I am going to use TensorRT to accelerate my inference step. For many issues, like the input data is a dict, it cannot be converted to ONNX.

For ONNX, dictionary inputs are not allowed. If there is a function like this:

def forward(self, data):
    x1 = data['label1']
    x2 = data['label2']
    return x1, x2
dummy_input = data

Then converting it to the following format would be suitable:

def forward(self, x1, x2):
    return x1, x2
dummy_input = data['label1'], data['label2']

In this pic, qcnet's input data is a nested dict, which means i need to flatten it to a all tensor parameter list according to your method.

def forward(self, data):
    scene_enc = self.encoder(data)
    pred = self.decoder(data, scene_enc)
    return pred

Editing the function:

def forward(self, x1, x2, x3, x4, x5):
    # Each para of x1,x2,x3,x4,x5 is a tensor in origin dict
    scene_enc = self.encoder(x1,x2,x3,x4,x5)
    pred = self.decoder(x1,x2,x3,x4,x5)
    return pred

Moreover, my model has been trained in original codes from author ZHOU. Is it necessary to rebuild the structures of network, to format dict to tensor in every layers, so that it is suitable to recieve pure tensor data.

Is it necessary to rebuild the structures of network, to format dict to tensor in every layers, so that it is suitable to recieve pure tensor data.

You're right. You need to change the model's input and other code using dictionaries to ensure the model uses pure tensors to forward.

Is it necessary to rebuild the structures of network, to format dict to tensor in every layers, so that it is suitable to recieve pure tensor data.

You're right. You need to change the model's input and other code using dictionaries to ensure the model uses pure tensors to forward.

Thanks for your reply. I will try to deal with it.
By the way, have you ever processed this job, and what's the result like? I am looking foward to your experience.

By the way, have you ever processed this job, and what's the result like? I am looking foward to your experience.

In my computer, the average inference time is approximately 10ms per scenario.

How operator operations in torch_geometric are converted to onn??

How operator operations in torch_geometric are converted to onn??

PyG and ONNX don't work very well together, especially with functions like torch scatter_add. However, the author has shared the excellent embedding codes that you can use to rewrite.

Hi @SunHaoOne have you produced the ONNX, Could you plz share some idea on the TensorRT process?

How operator operations in torch_geometric are converted to onn??

PyG and ONNX don't work very well together, especially with functions like torch scatter_add. However, the author has shared the excellent embedding codes that you can use to rewrite.

I encountered some issues while rewriting this code:
The input of a graph neural network is graph structured data, which is sparse data. When I want to replace PYG operations, I can use dense computing instead. However, dense computing increases the amount of data and reduces computational efficiency, which is not a solution

@xiaowuge1201 hi bro, have you find a solution? i got the same problem for dense computing. The inference speed is so damn slow with onnx-cpu version, and my gpu memory is not enough to run onnx-gpu version