Merge branch 'dev' into numpy2_reintro

monai/transforms/io/array.py

@@ -11,7 +11,6 @@
 """
 A collection of "vanilla" transforms for IO functions.
 """
-
 from __future__ import annotations
 
 import inspect

runtests.sh

@@ -728,7 +728,7 @@ if [ $doDistTests = true ]
 then
     echo "${separator}${blue}run distributed unit test cases${noColor}"
     torch_validate
-    for i in tests/test_*_dist.py
+    for i in  $(find ./tests/ -name "*_dist.py")
     do
         echo "$i"
         ${cmdPrefix}${cmd} "$i"
@@ -740,7 +740,7 @@ if [ $doNetTests = true ]
 then
     set +e  # disable exit on failure so that diagnostics can be given on failure
     echo "${separator}${blue}integration${noColor}"
-    for i in tests/*integration_*.py
+    for i in tests/integration/*.py
     do
         echo "$i"
         ${cmdPrefix}${cmd} "$i"

tests/apps/__init__.py

@@ -0,0 +1,10 @@
+# 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.

tests/apps/deepedit/__init__.py

@@ -0,0 +1,10 @@
+# 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.

tests/apps/deepgrow/__init__.py

@@ -0,0 +1,10 @@
+# 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.

tests/apps/deepgrow/transforms/__init__.py

@@ -0,0 +1,10 @@
+# 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.

tests/apps/detection/__init__.py

@@ -0,0 +1,10 @@
+# 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.

tests/apps/detection/metrics/__init__.py

@@ -0,0 +1,10 @@
+# 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.

tests/apps/detection/networks/__init__.py

@@ -0,0 +1,10 @@
+# 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.

tests/test_retinanet.py → tests/apps/detection/networks/test_retinanet.py

@@ -20,7 +20,7 @@
 from monai.networks import eval_mode
 from monai.networks.nets import resnet10, resnet18, resnet34, resnet50, resnet101, resnet152, resnet200
 from monai.utils import ensure_tuple, optional_import
-from tests.utils import SkipIfBeforePyTorchVersion, skip_if_quick, test_onnx_save, test_script_save
+from tests.test_utils import SkipIfBeforePyTorchVersion, skip_if_quick, test_onnx_save, test_script_save
 
 _, has_torchvision = optional_import("torchvision")
 
@@ -101,7 +101,6 @@
 @unittest.skipUnless(has_torchvision, "Requires torchvision")
 @skip_if_quick
 class TestRetinaNet(unittest.TestCase):
-
     @parameterized.expand(TEST_CASES)
     def test_retina_shape(self, model, input_param, input_shape):
         backbone = model(**input_param)

tests/test_retinanet_detector.py → tests/apps/detection/networks/test_retinanet_detector.py

@@ -21,7 +21,7 @@
 from monai.apps.detection.utils.anchor_utils import AnchorGeneratorWithAnchorShape
 from monai.networks import eval_mode, train_mode
 from monai.utils import optional_import
-from tests.utils import SkipIfBeforePyTorchVersion, skip_if_quick, test_script_save
+from tests.test_utils import SkipIfBeforePyTorchVersion, skip_if_quick, test_script_save
 
 _, has_torchvision = optional_import("torchvision")
 
@@ -93,7 +93,6 @@
 
 
 class NaiveNetwork(torch.nn.Module):
-
     def __init__(self, spatial_dims, num_classes, **kwargs):
         super().__init__()
         self.spatial_dims = spatial_dims
@@ -115,7 +114,6 @@ def forward(self, images):
 @unittest.skipUnless(has_torchvision, "Requires torchvision")
 @skip_if_quick
 class TestRetinaNetDetector(unittest.TestCase):
-
     @parameterized.expand(TEST_CASES)
     def test_retina_detector_resnet_backbone_shape(self, input_param, input_shape):
         returned_layers = [1]

tests/test_box_transform.py → tests/apps/detection/test_box_transform.py

@@ -36,7 +36,7 @@
 )
 from monai.data.meta_tensor import MetaTensor
 from monai.transforms import CastToTyped, Invertd
-from tests.utils import TEST_NDARRAYS, assert_allclose
+from tests.test_utils import TEST_NDARRAYS, assert_allclose
 
 TESTS_3D = []
 boxes = [[0, 0, 0, 0, 0, 0], [0, 1, 0, 2, 3, 3], [0, 1, 1, 2, 3, 4]]
@@ -79,7 +79,6 @@
 
 
 class TestBoxTransform(unittest.TestCase):
-
     @parameterized.expand(TESTS_2D_mask)
     def test_value_2d_mask(self, mask, expected_box_label):
         box_label = convert_mask_to_box(mask)

tests/apps/detection/utils/__init__.py

@@ -0,0 +1,10 @@
+# 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.

