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237 lines
8.1 KiB
237 lines
8.1 KiB
""" Transforms Factory
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Factory methods for building image transforms for use with TIMM (PyTorch Image Models)
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Hacked together by / Copyright 2020 Ross Wightman
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"""
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import math
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import torch
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from torchvision import transforms
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from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, DEFAULT_CROP_PCT
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from timm.data.auto_augment import rand_augment_transform, augment_and_mix_transform, auto_augment_transform
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from timm.data.transforms import _pil_interp, RandomResizedCropAndInterpolation, ToNumpy, ToTensor
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from timm.data.random_erasing import RandomErasing
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def transforms_noaug_train(
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img_size=224,
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interpolation='bilinear',
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use_prefetcher=False,
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mean=IMAGENET_DEFAULT_MEAN,
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std=IMAGENET_DEFAULT_STD,
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):
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if interpolation == 'random':
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# random interpolation not supported with no-aug
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interpolation = 'bilinear'
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tfl = [
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transforms.Resize(img_size, _pil_interp(interpolation)),
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transforms.CenterCrop(img_size)
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]
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if use_prefetcher:
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# prefetcher and collate will handle tensor conversion and norm
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tfl += [ToNumpy()]
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else:
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tfl += [
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transforms.ToTensor(),
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transforms.Normalize(
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mean=torch.tensor(mean),
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std=torch.tensor(std))
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]
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return transforms.Compose(tfl)
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def transforms_imagenet_train(
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img_size=224,
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scale=None,
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ratio=None,
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hflip=0.5,
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vflip=0.,
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color_jitter=0.4,
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auto_augment=None,
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interpolation='random',
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use_prefetcher=False,
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mean=IMAGENET_DEFAULT_MEAN,
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std=IMAGENET_DEFAULT_STD,
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re_prob=0.,
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re_mode='const',
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re_count=1,
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re_num_splits=0,
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separate=False,
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):
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"""
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If separate==True, the transforms are returned as a tuple of 3 separate transforms
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for use in a mixing dataset that passes
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* all data through the first (primary) transform, called the 'clean' data
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* a portion of the data through the secondary transform
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* normalizes and converts the branches above with the third, final transform
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"""
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scale = tuple(scale or (0.08, 1.0)) # default imagenet scale range
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ratio = tuple(ratio or (3./4., 4./3.)) # default imagenet ratio range
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primary_tfl = [
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RandomResizedCropAndInterpolation(img_size, scale=scale, ratio=ratio, interpolation=interpolation)]
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if hflip > 0.:
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primary_tfl += [transforms.RandomHorizontalFlip(p=hflip)]
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if vflip > 0.:
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primary_tfl += [transforms.RandomVerticalFlip(p=vflip)]
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secondary_tfl = []
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if auto_augment:
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assert isinstance(auto_augment, str)
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if isinstance(img_size, (tuple, list)):
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img_size_min = min(img_size)
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else:
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img_size_min = img_size
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aa_params = dict(
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translate_const=int(img_size_min * 0.45),
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img_mean=tuple([min(255, round(255 * x)) for x in mean]),
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)
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if interpolation and interpolation != 'random':
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aa_params['interpolation'] = _pil_interp(interpolation)
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if auto_augment.startswith('rand'):
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secondary_tfl += [rand_augment_transform(auto_augment, aa_params)]
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elif auto_augment.startswith('augmix'):
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aa_params['translate_pct'] = 0.3
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secondary_tfl += [augment_and_mix_transform(auto_augment, aa_params)]
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else:
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secondary_tfl += [auto_augment_transform(auto_augment, aa_params)]
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elif color_jitter is not None:
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# color jitter is enabled when not using AA
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if isinstance(color_jitter, (list, tuple)):
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# color jitter should be a 3-tuple/list if spec brightness/contrast/saturation
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# or 4 if also augmenting hue
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assert len(color_jitter) in (3, 4)
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else:
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# if it's a scalar, duplicate for brightness, contrast, and saturation, no hue
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color_jitter = (float(color_jitter),) * 3
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secondary_tfl += [transforms.ColorJitter(*color_jitter)]
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final_tfl = []
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if use_prefetcher:
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# prefetcher and collate will handle tensor conversion and norm
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final_tfl += [ToNumpy()]
