data/networks.py

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

# Acknowledgement to
# https://github.com/kuangliu/pytorch-cifar,
# https://github.com/BIGBALLON/CIFAR-ZOO,

# adapted from
# https://github.com/VICO-UoE/DatasetCondensation

''' MLP '''


class MLP(nn.Module):
    def __init__(self, channel, num_classes):
        super(MLP, self).__init__()
        self.fc_1 = nn.Linear(28 * 28 * 1 if channel == 1 else 32 * 32 * 3, 128)
        self.fc_2 = nn.Linear(128, 128)
        self.fc_3 = nn.Linear(128, num_classes)

    def forward(self, x):
        out = x.view(x.size(0), -1)
        out = F.relu(self.fc_1(out))
        out = F.relu(self.fc_2(out))
        out = self.fc_3(out)
        return out


''' ConvNet '''


class ConvNet(nn.Module):
    def __init__(self, channel, num_classes, net_width, net_depth, net_act, net_norm, net_pooling, im_size=(32, 32)):
        super(ConvNet, self).__init__()

        self.features, shape_feat = self._make_layers(channel, net_width, net_depth, net_norm, net_act, net_pooling,
                                                      im_size)
        num_feat = shape_feat[0] * shape_feat[1] * shape_feat[2]
        self.classifier = nn.Linear(num_feat, num_classes)

    def forward(self, x):
        # print("MODEL DATA ON: ", x.get_device(), "MODEL PARAMS ON: ", self.classifier.weight.data.get_device())
        out = self.features(x)
        out = out.view(out.size(0), -1)
        out = self.classifier(out)
        return out

    def _get_activation(self, net_act):
        if net_act == 'sigmoid':
            return nn.Sigmoid()
        elif net_act == 'relu':
            return nn.ReLU(inplace=True)
        elif net_act == 'leakyrelu':
            return nn.LeakyReLU(negative_slope=0.01)
        else:
            exit('unknown activation function: %s' % net_act)

    def _get_pooling(self, net_pooling):
        if net_pooling == 'maxpooling':
            return nn.MaxPool2d(kernel_size=2, stride=2)
        elif net_pooling == 'avgpooling':
            return nn.AvgPool2d(kernel_size=2, stride=2)
        elif net_pooling == 'none':
            return None
        else:
            exit('unknown net_pooling: %s' % net_pooling)

    def _get_normlayer(self, net_norm, shape_feat):
        # shape_feat = (c*h*w)
        if net_norm == 'batchnorm':
            return nn.BatchNorm2d(shape_feat[0], affine=True)
        elif net_norm == 'layernorm':
            return nn.LayerNorm(shape_feat, elementwise_affine=True)
        elif net_norm == 'instancenorm':
            return nn.GroupNorm(shape_feat[0], shape_feat[0], affine=True)
        elif net_norm == 'groupnorm':
            return nn.GroupNorm(4, shape_feat[0], affine=True)
        elif net_norm == 'none':
            return None
        else:
            exit('unknown net_norm: %s' % net_norm)

    def _make_layers(self, channel, net_width, net_depth, net_norm, net_act, net_pooling, im_size):
        layers = []
        in_channels = channel
        if im_size[0] == 28:
            im_size = (32, 32)
        shape_feat = [in_channels, im_size[0], im_size[1]]
        for d in range(net_depth):
            layers += [nn.Conv2d(in_channels, net_width, kernel_size=3, padding=3 if channel == 1 and d == 0 else 1)]
            shape_feat[0] = net_width
            if net_norm != 'none':
                layers += [self._get_normlayer(net_norm, shape_feat)]
            layers += [self._get_activation(net_act)]
            in_channels = net_width
            if net_pooling != 'none':
                layers += [self._get_pooling(net_pooling)]
                shape_feat[1] //= 2
                shape_feat[2] //= 2

        return nn.Sequential(*layers), shape_feat


''' ConvNet '''


class ConvNetGAP(nn.Module):
    def __init__(self, channel, num_classes, net_width, net_depth, net_act, net_norm, net_pooling, im_size=(32, 32)):
        super(ConvNetGAP, self).__init__()

