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