Type check + Lint in CI (#470)

* Try lint and typecheck in CI workflow * update * nit * continue on MyPy errors * test * correct * correct * correct Co-authored-by: Arie Matsliah <[email protected]>
talmolab · Feb 4, 2021 · 56660c0 · 56660c0
1 parent 5f95150
commit 56660c0
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Showing 5 changed files with 96 additions and 49 deletions.
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
@@ -8,7 +8,39 @@ on:
       - 'tests/**'
 
 jobs:
-  build:
+  type_check:
+    name: Type Check
+    runs-on: "ubuntu-18.04"
+    steps:
+    - uses: actions/checkout@v1
+    - name: Set up Python 3.6
+      uses: actions/setup-python@v1
+      with:
+        python-version: 3.6
+    - name: Install Dependencies
+      run: |
+        pip install mypy
+    - name: Run MyPy
+      # TODO: remove this once all MyPy errors get fixed
+      continue-on-error: true
+      run: |
+        mypy --follow-imports=skip --ignore-missing-imports sleap tests
+  lint:
+    name: Lint
+    runs-on: "ubuntu-18.04"
+    steps:
+    - uses: actions/checkout@v1
+    - name: Set up Python 3.6
+      uses: actions/setup-python@v1
+      with:
+        python-version: 3.6
+    - name: Install Dependencies
+      run: |
+        pip install black
+    - name: Run Black
+      run: |
+        black --check sleap tests
+  tests:
     name: Tests (${{ matrix.os }})
     runs-on: ${{ matrix.os }}
     strategy:

diff --git a/sleap/instance.py b/sleap/instance.py
@@ -534,7 +534,8 @@ def _node_to_index(self, node: Union[str, Node]) -> int:
         return self.skeleton.node_to_index(node)
 
     def __getitem__(
-        self, node: Union[List[Union[str, Node, int]], Union[str, Node, int], np.ndarray]
+        self,
+        node: Union[List[Union[str, Node, int]], Union[str, Node, int], np.ndarray],
     ) -> Union[List[Point], Point, np.ndarray]:
         """
         Get the Points associated with particular skeleton node(s).

diff --git a/sleap/skeleton.py b/sleap/skeleton.py
@@ -394,7 +394,9 @@ def symmetries(self) -> List[Tuple[Node, Node]]:
             if edge_type == EdgeType.SYMMETRY
         ]
         # Get rid of duplicates
-        symmetries = list(set([tuple(sorted(e, key=operator.attrgetter("name"))) for e in symmetries]))
+        symmetries = list(
+            set([tuple(sorted(e, key=operator.attrgetter("name"))) for e in symmetries])
+        )
         return symmetries
 
     @property

diff --git a/tests/nn/data/test_augmentation.py b/tests/nn/data/test_augmentation.py
@@ -52,49 +52,44 @@ def test_random_cropper(min_labels):
     assert "crop_bbox" in example
     offset = tf.stack([example["crop_bbox"][0, 1], example["crop_bbox"][0, 0]], axis=-1)
     assert tf.reduce_all(
-        example["instances"] == (
-            example_preaug["instances"] - tf.expand_dims(offset, axis=0)))
+        example["instances"]
+        == (example_preaug["instances"] - tf.expand_dims(offset, axis=0))
+    )
 
 
 def test_flip_instances_lr():
-    insts = tf.cast([
-        [[0, 1], [2, 3]],
-        [[4, 5], [6, 7]],
-    ], tf.float32)
+    insts = tf.cast(
+        [
+            [[0, 1], [2, 3]],
+            [[4, 5], [6, 7]],
+        ],
+        tf.float32,
+    )
 
     insts_flip = augmentation.flip_instances_lr(insts, 8)
-    np.testing.assert_array_equal(insts_flip, [
-        [[7, 1], [5, 3]],
-        [[3, 5], [1, 7]]
-    ])
+    np.testing.assert_array_equal(insts_flip, [[[7, 1], [5, 3]], [[3, 5], [1, 7]]])
 
     insts_flip1 = augmentation.flip_instances_lr(insts, 8, [[0, 1]])
     insts_flip2 = augmentation.flip_instances_lr(insts, 8, [[1, 0]])
-    np.testing.assert_array_equal(insts_flip1, [
-        [[5, 3], [7, 1]],
-        [[1, 7], [3, 5]]
-    ])
+    np.testing.assert_array_equal(insts_flip1, [[[5, 3], [7, 1]], [[1, 7], [3, 5]]])
     np.testing.assert_array_equal(insts_flip1, insts_flip2)
 
 
 def test_flip_instances_ud():
-    insts = tf.cast([
-        [[0, 1], [2, 3]],
-        [[4, 5], [6, 7]],
-    ], tf.float32)
+    insts = tf.cast(
+        [
+            [[0, 1], [2, 3]],
+            [[4, 5], [6, 7]],
+        ],
+        tf.float32,
+    )
 
     insts_flip = augmentation.flip_instances_ud(insts, 8)
-    np.testing.assert_array_equal(insts_flip, [
-        [[0, 6], [2, 4]],
-        [[4, 2], [6, 0]]
-    ])
+    np.testing.assert_array_equal(insts_flip, [[[0, 6], [2, 4]], [[4, 2], [6, 0]]])
 
     insts_flip1 = augmentation.flip_instances_ud(insts, 8, [[0, 1]])
     insts_flip2 = augmentation.flip_instances_ud(insts, 8, [[1, 0]])
-    np.testing.assert_array_equal(insts_flip1, [
-        [[2, 4], [0, 6]],
-        [[6, 0], [4, 2]]
-    ])
+    np.testing.assert_array_equal(insts_flip1, [[[2, 4], [0, 6]], [[6, 0], [4, 2]]])
     np.testing.assert_array_equal(insts_flip1, insts_flip2)
 
