Hi! My quantized segmentation model is slowing down on android , as well as on desktop cpu. Can’t figure out why. I tried different way  to quantize, used different layers. Here I attached a full example with QNNPACK
For android I use. Also I tried different versions here
  implementation 'org.pytorch:pytorch_android:1.5.0-SNAPSHOT'
   implementation 'org.pytorch:pytorch_android_torchvision:1.5.0-SNAPSHOT'

import torch
import os

from torch import nn
from torchvision.models.resnet import BasicBlock, ResNet
from torch.quantization import fuse_modules
from torch.nn import functional as F


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class QuantizableBasicBlock(BasicBlock):
    def __init__(self, *args, **kwargs):
        super(QuantizableBasicBlock, self).__init__(*args, **kwargs)
        self.add_relu = torch.nn.quantized.FloatFunctional()

    def forward(self, x):
        identity = 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:
            identity = self.downsample(x)

        out = self.add_relu.add_relu(out, identity)

        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 ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer
        shapes = (16, 32, 64, 128)
        block = QuantizableBasicBlock
        self.inplanes = shapes[0]
        self.dilation = 1
        if replace_stride_with_dilation is None:
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False, dilation=1)
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, shapes[0], layers[0])
        self.layer2 = self._make_layer(block, shapes[1], layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, shapes[2], layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, shapes[3], layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def _forward_impl(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)
        return x

    def forward(self, x):
        return self._forward_impl(x)


def _replace_relu(module):
    reassign = {}
    for name, mod in module.named_children():
        _replace_relu(mod)
        if type(mod) == nn.ReLU or type(mod) == nn.ReLU6:
            reassign[name] = nn.ReLU(inplace=False)

    for key, value in reassign.items():
        module._modules[key] = value


class QuantizableResNet(ResNet):

    def __init__(self, *args, **kwargs):
        super(QuantizableResNet, self).__init__(*args, **kwargs)

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

    def forward(self, x):
        x = self.quant(x)
        x = self._forward_impl(x)
        x = self.dequant(x)
        return x

    def fuse_model(self):
        fuse_modules(self, ['conv1', 'bn1', 'relu'], inplace=True)
        for m in self.modules():
            if isinstance(m, (QuantizableBasicBlock, DecoderBlock)):
                m.fuse_model()


class Conv2dReLU(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, padding=0,
                 stride=1, use_batchnorm=True, **batchnorm_params):
        super().__init__()
        layers = [
            nn.Conv2d(in_channels, out_channels, kernel_size,
                      stride=stride, padding=padding, bias=not (use_batchnorm), groups=1),
            nn.ReLU(),
        ]

        if use_batchnorm:
            layers.insert(1, nn.BatchNorm2d(out_channels, **batchnorm_params))

        self.block = nn.Sequential(*layers)

    def forward(self, x):
        return self.block(x)

    def fuse_model(self):
        fuse_modules(self, ['block.0', 'block.1', 'block.2'], inplace=True)
        for m in self.modules():
            if type(m) == QuantizableBasicBlock:
                m.fuse_model()


class Up(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        return F.upsample_bilinear(x, scale_factor=2)


class DecoderBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.block = nn.Sequential(
            Conv2dReLU(in_channels, in_channels // 4, kernel_size=1, use_batchnorm=True),
            Up(),
            Conv2dReLU(in_channels // 4, out_channels, kernel_size=1, use_batchnorm=True),
        )

    def forward(self, x):
        return self.block(x)

    def fuse_model(self):
        for m in self.modules():
            if isinstance(m, Conv2dReLU):
                m.fuse_model()


class ResNetUnet(QuantizableResNet):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.pretrained = False
        in_channels = (128, 64, 32, 16, 16)
        prefinal_channels = 16
        final_channels = 3
        self.block1 = DecoderBlock(in_channels[0], in_channels[1])
        self.block2 = DecoderBlock(in_channels[1], in_channels[2])
        self.block3 = DecoderBlock(in_channels[2], in_channels[3])
        self.block4 = DecoderBlock(in_channels[3], in_channels[4])
        self.block5 = DecoderBlock(in_channels[4], prefinal_channels)
        self.final_conv = nn.Conv2d(prefinal_channels, final_channels, kernel_size=(1, 1))
        self.linear = nn.Linear(128, 10)
        self.pool = nn.AdaptiveAvgPool2d(1)
        self.add = torch.nn.quantized.FloatFunctional()

        del self.fc

    def forward(self, x):
        x = self.quant(x)
        x, cls = self._forward_impl(x)
        x = self.dequant(x)
        cls = self.dequant(cls)
        return x, cls

    def _forward_impl(self, x):
        x0 = self.conv1(x)
        x0 = self.bn1(x0)
        x0 = self.relu(x0)
        x1 = self.maxpool(x0)
        x1 = self.layer1(x1)
        x2 = self.layer2(x1)
        x3 = self.layer3(x2)
        x4 = self.layer4(x3)
        cls_out = self.linear(self.pool(x4).view(x4.size(0), -1))
        x4 = self.block1(x4)
        x3 = self.add.add(x3, x4)
        x3 = self.block2(x3)
        x2 = self.add.add(x2, x3)
        x2 = self.block3(x2)
        x1 = self.add.add(x1, x2)
        x1 = self.block4(x1)
        x0 = self.add.add(x0, x1)
        x0 = self.block5(x0)
        x0 = self.final_conv(x0)

        return x0, cls_out


def print_size_of_model(model):
    torch.save(model.state_dict(), "temp.p")
    print('Size (MB):', os.path.getsize("temp.p") / 1e6)
    os.remove('temp.p')


def get_model():
    model = ResNetUnet(QuantizableBasicBlock, [2, 2, 2, 2])
    _replace_relu(model)

    return model


def quantize_model(model, backend):
    _dummy_input_data = torch.rand(1, 3, 224, 224)
    if backend not in torch.backends.quantized.supported_engines:
        raise RuntimeError("Quantized backend not supported ")
    torch.backends.quantized.engine = backend
    model.eval()
    if backend == 'fbgemm':
        model.qconfig = torch.quantization.QConfig(
            activation=torch.quantization.default_observer,
            weight=torch.quantization.default_per_channel_weight_observer)
    elif backend == 'qnnpack':
        model.qconfig = torch.quantization.QConfig(
            activation=torch.quantization.default_observer,
            weight=torch.quantization.default_weight_observer)

    model.fuse_model()
    torch.quantization.prepare(model, inplace=True)
    model(_dummy_input_data)
    torch.quantization.convert(model, inplace=True)


def evaltime(model):
    from time import time
    res = 0
    n = 500
    for _ in range(n):
        t = time()
        with torch.no_grad():
            model(torch.ones(1, 3, 224, 224))
        res += time() - t
    return res / n


torch.set_num_threads(1)
model = get_model()
model.eval()
print('time/image, initial model', evaltime(model)) # 0.025
print_size_of_model(model) # Size (MB): 2.89155
quantize_model(model, 'qnnpack')
print('time/image, quantized model', evaltime(model)) # 0.101
print_size_of_model(model) # Size (MB): 0.740964