save

aaa_helpers.txt

@@ -84,7 +84,6 @@ nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_tshirt" > nohup/iccv/pyr
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_skirt_skirt" > nohup/iccv/pyr/iccv_pyr_32_skirt_skirt.out 2>&1 &
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_skirt_dress" > nohup/iccv/pyr/iccv_pyr_32_skirt_dress.out 2>&1 &
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_skirt_batch" > nohup/iccv/pyr/iccv_pyr_32_skirt_batch.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_seq" > nohup/iccv/pyr/iccv_pyr_32_seq_2.out 2>&1 &
 
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64" > nohup/iccv/pyr/iccv_pyr_32_64.out 2>&1 &
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_skirt.out 2>&1 &
@@ -96,14 +95,6 @@ nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_reg" > nohup/iccv/pyr
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_reg_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_reg_skirt.out 2>&1 &
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_10p" > nohup/iccv/pyr/iccv_pyr_32_64_10p.out 2>&1 &
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_25p" > nohup/iccv/pyr/iccv_pyr_32_64_25p.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_tshirt" > nohup/iccv/pyr/iccv_pyr_32_64_tshirt.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_trousers" > nohup/iccv/pyr/iccv_pyr_32_64_trousers.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_top" > nohup/iccv/pyr/iccv_pyr_32_64_top.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_skirt_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_skirt_skirt.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_skirt_dress" > nohup/iccv/pyr/iccv_pyr_32_64_skirt_dress.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_jumpsuit" > nohup/iccv/pyr/iccv_pyr_32_64_jumpsuit.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_seq" > nohup/iccv/pyr/iccv_pyr_32_64_seq.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_seq_2" > nohup/iccv/pyr/iccv_pyr_32_64_seq_2.out 2>&1 &
 
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_cape_f" > nohup/iccv/pyr/iccv_pyr_64_cape_f.out 2>&1 &
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_cape_m" > nohup/iccv/pyr/iccv_pyr_64_cape_m.out 2>&1 &
@@ -125,14 +116,6 @@ nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_reg" > nohup/iccv
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_curv" > nohup/iccv/pyr/iccv_pyr_32_64_128_curv.out 2>&1 &
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_curv_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_128_curv_skirt.out 2>&1 &
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_var" > nohup/iccv/pyr/iccv_pyr_32_64_128_var.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_tshirt" > nohup/iccv/pyr/iccv_pyr_32_64_128_tshirt.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_trousers" > nohup/iccv/pyr/iccv_pyr_32_64_128_trousers.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_top" > nohup/iccv/pyr/iccv_pyr_32_64_128_top.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_jumpsuit" > nohup/iccv/pyr/iccv_pyr_32_64_128_jumpsuit.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_skirt_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_128_skirt_skirt.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_skirt_dress" > nohup/iccv/pyr/iccv_pyr_32_64_128_skirt_dress.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128" > nohup/iccv/pyr/iccv_pyr_32_64_128.out 2>&1 &
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128" > nohup/iccv/pyr/iccv_pyr_32_64_128.out 2>&1 &
 
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_128_cape_m_00032_SL" > nohup/iccv/pyr/iccv_pyr_64_128_cape_m_00032_SL.out 2>&1 &
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_128_cape_f_03223_SL" > nohup/iccv/pyr/iccv_pyr_64_128_cape_f_03223_SL.out 2>&1 &
@@ -144,6 +127,7 @@ nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_skin" > nohup
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_curv" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_curv.out 2>&1 &
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_curv_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_curv_skirt.out 2>&1 &
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_reg" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_reg.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_seq" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_seq.out 2>&1 &
 
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_512" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_512.out 2>&1 &
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_512_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_512_skirt.out 2>&1 &
@@ -172,22 +156,16 @@ nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_curv_skirt
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_10p > nohup/iccv/evaluate/iccv_pyr_32_64_10p.out 2>&1 &
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_25p > nohup/iccv/evaluate/iccv_pyr_32_64_25p.out 2>&1 &
 
-nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128 > nohcup/iccv/evaluate/iccv_pyr_32_64_128.out 2>&1 &
+nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128 > nohup/iccv/evaluate/iccv_pyr_32_64_128.out 2>&1 &
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_128_skirt.out 2>&1 &
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_skin > nohup/iccv/evaluate/iccv_pyr_32_64_128_skin.out 2>&1 &
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_skin_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_128_skin_skirt.out 2>&1 &
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_reg > nohup/iccv/evaluate/iccv_pyr_32_64_128_reg.out 2>&1 &
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_curv > nohup/iccv/evaluate/iccv_pyr_32_64_128_curv.out 2>&1 &
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_curv_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_128_curv_skirt.out 2>&1 &
-nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_trousers > nohup/iccv/evaluate/iccv_pyr_32_64_128_trousers.out 2>&1 &
-nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_tshirt > nohup/iccv/evaluate/iccv_pyr_32_64_128_tshirt.out 2>&1 &
-nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_top > nohup/iccv/evaluate/iccv_pyr_32_64_128_top.out 2>&1 &
-nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_jumpsuit > nohup/iccv/evaluate/iccv_pyr_32_64_128_jumpsuit.out 2>&1 &
-nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_skirt_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_128_skirt_skirt.out 2>&1 &
-nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_skirt_dress> nohup/iccv/evaluate/iccv_pyr_32_64_128_skirt_dress.out 2>&1 &
-nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128 > nohup/iccv/evaluate/iccv_pyr_32_64_128.out 2>&1 &
 
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256 > nohup/iccv/evaluate/iccv_pyr_32_64_128_256.out 2>&1 &
+nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256_seq > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_seq.out 2>&1 &
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_skirt.out 2>&1 &
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256_skin > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_skin.out 2>&1 &
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256_curv > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_curv.out 2>&1 &

calculate_error.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_abl_pyr_32_64_128_tshirt import config
+from configs.iccv.config_abl_pyr_32_64_128_seq import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -92,16 +92,9 @@ if __name__ == '__main__':
     frames = [frame for frame in frames if not frame.endswith('_fixed_coll.npy')]
     # frames = frames[:100]
 
-    # if 'frames_file_test' in config.keys():
+    # print('Reading bodies...')
-    #     with open(os.path.join(folder, config['frames_file_test']), 'r') as f:
+    # bodies_folder = '/home/workspace/local-data' + ('' if config['is_skirt'] else '_127') + '/test' + ('_skirt' if config['is_skirt'] else '') + '_ready' + ('_2' if config['is_skirt'] else '') + '/mesh_posed_body_no_orient'
-    #             lines = f.readlines()
+    # bodies = {frame : np.load(bodies_folder + '/' + frame[:-1] + 'z')['mesh_posed_body_no_orient'] for frame in tqdm(frames) }
-    #             lines = [line.strip() for line in lines]
-    #     frames = lines
-
-    if error_type == 'collision':
-        print('Reading bodies...')
-        bodies_folder = '/home/workspace/local-data' + ('' if config['is_skirt'] else '_127') + '/test' + ('_skirt' if config['is_skirt'] else '') + '_ready' + ('_2' if config['is_skirt'] else '') + '/mesh_posed_body_no_orient'
-        bodies = {frame : np.load(bodies_folder + '/' + frame[:-1] + 'z')['mesh_posed_body_no_orient'] for frame in tqdm(frames) }
 
     if config['remove_global_orientation']:
         gt_name = 'mesh_posed_no_orient'

configs/iccv/config_abl_pyr_32_64_128_256_512.py

@@ -1,6 +1,6 @@
 config = dict()
 
-config['gpu'] = '0'
+config['gpu'] = '4'
 config['name'] = 'iccv_pyr_32_64_128_256_512'
 config['notes'] = ''
 config['tags'] = ['pyr', 'iccv']
@@ -34,8 +34,8 @@ config['pyramid']['final_upscale_enhancer'] = ''
 config['train_dir'] = '/home/workspace/local-data/train'
 config['valid_dir'] = '/home/workspace/local-data/train'
 config['test_dir'] = '/home/workspace/local-data/test_ready'
-# config['frames_file'] = 'frames_00761_0.txt'
+config['frames_file'] = 'frames_00761_0.txt'
-# config['frames_file_valid'] = 'frames_00761_0.txt'
+config['frames_file_valid'] = 'frames_00761_0.txt'
 config['batch_size'] = 4
 config['is_skirt'] = False
 config['is_cape'] = False

configs/iccv/config_abl_pyr_32_64_128_256_512_skirt.py

@@ -1,6 +1,6 @@
 config = dict()
 
-config['gpu'] = '2'
+config['gpu'] = '4'
 config['name'] = 'iccv_pyr_32_64_128_256_512_skirt'
 config['notes'] = ''
 config['tags'] = ['pyr', 'iccv']

configs/iccv/config_abl_pyr_32_64_128_256_seq.py

@@ -1,7 +1,7 @@
 config = dict()
 
