<filename>models/wideresnet.py
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np

from modules import interleave


class BasicBlock(nn.Module):

    def __init__(self, in_planes, out_planes, stride, dropRate=0.0, activate_before_residual=False):
        super(BasicBlock, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes, momentum=0.001)
        self.relu1 = nn.LeakyReLU(negative_slope=0.1, inplace=True)
        self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_planes, momentum=0.001)
        self.relu2 = nn.LeakyReLU(negative_slope=0.1, inplace=True)
        self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.droprate = dropRate
        self.equalInOut = (in_planes == out_planes)
        self.convShortcut = (not self.equalInOut) and nn.Conv2d(
            in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False) or None
        self.activate_before_residual = activate_before_residual

    def forward(self, x):
        if not self.equalInOut and self.activate_before_residual == True:
            x = self.relu1(self.bn1(x))
        else:
            out = self.relu1(self.bn1(x))
        out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
        if self.droprate > 0:
            out = F.dropout(out, p=self.droprate, training=self.training)
        out = self.conv2(out)
        return torch.add(x if self.equalInOut else self.convShortcut(x), out)


class NetworkBlock(nn.Module):

    def __init__(self,
                 nb_layers,
                 in_planes,
                 out_planes,
                 block,
                 stride,
                 dropRate=0.0,
                 activate_before_residual=False):
        super(NetworkBlock, self).__init__()
        self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride, dropRate,
                                      activate_before_residual)

    def _make_layer(self, block, in_planes, out_planes, nb_layers, stride, dropRate,
                    activate_before_residual):
        layers = []
        for i in range(int(nb_layers)):
            layers.append(
                block(i == 0 and in_planes or out_planes, out_planes, i == 0 and stride or 1, dropRate,
                      activate_before_residual))
        return nn.Sequential(*layers)

    def forward(self, x):
        return self.layer(x)


class WideResNet(nn.Module):

    def __init__(self, num_classes, depth=28, widen_factor=2, dropRate=0.0):
        super(WideResNet, self).__init__()
        nChannels = [16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor]
        assert ((depth - 4) % 6 == 0)
        n = (depth - 4) / 6
        block = BasicBlock
        # 1st conv before any network block
        self.conv1 = nn.Conv2d(3, nChannels[0], kernel_size=3, stride=1, padding=1, bias=False)
        # 1st block
        self.block1 = NetworkBlock(n,
                                   nChannels[0],
                                   nChannels[1],
                                   block,
                                   1,
                                   dropRate,
                                   activate_before_residual=True)
        # 2nd block
        self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2, dropRate)
        # 3rd block
        self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2, dropRate)
        # global average pooling and classifier
        self.bn1 = nn.BatchNorm2d(nChannels[3], momentum=0.001)
        self.relu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
        self.fc = nn.Linear(nChannels[3], num_classes)
        self.nChannels = nChannels[3]

        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_()
            elif isinstance(m, nn.Linear):
                nn.init.xavier_normal_(m.weight.data)
                m.bias.data.zero_()

    def mixup(self, x1, x2, l):
        return l * x1 + (1 - l) * x2

    def forward(self, x, do_interleave=False, mix=False, layers_mix=None, idx=None, batch_size=None, l=None):

        out = x

        if do_interleave or mix:
            if isinstance(layers_mix, (list, tuple)):
                layer_mix = np.random.choice(layers_mix, 1)
            else:
                layer_mix = layers_mix

            if not mix:
                layer_mix = None

            if layer_mix == 0:
                out = self.mixup(out, out[idx], l)

            out = list(torch.split(out, batch_size))
            out = interleave(out, batch_size)
            out = [self.conv1(x) for x in out]

            if layer_mix == 1:
                out = interleave(out, batch_size)
                out = torch.cat(out, dim=0)
                out = self.mixup(out, out[idx], l)
                out = list(torch.split(out, batch_size))
                out = interleave(out, batch_size)
            out = [self.block1(x) for x in out]

            if layer_mix == 2:
                out = interleave(out, batch_size)
                out = torch.cat(out, dim=0)
                out = self.mixup(out, out[idx], l)
                out = list(torch.split(out, batch_size))
                out = interleave(out, batch_size)
            out = [self.block2(x) for x in out]

            outs = []
            for x in out:
                x = self.block3(x)
                x = self.relu(self.bn1(x))
                x = F.avg_pool2d(x, 8)
                x = x.view(-1, self.nChannels)
                x = self.fc(x)
                outs.append(x)
            outs = interleave(outs, batch_size)
            return outs
        else:
            out = self.conv1(out)
            out = self.block1(out)
            out = self.block2(out)
            out = self.block3(out)
            out = self.relu(self.bn1(out))
            out = F.avg_pool2d(out, 8)
            out = out.view(-1, self.nChannels)
            out = self.fc(out)
            return out

