Add GH workflow to run tests

.github/workflows/tests.yaml

@@ -0,0 +1,32 @@
+name: Tests
+
+on:
+  push:
+    branches: [ "master" ]
+  pull_request:
+    branches: [ "master" ]
+  workflow_dispatch:
+
+jobs:
+  tests:
+    if: github.event.pull_request.draft == false
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v4
+
+    - name: Set up Python Env
+      uses: actions/setup-python@v5
+      with:
+        python-version: "3.10"
+        cache: 'pip'
+
+    - name: Install Python Dependencies
+      run: pip install -r requirements.txt
+
+    - name: Install
+      run: python -m pip install -e .
+
+    - name: Test
+      run: python -m pytest $GITHUB_WORKSPACE/test/

requirements.txt

@@ -0,0 +1,15 @@
+--extra-index-url https://download.pytorch.org/whl/cpu
+torch
+torchvision
+scipy
+numpy
+opencv-python
+kornia
+matplotlib
+tqdm
+h5py
+onnx
+onnxruntime
+strenum
+tabulate
+pytest

scripts/export_model.py

@@ -42,7 +42,7 @@ def clear_denormals(state_dict, threshold=1.e-20):
             mask = torch.abs(v) > threshold
             n = torch.count_nonzero(~mask)
             if n:
-                print (f"{k:40s}: {n:10d} ({n/np.product(v.shape)*100}%)")
+                print (f"{k:40s}: {n:10d} ({n/np.prod(v.shape)*100}%)")
             v *= mask.to(torch.float32)
     return state_dict
 

test/test_dshdf5pose.py

@@ -40,7 +40,7 @@ def mytrafo(sample):
 
     ds = dshdf5pose.Hdf5PoseDataset(filename, transform = mytrafo, dataclass = 42)
 
-    assert np.alltrue(ds.sequence_starts == np.asarray(sequence_starts))
+    assert np.all(ds.sequence_starts == np.asarray(sequence_starts))
 
     sample = ds[0]
     assert isinstance(sample, batch.Batch)

test/test_models_sanity.py

@@ -22,7 +22,7 @@ def timing_and_output(net, x):
 
 
 def backprop(net):
-    device = 'cuda'
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
     B = 16
     net = copy.deepcopy(net)
     net.train()

trackertraincode/eval.py

@@ -382,7 +382,7 @@ def __init__(self, fov):
         self.f = 1. / math.tan(fov*math.pi/180.*0.5)
 
     def corrected_rotation(self, image_sizes : Tensor, coord : Tensor, pose : Tensor):
-        '''
+        r'''
             Explanation though top view
                                        ^ face-local z-axis
                          z-axis ^      |   ^ direction under which the CNN "sees" the face through it's crop