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save config files

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luni
2022-11-04 16:16:23 +00:00
parent 745193dc94
commit ebe8522905
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configs/config_abl_abs.py
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config = dict()
# 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 AVG GARMENT USED
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = True
config['use_curvature'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = False
config['use_norm_loss'] = False
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
assert ablation_count == 1 or ablation_count == 0
config['gpu'] = '3'
config['name'] = 'abl_abs'
config['notes'] = ''
config['avg_garment_path'] = '' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
if config['use_skinning']:
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['batch_size'] = 4
config['resolution'] = 512
config['is_skirt'] = False
config['dataset_in_memory'] = False
config['is_cape'] = False
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
config['static_model'] = 'checkpoints/static_1'
config['body_model'] = 'checkpoints/body_1'
config['curvature_model'] = 'checkpoints/curv_1'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['pretrained_model'] = ''
###############
config['lr'] = 0.0001# 0.0001
###############
config['starting_balancing_coeff'] = 0.0001
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (14475, 3,),
'mesh_posed': (14475, 3,),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (14475,),
'uv_mask': (512, 256,),
}
if config['use_skinning']:
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
config['signature_dynamic']['mesh_unposed'] = (14475, 3,)
else:
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
config['upscale_enhancer'] = ''
config['freeze_encoders'] = True
config['use_one_hot'] = False
config['cvae'] = dict()
config['cvae']['is_label'] = False
config['cvae']['latent_dim'] = config['latent_dim']
config['cvae']['add_noise'] = True
config['cvae']['noise_amount'] = 0.025
config['cvae']['kernel_size'] = 7
config['cvae']['encoder_depths'] = [3, 3, 9, 3]
config['cvae']['encoder_dims'] = [32, 64, 128, 256]
config['cvae']['decoder_input'] = (16, 8, 80)
config['cvae']['decoder_depths'] = [3, 3, 9, 3]
config['cvae']['decoder_dims'] = [256, 128, 64, 16]
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_abs_skirt.py
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config = dict()
# 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 AVG GARMENT USED
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = True
config['use_curvature'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = False
config['use_norm_loss'] = False
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
assert ablation_count == 1 or ablation_count == 0
config['gpu'] = '2'
config['name'] = 'abl_abs_skirt'
config['notes'] = ''
config['avg_garment_path'] = '' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
if config['use_skinning']:
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['batch_size'] = 4
config['resolution'] = 512
config['is_skirt'] = True
config['dataset_in_memory'] = False
config['is_cape'] = False
config['train_dir'] = '/home/workspace/local-data/train_skirt'
config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
config['static_model'] = 'checkpoints/static_1_skirt'
config['body_model'] = 'checkpoints/body_1_skirt_2_2'
config['curvature_model'] = 'checkpoints/curv_1_skirt'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['pretrained_model'] = ''
###############
config['lr'] = 0.0001# 0.0001
###############
config['starting_balancing_coeff'] = 0.0001
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (14475, 3,),
'mesh_posed': (14475, 3,),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (14475,),
'uv_mask': (512, 256,),
}
if config['use_skinning']:
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
config['signature_dynamic']['mesh_unposed'] = (14475, 3,)
else:
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
config['upscale_enhancer'] = ''
config['freeze_encoders'] = True
config['use_one_hot'] = False
config['cvae'] = dict()
config['cvae']['is_label'] = False
config['cvae']['latent_dim'] = config['latent_dim']
config['cvae']['add_noise'] = True
config['cvae']['noise_amount'] = 0.025
config['cvae']['kernel_size'] = 7
config['cvae']['encoder_depths'] = [3, 3, 9, 3]
config['cvae']['encoder_dims'] = [32, 64, 128, 256]
config['cvae']['decoder_input'] = (16, 8, 80)
config['cvae']['decoder_depths'] = [3, 3, 9, 3]
config['cvae']['decoder_dims'] = [256, 128, 64, 16]
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_all copy.py
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config = dict()
# 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 AVG GARMENT USED
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = True
config['use_curvature'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = True
config['use_norm_loss'] = False
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
assert ablation_count == 1 or ablation_count == 0
config['gpu'] = '0'
config['name'] = 'abl_all'
config['notes'] = ''
config['avg_garment_path'] = 'custom_data/train_no_shape_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
if config['use_skinning']:
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['batch_size'] = 4
config['resolution'] = 512
config['is_skirt'] = False
config['dataset_in_memory'] = False
config['is_cape'] = False
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
config['static_model'] = 'checkpoints/static_1'
config['body_model'] = 'checkpoints/body_1'
config['curvature_model'] = 'checkpoints/curv_1'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['pretrained_model'] = ''
###############
config['lr'] = 0.0001# 0.0001
###############
config['starting_balancing_coeff'] = 0.0001
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (14475, 3,),
'mesh_posed': (14475, 3,),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (14475,),
'uv_mask': (512, 256,),
}
if config['use_skinning']:
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
config['signature_dynamic']['mesh_unposed'] = (14475, 3,)
else:
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
config['upscale_enhancer'] = ''
config['freeze_encoders'] = True
config['use_one_hot'] = False
config['cvae'] = dict()
config['cvae']['is_label'] = False
config['cvae']['latent_dim'] = config['latent_dim']
config['cvae']['add_noise'] = True
config['cvae']['noise_amount'] = 0.025
config['cvae']['kernel_size'] = 7
config['cvae']['encoder_depths'] = [3, 3, 9, 3]
config['cvae']['encoder_dims'] = [32, 64, 128, 256]
config['cvae']['decoder_input'] = (16, 8, 80)
config['cvae']['decoder_depths'] = [3, 3, 9, 3]
config['cvae']['decoder_dims'] = [256, 128, 64, 16]
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_all.py
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config = dict()
# 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 AVG GARMENT USED
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = True
config['use_curvature'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = True
config['use_norm_loss'] = False
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
assert ablation_count == 1 or ablation_count == 0
config['gpu'] = '0'
config['name'] = 'abl_all'
config['notes'] = ''
config['avg_garment_path'] = 'custom_data/train_no_shape_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
if config['use_skinning']:
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['batch_size'] = 4
config['resolution'] = 512
config['is_skirt'] = False
config['dataset_in_memory'] = False
config['is_cape'] = False
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
config['static_model'] = 'checkpoints/static_1'
config['body_model'] = 'checkpoints/body_1'
config['curvature_model'] = 'checkpoints/curv_1'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['pretrained_model'] = ''
###############
config['lr'] = 0.0001# 0.0001
###############
config['starting_balancing_coeff'] = 0.0001
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (14475, 3,),
'mesh_posed': (14475, 3,),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (14475,),
'uv_mask': (512, 256,),
}
if config['use_skinning']:
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
config['signature_dynamic']['mesh_unposed'] = (14475, 3,)
else:
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
config['upscale_enhancer'] = ''
config['freeze_encoders'] = True
config['use_one_hot'] = False
config['cvae'] = dict()
config['cvae']['is_label'] = False
config['cvae']['latent_dim'] = config['latent_dim']
config['cvae']['add_noise'] = True
config['cvae']['noise_amount'] = 0.025
config['cvae']['kernel_size'] = 7
config['cvae']['encoder_depths'] = [3, 3, 9, 3]
config['cvae']['encoder_dims'] = [32, 64, 128, 256]
config['cvae']['decoder_input'] = (16, 8, 80)
config['cvae']['decoder_depths'] = [3, 3, 9, 3]
