[CI] Support Windows CI (open-mmlab#1448)

kennymckormick · web-flow · commit a94382c67c58 · 2022-02-18T13:26:05.000+08:00
* windows CI

* update

* update

* update CI

* fix windows CI

* fix CI

* fix CI

* update

* update

* update dependencies

* update
diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
@@ -2,18 +2,24 @@ name: build
 
 on:
   push:
-    paths:
-      - '!demo/**'
-      - '!docker/**'
-      - '!tools/**'
+    paths-ignore:
+      - ".github/**.md"
+      - "demo/**"
+      - "docker/**"
+      - "tools/**"
+      - "README.md"
+      - "README_zh-CN.md"
 
   pull_request:
-    paths:
-      - '!demo/**'
-      - '!docker/**'
-      - '!tools/**'
-      - '!docs/**'
-      - '!docs_zh_CN/**'
+    paths-ignore:
+      - ".github/**.md"
+      - "demo/**"
+      - "docker/**"
+      - "docs/**"
+      - "docs_zh-CN/**"
+      - "tools/**"
+      - "README.md"
+      - "README_zh-CN.md"
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
@@ -192,3 +198,43 @@ jobs:
           coverage run --branch --source mmaction -m pytest tests/
           coverage xml
           coverage report -m
+
+  test_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: ${{ matrix.python }}
+      - name: Upgrade pip
+        run: pip install pip --upgrade --user
+      - name: Install librosa and soundfile
+        run: python -m pip install librosa soundfile
+      - name: Install lmdb
+        run: python -m pip install lmdb
+      - name: Install PyTorch
+        # As a complement to Linux CI, we test on PyTorch LTS version
+        run: pip install torch==1.8.2+${{ matrix.platform }} torchvision==0.9.2+${{ matrix.platform }} -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
+      - name: Install MMCV
+        run: pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.8/index.html --only-binary mmcv-full
+      - name: Install mmaction dependencies
+        run: |
+          python -V
+          python -m pip install -q git+https://github.com/open-mmlab/mmdetection/
+          python -m pip install -q git+https://github.com/open-mmlab/mmclassification/
+          python -m pip install -r requirements.txt
+          python -c 'import mmcv; print(mmcv.__version__)'
+      - name: Install PytorchVideo
+        run: python -m pip install pytorchvideo
+      - name: Show pip list
+        run: pip list
+      - name: Build and install
+        run: pip install -e .
+      - name: Run unittests
+        run: coverage run --branch --source mmedit -m pytest tests -sv
diff --git a/mmaction/core/evaluation/accuracy.py b/mmaction/core/evaluation/accuracy.py
@@ -22,6 +22,8 @@ def confusion_matrix(y_pred, y_real, normalize=None):
 
     if isinstance(y_pred, list):
         y_pred = np.array(y_pred)
+        if y_pred.dtype == np.int32:
+            y_pred = y_pred.astype(np.int64)
     if not isinstance(y_pred, np.ndarray):
         raise TypeError(
             f'y_pred must be list or np.ndarray, but got {type(y_pred)}')
@@ -31,6 +33,8 @@ def confusion_matrix(y_pred, y_real, normalize=None):
 