-config['gpu'] = '2'
+config['gpu'] = '3'
-config['name'] = 'iccv_pyr_32_64_128_trousers'
+config['name'] = 'iccv_pyr_32_64_128_256_seq_2'
 config['notes'] = ''
 config['tags'] = ['pyr', 'iccv']
 
@@ -26,16 +26,13 @@ config['pretrained_model'] = ''
 config['pretrained_model_iteration'] = ''
 
 config['pyramid'] = dict()
-config['pyramid']['resolutions'] = [32, 64, 128]
+config['pyramid']['resolutions'] = [32, 64, 128, 256]
-config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_trousers'
+config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_128_seq_2'
 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/local-data/test_ready'
-config['frames_file'] = 'frames_trousers.txt'
-config['frames_file_valid'] = 'frames_trousers.txt'
-config['frames_file_test'] = 'frames_trousers.txt'
 
 config['batch_size'] = 4
 config['is_skirt'] = False

configs/iccv/config_abl_pyr_32_64_128_seq.py

@@ -1,7 +1,7 @@
 config = dict()
 
-config['gpu'] = '3'
+config['gpu'] = '1'
-config['name'] = 'iccv_pyr_32_64_128_jumpsuit'
+config['name'] = 'iccv_pyr_32_64_128_seq_2'
 config['notes'] = ''
 config['tags'] = ['pyr', 'iccv']
 
@@ -27,15 +27,12 @@ config['pretrained_model_iteration'] = ''
 
 config['pyramid'] = dict()
 config['pyramid']['resolutions'] = [32, 64, 128]
-config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_jumpsuit'
+config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_seq_2'
 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/local-data/test_ready'
-config['frames_file'] = 'frames_jumpsuit.txt'
-config['frames_file_valid'] = 'frames_jumpsuit.txt'
-config['frames_file_test'] = 'frames_jumpsuit.txt'
 
 config['batch_size'] = 4
 config['is_skirt'] = False

configs/iccv/config_abl_pyr_32_64_128_skirt_dress.py

@@ -1,190 +0,0 @@
-config = dict()
-
-config['gpu'] = '7'
-config['name'] = 'iccv_pyr_32_64_128_skirt_dress'
-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['use_curvature'] =             True
-
-config['pretrained_model'] = ''
-config['pretrained_model_iteration'] = ''
-
-config['pyramid'] = dict()
-config['pyramid']['resolutions'] = [32, 64, 128]
-config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_skirt_dress'
-config['pyramid']['final_upscale_enhancer'] = ''
-
-config['train_dir'] = '/home/workspace/local-data/train_skirt'
-config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
-config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
-config['frames_file'] = 'frames_dress.txt'
-config['frames_file_valid'] = 'frames_dress.txt'
-config['frames_file_test'] = 'frames_dress.txt'
-
-config['batch_size'] = 4
-config['is_skirt'] = True
-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['bottleneck_scale'] = 2
-
-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'] = False
-config['pyramid']['noise_amount'] = 0.0
-
-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/iccv/static_iccv_1_skirt'
-config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
-config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
-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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 7
-    level['encoder_depths'] = [3, 3, 3, 9, 3]
-    level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = ''
-    config['pyramid']['levels'].append(level)
-  elif res == 256:
-    level = dict()
-    level['resolution'] = 256
-    level['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [3, 3, 9, 3]
-    level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [9, 9, 9]
-    level['encoder_dims'] = [128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [9, 9]
-    level['encoder_dims'] = [64, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [16]
-    level['encoder_dims'] = [level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'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['bottleneck_scale']
-config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
-config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 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['bottleneck_scale']
-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['bottleneck_scale']
-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_abl_pyr_32_64_128_skirt_skirt.py

@@ -1,190 +0,0 @@
-config = dict()
-
-config['gpu'] = '3'
-config['name'] = 'iccv_pyr_32_64_128_skirt_skirt'
-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['use_curvature'] =             True
-
-config['pretrained_model'] = ''
-config['pretrained_model_iteration'] = ''
-
-config['pyramid'] = dict()
-config['pyramid']['resolutions'] = [32, 64, 128]
-config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_skirt_skirt'
-config['pyramid']['final_upscale_enhancer'] = ''
-
-config['train_dir'] = '/home/workspace/local-data/train_skirt'
-config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
-config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
-config['frames_file'] = 'frames_skirt.txt'
-config['frames_file_valid'] = 'frames_skirt.txt'
-config['frames_file_test'] = 'frames_skirt.txt'
-
-config['batch_size'] = 4
-config['is_skirt'] = True
-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['bottleneck_scale'] = 2
-
-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'] = False
-config['pyramid']['noise_amount'] = 0.0
-
-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/iccv/static_iccv_1_skirt'
-config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
-config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
-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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 7
-    level['encoder_depths'] = [3, 3, 3, 9, 3]
-    level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = ''
-    config['pyramid']['levels'].append(level)
-  elif res == 256:
-    level = dict()
-    level['resolution'] = 256
-    level['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [3, 3, 9, 3]
-    level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [9, 9, 9]
-    level['encoder_dims'] = [128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [9, 9]
-    level['encoder_dims'] = [64, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [16]
-    level['encoder_dims'] = [level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'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['bottleneck_scale']
-config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
-config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 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['bottleneck_scale']
-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['bottleneck_scale']
-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_abl_pyr_32_64_128_top.py

@@ -1,197 +0,0 @@
-config = dict()
-
-config['gpu'] = '3'
-config['name'] = 'iccv_pyr_32_64_128_top'
-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['use_curvature'] =             True
-
-config['pretrained_model'] = ''
-config['pretrained_model_iteration'] = ''
-
-config['pyramid'] = dict()
-config['pyramid']['resolutions'] = [32, 64, 128]
-config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_top'
-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/local-data/test_ready'
-config['train_dir_skirt'] = '/home/workspace/local-data_128/train_skirt'
-config['valid_dir_skirt'] = '/home/workspace/local-data/valid_skirt'
-config['test_dir_skirt'] = '/home/workspace/local-data/test_skirt_ready_2'
-config['frames_file'] = 'frames_top.txt'
-config['frames_file_valid'] = 'frames_top.txt'
-config['frames_file_test'] = 'frames_top.txt'
-config['frames_file_skirt'] = 'frames_top.txt'
-config['frames_file_valid_skirt'] = 'frames_top.txt'
-config['frames_file_test_skirt'] = 'frames_top.txt'
-
-
-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['bottleneck_scale'] = 2
-
-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'] = False
-config['pyramid']['noise_amount'] = 0.0
-
-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/iccv/static_iccv_1'
-config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
-config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 7
-    level['encoder_depths'] = [3, 3, 3, 9, 3]
-    level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = ''
-    config['pyramid']['levels'].append(level)
-  elif res == 256:
-    level = dict()
-    level['resolution'] = 256
-    level['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [3, 3, 9, 3]
-    level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [9, 9, 9]
-    level['encoder_dims'] = [128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [9, 9]
-    level['encoder_dims'] = [64, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [16]
-    level['encoder_dims'] = [level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'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['bottleneck_scale']
-config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
-config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 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['bottleneck_scale']
-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['bottleneck_scale']
-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_abl_pyr_32_64_128_top_skirt.py

@@ -1,197 +0,0 @@
-config = dict()
-
-config['gpu'] = '3'
-config['name'] = 'iccv_pyr_32_64_128_top_skirt'
-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['use_curvature'] =             True
-
-config['pretrained_model'] = ''
-config['pretrained_model_iteration'] = ''
-
-config['pyramid'] = dict()
-config['pyramid']['resolutions'] = [32, 64, 128]
-config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_top'
-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/local-data/test_skirt_ready_2'
-config['train_dir_skirt'] = '/home/workspace/local-data_128/train_skirt'
-config['valid_dir_skirt'] = '/home/workspace/local-data/valid_skirt'
-config['test_dir_skirt'] = '/home/workspace/local-data/test_skirt_ready_2'
-config['frames_file'] = 'frames_top.txt'
-config['frames_file_valid'] = 'frames_top.txt'
-config['frames_file_test'] = 'frames_top.txt'
-config['frames_file_skirt'] = 'frames_top.txt'
-config['frames_file_valid_skirt'] = 'frames_top.txt'
-config['frames_file_test_skirt'] = 'frames_top.txt'
-
-
-config['batch_size'] = 4
-config['is_skirt'] = True
-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['bottleneck_scale'] = 2
-
-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'] = False
-config['pyramid']['noise_amount'] = 0.0
-
-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/iccv/static_iccv_1'
-config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
-config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 7
-    level['encoder_depths'] = [3, 3, 3, 9, 3]
-    level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = ''
-    config['pyramid']['levels'].append(level)
-  elif res == 256:
-    level = dict()
-    level['resolution'] = 256
-    level['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [3, 3, 9, 3]
-    level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [9, 9, 9]
-    level['encoder_dims'] = [128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [9, 9]
-    level['encoder_dims'] = [64, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [16]
-    level['encoder_dims'] = [level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'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['bottleneck_scale']
-config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
-config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 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['bottleneck_scale']
-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['bottleneck_scale']
-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_abl_pyr_32_64_128_tshirt.py

