# Source code for archai.networks.wideresnet

    import torch.nn as nn
    import torch.nn.init as init
    import torch.nn.functional as F
    import numpy as np

    def conv3x3(in_planes, out_planes, stride=1):
        return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True)

    def conv_init(m):
        classname = m.__class__.__name__
        if classname.find('Conv') != -1:
            init.xavier_uniform_(m.weight, gain=np.sqrt(2))
            init.constant_(m.bias, 0)
        elif classname.find('BatchNorm') != -1:
            init.constant_(m.weight, 1)
            init.constant_(m.bias, 0)

    class WideBasic(nn.Module):
        def __init__(self, in_planes, planes, dropout_rate, stride=1):
            super(WideBasic, self).__init__()
            self.bn1 = nn.BatchNorm2d(in_planes, momentum=0.9)
            self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, bias=True)
            self.dropout = nn.Dropout(p=dropout_rate)
            self.bn2 = nn.BatchNorm2d(planes, momentum=0.9)
            self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=True)

            self.shortcut = nn.Sequential()
            if stride != 1 or in_planes != planes:
                self.shortcut = nn.Sequential(
                    nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=True),
                )

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

            return out

    class WideResNet(nn.Module):
        def __init__(self, depth, widen_factor, dropout_rate, n_classes):
            super(WideResNet, self).__init__()
            self.in_planes = 16

            assert ((depth - 4) % 6 == 0), 'Wide-resnet depth should be 6n+4'
            n = int((depth - 4) / 6)
            k = widen_factor

            nStages = [16, 16*k, 32*k, 64*k]

            self.conv1 = conv3x3(3, nStages[0])
            self.layer1 = self._wide_layer(WideBasic, nStages[1], n, dropout_rate, stride=1)
            self.layer2 = self._wide_layer(WideBasic, nStages[2], n, dropout_rate, stride=2)
            self.layer3 = self._wide_layer(WideBasic, nStages[3], n, dropout_rate, stride=2)
            self.bn1 = nn.BatchNorm2d(nStages[3], momentum=0.9)
            self.linear = nn.Linear(nStages[3], n_classes)

            # self.apply(conv_init)

        def _wide_layer(self, block, planes, num_blocks, dropout_rate, stride):
            strides = [stride] + [1]*(num_blocks-1)
            layers = []

            for stride in strides:
                layers.append(block(self.in_planes, planes, dropout_rate, stride))
                self.in_planes = planes

            return nn.Sequential(*layers)

        def forward(self, x):
            out = self.conv1(x)
            out = self.layer1(out)
            out = self.layer2(out)
            out = self.layer3(out)
            out = F.relu(self.bn1(out))
            # out = F.avg_pool2d(out, 8)
            out = F.adaptive_avg_pool2d(out, (1, 1))
            out = out.view(out.size(0), -1)
            out = self.linear(out)

            return out