Simplify your onnx model
ONNX is great, but sometimes too complicated.
One day I wanted to export the following simple reshape operation to ONNX:
class JustReshape(torch.nn.Module): def init(self): super(JustReshape, self).init()
def forward(self, x): return x.view((x.shape, x.shape, x.shape, x.shape))
net = JustReshape() model_name = 'just_reshape.onnx' dummy_input = torch.randn(2, 3, 4, 5) torch.onnx.export(net, dummy_input, model_name, input_names=['input'], output_names=['output'])
The input shape in this model is static, so what I expected is
However, I got the following complicated model even after polishing:
Moreover, there are also some operations performed on weights (like this), which can all be eliminated by offline computation.
ONNX Simplifier is presented to simplify the ONNX model. It infers the whole computation graph and then replaces the redundant operators with their constant outputs.
We have published ONNX Simplifier on https://convertmodel.com. It works out of the box and doesn't need any installation. Just open the webpage, choose ONNX as the output format, check the onnx simplifier and then select your model to simplify. Note that the web version is in its very early stage, if the web version doesn't work well for you, you can install the Python version following the instructions below.
pip3 install onnx-simplifier
python3 -m onnxsim input_onnx_model output_onnx_model
For more functions like skipping optimization and setting input shape manually (when input shape is dynamic itself), try the following command for help message
python3 -m onnxsim -h
An overall comparison between a complicated model and its simplified version:
If you would like to embed ONNX simplifier python package in another script, it is just that simple.
import onnx from onnxsim import simplify
load your predefined ONNX model
model = onnx.load(path + model_name + '.onnx')
model_simp, check = simplify(model)
assert check, "Simplified ONNX model could not be validated"
use model_simp as a standard ONNX model object
You can see more details of the API in onnxsim/__main__.py