I am trying to quantize a salient object detection model.
Originally, my ResNet class would look like:
import torch.nn as nn
import torch.nn.quantized as nnq
import torch.nn.functional as F
import torch.nn.quantized.functional as qF

class ResNet(nn.Module):
    def __init__(self, block, layers):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64,affine = affine_par)
        for i in self.bn1.parameters():
            i.requires_grad = False
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True) # changed to Quanti

        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation__ = 2)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, 0.01)
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
    def _make_layer(self, block, planes, blocks, stride=1,dilation__ = 1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion or dilation__ == 2 or dilation__ == 4:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion,affine = affine_par),

            )
        for i in downsample._modules['1'].parameters():
            i.requires_grad = False
        layers = []
        layers.append(block(self.inplanes, planes, stride,dilation_=dilation__, downsample = downsample ))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes,dilation_=dilation__))
        return nn.Sequential(*layers)
    def forward(self, x):
        tmp_x = []
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        tmp_x.append(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        tmp_x.append(x)
        x = self.layer2(x)
        tmp_x.append(x)
        x = self.layer3(x)
        tmp_x.append(x)
        x = self.layer4(x)
        tmp_x.append(x)
        return tmp_x

And it works just fine. But if I replace everything with quantized functions like:

class ResNet(nn.Module):
    def __init__(self, block, layers):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nnq.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nnq.BatchNorm2d(64) #,affine = affine_par
        for i in self.bn1.parameters():
            i.requires_grad = False
        self.relu = nnq.ReLU(inplace=False)

        #self.maxpool = F.nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True) # change 

        self.maxpool = qF.max_pool2d(x = ??? ,kernel_size=3, stride=2, padding=1, ceil_mode=True)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation__ = 2)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, 0.01)
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1,dilation__ = 1):

        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion or dilation__ == 2 or dilation__ == 4:
            downsample = nn.Sequential(
                nnq.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nnq.BatchNorm2d(planes * block.expansion), #,affine = affine_par
            )

        for i in downsample._modules['1'].parameters():
            i.requires_grad = False
        layers = []
        layers.append(block(self.inplanes, planes, stride,dilation_=dilation__, downsample = downsample ))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes,dilation_=dilation__))
        return nn.Sequential(*layers)
    def forward(self, x):
        tmp_x = []
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        tmp_x.append(x)

        x = qF.max_pool2d(x,kernel_size=3, stride=2, padding=1, ceil_mode=True)  #this certainly will not create a MaxPool layer.

        #x = self.maxpool(x)

        x = self.layer1(x)
        tmp_x.append(x)
        x = self.layer2(x)
        tmp_x.append(x)
        x = self.layer3(x)
        tmp_x.append(x)
        x = self.layer4(x)
        tmp_x.append(x)
        return tmp_x

Error:
TypeError: max_pool2d() missing 1 required positional argument: 'input'
So, I think the problem is in torch.nn.MaxPool2d does not need any input argument but torch.nn.quantized.functional.max_pool2d needs an input argument. DOES ANYONE KNOW ANY WAY ROUND? How can I successfully quantize like this one other custom classes?