tests/test_anchor_box.py → tests/apps/detection/utils/test_anchor_box.py

@@ -18,7 +18,7 @@
 
 from monai.apps.detection.utils.anchor_utils import AnchorGenerator, AnchorGeneratorWithAnchorShape
 from monai.utils import optional_import
-from tests.utils import SkipIfBeforePyTorchVersion, assert_allclose, test_script_save
+from tests.test_utils import SkipIfBeforePyTorchVersion, assert_allclose, test_script_save
 
 _, has_torchvision = optional_import("torchvision")
 
@@ -42,7 +42,6 @@
 @SkipIfBeforePyTorchVersion((1, 11))
 @unittest.skipUnless(has_torchvision, "Requires torchvision")
 class TestAnchorGenerator(unittest.TestCase):
-
     @parameterized.expand(TEST_CASES_2D)
     def test_anchor_2d(self, input_param, image_shape, feature_maps_shapes):
         torch_anchor_utils, _ = optional_import("torchvision.models.detection.anchor_utils")

tests/test_atss_box_matcher.py → tests/apps/detection/utils/test_atss_box_matcher.py

@@ -18,7 +18,7 @@
 
 from monai.apps.detection.utils.ATSS_matcher import ATSSMatcher
 from monai.data.box_utils import box_iou
-from tests.utils import assert_allclose
+from tests.test_utils import assert_allclose
 
 TEST_CASES = [
     [
@@ -33,7 +33,6 @@
 
 
 class TestATSS(unittest.TestCase):
-
     @parameterized.expand(TEST_CASES)
     def test_atss(self, input_param, boxes, anchors, num_anchors_per_level, num_anchors_per_loc, expected_matches):
         matcher = ATSSMatcher(**input_param, debug=True)

tests/test_box_coder.py → tests/apps/detection/utils/test_box_coder.py

@@ -17,11 +17,10 @@
 
 from monai.apps.detection.utils.box_coder import BoxCoder
 from monai.transforms import CastToType
-from tests.utils import assert_allclose
+from tests.test_utils import assert_allclose
 
 
 class TestBoxTransform(unittest.TestCase):
-
     def test_value(self):
         box_coder = BoxCoder(weights=[1, 1, 1, 1, 1, 1])
         test_dtype = [torch.float32, torch.float16]

tests/test_detector_boxselector.py → tests/apps/detection/utils/test_detector_boxselector.py

@@ -17,7 +17,7 @@
 from parameterized import parameterized
 
 from monai.apps.detection.utils.box_selector import BoxSelector
-from tests.utils import assert_allclose
+from tests.test_utils import assert_allclose
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 num_anchors = 7
@@ -56,7 +56,6 @@
 
 
 class TestBoxSelector(unittest.TestCase):
-
     @parameterized.expand(TEST_CASE)
     def test_box_selector(self, input_param, boxes, logits, image_shape, expected_results):
         box_selector = BoxSelector(**input_param)

tests/test_detector_utils.py → tests/apps/detection/utils/test_detector_utils.py

@@ -19,7 +19,7 @@
 
 from monai.apps.detection.utils.detector_utils import preprocess_images
 from monai.utils import ensure_tuple
-from tests.utils import assert_allclose
+from tests.test_utils import assert_allclose
 
 TEST_CASE_1 = [  # 3D, batch 3, 2 input channel
     {
@@ -79,7 +79,6 @@
 
 
 class TestDetectorUtils(unittest.TestCase):
-
     @parameterized.expand(TEST_CASES)
     def test_detector_utils(self, input_param, input_shape, expected_shape):
         size_divisible = 32 * ensure_tuple(input_param["conv1_t_stride"])[0]

tests/test_hardnegsampler.py → tests/apps/detection/utils/test_hardnegsampler.py

@@ -17,7 +17,7 @@
 from parameterized import parameterized
 
 from monai.apps.detection.utils.hard_negative_sampler import HardNegativeSampler
-from tests.utils import assert_allclose
+from tests.test_utils import assert_allclose
 
 TEST_CASE = [
     [[], [], [], [torch.tensor([]), torch.tensor([])], [torch.tensor([]), torch.tensor([])]],
@@ -37,7 +37,6 @@
 
 
 class TestSampleSlices(unittest.TestCase):
-
     @parameterized.expand(TEST_CASE)
     def test_shape(self, target_label0, target_label1, concat_fg_probs, expected_result_pos, expected_result_neg):
         compute_dtypes = [torch.float16, torch.float32]

tests/apps/maisi/networks/__init__.py

@@ -0,0 +1,10 @@
+# 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.

