tests(transforms): cover randomize_per_key across intensity and spatial dict transforms

Signed-off-by: Soumya Snigdha Kundu <soumya_snigdha.kundu@kcl.ac.uk>

Author: aymuos15

tests/transforms/test_rand_per_key.py

@@ -0,0 +1,82 @@
+# 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 unittest
+
+import torch
+from parameterized import parameterized
+
+from monai.transforms.intensity.dictionary import (
+    RandAdjustContrastd,
+    RandBiasFieldd,
+    RandGaussianNoised,
+    RandGaussianSharpend,
+    RandGaussianSmoothd,
+    RandGibbsNoised,
+    RandHistogramShiftd,
+    RandScaleIntensityd,
+    RandScaleIntensityFixedMeand,
+    RandShiftIntensityd,
+    RandStdShiftIntensityd,
+)
+from monai.transforms.spatial.dictionary import RandAxisFlipd, RandGridDistortiond, RandRotated, RandZoomd
+from tests.test_utils import assert_allclose
+
+KEYS = ["img1", "img2"]
+
+TESTS = [
+    (RandGaussianNoised, {}),
+    (RandShiftIntensityd, {"offsets": 0.5}),
+    (RandStdShiftIntensityd, {"factors": 0.5}),
+    (RandScaleIntensityd, {"factors": 0.5}),
+    (RandScaleIntensityFixedMeand, {"factors": 0.5}),
+    (RandBiasFieldd, {}),
+    (RandAdjustContrastd, {}),
+    (RandGaussianSmoothd, {}),
+    (RandGaussianSharpend, {}),
+    (RandHistogramShiftd, {"num_control_points": (5, 20)}),
+    (RandGibbsNoised, {}),
+    (RandAxisFlipd, {}),
+    (RandRotated, {"range_x": 1.0, "range_y": 1.0, "range_z": 1.0}),
+    (RandZoomd, {"min_zoom": 0.7, "max_zoom": 1.3}),
+    (RandGridDistortiond, {"num_cells": 3, "distort_limit": 0.2}),
+]
+
+
+class TestRandPerKey(unittest.TestCase):
+    @parameterized.expand([(cls.__name__, cls, kwargs) for cls, kwargs in TESTS])
+    def test_shared_default(self, _, cls, kwargs):
+        t = cls(keys=KEYS, prob=1.0, **kwargs)
+        t.set_random_state(0)
+        img = torch.rand(1, 8, 8, 8) + 1.0
+        out = t({k: img.clone() for k in KEYS})
+        assert_allclose(out["img1"], out["img2"], type_test=False)
+
+    @parameterized.expand([(cls.__name__, cls, kwargs) for cls, kwargs in TESTS])
+    def test_independent_per_key(self, _, cls, kwargs):
+        # independent draws may coincide for a single seed (e.g. RandAxisFlipd's discrete axis),
+        # so only require that some deterministic seed yields divergent per-key outputs
+        img = torch.rand(1, 8, 8, 8) + 1.0
+        differs = False
+        for seed in range(10):
+            t = cls(keys=KEYS, prob=1.0, randomize_per_key=True, **kwargs)
+            t.set_random_state(seed)
+            out = t({k: img.clone() for k in KEYS})
+            if not torch.allclose(out["img1"], out["img2"]):
+                differs = True
+                break
+        self.assertTrue(differs)
+
+
+if __name__ == "__main__":
+    unittest.main()