pytorch-image-models/timm/data/transforms_factory.py

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_imagenet_train(
        img_size=224,
        scale=(0.08, 1.0),
        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,
):

    primary_tfl = [
        RandomResizedCropAndInterpolation(
            img_size, scale=scale, interpolation=interpolation),
        transforms.RandomHorizontalFlip()
    ]

    secondary_tfl = []
    if auto_augment:
        assert isinstance(auto_augment, str)
        if isinstance(img_size, tuple):
            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):
        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,
        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):
        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:
            transform = transforms_imagenet_train(
                img_size,
                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