tests/test_autoencoderkl_maisi.py → tests/apps/maisi/networks/test_autoencoderkl_maisi.py

@@ -19,7 +19,7 @@
 from monai.apps.generation.maisi.networks.autoencoderkl_maisi import AutoencoderKlMaisi
 from monai.networks import eval_mode
 from monai.utils import optional_import
-from tests.utils import SkipIfBeforePyTorchVersion
+from tests.test_utils import SkipIfBeforePyTorchVersion
 
 tqdm, has_tqdm = optional_import("tqdm", name="tqdm")
 _, has_einops = optional_import("einops")
@@ -77,7 +77,6 @@
 
 
 class TestAutoencoderKlMaisi(unittest.TestCase):
-
     @parameterized.expand(CASES)
     def test_shape(self, input_param, input_shape, expected_shape, expected_latent_shape):
         net = AutoencoderKlMaisi(**input_param).to(device)

tests/test_controlnet_maisi.py → tests/apps/maisi/networks/test_controlnet_maisi.py

@@ -20,7 +20,7 @@
 from monai.apps.generation.maisi.networks.controlnet_maisi import ControlNetMaisi
 from monai.networks import eval_mode
 from monai.utils import optional_import
-from tests.utils import SkipIfBeforePyTorchVersion
+from tests.test_utils import SkipIfBeforePyTorchVersion
 
 _, has_einops = optional_import("einops")
 
@@ -129,7 +129,6 @@
 
 @SkipIfBeforePyTorchVersion((2, 0))
 class TestControlNet(unittest.TestCase):
-
     @parameterized.expand(TEST_CASES)
     @skipUnless(has_einops, "Requires einops")
     def test_shape_unconditioned_models(self, input_param, expected_num_down_blocks_residuals, expected_shape):

tests/apps/nuclick/__init__.py

@@ -0,0 +1,10 @@
+# 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.

tests/apps/pathology/__init__.py

@@ -0,0 +1,10 @@
+# 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.

tests/apps/pathology/handlers/__init__.py

@@ -0,0 +1,10 @@
+# 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.

tests/test_from_engine_hovernet.py → tests/apps/pathology/handlers/test_from_engine_hovernet.py

@@ -16,7 +16,7 @@
 from parameterized import parameterized
 
 from monai.apps.pathology.handlers.utils import from_engine_hovernet
-from tests.utils import assert_allclose
+from tests.test_utils import assert_allclose
 
 TEST_CASE_0 = [
     [{"A": {"C": 1, "D": 2}, "B": {"C": 2, "D": 2}}, {"A": {"C": 3, "D": 2}, "B": {"C": 4, "D": 2}}],
@@ -28,7 +28,6 @@
 
 
 class TestFromEngineHovernet(unittest.TestCase):
-
     @parameterized.expand(CASES)
     def test_results(self, input, expected):
         output = from_engine_hovernet(keys=["A", "B"], nested_key="C")(input)

tests/test_prepare_batch_hovernet.py → tests/apps/pathology/test_prepare_batch_hovernet.py

@@ -19,7 +19,7 @@
 from monai.apps.pathology.engines import PrepareBatchHoVerNet
 from monai.engines import SupervisedEvaluator
 from monai.utils.enums import HoVerNetBranch
-from tests.utils import assert_allclose
+from tests.test_utils import assert_allclose
 
 TEST_CASE_0 = [
     {"extra_keys": ["extra_label1", "extra_label2"]},
@@ -35,7 +35,6 @@ def forward(self, x: torch.Tensor):
 
 
 class TestPrepareBatchHoVerNet(unittest.TestCase):
-
     @parameterized.expand([TEST_CASE_0])
     def test_content(self, input_args, expected_value):
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

tests/test_sliding_window_hovernet_inference.py → tests/apps/pathology/test_sliding_window_hovernet_inference.py

@@ -21,7 +21,7 @@
 from monai.data import MetaTensor
 from monai.inferers import sliding_window_inference
 from monai.utils import optional_import
-from tests.test_sliding_window_inference import TEST_CASES
+from tests.inferers.test_sliding_window_inference import TEST_CASES
 
 _, has_tqdm = optional_import("tqdm")
 
@@ -36,7 +36,6 @@
 
 
 class TestSlidingWindowHoVerNetInference(unittest.TestCase):
-
     @parameterized.expand(TEST_CASES_PADDING)
     def test_sliding_window_with_padding(
         self, key, image_shape, roi_shape, sw_batch_size, overlap, mode, device, extra_input_padding