1404 Fixing a few code performance issues

.deepsource.toml

@@ -0,0 +1,22 @@
+version = 1
+
+test_patterns = ["tests/**"]
+
+[[analyzers]]
+name = "python"
+enabled = true
+
+  [analyzers.meta]
+  runtime_version = "3.x.x"
+
+[[analyzers]]
+name = "test-coverage"
+enabled = true
+
+[[analyzers]]
+name = "docker"
+enabled = true
+
+[[analyzers]]
+name = "shell"
+enabled = true

monai/apps/utils.py

@@ -146,7 +146,7 @@ def download_url(url: str, filepath: str, hash_val: Optional[str] = None, hash_t
             if has_tqdm:
                 pbar.close()
         except IOError as e:
-            logging.debug("IO Error - %s" % e)
+            logging.debug("IO Error - %s", e)
         finally:
             if file_size == os.path.getsize(tmp_file_path):
                 if hash_val and not check_hash(tmp_file_path, hash_val, hash_type):

monai/data/dataset.py

@@ -807,7 +807,7 @@ def __getitem__(self, index: int):
         def to_list(x):
             return list(x) if isinstance(x, (tuple, list)) else [x]
 
-        data = list()
+        data = []
         for dataset in self.data:
             data.extend(to_list(dataset[index]))
         if self.transform is not None:

monai/data/decathlon_datalist.py

@@ -136,7 +136,7 @@ def load_decathlon_properties(
     with open(data_property_file_path) as json_file:
         json_data = json.load(json_file)
 
-    properties = dict()
+    properties = {}
     for key in ensure_tuple(property_keys):
         if key not in json_data:
             raise KeyError(f"key {key} is not in the data property file.")

monai/data/image_reader.py

@@ -154,7 +154,7 @@ def read(self, data: Union[Sequence[str], str], **kwargs):
                 https://github.com/InsightSoftwareConsortium/ITK/blob/master/Wrapping/Generators/Python/itkExtras.py
 
         """
-        img_: List[Image] = list()
+        img_: List[Image] = []
 
         filenames: Sequence[str] = ensure_tuple(data)
         kwargs_ = self.kwargs.copy()
@@ -191,7 +191,7 @@ def get_data(self, img):
             img: a ITK image object loaded from a image file or a list of ITK image objects.
 
         """
-        img_array: List[np.ndarray] = list()
+        img_array: List[np.ndarray] = []
         compatible_meta: Dict = {}
 
         for i in ensure_tuple(img):
@@ -325,7 +325,7 @@ def read(self, data: Union[Sequence[str], str], **kwargs):
                 https://github.com/nipy/nibabel/blob/master/nibabel/loadsave.py
 
         """
-        img_: List[Nifti1Image] = list()
+        img_: List[Nifti1Image] = []
 
         filenames: Sequence[str] = ensure_tuple(data)
         kwargs_ = self.kwargs.copy()
@@ -348,7 +348,7 @@ def get_data(self, img):
             img: a Nibabel image object loaded from a image file or a list of Nibabel image objects.
 
         """
-        img_array: List[np.ndarray] = list()
+        img_array: List[np.ndarray] = []
         compatible_meta: Dict = {}
 
         for i in ensure_tuple(img):
@@ -456,7 +456,7 @@ def read(self, data: Union[Sequence[str], str], **kwargs):
                 https://numpy.org/doc/stable/reference/generated/numpy.load.html
 
         """
-        img_: List[Nifti1Image] = list()
+        img_: List[Nifti1Image] = []
 
         filenames: Sequence[str] = ensure_tuple(data)
         kwargs_ = self.kwargs.copy()
@@ -485,13 +485,13 @@ def get_data(self, img):
             img: a Numpy array loaded from a file or a list of Numpy arrays.
 
         """
-        img_array: List[np.ndarray] = list()
+        img_array: List[np.ndarray] = []
         compatible_meta: Dict = {}
         if isinstance(img, np.ndarray):
             img = (img,)
 
         for i in ensure_tuple(img):
-            header = dict()
+            header = {}
             if isinstance(i, np.ndarray):
                 header["spatial_shape"] = i.shape
             img_array.append(i)
@@ -540,7 +540,7 @@ def read(self, data: Union[Sequence[str], str, np.ndarray], **kwargs):
                 https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.open
 
         """
-        img_: List[PILImage.Image] = list()
+        img_: List[PILImage.Image] = []
 
         filenames: Sequence[str] = ensure_tuple(data)
         kwargs_ = self.kwargs.copy()
@@ -565,7 +565,7 @@ def get_data(self, img):
             img: a PIL Image object loaded from a file or a list of PIL Image objects.
 
