Fix path issues

Author: garciadias

tests/apps/pathology/test_sliding_window_hovernet_inference.py

@@ -121,19 +121,21 @@ def compute(self, data):
             sigma_scale=1.0,
         )
 
-        expected = np.array([
+        expected = np.array(
             [
                 [
-                    [3.0000, 3.0000, 3.0000, 3.0000, 3.0000, 3.0000, 3.0000],
-                    [3.0000, 3.0000, 3.0000, 3.0000, 3.0000, 3.0000, 3.0000],
-                    [3.3333, 3.3333, 3.3333, 3.3333, 3.3333, 3.3333, 3.3333],
-                    [3.6667, 3.6667, 3.6667, 3.6667, 3.6667, 3.6667, 3.6667],
-                    [4.3333, 4.3333, 4.3333, 4.3333, 4.3333, 4.3333, 4.3333],
-                    [4.5000, 4.5000, 4.5000, 4.5000, 4.5000, 4.5000, 4.5000],
-                    [5.0000, 5.0000, 5.0000, 5.0000, 5.0000, 5.0000, 5.0000],
+                    [
+                        [3.0000, 3.0000, 3.0000, 3.0000, 3.0000, 3.0000, 3.0000],
+                        [3.0000, 3.0000, 3.0000, 3.0000, 3.0000, 3.0000, 3.0000],
+                        [3.3333, 3.3333, 3.3333, 3.3333, 3.3333, 3.3333, 3.3333],
+                        [3.6667, 3.6667, 3.6667, 3.6667, 3.6667, 3.6667, 3.6667],
+                        [4.3333, 4.3333, 4.3333, 4.3333, 4.3333, 4.3333, 4.3333],
+                        [4.5000, 4.5000, 4.5000, 4.5000, 4.5000, 4.5000, 4.5000],
+                        [5.0000, 5.0000, 5.0000, 5.0000, 5.0000, 5.0000, 5.0000],
+                    ]
                 ]
             ]
-        ])
+        )
         np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)
         result = sliding_window_inference(
             inputs,
@@ -147,29 +149,31 @@ def compute(self, data):
             sigma_scale=1.0,
             progress=has_tqdm,
         )
-        expected = np.array([
+        expected = np.array(
             [
                 [
-                    [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0],
-                    [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0],
-                    [3.3271625, 3.3271623, 3.3271623, 3.3271623, 3.3271623, 3.3271623, 3.3271625],
-                    [3.6728377, 3.6728377, 3.6728377, 3.6728377, 3.6728377, 3.6728377, 3.6728377],
-                    [4.3271623, 4.3271623, 4.3271627, 4.3271627, 4.3271627, 4.3271623, 4.3271623],
-                    [4.513757, 4.513757, 4.513757, 4.513757, 4.513757, 4.513757, 4.513757],
-                    [4.9999995, 5.0, 5.0, 5.0, 5.0, 5.0, 4.9999995],
+                    [
+                        [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0],
+                        [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0],
+                        [3.3271625, 3.3271623, 3.3271623, 3.3271623, 3.3271623, 3.3271623, 3.3271625],
+                        [3.6728377, 3.6728377, 3.6728377, 3.6728377, 3.6728377, 3.6728377, 3.6728377],
+                        [4.3271623, 4.3271623, 4.3271627, 4.3271627, 4.3271627, 4.3271623, 4.3271623],
+                        [4.513757, 4.513757, 4.513757, 4.513757, 4.513757, 4.513757, 4.513757],
+                        [4.9999995, 5.0, 5.0, 5.0, 5.0, 5.0, 4.9999995],
+                    ]
                 ]
             ]
-        ])
+        )
         np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)
 
         result = SlidingWindowHoVerNetInferer(roi_shape, sw_batch_size, overlap=0.5, mode="gaussian", sigma_scale=1.0)(
             inputs, _Pred().compute
         )
         np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)
 
-        result = SlidingWindowHoVerNetInferer(roi_shape, sw_batch_size, overlap=0.5, mode="gaussian", sigma_scale=[1.0, 1.0])(
-            inputs, _Pred().compute
-        )
+        result = SlidingWindowHoVerNetInferer(
+            roi_shape, sw_batch_size, overlap=0.5, mode="gaussian", sigma_scale=[1.0, 1.0]
+        )(inputs, _Pred().compute)
         np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)
 
         result = SlidingWindowHoVerNetInferer(
@@ -200,7 +204,9 @@ def compute(data):
         expected = np.ones((1, 1, 3, 3)) * -6.0
         np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)
 