config['cvae']['decoder_dims'] = [256, 128, 64, 16]
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_all_skirt.py
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config = dict()
# 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 AVG GARMENT USED
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = True
config['use_curvature'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = True
config['use_norm_loss'] = False
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
assert ablation_count == 1 or ablation_count == 0
config['gpu'] = '5'
config['name'] = 'abl_all_skirt'
config['notes'] = ''
config['avg_garment_path'] = 'custom_data/train_no_shape_skirt_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
if config['use_skinning']:
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['batch_size'] = 4
config['resolution'] = 512
config['is_skirt'] = True
config['dataset_in_memory'] = False
config['is_cape'] = False
config['train_dir'] = '/home/workspace/local-data/train_skirt'
config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
config['static_model'] = 'checkpoints/static_1_skirt'
config['body_model'] = 'checkpoints/body_1_skirt_2_2'
config['curvature_model'] = 'checkpoints/curv_1_skirt'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['pretrained_model'] = ''
###############
config['lr'] = 0.0001# 0.0001
###############
config['starting_balancing_coeff'] = 0.0001
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (14475, 3,),
'mesh_posed': (14475, 3,),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (14475,),
'uv_mask': (512, 256,),
}
if config['use_skinning']:
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
config['signature_dynamic']['mesh_unposed'] = (14475, 3,)
else:
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
config['upscale_enhancer'] = ''
config['freeze_encoders'] = True
config['use_one_hot'] = False
config['cvae'] = dict()
config['cvae']['is_label'] = False
config['cvae']['latent_dim'] = config['latent_dim']
config['cvae']['add_noise'] = True
config['cvae']['noise_amount'] = 0.025
config['cvae']['kernel_size'] = 7
config['cvae']['encoder_depths'] = [3, 3, 9, 3]
config['cvae']['encoder_dims'] = [32, 64, 128, 256]
config['cvae']['decoder_input'] = (16, 8, 80)
config['cvae']['decoder_depths'] = [3, 3, 9, 3]
config['cvae']['decoder_dims'] = [256, 128, 64, 16]
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_curv.py
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config = dict()
# 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 AVG GARMENT USED
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = True
config['use_curvature'] = False
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = True
config['use_norm_loss'] = False
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
assert ablation_count == 1 or ablation_count == 0
config['gpu'] = '5'
config['name'] = 'abl_curv'
config['notes'] = ''
config['avg_garment_path'] = 'custom_data/train_no_shape_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
if config['use_skinning']:
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['batch_size'] = 4
config['resolution'] = 512
config['is_skirt'] = False
config['dataset_in_memory'] = False
config['is_cape'] = False
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
config['static_model'] = 'checkpoints/static_1'
config['body_model'] = 'checkpoints/body_1'
config['curvature_model'] = 'checkpoints/curv_1'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['pretrained_model'] = ''
###############
config['lr'] = 0.0001# 0.0001
###############
config['starting_balancing_coeff'] = 0.0001
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (14475, 3,),
'mesh_posed': (14475, 3,),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (14475,),
'uv_mask': (512, 256,),
}
if config['use_skinning']:
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
config['signature_dynamic']['mesh_unposed'] = (14475, 3,)
else:
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
config['upscale_enhancer'] = ''
config['freeze_encoders'] = True
config['use_one_hot'] = False
config['cvae'] = dict()
config['cvae']['is_label'] = False
config['cvae']['latent_dim'] = config['latent_dim']
config['cvae']['add_noise'] = True
config['cvae']['noise_amount'] = 0.025
config['cvae']['kernel_size'] = 7
config['cvae']['encoder_depths'] = [3, 3, 9, 3]
config['cvae']['encoder_dims'] = [32, 64, 128, 256]
config['cvae']['decoder_input'] = (16, 8, 80)
config['cvae']['decoder_depths'] = [3, 3, 9, 3]
config['cvae']['decoder_dims'] = [256, 128, 64, 16]
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_curv_skirt.py
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config = dict()
# 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 AVG GARMENT USED
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = True
config['use_curvature'] = False
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = True
config['use_norm_loss'] = False
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
assert ablation_count == 1 or ablation_count == 0
config['gpu'] = '6'
config['name'] = 'abl_curv_skirt'
config['notes'] = ''
config['avg_garment_path'] = 'custom_data/train_no_shape_skirt_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
if config['use_skinning']:
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['batch_size'] = 4
config['resolution'] = 512
config['is_skirt'] = True
config['dataset_in_memory'] = False
config['is_cape'] = False
config['train_dir'] = '/home/workspace/local-data/train_skirt'
config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
config['static_model'] = 'checkpoints/static_1_skirt'
config['body_model'] = 'checkpoints/body_1_skirt_2_2'
config['curvature_model'] = 'checkpoints/curv_1_skirt'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['pretrained_model'] = ''
###############
config['lr'] = 0.0001# 0.0001
###############
config['starting_balancing_coeff'] = 0.0001
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (14475, 3,),
'mesh_posed': (14475, 3,),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (14475,),
'uv_mask': (512, 256,),
}
if config['use_skinning']:
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
config['signature_dynamic']['mesh_unposed'] = (14475, 3,)
else:
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
config['upscale_enhancer'] = ''
config['freeze_encoders'] = True
config['use_one_hot'] = False
config['cvae'] = dict()
config['cvae']['is_label'] = False
config['cvae']['latent_dim'] = config['latent_dim']
config['cvae']['add_noise'] = True
config['cvae']['noise_amount'] = 0.025
config['cvae']['kernel_size'] = 7
config['cvae']['encoder_depths'] = [3, 3, 9, 3]
config['cvae']['encoder_dims'] = [32, 64, 128, 256]
config['cvae']['decoder_input'] = (16, 8, 80)
config['cvae']['decoder_depths'] = [3, 3, 9, 3]
config['cvae']['decoder_dims'] = [256, 128, 64, 16]
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_extra_loss.py
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config = dict()
# 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 AVG GARMENT USED
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = True
config['use_curvature'] = True
config['use_extra_losses'] = False # edge, collision, 3d mesh
config['use_offsets'] = True
config['use_norm_loss'] = False
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
assert ablation_count == 1 or ablation_count == 0
config['gpu'] = '4'
config['name'] = 'abl_extra_loss'
config['notes'] = ''
config['avg_garment_path'] = 'custom_data/train_no_shape_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
if config['use_skinning']:
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['batch_size'] = 4
config['resolution'] = 512
config['is_skirt'] = False
config['dataset_in_memory'] = False
config['is_cape'] = False
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
config['static_model'] = 'checkpoints/static_1'
config['body_model'] = 'checkpoints/body_1'
config['curvature_model'] = 'checkpoints/curv_1'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['pretrained_model'] = ''
###############
config['lr'] = 0.0001# 0.0001
###############
config['starting_balancing_coeff'] = 0.0001
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (14475, 3,),
'mesh_posed': (14475, 3,),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (14475,),
'uv_mask': (512, 256,),
}
if config['use_skinning']:
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
config['signature_dynamic']['mesh_unposed'] = (14475, 3,)
else:
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
config['upscale_enhancer'] = ''
config['freeze_encoders'] = True
config['use_one_hot'] = False
config['cvae'] = dict()
config['cvae']['is_label'] = False
config['cvae']['latent_dim'] = config['latent_dim']
config['cvae']['add_noise'] = True
config['cvae']['noise_amount'] = 0.025
config['cvae']['kernel_size'] = 7
config['cvae']['encoder_depths'] = [3, 3, 9, 3]
