from utils.convnext_utils import CNBlockConfig

config = dict()

config['gpu'] = '3'
config['name'] = 'static_torch_orig_2'
config['notes'] = ' ' 
config['tags'] = ['torch', 'static'] 

config['batch_size'] = 4
config['train_dir'] = '/data-local/127/train'
config['valid_dir'] = '/data-local/127/valid'
config['test_dir'] = '/data-net/dataset/test_ready'
config['train_frames'] = None
config['valid_frames'] = None

config['resolution'] = 512
config['is_skirt'] = False
config['dataset_in_memory'] = True
config['is_cape'] = False
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'] = False
config['cond_ae']['latent_dim'] = config['latent_dim']
config['cond_ae']['kernel_size'] = 7
config['cond_ae']['bottleneck_size'] = 8 * config['bottleneck_scale']

block_setting = [
    CNBlockConfig(8, 16, 3),
    CNBlockConfig(16, 32, 3),
    CNBlockConfig(32, 64, 3),
    CNBlockConfig(64, config['cond_ae']['bottleneck_size'], 9),
    CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3),
]

block_setting_reverse = [
    CNBlockConfig(config['cond_ae']['bottleneck_size'], 64, 3),
    CNBlockConfig(64, 32, 9),
    CNBlockConfig(32, 16, 3),
    CNBlockConfig(16, 8, 3),
    CNBlockConfig(8, 3, 3),
    # CNBlockConfig(3, 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'] = 0.0005
config['cond_ae']['upscale_output'] = False
config['pretrained_model'] = ''

config['signature'] = { 'id',
                        'frame_nr',
                        'uv_static',
                        'mesh_static',
                        'mesh_mask',
                        'uv_mask',
                        }