save

aaa_helpers.txt

@@ -44,7 +44,7 @@ nohup python cvae.py --name="lhunor_abl_skin_var" > nohup/iccv/ablation/abl_skin
 nohup python evaluate_ae.py --name abl_all > nohup/iccv/evaluate/abl_all.out 2>&1 &
 nohup python evaluate_ae.py --name abl_all_skirt > nohup/iccv/evaluate/abl_all_skirt.out 2>&1 &
 
-nohup python cvae_curv.py --name="lhunor_cvae_curv" > nohup/iccv/ablation/cvae_curv.out 2>&1 &
+nohup python cvae_curv.py --name="lhunor_cvae_curv" > nohup/iccv/ablation/cvae_curv_3.out 2>&1 &
 
 nohup python upscale_test.py --name="iccv_32_2" > nohup/iccv/upscale/iccv_32_2.out 2>&1 &
 nohup python upscale_test.py --name="iccv_64_2" > nohup/iccv/upscale/iccv_64_2.out 2>&1 &
@@ -86,6 +86,8 @@ nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_curv" > nohup/iccv/py
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_curv_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_curv_skirt.out 2>&1 &
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_reg" > nohup/iccv/pyr/iccv_pyr_32_64_reg.out 2>&1 &
 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_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 &
@@ -121,6 +123,7 @@ nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_512" > nohup/
 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 &
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_512_skin" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_512_skin.out 2>&1 &
 nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_512_curv" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_512_curv.out 2>&1 &
+nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_256_512_reg" > nohup/iccv/pyr/iccv_pyr_32_64_128_256_512_reg.out 2>&1 &
 
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32 > nohup/iccv/evaluate/iccv_pyr_32.out 2>&1 &
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_skirt > nohup/iccv/evaluate/iccv_pyr_32_skirt.out 2>&1 &
@@ -130,6 +133,8 @@ nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_reg > nohup/i
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_reg_skirt > nohup/iccv/evaluate/iccv_pyr_32_reg_skirt.out 2>&1 &
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_curv > nohup/iccv/evaluate/iccv_pyr_32_curv.out 2>&1 &
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_curv_skirt > nohup/iccv/evaluate/iccv_pyr_32_curv_skirt.out 2>&1 &
+nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_10p > nohup/iccv/evaluate/iccv_pyr_32_10p.out 2>&1 &
+nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_25p > nohup/iccv/evaluate/iccv_pyr_32_25p.out 2>&1 &
 
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64 > nohup/iccv/evaluate/iccv_pyr_32_64.out 2>&1 &
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_skin > nohup/iccv/evaluate/iccv_pyr_32_64_skin.out 2>&1 &
@@ -148,9 +153,15 @@ nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_curv_s
 
 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 &
+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 &
+nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256_reg > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_reg.out 2>&1 &
 
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256_512 > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_512.out 2>&1 &
 nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256_512_skirt > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_512_skirt.out 2>&1 &
+nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256_512_reg > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_512_reg.out 2>&1 &
+nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256_512_skin > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_512_skin.out 2>&1 &
+nohup python evaluate_ae_pyramid.py --name evaluate_ae_iccv_pyr_32_64_128_256_512_curv > nohup/iccv/evaluate/iccv_pyr_32_64_128_256_512_curv.out 2>&1 &
 
 nohup python cvae.py --name="lhunor_cape_f_03223_SL" > nohup/iccv/ablation/abl_all_cape_f_03223_SL.out 2>&1 &
 nohup python cvae.py --name="lhunor_cape_f_03223_SS" > nohup/iccv/ablation/abl_all_cape_f_03223_SS.out 2>&1 &

calculate_error.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_abl_pyr_32_64_128_curv_skirt import config
+from configs.iccv.config_abl_pyr_32_25p import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 # import tensorflow as tf

configs/iccv/config_abl_all_skirt_var.py

@@ -19,7 +19,7 @@ ablation_count = [config['use_skinning'], config['remove_global_orientation'], c
 assert ablation_count == 1 or ablation_count == 0
 
 config['gpu'] = '0'
-config['name'] = 'abl_all_skirt_var_2'
+config['name'] = 'abl_all_skirt_var'
 config['notes'] = ''
 config['tags'] = ['iccv', 'abl_all', 'skirt']
 

configs/iccv/config_abl_pyr_32_10p.py

@@ -1,6 +1,6 @@
 config = dict()
 
-config['gpu'] = '4'
+config['gpu'] = '2'
 config['name'] = 'iccv_pyr_32_10p'
 config['notes'] = ''
 config['tags'] = ['pyr', 'iccv']

configs/iccv/config_abl_pyr_32_25p.py

@@ -1,6 +1,6 @@
 config = dict()
 
-config['gpu'] = '5'
+config['gpu'] = '2'
 config['name'] = 'iccv_pyr_32_25p'
 config['notes'] = ''
 config['tags'] = ['pyr', 'iccv']

configs/iccv/config_abl_pyr_32_64_10p.py

@@ -0,0 +1,189 @@
+config = dict()
+
+config['gpu'] = '4'
+config['name'] = 'iccv_pyr_32_64_10p'
+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_10p'
+config['pyramid']['final_upscale_enhancer'] = ''
+
+config['train_dir'] = '/home/workspace/local-data_127/train'
+config['valid_dir'] = '/home/workspace/local-data_127/valid'
+config['test_dir'] = '/home/workspace/local-data_127/test_ready'
+
+config['frames_file'] = 'frames_10p.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_10p'
+config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_10p'
+config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_10p'
+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_256_512_curv.py

