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| import pytest | |
| from pathlib import Path | |
| from typing import List | |
| import numpy as np | |
| from PIL import Image | |
| import torch | |
| import torchvision.transforms as tvtfms | |
| from fastai.vision.data import PILImage | |
| import fastai.vision.augment as fastai_aug | |
| from deployment.transforms import resized_crop_pad, gpu_crop | |
| DATA_PATH = "data/200-bird-species-with-11788-images" | |
| def get_birds_images(path: Path) -> List[str]: | |
| with open(path / "images.txt", "r") as file: | |
| lines = [ | |
| path.resolve() / "images" / line.strip().split()[1] | |
| for line in file.readlines() | |
| ] | |
| return lines | |
| class TestTransforms: | |
| im_idx = 510 | |
| def img_paths(self) -> List[str]: | |
| path = Path(DATA_PATH) / "CUB_200_2011" | |
| return get_birds_images(path.resolve()) | |
| def im_fastai(self, img_paths: List[str]) -> PILImage: | |
| fname = img_paths[self.im_idx] | |
| return PILImage.create(fname) | |
| def im_pil(self, img_paths: List[str]) -> Image: | |
| fname = img_paths[self.im_idx] | |
| return Image.open(fname) | |
| def testImageFastaiEqualsPillow(self, im_fastai: PILImage, im_pil: Image): | |
| assert (np.array(im_fastai) == np.array(im_pil)).all() | |
| # RandomResizedCrop is not exactly equal to CropPad in validation | |
| # # def testRandomResizedCropEqualsCropPad(self, im_fastai: PILImage): | |
| # # crop_fastai = fastai_aug.CropPad((460, 460)) | |
| # # crop_rrc = fastai_aug.RandomResizedCrop((460, 460)) | |
| # # cropped_rrc = crop_rrc(im_fastai, split_idx=1) | |
| # # cropped_fastai = crop_fastai(im_fastai, split_idx=1) | |
| # # assert (np.array(cropped_rrc) == np.array(cropped_fastai)).all() | |
| def testRandomResizedCropEqualsCustomResizedCropPad( | |
| self, im_fastai: PILImage, im_pil: Image | |
| ): | |
| crop_rrc = fastai_aug.RandomResizedCrop((460, 460)) | |
| assert ( | |
| np.array(crop_rrc(im_fastai, split_idx=1)) | |
| == np.array(resized_crop_pad(im_pil, (460, 460))) | |
| ).all() | |
| def testFlipEqualsCustomGPUCrop(self, im_fastai: PILImage, im_pil: Image): | |
| # apply flip augmentation on validation | |
| tt_fastai = fastai_aug.ToTensor() | |
| i2f_fastai = fastai_aug.IntToFloatTensor() | |
| flip = fastai_aug.Flip(size=(224, 224)) | |
| result_im_fastai = flip( | |
| i2f_fastai(tt_fastai(im_fastai).unsqueeze(0)), split_idx=1 | |
| ) | |
| # apply custom gpu crop | |
| tt_torch = tvtfms.ToTensor() | |
| result_im_tv = gpu_crop(tt_torch(im_pil).unsqueeze(0), size=(224, 224)) | |
| assert torch.allclose(result_im_fastai, result_im_tv) | |
| assert (result_im_fastai == result_im_tv).all() | |
| def testFastaiTransformsEqualsCustom(self, im_fastai: PILImage, im_pil: Image): | |
| # fastai transforms | |
| crop_rrc = fastai_aug.RandomResizedCrop((460, 460)) | |
| tt_fastai = fastai_aug.ToTensor() | |
| i2f_fastai = fastai_aug.IntToFloatTensor() | |
| flip = fastai_aug.Flip(size=(224, 224)) | |
| brightness = fastai_aug.Brightness() | |
| norm_fastai = fastai_aug.Normalize.from_stats( | |
| *fastai_aug.imagenet_stats, cuda=False | |
| ) | |
| # custom transforms | |
| tt_torch = tvtfms.ToTensor() | |
| norm_torch = tvtfms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) | |
| # apply all fastai augmentations on validation (transformations) | |
| batch_im_fastai = tt_fastai(crop_rrc(im_fastai, split_idx=1)).unsqueeze(0) | |
| result_im_fastai = norm_fastai( | |
| brightness(flip(i2f_fastai(batch_im_fastai), split_idx=1), split_idx=1) | |
| ) | |
| # apply all custom transformations | |
| batch_im_tv = tt_torch(resized_crop_pad(im_pil, (460, 460))).unsqueeze(0) | |
| result_im_tv = norm_torch(gpu_crop(batch_im_tv, size=(224, 224))) | |
| assert torch.allclose(result_im_fastai, result_im_tv) | |
| assert (result_im_fastai == result_im_tv).all() | |