resnet.py

import torch.nn as nn
import torch

affine_par = True
k = 64
is_frozen = False


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, dilation_=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False)  # change
        self.bn1 = nn.BatchNorm2d(planes, affine=affine_par)
        padding = 1
        if dilation_ == 2:
            padding = 2
        elif dilation_ == 4:
            padding = 4
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1,  # change
                               padding=padding, bias=False, dilation=dilation_)
        self.bn2 = nn.BatchNorm2d(planes, affine=affine_par)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4, affine=affine_par)
        self.relu = nn.ReLU(inplace=True)
        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)
        out = self.relu(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)

        return out


class ResNet(nn.Module):
    def __init__(self, block, layers):
        self.inplanes = 64
        super(ResNet, self).__init__()
        # del self.fc
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)

        self.bn1 = nn.BatchNorm2d(64, affine=affine_par)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)  # change
        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)

    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),
            )
        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


def resnet50():
    model = ResNet(Bottleneck, [3, 4, 6, 3])
    return model