save

aaa_helpers.txt

@@ -172,13 +172,20 @@ 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 > 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 > 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_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_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_skirt.out 2>&1 &

calculate_error.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_abl_pyr_32_64_128_skin_skirt import config
+from configs.iccv.config_abl_pyr_32_64_128_tshirt import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -58,7 +58,7 @@ if __name__ == '__main__':
     # smpl = SMPL(use_one_hot=config['use_one_hot'])
     skirt_modifier = '_skirt' if config['is_skirt'] else ''
 
-    error_type = 'collision' # 'euclidean' 'collision' edge normal
+    error_type = 'euclidean' # 'euclidean' 'collision' edge normal
 
     folder = config['test_dir']
 
@@ -92,6 +92,13 @@ 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():
+    #     with open(os.path.join(folder, config['frames_file_test']), 'r') as f:
+    #             lines = f.readlines()
+    #             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) }

configs/iccv/config_abl_pyr_32_64_128_jumpsuit.py

@@ -32,7 +32,7 @@ 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'

configs/iccv/config_abl_pyr_32_64_128_skirt_skirt.py

@@ -31,7 +31,7 @@ config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_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['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'

configs/iccv/config_abl_pyr_32_64_128_top.py

@@ -32,10 +32,10 @@ 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['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['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'

configs/iccv/config_abl_pyr_32_64_128_top_skirt.py

@@ -0,0 +1,197 @@
+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_trousers.py

@@ -32,7 +32,7 @@ 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_trousers.txt'
 config['frames_file_valid'] = 'frames_trousers.txt'
 config['frames_file_test'] = 'frames_trousers.txt'

configs/iccv/config_abl_pyr_32_64_128_tshirt.py

@@ -32,7 +32,7 @@ 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['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'

configs/iccv/config_abl_pyr_32_64_128_tshirt_skirt.py

@@ -0,0 +1,196 @@
+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'

evaluate_ae_pyramid.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_abl_pyr_32_64_128_skin_skirt import config
+from configs.iccv.config_abl_pyr_32_64_128_tshirt_skirt 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_skin'
-    checkpoint_name_skirt = 'iccv_pyr_32_64_128_skin_skirt'
-    iteration = '75' + '000'
-    iteration_skirt : str = '155' + '000'
-    error_type = 'collision' # 'euclidean' 'collision' edge normal
+    checkpoint_name = 'iccv_pyr_32_64_128_2'
+    checkpoint_name_skirt = 'iccv_pyr_32_64_128_skirt_dress'
+    iteration = '125' + '000'
+    iteration_skirt : str = '175' + '000'
+    error_type = '-euclidean' # '-euclidean' '-collision' -edge -normal
 
 
     errors = []
@@ -21,7 +21,7 @@ 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]))
@@ -34,7 +34,7 @@ if __name__ == '__main__':
 
     # 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:
+    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]))
@@ -47,24 +47,24 @@ if __name__ == '__main__':
 
     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'])
+    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('Global tshirt avg: ' + str(cum_avg_shirt))
+    print('Global top avg: ' + str(cum_avg_top))
 
-    # print('\nGlobal per garment: ')
+    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])))
+    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))
+    print('Global avg: ' + str(np.mean(global_errors)))
+    print(len(global_errors))
     

sample_pro_2.py

@@ -41,13 +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_skin_var import config as config_main
+from configs.iccv.config_abl_all_skirt 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
@@ -296,52 +296,54 @@ 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))
-    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)
+    # 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([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']:
-        posed_mesh = [get_posed_mesh(uv, batch['pose_shape'], batch['mesh_body_posed'], mask) for uv in tf.unstack(out)]
-    else:
-        posed_mesh = [uv2mesh(uv[tf.newaxis,:,:,:]) for uv in tf.unstack(out)]
-    gt_mesh = batch['mesh_posed'][0].numpy()[mask, :]
+    # 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)]
+    # else:
+    #     posed_mesh = [uv2mesh(uv[tf.newaxis,:,:,:]) for uv in tf.unstack(out)]
+    # 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 = [mesh[0].numpy()[mask, :] 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]
 
-    # 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 = []
-    # fig = plot_fixed(gt_mesh, body, faces, camera)
-    # fig.write_image('/tmp/exp_temp_7.png')
-    # img = cv2.imread('/tmp/exp_temp_7.png')
-    # row_sampling.append(img[:, 100:700, :])
-    # for mesh in posed_mesh_fixed:
-    #     fig = plot_fixed(mesh, body, faces, camera)
-    #     fig.write_image('/tmp/exp_temp_7.png')
-    #     img = cv2.imread('/tmp/exp_temp_7.png')
-    #     row_sampling.append(img[:, 100:700, :])
+    # # row_sampling = []
+    # # fig = plot_fixed(gt_mesh, body, faces, camera)
+    # # fig.write_image('/tmp/exp_temp_7.png')
+    # # img = cv2.imread('/tmp/exp_temp_7.png')
+    # # row_sampling.append(img[:, 100:700, :])
+    # # for mesh in posed_mesh_fixed:
+    # #     fig = plot_fixed(mesh, body, faces, camera)
+    # #     fig.write_image('/tmp/exp_temp_7.png')
+    # #     img = cv2.imread('/tmp/exp_temp_7.png')
+    # #     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(gt_mesh, body, faces, camera).write_image(os.path.join(out_folder, frame, frame + '_gt.png'))
-    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(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'))
+    # 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'))
     
     # 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 + '_body.npy'), body)
-    np.save(os.path.join(out_folder, frame, frame + '_faces.npy'), faces.astype(np.int32))
-    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 + '_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 + '_faces.npy'), faces.astype(np.int32))
+    # for i in range(nr):
+    #     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()
@@ -417,6 +419,7 @@ def sampling_garment(frame, camera):
 
     print()
 
+
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='')
     parser.add_argument('--name', type=str)
@@ -476,9 +479,15 @@ if __name__ == '__main__':
     # frame = '04925_272'
 
     # frames = ['01070_13', '02276_232', '02978_242', '04008_295', '04173_193']
-    frames = ['02276_232', '02978_242', '04173_193']
+    # frames = ['00073_160', '00073_217', '00096_86', \
+    #           '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']
 
-    out_folder = 'examples/sampling_analysis'
+    frames = ['00265_257', '04070_263', '00239_9', '04608_170', '04372_289', '00077_106', '00205_164', '00323_97', '00185_7', '00079_250', \
+              '04143_56', '00240_136', '00185_33']
+    
+    
+    out_folder = 'examples/qualitative/bodies'
     os.makedirs(out_folder, exist_ok=True)
     for frame in tqdm.tqdm(frames):
         sampling_example(frame, camera_1)