@@ -1,196 +0,0 @@
-config = dict()
-
-config['gpu'] = '2'
-config['name'] = 'iccv_pyr_32_64_128_tshirt'
-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['use_curvature'] =             True
-
-config['pretrained_model'] = ''
-config['pretrained_model_iteration'] = ''
-
-config['pyramid'] = dict()
-config['pyramid']['resolutions'] = [32, 64, 128]
-config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_tshirt'
-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/local-data/test_ready'
-config['train_dir_skirt'] = '/home/workspace/local-data_128/train_skirt'
-config['valid_dir_skirt'] = '/home/workspace/local-data/valid_skirt'
-config['test_dir_skirt'] = '/home/workspace/local-data_128/test_skirt_ready_2'
-config['frames_file'] = 'frames_tshirt.txt'
-config['frames_file_valid'] = 'frames_tshirt.txt'
-config['frames_file_test'] = 'frames_tshirt.txt'
-config['frames_file_skirt'] = 'frames_tshirt.txt'
-config['frames_file_valid_skirt'] = 'frames_tshirt.txt'
-config['frames_file_test_skirt'] = 'frames_tshirt.txt'
-
-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['bottleneck_scale'] = 2
-
-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'] = False
-config['pyramid']['noise_amount'] = 0.0
-
-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/iccv/static_iccv_1'
-config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
-config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 7
-    level['encoder_depths'] = [3, 3, 3, 9, 3]
-    level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = ''
-    config['pyramid']['levels'].append(level)
-  elif res == 256:
-    level = dict()
-    level['resolution'] = 256
-    level['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [3, 3, 9, 3]
-    level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [9, 9, 9]
-    level['encoder_dims'] = [128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [9, 9]
-    level['encoder_dims'] = [64, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [16]
-    level['encoder_dims'] = [level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'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['bottleneck_scale']
-config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
-config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 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['bottleneck_scale']
-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['bottleneck_scale']
-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_abl_pyr_32_64_128_tshirt_skirt.py

@@ -1,196 +0,0 @@
-config = dict()
-
-config['gpu'] = '2'
-config['name'] = 'iccv_pyr_32_64_128_tshirt_skirt'
-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['use_curvature'] =             True
-
-config['pretrained_model'] = ''
-config['pretrained_model_iteration'] = ''
-
-config['pyramid'] = dict()
-config['pyramid']['resolutions'] = [32, 64, 128]
-config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_tshirt'
-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/local-data/test_skirt_ready_2'
-config['train_dir_skirt'] = '/home/workspace/local-data_128/train_skirt'
-config['valid_dir_skirt'] = '/home/workspace/local-data/valid_skirt'
-config['test_dir_skirt'] = '/home/workspace/local-data_128/test_skirt_ready_2'
-config['frames_file'] = 'frames_tshirt.txt'
-config['frames_file_valid'] = 'frames_tshirt.txt'
-config['frames_file_test'] = 'frames_tshirt.txt'
-config['frames_file_skirt'] = 'frames_tshirt.txt'
-config['frames_file_valid_skirt'] = 'frames_tshirt.txt'
-config['frames_file_test_skirt'] = 'frames_tshirt.txt'
-
-config['batch_size'] = 4
-config['is_skirt'] = True
-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['bottleneck_scale'] = 2
-
-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'] = False
-config['pyramid']['noise_amount'] = 0.0
-
-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/iccv/static_iccv_1'
-config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
-config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 7
-    level['encoder_depths'] = [3, 3, 3, 9, 3]
-    level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = ''
-    config['pyramid']['levels'].append(level)
-  elif res == 256:
-    level = dict()
-    level['resolution'] = 256
-    level['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [3, 3, 9, 3]
-    level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [9, 9, 9]
-    level['encoder_dims'] = [128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [9, 9]
-    level['encoder_dims'] = [64, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [16]
-    level['encoder_dims'] = [level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'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['bottleneck_scale']
-config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
-config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 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['bottleneck_scale']
-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['bottleneck_scale']
-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_abl_pyr_32_64_skirt_dress.py

@@ -1,190 +0,0 @@
-config = dict()
-
-config['gpu'] = '3'
-config['name'] = 'iccv_pyr_32_64_skirt_dress'
-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['use_curvature'] =             True
-
-config['pretrained_model'] = ''
-config['pretrained_model_iteration'] = ''
-
-config['pyramid'] = dict()
-config['pyramid']['resolutions'] = [32, 64]
-config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_skirt_dress'
-config['pyramid']['final_upscale_enhancer'] = ''
-
-config['train_dir'] = '/home/workspace/local-data/train_skirt'
-config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
-config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
-config['frames_file'] = 'frames_dress.txt'
-config['frames_file_valid'] = 'frames_dress.txt'
-config['frames_file_test'] = 'frames_dress.txt'
-
-config['batch_size'] = 4
-config['is_skirt'] = True
-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['bottleneck_scale'] = 2
-
-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'] = False
-config['pyramid']['noise_amount'] = 0.0
-
-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/iccv/static_iccv_1_skirt'
-config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
-config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
-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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 7
-    level['encoder_depths'] = [3, 3, 3, 9, 3]
-    level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = ''
-    config['pyramid']['levels'].append(level)
-  elif res == 256:
-    level = dict()
-    level['resolution'] = 256
-    level['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [3, 3, 9, 3]
-    level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [9, 9, 9]
-    level['encoder_dims'] = [128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [9, 9]
-    level['encoder_dims'] = [64, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [16]
-    level['encoder_dims'] = [level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'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['bottleneck_scale']
-config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
-config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 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['bottleneck_scale']
-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['bottleneck_scale']
-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_abl_pyr_32_64_skirt_skirt.py

@@ -1,190 +0,0 @@
-config = dict()
-
-config['gpu'] = '3'
-config['name'] = 'iccv_pyr_32_64_skirt_skirt'
-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['use_curvature'] =             True
-
-config['pretrained_model'] = ''
-config['pretrained_model_iteration'] = ''
-
-config['pyramid'] = dict()
-config['pyramid']['resolutions'] = [32, 64]
-config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_skirt_skirt'
-config['pyramid']['final_upscale_enhancer'] = ''
-
-config['train_dir'] = '/home/workspace/local-data/train_skirt'
-config['valid_dir'] = '/home/workspace/local-data_127/valid_skirt'
-config['test_dir'] = '/home/workspace/local-data/test_skirt_ready_2'
-config['frames_file'] = 'frames_skirt.txt'
-config['frames_file_valid'] = 'frames_skirt.txt'
-config['frames_file_test'] = 'frames_skirt.txt'
-
-config['batch_size'] = 4
-config['is_skirt'] = True
-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['bottleneck_scale'] = 2
-
-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'] = False
-config['pyramid']['noise_amount'] = 0.0
-
-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/iccv/static_iccv_1_skirt'
-config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_skirt'
-config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_skirt'
-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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 7
-    level['encoder_depths'] = [3, 3, 3, 9, 3]
-    level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = ''
-    config['pyramid']['levels'].append(level)
-  elif res == 256:
-    level = dict()
-    level['resolution'] = 256
-    level['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [3, 3, 9, 3]
-    level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2_skirt'#'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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [9, 9, 9]
-    level['encoder_dims'] = [128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_skirt'#'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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [9, 9]
-    level['encoder_dims'] = [64, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_skirt'#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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [16]
-    level['encoder_dims'] = [level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_skirt'#'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['bottleneck_scale']
-config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
-config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 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['bottleneck_scale']
-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['bottleneck_scale']
-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_abl_pyr_32_64_top.py

@@ -1,197 +0,0 @@
-config = dict()
-
-config['gpu'] = '3'
-config['name'] = 'iccv_pyr_32_64_top'
-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['use_curvature'] =             True
-
-config['pretrained_model'] = ''
-config['pretrained_model_iteration'] = ''
-
-config['pyramid'] = dict()
-config['pyramid']['resolutions'] = [32, 64]
-config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_top'
-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/local-data_127/test_ready'
-config['train_dir_skirt'] = '/home/workspace/local-data_128/train_skirt'
-config['valid_dir_skirt'] = '/home/workspace/local-data/valid_skirt'
-config['test_dir_skirt'] = '/home/workspace/local-data_128/test_skirt_ready_2'
-config['frames_file'] = 'frames_top.txt'
-config['frames_file_valid'] = 'frames_top.txt'
-config['frames_file_test'] = 'frames_top.txt'
-config['frames_file_skirt'] = 'frames_top.txt'
-config['frames_file_valid_skirt'] = 'frames_top.txt'
-config['frames_file_test_skirt'] = 'frames_top.txt'
-
-
-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['bottleneck_scale'] = 2
-
-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'] = False
-config['pyramid']['noise_amount'] = 0.0
-
-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/iccv/static_iccv_1'
-config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
-config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 7
-    level['encoder_depths'] = [3, 3, 3, 9, 3]
-    level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = ''
-    config['pyramid']['levels'].append(level)
-  elif res == 256:
-    level = dict()
-    level['resolution'] = 256
-    level['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [3, 3, 9, 3]
-    level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [9, 9, 9]
-    level['encoder_dims'] = [128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [9, 9]
-    level['encoder_dims'] = [64, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [16]
-    level['encoder_dims'] = [level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'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['bottleneck_scale']
-config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
-config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 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['bottleneck_scale']
-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['bottleneck_scale']
-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_abl_pyr_32_64_trousers.py

