ReWiS/resnet.py at main · Restuccia-Group/ReWiS · GitHub

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import torch.nn as nn
import torch
import torch.nn.functional as F
from torch.distributions import Bernoulli


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 SELayer(nn.Module):
    def __init__(self, channel, reduction=16):
        super(SELayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
                nn.Linear(channel, channel // reduction),
                nn.ReLU(inplace=True),
                nn.Linear(channel // reduction, channel),
                nn.Sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x * y


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, drop_rate=0.0, use_se=False):
        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
        self.drop_rate = drop_rate
        self.num_batches_tracked = 0
        self.use_se = use_se
        if self.use_se:
            self.se = SELayer(planes, 4)

    def forward(self, x):
        self.num_batches_tracked += 1

        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.use_se:
            out = self.se(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, block, n_blocks, keep_prob=1.0, avg_pool=False,
                 num_classes=-1, use_se=False):
        super(ResNet, self).__init__()

        self.inplanes = 1
        self.use_se = use_se
        self.layer1 = self._make_layer(block, n_blocks[0], 64,stride=2)
        self.layer2 = self._make_layer(block, n_blocks[1], 160,stride=2)
        self.layer3 = self._make_layer(block, n_blocks[2], 320,stride=2)
        self.layer4 = self._make_layer(block, n_blocks[3], 640,stride=2)
        if avg_pool:
            # self.avgpool = nn.AvgPool2d(5, stride=1)
            self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.keep_prob = keep_prob
        self.keep_avg_pool = avg_pool
        self.dropout = nn.Dropout(p=1 - self.keep_prob, inplace=False)

        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)

        self.num_classes = num_classes
        if self.num_classes > 0:
            self.classifier = nn.Linear(640, self.num_classes)

    def _make_layer(self, block, n_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 = []
        if n_block == 1:
            layer = block(self.inplanes, planes, stride, downsample, self.use_se)
        else:
            layer = block(self.inplanes, planes, stride, downsample, self.use_se)
        layers.append(layer)
        self.inplanes = planes * block.expansion

        for i in range(1, n_block):
            if i == n_block - 1:
                layer = block(self.inplanes, planes, use_se=self.use_se)
            else:
                layer = block(self.inplanes, planes, use_se=self.use_se)
            layers.append(layer)

        return nn.Sequential(*layers)

    def forward(self, x, is_feat=False):
        x = self.layer1(x)
        f0 = x
        x = self.layer2(x)
        f1 = x
        x = self.layer3(x)
        f2 = x
        x = self.layer4(x)
        f3 = x
        if self.keep_avg_pool:
            x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        feat = x
        if self.num_classes > 0:
            x = self.classifier(x)

        if is_feat:
            return [f0, f1, f2, f3, feat], x
        else:
            return x


def resnet12(keep_prob=1.0, avg_pool=False, **kwargs):
    """Constructs a ResNet-12 model.
    """
    model = ResNet(BasicBlock, [1, 1, 1, 1], keep_prob=keep_prob, avg_pool=avg_pool, **kwargs)
    return model

model = resnet12(avg_pool=True, num_classes = 4)