         """
-        img_array: List[np.ndarray] = list()
+        img_array: List[np.ndarray] = []
         compatible_meta: Dict = {}
 
         for i in ensure_tuple(img):

monai/data/utils.py

@@ -712,7 +712,7 @@ def partition_dataset_classes(
     for i, c in enumerate(classes):
         class_indices[c].append(i)
 
-    class_partition_indices: List[Sequence] = list()
+    class_partition_indices: List[Sequence] = []
     for _, per_class_indices in sorted(class_indices.items()):
         per_class_partition_indices = partition_dataset(
             data=per_class_indices,

monai/engines/evaluator.py

@@ -185,8 +185,8 @@ def _iteration(self, engine: Engine, batchdata: Dict[str, torch.Tensor]) -> Dict
         batch = self.prepare_batch(batchdata, engine.state.device, engine.non_blocking)
         if len(batch) == 2:
             inputs, targets = batch
-            args: Tuple = tuple()
-            kwargs: Dict = dict()
+            args: Tuple = ()
+            kwargs: Dict = {}
         else:
             inputs, targets, args, kwargs = batch
 
@@ -290,8 +290,8 @@ def _iteration(self, engine: Engine, batchdata: Dict[str, torch.Tensor]) -> Dict
         batch = self.prepare_batch(batchdata, engine.state.device, engine.non_blocking)
         if len(batch) == 2:
             inputs, targets = batch
-            args: Tuple = tuple()
-            kwargs: Dict = dict()
+            args: Tuple = ()
+            kwargs: Dict = {}
         else:
             inputs, targets, args, kwargs = batch
 

monai/engines/trainer.py

@@ -143,8 +143,8 @@ def _iteration(self, engine: Engine, batchdata: Dict[str, torch.Tensor]):
         batch = self.prepare_batch(batchdata, engine.state.device, engine.non_blocking)
         if len(batch) == 2:
             inputs, targets = batch
-            args: Tuple = tuple()
-            kwargs: Dict = dict()
+            args: Tuple = ()
+            kwargs: Dict = {}
         else:
             inputs, targets, args, kwargs = batch
 

monai/networks/nets/autoencoder.py

@@ -50,7 +50,7 @@ def __init__(
         self.norm = norm
         self.dropout = dropout
         self.num_inter_units = num_inter_units
-        self.inter_channels = inter_channels if inter_channels is not None else list()
+        self.inter_channels = inter_channels if inter_channels is not None else []
         self.inter_dilations = list(inter_dilations or [1] * len(self.inter_channels))
 
         # The number of channels and strides should match

monai/optimizers/utils.py

@@ -75,8 +75,8 @@ def _filter():
 
         return _filter
 
-    params = list()
-    _layers = list()
+    params = []
+    _layers = []
     for func, ty, lr in zip(layer_matches, match_types, lr_values):
         if ty.lower() == "select":
             layer_params = _get_select(func)

monai/transforms/adaptors.py

@@ -196,7 +196,7 @@ def _inner(ditems):
             if len(ret) != len(outputs):
                 raise ValueError("'outputs' must have the same length as the number of elements that were returned")
 
-            ret = {k: v for k, v in zip(op, ret)}
+            ret = dict(zip(op, ret))
         else:
             must_be_types("outputs", op, (str, list, tuple))
             if isinstance(op, (list, tuple)):

