From e6636324a72a04487ca904e3a5bb5f947898520c Mon Sep 17 00:00:00 2001
From: maturk <matiasturkulainen@gmail.com>
Date: Tue, 3 Oct 2023 13:33:35 +0300
Subject: [PATCH 1/2] refactor and move SH test

---
 examples/test_sh.py | 61 ---------------------------------------------
 tests/test_sh.py    | 50 +++++++++++++++++++++++++++++++++++++
 2 files changed, 50 insertions(+), 61 deletions(-)
 delete mode 100644 examples/test_sh.py
 create mode 100644 tests/test_sh.py

diff --git a/examples/test_sh.py b/examples/test_sh.py
deleted file mode 100644
index 2f1265264..000000000
--- a/examples/test_sh.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import torch
-from diff_rast._torch_impl import compute_sh_color
-from diff_rast.sh import num_sh_bases, SphericalHarmonics
-
-
-if __name__ == "__main__":
-    device = torch.device("cuda:0")
-    num_points = 1
-    degree = 4
-
-    gt_colors = torch.ones(num_points, 3, device=device) * 0.5
-    viewdirs = torch.randn(num_points, 3, device=device)
-    viewdirs /= torch.linalg.norm(viewdirs, dim=-1, keepdim=True)
-    # print("viewdirs", viewdirs)
-    sh_coeffs = torch.rand(
-        num_points, num_sh_bases(degree), 3, device=device, requires_grad=True
-    )
-    # print("sh_coeffs", sh_coeffs)
-    optim = torch.optim.Adam([sh_coeffs], lr=1e-2)
-
-    num_iters = 100
-    for _ in range(num_iters):
-        optim.zero_grad()
-        # compute python version's color and grad
-        check_colors = compute_sh_color(viewdirs, sh_coeffs)
-        check_loss = torch.square(check_colors - gt_colors).mean()
-        check_loss.backward()
-        check_grad = sh_coeffs.grad.detach()
-
-        optim.zero_grad()
-        # compute our colors and grads
-        colors = SphericalHarmonics.apply(degree, viewdirs, sh_coeffs)
-        loss = torch.square(colors - gt_colors).mean()
-        loss.backward()
-        grad = sh_coeffs.grad.detach()
-        optim.step()
-
-        diff_colors = (check_colors - colors).detach()
-        diff_grad = (check_grad - grad).detach()
-
-        print(f"LOSS {loss.item():.2e}")
-        # print("colors", colors)
-        # print("check_colors", check_colors)
-        # print("diff_colors", diff_colors)
-        # print("grad", grad)
-        # print("check grad", check_grad)
-        # print("diff grad", diff_grad)
-        print(f"colors min {colors.min().item():.2e} max {colors.max().item():.2e}")
-        print(
-            f"check_colors min {check_colors.min().item():.2e} max {check_colors.max().item():.2e}"
-        )
-        print(
-            f"diff_colors min {diff_colors.min().item():.2e} max {diff_colors.max().item():.2e}"
-        )
-        print(f"grad min {grad.min().item():.2e} max {grad.max().item():.2e}")
-        print(
-            f"check_grad min {check_grad.min().item():.2e} max {check_grad.max().item():.2e}"
-        )
-        print(
-            f"diff_grad min {diff_grad.min().item():.2e} max {diff_grad.max().item():.2e}"
-        )
diff --git a/tests/test_sh.py b/tests/test_sh.py
new file mode 100644
index 000000000..ba4d351a0
--- /dev/null
+++ b/tests/test_sh.py
@@ -0,0 +1,50 @@
+import pytest
+import torch
+
+
+device = torch.device("cuda:0")
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="No CUDA device")
+def test_sh():
+    from diff_rast import _torch_impl
+    from diff_rast import sh
+
+    num_points = 1
+    degree = 4
+    gt_colors = torch.ones(num_points, 3, device=device) * 0.5
+    viewdirs = torch.randn(num_points, 3, device=device)
+    viewdirs /= torch.linalg.norm(viewdirs, dim=-1, keepdim=True)
+    sh_coeffs = torch.rand(
+        num_points, sh.num_sh_bases(degree), 3, device=device, requires_grad=True
+    )
+    optim = torch.optim.Adam([sh_coeffs], lr=1e-2)
+
+    num_iters = 1000
+    for _ in range(num_iters):
+        optim.zero_grad()
+
+        # compute PyTorch's color and grad
+        check_colors = _torch_impl.compute_sh_color(viewdirs, sh_coeffs)
+        check_loss = torch.square(check_colors - gt_colors).mean()
+        check_loss.backward()
+        check_grad = sh_coeffs.grad.detach()
+
+        optim.zero_grad()
+        
+        # compute our colors and grads
+        colors = sh.SphericalHarmonics.apply(degree, viewdirs, sh_coeffs)
+        loss = torch.square(colors - gt_colors).mean()
+        loss.backward()
+        grad = sh_coeffs.grad.detach()
+        optim.step()
+
+        torch.testing.assert_close(check_grad, grad)
+        torch.testing.assert_close(check_colors, colors)
+
+    # check final optimized color
+    torch.testing.assert_close(check_colors, gt_colors)
+
+
+if __name__ == "__main__":
+    test_sh()

From cc51309ced0bc6e56f7430f798491ab5eb7ba104 Mon Sep 17 00:00:00 2001
From: maturk <matiasturkulainen@gmail.com>
Date: Tue, 3 Oct 2023 13:37:09 +0300
Subject: [PATCH 2/2] format

---
 tests/test_sh.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_sh.py b/tests/test_sh.py
index ba4d351a0..5bda5c951 100644
--- a/tests/test_sh.py
+++ b/tests/test_sh.py
@@ -31,7 +31,7 @@ def test_sh():
         check_grad = sh_coeffs.grad.detach()
 
         optim.zero_grad()
-        
+
         # compute our colors and grads
         colors = sh.SphericalHarmonics.apply(degree, viewdirs, sh_coeffs)
         loss = torch.square(colors - gt_colors).mean()