@@ -1,190 +0,0 @@
-config = dict()
-
-config['gpu'] = '2'
-config['name'] = 'iccv_pyr_32_64_trousers'
-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['use_curvature'] =             True
-
-config['pretrained_model'] = ''
-config['pretrained_model_iteration'] = ''
-
-config['pyramid'] = dict()
-config['pyramid']['resolutions'] = [32, 64]
-config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_trousers'
-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/local-data_127/test_ready'
-config['frames_file'] = 'frames_trousers.txt'
-config['frames_file_valid'] = 'frames_trousers.txt'
-config['frames_file_test'] = 'frames_trousers.txt'
-
-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['bottleneck_scale'] = 2
-
-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'] = False
-config['pyramid']['noise_amount'] = 0.0
-
-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/iccv/static_iccv_1'
-config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
-config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 7
-    level['encoder_depths'] = [3, 3, 3, 9, 3]
-    level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = ''
-    config['pyramid']['levels'].append(level)
-  elif res == 256:
-    level = dict()
-    level['resolution'] = 256
-    level['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [3, 3, 9, 3]
-    level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [9, 9, 9]
-    level['encoder_dims'] = [128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [9, 9]
-    level['encoder_dims'] = [64, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [16]
-    level['encoder_dims'] = [level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'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['bottleneck_scale']
-config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
-config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 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['bottleneck_scale']
-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['bottleneck_scale']
-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_abl_pyr_32_64_tshirt.py

@@ -1,196 +0,0 @@
-config = dict()
-
-config['gpu'] = '2'
-config['name'] = 'iccv_pyr_32_64_tshirt'
-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['use_curvature'] =             True
-
-config['pretrained_model'] = ''
-config['pretrained_model_iteration'] = ''
-
-config['pyramid'] = dict()
-config['pyramid']['resolutions'] = [32, 64]
-config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_tshirt'
-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/local-data_127/test_ready'
-config['train_dir_skirt'] = '/home/workspace/local-data_128/train_skirt'
-config['valid_dir_skirt'] = '/home/workspace/local-data/valid_skirt'
-config['test_dir_skirt'] = '/home/workspace/local-data_128/test_skirt_ready_2'
-config['frames_file'] = 'frames_tshirt.txt'
-config['frames_file_valid'] = 'frames_tshirt.txt'
-config['frames_file_test'] = 'frames_tshirt.txt'
-config['frames_file_skirt'] = 'frames_tshirt.txt'
-config['frames_file_valid_skirt'] = 'frames_tshirt.txt'
-config['frames_file_test_skirt'] = 'frames_tshirt.txt'
-
-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['bottleneck_scale'] = 2
-
-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'] = False
-config['pyramid']['noise_amount'] = 0.0
-
-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/iccv/static_iccv_1'
-config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
-config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 7
-    level['encoder_depths'] = [3, 3, 3, 9, 3]
-    level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = ''
-    config['pyramid']['levels'].append(level)
-  elif res == 256:
-    level = dict()
-    level['resolution'] = 256
-    level['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [3, 3, 9, 3]
-    level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [9, 9, 9]
-    level['encoder_dims'] = [128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [9, 9]
-    level['encoder_dims'] = [64, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [16]
-    level['encoder_dims'] = [level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'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['bottleneck_scale']
-config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
-config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 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['bottleneck_scale']
-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['bottleneck_scale']
-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_abl_pyr_32_seq.py

@@ -1,7 +1,7 @@
 config = dict()
 
-config['gpu'] = '3'
+config['gpu'] = '4'
-config['name'] = 'iccv_pyr_32_64_jumpsuit'
+config['name'] = 'iccv_pyr_32_seq_2'
 config['notes'] = ''
 config['tags'] = ['pyr', 'iccv']
 
@@ -27,15 +27,12 @@ config['pretrained_model_iteration'] = ''
 
 config['pyramid'] = dict()
 config['pyramid']['resolutions'] = [32, 64]
-config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_jumpsuit'
+config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32'
 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/local-data_127/test_ready'
+config['test_dir'] = '/home/workspace/local-data/test_ready'
-config['frames_file'] = 'frames_jumpsuit.txt'
-config['frames_file_valid'] = 'frames_jumpsuit.txt'
-config['frames_file_test'] = 'frames_jumpsuit.txt'
 
 config['batch_size'] = 4
 config['is_skirt'] = False

configs/iccv/config_abl_pyr_32_test.py

@@ -1,193 +0,0 @@
-config = dict()
-
-config['gpu'] = '2'
-config['name'] = 'iccv_pyr_32_test'
-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['use_curvature'] =             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/local-data_127/test_ready'
-config['train_dir_skirt'] = '/home/workspace/local-data_128/train_skirt'
-config['valid_dir_skirt'] = '/home/workspace/local-data/valid_skirt'
-config['test_dir_skirt'] = '/home/workspace/local-data_128/test_skirt_ready_2'
-config['frames_file'] = 'frames_top.txt'
-config['frames_file_valid'] = 'frames_top.txt'
-config['frames_file_test'] = 'frames_top.txt'
-
-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['bottleneck_scale'] = 2
-
-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'] = False
-config['pyramid']['noise_amount'] = 0.0
-
-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/iccv/static_iccv_1'
-config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1'
-config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 7
-    level['encoder_depths'] = [3, 3, 3, 9, 3]
-    level['encoder_dims'] = [16, 32, 64, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = ''
-    config['pyramid']['levels'].append(level)
-  elif res == 256:
-    level = dict()
-    level['resolution'] = 256
-    level['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [3, 3, 9, 3]
-    level['encoder_dims'] = [64, 128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_256_2'#'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'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 5
-    level['encoder_depths'] = [9, 9, 9]
-    level['encoder_dims'] = [128, 128, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2'#'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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [9, 9]
-    level['encoder_dims'] = [64, level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_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['latent_dim'] = 64 * config['bottleneck_scale']
-    level['kernel_size'] = 3
-    level['encoder_depths'] = [16]
-    level['encoder_dims'] = [level['latent_dim']]
-    level['decoder_depths'] = level['encoder_depths']
-    level['decoder_dims'] = level['encoder_dims']
-    level['avg_garment_path'] = ''
-    level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2'#'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['bottleneck_scale']
-config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
-config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 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['bottleneck_scale']
-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['bottleneck_scale']
-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'

cvae_pyramid.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_abl_pyr_32_64_128_skirt_dress import config
+from configs.iccv.config_abl_pyr_32_tshirt import config
 
 os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
@@ -39,13 +39,12 @@ else:
 from losses import uv_mask_loss, bend_loss, kl_loss, collision_ratio, normal_loss
 # from losses import collision_penalty as collision_penalty
 from losses import collision_loss as collision_penalty
-from custom_utils.custom_utils import batch_to_dict, load_model, save_stl
+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 PyramidNew as Pyramid
 from postprocessing import fix_collision
 from custom_utils.custom_utils import Accumulator
-from sample_pro_2 import postprocess
 
 
 def init_wandb():
@@ -683,10 +682,9 @@ def test():
 
 
 def generate_results():
-    load_model(model, 'checkpoints/iccv/' + config['name'])
+    load_model(model, 'checkpoints/' + config['name'])
-    F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
 
-    dataset_test = factory.get_dataset(folder=config['train_dir'], 
+    dataset_test = factory.get_dataset(folder=config['test_dir'], 
                                         batch_size=config['batch_size'], 
                                         # batch_size=1, 
                                         signature=config['signature_dynamic'], 
@@ -695,16 +693,15 @@ def generate_results():
                                         is_cape=config['is_cape'],
                                         frames_file=config['frames_file'] if 'frames_file' in config.keys() else None,
                                         do_repeat=False,
-                                        shuffle=True)
+                                        shuffle=False)
-    static_dataset_test = StaticDataset(folder=config['train_dir'], 
+    static_dataset_test = StaticDataset(folder=config['test_dir'], 
                                         signature=config['signature_static'], 
                                         resolutions=dataset_resolutions, 
                                         in_memory=True,
                                         is_cape=config['is_cape'])
-    folder = os.path.join('examples/qualitative', config['name'])
+    folder = os.path.join('/home/workspace/data/results/cape', config['name'])
     os.makedirs(folder, exist_ok=True)
    