config['cvae']['encoder_dims'] = [32, 64, 128, 256]
config['cvae']['decoder_input'] = (16, 8, 80)
config['cvae']['decoder_depths'] = [3, 3, 9, 3]
config['cvae']['decoder_dims'] = [256, 128, 64, 16]
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_extra_loss_skirt.py
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config = dict()
# 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 AVG GARMENT USED
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = True
config['use_curvature'] = True
config['use_extra_losses'] = False # edge, collision, 3d mesh
config['use_offsets'] = True
config['use_norm_loss'] = False
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
assert ablation_count == 1 or ablation_count == 0
config['gpu'] = '5'
config['name'] = 'abl_extra_loss_skirt'
config['notes'] = ''
config['avg_garment_path'] = 'custom_data/train_no_shape_skirt_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
if config['use_skinning']:
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['batch_size'] = 4
config['resolution'] = 512
config['is_skirt'] = True
config['dataset_in_memory'] = False
config['is_cape'] = False
config['train_dir'] = '/home/workspace/local-data/train_skirt'
config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
config['static_model'] = 'checkpoints/static_1_skirt'
config['body_model'] = 'checkpoints/body_1_skirt_2_2'
config['curvature_model'] = 'checkpoints/curv_1_skirt'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['pretrained_model'] = ''
###############
config['lr'] = 0.0001# 0.0001
###############
config['starting_balancing_coeff'] = 0.0001
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (14475, 3,),
'mesh_posed': (14475, 3,),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (14475,),
'uv_mask': (512, 256,),
}
if config['use_skinning']:
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
config['signature_dynamic']['mesh_unposed'] = (14475, 3,)
else:
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
config['upscale_enhancer'] = ''
config['freeze_encoders'] = True
config['use_one_hot'] = False
config['cvae'] = dict()
config['cvae']['is_label'] = False
config['cvae']['latent_dim'] = config['latent_dim']
config['cvae']['add_noise'] = True
config['cvae']['noise_amount'] = 0.025
config['cvae']['kernel_size'] = 7
config['cvae']['encoder_depths'] = [3, 3, 9, 3]
config['cvae']['encoder_dims'] = [32, 64, 128, 256]
config['cvae']['decoder_input'] = (16, 8, 80)
config['cvae']['decoder_depths'] = [3, 3, 9, 3]
config['cvae']['decoder_dims'] = [256, 128, 64, 16]
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_norm.py
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config = dict()
# 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 AVG GARMENT USED
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = True
config['use_curvature'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = True
config['use_norm_loss'] = True
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
assert ablation_count == 1 or ablation_count == 0
config['gpu'] = '2'
config['name'] = 'abl_norm'
config['notes'] = ''
config['avg_garment_path'] = 'custom_data/train_no_shape_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
if config['use_skinning']:
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['batch_size'] = 4
config['resolution'] = 512
config['is_skirt'] = False
config['dataset_in_memory'] = False
config['is_cape'] = False
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
config['static_model'] = 'checkpoints/static_1'
config['body_model'] = 'checkpoints/body_1'
config['curvature_model'] = 'checkpoints/curv_1'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['pretrained_model'] = ''
###############
config['lr'] = 0.0001# 0.0001
###############
config['starting_balancing_coeff'] = 0.0001
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (14475, 3,),
'mesh_posed': (14475, 3,),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (14475,),
'uv_mask': (512, 256,),
}
if config['use_skinning']:
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
config['signature_dynamic']['mesh_unposed'] = (14475, 3,)
else:
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
config['upscale_enhancer'] = ''
config['freeze_encoders'] = True
config['use_one_hot'] = False
config['cvae'] = dict()
config['cvae']['is_label'] = False
config['cvae']['latent_dim'] = config['latent_dim']
config['cvae']['add_noise'] = True
config['cvae']['noise_amount'] = 0.025
config['cvae']['kernel_size'] = 7
config['cvae']['encoder_depths'] = [3, 3, 9, 3]
config['cvae']['encoder_dims'] = [32, 64, 128, 256]
config['cvae']['decoder_input'] = (16, 8, 80)
config['cvae']['decoder_depths'] = [3, 3, 9, 3]
config['cvae']['decoder_dims'] = [256, 128, 64, 16]
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_one_hot_skirt.py
+116
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config = dict()
# 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 AVG GARMENT USED
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = True
config['use_curvature'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = True
config['use_one_hot'] = True
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
assert ablation_count == 1 or ablation_count == 0
config['gpu'] = '2'
config['name'] = 'abl_one_hot_skirt'
config['notes'] = ''
config['avg_garment_path'] = 'custom_data/train_no_shape_skirt_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
if config['use_skinning']:
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['batch_size'] = 4
config['resolution'] = 512
config['is_skirt'] = True
config['dataset_in_memory'] = False
config['is_cape'] = False
config['train_dir'] = '/home/workspace/local-data/train_skirt'
config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready'
config['static_model'] = 'checkpoints/static_1_skirt'
config['body_model'] = 'checkpoints/body_1_skirt_2_2'
config['curvature_model'] = 'checkpoints/curv_1_skirt'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['pretrained_model'] = ''
###############
config['lr'] = 0.0001# 0.0001
###############
config['starting_balancing_coeff'] = 0.0001
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (14475, 3,),
'mesh_posed': (14475, 3,),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (14475,),
'uv_mask': (512, 256,),
}
if config['use_skinning']:
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
config['signature_dynamic']['mesh_unposed'] = (14475, 3,)
else:
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
config['upscale_enhancer'] = ''
config['freeze_encoders'] = True
config['cvae'] = dict()
config['cvae']['is_label'] = False
config['cvae']['latent_dim'] = config['latent_dim']
config['cvae']['add_noise'] = True
config['cvae']['noise_amount'] = 0.025
config['cvae']['kernel_size'] = 7
config['cvae']['encoder_depths'] = [3, 3, 9, 3]
config['cvae']['encoder_dims'] = [32, 64, 128, 256]
config['cvae']['decoder_input'] = (16, 8, 80)
config['cvae']['decoder_depths'] = [3, 3, 9, 3]
config['cvae']['decoder_dims'] = [256, 128, 64, 16]
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_orient.py
+117
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config = dict()
# 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 AVG GARMENT USED
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = False
config['use_curvature'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = True
config['use_norm_loss'] = False
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
assert ablation_count == 1 or ablation_count == 0
config['gpu'] = '6'
config['name'] = 'abl_orient'
config['notes'] = ''
config['avg_garment_path'] = 'custom_data/train_no_shape_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
if config['use_skinning']:
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['batch_size'] = 4
config['resolution'] = 512
config['is_skirt'] = False
config['dataset_in_memory'] = False
config['is_cape'] = False
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
config['static_model'] = 'checkpoints/static_1'
config['body_model'] = 'checkpoints/body_1'
config['curvature_model'] = 'checkpoints/curv_1'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['pretrained_model'] = ''
###############
config['lr'] = 0.0001# 0.0001
###############
config['starting_balancing_coeff'] = 0.0001
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (14475, 3,),
'mesh_posed': (14475, 3,),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (14475,),
'uv_mask': (512, 256,),
}
if config['use_skinning']:
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
config['signature_dynamic']['mesh_unposed'] = (14475, 3,)
else:
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
config['upscale_enhancer'] = ''
config['freeze_encoders'] = True
config['use_one_hot'] = False
config['cvae'] = dict()
config['cvae']['is_label'] = False
config['cvae']['latent_dim'] = config['latent_dim']
config['cvae']['add_noise'] = True
config['cvae']['noise_amount'] = 0.025
config['cvae']['kernel_size'] = 7
