Repository: Project-MONAI/MONAI
File: tests/data/test_persistentdataset.py (Python)

Code before:
        """
        test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]).astype(float), np.eye(4))
        with tempfile.TemporaryDirectory() as tempdir:
            nib.save(test_image, os.path.join(tempdir, "test_image.nii.gz"))
            test_data = [{"image": os.path.join(tempdir, "test_image.nii.gz")}]
            transform = Compose([LoadImaged(keys=["image"])])
            cache_dir = os.path.join(os.path.join(tempdir, "cache"), "data")

            if expected_error:
                with self.assertRaises(ValueError):
                    PersistentDataset(
                        data=test_data,
                        transform=transform,
                        cache_dir=cache_dir,
                        track_meta=track_meta,
                        weights_only=weights_only,
                    )

            else:
                test_dataset = PersistentDataset(
                    data=test_data,
                    transform=transform,
                    cache_dir=cache_dir,
                    track_meta=track_meta,
                    weights_only=weights_only,
                )

                im = test_dataset[0]["image"]
                self.assertIsInstance(im, expected_type)


if __name__ == "__main__":
    unittest.main()


Review comment (performance):
```suggestion

            cm = self.assertRaises(ValueError) if expected_error else contextlib.nullcontext()
            with cm:
                test_dataset = PersistentDataset(
                    data=test_data,
                    transform=transform,
                    cache_dir=cache_dir,
                    track_meta=track_meta,
                    weights_only=weights_only,
                )
```
I think this works and is equivalent, you'll have to import `contextlib`.

Code after:
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#     http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

import contextlib
import os
import tempfile
import unittest

import nibabel as nib
import numpy as np
import torch
from parameterized import parameterized

from monai.data import MetaTensor, PersistentDataset, json_hashing
from monai.transforms import Compose, Flip, Identity, LoadImaged, SimulateDelayd, Transform

TEST_CASE_1 = [
    Compose(
        [
            LoadImaged(keys=["image", "label", "extra"]),
            SimulateDelayd(keys=["image", "label", "extra"], delay_time=[1e-7, 1e-6, 1e-5]),
        ]
    ),
    (128, 128, 128),
]

TEST_CASE_2 = [
    [
        LoadImaged(keys=["image", "label", "extra"]),
        SimulateDelayd(keys=["image", "label", "extra"], delay_time=[1e-7, 1e-6, 1e-5]),
    ],
    (128, 128, 128),
]

TEST_CASE_3 = [None, (128, 128, 128)]

TEST_CASE_4 = [True, False, False, MetaTensor]

TEST_CASE_5 = [True, True, True, None]

TEST_CASE_6 = [False, False, False, torch.Tensor]

TEST_CASE_7 = [False, True, False, torch.Tensor]


class _InplaceXform(Transform):
    def __call__(self, data):
        if data:
            data[0] = data[0] + np.pi
        else:
            data.append(1)
        return data


class TestDataset(unittest.TestCase):
    def test_cache(self):
        """testing no inplace change to the hashed item"""
        items = [[list(range(i))] for i in range(5)]

        with tempfile.TemporaryDirectory() as tempdir:
            ds = PersistentDataset(
                data=items,
                transform=_InplaceXform(),
                cache_dir=tempdir,
                pickle_module="pickle",
                # TODO: was pickle.HIGHEST_PROTOCOL but this wasn't compatible with torch.load, need to improve compatibility
                pickle_protocol=torch.serialization.DEFAULT_PROTOCOL,
            )
            self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])
            ds1 = PersistentDataset(items, transform=_InplaceXform(), cache_dir=tempdir)
            self.assertEqual(list(ds1), list(ds))
            self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])

            ds = PersistentDataset(items, transform=_InplaceXform(), cache_dir=tempdir, hash_func=json_hashing)
            self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])
            ds1 = PersistentDataset(items, transform=_InplaceXform(), cache_dir=tempdir, hash_func=json_hashing)
            self.assertEqual(list(ds1), list(ds))
            self.assertEqual(items, [[[]], [[0]], [[0, 1]], [[0, 1, 2]], [[0, 1, 2, 3]]])

    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])
    def test_shape(self, transform, expected_shape):
        test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]).astype(float), np.eye(4))
        with tempfile.TemporaryDirectory() as tempdir:
            nib.save(test_image, os.path.join(tempdir, "test_image1.nii.gz"))
            nib.save(test_image, os.path.join(tempdir, "test_label1.nii.gz"))
            nib.save(test_image, os.path.join(tempdir, "test_extra1.nii.gz"))
            nib.save(test_image, os.path.join(tempdir, "test_image2.nii.gz"))
            nib.save(test_image, os.path.join(tempdir, "test_label2.nii.gz"))
            nib.save(test_image, os.path.join(tempdir, "test_extra2.nii.gz"))
            test_data = [
                {
                    "image": os.path.join(tempdir, "test_image1.nii.gz"),
                    "label": os.path.join(tempdir, "test_label1.nii.gz"),
                    "extra": os.path.join(tempdir, "test_extra1.nii.gz"),
                },
                {
                    "image": os.path.join(tempdir, "test_image2.nii.gz"),
                    "label": os.path.join(tempdir, "test_label2.nii.gz"),
                    "extra": os.path.join(tempdir, "test_extra2.nii.gz"),
                },
            ]

