import logging from .constants import * logger = logging.getLogger(__name__) def resolve_data_config(args, default_cfg={}, model=None, verbose=True): new_config = {} default_cfg = default_cfg if not default_cfg and model is not None and hasattr(model, 'default_cfg'): default_cfg = model.default_cfg # Resolve input/image size in_chans = 3 if 'chans' in args and args['chans'] is not None: in_chans = args['chans'] input_size = (in_chans, 224, 224) if 'input_size' in args and args['input_size'] is not None: assert isinstance(args['input_size'], (tuple, list)) assert len(args['input_size']) == 3 input_size = tuple(args['input_size']) in_chans = input_size[0] # input_size overrides in_chans elif 'img_size' in args and args['img_size'] is not None: assert isinstance(args['img_size'], int) input_size = (in_chans, args['img_size'], args['img_size']) elif 'input_size' in default_cfg: input_size = default_cfg['input_size'] new_config['input_size'] = input_size # resolve interpolation method new_config['interpolation'] = 'bicubic' if 'interpolation' in args and args['interpolation']: new_config['interpolation'] = args['interpolation'] elif 'interpolation' in default_cfg: new_config['interpolation'] = default_cfg['interpolation'] # resolve dataset + model mean for normalization new_config['mean'] = IMAGENET_DEFAULT_MEAN if 'mean' in args and args['mean'] is not None: mean = tuple(args['mean']) if len(mean) == 1: mean = tuple(list(mean) * in_chans) else: assert len(mean) == in_chans new_config['mean'] = mean elif 'mean' in default_cfg: new_config['mean'] = default_cfg['mean'] # resolve dataset + model std deviation for normalization new_config['std'] = IMAGENET_DEFAULT_STD if 'std' in args and args['std'] is not None: std = tuple(args['std']) if len(std) == 1: std = tuple(list(std) * in_chans) else: assert len(std) == in_chans new_config['std'] = std elif 'std' in default_cfg: new_config['std'] = default_cfg['std'] # resolve default crop percentage new_config['crop_pct'] = DEFAULT_CROP_PCT if 'crop_pct' in args and args['crop_pct'] is not None: new_config['crop_pct'] = args['crop_pct'] elif 'crop_pct' in default_cfg: new_config['crop_pct'] = default_cfg['crop_pct'] if verbose: logger.info('Data processing configuration for current model + dataset:') for n, v in new_config.items(): logger.info('\t%s: %s' % (n, str(v))) return new_config