config['cvae']['encoder_depths'] = [3, 3, 9, 3]
config['cvae']['encoder_dims'] = [32, 64, 128, 256]
config['cvae']['decoder_input'] = (16, 8, 80)
config['cvae']['decoder_depths'] = [3, 3, 9, 3]
config['cvae']['decoder_dims'] = [256, 128, 64, 16]
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_orient_skirt.py
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config = dict()
# 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 AVG GARMENT USED
config['use_skinning'] = True # and deshaping
config['remove_global_orientation'] = False
config['use_curvature'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = True
config['use_norm_loss'] = False
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
assert ablation_count == 1 or ablation_count == 0
config['gpu'] = '1'
config['name'] = 'abl_orient_skirt'
config['notes'] = ''
config['avg_garment_path'] = 'custom_data/train_no_shape_skirt_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
if config['use_skinning']:
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['batch_size'] = 4
config['resolution'] = 512
config['is_skirt'] = True
config['dataset_in_memory'] = False
config['is_cape'] = False
config['train_dir'] = '/home/workspace/local-data/train_skirt'
config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
config['static_model'] = 'checkpoints/static_1_skirt'
config['body_model'] = 'checkpoints/body_1_skirt_2_2'
config['curvature_model'] = 'checkpoints/curv_1_skirt'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['pretrained_model'] = ''
###############
config['lr'] = 0.0001# 0.0001
###############
config['starting_balancing_coeff'] = 0.0001
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (14475, 3,),
'mesh_posed': (14475, 3,),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (14475,),
'uv_mask': (512, 256,),
}
if config['use_skinning']:
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
config['signature_dynamic']['mesh_unposed'] = (14475, 3,)
else:
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
config['upscale_enhancer'] = ''
config['freeze_encoders'] = True
config['use_one_hot'] = False
config['cvae'] = dict()
config['cvae']['is_label'] = False
config['cvae']['latent_dim'] = config['latent_dim']
config['cvae']['add_noise'] = True
config['cvae']['noise_amount'] = 0.025
config['cvae']['kernel_size'] = 7
config['cvae']['encoder_depths'] = [3, 3, 9, 3]
config['cvae']['encoder_dims'] = [32, 64, 128, 256]
config['cvae']['decoder_input'] = (16, 8, 80)
config['cvae']['decoder_depths'] = [3, 3, 9, 3]
config['cvae']['decoder_dims'] = [256, 128, 64, 16]
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_pyr_32.py
+180
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config = dict()
config['gpu'] = '0'
config['name'] = 'pyr_abl_32'
config['notes'] = ''
# 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['pyramid'] = dict()
config['pyramid']['resolutions'] = [32]
config['pyramid']['pretrained_pyramid'] = ''
config['pyramid']['final_upscale_enhancer'] = 'checkpoints/32_512_4'
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
config['batch_size'] = 4
config['is_skirt'] = False
config['is_cape'] = False
config['dataset_in_memory'] = False
config['resolution'] = None
config['latent_dim'] = 128
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.0001
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
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']['latent_dim'] = config['latent_dim']
config['pyramid']['add_noise'] = True
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/static_1'
config['pyramid']['body_model'] = 'checkpoints/body_1'
config['pyramid']['curvature_model'] = 'checkpoints/curv_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'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (16, 8, 80)
level['decoder_depths'] = [3, 3, 9, 3]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [9, 9, 9, 9]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (8, 4, 128)
level['decoder_depths'] = [9, 9, 9, 9]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [16, 9, 9, 9]
level['encoder_dims'] = [64, 128, 128, 256]
level['decoder_input'] = (4, 2, 128)
level['decoder_depths'] = [16, 9, 9, 9]
level['decoder_dims'] = [256, 128, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [64, 128, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9, 9]
level['decoder_dims'] = [256, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = False
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [256, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9]
level['decoder_dims'] = [256, 256]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_pyr_32_64.py
+180
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config = dict()
config['gpu'] = '4'
config['name'] = 'pyr_abl_32_64'
config['notes'] = ''
# 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['pyramid'] = dict()
config['pyramid']['resolutions'] = [32, 64]
config['pyramid']['pretrained_pyramid'] = 'checkpoints/pyr_abl_32_430k'
config['pyramid']['final_upscale_enhancer'] = 'checkpoints/64_512_2'
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
config['batch_size'] = 4
config['is_skirt'] = False
config['is_cape'] = False
config['dataset_in_memory'] = False
config['resolution'] = None
config['latent_dim'] = 128
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.0001
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
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']['latent_dim'] = config['latent_dim']
config['pyramid']['add_noise'] = True
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/static_1'
config['pyramid']['body_model'] = 'checkpoints/body_1'
config['pyramid']['curvature_model'] = 'checkpoints/curv_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'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (16, 8, 80)
level['decoder_depths'] = [3, 3, 9, 3]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [9, 9, 9, 9]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (8, 4, 128)
level['decoder_depths'] = [9, 9, 9, 9]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [16, 9, 9, 9]
level['encoder_dims'] = [64, 128, 128, 256]
level['decoder_input'] = (4, 2, 128)
level['decoder_depths'] = [16, 9, 9, 9]
level['decoder_dims'] = [256, 128, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [64, 128, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9, 9]
level['decoder_dims'] = [256, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = False
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [256, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9]
level['decoder_dims'] = [256, 256]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/32_512_4'#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_pyr_32_cape_f.py
+177
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config = dict()
config['gpu'] = '5'
config['name'] = 'pyr_abl_32_cape_f'
config['notes'] = ''
# 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['pyramid'] = dict()
config['pyramid']['resolutions'] = [32]
config['pyramid']['pretrained_pyramid'] = ''
config['pyramid']['final_upscale_enhancer'] = 'checkpoints/32_512_cape_f'
config['train_dir'] = '/home/workspace/local-data/cape_processed/train/female'
config['valid_dir'] = '/home/workspace/local-data/cape_processed/test/female'
config['test_dir'] = '/home/workspace/local-data/cape_processed/test/female'
config['batch_size'] = 4
config['is_skirt'] = False
config['is_cape'] = True
config['pyramid']['static_model'] = 'checkpoints/static_1_cape_f'
config['pyramid']['body_model'] = 'checkpoints/body_1_cape_f'
config['pyramid']['curvature_model'] = 'checkpoints/curv_1_cape_f'
config['dataset_in_memory'] = False
config['resolution'] = None
config['latent_dim'] = 128
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.0001
config['reg_loss_coeff'] = None # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
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']['latent_dim'] = config['latent_dim']
config['pyramid']['add_noise'] = True
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']['freeze_encoders'] = True
config['pyramid']['levels'] = []
for res in config['pyramid']['resolutions']:
if res == 512:
level = dict()
level['resolution'] = 512
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (16, 8, 80)
level['decoder_depths'] = [3, 3, 9, 3]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [9, 9, 9, 9]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (8, 4, 128)
level['decoder_depths'] = [9, 9, 9, 9]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [16, 9, 9, 9]
level['encoder_dims'] = [64, 128, 128, 256]
level['decoder_input'] = (4, 2, 128)
level['decoder_depths'] = [16, 9, 9, 9]
level['decoder_dims'] = [256, 128, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [64, 128, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9, 9]
level['decoder_dims'] = [256, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = False