@@ -1,6 +1,6 @@
 config = dict()
 
-config['gpu'] = '3'
+config['gpu'] = '2'
 config['name'] = 'iccv_pyr_32_64_128_256_512_curv'
 config['notes'] = ''
 config['tags'] = ['pyr', 'iccv', 'curv']
@@ -33,7 +33,7 @@ config['pyramid']['final_upscale_enhancer'] = ''
 
 config['train_dir'] = '/home/workspace/local-data_127/train'
 config['valid_dir'] = '/home/workspace/local-data_127/valid'
-config['test_dir'] = '/home/workspace/local-data_127/test_ready'
+config['test_dir'] = '/home/workspace/local-data/test_ready'
 config['batch_size'] = 4
 config['is_skirt'] = False
 config['is_cape'] = False

configs/iccv/config_abl_pyr_32_64_128_256_512_reg.py

@@ -0,0 +1,187 @@
+config = dict()
+
+config['gpu'] = '0'
+config['name'] = 'iccv_pyr_32_64_128_256_512_reg'
+config['notes'] = ''
+config['tags'] = ['pyr', 'iccv', 'reg']
+
+# 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'] =          False # edge, collision, 3d mesh
+config['use_offsets'] =               False
+config['use_norm_loss'] =             False
+config['use_curvature'] =             True
+
+config['pretrained_model'] = ''
+config['pretrained_model_iteration'] = ''
+
+config['pyramid'] = dict()
+config['pyramid']['resolutions'] = [32, 64, 128, 256, 512]
+config['pyramid']['pretrained_pyramid'] = 'checkpoints/iccv/iccv_pyr_32_64_128_256_reg'
+config['pyramid']['pretrained_pyramid_iteration'] = ''
+config['pyramid']['final_upscale_enhancer'] = ''
+
+config['train_dir'] = '/home/workspace/local-data_127/train'
+config['valid_dir'] = '/home/workspace/local-data_127/valid'
+config['test_dir'] = '/home/workspace/local-data/test_ready'
+config['batch_size'] = 4
+config['is_skirt'] = False
+config['is_cape'] = False
+
+config['dataset_in_memory'] = False
+config['resolution'] = None
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['lr'] = 0.0001
+config['starting_balancing_coeff'] = 0.001
+config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
+config['avg_garment_path'] = ''
+config['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_256_512_skin.py

@@ -1,6 +1,6 @@
 config = dict()
 
-config['gpu'] = '4'
+config['gpu'] = '1'
 config['name'] = 'iccv_pyr_32_64_128_256_512_skin'
 config['notes'] = ''
 config['tags'] = ['pyr', 'iccv']
@@ -33,7 +33,7 @@ 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['batch_size'] = 4
 config['is_skirt'] = False
 config['is_cape'] = False

configs/iccv/config_abl_pyr_32_64_128_256_curv.py

@@ -1,6 +1,6 @@
 config = dict()
 
-config['gpu'] = '3'
+config['gpu'] = '2'
 config['name'] = 'iccv_pyr_32_64_128_256_curv'
 config['notes'] = ''
 config['tags'] = ['pyr', 'iccv', 'curv']

configs/iccv/config_abl_pyr_32_64_128_256_reg.py

@@ -1,6 +1,6 @@
 config = dict()
 
-config['gpu'] = '1'
+config['gpu'] = '5'
 config['name'] = 'iccv_pyr_32_64_128_256_reg'
 config['notes'] = ''
 config['tags'] = ['pyr', 'iccv', 'reg']

configs/iccv/config_abl_pyr_32_64_25p.py

@@ -0,0 +1,189 @@
+config = dict()
+
+config['gpu'] = '5'
+config['name'] = 'iccv_pyr_32_64_25p'
+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_25p'
+config['pyramid']['final_upscale_enhancer'] = ''
+
+config['train_dir'] = '/home/workspace/local-data_127/train'
+config['valid_dir'] = '/home/workspace/local-data_127/valid'
+config['test_dir'] = '/home/workspace/local-data_127/test_ready'
+
+config['frames_file'] = 'frames_25p.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_25p'
+config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_25p'
+config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_25p'
+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_cvae_curv.py

@@ -19,7 +19,7 @@ ablation_count = [config['use_skinning'], config['remove_global_orientation'], c
 assert ablation_count == 1 or ablation_count == 0
 
 config['gpu'] = '0'
-config['name'] = 'cvae_curv'
+config['name'] = 'cvae_curv_3'
 config['notes'] = ''
 config['tags'] = ['iccv', 'cvae_curv']
 
@@ -50,12 +50,13 @@ if config['is_cape']:
 
 config['latent_dim'] = 128
 
-config['pretrained_model'] = ''
+config['pretrained_model'] = 'checkpoints/iccv/cvae_curv'
+config['pretrained_model_iteration'] = ''
 
