Add convenience options runtests (#496)

* Add subcommands to the individual codeformat test

One often wants to run the very fast codeformatting tests without
running any of the unit tests. Add a commands to allow this
behavior with "./runtests.sh [--black|--pytype|--mypy|--flake8]".

One often wants the `black` formatting tool to automatically
fix formatting violations without requiring explicit
manual call. Use "./runtests.sh --black-fix"

Add mypy static checking tool to monai

Run the complete mypy static checking tool during evaluations
of code format style checks.

Related to #476 subtask for enable mypy

Expand the documentation for runtest.sh script.  Provide more expressive
documentation for `-h`

The dryrun request to print the commands used for static
and format checking without actually running the commands.

* Temporarily add branch needed to trigger CI of this codebase.

* Suppress mypy warnings about incompatible superclass signature

Related to #495

* Suppress mypy error: Cannot assign to a method

Related to #494

* Mypy potential type mismatch resolved with assertion.

* Mypy type ambiguity resolved.

Author: hjmjohnson

.github/workflows/pythonapp.yml

@@ -5,7 +5,8 @@ on:
   push:
     branches:
       - master
-      - enable-pytype
+      # TODO: Remove ci trigger branch used for testing
+      - add-convenience-options-runtests
   pull_request:
 
 jobs:
@@ -21,16 +22,19 @@ jobs:
       run: |
         python -m pip install --upgrade pip wheel
         pip install -r requirements-dev.txt
-    - name: Lint with flake8
+    - name: Lint with black formater
       run: |
-        flake8 --version
-        python -c 'import monai; monai.config.print_config()'
-        MYPYPATH=$(pwd)/monai flake8 $(pwd) --count --statistics
-    - name: Type check with Pytype
+        $(pwd)/runtests.sh --nounittests --black
+    - name: Lint with flake8 code checker
+      run: |
+        $(pwd)/runtests.sh --nounittests --flake8
+    - name: Type check with mypy static analysis
+      run: |
+        $(pwd)/runtests.sh --nounittests --mypy
+    - name: Type check with Pytype static analysis
       run: |
-        pytype --version
         # Git hub actions have 2 cores, so parallize pytype
-        pytype -j 2 --python-version=$(python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')")
+        $(pwd)/runtests.sh --nounittests --pytype -j 2 
 
   quick-py3:
     runs-on: ${{ matrix.os }}

monai/handlers/segmentation_saver.py

@@ -65,6 +65,7 @@ def __init__(
             name (str): identifier of logging.logger to use, defaulting to `engine.logger`.
 
         """
+        self.saver: Union[NiftiSaver, PNGSaver]
         if output_ext in (".nii.gz", ".nii"):
             self.saver = NiftiSaver(output_dir, output_postfix, output_ext, resample, interp_order, mode, cval, dtype)
         elif output_ext == ".png":

monai/losses/dice.py

@@ -141,7 +141,9 @@ class MaskedDiceLoss(DiceLoss):
     Same as DiceLoss, but accepts a binary mask ([0,1]) indicating a region over which to compute the dice.
     """
 
-    def forward(self, input: torch.Tensor, target: torch.Tensor, mask: torch.Tensor = None, smooth: float = 1e-5):
+    def forward(  # type: ignore # see issue #495
+        self, input: torch.Tensor, target: torch.Tensor, mask: torch.Tensor = None, smooth: float = 1e-5
+    ):
         """
         Args:
             input (tensor): the shape should be BNH[WD].

monai/transforms/croppad/array.py

@@ -53,7 +53,7 @@ def _determine_data_pad_width(self, data_shape):
         else:
             return [(0, max(self.spatial_size[i] - data_shape[i], 0)) for i in range(len(self.spatial_size))]
 
-    def __call__(self, img, mode: Optional[str] = None):
+    def __call__(self, img, mode: Optional[str] = None):  # type: ignore # see issue #495
         data_pad_width = self._determine_data_pad_width(img.shape[1:])
         all_pad_width = [(0, 0)] + data_pad_width
         img = np.pad(img, all_pad_width, mode=mode or self.mode)

monai/transforms/post/array.py

@@ -38,7 +38,7 @@ def __init__(self, to_onehot: bool = False, num_classes: Optional[int] = None):
         self.to_onehot = to_onehot
         self.num_classes = num_classes
 
