I have a quantized model which is basically a resnet18. the quantization seems to go just fine until, when I try to load the quantized model from disk using sth like this :
def load_quantized(quantized_checkpoint_file_path):
    model = fvmodels.resnet18(pretrained=False, use_se=True)
    model.eval()
    model.fuse_model()
    # print(f'model: {model}')
    # Specify quantization configuration
    model.qconfig = torch.quantization.get_default_qconfig('fbgemm')
    # print(model.qconfig)
    torch.quantization.prepare(model, inplace=True)
    # Convert to quantized model
    torch.quantization.convert(model, inplace=True)
    checkpoint = torch.load(quantized_checkpoint_file_path, map_location=torch.device('cpu'))
    model.load_state_dict(checkpoint, strict=False)
    # model = torch.jit.load(quantized_checkpoint_file_path, map_location=torch.device('cpu'))
    fvmodels.print_size_of_model(model)
    return model

and while trying to use that :
model = load_quantized('path to model')
model.eval()
with torch.no_grad():
    for img, lbl in dtloader:
        features = model(img.unsqueeze(0))

I face the following error :
RuntimeError: Could not run 'aten::native_batch_norm' with arguments from the 'QuantizedCPUTensorId' backend. 'aten::native_batch_norm' is only available for these backends: [CPUTensorId, MkldnnCPUTensorId, VariableTensorId].

This seems to be casued by the fact that the batchnorm layer is not fused! and the issue is I dont know how to fuse it. to be more specific here is the resnet model I have at hand :
class ResNet(nn.Module):

    def __init__(self, block, layers, use_se=True):
        self.inplanes = 64
        self.use_se = use_se
        super().__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        # self.prelu = nn.PReLU()
        self.prelu = nn.ReLU()
        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
        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=2)
        self.bn2 = nn.BatchNorm2d(512)
        self.dropout = nn.Dropout()
        self.fc = nn.Linear(512 * 7 * 7, 512)
        self.bn3 = nn.BatchNorm1d(512)

        self.quant = torch.quantization.QuantStub()
        self.dequant = torch.quantization.DeQuantStub()

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.xavier_normal_(m.weight)
            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.xavier_normal_(m.weight)
                nn.init.constant_(m.bias, 0)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
        self.inplanes = planes
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, use_se=self.use_se))

        return nn.Sequential(*layers)

    def forward(self, x):
        
        x = self.quant(x)

        x = self.conv1(x)
        x = self.bn1(x)
        x = self.prelu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.bn2(x)
        x = self.dropout(x)
        # x = x.view(x.size(0), -1)
        x = x.reshape(x.size(0), -1)
        x = self.fc(x)
        x = self.bn3(x)

        x = self.dequant(x)
        return x

    def fuse_model(self):
        r"""Fuse conv/bn/relu modules in resnet models
        Fuse conv+bn+relu/ Conv+relu/conv+Bn modules to prepare for quantization.
        Model is modified in place.  Note that this operation does not change numerics
        and the model after modification is in floating point
        """

        fuse_modules(self, [['conv1', 'bn1', 'prelu'],
                            ['bn2'],
                            ['bn3']], inplace=True)
        for m in self.modules():
            # print(m)
            if type(m) == Bottleneck or type(m) == BasicBlock or type(m) == IRBlock:
                m.fuse_model()

as you can see in the forward pass we have :
...
x = self.bn2(x)
x = self.dropout(x)

which is followed by a dropout and unlike previous ones, doesnt come with neither conv or relu!
the same thing goes to bn3 a couple of lines later:
...
x = self.fc(x)
x = self.bn3(x)
x = self.dequant(x)
....

So I’m not sure how I’m supposed to get around this. obviously the way I’m fusing is wrong:
def fuse_model(self):
        fuse_modules(self, [['conv1', 'bn1', 'prelu'],
                            ['bn2'],
                            ['bn3']], inplace=True)
        for m in self.modules():
            # print(m)
            if type(m) == Bottleneck or type(m) == BasicBlock or type(m) == IRBlock:
                m.fuse_model()

For the sake of completeness here are the whole models :

def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)

class BasicBlock(nn.Module):
    expansion = 1
    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super().__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride
        
        self.add_relu = torch.nn.quantized.FloatFunctional()

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        # out += residual
        # out = self.relu(out)
        out = self.add_relu.add_relu(out, residual)

        return out

    def fuse_model(self):
        torch.quantization.fuse_modules(self, [['conv1', 'bn1', 'relu'],
                                               ['conv2', 'bn2']], inplace=True)
        if self.downsample:
            torch.quantization.fuse_modules(self.downsample, ['0', '1'], inplace=True)

class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu1 = nn.ReLU(inplace=False)
        self.relu2 = nn.ReLU(inplace=False)
        self.downsample = downsample
        self.stride = stride
        
        self.skip_add_relu = nn.quantized.FloatFunctional()