 ###############    
 config['lr'] = 0.0001#                 0.0001
 ###############
-config['starting_balancing_coeff'] = 0.01
+config['starting_balancing_coeff'] = 1.0
 
 config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
                           'pose_shape': (72 + 11,),

curvature_analysis.py

@@ -0,0 +1,297 @@
+import os
+import sys
+os.environ["CUDA_VISIBLE_DEVICES"] = '4'
+
+
+import tqdm
+import argparse
+import random
+import string
+import time
+import numpy as np
+import trimesh as tm
+import cmapy as cm
+import scipy as sp
+import scipy.io as sio
+import plotly.graph_objects as go
+from pyntcloud import PyntCloud
+from scipy.sparse import csr_matrix, coo_matrix
+from scipy.sparse.csgraph import floyd_warshall, dijkstra
+from plotly.subplots import make_subplots
+from custom_utils.custom_utils import _maskFaces
+from postprocessing import fix_collision
+
+from custom_utils.supersmpl_io import readPC2, readPC2Frame, writePC2, readOBJ
+from custom_utils.custom_utils import _maskFaces, save_stl
+from auto_encoder_curv import dice_dense
+
+
+def write_obj(file, vertices, faces, colors):
+    with open(file, 'w') as file:
+        for v, c in zip(vertices, colors):
+            line = 'v ' + ' '.join([str(_) for _ in v]) + ' ' + ' '.join([str(_) for _ in c]) + '\n'
+            file.write(line)
+        for f in faces:
+            line = 'f ' + ' '.join([str(_) for _ in f]) + '\n'
+            file.write(line)
+        
+
+def postprocess(mesh, faces, mask=None, body=None, do_smoothing=True):
+    if body is not None:
+        mesh_fixed = fix_collision(mesh, body[np.newaxis,:,:], mask[np.newaxis,:])[0].numpy()[mask, :]
+    else:
+        mesh_fixed = mesh
+
+    if do_smoothing:
+        m = tm.Trimesh(mesh_fixed, faces, process=False, validate=False)
+        outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
+        outliers = np.nonzero(outliers)[0]
+        outliers_n_neighbours = get_closest_n_points(mesh_fixed, outliers, 2)
+        outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
+        faces_fixed = np.concatenate((faces, outliers_faces), axis=0)
+        m = tm.Trimesh(mesh_fixed, faces_fixed, process=False, validate=False)
+        tm.smoothing.filter_humphrey(m, iterations=1, alpha=0.6, beta=0.4)
+
+        mesh = m.vertices
+    else:
+        mesh = mesh_fixed
+
+    return mesh
+
+def export_to_ply(mesh, file):
+    ply = tm.exchange.ply.export_ply(mesh, encoding='binary', vertex_normal=True, include_attributes=True)
+    output_file = open(file, "wb+")
+    output_file.write(ply)
+    output_file.close()
+
+
+def get_closest_n_points(verts, idx, n):
+    dist = sp.spatial.distance.cdist(verts[idx, :], verts)
+    dist[dist == 0] = 1e9
+    idx_sort = np.argsort(dist, axis=-1)
+    closest = idx_sort[:, :n]
+    return closest
+
+
+def calculate_dist(folder, neighbourhood):
+    out = dict()
+    frames = sorted(os.listdir(os.path.join(folder, 'mesh_posed')))[int(args.start):int(args.end)]
+
+    for frame in tqdm.tqdm(frames):
+        id = frame.split('_')[0]
+        if id in out.keys():
+            continue
+
+        mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
+        k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
+        out[id] = k_neighbors
+
+    return out
+
+
+def histedges_equalN(x, nbin):
+    npt = len(x)
+    return np.interp(np.linspace(0, npt, nbin + 1),
+                     np.arange(npt),
+                     np.sort(x))
+
+
+def get_bins(values, nr_of_bins):
+    flattened = []
+    for c in values:
+        flattened.extend(c.flatten())
+    flattened = np.array(flattened)
+    bins = histedges_equalN(flattened, nr_of_bins)
+    return bins
+
+
+def generate_curvatures(v, faces, mask, neighbourhood=25, nr_of_labels=6):
+    # id = frame.split('_')[0]
+    # frame_nr = int(frame.split('_')[1].split('.')[0])
+
+    # mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
+    # _, faces = _maskFaces(np.zeros((14475,3)), F, mask)
+    # v = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
+    
+    m = tm.Trimesh(v, faces, process=False, validate=False)
+
+    outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
+    outliers = np.nonzero(outliers)[0]
+    outliers_n_neighbours = get_closest_n_points(v, outliers, 2)
+    outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
+    faces = np.concatenate((faces, outliers_faces), axis=0)
+
+    verts = v
+
+    m = tm.Trimesh(verts, faces, process=False, validate=False)
+    export_to_ply(m, temp_file)
+
+    cloud = PyntCloud.from_file(temp_file)
+
+    k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
+    #k_neighbors = k_neighbors_cache[id] # np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
+    ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
+    cloud.add_scalar_field("curvature", ev=ev)
+    curvature = np.array(cloud.points["curvature(" + str(neighbourhood+1) + ")"])
+    
+    # bins = np.load(f'custom_data/train_bins.npy') # get_bins(curvature, nr_of_labels)
+    bins = get_bins(curvature, nr_of_labels)
+    labels = np.linspace(0, bins.shape[0] - 2, bins.shape[0] - 1)
+
+    frame_labels = np.zeros((curvature.shape[0]), dtype=np.float32)
+    for j in range(bins.shape[0] - 2):
+        frame_labels[(curvature >= bins[j]) * (curvature < bins[j+1])] = labels[j]
+    frame_labels[curvature >= bins[-2]] = labels[-1]
+       
+    return frame_labels
+
+import tensorflow as tf
+
+
+def visualize_curvature_labels(folder, results_folder, frame):
+    id = frame.split('_')[0]
+    frame_nr = frame.split('_')[1].split('.')[0]
+
+    mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
