[Fix] Fix UT to be compatible with pytorch 1.6 (#8707)

* Update * Update * force reinstall pycocotools * Fix build_cuda * docker install git * Update * comment other job to speedup process * update * uncomment * Update * Update * Add comments for --force-reinstall
open-mmlab · Sep 26, 2022 · 99ce362 · 99ce362
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commit 99ce362
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Showing 5 changed files with 20 additions and 16 deletions.
diff --git a/.circleci/test.yml b/.circleci/test.yml
@@ -66,10 +66,13 @@ jobs:
             - run: pip install "protobuf <= 3.20.1" && sudo apt-get update && sudo apt-get -y install libprotobuf-dev protobuf-compiler cmake
       - run:
           name: Install mmdet dependencies
+          # numpy may be downgraded after building pycocotools, which causes `ImportError: numpy.core.multiarray failed to import`
+          # force reinstall pycocotools to ensure pycocotools being built under the currenct numpy
           command: |
             python -m pip install git+ssh://git@github.com/open-mmlab/mmengine.git@main
             python -m pip install << parameters.mmcv >>
             pip install -r requirements/tests.txt -r requirements/optional.txt
+            pip install --force-reinstall pycocotools
             pip install albumentations>=0.3.2 --no-binary imgaug,albumentations
             pip install git+https://github.com/cocodataset/panopticapi.git
       - run:
@@ -111,17 +114,18 @@ jobs:
           command: |
             docker build .circleci/docker -t mmdetection:gpu --build-arg PYTORCH=<< parameters.torch >> --build-arg CUDA=<< parameters.cuda >> --build-arg CUDNN=<< parameters.cudnn >>
             docker run --gpus all -t -d -v /home/circleci/project:/mmdetection -v /home/circleci/mmengine:/mmengine -w /mmdetection --name mmdetection mmdetection:gpu
+            docker exec mmdetection apt-get install -y git
       - run:
           name: Install mmdet dependencies
           # pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch${{matrix.torch_version}}/index.html
           command: |
             docker exec mmdetection pip install -e /mmengine
             docker exec mmdetection pip install << parameters.mmcv >>
-            pip install -r requirements/tests.txt -r requirements/optional.txt
-            pip install pycocotools
-            pip install albumentations>=0.3.2 --no-binary imgaug,albumentations
-            pip install git+https://github.com/cocodataset/panopticapi.git
-            python -c 'import mmcv; print(mmcv.__version__)'
+            docker exec mmdetection pip install -r requirements/tests.txt -r requirements/optional.txt
+            docker exec mmdetection pip install pycocotools
+            docker exec mmdetection pip install albumentations>=0.3.2 --no-binary imgaug,albumentations
+            docker exec mmdetection pip install git+https://github.com/cocodataset/panopticapi.git
+            docker exec mmdetection python -c 'import mmcv; print(mmcv.__version__)'
       - run:
           name: Build and install
           command: |

diff --git a/mmdet/datasets/transforms/transforms.py b/mmdet/datasets/transforms/transforms.py
@@ -1136,7 +1136,7 @@ def transform(self, results: dict) -> dict:
                 if patch[2] == patch[0] or patch[3] == patch[1]:
                     continue
                 overlaps = boxes.overlaps(
-                    HorizontalBoxes(patch.reshape(-1, 4)),
+                    HorizontalBoxes(patch.reshape(-1, 4).astype(np.float32)),
                     boxes).numpy().reshape(-1)
                 if len(overlaps) > 0 and overlaps.min() < min_iou:
                     continue

diff --git a/tests/test_datasets/test_transforms/test_transforms.py b/tests/test_datasets/test_transforms/test_transforms.py
@@ -1495,7 +1495,7 @@ def setUp(self):
 
     def test_transform(self):
         transform = RandomErasing(
-            n_patches=(1, 5), ratio=(0.2, 0.5), img_border_value=0)
+            n_patches=(1, 5), ratio=(0.4, 0.8), img_border_value=0)
         results = transform(copy.deepcopy(self.results))
         self.assertTrue(results['img'].sum() < self.results['img'].sum())
 
@@ -1519,14 +1519,14 @@ def test_transform_with_boxlist(self):
         src_results['gt_bboxes'] = HorizontalBoxes(src_results['gt_bboxes'])
 
         transform = RandomErasing(
-            n_patches=(1, 5), ratio=(0.2, 0.5), img_border_value=0)
+            n_patches=(1, 5), ratio=(0.4, 0.8), img_border_value=0)
         results = transform(copy.deepcopy(src_results))
         self.assertTrue(results['img'].sum() < src_results['img'].sum())
 
         transform = RandomErasing(
             n_patches=1, ratio=0.999, img_border_value=255)
         results = transform(copy.deepcopy(src_results))
-        self.assertTrue(results['img'].sum() > self.results['img'].sum())
+        self.assertTrue(results['img'].sum() > src_results['img'].sum())
         # test empty results
         empty_results = copy.deepcopy(src_results)
         empty_results['gt_bboxes'] = HorizontalBoxes([], dtype=torch.float32)
@@ -1536,7 +1536,7 @@ def test_transform_with_boxlist(self):
         empty_results['gt_seg_map'] = np.ones_like(
             empty_results['gt_seg_map']) * 255
         results = transform(copy.deepcopy(empty_results))
-        self.assertTrue(results['img'].sum() > self.results['img'].sum())
+        self.assertTrue(results['img'].sum() > src_results['img'].sum())
 
     def test_repr(self):
         transform = RandomErasing(n_patches=(1, 5), ratio=(0, 0.2))

diff --git a/tests/test_engine/test_hooks/test_visualization_hook.py b/tests/test_engine/test_hooks/test_visualization_hook.py
@@ -16,12 +16,12 @@
 def _rand_bboxes(num_boxes, h, w):
     cx, cy, bw, bh = torch.rand(num_boxes, 4).T
 
-    tl_x = ((cx * w) - (w * bw / 2)).clip(0, w)
-    tl_y = ((cy * h) - (h * bh / 2)).clip(0, h)
-    br_x = ((cx * w) + (w * bw / 2)).clip(0, w)
-    br_y = ((cy * h) + (h * bh / 2)).clip(0, h)
+    tl_x = ((cx * w) - (w * bw / 2)).clamp(0, w)
+    tl_y = ((cy * h) - (h * bh / 2)).clamp(0, h)
+    br_x = ((cx * w) + (w * bw / 2)).clamp(0, w)
+    br_y = ((cy * h) + (h * bh / 2)).clamp(0, h)
 
-    bboxes = torch.vstack([tl_x, tl_y, br_x, br_y]).T
+    bboxes = torch.stack([tl_x, tl_y, br_x, br_y], dim=0).T
     return bboxes
 
 

diff --git a/tests/test_visualization/test_local_visualizer.py b/tests/test_visualization/test_local_visualizer.py
@@ -19,7 +19,7 @@ def _rand_bboxes(num_boxes, h, w):
     br_x = ((cx * w) + (w * bw / 2)).clamp(0, w)
     br_y = ((cy * h) + (h * bh / 2)).clamp(0, h)
 
-    bboxes = torch.vstack([tl_x, tl_y, br_x, br_y]).T
+    bboxes = torch.stack([tl_x, tl_y, br_x, br_y], dim=0).T
     return bboxes