-        result = SlidingWindowHoVerNetInferer(roi_shape, sw_batch_size, overlap=0.5, mode="constant", cval=-1)(inputs, compute)
+        result = SlidingWindowHoVerNetInferer(roi_shape, sw_batch_size, overlap=0.5, mode="constant", cval=-1)(
+            inputs, compute
+        )
         np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)
 
     def test_args_kwargs(self):
@@ -238,9 +244,9 @@ def compute(data, test1, test2):
         expected = np.ones((1, 1, 3, 3)) + 2.0
         np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)
 
-        result = SlidingWindowHoVerNetInferer(roi_shape, sw_batch_size, overlap=0.5, mode="constant", cval=-1, progress=has_tqdm)(
-            inputs, compute, t1, test2=t2
-        )
+        result = SlidingWindowHoVerNetInferer(
+            roi_shape, sw_batch_size, overlap=0.5, mode="constant", cval=-1, progress=has_tqdm
+        )(inputs, compute, t1, test2=t2)
         np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)
 
     @parameterized.expand(TEST_CASES_MULTIOUTPUT)
@@ -280,9 +286,9 @@ def compute_dict(data):
         for rr, _ in zip(result_dict, expected_dict):
             np.testing.assert_allclose(result_dict[rr].cpu().numpy(), expected_dict[rr], rtol=1e-4)
 
-        result = SlidingWindowHoVerNetInferer(roi_shape, sw_batch_size, overlap=0.5, mode="constant", cval=-1, progress=has_tqdm)(
-            inputs, compute
-        )
+        result = SlidingWindowHoVerNetInferer(
+            roi_shape, sw_batch_size, overlap=0.5, mode="constant", cval=-1, progress=has_tqdm
+        )(inputs, compute)
         for rr, ee in zip(result, expected):
             np.testing.assert_allclose(rr.cpu().numpy(), ee, rtol=1e-4)
 

tests/bundle/test_bundle_ckpt_export.py

@@ -75,7 +75,9 @@ def test_export(self, key_in_ckpt, use_trace):
         command_line_tests(full_cmd)
         self.assertTrue(os.path.exists(self.ts_file))
 
-        _, metadata, extra_files = load_net_with_metadata(self.ts_file, more_extra_files=["inference.json", "def_args.json"])
+        _, metadata, extra_files = load_net_with_metadata(
+            self.ts_file, more_extra_files=["inference.json", "def_args.json"]
+        )
         self.assertIn("schema", metadata)
         self.assertIn("meta_file", json.loads(extra_files["def_args.json"]))
         self.assertIn("network_def", json.loads(extra_files["inference.json"]))

tests/bundle/test_bundle_workflow.py

@@ -11,13 +11,13 @@
 
 from __future__ import annotations
 
-from pathlib import Path
 import os
 import shutil
 import sys
 import tempfile
 import unittest
 from copy import deepcopy
+from pathlib import Path
 
 import nibabel as nib
 import numpy as np

tests/bundle/test_config_parser.py

@@ -373,7 +373,9 @@ def test_parse_json_warn(self, config_string, extension, expected_unique_val, ex
 
     @parameterized.expand([TEST_CASE_MERGE_JSON, TEST_CASE_MERGE_YAML])
     @skipUnless(has_yaml, "Requires pyyaml")
-    def test_load_configs(self, config_string, config_string2, extension, expected_overridden_val, expected_merged_vals):
+    def test_load_configs(
+        self, config_string, config_string2, extension, expected_overridden_val, expected_merged_vals
+    ):
         with tempfile.TemporaryDirectory() as tempdir:
             config_path1 = Path(tempdir) / f"config1.{extension}"
             config_path2 = Path(tempdir) / f"config2.{extension}"

tests/data/test_dataset.py

@@ -52,10 +52,12 @@ def test_shape(self, expected_shape):
                     "extra": os.path.join(tempdir, "test_extra2.nii.gz"),
                 },
             ]
-            test_transform = Compose([
-                LoadImaged(keys=["image", "label", "extra"]),
-                SimulateDelayd(keys=["image", "label", "extra"], delay_time=[1e-7, 1e-6, 1e-5]),
-            ])
+            test_transform = Compose(
+                [
+                    LoadImaged(keys=["image", "label", "extra"]),
+                    SimulateDelayd(keys=["image", "label", "extra"], delay_time=[1e-7, 1e-6, 1e-5]),
+                ]
+            )
             dataset = Dataset(data=test_data, transform=test_transform)
             data1 = dataset[0]
             data2 = dataset[1]