-
     for batch in tqdm.tqdm(dataset_test):
         batch = static_dataset_test.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
 
@@ -721,28 +718,14 @@ def generate_results():
         mesh_pred = uv2mesh(pred)
         mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
         mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
-        mesh_gt = batch['mesh_posed'].numpy()
-        body = batch['mesh_body_posed'].numpy()
 
         for i in range(batch['id'].shape[0]):
             mask = batch['mesh_mask'][i].numpy().astype(bool)
             id, frame_nr = batch['id'][i].numpy().decode("utf-8"), batch['frame_nr'][i].numpy().decode("utf-8")
             unposed = mesh_pred[i].numpy()[mask, :]
             posed = mesh_pred_posed[i].numpy()[mask, :]
-            _, faces_garment = _maskFaces(np.zeros((14475, 3), np.float32), F, mask)
+            np.save(os.path.join(folder, id + '_' + frame_nr + '_posed.npy'), posed)
-            faces_garment = faces_garment.astype(np.int32)
+            np.save(os.path.join(folder, id + '_' + frame_nr + '_unposed.npy'), unposed)
-            posed_unmasked = np.zeros((1, 14475, 3), np.float32)
-            posed_unmasked[0, mask, :] = posed
-            posed = postprocess(posed_unmasked, body[i], faces_garment, mask, do_smoothing=True)
-            unposed_unmasked = np.zeros((1, 14475, 3), np.float32)
-            unposed_unmasked[0, mask, :] = unposed
-            unposed = postprocess(unposed_unmasked, body[i], faces_garment, mask, do_smoothing=True)
-            save_stl(os.path.join(folder, id + '_' + frame_nr + '_posed.stl'), posed, faces_garment)
-            save_stl(os.path.join(folder, id + '_' + frame_nr + '_unposed.stl'), unposed, faces_garment)
-            save_stl(os.path.join(folder, id + '_' + frame_nr + '_gt.stl'), mesh_gt[i][mask, :], faces_garment)
-            print(id + '_' + frame_nr)
-            # np.save(os.path.join(folder, id + '_' + frame_nr + '_posed.npy'), posed)
-            # np.save(os.path.join(folder, id + '_' + frame_nr + '_unposed.npy'), unposed)
 
     pass
 
@@ -789,7 +772,7 @@ if __name__ == '__main__':
                                         in_memory=config['dataset_in_memory'], 
                                         is_cape=config['is_cape'],
                                         frames_file=config['frames_file'] if 'frames_file' in config.keys() else None,
-                                        frames_file_skirt=config['frames_file_skirt'] if 'frames_file_skirt' in config.keys() else '',
+                                        frames_file_skirt=config['frames_file'] if 'frames_file' in config.keys() else '',
                                         folder_skirt=config['train_dir_skirt'] if 'train_dir_skirt' in config.keys() else '',
                                         )
     static_dataset_train = StaticDataset(folder=config['train_dir'], 
@@ -805,7 +788,7 @@ if __name__ == '__main__':
                                         in_memory=config['dataset_in_memory'], 
                                         is_cape=config['is_cape'],
                                         frames_file=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else None,
-                                        frames_file_skirt=config['frames_file_valid_skirt'] if 'frames_file_valid_skirt' in config.keys() else '',
+                                        frames_file_skirt=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else '',
                                         folder_skirt=config['valid_dir_skirt'] if 'valid_dir_skirt' in config.keys() else '',
                                         )
     static_dataset_valid = StaticDataset(folder=config['valid_dir'], 
@@ -829,8 +812,6 @@ if __name__ == '__main__':
 
     # generate_results()
 
-    
-
     ckpt = tf.train.Checkpoint(model=model)
     manager = tf.train.CheckpointManager(ckpt, 'checkpoints/iccv/' + config['name'], max_to_keep=20)
     train_accumulator = Accumulator()

docker/docker-compose.yml

@@ -1,24 +0,0 @@
-name: "lhunor_iccv23"
-services:
-  lhunor_tf:
-    image: lhunor_tf:2023.02.21
-    container_name: lhunor_iccv23
-    #command: python -c "import tensorflow as tf;tf.test.gpu_device_name()"
-    ipc: host
-    user: 11017:11000
-    tty: true
-    ulimits:
-      memlock: -1
-      stack: 67108864
-    volumes:
-      - '~/workspace:/workspace'
-      - '/data-net/guest/lhunor/data:/data-net'
-      - '/data-fast/126-data1/lhunor/data:/data-local/126'
-      - '/data-fast/127-data1/lhunor/data:/data-local/127'
-      - '/data-fast/128-data1/lhunor/data:/data-local/128'
-    working_dir: /data-net
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - capabilities: [gpu]

evaluate_ae_pyramid.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_abl_pyr_32_64_128_tshirt_skirt import config
+from configs.iccv.config_abl_pyr_32_64_128_seq import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf

get_error.py

@@ -8,11 +8,11 @@ if __name__ == '__main__':
     garment_type_table = np.load('custom_data/garment_type_table_test.npy', allow_pickle=True).item()
     folder = '/home/workspace/data/results/errors/iccv'
 
-    checkpoint_name = 'iccv_pyr_32_64_128_2'
+    checkpoint_name = 'iccv_pyr_32_64_128_256_seq_2'
-    checkpoint_name_skirt = 'iccv_pyr_32_64_128_skirt_dress'
+    checkpoint_name_skirt = 'iccv_pyr_32_64_128_skin_skirt'
-    iteration = '125' + '000'
+    iteration = '185' + '000'
-    iteration_skirt : str = '175' + '000'
+    iteration_skirt : str = '155' + '000'
-    error_type = '-euclidean' # '-euclidean' '-collision' -edge -normal
+    error_type = 'euclidean' # 'euclidean' 'collision' edge normal
 
 
     errors = []
@@ -21,50 +21,50 @@ if __name__ == '__main__':
     per_garment_errors_skirt  = { 'Tshirt': [], 'Dress': [], 'Skirt': [], 'Top': [] }
 
 
-    # with open(os.path.join(folder, f'{checkpoint_name}-{iteration}{error_type}.csv'), 'r') as f:
+    with open(os.path.join(folder, f'{checkpoint_name}-{iteration}-{error_type}.csv'), 'r') as f:
-    #     csv_reader = csv.reader(f, delimiter=',')
-    #     for row in csv_reader:
-    #         per_garment_errors[garment_type_table[row[0].split('_')[0]]].append(float(row[1]))
-
-    # print('\nNon-skirt:')
-    # for key in per_garment_errors.keys():
-    #     avg = np.mean(per_garment_errors[key])
-    #     errors.append(avg)
-    #     print(key + ': ' + str(avg))
-
-    # print('\nAvg non-skirt: ' + str(np.mean(errors)))
-
-    with open(os.path.join(folder, f'{checkpoint_name_skirt}-{iteration_skirt}{error_type}.csv'), 'r') as f:
         csv_reader = csv.reader(f, delimiter=',')
         for row in csv_reader:
-            per_garment_errors_skirt[garment_type_table[row[0].split('_')[0]]].append(float(row[1]))
+            per_garment_errors[garment_type_table[row[0].split('_')[0]]].append(float(row[1]))
 
-    print('\nSkirt:')
+    print('\nNon-skirt:')
-    for key in per_garment_errors_skirt.keys():
-        avg = np.mean(per_garment_errors_skirt[key])
-        errors_skirt.append(avg)
-        print(key + ': ' + str(avg))
-
-    print('Avg skirt: ' + str(np.mean(errors_skirt)))
-
-    cum_avg_shirt = np.mean(per_garment_errors['Tshirt'] + per_garment_errors_skirt['Tshirt'])
-    cum_avg_top = np.mean(per_garment_errors['Top'] + per_garment_errors_skirt['Top'])
-
-    print('Global tshirt avg: ' + str(cum_avg_shirt))
-    print('Global top avg: ' + str(cum_avg_top))
-
-    print('\nGlobal per garment: ')
-
-    global_errors = []
-    per_garment_errors['Tshirt'].extend(per_garment_errors_skirt['Tshirt'])
-    per_garment_errors['Top'].extend(per_garment_errors_skirt['Top'])
-    per_garment_errors['Skirt'] = per_garment_errors_skirt['Skirt']
-    per_garment_errors['Dress'] = per_garment_errors_skirt['Dress']
     for key in per_garment_errors.keys():
         avg = np.mean(per_garment_errors[key])
-        global_errors.append(avg)
+        errors.append(avg)
-        print(key + ': ' + str(avg), 'samples: ' + str(len(per_garment_errors[key])))
+        print(key + ': ' + str(avg))
 
