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pytorch-image-models/data/config.py

102 lines
3.1 KiB

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