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [256, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9]
level['decoder_dims'] = [256, 256]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_pyr_32_cape_m.py
+177
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config = dict()
config['gpu'] = '4'
config['name'] = 'pyr_abl_32_cape_m'
config['notes'] = ''
# 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['pyramid'] = dict()
config['pyramid']['resolutions'] = [32]
config['pyramid']['pretrained_pyramid'] = ''
config['pyramid']['final_upscale_enhancer'] = 'checkpoints/32_512_cape_m'
config['train_dir'] = '/home/workspace/local-data/cape_processed/train/male'
config['valid_dir'] = '/home/workspace/local-data/cape_processed/test/male'
config['test_dir'] = '/home/workspace/local-data/cape_processed/test/male'
config['batch_size'] = 4
config['is_skirt'] = False
config['is_cape'] = True
config['pyramid']['static_model'] = 'checkpoints/static_1_cape_m'
config['pyramid']['body_model'] = 'checkpoints/body_1_cape_m'
config['pyramid']['curvature_model'] = 'checkpoints/curv_1_cape_m'
config['dataset_in_memory'] = False
config['resolution'] = None
config['latent_dim'] = 128
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.0001
config['reg_loss_coeff'] = None # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
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']['latent_dim'] = config['latent_dim']
config['pyramid']['add_noise'] = True
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']['freeze_encoders'] = True
config['pyramid']['levels'] = []
for res in config['pyramid']['resolutions']:
if res == 512:
level = dict()
level['resolution'] = 512
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (16, 8, 80)
level['decoder_depths'] = [3, 3, 9, 3]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [9, 9, 9, 9]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (8, 4, 128)
level['decoder_depths'] = [9, 9, 9, 9]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [16, 9, 9, 9]
level['encoder_dims'] = [64, 128, 128, 256]
level['decoder_input'] = (4, 2, 128)
level['decoder_depths'] = [16, 9, 9, 9]
level['decoder_dims'] = [256, 128, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [64, 128, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9, 9]
level['decoder_dims'] = [256, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = False
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [256, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9]
level['decoder_dims'] = [256, 256]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_pyr_32_extra_loss_skirt.py
+181
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config = dict()
config['gpu'] = '6'
config['name'] = 'pyr_abl_32_extra_loss_skirt'
config['notes'] = ''
# 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['pyramid'] = dict()
config['pyramid']['resolutions'] = [32]
config['pyramid']['pretrained_pyramid'] = ''
config['pyramid']['final_upscale_enhancer'] = 'checkpoints/32_512_skirt'
config['train_dir'] = '/home/workspace/local-data/train_skirt'
config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
config['batch_size'] = 4
config['is_skirt'] = True
config['is_cape'] = False
config['pyramid']['static_model'] = 'checkpoints/static_1_skirt'
config['pyramid']['body_model'] = 'checkpoints/body_1_skirt_2_2'
config['pyramid']['curvature_model'] = 'checkpoints/curv_1_skirt'
config['dataset_in_memory'] = False
config['resolution'] = None
config['latent_dim'] = 128
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.0001
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
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']['latent_dim'] = config['latent_dim']
config['pyramid']['add_noise'] = True
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']['freeze_encoders'] = True
config['pyramid']['levels'] = []
for res in config['pyramid']['resolutions']:
if res == 512:
level = dict()
level['resolution'] = 512
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (16, 8, 80)
level['decoder_depths'] = [3, 3, 9, 3]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [9, 9, 9, 9]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (8, 4, 128)
level['decoder_depths'] = [9, 9, 9, 9]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [16, 9, 9, 9]
level['encoder_dims'] = [64, 128, 128, 256]
level['decoder_input'] = (4, 2, 128)
level['decoder_depths'] = [16, 9, 9, 9]
level['decoder_dims'] = [256, 128, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [64, 128, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9, 9]
level['decoder_dims'] = [256, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = False
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [256, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9]
level['decoder_dims'] = [256, 256]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_pyr_32_skin.py
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config = dict()
config['gpu'] = '3'
config['name'] = 'pyr_abl_32_skin'
config['notes'] = ''
# 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'] = False # and deshaping
config['use_offsets'] = False
config['remove_global_orientation'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['pyramid'] = dict()
config['pyramid']['resolutions'] = [32]
config['pyramid']['pretrained_pyramid'] = ''
config['pyramid']['final_upscale_enhancer'] = 'checkpoints/32_512_4'
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
config['batch_size'] = 4
config['is_skirt'] = False
config['is_cape'] = False
config['dataset_in_memory'] = False
config['resolution'] = None
config['latent_dim'] = 128
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.0001
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
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,),
'uv_curvature': (512, 256, 7),
}
if config['use_skinning']:
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
else:
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (config['smpl_res'],),
'uv_mask': (512, 256,),
}
config['pyramid']['is_label'] = False
config['pyramid']['latent_dim'] = config['latent_dim']
config['pyramid']['add_noise'] = True
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)
if config['use_skinning']:
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
else:
config['signature_dynamic']['uv_posed' + 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/static_1'
config['pyramid']['body_model'] = 'checkpoints/body_1'
config['pyramid']['curvature_model'] = '' #checkpoints/curv_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'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (16, 8, 80)
level['decoder_depths'] = [3, 3, 9, 3]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [9, 9, 9, 9]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (8, 4, 128)
level['decoder_depths'] = [9, 9, 9, 9]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [16, 9, 9, 9]
level['encoder_dims'] = [64, 128, 128, 256]
level['decoder_input'] = (4, 2, 128)
level['decoder_depths'] = [16, 9, 9, 9]
level['decoder_dims'] = [256, 128, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [64, 128, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9, 9]
level['decoder_dims'] = [256, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = False
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [256, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9]
level['decoder_dims'] = [256, 256]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_pyr_32_skin_offset.py
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config = dict()
config['gpu'] = '5'
config['name'] = 'pyr_abl_32_skin_offset'
config['notes'] = ''
# 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'] = False # and deshaping
config['use_offsets'] = True
config['remove_global_orientation'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['pyramid'] = dict()
config['pyramid']['resolutions'] = [32]
config['pyramid']['pretrained_pyramid'] = ''
config['pyramid']['final_upscale_enhancer'] = 'checkpoints/32_512_4'
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
config['batch_size'] = 4
config['is_skirt'] = False
config['is_cape'] = False
config['dataset_in_memory'] = False
config['resolution'] = None
config['latent_dim'] = 128
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.0001
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
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,),
'uv_curvature': (512, 256, 7),
}
if config['use_skinning']:
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
else:
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (config['smpl_res'],),
'uv_mask': (512, 256,),
}
config['pyramid']['is_label'] = False
config['pyramid']['latent_dim'] = config['latent_dim']
config['pyramid']['add_noise'] = True
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)
if config['use_skinning']:
config['signature_dynamic']['uv_unposed' + res_modifier] = (res, res//2, 3)
else:
config['signature_dynamic']['uv_posed' + 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/static_1'