     if isinstance(y_real, list):
         y_real = np.array(y_real)
+        if y_real.dtype == np.int32:
+            y_real = y_real.astype(np.int64)
     if not isinstance(y_real, np.ndarray):
         raise TypeError(
             f'y_real must be list or np.ndarray, but got {type(y_real)}')
diff --git a/requirements.txt b/requirements.txt
@@ -1,4 +1,3 @@
 -r requirements/build.txt
 -r requirements/optional.txt
--r requirements/runtime.txt
 -r requirements/tests.txt
diff --git a/requirements/build.txt b/requirements/build.txt
@@ -1,3 +1,8 @@
-# These must be installed before building mmaction2
+decord >= 0.4.1
+einops
+matplotlib
 numpy
+opencv-contrib-python
+Pillow
+scipy
 torch>=1.3
diff --git a/requirements/optional.txt b/requirements/optional.txt
@@ -1,6 +1,4 @@
 av
-decord >= 0.4.1
-einops
 imgaug
 librosa
 lmdb
diff --git a/requirements/runtime.txt b/requirements/runtime.txt
diff --git a/setup.py b/setup.py
@@ -186,11 +186,10 @@ def add_mim_extension():
         ],
         url='https://github.com/open-mmlab/mmaction2',
         license='Apache License 2.0',
-        install_requires=parse_requirements('requirements/runtime.txt'),
+        install_requires=parse_requirements('requirements/build.txt'),
         extras_require={
             'all': parse_requirements('requirements.txt'),
             'tests': parse_requirements('requirements/tests.txt'),
-            'build': parse_requirements('requirements/build.txt'),
             'optional': parse_requirements('requirements/optional.txt'),
         },
         zip_safe=False)
diff --git a/tests/test_data/test_pipelines/test_loadings/test_decode.py b/tests/test_data/test_pipelines/test_loadings/test_decode.py
@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import copy
+import platform
 
 import numpy as np
 from mmcv.utils import assert_dict_has_keys
@@ -408,34 +409,35 @@ def test_rawframe_decode(self):
                                              240, 320)
         assert results['original_shape'] == (240, 320)
 
-        # test frame selector in turbojpeg decoding backend
-        # when start_index = 0
-        inputs = copy.deepcopy(self.frame_results)
-        inputs['frame_inds'] = np.arange(0, self.total_frames, 5)
-        # since the test images start with index 1, we plus 1 to frame_inds
-        # in order to pass the CI
-        inputs['frame_inds'] = inputs['frame_inds'] + 1
-        frame_selector = RawFrameDecode(
-            io_backend='disk', decoding_backend='turbojpeg')
-        results = frame_selector(inputs)
-        assert assert_dict_has_keys(results, target_keys)
-        assert np.shape(results['imgs']) == (len(inputs['frame_inds']), 240,
-                                             320, 3)
-        assert results['original_shape'] == (240, 320)
-
-        # test frame selector in turbojpeg decoding backend
-        inputs = copy.deepcopy(self.frame_results)
-        inputs['frame_inds'] = np.arange(1, self.total_frames, 5)
-        frame_selector = RawFrameDecode(
-            io_backend='disk', decoding_backend='turbojpeg')
-        results = frame_selector(inputs)
-        assert assert_dict_has_keys(results, target_keys)
-        assert np.shape(results['imgs']) == (len(inputs['frame_inds']), 240,
-                                             320, 3)
-        assert results['original_shape'] == (240, 320)
-        assert repr(frame_selector) == (f'{frame_selector.__class__.__name__}('
-                                        f'io_backend=disk, '
-                                        f'decoding_backend=turbojpeg)')
+        if platform.system() != 'Windows':
+            # test frame selector in turbojpeg decoding backend
+            # when start_index = 0
+            inputs = copy.deepcopy(self.frame_results)
+            inputs['frame_inds'] = np.arange(0, self.total_frames, 5)
+            # since the test images start with index 1, we plus 1 to frame_inds
+            # in order to pass the CI
+            inputs['frame_inds'] = inputs['frame_inds'] + 1
+            frame_selector = RawFrameDecode(
+                io_backend='disk', decoding_backend='turbojpeg')
+            results = frame_selector(inputs)
+            assert assert_dict_has_keys(results, target_keys)
+            assert np.shape(results['imgs']) == (len(inputs['frame_inds']),
+                                                 240, 320, 3)
+            assert results['original_shape'] == (240, 320)
+
+            # test frame selector in turbojpeg decoding backend
+            inputs = copy.deepcopy(self.frame_results)
+            inputs['frame_inds'] = np.arange(1, self.total_frames, 5)
+            frame_selector = RawFrameDecode(
+                io_backend='disk', decoding_backend='turbojpeg')
+            results = frame_selector(inputs)
+            assert assert_dict_has_keys(results, target_keys)
+            assert np.shape(results['imgs']) == (len(inputs['frame_inds']),
+                                                 240, 320, 3)
+            assert results['original_shape'] == (240, 320)
+            assert repr(frame_selector) == (
+                f'{frame_selector.__class__.__name__}(io_backend=disk, '
+                f'decoding_backend=turbojpeg)')
 
