FCRSN.py

import torch
from torch import nn
def conv3x1(in_planes, out_planes, stride=1, groups=1):
    """3x3 convolution with padding"""
    return nn.Conv1d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, groups=groups, bias=False)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv1d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)

def conv5x1(in_planes,out_planes,stride=1):
    return nn.Conv1d(in_planes,out_planes,kernel_size=5,stride=stride,bias=False)

class SEModule(nn.Module):
    def __init__(self, channels, reduction=16):
        super(SEModule, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1, padding=0)
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1, padding=0)
        self.sigmoid = nn.Sigmoid()

    def forward(self, input):
        x = self.avg_pool(input)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.sigmoid(x)
        return input * x


class Res2NetBottleneck(nn.Module):
    expansion = 4
    def __init__(self, inplanes, planes, downsample=None,
                 gate=False,stride=1, scales=4, groups=1, se=False,  norm_layer=None):
        super(Res2NetBottleneck, self).__init__()
        if planes % scales != 0:
            raise ValueError('Planes must be divisible by scales')
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        bottleneck_planes = groups * planes

        self.conv1 = conv1x1(inplanes, bottleneck_planes, stride)
        self.bn1 = norm_layer(bottleneck_planes)
        self.conv2 = nn.ModuleList([conv3x1(bottleneck_planes // scales, bottleneck_planes // scales, groups=groups) for _ in range(scales-1)])
        self.bn2 = nn.ModuleList([norm_layer(bottleneck_planes // scales) for _ in range(scales-1)])
        self.conv3 = conv1x1(bottleneck_planes, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.gate= conv1x1(bottleneck_planes,bottleneck_planes//scales) if gate else None
        self.se = SEModule(planes * self.expansion) if se else None
        self.downsample = downsample
        self.stride = stride
        self.scales = scales

    def forward(self, x):
        identity = x

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

        xs = torch.chunk(out, self.scales, 1)
        ys = []
        gate=[]
        for s in range(self.scales):
            if s == 0:
                ys.append(xs[s])
            elif s == 1:
                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s]))))
            else:
                if gate:
                    value=self.gate(x)
                    ys.append(self.relu(self.bn2[s - 1](self.conv2[s - 1](xs[s] + value *ys[-1]))))
                else:
                    ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s] + ys[-1]))))
        out = torch.cat(ys, 1)

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

        if self.se is not None:
            out = self.se(out)

        if self.downsample is not None:
            identity = self.downsample(identity)

        out += identity
        out = self.relu(out)

        return out

class Res2RNN(nn.Module):
    def __init__(self, layers, maxlength, groups=1,
                  gate=False,width=8,scales=4, se=False, norm_layer=None,inplanes=26):
        super(Res2RNN, self).__init__()
        planes = [int(width * scales * 2 ** i) for i in range(4)]
        self.gate=gate
        self.pre=inplanes
        self.inplanes=planes[0]
        self.maxlength=maxlength
        self.pre=conv1x1(inplanes,planes[0])
        self.layer1 = self._make_layer(Res2NetBottleneck, planes[0], layers[0], scales=scales, groups=groups, gate=gate,
                                       se=se,
                                       norm_layer=norm_layer)
        self.layer2 = self._make_layer(Res2NetBottleneck, planes[1], layers[1], stride=2, scales=scales, groups=groups,
                                       gate=gate,se=se, norm_layer=norm_layer)
        self.layer3 = self._make_layer(Res2NetBottleneck, planes[2], layers[2], stride=2, scales=scales, groups=groups,
                                       gate=gate,se=se, norm_layer=norm_layer)
        self.layer4 = self._make_layer(Res2NetBottleneck, planes[3], layers[3], stride=2, scales=scales, groups=groups,
                                       gate=gate,se=se, norm_layer=norm_layer)
        self.presslayer=conv1x1(in_planes=1024,out_planes=maxlength)
        self.roi=nn.AdaptiveMaxPool1d(output_size=1)
    def _make_layer(self, block, planes, blocks, stride=1, scales=4, groups=1, gate=False,se=False, norm_layer=None):
        if norm_layer is None:
            norm_layer = nn.BatchNorm1d
        downsample = None
        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, downsample, stride=stride, gate=gate,scales=scales, groups=groups, se=se,
                            norm_layer=norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, scales=scales, groups=groups,gate=gate, se=se, norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def forward(self,x):
        x=  self.pre(x)
        x=  self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.roi(x)
        output= self.presslayer(x).squeeze()
        return output