PyTorch模型转换为ONNX格式实现过程详解

conda create -n onnx python=3.8
conda activate onnx

conda install pytorch torchvision torchaudio -c pytorch
pip install onnx

pip install onnxruntime

import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.onnx
import torchvision.transforms as transforms
import torchvision.datasets as datasets
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)
    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x
net = Net()
PATH = './model.pth'
torch.save(net.state_dict(), PATH)
model = Net()
model.load_state_dict(torch.load(PATH))
model.eval()

input_names = ["input"]
output_names = ["output"]
dummy_input = torch.randn(batch_size, input_channel_size, input_height, input_width)
# Export the model
torch.onnx.export(model, dummy_input, "model.onnx", verbose=True, 
                  input_names=input_names, output_names=output_names)

import onnxruntime as rt
import numpy as np
sess = rt.InferenceSession('model.onnx')
input_name = sess.get_inputs()[0].name
output_name = sess.get_outputs()[0].name
np.random.seed(123)
X = np.random.randn(batch_size, input_channel_size, input_height, input_width).astype(np.float32)
res = sess.run([output_name], {input_name: X})

# Test the model with PyTorch
model.eval()
with torch.no_grad():
    Y = model(torch.from_numpy(X)).numpy()
# Test the ONNX model with ONNX Runtime
sess = rt.InferenceSession('model.onnx')
res = sess.run(None, {input_name: X})[0]
# Compare the results
np.testing.assert_allclose(Y, res, rtol=1e-6, atol=1e-6)