     def test_audio_decode_init(self):
         target_keys = ['audios', 'length', 'sample_rate']
diff --git a/tests/test_metrics/test_accuracy.py b/tests/test_metrics/test_accuracy.py
@@ -320,7 +320,7 @@ def test_top_k_accurate_classes():
         np.array([0.25, 0.1, 0.3, 0.35]),  # 3
         np.array([0.2, 0.15, 0.3, 0.35]),  # 3
     ]
-    label = np.array([3, 2, 2, 1, 3, 3])
+    label = np.array([3, 2, 2, 1, 3, 3], dtype=np.int64)
 
     with pytest.raises(AssertionError):
         top_k_classes(scores, label, 1, mode='wrong')
@@ -333,7 +333,7 @@ def test_top_k_accurate_classes():
     assert results_top1 == [(3, 1.)]
     assert results_top3 == [(3, 1.), (2, 0.5), (1, 0.0)]
 
-    label = np.array([3, 2, 1, 1, 3, 0])
+    label = np.array([3, 2, 1, 1, 3, 0], dtype=np.int64)
     results_top1 = top_k_classes(scores, label, 1, mode='inaccurate')
     results_top3 = top_k_classes(scores, label, 3, mode='inaccurate')
     assert len(results_top1) == 1
diff --git a/tests/test_models/test_localizers/test_bmn.py b/tests/test_models/test_localizers/test_bmn.py
@@ -1,11 +1,15 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import platform
+
 import numpy as np
+import pytest
 import torch
 
 from mmaction.models import build_localizer
 from ..base import get_localizer_cfg
 
 
+@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
 def test_bmn():
     model_cfg = get_localizer_cfg(
         'bmn/bmn_400x100_2x8_9e_activitynet_feature.py')
diff --git a/tests/test_models/test_localizers/test_pem.py b/tests/test_models/test_localizers/test_pem.py
@@ -1,10 +1,14 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import platform
+
+import pytest
 import torch
 
 from mmaction.models import build_localizer
 from ..base import get_localizer_cfg
 
 
+@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
 def test_pem():
     model_cfg = get_localizer_cfg(
         'bsn/bsn_pem_400x100_1x16_20e_activitynet_feature.py')
diff --git a/tests/test_models/test_localizers/test_ssn.py b/tests/test_models/test_localizers/test_ssn.py
@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import copy
+import platform
 
 import mmcv
 import pytest
@@ -8,6 +9,7 @@
 from mmaction.models import build_localizer
 
 
+@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
 def test_ssn_train():
     train_cfg = mmcv.ConfigDict(
         dict(
@@ -105,6 +107,7 @@ def test_ssn_train():
     assert isinstance(losses, dict)
 
 
+@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
 def test_ssn_test():
     test_cfg = mmcv.ConfigDict(
         dict(
diff --git a/tests/test_models/test_localizers/test_tem.py b/tests/test_models/test_localizers/test_tem.py
@@ -1,10 +1,14 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import platform
+
+import pytest
 import torch
 
 from mmaction.models import build_localizer
 from ..base import get_localizer_cfg
 
 
+@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
 def test_tem():
     model_cfg = get_localizer_cfg(
         'bsn/bsn_tem_400x100_1x16_20e_activitynet_feature.py')

-Original file line number
+Diff line change
 av
 -decord >= 0.4.1
 -einops
 imgaug
 librosa
 lmdb