+    _, faces = _maskFaces(np.zeros((14475,3)), F, mask)
+    verts = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
+    pred = np.load(os.path.join(results_folder, frame + '.npy'))
+    pred = postprocess(pred, faces)
+
+    curvature_labels_gt = generate_curvatures(verts, faces, mask, 25)
+    curvature_labels_pred = generate_curvatures(pred, faces, mask, 25)
+    # dice = dice_dense(curvature_labels_pred[None,:], curvature_labels_gt[None,:])
+    dice = dice_dense(tf.one_hot(curvature_labels_pred, 7)[None,:], tf.one_hot(curvature_labels_gt, 7)[None,:]).numpy()
+    colors_binned_all = []
+
+    for curvature_labels in [curvature_labels_gt, curvature_labels_pred]:
+        colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, int(curvature_labels.max()+1))])
+        colors_binned = np.zeros((curvature_labels.shape[0], 3))
+        for i in range(int(curvature_labels.max())+1):
+            # print(np.count_nonzero(curvature_labels == i))
+            colors_binned[curvature_labels == i, :] = colors[i]
+        colors_binned_all.append(colors_binned)
+
+    fig = make_subplots(rows=1, cols=3, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(3)] ])
+
+    fig.add_trace(go.Mesh3d(
+                # 8 vertices of a cube
+                x=-pred[:,0],
+                y=pred[:,2],
+                z=pred[:,1],
+                # i, j and k give the vertices of triangles
+                i = faces[:,0],
+                j = faces[:,1],
+                k = faces[:,2],
+                vertexcolor=np.ones((verts.shape[0],3))*np.array([255,200,200])[None,:]
+            ),
+            row=1, col=2)
+
+    fig.add_trace(go.Mesh3d(
+                # 8 vertices of a cube
+                x=verts[:,0],
+                y=verts[:,1],
+                z=verts[:,2],
+                # i, j and k give the vertices of triangles
+                i = faces[:,0],
+                j = faces[:,1],
+                k = faces[:,2],
+                vertexcolor=colors_binned_all[0]
+            ),
+            row=1, col=1)
+    
+    fig.add_trace(go.Mesh3d(
+                # 8 vertices of a cube
+                x=-pred[:,0],
+                y=pred[:,2],
+                z=pred[:,1],
+                # i, j and k give the vertices of triangles
+                i = faces[:,0],
+                j = faces[:,1],
+                k = faces[:,2],
+                vertexcolor=colors_binned_all[1],
+                text=f'dice: {dice}'
+            ),
+            row=1, col=3)
+
+    fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),
+                    scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1),
+                    scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1),
+                    # scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1),
+                    # scene5_aspectmode='manual', scene5_aspectratio=dict(x=1, y=1, z=1),
+                    )
+    helper = dict(xaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
+                yaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
+                zaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),)
+        
+    fig.update_layout(scene = helper,
+                        scene2 = helper,
+                        scene3 = helper,
+                        # scene4 = helper,
+                        # scene5 = helper,
+                        )
+
+
+    # meshes = [go.Mesh3d(
+    #             x=x[:,0],
+    #             y=x[:,1],
+    #             z=x[:,2],
+    #             i = faces[:,0],
+    #             j = faces[:,1],
+    #             k = faces[:,2],
+    #             vertexcolor=colors_binned
+    #             )]
+
+    # fig = go.Figure(data=meshes)
+    # fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1))
+    # fig.update_layout(
+    #     scene = dict(
+    #         xaxis = dict(nticks=4, range=[-1,1],),
+    #                     yaxis = dict(nticks=4, range=[-1,1],),
+    #                     zaxis = dict(nticks=4, range=[-1,1],),)
+    #                     )
+
+    return fig, dice
+
+import wandb
+
+def init_wandb():
+    run = wandb.init(project='plots', name='curvatures', dir='/home/workspace/local-data/temp/')
+    artifact = wandb.Artifact('code', type='code')
+    artifact.add_file('curvature_analysis.py')
+    run.log_artifact(artifact)
+
+if __name__ == '__main__':
+    init_wandb()
+
+    is_skirt = False
+    skirt_modifier = '_skirt' if is_skirt else ''
+    F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
+    # k_neighbours(k=25)
+
+    folder = '/home/workspace/local-data/test_ready'
+    results_folder = '/home/workspace/data/results/iccv/iccv_pyr_32_64_128_256_512-120000'
+
+    # frames_file = 'frames_temp.txt'
+    # with open(os.path.join(folder, frames_file), 'r') as f:
+    #     lines = f.readlines()
+    #     lines = [line.strip() for line in lines]
+    # frames = lines
+    frames = os.listdir(os.path.join(folder, 'pose_shape'))
+    frames = [frame.split('.')[0] for frame in frames]
+
+    temp_file = '/home/workspace/local-data/temp/' + ''.join(random.choice(string.ascii_lowercase) for i in range(10)) + '.ply'
+    if not is_skirt:
+        dist = np.load('custom_data/dijkstra.npy')
+    else:
+        dist = np.load('custom_data/dijkstra_skirt.npy')
+
+    i = 0
+    while True:
+        frame = np.random.choice(frames)
+        # frame = '03265_193'
+        # frame = '03102_49'
+        fig, dice = visualize_curvature_labels(folder, results_folder, frame)
+        wandb.log({f'{frame}-dice:{dice}': fig}, step=i)
+        i += 1
+        print(frame)