config['pyramid']['body_model'] = 'checkpoints/body_1'
config['pyramid']['curvature_model'] = '' #checkpoints/curv_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'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (16, 8, 80)
level['decoder_depths'] = [3, 3, 9, 3]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [9, 9, 9, 9]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (8, 4, 128)
level['decoder_depths'] = [9, 9, 9, 9]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [16, 9, 9, 9]
level['encoder_dims'] = [64, 128, 128, 256]
level['decoder_input'] = (4, 2, 128)
level['decoder_depths'] = [16, 9, 9, 9]
level['decoder_dims'] = [256, 128, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [64, 128, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9, 9]
level['decoder_dims'] = [256, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = False
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [256, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9]
level['decoder_dims'] = [256, 256]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_pyr_32_skirt.py
+181
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config = dict()
config['gpu'] = '3'
config['name'] = 'pyr_abl_32_skirt_2'
config['notes'] = 'continued from pyr_abl_32_skirt-180k'
# 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['pyramid'] = dict()
config['pyramid']['resolutions'] = [32]
config['pyramid']['pretrained_pyramid'] = ''
config['pyramid']['final_upscale_enhancer'] = 'checkpoints/32_512_skirt'
config['train_dir'] = '/home/workspace/local-data/train_skirt'
config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
config['batch_size'] = 4
config['is_skirt'] = True
config['is_cape'] = False
config['pyramid']['static_model'] = 'checkpoints/static_1_skirt'
config['pyramid']['body_model'] = 'checkpoints/body_1_skirt_2_2'
config['pyramid']['curvature_model'] = 'checkpoints/curv_1_skirt'
config['dataset_in_memory'] = False
config['resolution'] = None
config['latent_dim'] = 128
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.0001
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
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']['latent_dim'] = config['latent_dim']
config['pyramid']['add_noise'] = True
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']['freeze_encoders'] = True
config['pyramid']['levels'] = []
for res in config['pyramid']['resolutions']:
if res == 512:
level = dict()
level['resolution'] = 512
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (16, 8, 80)
level['decoder_depths'] = [3, 3, 9, 3]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [9, 9, 9, 9]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (8, 4, 128)
level['decoder_depths'] = [9, 9, 9, 9]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [16, 9, 9, 9]
level['encoder_dims'] = [64, 128, 128, 256]
level['decoder_input'] = (4, 2, 128)
level['decoder_depths'] = [16, 9, 9, 9]
level['decoder_dims'] = [256, 128, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [64, 128, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9, 9]
level['decoder_dims'] = [256, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = False
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [256, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9]
level['decoder_dims'] = [256, 256]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_pyr_64_128_256_512_cape_f.py
+179
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config = dict()
config['gpu'] = '0'
config['name'] = 'pyr_abl_64_128_256_512_cape_f_2'
config['notes'] = 'removing activation from last layer and dividing output by 100, 80k pretrained prev level'
# 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['pyramid'] = dict()
config['pyramid']['resolutions'] = [64, 128, 256, 512]
config['pyramid']['pretrained_pyramid'] = 'checkpoints/pyr_abl_64_128_256_cape_f_2'
config['pyramid']['final_upscale_enhancer'] = ''
config['train_dir'] = '/home/workspace/local-data/cape_processed/train/female'
config['valid_dir'] = '/home/workspace/local-data/cape_processed/test/female'
config['test_dir'] = '/home/workspace/local-data/cape_processed/test/female'
config['batch_size'] = 4
config['is_skirt'] = False
config['is_cape'] = True
config['pyramid']['static_model'] = 'checkpoints/static_1_cape_f'
config['pyramid']['body_model'] = 'checkpoints/body_1_cape_f'
config['pyramid']['curvature_model'] = 'checkpoints/curv_1_cape_f'
config['dataset_in_memory'] = False
config['resolution'] = None
config['latent_dim'] = 128
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.0001
config['reg_loss_coeff'] = None # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
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']['latent_dim'] = config['latent_dim']
config['pyramid']['add_noise'] = True
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']['freeze_encoders'] = True
config['pyramid']['levels'] = []
for res in config['pyramid']['resolutions']:
if res == 512:
level = dict()
level['resolution'] = 512
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (16, 8, 80)
level['decoder_depths'] = [3, 3, 9, 3]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [9, 9, 9, 9]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (8, 4, 128)
level['decoder_depths'] = [9, 9, 9, 9]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/256_512_cape_f'#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [16, 9, 9, 9]
level['encoder_dims'] = [64, 128, 128, 256]
level['decoder_input'] = (4, 2, 128)
level['decoder_depths'] = [16, 9, 9, 9]
level['decoder_dims'] = [256, 128, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/128_512_cape_f'#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [64, 128, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9, 9]
level['decoder_dims'] = [256, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/64_512_cape_f'#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = False
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [256, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9]
level['decoder_dims'] = [256, 256]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_pyr_64_128_256_cape_f.py
+177
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@@ -0,0 +1,177 @@
config = dict()
config['gpu'] = '4'
config['name'] = 'pyr_abl_64_128_256_cape_f_2'
config['notes'] = 'removing activation from last layer and dividing output by 100'
# 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['pyramid'] = dict()
config['pyramid']['resolutions'] = [64, 128, 256]
config['pyramid']['pretrained_pyramid'] = 'checkpoints/pyr_abl_64_128_cape_f_2'
config['pyramid']['final_upscale_enhancer'] = 'checkpoints/256_512_cape_f'
config['train_dir'] = '/home/workspace/local-data/cape_processed/train/female'
config['valid_dir'] = '/home/workspace/local-data/cape_processed/test/female'
config['test_dir'] = '/home/workspace/local-data/cape_processed/test/female'
config['batch_size'] = 4
config['is_skirt'] = False
config['is_cape'] = True
config['pyramid']['static_model'] = 'checkpoints/static_1_cape_f'
config['pyramid']['body_model'] = 'checkpoints/body_1_cape_f'
config['pyramid']['curvature_model'] = 'checkpoints/curv_1_cape_f'
config['dataset_in_memory'] = False
config['resolution'] = None
config['latent_dim'] = 128
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.0001
config['reg_loss_coeff'] = None # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
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']['latent_dim'] = config['latent_dim']
config['pyramid']['add_noise'] = True
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']['freeze_encoders'] = True
config['pyramid']['levels'] = []
for res in config['pyramid']['resolutions']:
if res == 512:
level = dict()
level['resolution'] = 512
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (16, 8, 80)
level['decoder_depths'] = [3, 3, 9, 3]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [9, 9, 9, 9]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (8, 4, 128)
level['decoder_depths'] = [9, 9, 9, 9]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [16, 9, 9, 9]
level['encoder_dims'] = [64, 128, 128, 256]
level['decoder_input'] = (4, 2, 128)
level['decoder_depths'] = [16, 9, 9, 9]
level['decoder_dims'] = [256, 128, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/128_512_cape_f'#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [64, 128, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9, 9]
level['decoder_dims'] = [256, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/64_512_cape_f'#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = False
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [256, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9]
level['decoder_dims'] = [256, 256]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_pyr_64_128_cape_f.py
+177
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config = dict()
config['gpu'] = '4'
config['name'] = 'pyr_abl_64_128_cape_f_2'
