mirror of
https://github.com/huggingface/pytorch-image-models.git
synced 2025-06-03 15:01:08 +08:00
78fa0772cc
with this update one can tune the kind of logs generated by timm but training and inference traces are unchanged
76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
import logging
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from .constants import *
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logger = logging.getLogger(__name__)
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def resolve_data_config(args, default_cfg={}, model=None, verbose=True):
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new_config = {}
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default_cfg = default_cfg
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if not default_cfg and model is not None and hasattr(model, 'default_cfg'):
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default_cfg = model.default_cfg
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# Resolve input/image size
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in_chans = 3
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if 'chans' in args and args['chans'] is not None:
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in_chans = args['chans']
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input_size = (in_chans, 224, 224)
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if 'input_size' in args and args['input_size'] is not None:
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assert isinstance(args['input_size'], (tuple, list))
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assert len(args['input_size']) == 3
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input_size = tuple(args['input_size'])
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in_chans = input_size[0] # input_size overrides in_chans
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elif 'img_size' in args and args['img_size'] is not None:
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assert isinstance(args['img_size'], int)
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input_size = (in_chans, args['img_size'], args['img_size'])
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elif 'input_size' in default_cfg:
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input_size = default_cfg['input_size']
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new_config['input_size'] = input_size
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# resolve interpolation method
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new_config['interpolation'] = 'bicubic'
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if 'interpolation' in args and args['interpolation']:
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new_config['interpolation'] = args['interpolation']
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elif 'interpolation' in default_cfg:
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new_config['interpolation'] = default_cfg['interpolation']
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# resolve dataset + model mean for normalization
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new_config['mean'] = IMAGENET_DEFAULT_MEAN
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if 'mean' in args and args['mean'] is not None:
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mean = tuple(args['mean'])
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if len(mean) == 1:
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mean = tuple(list(mean) * in_chans)
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else:
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assert len(mean) == in_chans
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new_config['mean'] = mean
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elif 'mean' in default_cfg:
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new_config['mean'] = default_cfg['mean']
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# resolve dataset + model std deviation for normalization
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new_config['std'] = IMAGENET_DEFAULT_STD
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if 'std' in args and args['std'] is not None:
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std = tuple(args['std'])
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if len(std) == 1:
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std = tuple(list(std) * in_chans)
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else:
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assert len(std) == in_chans
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new_config['std'] = std
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elif 'std' in default_cfg:
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new_config['std'] = default_cfg['std']
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# resolve default crop percentage
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new_config['crop_pct'] = DEFAULT_CROP_PCT
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if 'crop_pct' in args and args['crop_pct'] is not None:
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new_config['crop_pct'] = args['crop_pct']
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elif 'crop_pct' in default_cfg:
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new_config['crop_pct'] = default_cfg['crop_pct']
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if verbose:
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logger.info('Data processing configuration for current model + dataset:')
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for n, v in new_config.items():
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logger.info('\t%s: %s' % (n, str(v)))
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return new_config
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