RCL_FS_BSED/models.py at master · ilyassmoummad/RCL_FS_BSED · GitHub

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import torch.nn as nn
import torch.nn.functional as F
import torch

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(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.LeakyReLU(0.1)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = conv3x3(planes, planes)
        self.bn3 = nn.BatchNorm2d(planes)
        self.maxpool = nn.MaxPool2d(stride)
        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)
        out = self.maxpool(out)

        return out


class ResNet(nn.Module):

    def __init__(self, method='scl', block=BasicBlock, avg_pool=True, num_classes=46):
        self.inplanes = 1
        super(ResNet, self).__init__()

        self.layer1 = self._make_layer(block, 64, stride=(2,2))
        self.layer2 = self._make_layer(block, 128, stride=(2,2))
        self.layer3 = self._make_layer(block, 256, stride=(1,2))

        if avg_pool:
            self.avgpool = nn.AvgPool2d(5, stride=1)
        self.keep_avg_pool = avg_pool

        self.pool = nn.AdaptiveMaxPool2d((8, 1))
        if method in ['scl', 'ssl']:
            self.lin = nn.Sequential(nn.Linear(2048, 2048), nn.ReLU(inplace=True), nn.Linear(2048, 512)) # 2048 = 256 * 8, the dimension of the latent space
        else:
            self.lin = nn.Linear(2048, num_classes)

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

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

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion

        return nn.Sequential(*layers)

    def forward(self, x):

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

        x = self.pool(x)

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

        out = self.lin(x)

        return x, out