from torch.autograd import Variable
import torch
import torch.nn.functional as F
import torch.nn as nn
 
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5, 1)
        self.conv2 = nn.Conv2d(20, 50, 5, 1)
        self.fc1 = nn.Linear(4*4*50, 500)
        self.fc2 = nn.Linear(500, 10)
 
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2, 2)
        x = x.view(-1, 4*4*50)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)
 
trained_model = Net()
trained_model.load_state_dict(torch.load('PyTorch_Model.pt'))
dummy_input = Variable(torch.randn(1, 1, 28, 28)) 
torch.onnx.export(trained_model, dummy_input, "ONNX_Model.onnx") 