auto test and doc (#19)

* auto test and doc

* black

* update workflow

* inst

* dep in workflow

* updates

* a bit instr

---------

Co-authored-by: Ruilong Li <[email protected]>

.github/workflows/core_tests.yml

@@ -21,6 +21,10 @@ jobs:
           python-version: "3.8.12"
       - name: Install dependencies
         run: |
-          pip install black[jupyter]==22.3.0
+          pip install black[jupyter]==22.3.0 pytest
+          pip install torch==2.0.0 --index-url https://download.pytorch.org/whl/cpu
+          BUILD_NO_CUDA=1 pip install .
       - name: Run Black Format Check
         run: black . diff_rast/ tests/ examples/ --check
+      - name: Run Tests.
+        run: pytest tests/

README.md

@@ -2,10 +2,11 @@
 
 Our version of differentiable gaussian rasterizer
 
-# Installation
+## Installation
+
 Clone the repository and submodules with
 
-```
+```bash
 git clone --recurse-submodules URL
 ```
 
@@ -15,31 +16,44 @@ first run. The benefit of JIT compiling is that it does incremental compiling as
 you modify your cuda code so it is much faster than re-compile through pip. Note
 the JIT compiled library can be found under `~/.cache/torch_extensions/py*-cu*/`.
 
-```
+```bash
 BUILD_NO_CUDA=1 pip install -e .[dev]
 ```
 
 If you won't touch the underlying CUDA code, you can just install with compiling:
 
-```
+```bash
 pip install -e .[dev]
 ```
 
-# Development
+## Development
 
 ## Protect Main Branch over Pull Request.
-It is recommend to commit the code into the main branch as a PR over a hard push, as the
-PR would protect the main branch if the code break tests but a hard push won't.
+
+It is recommended to commit the code into the main branch as a PR over a hard push, as the PR would protect the main branch if the code break tests but a hard push won't. Also squash the commits before merging the PR so it won't span the git history.
 
 The curret tests that will be triggered by PR:
-- `.github/workflows/core_tests.yml`: Black formating.
+
+- `.github/workflows/core_tests.yml`: Black formating. Pytests.
 - `.github/workflows/doc.yml`: Doc build.
 
 Because we check for black formatting, it is recommend to run black before commit in the code:
+
+```bash
+black . diff_rast/ tests/ examples/
 ```
-black . diff_rast/ tests/ examples/ --check
+
+Since there is no GPU supported on github workflow container, we test against those cuda unit tests under `tests/`. So it is recommended to check test pass locally before committing:
+
+```bash
+pytest tests/  # check for all tests
+pytest tests/test_cov2d_bounds.py  # check for a single test file.
 ```
 
+Note that `pytest` recognizes and runs all functions named as `test_*`, so you should name the
+test functions in this pattern. See `test_cov2d_bounds.py` as an example.
+
+
 ## Build the Doc Locally
 If you want to contribute to the doc, here is the way to build it locally. The source code of the doc can be found in `docs/source` and the built doc will be in `_build/`. Here are some examples on documentation: [viser](https://github.com/nerfstudio-project/viser/tree/main/docs/source), [nerfstudio](https://github.com/nerfstudio-project/nerfstudio/tree/main/docs), [nerfacc](https://github.com/KAIR-BAIR/nerfacc/tree/master/docs/source).
 

diff_rast/_torch_impl.py

@@ -112,7 +112,7 @@ def eval_sh_bases(basis_dim: int, dirs: torch.Tensor):
 
 def quat_to_rotmat(quat: Tensor) -> Tensor:
     assert quat.shape[-1] == 4, quat.shape
-    x, y, z, w = torch.unbind(F.normalize(quat, dim=-1), dim=-1)
+    w, x, y, z = torch.unbind(F.normalize(quat, dim=-1), dim=-1)
     return torch.stack(
         [
             torch.stack(
@@ -163,6 +163,9 @@ def project_cov3d_ewa(
     W = viewmat[..., :3, :3]  # (..., 3, 3)
     p = viewmat[..., :3, 3]  # (..., 3)
     t = torch.matmul(W, mean3d[..., None])[..., 0] + p  # (..., 3)
+    raise NotImplementedError(
+        "Need to incorporate changes from this commit: 85e76e1c8b8e102145922f561800a74262ceb196!"
+    )
     rz = 1.0 / t[..., 2]  # (...,)
     rz2 = rz**2  # (...,)
     J = torch.stack(

tests/test_cov2d_bounds.py

@@ -5,8 +5,8 @@
 device = torch.device("cuda:0")
 
 
-@pytest.mark.skipif(not torch.cuda.is_available, reason="No CUDA device")
-def compare_binding_to_pytorch():
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="No CUDA device")
+def test_compare_binding_to_pytorch():
     from diff_rast._torch_impl import compute_cov2d_bounds as _compute_cov2d_bounds
     from diff_rast.cov2d_bounds import compute_cov2d_bounds
 
@@ -27,13 +27,13 @@ def compare_binding_to_pytorch():
     )
 
     conic, radii = compute_cov2d_bounds.apply(covs2d)
-    _conic, _radii, _ = _compute_cov2d_bounds(_covs2d)
+    _conic, _radii, _mask = _compute_cov2d_bounds(_covs2d)
 
     radii = radii.squeeze(-1)
 
-    torch.testing.assert_close(conic, _conic)
-    torch.testing.assert_close(radii, _radii)
+    torch.testing.assert_close(conic[_mask], _conic[_mask])
+    torch.testing.assert_close(radii[_mask], _radii[_mask])
 
 
 if __name__ == "__main__":
-    compare_binding_to_pytorch()
+    test_compare_binding_to_pytorch()