()
-    # allow_memory_growth()
+    allow_memory_growth()
     parser = argparse.ArgumentParser(description='')
     parser.add_argument('--name', type=str)
     args = vars(parser.parse_args())
@@ -223,6 +214,7 @@ if __name__ == '__main__':
     # uv_mask_body = np.load('custom_utils/mask_256x512.npy')
     # uv_mask_body = cv2.dilate(uv_mask_body.astype(np.float32), cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2)
 
+    low_res = 32
     high_res = 512
     uv_mask_body = tf.ones((low_res, low_res//2))
     low_resolution_modifier = '_' + str(low_res)
@@ -242,37 +234,24 @@ if __name__ == '__main__':
                          'uv_mask' + low_resolution_modifier: (low_res, low_res//2,),
                         }
 
-    if not is_skirt:
     dataset_train = factory.get_dataset(folder='/home/workspace/local-data/train', batch_size=8, signature=signature_dynamic, resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
     static_dataset_train = StaticDataset(folder='/home/workspace/local-data/train', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
-        dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/valid', batch_size=8, signature=signature_dynamic,resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
-        static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/valid', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
-
-    if is_skirt:
-        dataset_train = factory.get_dataset(folder='/home/workspace/data/dataset/local-cache/train_skirt', batch_size=8, signature=signature_dynamic, resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
-        static_dataset_train = StaticDataset(folder='/home/workspace/data/dataset/local-cache/train_skirt', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
     dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/valid_skirt', batch_size=8, signature=signature_dynamic,resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
     static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/valid_skirt', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
 
-    if is_cape:
+    # dataset_train = factory.get_dataset(folder='/home/workspace/local-data/cape_processed/train/male', batch_size=8, signature=signature_dynamic, resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
-        dataset_train = factory.get_dataset(folder='/home/workspace/local-data/cape_processed/train/male', batch_size=8, signature=signature_dynamic, resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
+    # static_dataset_train = StaticDataset(folder='/home/workspace/local-data/cape_processed/train/male', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
-        static_dataset_train = StaticDataset(folder='/home/workspace/local-data/cape_processed/train/male', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
+    # dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/cape_processed/test/male', batch_size=8, signature=signature_dynamic, resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
-        dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/cape_processed/test/male', batch_size=8, signature=signature_dynamic, resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
+    # static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/cape_processed/test/male', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
-        static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/cape_processed/test/male', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
+
 
-    if low_res == 32 or low_res == 64:
     model = Upscaler(low_res=low_res, kernel_size=3)
-    elif low_res == 128 or low_res == 256:
-        model = Upscaler(low_res=low_res, kernel_size=5)
-    else:
-        model = Upscaler(low_res=low_res, kernel_size=7)
-
 
     optimizer = tf.optimizers.Adam(learning_rate=0.0001)
     folder = 'models/ae_logs/'
 
     ckpt = tf.train.Checkpoint(model=model)
-    manager = tf.train.CheckpointManager(ckpt, 'checkpoints/iccv/' + name, max_to_keep=20)
+    manager = tf.train.CheckpointManager(ckpt, 'checkpoints/' + name, max_to_keep=20)
-    # load_model(model, f'checkpoints/iccv/iccv_{low_res}' + skirt_modifier)
+    load_model(model, 'checkpoints/32_512_4')
 
     train()