<filename>models/wideresnet.py
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np

from modules import interleave


class BasicBlock(nn.Module):

    def __init__(self, in_planes, out_planes, stride, dropRate=0.0, activate_before_residual=False):
        super(BasicBlock, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes, momentum=0.001)
        self.relu1 = nn.LeakyReLU(negative_slope=0.1, inplace=True)
        self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_planes, momentum=0.001)
        self.relu2 = nn.LeakyReLU(negative_slope=0.1, inplace=True)
        self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.droprate = dropRate
        self.equalInOut = (in_planes == out_planes)
        self.convShortcut = (not self.equalInOut) and nn.Conv2d(
            in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False) or None
        self.activate_before_residual = activate_before_residual

    def forward(self, x):
        if not self.equalInOut and self.activate_before_residual == True:
            x = self.relu1(self.bn1(x))
        else:
            out = self.relu1(self.bn1(x))
        out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
        if self.droprate > 0:
            out = F.dropout(out, p=self.droprate, training=self.training)
        out = self.conv2(out)
        return torch.add(x if self.equalInOut else self.convShortcut(x), out)


class NetworkBlock(nn.Module):

    def __init__(self,
                 nb_layers,
                 in_planes,
                 out_planes,
                 block,
                 stride,
                 dropRate=0.0,
                 activate_before_residual=False):
        super(NetworkBlock, self).__init__()
        self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride, dropRate,
                                      activate_before_residual)

    def _make_layer(self, block, in_planes, out_planes, nb_layers, stride, dropRate,
                    activate_before_residual):
        layers = []
        for i in range(int(nb_layers)):
            layers.append(
                block(i == 0 and in_planes or out_planes, out_planes, i == 0 and stride or 1, dropRate,
                      activate_before_residual))
        return nn.Sequential(*layers)

    def forward(self, x):
        return self.layer(x)


class WideResNet(nn.Module):

    def __init__(self, num_classes, depth=28, widen_factor=2, dropRate=0.0):
        super(WideResNet, self).__init__()
        nChannels = [16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor]
        assert ((depth - 4) % 6 == 0)
        n = (depth - 4) / 6
        block = BasicBlock
        # 1st conv before any network block
        self.conv1 = nn.Conv2d(3, nChannels[0], kernel_size=3, stride=1, padding=1, bias=False)
        # 1st block
        self.block1 = NetworkBlock(n,
                                   nChannels[0],
                                   nChannels[1],
                                   block,
                                   1,
                                   dropRate,
                                   activate_before_residual=True)
        # 2nd block
        self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2, dropRate)
        # 3rd block
        self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2, dropRate)
        # global average pooling and classifier
        self.bn1 = nn.BatchNorm2d(nChannels[3], momentum=0.001)
        self.relu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
        self.fc = nn.Linear(nChannels[3], num_classes)
        self.nChannels = nChannels[3]

        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_()
            elif isinstance(m, nn.Linear):
                nn.init.xavier_normal_(m.weight.data)
                m.bias.data.zero_()

    def mixup(self, x1, x2, l):
        return l * x1 + (1 - l) * x2

    def forward(self, x, do_interleave=False, mix=False, layers_mix=None, idx=None, batch_size=None, l=None):

        out = x

        if do_interleave or mix:
            if isinstance(layers_mix, (list, tuple)):
                layer_mix = np.random.choice(layers_mix, 1)
            else:
                layer_mix = layers_mix

            if not mix:
                layer_mix = None

            if layer_mix == 0:
                out = self.mixup(out, out[idx], l)

            out = list(torch.split(out, batch_size))
            out = interleave(out, batch_size)
            out = [self.conv1(x) for x in out]

            if layer_mix == 1:
                out = interleave(out, batch_size)
                out = torch.cat(out, dim=0)
                out = self.mixup(out, out[idx], l)
                out = list(torch.split(out, batch_size))
                out = interleave(out, batch_size)
            out = [self.block1(x) for x in out]

            if layer_mix == 2:
                out = interleave(out, batch_size)
                out = torch.cat(out, dim=0)
                out = self.mixup(out, out[idx], l)
                out = list(torch.split(out, batch_size))
                out = interleave(out, batch_size)
            out = [self.block2(x) for x in out]

            outs = []
            for x in out:
                x = self.block3(x)
                x = self.relu(self.bn1(x))
                x = F.avg_pool2d(x, 8)
                x = x.view(-1, self.nChannels)
                x = self.fc(x)
                outs.append(x)
            outs = interleave(outs, batch_size)
            return outs
        else:
            out = self.conv1(out)
            out = self.block1(out)
            out = self.block2(out)
            out = self.block3(out)
            out = self.relu(self.bn1(out))
            out = F.avg_pool2d(out, 8)
            out = out.view(-1, self.nChannels)
            out = self.fc(out)
            return out

# 1st conv before any network block # 1st block # 2nd block # 3rd block # global average pooling and classifier