ArrayDataset only applies same transform if transforms match #2111
Description
Describe the bug
Noticed when looking into #2052.
ArrayDataset gives matching results if img_transform and label_transform match each other. However results diverge when they don't match. At which point, I don't fully understand the utility of the ArrayDataset over using dictionary transforms. Perhaps I've misunderstood something.
To Reproduce
The following code is fine if t1 matches t2, but fails if they are different.
works:
t1 = RandSpatialCropSamples(roi_size=(10, 10, 10), num_samples=num_samples, random_size=False)
t2 = RandSpatialCropSamples(roi_size=(10, 10, 10), num_samples=num_samples, random_size=False)doesn't work
t1 = Compose([RandSpatialCropSamples(roi_size=(10, 10, 10), num_samples=num_samples, random_size=False)])
t2 = RandSpatialCropSamples(roi_size=(10, 10, 10), num_samples=num_samples, random_size=False)also doesn't work
t1 = Compose([Lambda(lambda x: x), RandSpatialCropSamples(roi_size=(10, 10, 10), num_samples=num_samples, random_size=False)])
t2 = RandSpatialCropSamples(roi_size=(10, 10, 10), num_samples=num_samples, random_size=False)import sys
import unittest
import numpy as np
from monai.data import ArrayDataset, DataLoader
from monai.transforms import Compose, RandSpatialCropSamples
class TestArrayDatasetTransforms(unittest.TestCase):
def test_same_transforms(self):
im = np.arange(0, 100 ** 3).reshape(1, 100, 100, 100)
a1 = [np.copy(im) for _ in range(20)]
a2 = [np.copy(im) for _ in range(20)]
num_samples = 10
t1 = Compose([RandSpatialCropSamples(roi_size=(10, 10, 10), num_samples=num_samples, random_size=False)])
t2 = RandSpatialCropSamples(roi_size=(10, 10, 10), num_samples=num_samples, random_size=False)
dataset = ArrayDataset(a1, t1, a2, t2)
self.assertEqual(len(dataset), len(a1))
dataset.set_random_state(1234)
n_workers = 0 if sys.platform != "linux" else 2
batch_size = 2
loader = DataLoader(dataset, batch_size=batch_size, num_workers=n_workers)
batch_data = next(iter(loader))
self.assertEqual(len(batch_data), num_samples * 2)
self.assertEqual(batch_data[0].shape[0], batch_size)
for i in range(num_samples):
out1, out2 = batch_data[i], batch_data[i + num_samples]
np.testing.assert_array_equal(out1, out2)
if __name__ == "__main__":
unittest.main()