            cache_dir = os.path.join(os.path.join(tempdir, "cache"), "data")
            dataset_precached = PersistentDataset(data=test_data, transform=transform, cache_dir=cache_dir)
            data1_precached = dataset_precached[0]
            data2_precached = dataset_precached[1]

            dataset_postcached = PersistentDataset(data=test_data, transform=transform, cache_dir=cache_dir)
            data1_postcached = dataset_postcached[0]
            data2_postcached = dataset_postcached[1]
            data3_postcached = dataset_postcached[0:2]

            if transform is None:
                self.assertEqual(data1_precached["image"], os.path.join(tempdir, "test_image1.nii.gz"))
                self.assertEqual(data2_precached["label"], os.path.join(tempdir, "test_label2.nii.gz"))
                self.assertEqual(data1_postcached["image"], os.path.join(tempdir, "test_image1.nii.gz"))
                self.assertEqual(data2_postcached["extra"], os.path.join(tempdir, "test_extra2.nii.gz"))
            else:
                self.assertTupleEqual(data1_precached["image"].shape, expected_shape)
                self.assertTupleEqual(data1_precached["label"].shape, expected_shape)
                self.assertTupleEqual(data1_precached["extra"].shape, expected_shape)
                self.assertTupleEqual(data2_precached["image"].shape, expected_shape)
                self.assertTupleEqual(data2_precached["label"].shape, expected_shape)
                self.assertTupleEqual(data2_precached["extra"].shape, expected_shape)

                self.assertTupleEqual(data1_postcached["image"].shape, expected_shape)
                self.assertTupleEqual(data1_postcached["label"].shape, expected_shape)
                self.assertTupleEqual(data1_postcached["extra"].shape, expected_shape)
                self.assertTupleEqual(data2_postcached["image"].shape, expected_shape)
                self.assertTupleEqual(data2_postcached["label"].shape, expected_shape)
                self.assertTupleEqual(data2_postcached["extra"].shape, expected_shape)
                for d in data3_postcached:
                    self.assertTupleEqual(d["image"].shape, expected_shape)

            # update the data to cache
            test_data_new = [
                {
                    "image": os.path.join(tempdir, "test_image1_new.nii.gz"),
                    "label": os.path.join(tempdir, "test_label1_new.nii.gz"),
                    "extra": os.path.join(tempdir, "test_extra1_new.nii.gz"),
                },
                {
                    "image": os.path.join(tempdir, "test_image2_new.nii.gz"),
                    "label": os.path.join(tempdir, "test_label2_new.nii.gz"),
                    "extra": os.path.join(tempdir, "test_extra2_new.nii.gz"),
                },
            ]
            dataset_postcached.set_data(data=test_data_new)
            # test new exchanged cache content
            if transform is None:
                self.assertEqual(dataset_postcached[0]["image"], os.path.join(tempdir, "test_image1_new.nii.gz"))
                self.assertEqual(dataset_postcached[0]["label"], os.path.join(tempdir, "test_label1_new.nii.gz"))
                self.assertEqual(dataset_postcached[1]["extra"], os.path.join(tempdir, "test_extra2_new.nii.gz"))

    def test_different_transforms(self):
        """
        Different instances of `PersistentDataset` with the same cache_dir,
        same input data, but different transforms should give different results.
        """
        shape = (1, 10, 9, 8)
        im = np.arange(0, np.prod(shape)).reshape(shape)
        with tempfile.TemporaryDirectory() as path:
            im1 = PersistentDataset([im], Identity(), cache_dir=path, hash_transform=json_hashing)[0]
            im2 = PersistentDataset([im], Flip(1), cache_dir=path, hash_transform=json_hashing)[0]
            l2 = ((im1 - im2) ** 2).sum() ** 0.5
            self.assertGreater(l2, 1)

    @parameterized.expand([TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7])
    def test_track_meta_and_weights_only(self, track_meta, weights_only, expected_error, expected_type):
        """
        Ensure expected behavior for all combinations of `track_meta` and `weights_only`.
        """
        test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]).astype(float), np.eye(4))
        with tempfile.TemporaryDirectory() as tempdir:
            nib.save(test_image, os.path.join(tempdir, "test_image.nii.gz"))
            test_data = [{"image": os.path.join(tempdir, "test_image.nii.gz")}]
            transform = Compose([LoadImaged(keys=["image"])])
            cache_dir = os.path.join(os.path.join(tempdir, "cache"), "data")

            cm = self.assertRaises(ValueError) if expected_error else contextlib.nullcontext()
            with cm:
                test_dataset = PersistentDataset(
                    data=test_data,
                    transform=transform,
                    cache_dir=cache_dir,
                    track_meta=track_meta,
                    weights_only=weights_only,
                )

                im = test_dataset[0]["image"]
                self.assertIsInstance(im, expected_type)


if __name__ == "__main__":
    unittest.main()