Torch to ONNX and Beyond: Unlocking Dynamic Input Sizes with ONNX Runtime SDK

Are you tired of being stuck in the dark ages of model deployment, struggling to transfer your torch model to ONNX and make it work with dynamic input sizes? Fear not, dear reader, for today we’re going to light the way and guide you through the process of transferring a torch model to ONNX and successfully inferencing it with the C++ ONNX Runtime SDK, even with dynamic input sizes.

What’s the Issue?

When you try to transfer a torch model to ONNX with dynamic input size, things can get a bit hairy. You might encounter errors like “Invalid input shape” or “Runtime error” when trying to infer the model using the C++ ONNX Runtime SDK. This is because the ONNX model doesn’t know how to handle dynamic input sizes by default.

Step 1: Preparing Your Torch Model

Before you can transfer your torch model to ONNX, you need to make sure it’s ready for the journey. Here are a few things to check:

• Make sure your torch model is in eval() mode. You can do this by calling model.eval() before exporting the model to ONNX.

• Verify that your model doesn’t contain any PyTorch modules that aren’t supported by the ONNX converter. You can check the ONNX documentation for a list of supported modules.

• Ensure that your model’s input shape is defined correctly. In this case, we’re dealing with dynamic input sizes, so we’ll need to specify the input shape as a range of values.

Step 2: Exporting the Torch Model to ONNX

Now that your torch model is ready, it’s time to export it to ONNX. You can do this using the torch.onnx module. Here’s an example:

import torch
import torch.onnx

# assume 'model' is your torch model
input_names = ['input']
output_names = ['output']

dynamic_axes = {
    'input': {0: 'batch_size', 1: 'seq_len'},
    'output': {0: 'batch_size', 1: 'seq_len'}
}

torch.onnx.export(
    model,
    torch.randn(1, 10),  # dummy input
    'model.onnx',
    input_names=input_names,
    output_names=output_names,
    dynamic_axes=dynamic_axes,
    opset_version=11
)

In this example, we’re exporting the model to an ONNX file called model.onnx. We’re also specifying the input and output names, as well as the dynamic axes for the input and output tensors.

Step 3: Loading the ONNX Model with C++ ONNX Runtime SDK

Now that we have our ONNX model, it’s time to load it using the C++ ONNX Runtime SDK. Here’s an example:

#include 

// create an OrtEnv object
OrtEnv* env;
OrtStatus* status = OrtCreateEnv(ORT_LOGLEVEL_INFO, "test", &env);
if (status != nullptr) {
    std::cerr << " OrtCreateEnv failed" << std::endl;
    return 1;
}

// load the ONNX model
 OrtSessionOptions* session_options;
status = OrtCreateSessionOptions(&session_options);
if (status != nullptr) {
    std::cerr << " OrtCreateSessionOptions failed" << std::endl;
    return 1;
}

OrtSession* session;
status = OrtCreateSession(env, "model.onnx", session_options, &session);
if (status != nullptr) {
    std::cerr << " OrtCreateSession failed" << std::endl;
    return 1;
}

In this example, we're loading the ONNX model using the OrtCreateSession function, which takes the environment object, the path to the ONNX file, the session options, and a pointer to the session object as arguments.

Step 4: Creating Input Tensors with Dynamic Shapes

Now that we have our ONNX model loaded, it's time to create input tensors with dynamic shapes. Since we're dealing with dynamic input sizes, we need to specify the shape of the input tensor at runtime. Here's an example:

int64_t shape[] = {1, 10};  // batch size 1, sequence length 10
OrtAllocator* allocator;
status = OrtCreateAllocator(ORT_ALLOCATOR_DEFAULT, &allocator);
if (status != nullptr) {
    std::cerr << " OrtCreateAllocator failed" << std::endl;
    return 1;
}

OrtValue* input_tensor;
status = OrtCreateTensorWithDataAsOrtValue(allocator, shape, 2, ONNX.TensorProto_DataType_FLOAT, nullptr, 0, &input_tensor);
if (status != nullptr) {
    std::cerr << " OrtCreateTensorWithDataAsOrtValue failed" << std::endl;
    return 1;
}

In this example, we're creating an input tensor with a shape of [1, 10], which means a batch size of 1 and a sequence length of 10. We're using the OrtCreateTensorWithDataAsOrtValue function to create the tensor, which takes the allocator, shape, data type, and a pointer to the tensor object as arguments.

Step 5: Running Inference with the ONNX Model

Finally, it's time to run inference with the ONNX model. Here's an example:

OrtValue* output_tensor;
status = OrtRun(session, nullptr, input_names, &input_tensor, 1, output_names, 1, &output_tensor);
if (status != nullptr) {
    std::cerr << " OrtRun failed" << std::endl;
    return 1;
}

// get the output tensor data
float* output_data;
status = OrtGetTensorMutableData(output_tensor, (void**)&output_data);
if (status != nullptr) {
    std::cerr << " OrtGetTensorMutableData failed" << std::endl;
    return 1;
}

// print the output tensor data
std::cout << "Output tensor data:" << std::endl;
for (int i = 0; i < 10; i++) {
    std::cout << output_data[i] << " ";
}
std::cout << std::endl;

In this example, we're running inference using the OrtRun function, which takes the session object, input names, input tensors, output names, and a pointer to the output tensor object as arguments. We're then getting the output tensor data using the OrtGetTensorMutableData function and printing it to the console.

Troubleshooting Common Issues

When working with dynamic input sizes, things can get a bit tricky. Here are some common issues you might encounter and how to troubleshoot them:

Error Message	Solution
Invalid input shape	Check that the input shape is defined correctly in the ONNX model. Make sure the dynamic axes are specified correctly.
Runtime error	Check that the input tensor shape matches the shape defined in the ONNX model. Make sure the data type is correct.
Segmentation fault	Check that the input tensor data is allocated correctly. Make sure the allocator is created correctly.

Conclusion

Transferring a torch model to ONNX and running inference with dynamic input sizes can be a bit of a challenge, but with the right steps and troubleshooting techniques, you can overcome these hurdles and unlock the power of ONNX Runtime SDK. Remember to prepare your torch model correctly, export it to ONNX with dynamic axes, load the ONNX model with the C++ ONNX Runtime SDK, create input tensors with dynamic shapes, and run inference with the ONNX model.

By following these steps and troubleshooting common issues, you'll be able to successfully deploy your torch model with dynamic input sizes using the C++ ONNX Runtime SDK. Happy deploying!

Frequently Asked Question

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