gemini-code-assist[bot] commented on code in PR #18550:
URL: https://github.com/apache/tvm/pull/18550#discussion_r2593399949
##########
tests/python/relax/test_frontend_from_exported_program.py:
##########
@@ -4900,6 +4900,67 @@ def main(
verify_model(InterpolateBilinearAA(), example_args, {},
expected_bilinear_aa)
+def test_interpolate_scale_factor_list():
+ class InterpolateNearestScalar(Module):
+ """Nearest interpolation with scalar scale_factor."""
+
+ def forward(self, input):
+ return torch.nn.functional.interpolate(input, scale_factor=2.0,
mode="nearest")
+
+ class InterpolateNearestList(Module):
+ """Nearest interpolation with list scale_factor (different H and W
scaling)."""
+
+ def forward(self, input):
+ return torch.nn.functional.interpolate(input, scale_factor=[2.0,
3.0], mode="nearest")
+
+ class InterpolateBilinearScalar(Module):
+ """Bilinear interpolation with scalar scale_factor."""
+
+ def forward(self, input):
+ return torch.nn.functional.interpolate(
+ input, scale_factor=2.0, mode="bilinear", align_corners=False
+ )
+
+ class InterpolateBilinearList(Module):
+ """Bilinear interpolation with list scale_factor (different H and W
scaling)."""
+
+ def forward(self, input):
+ return torch.nn.functional.interpolate(
+ input, scale_factor=[2.0, 3.0], mode="bilinear",
align_corners=False
+ )
+
+ class InterpolateBicubicScalar(Module):
+ """Bicubic interpolation with scalar scale_factor."""
+
+ def forward(self, input):
+ return torch.nn.functional.interpolate(
+ input, scale_factor=2.0, mode="bicubic", align_corners=False
+ )
+
+ class InterpolateBicubicList(Module):
+ """Bicubic interpolation with list scale_factor (different H and W
scaling)."""
+
+ def forward(self, input):
+ return torch.nn.functional.interpolate(
+ input, scale_factor=[2.0, 3.0], mode="bicubic",
align_corners=False
+ )
+
+ # Test with 32x32 input
+ example_args = (torch.randn(1, 3, 32, 32, dtype=torch.float32),)
+
+ # Test nearest mode with scalar and list scale_factor
+ verify_model_numerically(InterpolateNearestScalar(), example_args,
rtol=1e-5, atol=1e-5)
+ verify_model_numerically(InterpolateNearestList(), example_args,
rtol=1e-5, atol=1e-5)
+
+ # Test bilinear mode with scalar and list scale_factor
+ verify_model_numerically(InterpolateBilinearScalar(), example_args,
rtol=1e-5, atol=1e-5)
+ verify_model_numerically(InterpolateBilinearList(), example_args,
rtol=1e-5, atol=1e-5)
+
+ # Test bicubic mode with scalar and list scale_factor
+ verify_model_numerically(InterpolateBicubicScalar(), example_args,
rtol=1e-5, atol=1e-5)
+ verify_model_numerically(InterpolateBicubicList(), example_args,
rtol=1e-5, atol=1e-5)
+
Review Comment:
This test is great for covering the different interpolation modes with both
scalar and list `scale_factor`s. However, there's a lot of repeated code
between the different test cases. You can significantly simplify this by using
`pytest.mark.parametrize` to iterate through the different modes and scale
factors. This will make the test more concise and easier to maintain or extend
in the future.
```python
@pytest.mark.parametrize(
"mode, scale_factor",
[
("nearest", 2.0),
("nearest", [2.0, 3.0]),
("bilinear", 2.0),
("bilinear", [2.0, 3.0]),
("bicubic", 2.0),
("bicubic", [2.0, 3.0]),
],
)
def test_interpolate_scale_factor_list(mode, scale_factor):
"""Test interpolation with various modes and scale factors."""
class InterpolateModel(Module):
def forward(self, input_tensor):
kwargs = {"scale_factor": scale_factor, "mode": mode}
if mode != "nearest":
kwargs["align_corners"] = False
return torch.nn.functional.interpolate(input_tensor, **kwargs)
# Test with 32x32 input
example_args = (torch.randn(1, 3, 32, 32, dtype=torch.float32),)
verify_model_numerically(InterpolateModel(), example_args, rtol=1e-5,
atol=1e-5)
```
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