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This commit is contained in:
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@@ -86,7 +86,7 @@ nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_cape_f_03223_SL" > nohup
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_cape_f_03223_SS" > nohup/iccv/pyr/iccv_pyr_64_cape_f_03223_SS.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_128_cape_f" > nohup/iccv/pyr/iccv_pyr_64_128_cape_f.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_64_pre_cape_f" > nohup/iccv/pyr/iccv_pyr_64_pre_cape_f.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_pre_cape_f" > nohup/iccv/pyr/iccv_pyr_32_64_128_pre_cape_f.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128" > nohup/iccv/pyr/iccv_pyr_32_64_128.out 2>&1 &
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nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_64_128_skirt" > nohup/iccv/pyr/iccv_pyr_32_64_128_skirt.out 2>&1 &
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+1
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@@ -1,6 +1,6 @@
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import os
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import sys
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from configs.iccv.config_abl_pyr_32_64_skin import config
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from configs.iccv.config_abl_pyr_32_64_128_skirt import config
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os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
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# import tensorflow as tf
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@@ -0,0 +1,188 @@
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config = dict()
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config['gpu'] = '2'
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config['name'] = 'iccv_pyr_32_64_128_pre_cape_f'
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config['notes'] = 'load pretrained on cloth3d'
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config['tags'] = ['pyr', 'iccv', 'cape_f']
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# DON'T FORGET TO RESET SSIM LOSS USAGE
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# DON'T FORGET TO CHECK MODEL LOADING
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# DON'T FORGET TO CHECK IF STORE IN MEMORY
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# DON'T FORGET TO CHECK REG LOSS COEFF
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# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
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# DON'T FORGET TO CHECK IF SKIRT
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# DON'T FORGET TO CHECK IF CAPE
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# DON'T FORGET TO CHECK IF USING DOWNSCALE NONE OR _2
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# DON'T FORGET TO CHECK UPSCALING and ENCODER CHECKPOINTS
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config['use_skinning'] = True # and deshaping
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config['remove_global_orientation'] = True
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config['use_extra_losses'] = True # edge, collision, 3d mesh
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config['use_offsets'] = False
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config['use_norm_loss'] = True
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config['use_curvature'] = True
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config['pretrained_model'] = 'checkpoints/iccv/iccv_pyr_32_64_128'
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config['pretrained_model_iteration'] = ''
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config['pyramid'] = dict()
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config['pyramid']['resolutions'] = [32, 64, 128]
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config['pyramid']['pretrained_pyramid'] = ''
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config['pyramid']['final_upscale_enhancer'] = ''
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config['train_dir'] = '/home/workspace/local-data/cape_processed/train/female'
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config['valid_dir'] = '/home/workspace/local-data/cape_processed/test/female'
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config['test_dir'] = '/home/workspace/local-data_127/cape_processed/test/female'
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config['batch_size'] = 4
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config['is_skirt'] = False
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config['is_cape'] = True
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config['dataset_in_memory'] = False
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config['resolution'] = None
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config['smpl_res'] = 14475
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if config['is_cape']:
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config['smpl_res'] = 3638
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config['lr'] = 0.00001
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config['starting_balancing_coeff'] = 0.001
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config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
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config['avg_garment_path'] = ''
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config['bottleneck_scale'] = 2
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config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
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'pose_shape': (72 + 11,),
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'uv_body_posed': (512, 256, 3),
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'mesh_body_posed': (config['smpl_res'], 3,),
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'mesh_posed': (config['smpl_res'], 3,),
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'mesh_unposed': (config['smpl_res'], 3,),
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'uv_unposed': (512, 256, 3),
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'uv_curvature': (512, 256, 7),
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}
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config['signature_static'] = { 'uv_static': (512, 256, 3),
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'mesh_mask': (config['smpl_res'],),
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'uv_mask': (512, 256,),
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}
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config['pyramid']['is_label'] = False
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config['pyramid']['add_noise'] = False
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config['pyramid']['noise_amount'] = 0.0
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config['pyramid']['loss_weights'] = [1,1,1,1,1]
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for res in config['pyramid']['resolutions']:
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if res != 512:
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res_modifier = '_' + str(res)
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config['signature_dynamic']['uv_body_posed' + res_modifier] = (res, res//2, 3)
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config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
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config['signature_dynamic']['uv_curvature' + res_modifier] = (res, res//2, 7)
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config['signature_static']['uv_static' + res_modifier] = (res, res//2, 3)
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config['signature_static']['uv_mask' + res_modifier] = (res, res//2)
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config['pyramid']['static_model'] = 'checkpoints/iccv/static_iccv_1_cape_f'
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config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_cape_f'
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config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_cape_f'
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config['pyramid']['freeze_encoders'] = True
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config['pyramid']['levels'] = []
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for res in config['pyramid']['resolutions']:
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if res == 512:
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level = dict()
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level['resolution'] = 512
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level['latent_dim'] = 64 * config['bottleneck_scale']
