[Enhance] Add Windows CI after moving ops (#1345)

* lock mmcv version

* change to cpu

* change int to int64

* fix tempfile error

* change tensor

* change tensor

* change cpu

* complete windows ci

* change dtype

* change data type to numpy

* change tensor to numpy

* fix comments

.github/workflows/build.yml

@@ -103,3 +103,44 @@ jobs:
           env_vars: OS,PYTHON
           name: codecov-umbrella
           fail_ci_if_error: false
+
+  build_windows:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [windows-2022]
+        python: [3.8]
+        platform: [cpu]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python }}
+      - name: Upgrade pip
+        run: pip install pip --upgrade --user
+      - name: Install PyTorch
+        # As a complement to Linux CI, we test on PyTorch LTS version
+        run: pip install torch==1.8.2+${{ matrix.platform }} torchvision==0.9.2+${{ matrix.platform }} -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
+      - name: Install mmdet3d dependencies
+        run: |
+          pip install mmcv-full==1.4.7 -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.8/index.html --only-binary mmcv-full
+          python -m pip install mmdet==2.19.0
+          python -m pip install mmsegmentation==0.20.0
+          python -m pip install -r requirements/build.txt -r requirements/runtime.txt -r requirements/tests.txt
+      - name: Build and install
+        run: pip install -e .
+      - name: Run unittests and generate coverage report
+        run: coverage run --branch --source mmdet3d -m pytest tests/
+      - name: Generate coverage report
+        run: |
+          coverage xml
+          coverage report -m
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v2
+        with:
+          file: ./coverage.xml
+          flags: unittests
+          env_vars: OS,PYTHON
+          name: codecov-umbrella
+          fail_ci_if_error: false

mmdet3d/datasets/pipelines/loading.py

@@ -518,7 +518,7 @@ class LoadAnnotations3D(LoadAnnotations):
                  with_seg=False,
                  with_bbox_depth=False,
                  poly2mask=True,
-                 seg_3d_dtype='int',
+                 seg_3d_dtype=np.int64,
                  file_client_args=dict(backend='disk')):
         super().__init__(
             with_bbox,
@@ -600,7 +600,7 @@ class LoadAnnotations3D(LoadAnnotations):
             self.file_client = mmcv.FileClient(**self.file_client_args)
         try:
             mask_bytes = self.file_client.get(pts_instance_mask_path)
-            pts_instance_mask = np.frombuffer(mask_bytes, dtype=np.int)
+            pts_instance_mask = np.frombuffer(mask_bytes, dtype=np.int64)
         except ConnectionError:
             mmcv.check_file_exist(pts_instance_mask_path)
             pts_instance_mask = np.fromfile(

tests/test_data/test_datasets/test_lyft_dataset.py

@@ -118,12 +118,12 @@ def test_getitem():
     assert lyft_dataset.CLASSES == ('car', 'pedestrian')
 
     import tempfile
-    tmp_file = tempfile.NamedTemporaryFile()
-    with open(tmp_file.name, 'w') as f:
-        f.write('car\npedestrian\n')
+    with tempfile.TemporaryDirectory() as tmpdir:
+        path = tmpdir + 'classes.txt'
+        with open(path, 'w') as f:
+            f.write('car\npedestrian\n')
 
-    lyft_dataset = LyftDataset(
-        ann_file, None, root_path, classes=tmp_file.name)
+    lyft_dataset = LyftDataset(ann_file, None, root_path, classes=path)
     assert lyft_dataset.CLASSES != original_classes
     assert lyft_dataset.CLASSES == ['car', 'pedestrian']
 

tests/test_data/test_datasets/test_s3dis_dataset.py

@@ -179,15 +179,16 @@ def test_seg_getitem():
 
     # test load classes from file
     import tempfile
-    tmp_file = tempfile.NamedTemporaryFile()
-    with open(tmp_file.name, 'w') as f:
-        f.write('beam\nwindow\n')
+    with tempfile.TemporaryDirectory() as tmpdir:
+        path = tmpdir + 'classes.txt'
+        with open(path, 'w') as f:
+            f.write('beam\nwindow\n')
 
     s3dis_dataset = S3DISSegDataset(
         data_root=root_path,
         ann_files=ann_file,
         pipeline=None,
-        classes=tmp_file.name,
+        classes=path,
         scene_idxs=scene_idxs)
     assert s3dis_dataset.CLASSES != original_classes
     assert s3dis_dataset.CLASSES == ['beam', 'window']

tests/test_data/test_datasets/test_scannet_dataset.py

@@ -112,12 +112,13 @@ def test_getitem():
 