-    print('Global avg: ' + str(np.mean(global_errors)))
+    print('\nAvg non-skirt: ' + str(np.mean(errors)))
-    print(len(global_errors))
+
+    # with open(os.path.join(folder, f'{checkpoint_name_skirt}-{iteration_skirt}-{error_type}.csv'), 'r') as f:
+    #     csv_reader = csv.reader(f, delimiter=',')
+    #     for row in csv_reader:
+    #         per_garment_errors_skirt[garment_type_table[row[0].split('_')[0]]].append(float(row[1]))
+
+    # print('\nSkirt:')
+    # for key in per_garment_errors_skirt.keys():
+    #     avg = np.mean(per_garment_errors_skirt[key])
+    #     errors_skirt.append(avg)
+    #     print(key + ': ' + str(avg))
+
+    # print('Avg skirt: ' + str(np.mean(errors_skirt)))
+
+    # cum_avg_shirt = np.mean(per_garment_errors['Tshirt'] + per_garment_errors_skirt['Tshirt'])
+    # cum_avg_top = np.mean(per_garment_errors['Top'] + per_garment_errors_skirt['Top'])
+
+    # print('Global tshirt avg: ' + str(cum_avg_shirt))
+    # print('Global top avg: ' + str(cum_avg_top))
+
+    # print('\nGlobal per garment: ')
+
+    # global_errors = []
+    # per_garment_errors['Tshirt'].extend(per_garment_errors_skirt['Tshirt'])
+    # per_garment_errors['Top'].extend(per_garment_errors_skirt['Top'])
+    # per_garment_errors['Skirt'] = per_garment_errors_skirt['Skirt']
+    # per_garment_errors['Dress'] = per_garment_errors_skirt['Dress']
+    # for key in per_garment_errors.keys():
+    #     avg = np.mean(per_garment_errors[key])
+    #     global_errors.append(avg)
+    #     print(key + ': ' + str(avg), 'samples: ' + str(len(per_garment_errors[key])))
+
+    # print('Global avg: ' + str(np.mean(global_errors)))
+    # print(len(global_errors))
     

models/pyramid_level.py

@@ -155,15 +155,18 @@ class PyramidLevelNew(tf.keras.Model):
         return out
 
 
-    def call(self, inputs, z_cond, training=None):
+    def call(self, inputs, z_cond, prev_latent=None, training=None):
         uv_unposed, uv_template, uv_body_posed, uv_curv, uv_mask, uv_mask_high = inputs
 
         x = tf.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], axis=-1)
         mean, logvar = self.encoder(x, training=training)
         z = self.reparameterize(mean, logvar, training)
-        z = self.reshape(z)
+        z_latent = self.reshape(z)
     
-        z = tf.concat([z, z_cond], axis=-1)
+        if prev_latent is not None:
+            z_latent = tf.concat([z_latent, prev_latent], axis=-1)
+            
+        z = tf.concat([z_latent, z_cond], axis=-1)
         x = self.decoder(z, training=training)
 
         if self.avg_garment is not None:
@@ -177,4 +180,4 @@ class PyramidLevelNew(tf.keras.Model):
         
         # x = x * uv_mask_high[:,:,:,tf.newaxis]
 
-        return x, mean, logvar
+        return x, mean, logvar, z_latent

models/pyramid_model_optimized.py

@@ -12,244 +12,6 @@ from models.pyramid_level import PyramidLevel, PyramidLevelNew
 from models.upscaler import UpscalerBasic, UpscalerBasicConv, UpscalerBasicNew
 
