from utils.convnext_utils import CNBlockConfig

config = dict()

config['gpu'] = '4'
config['name'] = 'direct_6'
config['notes'] = 'with normals input but only single garment sequence with msssim and l2 mesh loss only' 
config['tags'] = ['torch', 'direct'] 
config['script'] = 'scripts/direct_train.py'

config['pretrained_model'] = ''
config['use_curvature'] = False
config['use_normals'] = True
config['use_skinning'] = True

# assert that only use curvature or normals
assert not (config['use_curvature'] and config['use_normals'])

config['batch_size'] = 4
config['train_dir'] = '/data-local/127/train'
config['valid_dir'] = '/data-local/127/train'
config['test_dir'] = '/data-net/dataset/test_ready'
config['train_frames'] = 'frames_00761.txt'
config['valid_frames'] = 'frames_00761.txt'

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['input_features'] = 9

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

config['latent_dim'] = 128

config['bottleneck_scale'] = 2

config['is_label'] = False
config['kernel_size'] = 7
config['bottleneck_size'] = 64 * config['bottleneck_scale']

block_setting = [
    CNBlockConfig(16, 32, 3),
    CNBlockConfig(32, 128, 3),
    CNBlockConfig(128, 256, 9),
    CNBlockConfig(256, config['bottleneck_size'], 9),
    CNBlockConfig(config['bottleneck_size'], None, 3),
]

block_setting_reverse = [
    CNBlockConfig(config['bottleneck_size'], 256, 3),
    CNBlockConfig(256, 128, 9),
    CNBlockConfig(128, 32, 9),
    CNBlockConfig(32, 16, 3),
    CNBlockConfig(16, 3, 3),
    # CNBlockConfig(3, 3, 3),
]

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


config['lr'] = 0.0001
config['reg_loss_coeff'] = 0.1

config['signature'] = { 'id',
                        'frame_nr',
                        'pose_shape',
                        'uv_normals',
                        'mesh_mask',
                        'mesh_body_posed',
                        'mesh_posed',
                        'uv_unposed',
                        'mesh_unposed',
                        'uv_static',
                        'uv_mask',
                        'uv_body_posed',
                        }