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| import logging | |
| from .constants import * | |
| _logger = logging.getLogger(__name__) | |
| def resolve_data_config(args, default_cfg={}, model=None, use_test_size=False, verbose=False): | |
| 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']) | |
| else: | |
| if use_test_size and 'test_input_size' in default_cfg: | |
| input_size = default_cfg['test_input_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 | |
| crop_pct = DEFAULT_CROP_PCT | |
| if 'crop_pct' in args and args['crop_pct'] is not None: | |
| crop_pct = args['crop_pct'] | |
| else: | |
| if use_test_size and 'test_crop_pct' in default_cfg: | |
| crop_pct = default_cfg['test_crop_pct'] | |
| elif 'crop_pct' in default_cfg: | |
| crop_pct = default_cfg['crop_pct'] | |
| new_config['crop_pct'] = 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 | |