Can`t export mobilenetv3 model to onnx

model = models.mobilenet_v3_small(pretrained=True) input_np = np.random.uniform(0, 1, (1, 3, 224, 224)) input_var = torch.FloatTensor(input_np) torch.onnx.export(model, args=(input_var), f="cnn.onnx", verbose=False, input_names=["input"], output_names=["output"])

Error: RuntimeError: Exporting the operator hardsigmoid to ONNX opset version 9 is not supported. Please feel free to request support or submit a pull request on PyTorch GitHub.

cc @neginraoof

[zhiqwang]

Hi @jkparuchuri

Sorry, I haven’t fully tested my proposal and you are right. I only tested exporting nn.Hardswish before. I’ve missed the Hardsigmoid in

https://github.com/pytorch/vision/blob/c991db82abba12e664eeac14c9b643d0f1f1a7df/torchvision/models/mobilenetv3.py#L35

As you mentioned, torch.export doesn’t support this operator,

I replaced this operator with F.hardtanh to solve this problem (I’ve tested the whole model now). You can do something as following to address this error.

diff --git a/torchvision/models/mobilenetv3.py b/torchvision/models/mobilenetv3.py
index 1e2606d..fdf1f6d 100644
--- a/torchvision/models/mobilenetv3.py
+++ b/torchvision/models/mobilenetv3.py
@@ -32,7 +32,7 @@ class SqueezeExcitation(nn.Module):
         scale = self.fc1(scale)
         scale = self.relu(scale)
         scale = self.fc2(scale)
-        return F.hardsigmoid(scale, inplace=inplace)
+        return F.hardtanh(scale + 3, 0., 6., inplace=inplace) / 6.
 
     def forward(self, input: Tensor) -> Tensor:
         scale = self._scale(input, True)

Now there are two ways to solve this bug,

1. Native support exporting F.hardsigmoid to onnx.
2. Replace F.hardsigmoid with F.hardtanh that is friendly for exporting and equal numerically as I did above.

And the export of mobilenetv3 to onnx is missing in the unit-test, maybe we could add a test like test_shufflenet_v2_dynamic_axes.

cc @datumbox @fmassa What is your suggestion?

[zhiqwang]

Hi

It seems that the nn.Hardswish caused this problem, actually the nightly version of PyTorch has addressed this problem, so the unit-test is passed.

If you are using PyTorch 1.7.x, you can replace it to an export friendly version of Hardswish as below and set ONNX opset version to 11.

class Hardswish(nn.Module):
    """
    Export-friendly version of nn.Hardswish()
    """
    def __init__(self):
        super().__init__()

    def forward(self, x):
        return x * F.hardtanh(x + 3, 0., 6.) / 6.