def validate():
trainloader, testloader = get_train_test_loaders()
net = Net().float().eval()
pretrained_model = torch.load("checkpoint.pth")
net.load_state_dict(pretrained_model)
print('=' * 10, 'PyTorch', '=' * 10)
train_acc = batch_evaluate(net, trainloader) * 100.
print('Training accuracy: %.1f' % train_acc)
test_acc = batch_evaluate(net, testloader) * 100.
print('Validation accuracy: %.1f' % test_acc)
trainloader, testloader = get_train_test_loaders(1)
# export to onnx
fname = "signlanguage.onnx"
dummy = torch.randn(1, 1, 28, 28)
torch.onnx.export(net, dummy, fname, input_names=['input'])
# check exported model
model = onnx.load(fname)
onnx.checker.check_model(model) # check model is well-formed
# create runnable session with exported model
ort_session = ort.InferenceSession(fname)
net = lambda inp: ort_session.run(None, {'input': inp.data.numpy()})[0]
print('=' * 10, 'ONNX', '=' * 10)
train_acc = batch_evaluate(net, trainloader) * 100.
print('Training accuracy: %.1f' % train_acc)
test_acc = batch_evaluate(net, testloader) * 100.
print('Validation accuracy: %.1f' % test_acc)
def main():
net = Net().float()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)
trainloader, _ = get_train_test_loaders()
for epoch in range(12): # loop over the dataset multiple times
train(net, criterion, optimizer, trainloader, epoch)
scheduler.step()
torch.save(net.state_dict(), "checkpoint.pth")