PyTorch to ONNX to CNTK Tutorial

Note

We no longer include the CNTK, Caffe, Caffe2 and Theano Conda environments in the AWS Deep Learning AMI starting with the v28 release. Previous releases of the AWS Deep Learning AMI that contain these environments will continue to be available. However, we will only provide updates to these environments if there are security fixes published by the open source community for these frameworks.

ONNX Overview

The Open Neural Network Exchange (ONNX) is an open format used to represent deep learning models. ONNX is supported by Amazon Web Services, Microsoft, Facebook, and several other partners. You can design, train, and deploy deep learning models with any framework you choose. The benefit of ONNX models is that they can be moved between frameworks with ease.

This tutorial shows you how to use the Deep Learning AMI with Conda with ONNX. By following these steps, you can train a model or load a pre-trained model from one framework, export this model to ONNX, and then import the model in another framework.

ONNX Prerequisites

To use this ONNX tutorial, you must have access to a Deep Learning AMI with Conda version 12 or later. For more information about how to get started with a Deep Learning AMI with Conda, see Deep Learning AMI with Conda.

Important

These examples use functions that might require up to 8 GB of memory (or more). Be sure to choose an instance type with enough memory.

Launch a terminal session with your Deep Learning AMI with Conda to begin the following tutorial.

Convert a PyTorch Model to ONNX, then Load the Model into CNTK

First, activate the PyTorch environment:

$ source activate pytorch_p36
         

Create a new file with your text editor, and use the following program in a script to train a mock model in PyTorch, then export it to the ONNX format.

# Build a Mock Model in Pytorch with a convolution and a reduceMean layer\
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import torch.onnx as torch_onnx

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=(3,3), stride=1, padding=0, bias=False)

    def forward(self, inputs):
        x = self.conv(inputs)
        #x = x.view(x.size()[0], x.size()[1], -1)
        return torch.mean(x, dim=2)

# Use this an input trace to serialize the model
input_shape = (3, 100, 100)
model_onnx_path = "torch_model.onnx"
model = Model()
model.train(False)

# Export the model to an ONNX file
dummy_input = Variable(torch.randn(1, *input_shape))
output = torch_onnx.export(model, 
                          dummy_input, 
                          model_onnx_path, 
                          verbose=False)

After you run this script, you will see the newly created .onnx file in the same directory. Now, switch to the CNTK Conda environment to load the model with CNTK.

Next, activate the CNTK environment:

$ source deactivate
$ source activate cntk_p36      

Create a new file with your text editor, and use the following program in a script to open ONNX format file in CNTK.

import cntk as C
# Import the PyTorch model into CNTK via the CNTK import API
z = C.Function.load("torch_model.onnx", device=C.device.cpu(), format=C.ModelFormat.ONNX)

After you run this script, CNTK will have loaded the model.

You may also export to ONNX using CNTK by appending the following to your previous script then running it.

# Export the model to ONNX via the CNTK export API
z.save("cntk_model.onnx", format=C.ModelFormat.ONNX)

ONNX Tutorials

• Apache MXNet to ONNX to CNTK Tutorial

• Chainer to ONNX to CNTK Tutorial

• Chainer to ONNX to MXNet Tutorial

• PyTorch to ONNX to MXNet Tutorial

• PyTorch to ONNX to CNTK Tutorial