tests/data/test_threadcontainer.py

@@ -12,10 +12,10 @@
 from __future__ import annotations
 
 import os
-from pathlib import Path
 import tempfile
 import time
 import unittest
+from pathlib import Path
 
 import torch
 

tests/handlers/test_handler_prob_map_producer.py

@@ -31,9 +31,7 @@
 
 
 class TestDataset(Dataset):
-    __test__ = (
-        False  # indicate to pytest that this class is not intended for collection
-    )
+    __test__ = False  # indicate to pytest that this class is not intended for collection
 
     def __init__(self, name, size):
         super().__init__(
@@ -68,9 +66,7 @@ def __getitem__(self, index):
 
 
 class TestEvaluator(Evaluator):
-    __test__ = (
-        False  # indicate to pytest that this class is not intended for collection
-    )
+    __test__ = False  # indicate to pytest that this class is not intended for collection
 
     def _iteration(self, engine, batchdata):
         return batchdata
@@ -96,10 +92,7 @@ def inference(engine, batch):
         prob_map_gen = ProbMapProducer(output_dir=output_dir)
 
         evaluator = TestEvaluator(
-            torch.device("cpu:0"),
-            data_loader,
-            np.ceil(size / batch_size),
-            val_handlers=[prob_map_gen],
+            torch.device("cpu:0"), data_loader, np.ceil(size / batch_size), val_handlers=[prob_map_gen]
         )
 
         # set up validation handler
@@ -110,12 +103,8 @@ def inference(engine, batch):
         engine.run(data_loader)
 
         prob_map = np.load(os.path.join(output_dir, name + ".npy"))
-        self.assertListEqual(
-            np.vstack(prob_map.nonzero()).T.tolist(), [[i, i + 1] for i in range(size)]
-        )
-        self.assertListEqual(
-            prob_map[prob_map.nonzero()].tolist(), [i + 1 for i in range(size)]
-        )
+        self.assertListEqual(np.vstack(prob_map.nonzero()).T.tolist(), [[i, i + 1] for i in range(size)])
+        self.assertListEqual(prob_map[prob_map.nonzero()].tolist(), [i + 1 for i in range(size)])
 
 
 if __name__ == "__main__":

tests/transforms/test_resize_with_pad_or_cropd.py

@@ -49,7 +49,9 @@ class TestResizeWithPadOrCropd(unittest.TestCase):
     @parameterized.expand(TEST_CASES)
     def test_pad_shape(self, input_param, input_data, expected_val):
         for p in TEST_NDARRAYS_ALL:
-            if isinstance(p(0), torch.Tensor) and ("constant_values" in input_param or input_param["mode"] == "reflect"):
+            if isinstance(p(0), torch.Tensor) and (
+                "constant_values" in input_param or input_param["mode"] == "reflect"
+            ):
                 continue
             padcropper = ResizeWithPadOrCropd(**input_param)
             input_data_ = deepcopy(input_data)

tests/visualize/utils/test_matshow3d.py

@@ -12,9 +12,9 @@
 from __future__ import annotations
 
 import os
-from pathlib import Path
 import tempfile
 import unittest
+from pathlib import Path
 
 import numpy as np
 
@@ -41,95 +41,71 @@ def test_3d(self):
         testing_dir = os.path.join(test_root, "testing_data")
         print("test_root: ", testing_dir)
         keys = "image"
-        xforms = Compose([
-            LoadImaged(keys=keys),
-            EnsureChannelFirstd(keys=keys, channel_dim="no_channel"),
-            ScaleIntensityd(keys=keys),
-        ])
+        xforms = Compose(
+            [
+                LoadImaged(keys=keys),
+                EnsureChannelFirstd(keys=keys, channel_dim="no_channel"),
+                ScaleIntensityd(keys=keys),
+            ]
+        )
         image_path = os.path.join(testing_dir, "anatomical.nii")
         ims = xforms({keys: image_path})
 
         fig = pyplot.figure()  # external figure
-        fig, _ = matshow3d(
-            ims[keys],
-            fig=fig,
-            figsize=(2, 2),
-            frames_per_row=5,
-            every_n=2,
-            frame_dim=-1,
-            show=False,
-        )
+        fig, _ = matshow3d(ims[keys], fig=fig, figsize=(2, 2), frames_per_row=5, every_n=2, frame_dim=-1, show=False)
 
         with tempfile.TemporaryDirectory() as tempdir:
             tempimg = f"{tempdir}/matshow3d_test.png"
             fig.savefig(tempimg)
             comp = compare_images(f"{testing_dir}/matshow3d_test.png", tempimg, 5e-2)
-            self.assertIsNone(
-                comp, f"value of comp={comp}"
-            )  # None indicates test passed
+            self.assertIsNone(comp, f"value of comp={comp}")  # None indicates test passed
 
