tests for transformation. centercrop transform failing.

.vscode/settings.json

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+{
+    "python.testing.pytestArgs": [
+        "tests"
+    ],
+    "python.testing.unittestEnabled": false,
+    "python.testing.pytestEnabled": true,
+    "python.analysis.inlayHints.pytestParameters": true
+}

deployment/transforms.py

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+from typing import Literal
+import torchvision.transforms as tvtfms
+
+
+def CenterCropPad(size: tuple[Literal[460], Literal[460]]):
+    return tvtfms.CenterCrop(size)

tests/test_transforms.py

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+import pytest
+
+from pathlib import Path
+from typing import List
+import numpy as np
+from PIL import Image
+from fastai.vision.data import PILImage
+import fastai.vision.augment as fastai_aug
+
+from deployment.transforms import CenterCropPad
+
+DATA_PATH = "data/kaggle/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 = 0
+
+    @pytest.fixture
+    def img_paths(self) -> List[str]:
+        path = Path(DATA_PATH) / "CUB_200_2011"
+        return get_birds_images(path.resolve())
+
+    @pytest.fixture
+    def im_fastai(self, img_paths) -> PILImage:
+        fname = img_paths[self.im_idx]
+        return PILImage.create(fname)
+
+    @pytest.fixture
+    def im_pil(self, img_paths) -> Image:
+        fname = img_paths[self.im_idx]
+        return Image.open(fname)
+
+    def testImageFastaiEqualsPillow(self, im_fastai, im_pil):
+        assert (np.array(im_fastai) == np.array(im_pil)).all()
+
+    def testCropPadFastaiEqualsTorch(self, im_fastai, im_pil):
+        crop_fastai = fastai_aug.CropPad((460, 460))
+        crop_torch = CenterCropPad((460, 460))
+
+        assert (np.array(crop_fastai(im_fastai)) == np.array(crop_torch(im_pil))).all()