     # Test load classes from file
     import tempfile
-    tmp_file = tempfile.NamedTemporaryFile()
-    with open(tmp_file.name, 'w') as f:
-        f.write('cabinet\nbed\n')
+    with tempfile.TemporaryDirectory() as tmpdir:
+        path = tmpdir + 'classes.txt'
+        with open(path, 'w') as f:
+            f.write('cabinet\nbed\n')
 
     scannet_dataset = ScanNetDataset(
-        root_path, ann_file, pipeline=None, classes=tmp_file.name)
+        root_path, ann_file, pipeline=None, classes=path)
     assert scannet_dataset.CLASSES != original_classes
     assert scannet_dataset.CLASSES == ['cabinet', 'bed']
 
@@ -497,15 +498,16 @@ def test_seg_getitem():
 
     # test load classes from file
     import tempfile
-    tmp_file = tempfile.NamedTemporaryFile()
-    with open(tmp_file.name, 'w') as f:
-        f.write('cabinet\nchair\n')
+    with tempfile.TemporaryDirectory() as tmpdir:
+        path = tmpdir + 'classes.txt'
+        with open(path, 'w') as f:
+            f.write('cabinet\nchair\n')
 
     scannet_dataset = ScanNetSegDataset(
         data_root=root_path,
         ann_file=ann_file,
         pipeline=None,
-        classes=tmp_file.name,
+        classes=path,
         scene_idxs=scene_idxs)
     assert scannet_dataset.CLASSES != original_classes
     assert scannet_dataset.CLASSES == ['cabinet', 'chair']
@@ -744,15 +746,18 @@ def test_instance_seg_getitem():
                                         -4.3207e-01, 1.8154e+00, 1.7455e-01,
                                         4.0392e-01, 3.8039e-01, 4.1961e-01
                                     ]])
-    expected_semantic_mask = torch.tensor([11, 18, 18, 0, 4]).long()
-    expected_instance_mask = torch.tensor([6, 56, 10, 9, 35]).long()
 
     data = scannet_dataset[0]
-    assert torch.allclose(data['points']._data[:5], expected_points, 1e-2)
-    assert torch.allclose(data['pts_semantic_mask']._data[:5],
-                          expected_semantic_mask)
-    assert torch.allclose(data['pts_instance_mask']._data[:5],
-                          expected_instance_mask)
+
+    points = data['points']._data[:5]
+    pts_semantic_mask = data['pts_semantic_mask']._data[:5]
+    pts_instance_mask = data['pts_instance_mask']._data[:5]
+    expected_semantic_mask = np.array([11, 18, 18, 0, 4])
+    expected_instance_mask = np.array([6, 56, 10, 9, 35])
+
+    assert torch.allclose(points, expected_points, 1e-2)
+    assert np.all(pts_semantic_mask.numpy() == expected_semantic_mask)
+    assert np.all(pts_instance_mask.numpy() == expected_instance_mask)
 
 
 def test_instance_seg_evaluate():

tests/test_data/test_datasets/test_sunrgbd_dataset.py

@@ -89,6 +89,9 @@ def _generate_sunrgbd_multi_modality_dataset_config():
 
 
 def test_getitem():
+
+    from os import path as osp
+
     np.random.seed(0)
     root_path, ann_file, class_names, pipelines, modality = \
         _generate_sunrgbd_dataset_config()
@@ -107,7 +110,8 @@ def test_getitem():
     pcd_rotation_expected = np.array([[0.99889565, 0.04698427, 0.],
                                       [-0.04698427, 0.99889565, 0.],
                                       [0., 0., 1.]])
-    assert file_name == './tests/data/sunrgbd/points/000001.bin'
+    expected_file_name = osp.join('./tests/data/sunrgbd', 'points/000001.bin')
+    assert file_name == expected_file_name
     assert pcd_horizontal_flip is False
     assert abs(pcd_scale_factor - 0.9770964398016714) < 1e-5
     assert np.allclose(pcd_rotation, pcd_rotation_expected, 1e-3)
@@ -142,12 +146,13 @@ def test_getitem():
     assert SUNRGBD_dataset.CLASSES == ('bed', 'table')
 
     import tempfile
-    tmp_file = tempfile.NamedTemporaryFile()
-    with open(tmp_file.name, 'w') as f:
-        f.write('bed\ntable\n')
+    with tempfile.TemporaryDirectory() as tmpdir:
+        path = tmpdir + 'classes.txt'
+        with open(path, 'w') as f:
+            f.write('bed\ntable\n')
 
     SUNRGBD_dataset = SUNRGBDDataset(
-        root_path, ann_file, pipeline=None, classes=tmp_file.name)
+        root_path, ann_file, pipeline=None, classes=path)
     assert SUNRGBD_dataset.CLASSES != original_classes
     assert SUNRGBD_dataset.CLASSES == ['bed', 'table']