Merge pull request #205 from Visual-Behavior/github_actions

GitHub actions

Author: thibo73800

.github/workflows/action.yml

@@ -0,0 +1,48 @@
+name: Aloception unit tests
+on:
+  push:
+    branches:
+      - 'master'
+  pull_request:
+    branches:
+      - 'master'
+
+jobs:
+  unit_tests:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v3
+    - name: Install packages
+      run : |
+        pip install numpy==1.22.2
+        pip install torch==1.10.2+cpu torchvision==0.11.3+cpu torchaudio==0.10.2 -f https://download.pytorch.org/whl/torch_stable.html
+        pip install -r requirements.txt
+      shell: bash
+    - name: Run python unit tests
+      run: |
+        invalid=0
+        for file in unittest/*;
+        do
+          echo "Testing : $file"
+          res=$(grep "GithubActionsIgnore" "$file" | wc -l || true)
+          if [ "$res" = "0" ]
+          then
+            if PYTHONPATH=${GITHUB_WORKSPACE} python3 "$file"; then
+              echo "::notice file=$file::Tests : ok"
+            else
+              echo "::error file=$file::Something wrong happened during execution"
+              invalid=$((invalid+1))
+            fi
+          else
+            echo "::warning file=$file::This file should be ignore on github actions"
+          fi
+        done
+        if [ "$invalid" -eq 0 ]
+        then
+            echo "::notice title="All possible tests are valid"::Congratulation !"
+            exit 0
+        else
+            echo "::error title="Final review"::Some tests return errors"
+            exit 1
+        fi
+      shell: bash

aloscene/tensors/augmented_tensor.py

@@ -299,10 +299,11 @@ def __getitem__(self, idx):
                 )
 
         if isinstance(idx, torch.Tensor):
-            if not idx.dtype == torch.bool:
-                if not torch.equal(idx ** 3, idx):
-                    raise IndexError(f"Unvalid mask. Expected mask elements to be in [0, 1, True, False]")
-            tensor = self * idx
+            tensor = torch.Tensor.__getitem__(self.rename(None), idx).reset_names()
+            #if not idx.dtype == torch.bool:
+            #    if not torch.equal(idx ** 3, idx):
+            #        raise IndexError(f"Unvalid mask. Expected mask elements to be in [0, 1, True, False]")
+            #tensor = self * idx
         else:
             tensor = torch.Tensor.__getitem__(self, idx)
 

aloscene/utils/rotated_iou/cuda_op/cuda_ext.py

@@ -4,8 +4,9 @@
 try:
     import sort_vertices
     sort_vertices_error = None
-except Exception as sort_vertices_error:
+except Exception as e:
     sort_vertices= None
+    sort_vertices_error = e
     pass
 
 
@@ -14,8 +15,7 @@ class SortVertices(Function):
     def forward(ctx, vertices, mask, num_valid):
 
         if sort_vertices is None:
-            print(sort_vertices_error)
-            raise Exception("To install: cd aloception/aloscene/utils/rotated_iou/cuda_op; python setup.py install --user ")
+            raise Exception(f"To install: cd aloception/aloscene/utils/rotated_iou/cuda_op; python setup.py install --user. Error={sort_vertices_error}")
 
         idx = sort_vertices.sort_vertices_forward(vertices, mask, num_valid)
         ctx.mark_non_differentiable(idx)

unittest/test_boxes.py

@@ -384,10 +384,7 @@ def test_crop_abs():
         boxes, boxes_format="xcyc", frame_size=(frame.H, frame.W), absolute=True, labels=labels
     )
     frame.append_boxes2d(boxes)
-    # frame.get_view().render()
-    frame = frame.crop(H_crop=(0.0, 0.5), W_crop=(0.0, 0.5))
-    # frame.get_view().render()
-    # frame.get_view().render()
+    frame = frame.crop(H_crop=(0.0, 0.4), W_crop=(0.0, 0.4))
     assert torch.allclose(frame.boxes2d[0].as_tensor(), boxes[0].as_tensor())
     assert np.allclose(frame.boxes2d.frame_size[0], frame.HW[0])
     assert np.allclose(frame.boxes2d.frame_size[1], frame.HW[1])

unittest/test_boxes_3d.py

@@ -6,7 +6,7 @@
 
 TEST_FRAME = 0
 CAMERAS = ["front", "front_left", "front_right", "side_left", "side_right"]
-device = torch.device("cuda")
+device = torch.device("cpu")
 
 waymo_dataset = WaymoDataset(sample=True)
 
@@ -133,7 +133,9 @@ def test_giou3d_2():
     test_shape_vertices_3d_proj()
     test_shape_boxes_3d_proj()
     test_hflip()
-    test_giou3d_same_box()
-    test_giou3d_same_face()
-    test_giou3d_1()
-    test_giou3d_2()
+    # if cuda is available, run the tests on cuda. (giou use custom cuda op to compile)
+    if torch.cuda.is_available():
+        test_giou3d_same_box()
+        test_giou3d_same_face()
+        test_giou3d_1()
+        test_giou3d_2()

unittest/test_frame.py

@@ -504,14 +504,15 @@ def test_device_propagation():
     assert frame.disparity.occlusion["occ2"].device == torch.device("cpu")
     assert frame.flow[0].occlusion.device == torch.device("cpu")
 
