You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
87 lines
3.3 KiB
87 lines
3.3 KiB
import logging
|
|
from .constants import *
|
|
|
|
|
|
_logger = logging.getLogger(__name__)
|
|
|
|
|
|
def resolve_data_config(args, default_cfg={}, model=None, use_test_size=False, verbose=False):
|
|
new_config = {}
|
|
default_cfg = default_cfg
|
|
if not default_cfg and model is not None and hasattr(model, 'default_cfg'):
|
|
default_cfg = model.default_cfg
|
|
|
|
# Resolve input/image size
|
|
in_chans = 3
|
|
if 'chans' in args and args['chans'] is not None:
|
|
in_chans = args['chans']
|
|
|
|
input_size = (in_chans, 224, 224)
|
|
if 'input_size' in args and args['input_size'] is not None:
|
|
assert isinstance(args['input_size'], (tuple, list))
|
|
assert len(args['input_size']) == 3
|
|
input_size = tuple(args['input_size'])
|
|
in_chans = input_size[0] # input_size overrides in_chans
|
|
elif 'img_size' in args and args['img_size'] is not None:
|
|
assert isinstance(args['img_size'], int)
|
|
input_size = (in_chans, args['img_size'], args['img_size'])
|
|
else:
|
|
if use_test_size and 'test_input_size' in default_cfg:
|
|
input_size = default_cfg['test_input_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'] = 'bicubic'
|
|
if 'interpolation' in args and 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'] = IMAGENET_DEFAULT_MEAN
|
|
if 'mean' in args and 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'] = IMAGENET_DEFAULT_STD
|
|
if 'std' in args and 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 args and args['crop_pct'] is not None:
|
|
new_config['crop_pct'] = args['crop_pct']
|
|
elif 'crop_pct' in default_cfg:
|
|
new_config['crop_pct'] = default_cfg['crop_pct']
|
|
|
|
if getattr(args, 'mixup', 0) > 0 \
|
|
or getattr(args, 'cutmix', 0) > 0. \
|
|
or getattr(args, 'cutmix_minmax', None) is not None:
|
|
new_config['mixup'] = dict(
|
|
mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax,
|
|
prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode,
|
|
label_smoothing=args.smoothing, num_classes=args.num_classes)
|
|
|
|
if verbose:
|
|
_logger.info('Data processing configuration for current model + dataset:')
|
|
for n, v in new_config.items():
|
|
_logger.info('\t%s: %s' % (n, str(v)))
|
|
|
|
return new_config
|