cvae_curv.py

@@ -153,11 +153,11 @@ def dice_dense(prediction, ground_truth, weight_map=None):
     return tf.reduce_mean(dice_score)
 
 
-# @tf.function
+@tf.function
 def train_step(batch):
     with tf.GradientTape() as tape:
 
-        pred, mean, logvar = model([batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']])
+        pred, mean, logvar = model([batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=True)
 
         ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)
         dice = dice_dense(pred, batch['uv_curvature'])
@@ -189,7 +189,7 @@ def train_step(batch):
 
 @tf.function
 def valid_step(batch):
-    pred, mean, logvar = model([batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']])
+    pred, mean, logvar = model([batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
 
     ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)
     loss =  ce_uv_loss
@@ -216,7 +216,7 @@ def valid_step(batch):
 
 
 def plot_everything(batch: dict, type: str):
-    pred, mean, logvar = model([batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']])
+    pred, mean, logvar = model([batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
     
     pred = np.argmax(pred.numpy(), axis=-1)
     mesh_pred = uv2mesh(pred)
@@ -252,27 +252,51 @@ def plot_everything(batch: dict, type: str):
 
 
 def sample():
-    iteration = load_model(model, f'checkpoints/iccv/cvae_curv', return_iteration=True)
+    iteration = load_model(model, f'checkpoints/iccv/cvae_curv_3', return_iteration=True)
     for batch in dataset_train:
         batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
         nr_of_samples = 4
 
-        # pred = model.sample(batch['uv_static'], batch['uv_body_posed'], batch['uv_mask'])
+        for i in range(4):
-        # pred = model.sample_single(batch['uv_static'][0], batch['uv_body_posed'][0], batch['uv_mask'][0], nr_of_samples=nr_of_samples)
+            # pred = model.sample(batch['uv_static'], batch['uv_body_posed'], batch['uv_mask'])
-        pred = model.sample(tf.tile(batch['uv_static'][0][None,:,:,:], [nr_of_samples,1,1,1]), tf.tile(batch['uv_body_posed'][0][None,:,:,:], [nr_of_samples,1,1,1]), tf.tile(batch['uv_mask'][0][None,:,:], [nr_of_samples,1,1]))
+            # pred = model.sample_single(batch['uv_static'][0], batch['uv_body_posed'][0], batch['uv_mask'][0], nr_of_samples=nr_of_samples)
-        # pred,_,_ = model([tf.tile(batch['uv_static'][0][None,:,:,:], [nr_of_samples,1,1,1]), tf.tile(batch['uv_body_posed'][0][None,:,:,:], [nr_of_samples,1,1,1]), tf.tile(batch['uv_mask'][0][None,:,:], [nr_of_samples,1,1])], training=False)
+            pred = model.sample(tf.tile(batch['uv_static'][i][None,:,:,:], [nr_of_samples,1,1,1]), tf.tile(batch['uv_body_posed'][i][None,:,:,:], [nr_of_samples,1,1,1]), tf.tile(batch['uv_mask'][i][None,:,:], [nr_of_samples,1,1]))
+            # pred,_,_ = model([tf.tile(batch['uv_static'][0][None,:,:,:], [nr_of_samples,1,1,1]), tf.tile(batch['uv_body_posed'][0][None,:,:,:], [nr_of_samples,1,1,1]), tf.tile(batch['uv_mask'][0][None,:,:], [nr_of_samples,1,1])], training=False)
+            # pred = model.call_sample([batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']])
         
-        orig_img = np.argmax(batch['uv_curvature'][0].numpy(), axis=-1)
+            orig_img = np.argmax(batch['uv_curvature'][i].numpy(), axis=-1)
-        pred_img = np.argmax(pred.numpy(), axis=-1)
+            pred_img = np.argmax(pred.numpy(), axis=-1)
-        img = np.concatenate((orig_img, np.concatenate(pred_img, axis=1)), axis=1)
+            img = np.concatenate((orig_img, np.concatenate(pred_img, axis=1)), axis=1)
-
-        colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, 6+1)])
-        color_img = np.zeros((img.shape[0], img.shape[1], 3), np.uint8)
-        for i in range(7):
-            color_img[img == i, :] = colors[i]
 
