ONNX is great, but sometimes too complicated.
One day I wanted to export the following simple reshape operation to ONNX:
import torch
class JustReshape(torch.nn.Module):
def __init__(self):
super(JustReshape, self).__init__()
def forward(self, x):
return x.view((x.shape[0], x.shape[1], x.shape[3], x.shape[2]))
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.
Install it via pip (Python >= 3.5)
pip3 install onnx-simplifier
Then
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:
