from utils.convnext_utils import CNBlockConfig

config = dict()

config['gpu'] = '5'
config['name'] = 'curv_torch_cape_f'
config['notes'] = ' ' 
config['tags'] = ['torch', 'curv'] 
config['script'] = 'scripts/ae_curv_train.py'

config['pretrained_model'] = ''

config['batch_size'] = 4
config['train_dir'] = '/data-local/127/cape_processed/train/female'
config['valid_dir'] = '/data-local/127/cape_processed/test/female'
config['test_dir'] = '/data-local/127/cape_processed/test/female'
config['train_frames'] = None
config['valid_frames'] = None

config['resolution'] = 512
config['is_skirt'] = False
config['dataset_in_memory'] = False
config['is_cape'] = True
config['remove_global_orientation'] = True
config['device'] = 'cuda'

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

config['latent_dim'] = 128

config['bottleneck_scale'] = 2

config['cond_ae'] = dict()
config['cond_ae']['device'] = config['device']
config['cond_ae']['is_label'] = True
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 40 * config['bottleneck_scale']

block_setting = [
    CNBlockConfig(16, 32, 3),
    CNBlockConfig(32, 64, 3),
    CNBlockConfig(64, 128, 9),
    CNBlockConfig(128, config['cond_ae']['bottleneck_size'], 9),
    CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
]

block_setting_reverse = [
    CNBlockConfig(config['cond_ae']['bottleneck_size'], 128, 3),
    CNBlockConfig(128, 64, 9),
    CNBlockConfig(64, 32, 9),
    CNBlockConfig(32, 16, 3),
    CNBlockConfig(16, 3, 3),
]

config['cond_ae']['encoder_block_settings'] = block_setting
config['cond_ae']['decoder_block_settings'] = block_setting_reverse
config['cond_ae']['activation'] = 'tanh'


config['lr'] = 0.001
config['cond_ae']['upscale_output'] = False

config['signature'] = { 'id',
                        'frame_nr',
                        'uv_curvature',
                        'mesh_curvature',
                        'mesh_mask',
                        'uv_mask',
                        }