onnx-pytorch
Generating pytorch code from ONNX.
Currently support onnx==1.9.0 and torch==1.8.1.
Installation
- From PyPI
pip install onnx-pytorch
- From source
git clone https://github.com/fumihwh/onnx-pytorch.git
pip install -r requirements.txt
pip install -e .
Usage
By Command Line
python -m onnx_pytorch.code_gen -h
usage: code_gen.py [-h] [--onnx_model_path ONNX_MODEL_PATH] [--output_dir OUTPUT_DIR] [--overwrite OVERWRITE] [--tensor_inplace TENSOR_INPLACE] [--continue_on_error CONTINUE_ON_ERROR] [--simplify_names SIMPLIFY_NAMES]
optional arguments:
-h, --help show this help message and exit
--onnx_model_path ONNX_MODEL_PATH
The onnx model path.
--output_dir OUTPUT_DIR
The output dir
--overwrite OVERWRITE
Should overwrite the output dir.
--tensor_inplace TENSOR_INPLACE
Try best to inplace tensor.
--continue_on_error CONTINUE_ON_ERROR
Continue on error.
--simplify_names SIMPLIFY_NAMES
Use indexing shorten name instead of original name.
By Python
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
- Download resnet18 onnx model
wget https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet18-v2-7.onnx
- Use onnx-pytorch to generate pytorch code and variables.
from onnx_pytorch import code_gen
code_gen.gen("resnet18-v2-7.onnx", "./")
- 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))