config['notes'] = 'removing activation from last layer and dividing output by 100'
# 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['pyramid'] = dict()
config['pyramid']['resolutions'] = [64, 128]
config['pyramid']['pretrained_pyramid'] = 'checkpoints/pyr_abl_64_cape_f'
config['pyramid']['final_upscale_enhancer'] = 'checkpoints/128_512_cape_f'
config['train_dir'] = '/home/workspace/local-data/cape_processed/train/female'
config['valid_dir'] = '/home/workspace/local-data/cape_processed/test/female'
config['test_dir'] = '/home/workspace/local-data/cape_processed/test/female'
config['batch_size'] = 4
config['is_skirt'] = False
config['is_cape'] = True
config['pyramid']['static_model'] = 'checkpoints/static_1_cape_f'
config['pyramid']['body_model'] = 'checkpoints/body_1_cape_f'
config['pyramid']['curvature_model'] = 'checkpoints/curv_1_cape_f'
config['dataset_in_memory'] = False
config['resolution'] = None
config['latent_dim'] = 128
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.0001
config['reg_loss_coeff'] = None # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
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']['latent_dim'] = config['latent_dim']
config['pyramid']['add_noise'] = True
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']['freeze_encoders'] = True
config['pyramid']['levels'] = []
for res in config['pyramid']['resolutions']:
if res == 512:
level = dict()
level['resolution'] = 512
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (16, 8, 80)
level['decoder_depths'] = [3, 3, 9, 3]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [9, 9, 9, 9]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (8, 4, 128)
level['decoder_depths'] = [9, 9, 9, 9]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [16, 9, 9, 9]
level['encoder_dims'] = [64, 128, 128, 256]
level['decoder_input'] = (4, 2, 128)
level['decoder_depths'] = [16, 9, 9, 9]
level['decoder_dims'] = [256, 128, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [64, 128, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9, 9]
level['decoder_dims'] = [256, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/64_512_cape_f'#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = False
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [256, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9]
level['decoder_dims'] = [256, 256]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_pyr_64_128_cape_m.py
+182
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config = dict()
config['gpu'] = '1'
config['name'] = 'pyr_abl_64_128_cape_m'
config['notes'] = ''
# 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['pyramid'] = dict()
config['pyramid']['resolutions'] = [64, 128]
config['pyramid']['pretrained_pyramid'] = 'checkpoints/pyr_abl_64_cape_m'
config['pyramid']['final_upscale_enhancer'] = 'checkpoints/128_512_cape_m'
config['train_dir'] = '/home/workspace/local-data/cape_processed/train/male'
config['valid_dir'] = '/home/workspace/local-data/cape_processed/test/male'
config['test_dir'] = '/home/workspace/local-data/cape_processed/test/male'
config['batch_size'] = 4
config['is_skirt'] = False
config['is_cape'] = True
config['pyramid']['static_model'] = 'checkpoints/static_1_cape_m'
config['pyramid']['body_model'] = 'checkpoints/body_1_cape_m'
config['pyramid']['curvature_model'] = 'checkpoints/curv_1_cape_m'
config['dataset_in_memory'] = False
config['resolution'] = None
config['latent_dim'] = 128
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.0001
config['reg_loss_coeff'] = None # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
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']['latent_dim'] = config['latent_dim']
config['pyramid']['add_noise'] = True
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']['freeze_encoders'] = True
config['pyramid']['levels'] = []
for res in config['pyramid']['resolutions']:
if res == 512:
level = dict()
level['resolution'] = 512
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (16, 8, 80)
level['decoder_depths'] = [3, 3, 9, 3]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [9, 9, 9, 9]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (8, 4, 128)
level['decoder_depths'] = [9, 9, 9, 9]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [16, 9, 9, 9]
level['encoder_dims'] = [64, 128, 128, 256]
level['decoder_input'] = (4, 2, 128)
level['decoder_depths'] = [16, 9, 9, 9]
level['decoder_dims'] = [256, 128, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [64, 128, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9, 9]
level['decoder_dims'] = [256, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = 'checkpoints/64_512_cape_m'#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = False
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [256, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9]
level['decoder_dims'] = [256, 256]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_pyr_64_cape_f.py
+177
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config = dict()
config['gpu'] = '3'
config['name'] = 'pyr_abl_64_cape_f'
config['notes'] = ''
# 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['pyramid'] = dict()
config['pyramid']['resolutions'] = [64]
config['pyramid']['pretrained_pyramid'] = ''
config['pyramid']['final_upscale_enhancer'] = 'checkpoints/64_512_cape_f'
config['train_dir'] = '/home/workspace/local-data/cape_processed/train/female'
config['valid_dir'] = '/home/workspace/local-data/cape_processed/test/female'
config['test_dir'] = '/home/workspace/local-data/cape_processed/test/female'
config['batch_size'] = 4
config['is_skirt'] = False
config['is_cape'] = True
config['pyramid']['static_model'] = 'checkpoints/static_1_cape_f'
config['pyramid']['body_model'] = 'checkpoints/body_1_cape_f'
config['pyramid']['curvature_model'] = 'checkpoints/curv_1_cape_f'
config['dataset_in_memory'] = False
config['resolution'] = None
config['latent_dim'] = 128
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.0001
config['reg_loss_coeff'] = None # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
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']['latent_dim'] = config['latent_dim']
config['pyramid']['add_noise'] = True
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']['freeze_encoders'] = True
config['pyramid']['levels'] = []
for res in config['pyramid']['resolutions']:
if res == 512:
level = dict()
level['resolution'] = 512
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (16, 8, 80)
level['decoder_depths'] = [3, 3, 9, 3]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [9, 9, 9, 9]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (8, 4, 128)
level['decoder_depths'] = [9, 9, 9, 9]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [16, 9, 9, 9]
level['encoder_dims'] = [64, 128, 128, 256]
level['decoder_input'] = (4, 2, 128)
level['decoder_depths'] = [16, 9, 9, 9]
level['decoder_dims'] = [256, 128, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [64, 128, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9, 9]
level['decoder_dims'] = [256, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = False
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [256, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9]
level['decoder_dims'] = [256, 256]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_pyr_64_cape_m.py
+177
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config = dict()
config['gpu'] = '3'
config['name'] = 'pyr_abl_64_cape_m'
config['notes'] = ''
# 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['pyramid'] = dict()
config['pyramid']['resolutions'] = [64]
config['pyramid']['pretrained_pyramid'] = ''
config['pyramid']['final_upscale_enhancer'] = 'checkpoints/64_512_cape_m'
config['train_dir'] = '/home/workspace/local-data/cape_processed/train/male'
config['valid_dir'] = '/home/workspace/local-data/cape_processed/test/male'
config['test_dir'] = '/home/workspace/local-data/cape_processed/test/male'
config['batch_size'] = 4
config['is_skirt'] = False
config['is_cape'] = True
config['pyramid']['static_model'] = 'checkpoints/static_1_cape_m'
config['pyramid']['body_model'] = 'checkpoints/body_1_cape_m'
config['pyramid']['curvature_model'] = 'checkpoints/curv_1_cape_m'
config['dataset_in_memory'] = False
config['resolution'] = None
config['latent_dim'] = 128
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['lr'] = 0.0001
config['starting_balancing_coeff'] = 0.0001
config['reg_loss_coeff'] = None # set to 0 if don't want to use edge+collision
config['avg_garment_path'] = ''
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']['latent_dim'] = config['latent_dim']
config['pyramid']['add_noise'] = True
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']['freeze_encoders'] = True
config['pyramid']['levels'] = []
for res in config['pyramid']['resolutions']:
if res == 512:
level = dict()
level['resolution'] = 512