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level['kernel_size'] = 7
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level['encoder_depths'] = [3, 3, 9, 3]
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level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
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level['decoder_input'] = (16, 8, 80)
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level['decoder_depths'] = [3, 3, 9, 3]
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level['decoder_dims'] = [256, 128, 64, 16]
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level['avg_garment_path'] = ''
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level['upscale_enhancer'] = ''
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config['pyramid']['levels'].append(level)
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elif res == 256:
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level = dict()
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level['resolution'] = 256
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level['latent_dim'] = 64 * config['bottleneck_scale']
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level['kernel_size'] = 5
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level['encoder_depths'] = [9, 9, 9, 9]
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level['encoder_dims'] = [32, 64, 128, level['latent_dim']]
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level['decoder_input'] = (8, 4, 128)
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level['decoder_depths'] = [9, 9, 9, 9]
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level['decoder_dims'] = [256, 128, 64, 16]
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level['avg_garment_path'] = ''
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level['upscale_enhancer'] = 'checkpoints/iccv/iccv_'#'checkpoints/up_3_d256_512_2'
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level['upscale_enhancer_freeze'] = True
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config['pyramid']['levels'].append(level)
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elif res == 128:
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level = dict()
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level['resolution'] = 128
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level['latent_dim'] = 64 * config['bottleneck_scale']
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level['kernel_size'] = 5
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level['encoder_depths'] = [9, 9, 9]
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level['encoder_dims'] = [128, 128, level['latent_dim']]
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level['decoder_depths'] = level['encoder_depths']
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level['decoder_dims'] = level['encoder_dims']
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level['avg_garment_path'] = ''
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level['upscale_enhancer'] = 'checkpoints/iccv/iccv_128_2_cape_f'#'checkpoints/up_3_d128_256_2'
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level['upscale_enhancer_freeze'] = True
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config['pyramid']['levels'].append(level)
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elif res == 64:
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level = dict()
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level['resolution'] = 64
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level['latent_dim'] = 64 * config['bottleneck_scale']
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level['kernel_size'] = 3
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level['encoder_depths'] = [9, 9]
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level['encoder_dims'] = [64, level['latent_dim']]
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level['decoder_depths'] = level['encoder_depths']
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level['decoder_dims'] = level['encoder_dims']
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level['avg_garment_path'] = ''
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level['upscale_enhancer'] = 'checkpoints/iccv/iccv_64_2_cape_f'#checkpoints/up_3_d64_128_2'
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level['upscale_enhancer_freeze'] = True
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config['pyramid']['levels'].append(level)
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elif res == 32:
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level = dict()
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level['resolution'] = 32
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level['latent_dim'] = 64 * config['bottleneck_scale']
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level['kernel_size'] = 3
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level['encoder_depths'] = [16]
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level['encoder_dims'] = [level['latent_dim']]
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level['decoder_depths'] = level['encoder_depths']
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level['decoder_dims'] = level['encoder_dims']
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level['avg_garment_path'] = ''
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level['upscale_enhancer'] = 'checkpoints/iccv/iccv_32_2_cape_f'#'checkpoints/up_3_d32_64_2'
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level['upscale_enhancer_freeze'] = True
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config['pyramid']['levels'].append(level)
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else:
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raise NotImplementedError
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config['cond_ae_static'] = dict()
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config['cond_ae_static']['is_label'] = False
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config['cond_ae_static']['kernel_size'] = 7
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config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
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config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
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config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
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config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
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config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
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config['cond_ae_static']['activation'] = 'tanh'
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config['cond_ae_curv'] = dict()
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config['cond_ae_curv']['is_label'] = True
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config['cond_ae_curv']['kernel_size'] = 7
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config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
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config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
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config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
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config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
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config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
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config['cond_ae_curv']['activation'] = 'tanh'
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config['cond_ae_body'] = dict()
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config['cond_ae_body']['is_label'] = False
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config['cond_ae_body']['kernel_size'] = 7
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config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
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config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
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config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
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config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
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config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
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config['cond_ae_body']['activation'] = 'tanh'
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+98
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@@ -1,6 +1,6 @@
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import os
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import sys