 
@@ -103,53 +98,71 @@ def test_random_flipper():
         "tests/data/json_format_v1/centered_pair_low_quality.mp4"
     )
     skel = sleap.Skeleton.from_names_and_edge_inds(["A", "BL", "BR"], [[0, 1], [0, 2]])
-    labels = sleap.Labels([sleap.LabeledFrame(video=vid, frame_idx=0, instances=[
-        sleap.Instance.from_pointsarray([[25, 50], [50, 25], [25, 25]], skeleton=skel),
-        sleap.Instance.from_pointsarray([[125, 150], [150, 125], [125, 125]], skeleton=skel),
-    ])])
-
+    labels = sleap.Labels(
+        [
+            sleap.LabeledFrame(
+                video=vid,
+                frame_idx=0,
+                instances=[
+                    sleap.Instance.from_pointsarray(
+                        [[25, 50], [50, 25], [25, 25]], skeleton=skel
+                    ),
+                    sleap.Instance.from_pointsarray(
+                        [[125, 150], [150, 125], [125, 125]], skeleton=skel
+                    ),
+                ],
+            )
+        ]
+    )
+
     p = labels.to_pipeline()
     p += sleap.nn.data.augmentation.RandomFlipper.from_skeleton(
-        skel, horizontal=True, probability=1.)
+        skel, horizontal=True, probability=1.0
+    )
     ex = p.peek()
     np.testing.assert_array_equal(ex["image"], vid[0][0][:, ::-1])
     np.testing.assert_array_equal(
         ex["instances"],
-        [[[358.,  50.], [333.,  25.], [358.,  25.]],
-         [[258., 150.], [233., 125.], [258., 125.]]]
+        [
+            [[358.0, 50.0], [333.0, 25.0], [358.0, 25.0]],
+            [[258.0, 150.0], [233.0, 125.0], [258.0, 125.0]],
+        ],
     )
 
     skel.add_symmetry("BL", "BR")
 
     p = labels.to_pipeline()
     p += sleap.nn.data.augmentation.RandomFlipper.from_skeleton(
-        skel, horizontal=True, probability=1.)
+        skel, horizontal=True, probability=1.0
+    )
     ex = p.peek()
     np.testing.assert_array_equal(ex["image"], vid[0][0][:, ::-1])
     np.testing.assert_array_equal(
         ex["instances"],
-        [[[358.,  50.], [358.,  25.], [333.,  25.]],
-         [[258., 150.], [258., 125.], [233., 125.]]]
+        [
+            [[358.0, 50.0], [358.0, 25.0], [333.0, 25.0]],
+            [[258.0, 150.0], [258.0, 125.0], [233.0, 125.0]],
+        ],
     )
 
     p = labels.to_pipeline()
     p += sleap.nn.data.augmentation.RandomFlipper.from_skeleton(
-        skel, horizontal=True, probability=0.)
+        skel, horizontal=True, probability=0.0
+    )
     ex = p.peek()
     np.testing.assert_array_equal(ex["image"], vid[0][0])
     np.testing.assert_array_equal(
         ex["instances"],
-        [[[25, 50], [50, 25], [25, 25]],
-         [[125, 150], [150, 125], [125, 125]]]
+        [[[25, 50], [50, 25], [25, 25]], [[125, 150], [150, 125], [125, 125]]],
     )
 
     p = labels.to_pipeline()
     p += sleap.nn.data.augmentation.RandomFlipper.from_skeleton(
-        skel, horizontal=False, probability=1.)
+        skel, horizontal=False, probability=1.0
+    )
     ex = p.peek()
     np.testing.assert_array_equal(ex["image"], vid[0][0][::-1, :])
     np.testing.assert_array_equal(
         ex["instances"],
-        [[[25, 333], [25, 358], [50, 358]],
-         [[125, 233], [125, 258], [150, 258]]]
+        [[[25, 333], [25, 358], [50, 358]], [[125, 233], [125, 258], [150, 258]]],
     )
diff --git a/tests/test_instance.py b/tests/test_instance.py
@@ -59,10 +59,9 @@ def test_instance_node_multi_get_set_item(skeleton):
 
     assert np.allclose(x_values, [1, 2, 3])
     assert np.allclose(y_values, [4, 5, 6])
-    
+
     np.testing.assert_array_equal(
-        instance1[np.array([0, 2, 4])],
-        [[1, 4], [np.nan, np.nan], [3, 6]]
+        instance1[np.array([0, 2, 4])], [[1, 4], [np.nan, np.nan], [3, 6]]
     )
 
     instance1[np.array([0, 1])] = [[1, 2], [3, 4]]