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76 lines
2.7 KiB
76 lines
2.7 KiB
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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