+            colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, 6+1)])
+            color_img = np.zeros((img.shape[0], img.shape[1], 3), np.uint8)
+            for i in range(7):
+                color_img[img == i, :] = colors[i]
+            ex.imshow(color_img).show()
+            print()
         print()
 
+def sample_2(batch):
+    nr_of_samples = 4
+
+    # pred = model.sample(batch['uv_static'], batch['uv_body_posed'], batch['uv_mask'])
+    # pred = model.sample_single(batch['uv_static'][0], batch['uv_body_posed'][0], batch['uv_mask'][0], nr_of_samples=nr_of_samples)
+    # pred = model.sample(tf.tile(batch['uv_static'][0][None,:,:,:], [nr_of_samples,1,1,1]), tf.tile(batch['uv_body_posed'][0][None,:,:,:], [nr_of_samples,1,1,1]), tf.tile(batch['uv_mask'][0][None,:,:], [nr_of_samples,1,1]))
+    pred,_,_ = model([tf.tile(batch['uv_static'][0][None,:,:,:], [nr_of_samples,1,1,1]), tf.tile(batch['uv_body_posed'][0][None,:,:,:], [nr_of_samples,1,1,1]),tf.tile(batch['uv_curvature'][0][None,:,:,:], [nr_of_samples,1,1,1]), tf.tile(batch['uv_mask'][0][None,:,:], [nr_of_samples,1,1])], training=False)
+    # pred = model.call_sample([batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']])
+
+    orig_img = np.argmax(batch['uv_curvature'][0].numpy(), axis=-1)
+    pred_img = np.argmax(pred.numpy(), axis=-1)
+    img = np.concatenate((orig_img, np.concatenate(pred_img, axis=1)), axis=1)
+
+    colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, 6+1)])
+    color_img = np.zeros((img.shape[0], img.shape[1], 3), np.uint8)
+    for i in range(7):
+        color_img[img == i, :] = colors[i]
+    ex.imshow(color_img).show()
+    print()
+
+
 if __name__ == '__main__':
     # init_wandb()
     parser = argparse.ArgumentParser(description='')

cvae_pyramid.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_abl_pyr_32_64_128_256_512_curv import config
+from configs.iccv.config_abl_pyr_32_64_25p import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf

evaluate_ae_pyramid.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_abl_pyr_32_64_128_256_512_skirt import config
+from configs.iccv.config_abl_pyr_32_64_128_256_512_curv import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf

get_error.py

@@ -7,10 +7,10 @@ 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_skin'
+    checkpoint_name = 'iccv_pyr_32_64_128_256_512'
-    checkpoint_name_skirt = 'iccv_pyr_32_64_skin_skirt'
+    checkpoint_name_skirt = 'iccv_pyr_32_64_128_256_512_skirt'
-    iteration = '130000'
+    iteration = '120000'
-    iteration_skirt : str = '65000'
+    iteration_skirt : str = '115000'
 
     # checkpoint_name = 'abl_skin'
     # checkpoint_name_skirt = checkpoint_name + '_skirt'
@@ -28,23 +28,25 @@ if __name__ == '__main__':
         for row in csv_reader:
             per_garment_errors[garment_type_table[row[0].split('_')[0]]].append(float(row[1]))
 
-    with open(os.path.join(folder, checkpoint_name_skirt + '-' + iteration_skirt + '.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('\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, checkpoint_name_skirt + '-' + iteration_skirt + '.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('\nAvg non-skirt: ' + str(np.mean(errors)))
     print('Avg skirt: ' + str(np.mean(errors_skirt)))
 
     cum_avg_shirt = np.mean(per_garment_errors['Tshirt'] + per_garment_errors_skirt['Tshirt'])

models/cvae_curv_model.py

@@ -50,7 +50,7 @@ class CVAE(tf.keras.Model):
     # @tf.function
     def sample_single(self, uv_template, uv_body_posed, uv_mask, eps=None, nr_of_samples=4):
         if eps is None:
-            eps = tf.random.normal(shape=(nr_of_samples, self.latent_dim))
+            eps = tf.random.normal(shape=(nr_of_samples, 8, 4, self.latent_dim))
 
         uv_template = tf.tile(tf.transpose(uv_template, [1,2,0])[tf.newaxis,:,:,:], [nr_of_samples,1,1,1])
         uv_body_posed = tf.tile(tf.transpose(uv_body_posed, [1,2,0])[tf.newaxis,:,:,:], [nr_of_samples,1,1,1])
@@ -149,3 +149,15 @@ class CVAE(tf.keras.Model):
         # x = tf.math.softmax(x)
 
         return x, mean, logvar
+
+    def call_sample(self, inputs):
+        uv_template, uv_body_posed, uv_curv, uv_mask = inputs
+
+        z = tf.random.normal(shape=(uv_template.shape[0], 8, 4, self.latent_dim))
+
+        z_cond = self.encode_conds(uv_template, uv_body_posed, training=False)
+
+        z = tf.concat([z, z_cond], axis=-1)
+        x = self.decoder([z, uv_mask], training=False)
+
+        return x

plot_ablation_pyramid.py

@@ -1,5 +1,5 @@
 import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "5"
+os.environ["CUDA_VISIBLE_DEVICES"] = "4"
 
 import tensorflow as tf
 
@@ -30,15 +30,15 @@ from plotly.subplots import make_subplots
 from scipy.spatial.transform import Rotation
 
 from dataset import DatasetFactory, StaticDataset
-from custom_utils.custom_utils import batch_to_dict, UV2Mesh, _maskFaces, load_model, group_inputs_for_levels
+from custom_utils.custom_utils import batch_to_dict, UV2Mesh, _maskFaces, load_model, group_inputs_for_levels, save_stl
 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 PyramidNew as Pyramid
 
