# Source code for archai.networks.pyramidnet

    import torch
    import torch.nn as nn
    import math

    from .shakedrop import ShakeDrop

    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):
        outchannel_ratio = 1

        def __init__(self, inplanes, planes, stride=1, downsample=None, p_shakedrop=1.0):
            super(BasicBlock, self).__init__()
            self.bn1 = nn.BatchNorm2d(inplanes)
            self.conv1 = conv3x3(inplanes, planes, stride)
            self.bn2 = nn.BatchNorm2d(planes)
            self.conv2 = conv3x3(planes, planes)
            self.bn3 = nn.BatchNorm2d(planes)
            self.relu = nn.ReLU(inplace=True)
            self.downsample = downsample
            self.stride = stride
            self.shake_drop = ShakeDrop(p_shakedrop)

        def forward(self, x):

            out = self.bn1(x)
            out = self.conv1(out)
            out = self.bn2(out)
            out = self.relu(out)
            out = self.conv2(out)
            out = self.bn3(out)

            out = self.shake_drop(out)

            if self.downsample is not None:
                shortcut = self.downsample(x)
                featuremap_size = shortcut.size()[2:4]
            else:
                shortcut = x
                featuremap_size = out.size()[2:4]

            batch_size = out.size()[0]
            residual_channel = out.size()[1]
            shortcut_channel = shortcut.size()[1]

            if residual_channel != shortcut_channel:
                padding = torch.autograd.Variable(
                    torch.cuda.FloatTensor(batch_size, residual_channel - shortcut_channel, featuremap_size[0],
                                           featuremap_size[1]).fill_(0))
                out += torch.cat((shortcut, padding), dim=1)
            else:
                out += shortcut

            return out

    class Bottleneck(nn.Module):
        outchannel_ratio = 4

        def __init__(self, inplanes, planes, stride=1, downsample=None, p_shakedrop=1.0):
            super(Bottleneck, self).__init__()
            self.bn1 = nn.BatchNorm2d(inplanes)
            self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
            self.bn2 = nn.BatchNorm2d(planes)
            self.conv2 = nn.Conv2d(planes, (planes * 1), kernel_size=3, stride=stride,
                                   padding=1, bias=False)
            self.bn3 = nn.BatchNorm2d((planes * 1))
            self.conv3 = nn.Conv2d((planes * 1), planes * Bottleneck.outchannel_ratio, kernel_size=1, bias=False)
            self.bn4 = nn.BatchNorm2d(planes * Bottleneck.outchannel_ratio)
            self.relu = nn.ReLU(inplace=True)
            self.downsample = downsample
            self.stride = stride
            self.shake_drop = ShakeDrop(p_shakedrop)

        def forward(self, x):

            out = self.bn1(x)
            out = self.conv1(out)

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

            out = self.bn3(out)
            out = self.relu(out)
            out = self.conv3(out)

            out = self.bn4(out)

            out = self.shake_drop(out)

            if self.downsample is not None:
                shortcut = self.downsample(x)
                featuremap_size = shortcut.size()[2:4]
            else:
                shortcut = x
                featuremap_size = out.size()[2:4]

            batch_size = out.size()[0]
            residual_channel = out.size()[1]
            shortcut_channel = shortcut.size()[1]

            if residual_channel != shortcut_channel:
                padding = torch.autograd.Variable(
                    torch.cuda.FloatTensor(batch_size, residual_channel - shortcut_channel, featuremap_size[0],
                                           featuremap_size[1]).fill_(0))
                out += torch.cat((shortcut, padding), dim=1)
            else:
                out += shortcut

            return out

    class PyramidNet(nn.Module):

        def __init__(self, dataset, depth, alpha, n_classes, bottleneck=True):
            super(PyramidNet, self).__init__()
            self.dataset = dataset
            if self.dataset.startswith('cifar'):
                self.inplanes = 16
                if bottleneck:
                    n = int((depth - 2) / 9)
                    block = Bottleneck
                else:
                    n = int((depth - 2) / 6)
                    block = BasicBlock

                self.addrate = alpha / (3 * n * 1.0)
                self.ps_shakedrop = [1. - (1.0 - (0.5 / (3 * n)) * (i + 1)) for i in range(3 * n)]

                self.input_featuremap_dim = self.inplanes
                self.conv1 = nn.Conv2d(3, self.input_featuremap_dim, kernel_size=3, stride=1, padding=1, bias=False)
                self.bn1 = nn.BatchNorm2d(self.input_featuremap_dim)