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from configs.iccv.config_abl_pyr_64_pre_cape_f import config
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from configs.iccv.config_abl_pyr_32_64_128_pre_cape_f import config
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os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
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import tensorflow as tf
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@@ -220,20 +220,104 @@ def train_step(batch):
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out_levels = model([levels_input, conds], training=True)
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if config['use_skinning']:
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mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
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mesh_orig = batch['mesh_unposed']
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else:
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mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
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mesh_orig = batch['mesh_posed']
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output = dict()
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reconstr_loss_total = 0
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kl_total = 0
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#for i, res in enumerate(config['pyramid']['resolutions']):
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i = -1
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res = config['pyramid']['resolutions'][-1]
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for i, res in enumerate(config['pyramid']['resolutions']):
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# i = -1
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res = config['pyramid']['resolutions'][i] if i != len(config['pyramid']['resolutions']) - 1 else 512
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res_modifier = '_' + str(res) if res != 512 else ''
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pred, mean, logvar = out_levels[i]
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if config['use_skinning']:
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uv_orig = batch['uv_unposed' + res_modifier]
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else:
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uv_orig = batch['uv_posed' + res_modifier]
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mesh_pred = uv2mesh(pred)
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if config['use_skinning']:
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shape = batch['pose_shape'][:, 72:]
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gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
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shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
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mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
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mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
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else:
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mesh_pred_posed = mesh_pred
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# is the output already masked?
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e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
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c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
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c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
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l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1) * batch['uv_mask' + res_modifier], axis=[1, 2]))
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# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
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# ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
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l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
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n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'])
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kl = kl_loss(mean, logvar)
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if config['use_extra_losses']:
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reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
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if not config['is_cape']:
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reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff']
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else:
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reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
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if config['use_norm_loss'] and step > 2000:
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reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
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kl_total += kl
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mean = tf.reduce_mean(mean)
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logvar = tf.reduce_mean(logvar)
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metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
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output = { **output, **{ 'train/edge_loss' + res_modifier: e_loss, 'train/collision_loss' + res_modifier: c_loss,'train/euclid_error' + res_modifier: metric,
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'train/mean' + res_modifier: mean, 'train/logvar' + res_modifier: logvar, 'train/kl_loss' + res_modifier: kl,
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'train/var' + res_modifier: np.e ** logvar, 'train/l2_loss' + res_modifier: l2_loss, 'train/normal_loss': n_loss,
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'train/euclid_error': metric, 'train/l2_mesh_loss': l2_mesh_loss } } # 'train/ms_ssim' + res_modifier: ms_ssim,
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loss = reconstr_loss_total + balancing_coeff * kl_total
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gradients = tape.gradient(loss, model.trainable_variables)
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optimizer.apply_gradients(zip(gradients, model.trainable_variables))
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return { **{ 'train/loss': loss, 'train/reconstr_loss_total': reconstr_loss_total, 'train/kl_total': kl_total }, **output }
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@tf.function
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def valid_step(batch):
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levels_input, conds = group_inputs_for_levels(batch, config)
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out_levels = model([levels_input, conds], training=False)
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if config['use_skinning']:
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mesh_orig = batch['mesh_unposed']
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else:
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mesh_orig = batch['mesh_posed']
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output = dict()
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reconstr_loss_total = 0
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kl_total = 0
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for i, res in enumerate(config['pyramid']['resolutions']):
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# i = -1
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res = config['pyramid']['resolutions'][i] if i != len(config['pyramid']['resolutions']) - 1 else 512
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res_modifier = '_' + str(res) if res != 512 else ''
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pred, mean, logvar = out_levels[i]
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if config['use_skinning']:
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uv_orig = batch['uv_unposed' + res_modifier]
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else:
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uv_orig = batch['uv_posed' + res_modifier]
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mesh_pred = uv2mesh(pred)
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if config['use_skinning']:
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@@ -245,13 +329,12 @@ def train_step(batch):
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mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
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else:
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mesh_pred_posed = mesh_pred
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# is the output already masked?
