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'] =             True
config['only_curvatures'] =           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_all_gnn_3'
config['notes'] = ''
config['tags'] = ['iccv', 'abl_all_gnn']

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/iccv/static_iccv_1'
config['body_model'] = 'checkpoints/iccv/body_iccv_1'
config['curvature_model'] = 'checkpoints/iccv/curv_iccv_1'

# config['static_model'] = 'checkpoints/post/static_post_2'
# config['body_model'] = 'checkpoints/post/body_post_2'
# config['curvature_model'] = 'checkpoints/post/curv_post_1'


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


config['pretrained_model'] = 'checkpoints/iccv/abl_skin'

###############    
config['lr'] = 0.0001#                 0.0001
###############
config['starting_balancing_coeff'] = 0.001

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),
                        }
config['signature_static'] = { 'uv_static': (512, 256, 3),
                              'mesh_mask': (config['smpl_res'],),
                              'uv_mask': (512, 256,),
                              }

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['upscale_enhancer'] = ''
config['freeze_encoders'] = True
config['use_one_hot'] = False
config['bottleneck_scale'] = 2

config['cvae'] = dict()
config['cvae']['is_label'] = False
config['cvae']['latent_dim'] = 64 * config['bottleneck_scale']
config['cvae']['add_noise'] = False
config['cvae']['noise_amount'] = 0.0

config['cvae']['kernel_size'] = 7
config['cvae']['encoder_depths'] = [3, 3, 3, 9, 3]
config['cvae']['encoder_dims'] = [32, 64, 128, 256, config['cvae']['latent_dim']]
config['cvae']['decoder_depths'] = config['cvae']['encoder_depths']
config['cvae']['decoder_dims'] = config['cvae']['encoder_dims']


config['cond_ae_static'] = dict()
config['cond_ae_static']['is_label'] = False
config['cond_ae_static']['kernel_size'] = 7
config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
config['cond_ae_static']['encoder_depths'] = [3, 3, 3, 27, 3]
config['cond_ae_static']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae_static']['bottleneck_size']]
config['cond_ae_static']['decoder_depths'] = config['cond_ae_static']['encoder_depths']
config['cond_ae_static']['decoder_dims'] = config['cond_ae_static']['encoder_dims']
config['cond_ae_static']['activation'] = 'tanh'

config['cond_ae_curv'] = dict()
config['cond_ae_curv']['is_label'] = True
config['cond_ae_curv']['kernel_size'] = 7
config['cond_ae_curv']['bottleneck_size'] = 40 * config['bottleneck_scale']
config['cond_ae_curv']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae_curv']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_curv']['bottleneck_size']]
config['cond_ae_curv']['decoder_depths'] = config['cond_ae_curv']['encoder_depths']
config['cond_ae_curv']['decoder_dims'] = config['cond_ae_curv']['encoder_dims']
config['cond_ae_curv']['activation'] = 'tanh'

config['cond_ae_body'] = dict()
config['cond_ae_body']['is_label'] = False
config['cond_ae_body']['kernel_size'] = 7
config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3]
config['cond_ae_body']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae_body']['bottleneck_size']]
config['cond_ae_body']['decoder_depths'] = config['cond_ae_body']['encoder_depths']
config['cond_ae_body']['decoder_dims'] = config['cond_ae_body']['encoder_dims']
config['cond_ae_body']['activation'] = 'tanh'