config_a = 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_a['use_skinning'] =              False # and deshaping
config_a['remove_global_orientation'] = True
config_a['use_curvature'] =             True
config_a['use_extra_losses'] =          True # edge, collision, 3d mesh
config_a['use_offsets'] =               True

config_a['use_norm_loss'] =             False

ablation_count = [config_a['use_skinning'], config_a['remove_global_orientation'], config_a['use_curvature'], config_a['use_extra_losses'], config_a['use_offsets']].count(False)

config_a['gpu'] = '4'
config_a['name'] = 'abl_skin_skirt'
config_a['notes'] = ''

config_a['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_a['use_skinning']:
  assert (config_a['use_offsets'] and config_a['avg_garment_path'] != '') or (not config_a['use_offsets'] and config_a['avg_garment_path'] == '')

config_a['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision

config_a['batch_size'] = 4
config_a['resolution'] = 512
config_a['is_skirt'] = True
config_a['dataset_in_memory'] = False
config_a['is_cape'] = False

config_a['train_dir'] = '/home/workspace/local-data/train_skirt'
config_a['valid_dir'] = '/home/workspace/local-data/valid_skirt'
config_a['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'

config_a['static_model'] = 'checkpoints/static_1_skirt'
config_a['body_model'] = 'checkpoints/body_1_skirt_2_2'
config_a['curvature_model'] = 'checkpoints/curv_1_skirt'


config_a['smpl_res'] = 14475
if config_a['is_cape']:
  config_a['smpl_res'] = 3638

config_a['latent_dim'] = 128

config_a['pretrained_model'] = ''

###############    
config_a['lr'] = 0.0001#                 0.0001
###############
config_a['starting_balancing_coeff'] = 0.0001

config_a['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_a['signature_static'] = { 'uv_static': (512, 256, 3),
                              'mesh_mask': (14475,),
                              'uv_mask': (512, 256,),
                              }

if config_a['use_skinning']:
  config_a['signature_dynamic']['uv_unposed'] = (512, 256, 3)
  config_a['signature_dynamic']['mesh_unposed'] = (14475, 3,)
else:
  config_a['signature_dynamic']['uv_posed'] = (512, 256, 3)


config_a['upscale_enhancer'] = ''
config_a['freeze_encoders'] = True
config_a['use_one_hot'] = False

config_a['cvae'] = dict()
config_a['cvae']['is_label'] = False
config_a['cvae']['latent_dim'] = config_a['latent_dim']
config_a['cvae']['add_noise'] = True
config_a['cvae']['noise_amount'] = 0.025

config_a['cvae']['kernel_size'] = 7
config_a['cvae']['encoder_depths'] = [3, 3, 9, 3]
config_a['cvae']['encoder_dims'] = [32, 64, 128, 256]
config_a['cvae']['decoder_input'] = (16, 8, 80)
config_a['cvae']['decoder_depths'] = [3, 3, 9, 3]
config_a['cvae']['decoder_dims'] = [256, 128, 64, 16]


config_a['cond_ae'] = dict()
config_a['cond_ae']['is_label'] = False
config_a['cond_ae']['upscale_output'] = False
config_a['cond_ae']['latent_dim'] = config_a['latent_dim']
config_a['cond_ae']['kernel_size'] = 7
config_a['cond_ae']['bottleneck_size'] = 64
config_a['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
config_a['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config_a['cond_ae']['bottleneck_size']]
config_a['cond_ae']['decoder_input'] = (8, 4, config_a['cond_ae']['bottleneck_size'])
config_a['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
config_a['cond_ae']['decoder_dims'] = [config_a['cond_ae']['bottleneck_size'], 256, 128, 64, 16]

config_a['cond_ae_body'] = dict()
config_a['cond_ae_body']['latent_dim'] = config_a['latent_dim']
config_a['cond_ae_body']['kernel_size'] = 7
config_a['cond_ae_body']['upscale_output'] = True
config_a['cond_ae_body']['bottleneck_size'] = 512
config_a['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
config_a['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config_a['cond_ae_body']['bottleneck_size']]
config_a['cond_ae_body']['decoder_input'] = (4, 2, config_a['cond_ae_body']['bottleneck_size'])
config_a['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
config_a['cond_ae_body']['decoder_dims'] = [config_a['cond_ae_body']['bottleneck_size'], 128, 64, 16]