        self.features, shape_feat = self._make_layers(channel, net_width, net_depth, net_norm, net_act, net_pooling,
                                                      im_size)
        num_feat = shape_feat[0] * shape_feat[1] * shape_feat[2]
        # self.classifier = nn.Linear(num_feat, num_classes)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.classifier = nn.Linear(shape_feat[0], num_classes)

    def forward(self, x):
        out = self.features(x)
        out = self.avgpool(out)
        out = out.view(out.size(0), -1)
        out = self.classifier(out)
        return out

    def _get_activation(self, net_act):
        if net_act == 'sigmoid':
            return nn.Sigmoid()
        elif net_act == 'relu':
            return nn.ReLU(inplace=True)
        elif net_act == 'leakyrelu':
            return nn.LeakyReLU(negative_slope=0.01)
        else:
            exit('unknown activation function: %s' % net_act)

    def _get_pooling(self, net_pooling):
        if net_pooling == 'maxpooling':
            return nn.MaxPool2d(kernel_size=2, stride=2)
        elif net_pooling == 'avgpooling':
            return nn.AvgPool2d(kernel_size=2, stride=2)
        elif net_pooling == 'none':
            return None
        else:
            exit('unknown net_pooling: %s' % net_pooling)

    def _get_normlayer(self, net_norm, shape_feat):
        # shape_feat = (c*h*w)
        if net_norm == 'batchnorm':
            return nn.BatchNorm2d(shape_feat[0], affine=True)
        elif net_norm == 'layernorm':
            return nn.LayerNorm(shape_feat, elementwise_affine=True)
        elif net_norm == 'instancenorm':
            return nn.GroupNorm(shape_feat[0], shape_feat[0], affine=True)
        elif net_norm == 'groupnorm':
            return nn.GroupNorm(4, shape_feat[0], affine=True)
        elif net_norm == 'none':
            return None
        else:
            exit('unknown net_norm: %s' % net_norm)

    def _make_layers(self, channel, net_width, net_depth, net_norm, net_act, net_pooling, im_size):
        layers = []
        in_channels = channel
        if im_size[0] == 28:
            im_size = (32, 32)
        shape_feat = [in_channels, im_size[0], im_size[1]]
        for d in range(net_depth):
            layers += [nn.Conv2d(in_channels, net_width, kernel_size=3, padding=3 if channel == 1 and d == 0 else 1)]
            shape_feat[0] = net_width
            if net_norm != 'none':
                layers += [self._get_normlayer(net_norm, shape_feat)]
            layers += [self._get_activation(net_act)]
            in_channels = net_width
            if net_pooling != 'none':
                layers += [self._get_pooling(net_pooling)]
                shape_feat[1] //= 2
                shape_feat[2] //= 2

        return nn.Sequential(*layers), shape_feat


''' LeNet '''


class LeNet(nn.Module):
    def __init__(self, channel, num_classes):
        super(LeNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(channel, 6, kernel_size=5, padding=2 if channel == 1 else 0),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(6, 16, kernel_size=5),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.fc_1 = nn.Linear(16 * 5 * 5, 120)
        self.fc_2 = nn.Linear(120, 84)
        self.fc_3 = nn.Linear(84, num_classes)

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = F.relu(self.fc_1(x))
        x = F.relu(self.fc_2(x))
        x = self.fc_3(x)
        return x


''' AlexNet '''


class AlexNet(nn.Module):
    def __init__(self, channel, num_classes):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(channel, 128, kernel_size=5, stride=1, padding=4 if channel == 1 else 2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(128, 192, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(192, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 192, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(192, 192, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.fc = nn.Linear(192 * 4 * 4, num_classes)

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x


''' VGG '''
cfg_vgg = {
    'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
    'VGG19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}


class VGG(nn.Module):
    def __init__(self, vgg_name, channel, num_classes, norm='instancenorm'):
        super(VGG, self).__init__()
        self.channel = channel
        self.features = self._make_layers(cfg_vgg[vgg_name], norm)
        self.classifier = nn.Linear(512 if vgg_name != 'VGGS' else 128, num_classes)