monai/transforms/croppad/array.py

@@ -74,7 +74,7 @@ def __init__(
     def _determine_data_pad_width(self, data_shape: Sequence[int]) -> List[Tuple[int, int]]:
         self.spatial_size = fall_back_tuple(self.spatial_size, data_shape)
         if self.method == Method.SYMMETRIC:
-            pad_width = list()
+            pad_width = []
             for i in range(len(self.spatial_size)):
                 width = max(self.spatial_size[i] - data_shape[i], 0)
                 pad_width.append((width // 2, width - (width // 2)))
@@ -601,7 +601,7 @@ def __call__(
             else:
                 fg_indices, bg_indices = map_binary_to_indices(label, image, self.image_threshold)
         self.randomize(label, fg_indices, bg_indices, image)
-        results: List[np.ndarray] = list()
+        results: List[np.ndarray] = []
         if self.centers is not None:
             for center in self.centers:
                 cropper = SpatialCrop(roi_center=tuple(center), roi_size=self.spatial_size)
@@ -680,7 +680,7 @@ def __call__(self, img: np.ndarray) -> np.ndarray:
         """
         See also: :py:class:`monai.transforms.utils.generate_spatial_bounding_box`.
         """
-        bbox = list()
+        bbox = []
 
         for channel in range(img.shape[0]):
             start_, end_ = generate_spatial_bounding_box(img, select_fn=self.select_fn, channel_indices=channel)

monai/transforms/croppad/dictionary.py

@@ -462,7 +462,7 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> List[Dict[Hashable, n
         self.randomize(d[self.w_key])
         _spatial_size = fall_back_tuple(self.spatial_size, d[self.w_key].shape[1:])
 
-        results: List[Dict[Hashable, np.ndarray]] = [dict() for _ in range(self.num_samples)]
+        results: List[Dict[Hashable, np.ndarray]] = [{} for _ in range(self.num_samples)]
         for key in data.keys():
             if key in self.keys:
                 img = d[key]
@@ -575,7 +575,7 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> List[Dict[Hashable, n
         self.randomize(label, fg_indices, bg_indices, image)
         assert isinstance(self.spatial_size, tuple)
         assert self.centers is not None
-        results: List[Dict[Hashable, np.ndarray]] = [dict() for _ in range(self.num_samples)]
+        results: List[Dict[Hashable, np.ndarray]] = [{} for _ in range(self.num_samples)]
         for key in data.keys():
             if key in self.keys:
                 img = d[key]

monai/transforms/io/array.py

@@ -171,8 +171,8 @@ def __call__(self, filename: Union[Sequence[Union[Path, str]], Path, str]):
             filename: path file or file-like object or a list of files.
         """
         filename = ensure_tuple(filename)
-        img_array = list()
-        compatible_meta: Dict = dict()
+        img_array = []
+        compatible_meta: Dict = {}
         for name in filename:
             img = nib.load(name)
             img = correct_nifti_header_if_necessary(img)
@@ -240,7 +240,7 @@ def __call__(self, filename: Union[Sequence[Union[Path, str]], Path, str]):
             filename: path file or file-like object or a list of files.
         """
         filename = ensure_tuple(filename)
-        img_array = list()
+        img_array = []
         compatible_meta = None
         for name in filename:
             img = Image.open(name)
@@ -252,7 +252,7 @@ def __call__(self, filename: Union[Sequence[Union[Path, str]], Path, str]):
             if self.image_only:
                 continue
 
-            meta = dict()
+            meta = {}
             meta["filename_or_obj"] = name
             meta["spatial_shape"] = data.shape[:2]
             meta["format"] = img.format
@@ -314,13 +314,13 @@ def __call__(self, filename: Union[Sequence[Union[Path, str]], Path, str]):
                 if name.endswith(".npz"):
                     raise ValueError("Cannot load a sequence of npz files.")
         filename = ensure_tuple(filename)
-        data_array: List = list()
+        data_array: List = []
         compatible_meta = None
 
         def _save_data_meta(data_array, name, data, compatible_meta):
             data_array.append(data if self.dtype is None else data.astype(self.dtype))
             if not self.data_only:
-                meta = dict()
+                meta = {}
                 meta["filename_or_obj"] = name
                 meta["spatial_shape"] = data.shape
                 if not compatible_meta:

monai/transforms/spatial/array.py

@@ -299,7 +299,7 @@ def __call__(self, img: np.ndarray) -> np.ndarray:
         Args:
             img: channel first array, must have shape: (num_channels, H[, W, ..., ]),
         """
-        flipped = list()
+        flipped = []
         for channel in img:
             flipped.append(np.flip(channel, self.spatial_axis))
         return np.stack(flipped).astype(img.dtype)
@@ -584,7 +584,7 @@ def __call__(self, img: np.ndarray) -> np.ndarray:
         Args:
             img: channel first array, must have shape: (num_channels, H[, W, ..., ]),
         """
-        rotated = list()
+        rotated = []
         for channel in img:
             rotated.append(np.rot90(channel, self.k, self.spatial_axes))
         return np.stack(rotated).astype(img.dtype)

