<reponame>daintlab/unknown-detection-benchmarks
import torch
import torch.nn as nn
import torch.nn.functional as F

import math


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, dropRate=0.0):
        super(BasicBlock, self).__init__()
        self.dropRate = dropRate
        self.conv1 = conv3x3(inplanes, planes, 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)
        
        if self.dropRate > 0:
            out = F.dropout(out, p=self.dropRate, inplace=False, training=True)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)
        
        if self.dropRate > 0:
            out = F.dropout(out, p=self.dropRate, inplace=False, training=True)

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

        return out


class Bottleneck(nn.Module):
    expansion=4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes*4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes*4)
        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_Cifar(nn.Module):

    def __init__(self, block, layers, num_classes=40, dropRate=0.0):
        super(ResNet_Cifar, self).__init__()
        self.dropRate = dropRate
        self.inplanes = 16
        self.conv1 = nn.Conv2d(3, 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)
        self.layer3 = self._make_layer(block, 64, layers[2], stride=2)
        self.avgpool = nn.AvgPool2d(8, stride=1)
        self.fc = nn.Linear(64 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

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

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

        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 = x.view(x.size(0), -1)
        
        if self.dropRate > 0:
            x = F.dropout(x, p=0.1, inplace=False, training=True)
        
        x = self.fc(x)

        return x
    
    # function to extract the multiple features
    def feature_list(self, x):
        out_list = []
        out = self.relu(self.bn1(self.conv1(x)))
        out_list.append(out)
        out = self.layer1(out)
        out_list.append(out)
        out = self.layer2(out)
        out_list.append(out)
        out = self.layer3(out)
        out_list.append(out)
        out = self.avgpool(out)
        out = out.view(out.size(0), -1)
        y = self.fc(out)

        return y, out_list

    # function to extract a specific feature
    def intermediate_forward(self, x, layer_index):
        out = F.relu(self.bn1(self.conv1(x)))
        if layer_index == 1:
            out = self.layer1(out)
        elif layer_index == 2:
            out = self.layer1(out)
            out = self.layer2(out)
        elif layer_index == 3:
            out = self.layer1(out)
            out = self.layer2(out)
            out = self.layer3(out)

        return out

    # function to extract the penultimate features
    def penultimate_forward(self, x):
        out = self.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        penultimate = self.layer3(out)
        out = self.avgpool(penultimate)
        out = out.view(out.size(0), -1)
        y = self.fc(out)

        return y, penultimate



def resnet20(**kwargs):
    model = ResNet_Cifar(BasicBlock, [3, 3, 3], **kwargs)
    return model

def resnet110(**kwargs):
    model = ResNet_Cifar(BasicBlock, [18, 18, 18], **kwargs)
    return model

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

import math


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, dropRate=0.0):
        super(BasicBlock, self).__init__()
        self.dropRate = dropRate
        self.conv1 = conv3x3(inplanes, planes, 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)
        
        if self.dropRate > 0:
            out = F.dropout(out, p=self.dropRate, inplace=False, training=True)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)
        
        if self.dropRate > 0:
            out = F.dropout(out, p=self.dropRate, inplace=False, training=True)

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

        return out


class Bottleneck(nn.Module):
    expansion=4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes*4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes*4)
        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_Cifar(nn.Module):

    def __init__(self, block, layers, num_classes=40, dropRate=0.0):
        super(ResNet_Cifar, self).__init__()
        self.dropRate = dropRate
        self.inplanes = 16
        self.conv1 = nn.Conv2d(3, 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)
        self.layer3 = self._make_layer(block, 64, layers[2], stride=2)
        self.avgpool = nn.AvgPool2d(8, stride=1)
        self.fc = nn.Linear(64 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

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

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

        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 = x.view(x.size(0), -1)
        
        if self.dropRate > 0:
            x = F.dropout(x, p=0.1, inplace=False, training=True)
        
        x = self.fc(x)

        return x
    
    # function to extract the multiple features
    def feature_list(self, x):
        out_list = []
        out = self.relu(self.bn1(self.conv1(x)))
        out_list.append(out)
        out = self.layer1(out)
        out_list.append(out)
        out = self.layer2(out)
        out_list.append(out)
        out = self.layer3(out)
        out_list.append(out)
        out = self.avgpool(out)
        out = out.view(out.size(0), -1)
        y = self.fc(out)

        return y, out_list

    # function to extract a specific feature
    def intermediate_forward(self, x, layer_index):
        out = F.relu(self.bn1(self.conv1(x)))
        if layer_index == 1:
            out = self.layer1(out)
        elif layer_index == 2:
            out = self.layer1(out)
            out = self.layer2(out)
        elif layer_index == 3:
            out = self.layer1(out)
            out = self.layer2(out)
            out = self.layer3(out)

        return out

    # function to extract the penultimate features
    def penultimate_forward(self, x):
        out = self.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        penultimate = self.layer3(out)
        out = self.avgpool(penultimate)
        out = out.view(out.size(0), -1)
        y = self.fc(out)

        return y, penultimate



def resnet20(**kwargs):
    model = ResNet_Cifar(BasicBlock, [3, 3, 3], **kwargs)
    return model

def resnet110(**kwargs):
    model = ResNet_Cifar(BasicBlock, [18, 18, 18], **kwargs)
    return model
# function to extract the multiple features # function to extract a specific feature # function to extract the penultimate features