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102 lines
3.1 KiB
102 lines
3.1 KiB
5 years ago
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from .constants import *
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6 years ago
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def resolve_data_config(model, args, default_cfg={}, 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 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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# FIXME grayscale/chans arg to use different # channels?
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in_chans = 3
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input_size = (in_chans, 224, 224)
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if args.img_size is not None:
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# FIXME support passing img_size as tuple, non-square
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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'] = 'bilinear'
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if 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'] = get_mean_by_model(args.model)
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if 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'] = get_std_by_model(args.model)
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if 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 default_cfg:
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new_config['crop_pct'] = default_cfg['crop_pct']
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if verbose:
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print('Data processing configuration for current model + dataset:')
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for n, v in new_config.items():
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print('\t%s: %s' % (n, str(v)))
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return new_config
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def get_mean_by_name(name):
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if name == 'dpn':
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return IMAGENET_DPN_MEAN
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elif name == 'inception' or name == 'le':
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return IMAGENET_INCEPTION_MEAN
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else:
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return IMAGENET_DEFAULT_MEAN
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def get_std_by_name(name):
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if name == 'dpn':
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return IMAGENET_DPN_STD
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elif name == 'inception' or name == 'le':
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return IMAGENET_INCEPTION_STD
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else:
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return IMAGENET_DEFAULT_STD
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def get_mean_by_model(model_name):
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model_name = model_name.lower()
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if 'dpn' in model_name:
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return IMAGENET_DPN_STD
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elif 'ception' in model_name or 'nasnet' in model_name:
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return IMAGENET_INCEPTION_MEAN
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else:
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return IMAGENET_DEFAULT_MEAN
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def get_std_by_model(model_name):
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model_name = model_name.lower()
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if 'dpn' in model_name:
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return IMAGENET_DEFAULT_STD
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elif 'ception' in model_name or 'nasnet' in model_name:
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return IMAGENET_INCEPTION_STD
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else:
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return IMAGENET_DEFAULT_STD
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