Trying to export the encoder model based on this project 1, since the onnx export function do works well with list output, I change the forward function of the encoder from
def forward(self, input_image):
        self.features = []
        x = (input_image - 0.45) / 0.225
        x = self.encoder.conv1(x)
        x = self.encoder.bn1(x)
        self.features.append(self.encoder.relu(x))
        self.features.append(self.encoder.layer1(self.encoder.maxpool(self.features[-1])))
        self.features.append(self.encoder.layer2(self.features[-1]))
        self.features.append(self.encoder.layer3(self.features[-1]))
        self.features.append(self.encoder.layer4(self.features[-1]))

        return self.features

To
def forward(self, input_image):        
        x = (input_image - 0.45) / 0.225
        x = self.encoder.conv1(x)
        x = self.encoder.bn1(x)		
        output1 = self.encoder.relu(x)
        output2 = self.encoder.layer1(self.encoder.maxpool(output1))
        output3 = self.encoder.layer2(output2)
        output4 = self.encoder.layer3(output3)
        output5 = self.encoder.layer4(output4)
		
        output_tensor = torch.ones(2887680)
        #guess I do not need to call clone?
        output_tensor[0:1966080] = output1.flatten()
        output_tensor[1966080:2457600] = output2.flatten()
        output_tensor[2457600:2703360] = output3.flatten()
        output_tensor[2703360:2826240] = output4.flatten()
        output_tensor[2826240:] = output5.flatten()

        return output_tensor

It show me warning
TracerWarning: There are 2 live references to the data region being modified when tracing in-place operator copy_ (possibly due to an assignment). This might cause the trace to be incorrect, because all other views that also reference this data will not reflect this change in the trace! On the other hand, if all other views use the same memory chunk, but are disjoint (e.g. are outputs of torch.split), this might still be safe.
Is this safe?