 
-class Pyramid(tf.keras.Model):
-    
-    def __init__(self, config):
-        super(Pyramid, self).__init__()
-
-        self.static_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
-        if config['pyramid']['static_model'] != '':
-            load_model(self.static_enc, config['pyramid']['static_model'] + '_enc')
-
-        self.body_enc : AEEncoder = AEEncoder(config['cond_ae_body']['latent_dim'], config['cond_ae_body']['encoder_depths'], config['cond_ae_body']['encoder_dims'], config['cond_ae_body']['bottleneck_size'], config['cond_ae_body']['kernel_size'])
-        if config['pyramid']['body_model'] != '':
-            load_model(self.body_enc, config['pyramid']['body_model'] + '_enc')
-
-        self.curv_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
-        if config['pyramid']['curvature_model'] != '':
-            load_model(self.curv_enc, config['pyramid']['curvature_model'] + '_enc')
-
-        if config['pyramid']['freeze_encoders']:
-            self.static_enc.trainable = False
-            self.body_enc.trainable = False
-            self.curv_enc.trainable = False
-
-        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.Dense(config['pyramid']['latent_dim'], activation='tanh'),])
-
-        self.levels = [PyramidLevel({ **config['pyramid']['levels'][i], **{ 'cond_ae': config['cond_ae']} }) for i in range(len(config['pyramid']['levels']))]
-
-        # if config['is_cape']:
-        #     if len(config['pyramid']['resolutions']) == 2:
-        #         self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
-        #     if len(config['pyramid']['resolutions']) == 3:
-        #         self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
-        #         self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
-        #     if len(config['pyramid']['resolutions']) == 4:
-        #         self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
-        #         self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
-        #         self.levels[-3].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
-        # else:
-        if len(config['pyramid']['resolutions']) == 2:
-            self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
-        if len(config['pyramid']['resolutions']) == 3:
-            self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
-            self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
-        if len(config['pyramid']['resolutions']) == 4:
-            self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 10000)])
-            self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
-            self.levels[-3].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
-        if len(config['pyramid']['resolutions']) == 5:
-            self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100000)])
-            self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 10000)])
-            self.levels[-3].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
-            self.levels[-4].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
-
-        self.add_noise = config['pyramid']['add_noise']
-        self.noise_amount = config['pyramid']['noise_amount']
-        self.use_offsets = config['use_offsets']
- 
-        self.upscale_enhancer = None
-        if config['pyramid']['final_upscale_enhancer'] != '':
-            k_size = config['pyramid']['final_upscale_enhancer_kernel_size'] if 'final_upscale_enhancer_kernel_size' in config['pyramid'].keys() else ((3 if config['pyramid']['resolutions'][-1] <= 64 else 5))
-            self.upscale_enhancer = UpscalerBasicConv(low_res=config['pyramid']['resolutions'][-1], kernel_size=k_size)
-            load_model(self.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
-            self.upscale_enhancer.trainable = False
-
-    def enhance(self, low, mask, training):
-        x = self.upscale_enhancer([low, mask], training=training)
-        return x
-
-    def encode_conds(self, uv_template, uv_body_posed, uv_curv, training):
-        z_static = self.static_enc(uv_template, training)
-        z_body = self.body_enc(uv_body_posed, training)
-        z_curv = self.curv_enc(uv_curv, training)
-
-        z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
-        z_cond = self.cond_encoder(z_cond, training=training)
-
-        return z_cond
-
-    def call(self, inputs, training):
-        levels_input, conds = inputs
-        uv_static, uv_body, uv_curv = conds[0], conds[1], conds[2]
-
-        z_cond = self.encode_conds(uv_static, uv_body, uv_curv, training)
-
-        if self.add_noise == True and training == True:
-            z_cond = z_cond + (tf.random.uniform(z_cond.shape, maxval=self.noise_amount * 2) - self.noise_amount)
-
-        out_levels = []
-        pred, mean, logvar = self.levels[0](levels_input[0], z_cond, training=training)
-        if self.use_offsets:
-            pred = pred + levels_input[0][2] # uv_body_posed_32
-        if 1 != len(self.levels):
-            pred = self.levels[0].enhance(pred, levels_input[0][-2], training=training)  # levels_input[i][-2]=uv_mask_res
-            pred = tf.image.resize(pred, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.LANCZOS5)
-            out_levels.append([pred, mean, logvar])
-            pred = pred * levels_input[1][-2][:,:,:,tf.newaxis]
-        else:
-            pred = self.enhance(pred, levels_input[0][-2], training=training)
-            pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.LANCZOS5)
-            pred = pred * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
-            out_levels.append([pred, mean, logvar])
-
-        for i in range(1, len(self.levels)):
-            pred_level, mean, logvar = self.levels[i](levels_input[i], z_cond, training=training)
-            pred = pred + pred_level
-            if i != len(self.levels) - 1:
-                pred = self.levels[i].enhance(pred, levels_input[i][-2], training=training)
-                out_levels.append([pred, mean, logvar])
-                pred = tf.image.resize(pred, (self.levels[i+1].size, self.levels[i+1].size//2), tf.image.ResizeMethod.LANCZOS5)
-                pred = pred * levels_input[i+1][-2][:,:,:,tf.newaxis]
-            else:
-                if self.upscale_enhancer is not None:
-                    pred = self.enhance(pred, levels_input[-1][-2], training=training)
-                    pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.LANCZOS5)
-                pred = pred * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
-                out_levels.append([pred, mean, logvar])
-
-        return out_levels
-
-        # # 32
-        # pred_0, mean_0, logvar_0 = self.levels[0](levels_input[0], z_cond, training=training)
-        # pred_0 = self.enhance(pred_0, levels_input[0][-2], training=training)
-        # pred_0 = tf.image.resize(pred_0, (512, 256), tf.image.ResizeMethod.LANCZOS5)
-        # pred_0 = pred_0 * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
-        # return [ [pred_0, mean_0, logvar_0] ]
-
-        # # 32 64
-        # pred_0, mean_0, logvar_0 = self.levels[0](levels_input[0], z_cond, training=training)
-        # pred_0 = self.levels[0].enhance(pred_0, levels_input[0][-2], training=training)  # levels_input[i][-2]=uv_mask_res
-        # pred_0 = tf.image.resize(pred_0, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.LANCZOS5)
-        # pred = pred_0 * levels_input[1][-2][:,:,:,tf.newaxis]
-
-        # pred_1, mean_1, logvar_1 = self.levels[1](levels_input[1], z_cond, training=training)
-        # pred_1 = pred + pred_1
-        # pred_1 = self.enhance(pred_1, levels_input[-1][-2], training=training)
-        # pred_1 = tf.image.resize(pred_1, (512, 256), tf.image.ResizeMethod.LANCZOS5)
-        # pred_1 = pred_1 * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
-
-        # return [ [pred_0, mean_0, logvar_0], [pred_1, mean_1, logvar_1] ]
-
-
-# class Pyramid(tf.keras.Model):
-    
-#     def __init__(self, config):
-#         super(Pyramid, self).__init__()
-
-#         self.static_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
-#         if config['pyramid']['static_model'] != '':
-#             load_model(self.static_enc, config['pyramid']['static_model'] + '_enc')
-
-#         self.body_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
-#         if config['pyramid']['body_model'] != '':
-#             load_model(self.body_enc, config['pyramid']['body_model'] + '_enc')
-
-#         self.curv_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
-#         if config['pyramid']['curvature_model'] != '':
-#             load_model(self.curv_enc, config['pyramid']['curvature_model'] + '_enc')
-
-#         if config['pyramid']['freeze_encoders']:
-#             self.static_enc.trainable = False
-#             self.body_enc.trainable = False
-#             self.curv_enc.trainable = False
-
-#         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.Dense(config['pyramid']['latent_dim']),])
-
-#         self.levels = [PyramidLevel({ **config['pyramid']['levels'][i], **{ 'cond_ae': config['cond_ae']} }) for i in range(len(config['pyramid']['levels']))]
-
- 
-#         self.upscale_enhancer = None
-#         if config['pyramid']['final_upscale_enhancer'] != '':
-#             self.upscale_enhancer = UpscalerBasicNew(low_res=config['pyramid']['resolutions'][-1])
-#             load_model(self.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
-#             self.upscale_enhancer.trainable = False
-
-#     def enhance(self, low, mask, training):
-#         x = self.upscale_enhancer([low, mask], training=training)
-#         return x
-
-#     def encode_conds(self, uv_template, uv_body_posed, uv_curv):
-#         z_static = self.static_enc(uv_template)
-#         z_body = self.body_enc(uv_body_posed)
-#         z_curv = self.curv_enc(uv_curv)
-
-#         z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
-#         z_cond = self.cond_encoder(z_cond)
-
-#         return z_cond
-
-#     def call(self, inputs, training):
-#         levels_input, conds = inputs
-#         uv_static, uv_body, uv_curv = conds[0], conds[1], conds[2]
-#         z_cond = self.encode_conds(uv_static, uv_body, uv_curv)
-
-#         out_levels = []
-#         pred_0, mean_0, logvar_0 = self.levels[0](levels_input[0], z_cond, training=training)
-#         pred_0 = self.levels[0].enhance(pred_0, levels_input[0][-2], training=training)  # levels_input[i][-2]=uv_mask_res
-#         # pred_0 = self.enhance(pred_0, levels_input[0][-2], training=training)
-            
-#         ##################    
-#         # if more than 1 level
-#         # x = tf.image.resize(pred_0, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.LANCZOS5)
-#         # x = x * levels_input[1][-2][:,:,:,tf.newaxis]
-
-#         # if only 1 level
-#         pred_0 = tf.image.resize(pred_0, (512, 256), tf.image.ResizeMethod.LANCZOS5)
-#         ##################
-
-
-#         # pred_1, mean_1, logvar_1 = self.levels[1](levels_input[1], z_cond, training=training)
-#         # pred_1 = pred_0 + pred_1
-#         # pred_1 = self.levels[1].enhance(pred, levels_input[1][-2], training=training)
-
-#         # for i in range(1, len(self.levels)):
-#         #     pred_level, mean, logvar = self.levels[i](levels_input[i], z_cond, training=training)
-#         #     pred = pred + pred_level
-#         #     if i != len(self.levels) - 1:
-#         #         pred = self.levels[i].enhance(pred, levels_input[i][-2], training=training)
-#         #         out_levels.append([pred, mean, logvar])
-#         #         pred = tf.image.resize(pred, (self.levels[i+1].size, self.levels[i+1].size//2), tf.image.ResizeMethod.LANCZOS5)
-#         #         pred = pred * levels_input[i+1][-2][:,:,:,tf.newaxis]
-#         #     else:
-#         #         if self.upscale_enhancer is not None:
-#         #             pred = self.enhance(pred, levels_input[-1][-2], training=training)
-#         #             pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.LANCZOS5)
-#         #         out_levels.append([pred, mean, logvar])
-#         #         pred = pred * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
-
-#         out_levels = [
-#             [pred_0, mean_0, logvar_0]
-#         ]
-
-#         return out_levels
-
 
 class PyramidNew(tf.keras.Model):
     
@@ -278,21 +40,6 @@ class PyramidNew(tf.keras.Model):
         self.levels = [PyramidLevelNew({ **config['pyramid']['levels'][i] }) for i in range(len(config['pyramid']['levels']))]
 
 
-        # if len(config['pyramid']['resolutions']) == 2:
-        #     self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
-        # if len(config['pyramid']['resolutions']) == 3:
-        #     self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
-        #     self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
-        # if len(config['pyramid']['resolutions']) == 4:
-        #     self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 10000)])
-        #     self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
-        #     self.levels[-3].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
-        # if len(config['pyramid']['resolutions']) == 5:
-        #     self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100000)])
-        #     self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 10000)])
-        #     self.levels[-3].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
-        #     self.levels[-4].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
-
         self.add_noise = config['pyramid']['add_noise']
         self.noise_amount = config['pyramid']['noise_amount']
         self.use_offsets = config['use_offsets']
@@ -411,7 +158,7 @@ class PyramidNew(tf.keras.Model):
             z_cond = z_cond + (tf.random.uniform(z_cond.shape, maxval=self.noise_amount * 2) - self.noise_amount)
 
         out_levels = []
-        pred, mean, logvar = self.levels[0](levels_input[0], z_cond, training=training)
+        pred, mean, logvar, z_latent = self.levels[0](levels_input[0], z_cond, training=training)
         if self.use_offsets:
             pred = pred + levels_input[0][2] # uv_body_posed_32
         if 1 != len(self.levels):
@@ -429,7 +176,7 @@ class PyramidNew(tf.keras.Model):
         for i in range(1, len(self.levels)):
             input = levels_input[i]
             input[0] = input[0] - pred
-            pred_level, mean, logvar = self.levels[i](input, z_cond, training=training)
+            pred_level, mean, logvar, z_latent = self.levels[i](input, z_cond, z_latent, training=training)
             pred = pred + pred_level
             if i != len(self.levels) - 1:
                 pred = self.levels[i].enhance(pred, levels_input[i][-2], training=training)

sample_pro_2.py

@@ -41,14 +41,13 @@ from custom_utils.smpl import SMPL
 from convert_to_uv_api import convert_to_uv_api
 from models.upscaler import UpscalerBasicConv as Upscaler
 from postprocessing import fix_collision
-# from models.pyramid_model_optimized import Pyramid
+from models.pyramid_model_optimized import Pyramid
 
 # from configs.config_abl_overfit_dress_var_3 import config as config_main
 # from configs.config_abl_overfit_jumpsuit_2 import config as config_main
 # from configs.config_cvae_curv import config as config_curv
 # from configs.iccv.config_abl_all_skirt_var import config as config_main
-# from configs.iccv.config_abl_all_skirt import config as config_main
+from configs.iccv.config_abl_skin_var import config as config_main
-from configs.iccv.config_abl_all import config as config_main
 from configs.iccv.config_cvae_curv import config as config_curv
 # from models.cvae_model_overfit_jumpsuit import CVAE
 from models.cvae_model import CVAENew as CVAE
@@ -297,54 +296,52 @@ def sampling_example(frame, camera, nr=15):
     mask = batch['mesh_mask'][0].numpy().astype(bool)
     _, faces = _maskFaces(np.zeros((14475, 3)), F, mask)
 
