from configs.config_base import config

config['gpu'] = '0'
config['name'] = 'v2_7_4_3'
config['notes'] = '\
offsets over average unposed garment. remove global orientation (in posed body input and in pose_shape parameters. | \
retrained body encoder with deoriented bodies | \
use mean l2 loss. small batch. l2 * 1000, balancing to 1 from 0.01 | \
fixed gender issue and retrained body encoder, FINAL | \
remove shape from unposed garment | \
training on 100 garments with offsets over the average of these | \
fixed autoencoders!!!!!!!!! | \
cvae only loads encoders instead AEs | \
increase balancing coeff sooner | \
use 3d mesh loss | \
add final gc layer | \
starting from v2_5_2, add tanh to cond_enc last layer | \
NOT using one-hot skinning | \
using new average garment with one-hot versions | \
fix collision for plotting | \
use 3d mesh loss | \
'
config['dataset_in_memory'] = False


# 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['pretrained_model'] = 'checkpoints/v2_5_2'

config['resolution'] = 512
config['is_skirt'] = False
config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
config['l2_coeff'] = 1

if config['resolution'] != 512:
  config['notes'] += '\
using custom enhancer for upscaling, using not filled versions\
'
    
###############    
config['lr'] = 0.0001#                 0.0001
###############
config['starting_balancing_coeff'] = 0.0001

config['signature_dynamic'] = { 'id': (),
                          'pose_shape': (72 + 11,),
                          'uv_body_posed': (512, 256, 3),
                          'mesh_body_posed': (14475, 3,),
                          'mesh_posed': (14475, 3,),
                          'mesh_unposed': (14475, 3,),
                          'uv_unposed': (512, 256, 3),
                          'uv_curvature': (512, 256, 7),
                        }
config['signature_static'] = { 'uv_static': (512, 256, 3),
                              'mesh_mask': (14475,),
                              'uv_mask': (512, 256,),
                              }

if config['resolution'] != 512:
  res_modifier = '_' + str(config['resolution'])
  config['signature_dynamic']['uv_body_posed' + res_modifier] = (config['resolution'], config['resolution']//2, 3)
  config['signature_dynamic']['uv_unposed' + res_modifier] = (config['resolution'], config['resolution']//2, 3)
  config['signature_dynamic']['uv_curvature' + res_modifier] = (config['resolution'], config['resolution']//2)
  config['signature_static']['uv_static' + res_modifier] = (config['resolution'], config['resolution']//2, 3)
  config['signature_static']['uv_mask' + res_modifier] = (config['resolution'], config['resolution']//2)

if config['resolution'] == 512:
  config['avg_garment_path'] = 'custom_data/train_no_shape_avg_uv.npy' # train_no_shape_avg_uv  train_no_shape_one_hot_avg_uv
else:
  config['avg_garment_path'] = ''

config['static_model'] = 'checkpoints/static_1'
config['body_model'] = 'checkpoints/body_1'
config['curvature_model'] = 'checkpoints/curv_1'
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

if config['resolution'] == 512:
  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]
elif config['resolution'] == 128:
  config['cvae']['kernel_size'] = 5
  config['cvae']['encoder_depths'] = [16, 16, 9, 9]
  config['cvae']['encoder_dims'] = [64, 128, 256, 256]
  config['cvae']['decoder_input'] = (4, 2, 128)
  config['cvae']['decoder_depths'] = [16, 16, 9, 9]
  config['cvae']['decoder_dims'] = [256, 256, 128, 64]
elif config['resolution'] == 32:
  config['cvae']['kernel_size'] = 3
  config['cvae']['encoder_depths'] = [27, 27]
  config['cvae']['encoder_dims'] = [256, 256]
  config['cvae']['decoder_input'] = (4, 2, 256)
  config['cvae']['decoder_depths'] = [27, 27]
  config['cvae']['decoder_dims'] = [256, 256]
else:
  raise NotImplementedError