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [3, 3, 9, 3]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (16, 8, 80)
level['decoder_depths'] = [3, 3, 9, 3]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''
config['pyramid']['levels'].append(level)
elif res == 256:
level = dict()
level['resolution'] = 256
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 7
level['encoder_depths'] = [9, 9, 9, 9]
level['encoder_dims'] = [32, 64, 128, 256]
level['decoder_input'] = (8, 4, 128)
level['decoder_depths'] = [9, 9, 9, 9]
level['decoder_dims'] = [256, 128, 64, 16]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [16, 9, 9, 9]
level['encoder_dims'] = [64, 128, 128, 256]
level['decoder_input'] = (4, 2, 128)
level['decoder_depths'] = [16, 9, 9, 9]
level['decoder_dims'] = [256, 128, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'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'] = config['latent_dim']
level['kernel_size'] = 5
level['encoder_depths'] = [9, 9, 9]
level['encoder_dims'] = [64, 128, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9, 9]
level['decoder_dims'] = [256, 128, 64]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#checkpoints/up_3_d64_128_2'
level['upscale_enhancer_freeze'] = False
config['pyramid']['levels'].append(level)
elif res == 32:
level = dict()
level['resolution'] = 32
level['latent_dim'] = config['latent_dim']
level['kernel_size'] = 3
level['encoder_depths'] = [9, 9]
level['encoder_dims'] = [256, 256]
level['decoder_input'] = (4, 2, 256)
level['decoder_depths'] = [9, 9]
level['decoder_dims'] = [256, 256]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
raise NotImplementedError
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_skin.py
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config = dict()
# 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 AVG GARMENT USED
config['use_skinning'] = False # and deshaping
config['remove_global_orientation'] = True
config['use_curvature'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = True
config['use_norm_loss'] = False
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
config['gpu'] = '6'
config['name'] = 'abl_skin'
config['notes'] = ''
config['avg_garment_path'] = '' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
if config['use_skinning']:
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['batch_size'] = 4
config['resolution'] = 512
config['is_skirt'] = False
config['dataset_in_memory'] = False
config['is_cape'] = False
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
config['static_model'] = 'checkpoints/static_1'
config['body_model'] = 'checkpoints/body_1'
config['curvature_model'] = 'checkpoints/curv_1'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['pretrained_model'] = ''
###############
config['lr'] = 0.0001# 0.0001
###############
config['starting_balancing_coeff'] = 0.0001
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (14475, 3,),
'mesh_posed': (14475, 3,),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (14475,),
'uv_mask': (512, 256,),
}
if config['use_skinning']:
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
config['signature_dynamic']['mesh_unposed'] = (14475, 3,)
else:
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
config['upscale_enhancer'] = ''
config['freeze_encoders'] = True
config['use_one_hot'] = False
config['cvae'] = dict()
config['cvae']['is_label'] = False
config['cvae']['latent_dim'] = config['latent_dim']
config['cvae']['add_noise'] = True
config['cvae']['noise_amount'] = 0.025
config['cvae']['kernel_size'] = 7
config['cvae']['encoder_depths'] = [3, 3, 9, 3]
config['cvae']['encoder_dims'] = [32, 64, 128, 256]
config['cvae']['decoder_input'] = (16, 8, 80)
config['cvae']['decoder_depths'] = [3, 3, 9, 3]
config['cvae']['decoder_dims'] = [256, 128, 64, 16]
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_skin_no_orient.py
+114
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config = dict()
# 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 AVG GARMENT USED
config['use_skinning'] = False # and deshaping
config['remove_global_orientation'] = False
config['use_curvature'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = True
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
config['gpu'] = '2'
config['name'] = 'abl_skin_no_orient'
config['notes'] = ''
config['avg_garment_path'] = '' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
if config['use_skinning']:
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['batch_size'] = 4
config['resolution'] = 512
config['is_skirt'] = False
config['dataset_in_memory'] = False
config['is_cape'] = False
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
config['test_dir'] = '/home/workspace/data/dataset/test_ready'
config['static_model'] = 'checkpoints/static_1'
config['body_model'] = 'checkpoints/body_1'
config['curvature_model'] = 'checkpoints/curv_1'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['pretrained_model'] = ''
###############
config['lr'] = 0.0001# 0.0001
###############
config['starting_balancing_coeff'] = 0.0001
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (14475, 3,),
'mesh_posed': (14475, 3,),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (14475,),
'uv_mask': (512, 256,),
}
if config['use_skinning']:
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
config['signature_dynamic']['mesh_unposed'] = (14475, 3,)
else:
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
config['upscale_enhancer'] = ''
config['freeze_encoders'] = True
config['use_one_hot'] = False
config['cvae'] = dict()
config['cvae']['is_label'] = False
config['cvae']['latent_dim'] = config['latent_dim']
config['cvae']['add_noise'] = True
config['cvae']['noise_amount'] = 0.025
config['cvae']['kernel_size'] = 7
config['cvae']['encoder_depths'] = [3, 3, 9, 3]
config['cvae']['encoder_dims'] = [32, 64, 128, 256]
config['cvae']['decoder_input'] = (16, 8, 80)
config['cvae']['decoder_depths'] = [3, 3, 9, 3]
config['cvae']['decoder_dims'] = [256, 128, 64, 16]
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]
configs/config_abl_skin_skirt.py
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config = dict()
# 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 AVG GARMENT USED
config['use_skinning'] = False # and deshaping
config['remove_global_orientation'] = True
config['use_curvature'] = True
config['use_extra_losses'] = True # edge, collision, 3d mesh
config['use_offsets'] = True
config['use_norm_loss'] = False
ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
config['gpu'] = '4'
config['name'] = 'abl_skin_skirt'
config['notes'] = ''
config['avg_garment_path'] = '' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
if config['use_skinning']:
assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['batch_size'] = 4
config['resolution'] = 512
config['is_skirt'] = True
config['dataset_in_memory'] = False
config['is_cape'] = False
config['train_dir'] = '/home/workspace/local-data/train_skirt'
config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
config['static_model'] = 'checkpoints/static_1_skirt'
config['body_model'] = 'checkpoints/body_1_skirt_2_2'
config['curvature_model'] = 'checkpoints/curv_1_skirt'
config['smpl_res'] = 14475
if config['is_cape']:
config['smpl_res'] = 3638
config['latent_dim'] = 128
config['pretrained_model'] = ''
###############
config['lr'] = 0.0001# 0.0001
###############
config['starting_balancing_coeff'] = 0.0001
config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
'pose_shape': (72 + 11,),
'uv_body_posed': (512, 256, 3),
'mesh_body_posed': (14475, 3,),
'mesh_posed': (14475, 3,),
'uv_curvature': (512, 256, 7),
}
config['signature_static'] = { 'uv_static': (512, 256, 3),
'mesh_mask': (14475,),
'uv_mask': (512, 256,),
}
if config['use_skinning']:
config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
config['signature_dynamic']['mesh_unposed'] = (14475, 3,)
else:
config['signature_dynamic']['uv_posed'] = (512, 256, 3)
config['upscale_enhancer'] = ''
config['freeze_encoders'] = True
config['use_one_hot'] = False
config['cvae'] = dict()
config['cvae']['is_label'] = False
config['cvae']['latent_dim'] = config['latent_dim']
config['cvae']['add_noise'] = True
config['cvae']['noise_amount'] = 0.025
config['cvae']['kernel_size'] = 7
config['cvae']['encoder_depths'] = [3, 3, 9, 3]
config['cvae']['encoder_dims'] = [32, 64, 128, 256]
config['cvae']['decoder_input'] = (16, 8, 80)
config['cvae']['decoder_depths'] = [3, 3, 9, 3]
config['cvae']['decoder_dims'] = [256, 128, 64, 16]
config['cond_ae'] = dict()
config['cond_ae']['is_label'] = False
config['cond_ae']['upscale_output'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 64
config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
config['cond_ae_body'] = dict()
config['cond_ae_body']['latent_dim'] = config['latent_dim']
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['upscale_output'] = True
config['cond_ae_body']['bottleneck_size'] = 512
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]