# Source code for archai.networks.shakeshake.shake_resnet

    # -*- coding: utf-8 -*-

    import math

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

    from archai.networks.shakeshake.shakeshake import ShakeShake
    from archai.networks.shakeshake.shakeshake import Shortcut

    class ShakeBlock(nn.Module):

        def __init__(self, in_ch, out_ch, stride=1):
            super(ShakeBlock, self).__init__()
            self.equal_io = in_ch == out_ch
            self.shortcut = self.equal_io and None or Shortcut(in_ch, out_ch, stride=stride)

            self.branch1 = self._make_branch(in_ch, out_ch, stride)
            self.branch2 = self._make_branch(in_ch, out_ch, stride)

        def forward(self, x):
            h1 = self.branch1(x)
            h2 = self.branch2(x)
            h = ShakeShake.apply(h1, h2, self.training)
            h0 = x if self.equal_io else self.shortcut(x)
            return h + h0

        def _make_branch(self, in_ch, out_ch, stride=1):
            return nn.Sequential(
                nn.ReLU(inplace=False),
                nn.Conv2d(in_ch, out_ch, 3, padding=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_ch),
                nn.ReLU(inplace=False),
                nn.Conv2d(out_ch, out_ch, 3, padding=1, stride=1, bias=False),
                nn.BatchNorm2d(out_ch))

    class ShakeResNet(nn.Module):

        def __init__(self, depth, w_base, label):
            super(ShakeResNet, self).__init__()
            n_units = (depth - 2) / 6

            in_chs = [16, w_base, w_base * 2, w_base * 4]
            self.in_chs = in_chs

            self.c_in = nn.Conv2d(3, in_chs[0], 3, padding=1)
            self.layer1 = self._make_layer(n_units, in_chs[0], in_chs[1])
            self.layer2 = self._make_layer(n_units, in_chs[1], in_chs[2], 2)
            self.layer3 = self._make_layer(n_units, in_chs[2], in_chs[3], 2)
            self.fc_out = nn.Linear(in_chs[3], label)

            # Initialize paramters
            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):
                    m.bias.data.zero_()

        def forward(self, x):
            h = self.c_in(x)
            h = self.layer1(h)
            h = self.layer2(h)
            h = self.layer3(h)
            h = F.relu(h)
            h = F.avg_pool2d(h, 8)
            h = h.view(-1, self.in_chs[3])
            h = self.fc_out(h)
            return h

        def _make_layer(self, n_units, in_ch, out_ch, stride=1):
            layers = []
            for i in range(int(n_units)):
                layers.append(ShakeBlock(in_ch, out_ch, stride=stride))
                in_ch, stride = out_ch, 1
            return nn.Sequential(*layers)