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else:
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final_tfl += [
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transforms.ToTensor(),
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transforms.Normalize(
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mean=torch.tensor(mean),
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std=torch.tensor(std))
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]
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if re_prob > 0.:
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final_tfl.append(
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RandomErasing(re_prob, mode=re_mode, max_count=re_count, num_splits=re_num_splits, device='cpu'))
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if separate:
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return transforms.Compose(primary_tfl), transforms.Compose(secondary_tfl), transforms.Compose(final_tfl)
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else:
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return transforms.Compose(primary_tfl + secondary_tfl + final_tfl)
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def transforms_imagenet_eval(
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img_size=224,
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crop_pct=None,
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interpolation='bilinear',
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use_prefetcher=False,
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mean=IMAGENET_DEFAULT_MEAN,
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std=IMAGENET_DEFAULT_STD):
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crop_pct = crop_pct or DEFAULT_CROP_PCT
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if isinstance(img_size, (tuple, list)):
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assert len(img_size) == 2
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if img_size[-1] == img_size[-2]:
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# fall-back to older behaviour so Resize scales to shortest edge if target is square
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scale_size = int(math.floor(img_size[0] / crop_pct))
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else:
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scale_size = tuple([int(x / crop_pct) for x in img_size])
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else:
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scale_size = int(math.floor(img_size / crop_pct))
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tfl = [
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transforms.Resize(scale_size, _pil_interp(interpolation)),
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transforms.CenterCrop(img_size),
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]
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if use_prefetcher:
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# prefetcher and collate will handle tensor conversion and norm
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tfl += [ToNumpy()]
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else:
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tfl += [
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transforms.ToTensor(),
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transforms.Normalize(
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mean=torch.tensor(mean),
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std=torch.tensor(std))
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]
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return transforms.Compose(tfl)
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def create_transform(
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input_size,
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is_training=False,
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use_fetcher=False,
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no_aug=False,
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scale=None,
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ratio=None,
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hflip=0.5,
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vflip=0.,
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color_jitter=0.4,
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auto_augment=None,
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interpolation='bilinear',
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mean=IMAGENET_DEFAULT_MEAN,
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std=IMAGENET_DEFAULT_STD,
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re_prob=0.,
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re_mode='const',
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re_count=1,
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re_num_splits=0,
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crop_pct=None,
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tf_preprocessing=False,
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separate=False):
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if isinstance(input_size, (tuple, list)):
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img_size = input_size[-2:]
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else:
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img_size = input_size
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if tf_preprocessing and use_fetcher:
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assert not separate, "Separate transforms not supported for TF preprocessing"
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from timm.data.tf_preprocessing import TfPreprocessTransform
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transform = TfPreprocessTransform(
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is_training=is_training, size=img_size, interpolation=interpolation)
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else:
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if is_training and no_aug:
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assert not separate, "Cannot perform split augmentation with no_aug"
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transform = transforms_noaug_train(
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img_size,
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interpolation=interpolation,
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use_prefetcher=use_fetcher,
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mean=mean,
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std=std)
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elif is_training:
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transform = transforms_imagenet_train(
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img_size,
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scale=scale,
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ratio=ratio,
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hflip=hflip,
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vflip=vflip,
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color_jitter=color_jitter,
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auto_augment=auto_augment,
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interpolation=interpolation,
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use_prefetcher=use_fetcher,
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mean=mean,
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std=std,
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re_prob=re_prob,
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re_mode=re_mode,
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re_count=re_count,
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re_num_splits=re_num_splits,
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separate=separate)
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else:
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assert not separate, "Separate transforms not supported for validation preprocessing"
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transform = transforms_imagenet_eval(
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img_size,
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interpolation=interpolation,
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use_prefetcher=use_fetcher,
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mean=mean,
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std=std,
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crop_pct=crop_pct)
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return transform
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