                self.featuremap_dim = self.input_featuremap_dim
                self.layer1 = self.pyramidal_make_layer(block, n)
                self.layer2 = self.pyramidal_make_layer(block, n, stride=2)
                self.layer3 = self.pyramidal_make_layer(block, n, stride=2)

                self.final_featuremap_dim = self.input_featuremap_dim
                self.bn_final = nn.BatchNorm2d(self.final_featuremap_dim)
                self.relu_final = nn.ReLU(inplace=True)
                self.avgpool = nn.AvgPool2d(8)
                self.fc = nn.Linear(self.final_featuremap_dim, n_classes)

            elif dataset == 'imagenet':
                blocks = {18: BasicBlock, 34: BasicBlock, 50: Bottleneck, 101: Bottleneck, 152: Bottleneck, 200: Bottleneck}
                layers = {18: [2, 2, 2, 2], 34: [3, 4, 6, 3], 50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3],
                          200: [3, 24, 36, 3]}

                if layers.get(depth) is None:
                    if bottleneck == True:
                        blocks[depth] = Bottleneck
                        temp_cfg = int((depth - 2) / 12)
                    else:
                        blocks[depth] = BasicBlock
                        temp_cfg = int((depth - 2) / 8)

                    layers[depth] = [temp_cfg, temp_cfg, temp_cfg, temp_cfg]
                    #logger.info('=> the layer configuration for each stage is set to', layers[depth])

                self.inplanes = 64
                self.addrate = alpha / (sum(layers[depth]) * 1.0)

                self.input_featuremap_dim = self.inplanes
                self.conv1 = nn.Conv2d(3, self.input_featuremap_dim, kernel_size=7, stride=2, padding=3, bias=False)
                self.bn1 = nn.BatchNorm2d(self.input_featuremap_dim)
                self.relu = nn.ReLU(inplace=True)
                self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

                self.featuremap_dim = self.input_featuremap_dim
                self.layer1 = self.pyramidal_make_layer(blocks[depth], layers[depth][0])
                self.layer2 = self.pyramidal_make_layer(blocks[depth], layers[depth][1], stride=2)
                self.layer3 = self.pyramidal_make_layer(blocks[depth], layers[depth][2], stride=2)
                self.layer4 = self.pyramidal_make_layer(blocks[depth], layers[depth][3], stride=2)

                self.final_featuremap_dim = self.input_featuremap_dim
                self.bn_final = nn.BatchNorm2d(self.final_featuremap_dim)
                self.relu_final = nn.ReLU(inplace=True)
                self.avgpool = nn.AvgPool2d(7)
                self.fc = nn.Linear(self.final_featuremap_dim, n_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_()

            assert len(self.ps_shakedrop) == 0, self.ps_shakedrop

        def pyramidal_make_layer(self, block, block_depth, stride=1):
            downsample = None
            if stride != 1:  # or self.inplanes != int(round(featuremap_dim_1st)) * block.outchannel_ratio:
                downsample = nn.AvgPool2d((2, 2), stride=(2, 2), ceil_mode=True)

            layers = []
            self.featuremap_dim = self.featuremap_dim + self.addrate
            layers.append(block(self.input_featuremap_dim, int(round(self.featuremap_dim)), stride, downsample, p_shakedrop=self.ps_shakedrop.pop(0)))
            for i in range(1, block_depth):
                temp_featuremap_dim = self.featuremap_dim + self.addrate
                layers.append(
                    block(int(round(self.featuremap_dim)) * block.outchannel_ratio, int(round(temp_featuremap_dim)), 1, p_shakedrop=self.ps_shakedrop.pop(0)))
                self.featuremap_dim = temp_featuremap_dim
            self.input_featuremap_dim = int(round(self.featuremap_dim)) * block.outchannel_ratio

            return nn.Sequential(*layers)

        def forward(self, x):
            if self.dataset == 'cifar10' or self.dataset == 'cifar100':
                x = self.conv1(x)
                x = self.bn1(x)

                x = self.layer1(x)
                x = self.layer2(x)
                x = self.layer3(x)

                x = self.bn_final(x)
                x = self.relu_final(x)
                x = self.avgpool(x)
                x = x.view(x.size(0), -1)
                x = self.fc(x)

            elif self.dataset == 'imagenet':
                x = self.conv1(x)
                x = self.bn1(x)
                x = self.relu(x)
                x = self.maxpool(x)

                x = self.layer1(x)
                x = self.layer2(x)
                x = self.layer3(x)
                x = self.layer4(x)

                x = self.bn_final(x)
                x = self.relu_final(x)
                x = self.avgpool(x)
                x = x.view(x.size(0), -1)
                x = self.fc(x)

            return x