3347 fixes mednist dataset

monai/apps/datasets.py

@@ -114,12 +114,9 @@ def _generate_data_list(self, dataset_dir: PathLike) -> List[Dict]:
 
         """
         dataset_dir = Path(dataset_dir)
-        class_names = sorted(f"{x}" for x in dataset_dir.iterdir() if (dataset_dir / x).is_dir())
+        class_names = sorted(f"{x.name}" for x in dataset_dir.iterdir() if x.is_dir())  # folder name as the class name
         self.num_class = len(class_names)
-        image_files = [
-            [f"{dataset_dir.joinpath(class_names[i], x)}" for x in (dataset_dir / class_names[i]).iterdir()]
-            for i in range(self.num_class)
-        ]
+        image_files = [[f"{x}" for x in (dataset_dir / class_names[i]).iterdir()] for i in range(self.num_class)]
         num_each = [len(image_files[i]) for i in range(self.num_class)]
         image_files_list = []
         image_class = []
@@ -145,7 +142,6 @@ def _generate_data_list(self, dataset_dir: PathLike) -> List[Dict]:
             raise ValueError(
                 f'Unsupported section: {self.section}, available options are ["training", "validation", "test"].'
             )
-
         # the types of label and class name should be compatible with the pytorch dataloader
         return [
             {"image": image_files_list[i], "label": image_class[i], "class_name": class_name[i]}

monai/apps/utils.py

@@ -241,7 +241,7 @@ def extractall(
         cache_dir = Path(output_dir, _basename(filepath).split(".")[0])
     else:
         cache_dir = Path(output_dir)
-    if cache_dir.exists() and len(list(cache_dir.iterdir())) > 0:
+    if cache_dir.exists() and next(cache_dir.iterdir(), None) is not None:
         logger.info(f"Non-empty folder exists in {cache_dir}, skipped extracting.")
         return
     filepath = Path(filepath)

tests/test_decathlondataset.py

@@ -60,6 +60,8 @@ def _test_dataset(dataset):
             root_dir=testing_dir, task="Task04_Hippocampus", transform=transform, section="validation", download=False
         )
         _test_dataset(data)
+        self.assertTrue(data[0]["image_meta_dict"]["filename_or_obj"].endswith("hippocampus_163.nii.gz"))
+        self.assertTrue(data[0]["label_meta_dict"]["filename_or_obj"].endswith("hippocampus_163.nii.gz"))
         # test validation without transforms
         data = DecathlonDataset(root_dir=testing_dir, task="Task04_Hippocampus", section="validation", download=False)
         self.assertTupleEqual(data[0]["image"].shape, (36, 47, 44))
@@ -77,7 +79,7 @@ def _test_dataset(dataset):
 
         shutil.rmtree(os.path.join(testing_dir, "Task04_Hippocampus"))
         try:
-            data = DecathlonDataset(
+            DecathlonDataset(
                 root_dir=testing_dir,
                 task="Task04_Hippocampus",
                 transform=transform,

tests/test_mednistdataset.py

@@ -55,16 +55,18 @@ def _test_dataset(dataset):
 
         # testing from
         data = MedNISTDataset(root_dir=Path(testing_dir), transform=transform, section="test", download=False)
-        data.get_num_classes()
+        self.assertEqual(data.get_num_classes(), 6)
         _test_dataset(data)
         data = MedNISTDataset(root_dir=testing_dir, section="test", download=False)
         self.assertTupleEqual(data[0]["image"].shape, (64, 64))
         # test same dataset length with different random seed
         data = MedNISTDataset(root_dir=testing_dir, transform=transform, section="test", download=False, seed=42)
         _test_dataset(data)
+        self.assertEqual(data[0]["class_name"], "AbdomenCT")
+        self.assertEqual(data[0]["label"].cpu().item(), 0)
         shutil.rmtree(os.path.join(testing_dir, "MedNIST"))
         try:
-            data = MedNISTDataset(root_dir=testing_dir, transform=transform, section="test", download=False)
+            MedNISTDataset(root_dir=testing_dir, transform=transform, section="test", download=False)
         except RuntimeError as e:
             print(str(e))
             self.assertTrue(str(e).startswith("Cannot find dataset directory"))