onnx-pytorch

Generates PyTorch code from ONNX. Currently supports onnx==1.9.0 and torch==1.8.1.

Usage

from onnx_pytorch import code_gen
code_gen.gen("/path/to/onnx_model", "/path/to/output_dir")

A model.py file and variables/ folder will be created under output_dir/.

Tutorial

1. Download resnet18 ONNX model.

wget https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet18-v2-7.onnx

2. Use onnx-pytorch to generate PyTorch code and variables.

from onnx_pytorch import code_gen
code_gen.gen("resnet18-v2-7.onnx", "./")

3. Test result.

import numpy as np
import onnx
import onnxruntime
import torch
torch.set_printoptions(8)

from model import Model

model = Model()
model.eval()
inp = np.random.randn(1, 3, 224, 224).astype(np.float32)
with torch.no_grad():
  torch_outputs = model(torch.from_numpy(inp))

onnx_model = onnx.load("resnet18-v2-7.onnx")
sess_options = onnxruntime.SessionOptions()
session = onnxruntime.InferenceSession(onnx_model.SerializeToString(),
                                       sess_options)
inputs = {"data": inp}
ort_outputs = session.run(None, inputs)

print(
    "Comparison result:",
    np.allclose(torch_outputs.detach().numpy(),
                ort_outputs[0],
                atol=1e-5,
                rtol=1e-5))