QAT_torch_quantizer.py

import torch
import torch.nn.functional as F
from torchvision import datasets, transforms
from nni.compression.torch import QAT_Quantizer


class Mnist(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = torch.nn.Conv2d(1, 20, 5, 1)
        self.conv2 = torch.nn.Conv2d(20, 50, 5, 1)
        self.fc1 = torch.nn.Linear(4 * 4 * 50, 500)
        self.fc2 = torch.nn.Linear(500, 10)
        self.relu1 = torch.nn.ReLU6()
        self.relu2 = torch.nn.ReLU6()
        self.relu3 = torch.nn.ReLU6()

    def forward(self, x):
        x = self.relu1(self.conv1(x))
        x = F.max_pool2d(x, 2, 2)
        x = self.relu2(self.conv2(x))
        x = F.max_pool2d(x, 2, 2)
        x = x.view(-1, 4 * 4 * 50)
        x = self.relu3(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)


def train(model, quantizer, device, train_loader, optimizer):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 100 == 0:
            print('{:2.0f}%  Loss {}'.format(100 * batch_idx / len(train_loader), loss.item()))

def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.nll_loss(output, target, reduction='sum').item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()
    test_loss /= len(test_loader.dataset)

    print('Loss: {}  Accuracy: {}%)\n'.format(
        test_loss, 100 * correct / len(test_loader.dataset)))

def main():
    torch.manual_seed(0)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    trans = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
    train_loader = torch.utils.data.DataLoader(
        datasets.MNIST('data', train=True, download=True, transform=trans),
        batch_size=64, shuffle=True)
    test_loader = torch.utils.data.DataLoader(
        datasets.MNIST('data', train=False, transform=trans),
        batch_size=1000, shuffle=True)

    model = Mnist()
    '''you can change this to DoReFaQuantizer to implement it
    DoReFaQuantizer(configure_list).compress(model)
    '''
    configure_list = [{
        'quant_types': ['weight'],
        'quant_bits': {
            'weight': 8,
        }, # you can just use `int` here because all `quan_types` share same bits length, see config for `ReLu6` below.
        'op_types':['Conv2d', 'Linear']
    }, {
        'quant_types': ['output'],
        'quant_bits': 8,
        'quant_start_step': 7000,
        'op_types':['ReLU6']
    }]
    quantizer = QAT_Quantizer(model, configure_list)
    quantizer.compress()

    model.to(device)
    optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
    for epoch in range(10):
        print('# Epoch {} #'.format(epoch))
        train(model, quantizer, device, train_loader, optimizer)
        test(model, device, test_loader)


if __name__ == '__main__':
    main()