resnet_cnn.py

# 2020.01.10-Replaced conv with adder
#            Huawei Technologies Co., Ltd. <foss@huawei.com>

from adder.adder_gai import Adder2D
import torch.nn as nn
from torchsummary import summary


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


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=(1, 1), downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride=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

    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)

        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=6):
        super(ResNet, self).__init__()
        self.inplanes = 16
        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(16)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self._make_layer(block, 16, layers[0])
        self.layer2 = self._make_layer(block, 32, layers[1], stride=(2, 2))  # change h
        self.layer3 = self._make_layer(block, 64, layers[2], stride=(2, 2))  # change h
        self.avgpool = nn.MaxPool2d(3, stride=1)  # need transform
        self.fc = nn.Conv2d(64 * block.expansion, num_classes, 1, bias=False)
        self.bn2 = nn.BatchNorm2d(num_classes)

        for m in self.modules():
            if isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=(1, 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, 1), stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion)
            )

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

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

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

        x = self.avgpool(x)
        x = self.fc(x)
        x = self.bn2(x)

        return x.view(x.size(0), -1)


def resnet20(**kwargs):
    return ResNet(BasicBlock, [3, 3, 3], **kwargs)


# net = resnet20().cuda()
#
# summary(net, (1, 128, 9))


def resnet32_CNN(**kwargs):
    return ResNet(BasicBlock, [5, 5, 5], **kwargs)


# net = resnet32().cuda()
#
# summary(net, (1, 200, 3))


def resnet5(**kwargs):
    return ResNet(BasicBlock, [1, 1, 1], **kwargs)

def resnet_test(**kwargs):
    return ResNet(BasicBlock, [2, 3, 3], **kwargs)