-    cuda_frame = frame.cuda()
-    assert cuda_frame.boxes2d["boxes1"][0].device != torch.device("cpu")
-    assert cuda_frame.boxes2d["boxes2"][0].device != torch.device("cpu")
-    assert cuda_frame.disparity.device != torch.device("cpu")
-    assert cuda_frame.flow[0].device != torch.device("cpu")
-    assert cuda_frame.disparity.occlusion["occ1"].device != torch.device("cpu")
-    assert cuda_frame.disparity.occlusion["occ2"].device != torch.device("cpu")
-    assert cuda_frame.flow[0].occlusion.device != torch.device("cpu")
+    if torch.cuda.is_available():
+        cuda_frame = frame.cuda()
+        assert cuda_frame.boxes2d["boxes1"][0].device != torch.device("cpu")
+        assert cuda_frame.boxes2d["boxes2"][0].device != torch.device("cpu")
+        assert cuda_frame.disparity.device != torch.device("cpu")
+        assert cuda_frame.flow[0].device != torch.device("cpu")
+        assert cuda_frame.disparity.occlusion["occ1"].device != torch.device("cpu")
+        assert cuda_frame.disparity.occlusion["occ2"].device != torch.device("cpu")
+        assert cuda_frame.flow[0].occlusion.device != torch.device("cpu")
 
     cpu_frame = frame.cpu()
     assert cpu_frame.boxes2d["boxes1"][0].device == torch.device("cpu")

unittest/test_oriented_boxes_2d.py

@@ -3,7 +3,7 @@
 import aloscene
 from aloscene import OrientedBoxes2D
 
-device = torch.device("cuda")
+device = torch.device("cpu")
 
 
 def tensor_equal(tensor1, tensor2, threshold=1e-4):
@@ -14,6 +14,8 @@ def tensor_equal(tensor1, tensor2, threshold=1e-4):
 
 
 def test_same_box():
+    # if cuda not avaible
+    if not torch.cuda.is_available(): return
     box1 = OrientedBoxes2D(torch.tensor([[0.0, 0.0, 2.0, 2.0, 0.0]], device=device))
     box2 = OrientedBoxes2D(torch.tensor([[0.0, 0.0, 2.0, 2.0, 0.0]], device=device))
     expected_iou = torch.tensor([1.0], device=device)
@@ -24,6 +26,8 @@ def test_same_box():
 
 
 def test_same_edge():
+    # if cuda not avaible
+    if not torch.cuda.is_available(): return
     box1 = OrientedBoxes2D(torch.tensor([[0.0, 0.0, 2.0, 2.0, 0.0]], device=device))
     box2 = OrientedBoxes2D(torch.tensor([[2.0, 0.0, 2.0, 2.0, 0.0]], device=device))
     expected_iou = torch.tensor([0.0], device=device)
@@ -35,6 +39,8 @@ def test_same_edge():
 
 
 def test_1():
+    # if cuda not avaible
+    if not torch.cuda.is_available(): return
     box1 = OrientedBoxes2D(torch.tensor([[0.0, 0.0, 2.0, 2.0, 0.0]], device=device))
     box2 = OrientedBoxes2D(torch.tensor([[1.0, 0.0, 2.0, 2.0, 0.0]], device=device))
     expected_iou = torch.tensor([1 / 3], device=device)
@@ -45,6 +51,8 @@ def test_1():
 
 
 def test_2():
+    # if cuda not avaible
+    if not torch.cuda.is_available(): return
     box1 = OrientedBoxes2D(torch.tensor([[0.0, 0.0, 2.0, 2.0, 0.0]], device=device))
     box2 = OrientedBoxes2D(torch.tensor([[1.0, 1.0, 2.0, 2.0, 0.0]], device=device))
     expected_iou = torch.tensor([1 / 7], device=device)
@@ -55,6 +63,8 @@ def test_2():
 
 
 def test_3():
+    # if cuda not avaible
+    if not torch.cuda.is_available(): return
     box1 = OrientedBoxes2D(torch.tensor([[0.0, 0.0, 2.0, 2.0, 0.0]], device=device))
     box2 = OrientedBoxes2D(torch.tensor([[1.0, 1.0, 2.0, 2.0, np.pi / 2]], device=device))
     expected_iou = torch.tensor([1 / 7], device=device)
@@ -65,6 +75,8 @@ def test_3():
 
 
 def test_4():
+    # if cuda not avaible
+    if not torch.cuda.is_available(): return
     box1 = OrientedBoxes2D(torch.tensor([[0.0, 0.0, 2.0, 2.0, 0.0]], device=device))
     box2 = OrientedBoxes2D(
         torch.tensor([[1.0, 1.0, np.sqrt(2), np.sqrt(2), np.pi / 4]], device=device, dtype=torch.float)

unittest/test_train.py

@@ -1,6 +1,7 @@
 import argparse
 from unittest import mock
 
+# GithubActionsIgnore : cuda
 
 def get_argparse_defaults(parser):
     defaults = {}