189 lines
7.8 KiB
Python
189 lines
7.8 KiB
Python
config = dict()
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config['gpu'] = '2'
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config['name'] = 'iccv_pyr_32_10p'
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config['notes'] = ''
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config['tags'] = ['pyr', 'iccv']
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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'] = ''
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config['pretrained_model_iteration'] = ''
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config['pyramid'] = dict()
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config['pyramid']['resolutions'] = [32]
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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_127/train'
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config['valid_dir'] = '/home/workspace/local-data_127/valid'
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config['test_dir'] = '/home/workspace/local-data_127/test_ready'
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config['frames_file'] = 'frames_10p.txt'
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config['batch_size'] = 4
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config['is_skirt'] = False
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config['is_cape'] = False
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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'] = 0.0001
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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_10p'
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config['pyramid']['body_model'] = 'checkpoints/iccv/body_iccv_1_10p'
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config['pyramid']['curvature_model'] = 'checkpoints/iccv/curv_iccv_1_10p'
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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, 3, 9, 3]
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level['encoder_dims'] = [16, 32, 64, 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'] = ''
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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'] = [3, 3, 9, 3]
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level['encoder_dims'] = [64, 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_256_2'#'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'#'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'#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'#'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' |