-from configs.config_abl_2_pyr_32_64_128_256_512 import config as config_regular
+from configs.iccv.config_abl_pyr_32_64_128_256_512 import config as config_regular
-from configs.config_abl_2_pyr_32_64_128_256_512_skirt import config as config_skirt
+from configs.iccv.config_abl_pyr_32_64_128_256_512_skirt import config as config_skirt
 
 
 def color_labels(img):
@@ -182,7 +182,7 @@ def convert_to_mesh(uvs, is_skirt, mask):
     out = []
     for i in range(len(uvs)):
         if uvs[i].shape[1] != 512:
-            uvs[i] = tf.image.resize(uvs[i], (512, 256), tf.image.ResizeMethod.LANCZOS5) 
+            uvs[i] = tf.image.resize(uvs[i], (512, 256), tf.image.ResizeMethod.BILINEAR) 
         if is_skirt:
             mesh = uv2mesh_skirt(uvs[i])
         else:
@@ -239,9 +239,9 @@ def get_closest_n_points(verts, idx, n):
 
 def get_experiment_plots(frames, skirt_mods, bodies, faces, masks, cameras):
     model: Pyramid = Pyramid(config_regular)
-    load_model(model, 'checkpoints/' + config_regular['name'])
+    load_model(model, 'checkpoints/iccv/' + config_regular['name'])
     model_skirt: Pyramid = Pyramid(config_skirt)
-    load_model(model_skirt, 'checkpoints/' + config_skirt['name'])
+    load_model(model_skirt, 'checkpoints/iccv/' + config_skirt['name'])
     result = []
     for frame in frames:
         id = frame.split('_')[0]
@@ -267,6 +267,10 @@ def get_experiment_plots(frames, skirt_mods, bodies, faces, masks, cameras):
             img = cv2.imread('/tmp/exp_temp.png')
             frame_out.append(img[:, 50:-50, :])
 
+        for i, mesh in enumerate(meshes):
+            save_stl(os.path.join(output_folder, f'{frame}_{i+1}.stl'), mesh, faces[id].astype(np.int32))
+        save_stl(os.path.join(output_folder, f'{frame}_final.stl'), final_mesh_fixed, faces[id].astype(np.int32))
+
         # fig = plot_fixed(final_mesh, faces[id], cameras[frame], bodies[frame])
         # fig.write_image('/tmp/exp_temp.png')
         # img = cv2.imread('/tmp/exp_temp.png')
@@ -297,6 +301,10 @@ def get_gt_image(frames, bodies, faces, cameras, skirt_mods):
         fig.write_image('/tmp/exp_temp.png')
         img_unposed = cv2.imread('/tmp/exp_temp.png')
 
+        save_stl(os.path.join(output_folder, f'{frame}_gt.stl'), gt_unposed[mask,:], faces[id].astype(np.int32))
+        save_stl(os.path.join(output_folder, f'{frame}_gt_unposed.stl'), gt[mask,:], faces[id].astype(np.int32))
+        save_stl(os.path.join(output_folder, f'{frame}_body.stl'), bodies[frame], F)
+
         out.append(np.concatenate((img[:, 50:-50, :], img_unposed[:, 50:-50, :]), axis=1))
 
     return out
@@ -322,33 +330,36 @@ if __name__ == '__main__':
     # garment_type_table = np.load('custom_data/garment_type_table_test.npy', allow_pickle=True).item()
     garment_type_table = np.load('custom_data/garment_type_table_train.npy', allow_pickle=True).item()
 
+    camera_1 =  dict(up=dict(x=0, y=0, z=1), center=dict(x=0, y=0, z=-0.05), eye=dict(x=0.3, y=1.0, z=0.05))
+    camera_2 =  dict(up=dict(x=0, y=0, z=1), center=dict(x=0, y=0, z=-0.05), eye=dict(x=-0.9, y=0.7, z=0.05))
 