-    # noise = tf.random.normal(shape=(nr, 8, 4, 128))
+    noise = tf.random.normal(shape=(nr, 8, 4, 128))
-    # out = model.sample(tf.tile(batch['uv_static'], [nr,1,1,1]), tf.tile(batch['uv_body_posed'], [nr,1,1,1]), tf.tile(batch['uv_curvature'], [nr,1,1,1]), tf.tile(batch['uv_mask'], [nr,1,1]), noise)
+    out = model.sample(tf.tile(batch['uv_static'], [nr,1,1,1]), tf.tile(batch['uv_body_posed'], [nr,1,1,1]), tf.tile(batch['uv_curvature'], [nr,1,1,1]), tf.tile(batch['uv_mask'], [nr,1,1]), noise)
-    # # out,_,_ = model([tf.tile(batch['uv_unposed'], [nr,1,1,1]), tf.tile(batch['uv_static'], [nr,1,1,1]), tf.tile(batch['uv_body_posed'], [nr,1,1,1]), tf.tile(batch['uv_curvature'], [nr,1,1,1]), tf.tile(batch['uv_mask'], [nr,1,1])], training=False)
+    # out,_,_ = model([tf.tile(batch['uv_unposed'], [nr,1,1,1]), tf.tile(batch['uv_static'], [nr,1,1,1]), tf.tile(batch['uv_body_posed'], [nr,1,1,1]), tf.tile(batch['uv_curvature'], [nr,1,1,1]), tf.tile(batch['uv_mask'], [nr,1,1])], training=False)
     
-    # if config_main['use_skinning']:
+    if config_main['use_skinning']:
-    #     posed_mesh = [get_posed_mesh(uv, batch['pose_shape'], batch['mesh_body_posed'], mask) for uv in tf.unstack(out)]
+        posed_mesh = [get_posed_mesh(uv, batch['pose_shape'], batch['mesh_body_posed'], mask) for uv in tf.unstack(out)]
-    # else:
+    else:
-    #     posed_mesh = [uv2mesh(uv[tf.newaxis,:,:,:]) for uv in tf.unstack(out)]
+        posed_mesh = [uv2mesh(uv[tf.newaxis,:,:,:]) for uv in tf.unstack(out)]
-    # gt_mesh = batch['mesh_posed'][0].numpy()[mask, :]
+    gt_mesh = batch['mesh_posed'][0].numpy()[mask, :]
 
     body = batch['mesh_body_posed'][0].numpy()
-    # posed_mesh_fixed = [postprocess(mesh, body, faces, mask, do_smoothing=True) for mesh in posed_mesh]
+    posed_mesh_fixed = [postprocess(mesh, body, faces, mask, do_smoothing=True) for mesh in posed_mesh]
-    # # posed_mesh_fixed = [mesh[0].numpy()[mask, :] for mesh in posed_mesh]
+    # posed_mesh_fixed = [mesh[0].numpy()[mask, :] for mesh in posed_mesh]
 
-    # # error = [np.sqrt(((gt_mesh-m) ** 2).sum(-1)).mean() for m in posed_mesh_fixed]
+    # error = [np.sqrt(((gt_mesh-m) ** 2).sum(-1)).mean() for m in posed_mesh_fixed]
 
-    # # row_sampling = []
+    # row_sampling = []
-    # # fig = plot_fixed(gt_mesh, body, faces, camera)
+    # fig = plot_fixed(gt_mesh, body, faces, camera)
-    # # fig.write_image('/tmp/exp_temp_7.png')
+    # fig.write_image('/tmp/exp_temp_7.png')
-    # # img = cv2.imread('/tmp/exp_temp_7.png')
+    # img = cv2.imread('/tmp/exp_temp_7.png')
-    # # row_sampling.append(img[:, 100:700, :])
+    # row_sampling.append(img[:, 100:700, :])
-    # # for mesh in posed_mesh_fixed:
+    # for mesh in posed_mesh_fixed:
-    # #     fig = plot_fixed(mesh, body, faces, camera)
+    #     fig = plot_fixed(mesh, body, faces, camera)
-    # #     fig.write_image('/tmp/exp_temp_7.png')
+    #     fig.write_image('/tmp/exp_temp_7.png')
-    # #     img = cv2.imread('/tmp/exp_temp_7.png')
+    #     img = cv2.imread('/tmp/exp_temp_7.png')
-    # #     row_sampling.append(img[:, 100:700, :])
+    #     row_sampling.append(img[:, 100:700, :])
 
 
     # custom_imshow(np.concatenate(row_sampling, axis=1)).show()
-    # os.makedirs(os.path.join(out_folder, frame), exist_ok=True)
+    os.makedirs(os.path.join(out_folder, frame), exist_ok=True)
 
-    # plot_fixed(np.zeros_like(gt_mesh), body, faces, camera).write_image(os.path.join(out_folder, frame, frame + '_body.png'))
+    plot_fixed(np.zeros_like(gt_mesh), body, faces, camera).write_image(os.path.join(out_folder, frame, frame + '_body.png'))
-    # plot_fixed(gt_mesh, body, faces, camera).write_image(os.path.join(out_folder, frame, frame + '_gt.png'))
+    plot_fixed(gt_mesh, body, faces, camera).write_image(os.path.join(out_folder, frame, frame + '_gt.png'))
-    # for i in range(nr):
+    for i in range(nr):
-    #     plot_fixed(posed_mesh_fixed[i], body, faces, camera).write_image(os.path.join(out_folder, frame, frame + '_' + str(i) + '.png'))
+        plot_fixed(posed_mesh_fixed[i], body, faces, camera).write_image(os.path.join(out_folder, frame, frame + '_' + str(i) + '.png'))
     
     # save_stl(os.path.join(out_folder, frame, frame + '_body.stl'), body, F_body)
     # save_stl(os.path.join(out_folder, frame, frame + '_gt.stl'), gt_mesh, faces.astype(np.int32))
     # for i in range(nr):
     #     save_stl(os.path.join(out_folder, frame, frame + '_' + str(i) + '.stl'), posed_mesh_fixed[i], faces.astype(np.int32))
-    # np.save(os.path.join(out_folder, frame, frame + '_gt.npy'), gt_mesh)
+    np.save(os.path.join(out_folder, frame, frame + '_gt.npy'), gt_mesh)
-    # np.save(os.path.join(out_folder, frame, frame + '_body.npy'), body)
+    np.save(os.path.join(out_folder, frame, frame + '_body.npy'), body)
-    # np.save(os.path.join(out_folder, frame, frame + '_faces.npy'), faces.astype(np.int32))
+    np.save(os.path.join(out_folder, frame, frame + '_faces.npy'), faces.astype(np.int32))
-    # for i in range(nr):
+    for i in range(nr):
-    #     np.save(os.path.join(out_folder, frame, frame + '_' + str(i) + '.npy'), posed_mesh_fixed[i])
+        np.save(os.path.join(out_folder, frame, frame + '_' + str(i) + '.npy'), posed_mesh_fixed[i])
 
-    save_stl(os.path.join(out_folder, frame + '_gt_body.stl'), body, F_body)
-    save_stl(os.path.join(out_folder, frame + '_posed_body.stl'), body, F_body)
 
     # custom_imshow(np.concatenate(row_sampling, axis=1)).write_image(os.path.join(out_folder, frame + '_sampling.png'))
     print()
@@ -420,7 +417,6 @@ def sampling_garment(frame, camera):
 
     print()
 
-
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='')
     parser.add_argument('--name', type=str)
@@ -480,16 +476,9 @@ if __name__ == '__main__':
     # frame = '04925_272'
 
     # frames = ['01070_13', '02276_232', '02978_242', '04008_295', '04173_193']
-    # frames = ['00073_160', '00073_217', '00096_86', \
+    frames = ['02276_232', '02978_242', '04173_193']
-    #           '04347_100', '04360_2', '04404_41', '04442_85', '04466_59', '04620_68', '04620_129', \
-    #           '04655_28', '04668_255', '04682_96', '04709_37', '06117_123', '06141_162']
     
-    # frames = ['00265_257', '04070_263', '00239_9', '04608_170', '04372_289', '00077_106', '00205_164', '00323_97', '00185_7', '00079_250', \
+    out_folder = 'examples/sampling_analysis'
-    #           '04143_56', '00240_136', '00185_33']
-    
-    frames = ['009042_205', '009152_33', '010352_168', '012002_4', '03316_168', '03365_230']
-    
-    out_folder = 'examples/qualitative/bodies3'
     os.makedirs(out_folder, exist_ok=True)
     for frame in tqdm.tqdm(frames):
         sampling_example(frame, camera_1)