monai/transforms/utility/array.py

@@ -189,7 +189,7 @@ def __call__(self, img: Union[np.ndarray, torch.Tensor]) -> List[Union[np.ndarra
         if n_classes <= 1:
             raise RuntimeError("input image does not contain multiple channels.")
 
-        outputs = list()
+        outputs = []
         slices = [slice(None)] * len(img.shape)
         for i in range(n_classes):
             slices[channel_dim] = slice(i, i + 1)

monai/transforms/utility/dictionary.py

@@ -535,7 +535,7 @@ def __call__(self, data):
 
         """
         d = dict(data)
-        output = list()
+        output = []
         data_type = None
         for key in self.keys:
             if data_type is None:
@@ -674,7 +674,7 @@ class ConvertToMultiChannelBasedOnBratsClassesd(MapTransform):
     def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]:
         d = dict(data)
         for key in self.keys:
-            result = list()
+            result = []
             # merge labels 1 (tumor non-enh) and 4 (tumor enh) to TC
             result.append(np.logical_or(d[key] == 1, d[key] == 4))
             # merge labels 1 (tumor non-enh) and 4 (tumor enh) and 2 (large edema) to WT

monai/utils/misc.py

@@ -265,7 +265,7 @@ def _parse_var(s):
             value = items[1].strip(" \n\r\t'")
         return key, value
 
-    d = dict()
+    d = {}
     if items:
         for item in items:
             key, value = _parse_var(item)

tests/runner.py

@@ -18,15 +18,15 @@
 
 from monai.utils import PerfContext
 
-results: dict = dict()
+results: dict = {}
 
 
 class TimeLoggingTestResult(unittest.TextTestResult):
     """Overload the default results so that we can store the results."""
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.timed_tests = dict()
+        self.timed_tests = {}
 
     def startTest(self, test):  # noqa: N802
         """Start timer, print test name, do normal test."""

tests/test_compute_confusion_matrix.py

@@ -220,7 +220,7 @@ def test_value(self, input_data, expected_value):
     @parameterized.expand(TEST_CASES_COMPUTE_SAMPLE)
     def test_compute_sample(self, input_data, expected_value):
         params = input_data.copy()
-        vals = dict()
+        vals = {}
         vals["y_pred"] = params.pop("y_pred")
         vals["y"] = params.pop("y")
         metric = ConfusionMatrixMetric(**params)
@@ -230,7 +230,7 @@ def test_compute_sample(self, input_data, expected_value):
     @parameterized.expand(TEST_CASES_COMPUTE_SAMPLE_MULTI_METRICS)
     def test_compute_sample_multiple_metrics(self, input_data, expected_values):
         params = input_data.copy()
-        vals = dict()
+        vals = {}
         vals["y_pred"] = params.pop("y_pred")
         vals["y"] = params.pop("y")
         metric = ConfusionMatrixMetric(**params)
@@ -243,7 +243,7 @@ def test_compute_sample_multiple_metrics(self, input_data, expected_values):
     @parameterized.expand(TEST_CASES_COMPUTE_SAMPLE_NAN)
     def test_compute_sample_with_nan(self, input_data, expected_value, expected_not_nans):
         params = input_data.copy()
-        vals = dict()
+        vals = {}
         vals["y_pred"] = params.pop("y_pred")
         vals["y"] = params.pop("y")
         metric = ConfusionMatrixMetric(**params)
@@ -254,7 +254,7 @@ def test_compute_sample_with_nan(self, input_data, expected_value, expected_not_
     @parameterized.expand([TEST_CASES_CLF])
     def test_clf_with_nan(self, input_data, expected_value):
         params = input_data.copy()
-        vals = dict()
+        vals = {}
         vals["y_pred"] = params.pop("y_pred")
         vals["y"] = params.pop("y")
         metric = ConfusionMatrixMetric(**params)