         _, axes = pyplot.subplots()
-        matshow3d(
-            ims[keys],
-            fig=axes,
-            figsize=(2, 2),
-            frames_per_row=5,
-            every_n=2,
-            frame_dim=-1,
-            show=False,
-        )
+        matshow3d(ims[keys], fig=axes, figsize=(2, 2), frames_per_row=5, every_n=2, frame_dim=-1, show=False)
 
     def test_samples(self):
         test_root = Path(__file__).parents[2]
         testing_dir = os.path.join(test_root, "testing_data")
         keys = "image"
-        xforms = Compose([
-            LoadImaged(keys=keys),
-            EnsureChannelFirstd(keys=keys),
-            ScaleIntensityd(keys=keys),
-            RandSpatialCropSamplesd(keys=keys, roi_size=(8, 8, 5), random_size=True, num_samples=10),
-        ])
+        xforms = Compose(
+            [
+                LoadImaged(keys=keys),
+                EnsureChannelFirstd(keys=keys),
+                ScaleIntensityd(keys=keys),
+                RandSpatialCropSamplesd(keys=keys, roi_size=(8, 8, 5), random_size=True, num_samples=10),
+            ]
+        )
         image_path = os.path.join(testing_dir, "anatomical.nii")
         xforms.set_random_state(0)
         ims = xforms({keys: image_path})
         fig, mat = matshow3d(
-            [im[keys] for im in ims],
-            title=f"testing {keys}",
-            figsize=(2, 2),
-            frames_per_row=5,
-            every_n=2,
-            show=False,
+            [im[keys] for im in ims], title=f"testing {keys}", figsize=(2, 2), frames_per_row=5, every_n=2, show=False
         )
         self.assertEqual(mat.dtype, np.float32)
 
         with tempfile.TemporaryDirectory() as tempdir:
             tempimg = f"{tempdir}/matshow3d_patch_test.png"
             fig.savefig(tempimg)
-            comp = compare_images(
-                f"{testing_dir}/matshow3d_patch_test.png",
-                tempimg,
-                5e-2,
-                in_decorator=True,
-            )
+            comp = compare_images(f"{testing_dir}/matshow3d_patch_test.png", tempimg, 5e-2, in_decorator=True)
             if comp:
                 print("not none comp: ", comp)  # matplotlib 3.2.2
                 np.testing.assert_allclose(comp["rms"], 30.786983, atol=1e-3, rtol=1e-3)
             else:
-                self.assertIsNone(
-                    comp, f"value of comp={comp}"
-                )  # None indicates test passed
+                self.assertIsNone(comp, f"value of comp={comp}")  # None indicates test passed
 
     def test_3d_rgb(self):
         test_dir = Path(__file__).parents[2].as_posix()
         testing_dir = os.path.join(test_dir, "testing_data")
         keys = "image"
-        xforms = Compose([
-            LoadImaged(keys=keys),
-            EnsureChannelFirstd(keys=keys, channel_dim="no_channel"),
-            ScaleIntensityd(keys=keys),
-            # change to RGB color image
-            RepeatChanneld(keys=keys, repeats=3),
-        ])
+        xforms = Compose(
+            [
+                LoadImaged(keys=keys),
+                EnsureChannelFirstd(keys=keys, channel_dim="no_channel"),
+                ScaleIntensityd(keys=keys),
+                # change to RGB color image
+                RepeatChanneld(keys=keys, repeats=3),
+            ]
+        )
         image_path = os.path.join(testing_dir, "anatomical.nii")
         ims = xforms({keys: image_path})
 
@@ -149,12 +125,8 @@ def test_3d_rgb(self):
         with tempfile.TemporaryDirectory() as tempdir:
             tempimg = f"{tempdir}/matshow3d_rgb_test.png"
             fig.savefig(tempimg)
-            comp = compare_images(
-                f"{testing_dir}/matshow3d_rgb_test.png", tempimg, 5e-2
-            )
-            self.assertIsNone(
-                comp, f"value of comp={comp}"
-            )  # None indicates test passed
+            comp = compare_images(f"{testing_dir}/matshow3d_rgb_test.png", tempimg, 5e-2)
+            self.assertIsNone(comp, f"value of comp={comp}")  # None indicates test passed
 
 
 if __name__ == "__main__":