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x

    def _make_layers(self, cfg, norm):
        layers = []
        in_channels = self.channel
        for ic, x in enumerate(cfg):
            if x == 'M':
                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
            else:
                layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=3 if self.channel == 1 and ic == 0 else 1),
                           nn.GroupNorm(x, x, affine=True) if norm == 'instancenorm' else nn.BatchNorm2d(x),
                           nn.ReLU(inplace=True)]
                in_channels = x
        layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
        return nn.Sequential(*layers)


def VGG11(channel, num_classes):
    return VGG('VGG11', channel, num_classes)


def VGG11BN(channel, num_classes):
    return VGG('VGG11', channel, num_classes, norm='batchnorm')


def VGG13(channel, num_classes):
    return VGG('VGG13', channel, num_classes)


def VGG16(channel, num_classes):
    return VGG('VGG16', channel, num_classes)


def VGG19(channel, num_classes):
    return VGG('VGG19', channel, num_classes)


''' ResNet_AP '''


# The conv(stride=2) is replaced by conv(stride=1) + avgpool(kernel_size=2, stride=2)

class BasicBlock_AP(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1, norm='instancenorm'):
        super(BasicBlock_AP, self).__init__()
        self.norm = norm
        self.stride = stride
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=1, padding=1, bias=False)  # modification
        self.bn1 = nn.GroupNorm(planes, planes, affine=True) if self.norm == 'instancenorm' else nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.GroupNorm(planes, planes, affine=True) if self.norm == 'instancenorm' else nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=1, bias=False),
                nn.AvgPool2d(kernel_size=2, stride=2),  # modification
                nn.GroupNorm(self.expansion * planes, self.expansion * planes,
                             affine=True) if self.norm == 'instancenorm' else nn.BatchNorm2d(self.expansion * planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        if self.stride != 1:  # modification
            out = F.avg_pool2d(out, kernel_size=2, stride=2)
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class Bottleneck_AP(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1, norm='instancenorm'):
        super(Bottleneck_AP, self).__init__()
        self.norm = norm
        self.stride = stride
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.GroupNorm(planes, planes, affine=True) if self.norm == 'instancenorm' else nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)  # modification
        self.bn2 = nn.GroupNorm(planes, planes, affine=True) if self.norm == 'instancenorm' else nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False)
        self.bn3 = nn.GroupNorm(self.expansion * planes, self.expansion * planes,
                                affine=True) if self.norm == 'instancenorm' else nn.BatchNorm2d(self.expansion * planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=1, bias=False),
                nn.AvgPool2d(kernel_size=2, stride=2),  # modification
                nn.GroupNorm(self.expansion * planes, self.expansion * planes,
                             affine=True) if self.norm == 'instancenorm' else nn.BatchNorm2d(self.expansion * planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        if self.stride != 1:  # modification
            out = F.avg_pool2d(out, kernel_size=2, stride=2)
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class ResNet_AP(nn.Module):
    def __init__(self, block, num_blocks, channel=3, num_classes=10, norm='instancenorm'):
        super(ResNet_AP, self).__init__()
        self.in_planes = 64
        self.norm = norm

        self.conv1 = nn.Conv2d(channel, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.GroupNorm(64, 64, affine=True) if self.norm == 'instancenorm' else nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.classifier = nn.Linear(512 * block.expansion * 3 * 3 if channel == 1 else 512 * block.expansion * 4 * 4,
                                    num_classes)  # modification

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride, self.norm))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, kernel_size=1, stride=1)  # modification
        out = out.view(out.size(0), -1)
        out = self.classifier(out)
        return out


def ResNet18BN_AP(channel, num_classes):
    return ResNet_AP(BasicBlock_AP, [2, 2, 2, 2], channel=channel, num_classes=num_classes, norm='batchnorm')


def ResNet18_AP(channel, num_classes):
    return ResNet_AP(BasicBlock_AP, [2, 2, 2, 2], channel=channel, num_classes=num_classes)


