8185 - Refactor test

.gitignore

@@ -130,6 +130,7 @@ temp/
 .DS_Store
 
 # temporary testing data MedNIST
+tests/integration/testing_data/MedNIST*
 tests/testing_data/MedNIST*
 tests/testing_data/*Hippocampus*
 tests/testing_data/*.tiff

runtests.sh

@@ -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/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.utils.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.utils.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.utils.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/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.utils.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.utils.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.utils.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.utils.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.utils.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.utils.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/test_autoencoderkl_maisi.py → tests/apps/generation/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.utils.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/generation/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.utils.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/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.utils.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.utils.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

tests/test_generate_distance_map.py → tests/apps/pathology/transforms/post/test_generate_distance_map.py

@@ -18,7 +18,7 @@
 
 from monai.apps.pathology.transforms.post.array import GenerateDistanceMap
 from monai.transforms.intensity.array import GaussianSmooth
-from tests.utils import TEST_NDARRAYS
+from tests.utils.utils import TEST_NDARRAYS
 
 EXCEPTION_TESTS = []
 TESTS = []
@@ -36,7 +36,6 @@
 
 
 class TestGenerateDistanceMap(unittest.TestCase):
-
     @parameterized.expand(EXCEPTION_TESTS)
     def test_value(self, arguments, mask, probmap, exception_type):
         with self.assertRaises(exception_type):

tests/test_generate_distance_mapd.py → tests/apps/pathology/transforms/post/test_generate_distance_mapd.py

@@ -18,7 +18,7 @@
 
 from monai.apps.pathology.transforms.post.dictionary import GenerateDistanceMapd
 from monai.transforms.intensity.array import GaussianSmooth
-from tests.utils import TEST_NDARRAYS
+from tests.utils.utils import TEST_NDARRAYS
 
 EXCEPTION_TESTS = []
 TESTS = []
@@ -55,7 +55,6 @@
 
 
 class TestGenerateDistanceMapd(unittest.TestCase):
-
     @parameterized.expand(EXCEPTION_TESTS)
     def test_value(self, arguments, mask, border_map, exception_type):
         with self.assertRaises(exception_type):

tests/test_generate_instance_border.py → tests/apps/pathology/transforms/post/test_generate_instance_border.py

@@ -17,7 +17,7 @@
 from parameterized import parameterized
 
 from monai.apps.pathology.transforms.post.array import GenerateInstanceBorder
-from tests.utils import TEST_NDARRAYS
+from tests.utils.utils import TEST_NDARRAYS
 
 EXCEPTION_TESTS = []
 TESTS = []
@@ -34,7 +34,6 @@
 
 
 class TestGenerateInstanceBorder(unittest.TestCase):
-
     @parameterized.expand(EXCEPTION_TESTS)
     def test_value(self, arguments, mask, hover_map, exception_type):
         with self.assertRaises(exception_type):

tests/test_generate_instance_borderd.py → tests/apps/pathology/transforms/post/test_generate_instance_borderd.py

@@ -17,7 +17,7 @@
 from parameterized import parameterized
 
 from monai.apps.pathology.transforms.post.dictionary import GenerateInstanceBorderd
-from tests.utils import TEST_NDARRAYS
+from tests.utils.utils import TEST_NDARRAYS
 
 EXCEPTION_TESTS = []
 TESTS = []
@@ -44,7 +44,6 @@
 
 
 class TestGenerateInstanceBorderd(unittest.TestCase):
-
     @parameterized.expand(EXCEPTION_TESTS)
     def test_value(self, arguments, mask, hover_map, exception_type):
         with self.assertRaises(exception_type):

tests/test_generate_instance_centroid.py → tests/apps/pathology/transforms/post/test_generate_instance_centroid.py

@@ -19,7 +19,7 @@
 from monai.apps.pathology.transforms.post.array import GenerateInstanceCentroid
 from monai.transforms import BoundingRect
 from monai.utils import min_version, optional_import
-from tests.utils import TEST_NDARRAYS, assert_allclose
+from tests.utils.utils import TEST_NDARRAYS, assert_allclose
 
 _, has_skimage = optional_import("skimage", "0.19.3", min_version)
 
@@ -41,7 +41,6 @@
 
 @unittest.skipUnless(has_skimage, "Requires scikit-image library.")
 class TestGenerateInstanceCentroid(unittest.TestCase):
-
     @parameterized.expand(TEST_CASE)
     def test_shape(self, in_type, test_data, offset, expected):
         inst_bbox = get_bbox(test_data[None])

tests/test_generate_instance_centroidd.py → tests/apps/pathology/transforms/post/test_generate_instance_centroidd.py

@@ -19,7 +19,7 @@
 from monai.apps.pathology.transforms.post.dictionary import GenerateInstanceCentroidd
 from monai.transforms import BoundingRect
 from monai.utils import min_version, optional_import
-from tests.utils import TEST_NDARRAYS, assert_allclose
+from tests.utils.utils import TEST_NDARRAYS, assert_allclose
 
 _, has_skimage = optional_import("skimage", "0.19.3", min_version)
 
@@ -41,7 +41,6 @@
 
 @unittest.skipUnless(has_skimage, "Requires scikit-image library.")
 class TestGenerateInstanceCentroidd(unittest.TestCase):
-
     @parameterized.expand(TEST_CASE)
     def test_shape(self, in_type, test_data, offset, expected):
         inst_bbox = get_bbox(test_data[None])

tests/test_generate_instance_contour.py → tests/apps/pathology/transforms/post/test_generate_instance_contour.py

@@ -19,7 +19,7 @@
 from monai.apps.pathology.transforms.post.array import GenerateInstanceContour
 from monai.transforms import BoundingRect
 from monai.utils import min_version, optional_import
-from tests.utils import TEST_NDARRAYS, assert_allclose
+from tests.utils.utils import TEST_NDARRAYS, assert_allclose
 
 _, has_skimage = optional_import("skimage", "0.19.3", min_version)
 
@@ -46,7 +46,6 @@
 
 @unittest.skipUnless(has_skimage, "Requires scikit-image library.")
 class TestGenerateInstanceContour(unittest.TestCase):
-
     @parameterized.expand(TEST_CASE)
     def test_shape(self, in_type, test_data, min_num_points, offset, expected):
         inst_bbox = get_bbox(test_data[None])

tests/test_generate_instance_contourd.py → tests/apps/pathology/transforms/post/test_generate_instance_contourd.py

@@ -19,7 +19,7 @@
 from monai.apps.pathology.transforms.post.dictionary import GenerateInstanceContourd
 from monai.transforms import BoundingRect
 from monai.utils import min_version, optional_import
-from tests.utils import TEST_NDARRAYS, assert_allclose
+from tests.utils.utils import TEST_NDARRAYS, assert_allclose
 
 _, has_skimage = optional_import("skimage", "0.19.3", min_version)
 
@@ -46,7 +46,6 @@
 
 @unittest.skipUnless(has_skimage, "Requires scikit-image library.")
 class TestGenerateInstanceContourd(unittest.TestCase):
-
     @parameterized.expand(TEST_CASE)
     def test_shape(self, in_type, test_data, min_num_points, offset, expected):
         inst_bbox = get_bbox(test_data[None])