mirror of
https://github.com/huggingface/pytorch-image-models.git
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76539d905e
* factor out data related constants to own file * move data related config helpers to own file * add a variant of RandomResizeCrop that randomizes interpolation method * remove old Numpy version of RandomErasing * cleanup torch version of RandomErasing and use it in either GPU loader batch mode or single image cpu Transform
102 lines
3.1 KiB
Python
102 lines
3.1 KiB
Python
from data.constants import *
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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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