''' ResNet '''


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1, norm='instancenorm'):
        super(BasicBlock, self).__init__()
        self.norm = norm
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.GroupNorm(planes, planes, affine=True) if self.norm == 'instancenorm' else nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.GroupNorm(planes, planes, affine=True) if self.norm == 'instancenorm' else nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
                nn.GroupNorm(self.expansion * planes, self.expansion * planes,
                             affine=True) if self.norm == 'instancenorm' else nn.BatchNorm2d(self.expansion * planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1, norm='instancenorm'):
        super(Bottleneck, self).__init__()
        self.norm = norm
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.GroupNorm(planes, planes, affine=True) if self.norm == 'instancenorm' else nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.GroupNorm(planes, planes, affine=True) if self.norm == 'instancenorm' else nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False)
        self.bn3 = nn.GroupNorm(self.expansion * planes, self.expansion * planes,
                                affine=True) if self.norm == 'instancenorm' else nn.BatchNorm2d(self.expansion * planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
                nn.GroupNorm(self.expansion * planes, self.expansion * planes,
                             affine=True) if self.norm == 'instancenorm' else nn.BatchNorm2d(self.expansion * planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, channel=3, num_classes=10, norm='instancenorm'):
        super(ResNet, self).__init__()
        self.in_planes = 64
        self.norm = norm

        self.conv1 = nn.Conv2d(channel, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.GroupNorm(64, 64, affine=True) if self.norm == 'instancenorm' else nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.classifier = nn.Linear(512 * block.expansion, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride, self.norm))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.classifier(out)
        return out


class ResNetImageNet(nn.Module):
    def __init__(self, block, num_blocks, channel=3, num_classes=10, norm='instancenorm'):
        super(ResNetImageNet, self).__init__()
        self.in_planes = 64
        self.norm = norm

        self.conv1 = nn.Conv2d(channel, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.GroupNorm(64, 64, affine=True) if self.norm == 'instancenorm' else nn.BatchNorm2d(64)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.classifier = nn.Linear(512 * block.expansion, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride, self.norm))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.maxpool(out)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        # out = F.avg_pool2d(out, 4)
        # out = out.view(out.size(0), -1)
        out = self.avgpool(out)
        out = torch.flatten(out, 1)
        out = self.classifier(out)
        return out


from backbone import AttU_Net, R2AttU_Net, UNet, VisionTransformer


def Unet(channel, num_classes):
    return UNet(n_channels=channel, n_classes=num_classes)


def AttnUnet(channel, num_classes):
    return AttU_Net(img_ch=channel, output_ch=num_classes)


def R2AttnUnet(channel, num_classes):
    return R2AttU_Net(img_ch=channel, output_ch=num_classes)


def TransUnet(channel, num_classes):
    return VisionTransformer(num_classes=num_classes)

def ResNet18BN(channel, num_classes):
    return ResNet(BasicBlock, [2, 2, 2, 2], channel=channel, num_classes=num_classes, norm='batchnorm')


def ResNet18(channel, num_classes):
    return ResNet(BasicBlock, [2, 2, 2, 2], channel=channel, num_classes=num_classes)


def ResNet34(channel, num_classes):
    return ResNet(BasicBlock, [3, 4, 6, 3], channel=channel, num_classes=num_classes)


def ResNet50(channel, num_classes):
    return ResNet(Bottleneck, [3, 4, 6, 3], channel=channel, num_classes=num_classes)


def ResNet101(channel, num_classes):
    return ResNet(Bottleneck, [3, 4, 23, 3], channel=channel, num_classes=num_classes)


def ResNet152(channel, num_classes):
    return ResNet(Bottleneck, [3, 8, 36, 3], channel=channel, num_classes=num_classes)


def ResNet18ImageNet(channel, num_classes):
    return ResNetImageNet(BasicBlock, [2, 2, 2, 2], channel=channel, num_classes=num_classes)


def ResNet6ImageNet(channel, num_classes):
    return ResNetImageNet(BasicBlock, [1, 1, 1, 1], channel=channel, num_classes=num_classes)