-    def __call__(self, img, to_onehot: Optional[bool] = None, num_classes: Optional[int] = None):
+    def __call__(self, img, to_onehot: Optional[bool] = None, num_classes: Optional[int] = None):  # type: ignore # see issue #495
         if to_onehot or self.to_onehot:
             if num_classes is None:
                 num_classes = self.num_classes
@@ -69,7 +69,7 @@ def __init__(self, sigmoid: bool = False, softmax: bool = False, other: Optional
         self.softmax = softmax
         self.other = other
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self, img, sigmoid: Optional[bool] = None, softmax: Optional[bool] = None, other: Optional[Callable] = None
     ):
         if sigmoid is True and softmax is True:
@@ -118,7 +118,7 @@ def __init__(
         self.threshold_values = threshold_values
         self.logit_thresh = logit_thresh
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         img,
         argmax: Optional[bool] = None,
@@ -131,7 +131,9 @@ def __call__(
             img = torch.argmax(img, dim=1, keepdim=True)
 
         if to_onehot or self.to_onehot:
-            img = one_hot(img, self.n_classes if n_classes is None else n_classes)
+            _nclasses = self.n_classes if n_classes is None else n_classes
+            assert isinstance(_nclasses, int), "One of self.n_classes or n_classes must be an integer"
+            img = one_hot(img, _nclasses)
 
         if threshold_values or self.threshold_values:
             img = img >= (self.logit_thresh if logit_thresh is None else logit_thresh)

monai/transforms/spatial/array.py

@@ -79,7 +79,7 @@ def __init__(
         self.cval = cval
         self.dtype = dtype
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         data_array: np.ndarray,
         affine=None,
@@ -162,7 +162,7 @@ def __init__(self, axcodes=None, as_closest_canonical: bool = False, labels=tupl
         self.as_closest_canonical = as_closest_canonical
         self.labels = labels
 
-    def __call__(self, data_array: np.ndarray, affine=None):
+    def __call__(self, data_array: np.ndarray, affine=None):  # type: ignore # see issue #495
         """
         original orientation of `data_array` is defined by `affine`.
 
@@ -261,7 +261,7 @@ def __init__(
         self.preserve_range = preserve_range
         self.anti_aliasing = anti_aliasing
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         img,
         order=None,
@@ -329,7 +329,7 @@ def __init__(
         self.cval = cval
         self.prefilter = prefilter
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         img,
         order=None,
@@ -471,7 +471,7 @@ def __init__(self, k: int = 1, spatial_axes=(0, 1)):
         self.k = k
         self.spatial_axes = spatial_axes
 
-    def __call__(self, img: np.ndarray):
+    def __call__(self, img: np.ndarray):  # type: ignore # see issue #495
         """
         Args:
             img (ndarray): channel first array, must have shape: (num_channels, H[, W, ..., ]),
@@ -567,7 +567,7 @@ def randomize(self):
         self._do_transform = self.R.random_sample() < self.prob
         self.angle = self.R.uniform(low=self.degrees[0], high=self.degrees[1])
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         img,
         order=None,
@@ -671,7 +671,7 @@ def randomize(self):
         else:
             self._zoom = self.R.uniform(self.min_zoom, self.max_zoom)
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         img,
         order=None,
@@ -887,7 +887,7 @@ def __init__(
         self.as_tensor_output = as_tensor_output
         self.device = device
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         img: Union[np.ndarray, torch.Tensor],
         grid: Union[np.ndarray, torch.Tensor],
@@ -977,7 +977,7 @@ def __init__(
         self.padding_mode = padding_mode
         self.mode = mode
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         img: Union[np.ndarray, torch.Tensor],
         spatial_size=None,
@@ -1061,7 +1061,7 @@ def randomize(self):
         self.do_transform = self.R.rand() < self.prob
         self.rand_affine_grid.randomize()
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         img: Union[np.ndarray, torch.Tensor],
         spatial_size=None,
@@ -1158,7 +1158,7 @@ def randomize(self, spatial_size):
         self.deform_grid.randomize(spatial_size)
         self.rand_affine_grid.randomize()
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         img: Union[np.ndarray, torch.Tensor],
         spatial_size=None,

monai/transforms/utility/array.py

@@ -118,7 +118,7 @@ def __init__(self, dtype: np.dtype = np.float32):
         """
         self.dtype = dtype
 
-    def __call__(self, img: np.ndarray):
+    def __call__(self, img: np.ndarray):  # type: ignore # see issue #495
         assert isinstance(img, np.ndarray), "image must be numpy array."
         return img.astype(self.dtype)
 
@@ -161,7 +161,7 @@ def __init__(self, dim: Optional[int] = None):
             assert isinstance(dim, int) and dim >= -1, "invalid channel dimension."
         self.dim = dim
 
-    def __call__(self, img: np.ndarray):
+    def __call__(self, img: np.ndarray):  # type: ignore # see issue #495
         """
         Args:
             img (ndarray): numpy arrays with required dimension `dim` removed
@@ -209,7 +209,7 @@ def __init__(
         if logger_handler is not None:
             self._logger.addHandler(logger_handler)
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         img,
         prefix: Optional[str] = None,

requirements-dev.txt

@@ -14,3 +14,4 @@ coverage
 parameterized
 black
 pytype>=2020.6.1
+mypy