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e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
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c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
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c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
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l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
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l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1) * batch['uv_mask' + res_modifier], axis=[1, 2]))
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
|
||||
l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
|
||||
@@ -275,83 +358,10 @@ def train_step(batch):
|
||||
|
||||
metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
|
||||
|
||||
output = { **output, **{ 'train/edge_loss' + res_modifier: e_loss, 'train/collision_loss' + res_modifier: c_loss,'train/euclid_error' + res_modifier: metric,
|
||||
'train/mean' + res_modifier: mean, 'train/logvar' + res_modifier: logvar, 'train/kl_loss' + res_modifier: kl,
|
||||
'train/var' + res_modifier: np.e ** logvar, 'train/l2_loss' + res_modifier: l2_loss, 'train/normal_loss': n_loss,
|
||||
'train/euclid_error': metric, 'train/l2_mesh_loss': l2_mesh_loss } } # 'train/ms_ssim' + res_modifier: ms_ssim,
|
||||
|
||||
loss = reconstr_loss_total + balancing_coeff * kl_total
|
||||
|
||||
gradients = tape.gradient(loss, model.trainable_variables)
|
||||
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
|
||||
return { **{ 'train/loss': loss, 'train/reconstr_loss_total': reconstr_loss_total, 'train/kl_total': kl_total }, **output }
|
||||
|
||||
|
||||
@tf.function
|
||||
def valid_step(batch):
|
||||
levels_input, conds = group_inputs_for_levels(batch, config)
|
||||
out_levels = model([levels_input, conds], training=False)
|
||||
|
||||
|
||||
if config['use_skinning']:
|
||||
mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
|
||||
else:
|
||||
mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
|
||||
|
||||
output = dict()
|
||||
reconstr_loss_total = 0
|
||||
kl_total = 0
|
||||
|
||||
#for i, res in enumerate(config['pyramid']['resolutions']):
|
||||
i = -1
|
||||
res = config['pyramid']['resolutions'][-1]
|
||||
res_modifier = '_' + str(res) if res != 512 else ''
|
||||
pred, mean, logvar = out_levels[i]
|
||||
|
||||
mesh_pred = uv2mesh(pred)
|
||||
|
||||
if config['use_skinning']:
|
||||
shape = batch['pose_shape'][:, 72:]
|
||||
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
|
||||
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
|
||||
|
||||
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
|
||||
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
|
||||
else:
|
||||
mesh_pred_posed = mesh_pred
|
||||
# is the output already masked?
|
||||
|
||||
e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
|
||||
c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
|
||||
c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
|
||||
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
|
||||
l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
|
||||
n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'])
|
||||
kl = kl_loss(mean, logvar)
|
||||
|
||||
if config['use_extra_losses']:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
|
||||
if not config['is_cape']:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff']
|
||||
else:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
|
||||
|
||||
if config['use_norm_loss'] and step > 2000:
|
||||
reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
|
||||
|
||||
kl_total += kl
|
||||
|
||||
mean = tf.reduce_mean(mean)
|
||||
logvar = tf.reduce_mean(logvar)
|
||||
|
||||
metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
|
||||
|
||||
output = { **output, **{ 'valid/edge_loss' + res_modifier: e_loss, 'valid/collision_loss' + res_modifier: c_loss,'valid/euclid_error' + res_modifier: metric,
|
||||
'valid/mean' + res_modifier: mean, 'valid/logvar' + res_modifier: logvar, 'valid/kl_loss' + res_modifier: kl,
|
||||
'valid/var' + res_modifier: np.e ** logvar, 'valid/l2_loss' + res_modifier: l2_loss, 'valid/normal_loss': n_loss,
|
||||
'valid/euclid_error': metric, 'valid/l2_mesh_loss': l2_mesh_loss } } # 'valid/ms_ssim' + res_modifier: ms_ssim,
|
||||
output = { **output, **{ 'valid/edge_loss' + res_modifier: e_loss, 'valid/collision_loss' + res_modifier: c_loss,'valid/euclid_error' + res_modifier: metric,
|
||||
'valid/mean' + res_modifier: mean, 'valid/logvar' + res_modifier: logvar, 'valid/kl_loss' + res_modifier: kl,
|
||||
'valid/var' + res_modifier: np.e ** logvar, 'valid/l2_loss' + res_modifier: l2_loss, 'valid/normal_loss': n_loss,
|
||||
'valid/euclid_error': metric, 'valid/l2_mesh_loss': l2_mesh_loss } } # 'valid/ms_ssim' + res_modifier: ms_ssim,
|
||||
|
||||
loss = reconstr_loss_total + balancing_coeff * kl_total
|
||||
|
||||
@@ -810,7 +820,7 @@ if __name__ == '__main__':
|
||||
if config['use_curvature']:
|
||||
model.curv_enc.trainable = False
|
||||
else:
|
||||
load_model(model, config['pyramid']['pretrained_pyramid'], iteration=(config['pyramid']['pretrained_pyramid_iteration'] if 'pretrained_pyramid_iteration' in config.keys() else ''))
|
||||
# load_model(model, config['pyramid']['pretrained_pyramid'], iteration=(config['pyramid']['pretrained_pyramid_iteration'] if 'pretrained_pyramid_iteration' in config.keys() else ''))
|
||||
model.static_enc.trainable = False
|
||||
model.body_enc.trainable = False
|
||||
if config['use_curvature']:
|
||||
@@ -828,8 +838,8 @@ if __name__ == '__main__':
|
||||
# load_model(model.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
|
||||
# model.upscale_enhancer.trainable = False
|
||||
|
||||
for i in range(len(model.levels)-1):
|
||||
model.levels[i].trainable = False
|
||||
# for i in range(len(model.levels)-1):
|
||||
# model.levels[i].trainable = False
|
||||
|
||||
# load_model(model.cond_encoder, config['pyramid']['pretrained_pyramid'])
|
||||
# load_model(model.levels[0], config['pyramid']['pretrained_pyramid'])
|
||||
|
||||
@@ -335,8 +335,8 @@ class PyramidNew(tf.keras.Model):
|
||||
pred = pred + levels_input[0][2] # uv_body_posed_32
|
||||
if 1 != len(self.levels):
|
||||
pred = self.levels[0].enhance(pred, levels_input[0][-2], training=training) # levels_input[i][-2]=uv_mask_res
|
||||
pred = tf.image.resize(pred, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.BILINEAR)
|
||||
out_levels.append([pred, mean, logvar])
|
||||
pred = tf.image.resize(pred, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.BILINEAR)
|
||||
pred = pred * levels_input[1][-2][:,:,:,tf.newaxis]
|
||||
else:
|
||||
# pred = self.enhance(pred, levels_input[0][-2], training=training)
|
||||
|
||||
Reference in New Issue
Block a user