-    frames = ['05656_221', '00300_69', '06955_117']
+    frames = ['01070_13']
+    cameras = {'01070_13': camera_1}
+    # frames = ['05656_221', '00300_69', '06955_117']
     # frames = ['02184_157', '02241_142', '06955_117', '06804_192']
     # frames = ['01763_259', '007292_282', '00539_296', '00075_4']
     # frames = [ '01763_259']
-    camera_1 =  dict(up=dict(x=0, y=0, z=1), center=dict(x=0, y=0, z=-0.05), eye=dict(x=0.3, y=1.0, z=0.05))
-    camera_2 =  dict(up=dict(x=0, y=0, z=1), center=dict(x=0, y=0, z=-0.05), eye=dict(x=-0.9, y=0.7, z=0.05))
     # cameras = {'01763_259': camera_1, '007292_282': camera_1, '00539_296': camera_1, '00075_4': camera_2}
-    cameras = {'05656_221': camera_1, '00300_69': camera_1, '06955_117': camera_1}
+    # cameras = {'05656_221': camera_1, '00300_69': camera_1, '06955_117': camera_1}
     # cameras = {'02184_157': camera_1, '02241_142': camera_1, '06955_117': camera_1, '06804_192': camera_1}
 
     # abl_all replace abl_norm maybe
-    experiment = 'abl_2_pyr_32_64_128_256_512'
+    experiment = 'abl_pyr_32_64_128_256_512'
     skirt_mods = { frame.split('_')[0]: ('_skirt' if (garment_type_table[frame.split('_')[0]] == 'Skirt' or garment_type_table[frame.split('_')[0]] == 'Dress') else '') for frame in frames }
 
     data_folder = '/home/workspace/local-data/train'
-    data_folder_skirt = '/home/workspace/local-data/train_skirt'
+    data_folder_skirt = '/home/workspace/local-data_128/train_skirt'
+
+    results_folder_base = 'examples/pyramid'
+
+    output_folder = os.path.join(results_folder_base)
+    os.makedirs(output_folder, exist_ok=True)
     
     bodies = get_bodies(frames, skirt_mods)
     masks = get_masks(frames)
     faces = { frame.split('_')[0]: _maskFaces(np.zeros((14475,3)), F_skirt if skirt_mods[frame.split('_')[0]] == '_skirt' else F, masks[frame.split('_')[0]])[1] for frame in frames }
     gts = get_gt_image(frames, bodies, faces, cameras, skirt_mods)
 
-    results_folder_base = '/home/workspace/data/results'
-
-    output_folder = os.path.join(results_folder_base, 'ablation_plots')
-    os.makedirs(output_folder, exist_ok=True)
 
     images = get_experiment_plots(frames, skirt_mods, bodies, faces, masks, cameras)
 

sample_pro_2.py

@@ -46,7 +46,7 @@ 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_skin_var import config as config_main
+from configs.iccv.config_abl_all_skirt_var 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
@@ -289,7 +289,7 @@ def postprocess(mesh, body, faces, mask, do_smoothing=True):
     return mesh
 
 
-def sampling_example(frame, camera, nr=10):
+def sampling_example(frame, camera, nr=15):
 
     batch = get_temp_batch(frame, config_main)
     mask = batch['mesh_mask'][0].numpy().astype(bool)
@@ -417,19 +417,25 @@ if __name__ == '__main__':
     args = vars(parser.parse_args())
     
     smpl = SMPL()
-    F = np.load("custom_utils/smpl_faces.npy")
+
+    if config_main['is_skirt']:
+        F = np.load("custom_utils/smpl_faces_skirt.npy")
+        folder = os.path.join('/home/workspace/local-data_128/train_skirt')
+    else:
+        F = np.load("custom_utils/smpl_faces.npy")
+        folder = os.path.join('/home/workspace/local-data/train')
     # F = np.load("custom_utils/smpl_faces_skirt.npy")
     F_body = np.load("custom_utils/smpl_faces.npy")
     # folder = os.path.join('/home/workspace/data/dataset', ('test_skirt_ready_2' if is_skirt else 'test_ready'))
     # folder = os.path.join('/home/workspace/local-data/train_skirt')
-    folder = os.path.join('/home/workspace/local-data/train')
     
 
     model = CVAE(config_main)
-    curv_model = CVAECurv(config_curv)
+    # curv_model = CVAECurv(config_curv)
 
     load_model(model, 'checkpoints/iccv/' + config_main['name'])
-    load_model(curv_model, 'checkpoints/iccv/' + config_curv['name'])
+    # load_model(curv_model, 'checkpoints/iccv/' + config_curv['name'])
+    
 
     camera_1 =  dict(up=dict(x=0, y=0, z=1), center=dict(x=0, y=0, z=-0.05), eye=dict(x=0.3, y=1.0, z=0.05))
     # faces_folder = '/home/workspace/data/results/support/faces'
@@ -464,10 +470,12 @@ if __name__ == '__main__':
     # frame = '04925_272'
 
     # frames = ['01070_13', '02276_232', '02978_242', '04008_295', '04173_193']
-    # out_folder = 'examples/sampling'
+    frames = ['00214_111', '01661_210', '02144_249', '02151_37', '02930_275', '03812_3']
-    # os.makedirs(out_folder, exist_ok=True)
+    
-    # for frame in tqdm.tqdm(frames):
+    out_folder = 'examples/sampling_skirt'
-    #     sampling_example(frame, camera_1)
+    os.makedirs(out_folder, exist_ok=True)
+    for frame in tqdm.tqdm(frames):
+        sampling_example(frame, camera_1)
     
     # sampling_poses(frame, camera_01082_109)
     # sampling_garment(frame, camera_1)