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu1(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu2(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)
        # out += residual
        # out = self.relu(out)
        out = self.skip_add_relu.add_relu(out, residual)
        return out

    def fuse_model(self):
        fuse_modules(self, [['conv1', 'bn1', 'relu1'],
                            ['conv2', 'bn2', 'relu2'],
                            ['conv3', 'bn3']], inplace=True)
        if self.downsample:
            torch.quantization.fuse_modules(self.downsample, ['0', '1'], inplace=True)

class SEBlock(nn.Module):
    def __init__(self, channel, reduction=16):
        super().__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.mult_xy = nn.quantized.FloatFunctional()

        self.fc = nn.Sequential(
            nn.Linear(channel, channel // reduction),
            # nn.PReLU(),
            nn.ReLU(),
            nn.Linear(channel // reduction, channel),
            nn.Sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        # out = x*y 
        out = self.mult_xy.mul(x, y)
        return out

class IRBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
        super().__init__()
        self.bn0 = nn.BatchNorm2d(inplanes)
        self.conv1 = conv3x3(inplanes, inplanes)
        self.bn1 = nn.BatchNorm2d(inplanes)
        # self.prelu = nn.PReLU()
        self.prelu = nn.ReLU()
        self.conv2 = conv3x3(inplanes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride
        self.use_se = use_se
        if self.use_se:
            self.se = SEBlock(planes)

        self.add_residual_relu = nn.quantized.FloatFunctional()

    def forward(self, x):
        residual = x

        out = self.bn0(x)
        out = self.conv1(out)
        out = self.bn1(out)
        out = self.prelu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        if self.use_se:
            out = self.se(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        # out += residual
        # out = self.prelu(out)

        # we may need to change prelu into relu and this, instead of add, use add_relu here
        out = self.add_residual_relu.add_relu(out, residual)
        # out = self.prelu(out)
        return out

    def fuse_model(self):
        fuse_modules(self, [['conv1', 'bn1', 'prelu'],
                            ['conv2', 'bn2']], inplace=True)
        if self.downsample:
            torch.quantization.fuse_modules(self.downsample, ['0', '1'], inplace=True)

class ResNet(nn.Module):

    def __init__(self, block, layers, use_se=True):
        self.inplanes = 64
        self.use_se = use_se
        super().__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        # self.prelu = nn.PReLU()
        self.prelu = nn.ReLU()
        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
        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=2)
        self.bn2 = nn.BatchNorm2d(512)
        self.dropout = nn.Dropout()
        self.fc = nn.Linear(512 * 7 * 7, 512)
        self.bn3 = nn.BatchNorm1d(512)

        self.quant = torch.quantization.QuantStub()
        self.dequant = torch.quantization.DeQuantStub()

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.xavier_normal_(m.weight)
            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.xavier_normal_(m.weight)
                nn.init.constant_(m.bias, 0)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
        self.inplanes = planes
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, use_se=self.use_se))

        return nn.Sequential(*layers)

    def forward(self, x):
        
        x = self.quant(x)

        x = self.conv1(x)
        x = self.bn1(x)
        x = self.prelu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.bn2(x)
        x = self.dropout(x)
        # x = x.view(x.size(0), -1)
        x = x.reshape(x.size(0), -1)
        x = self.fc(x)
        x = self.bn3(x)

        x = self.dequant(x)
        return x

    def fuse_model(self):
        r"""Fuse conv/bn/relu modules in resnet models
        Fuse conv+bn+relu/ Conv+relu/conv+Bn modules to prepare for quantization.
        Model is modified in place.  Note that this operation does not change numerics
        and the model after modification is in floating point
        """

        fuse_modules(self, [['conv1', 'bn1', 'prelu'],
                            ['bn2'],
                            ['bn3']], inplace=True)
        for m in self.modules():
            # print(m)
            if type(m) == Bottleneck or type(m) == BasicBlock or type(m) == IRBlock:
                m.fuse_model()

def resnet18(pretrained, use_se, **kwargs):
    model = ResNet(IRBlock, [2, 2, 2, 2], use_se=use_se, **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
    return model


side note:
Also the actual model (resnet18 can be found from this link 1 in case someone might need it)
Config:
Im using Pytorch 1.5.0+cpu on windows 10 x64 v1803
Any help is greatly appreciated