Quantized PreActResNet-18 for QAT

CCL: <p>I’m trying to train PreActResNet-18 with QAT (quantization aware training). Is this the correct way to insert <code>quant</code> and <code>dequant</code> stubs into the <code>PreActBlock</code> and <code>CIFAR_ResNet</code> definitions? <strong>In particular, I am unsure about the lines to do with <code>shortcut</code> connections in PreActBlock.</strong> Thanks!</p>
<pre><code class="lang-auto">class PreActBlock(nn.Module):
    '''Pre-activation version of the BasicBlock.'''
    expansion = 1

    def __init__(self, in_planes, planes, stride=1, quant=False):
        super(PreActBlock, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv1 = conv3x3(in_planes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3(planes, planes)
        self.relu = nn.ReLU(inplace=True)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False)
            )
        self.Q = quant
        if quant:
            self.quant = torch.ao.quantization.QuantStub()
            self.dequant = torch.ao.quantization.DeQuantStub()

    def forward(self, x):
        out = self.relu(self.bn1(x))
        shortcut = self.shortcut(out)
        out = self.conv1(out)
        out = self.conv2(self.relu(self.bn2(out)))
        if self.Q:
            out = self.dequant(out)
            shortcut = self.dequant(shortcut)
        out += shortcut
        if self.Q:
            out = self.quant(out)
        return out

class CIFAR_ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10, bias=True, in_channels=3, zero_init_residual=False, quant=False):
        super(CIFAR_ResNet, self).__init__()
        self.Q = quant
        self.in_planes = 64
        self.conv1 = conv3x3(in_channels,64)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.gap = nn.AvgPool2d(4)
        self.linear = nn.Linear(512*block.expansion, num_classes, bias=bias)
        if quant:
            self.quant = torch.ao.quantization.QuantStub()
            self.dequant = torch.ao.quantization.DeQuantStub()

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride, quant=self.Q))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x, lin=0, lout=5):
        bb = x.size(0)
        if self.Q:
            x = self.quant(x)
        x = self.relu(self.bn1(self.conv1(x)))
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.gap(x)
        x = x.view(x.size(0), -1)
        x = self.linear(x)
        if self.Q:
            x = self.dequant(x)
        return x
</code></pre>