# Source code for archai.networks.resnet

    # Original code: https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py

    import torch.nn as nn
    import math

    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):
        expansion = 1

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

            self.downsample = downsample
            self.stride = stride

        def forward(self, x):
            residual = x

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

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

            if self.downsample is not None:
                residual = self.downsample(x)

            out += residual
            out = self.relu(out)

            return out

    class Bottleneck(nn.Module):
        expansion = 4

        def __init__(self, inplanes, planes, stride=1, downsample=None):
            super(Bottleneck, self).__init__()

            self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
            self.bn1 = nn.BatchNorm2d(planes)
            self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
            self.bn2 = nn.BatchNorm2d(planes)
            self.conv3 = nn.Conv2d(planes, planes * Bottleneck.expansion, kernel_size=1, bias=False)
            self.bn3 = nn.BatchNorm2d(planes * Bottleneck.expansion)
            self.relu = nn.ReLU(inplace=True)

            self.downsample = downsample
            self.stride = stride

        def forward(self, x):
            residual = x

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

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

            out = self.conv3(out)
            out = self.bn3(out)
            if self.downsample is not None:
                residual = self.downsample(x)

            out += residual
            out = self.relu(out)

            return out

    class ResNet(nn.Module):
        def __init__(self, dataset, depth, n_classes, bottleneck=False):
            super(ResNet, self).__init__()
            self.dataset = dataset
            if self.dataset.startswith('cifar'):
                self.inplanes = 16
                #logger.info(bottleneck)
                if bottleneck == True:
                    n = int((depth - 2) / 9)
                    block = Bottleneck
                else:
                    n = int((depth - 2) / 6)
                    block = BasicBlock

                self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
                self.bn1 = nn.BatchNorm2d(self.inplanes)
                self.relu = nn.ReLU(inplace=True)
                self.layer1 = self._make_layer(block, 16, n)
                self.layer2 = self._make_layer(block, 32, n, stride=2)
                self.layer3 = self._make_layer(block, 64, n, stride=2)
                # self.avgpool = nn.AvgPool2d(8)
                self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
                self.fc = nn.Linear(64 * block.expansion, 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]}
                assert layers[depth], 'invalid detph for ResNet (depth should be one of 18, 34, 50, 101, 152, and 200)'

                self.inplanes = 64
                self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)
                self.bn1 = nn.BatchNorm2d(64)
                self.relu = nn.ReLU(inplace=True)
                self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
                self.layer1 = self._make_layer(blocks[depth], 64, layers[depth][0])
                self.layer2 = self._make_layer(blocks[depth], 128, layers[depth][1], stride=2)
                self.layer3 = self._make_layer(blocks[depth], 256, layers[depth][2], stride=2)
                self.layer4 = self._make_layer(blocks[depth], 512, layers[depth][3], stride=2)
                # self.avgpool = nn.AvgPool2d(7)
                self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
                self.fc = nn.Linear(512 * blocks[depth].expansion, 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_()

        def _make_layer(self, block, planes, blocks, stride=1):
            downsample = None
            if stride != 1 or self.inplanes != planes * block.expansion:
                downsample = nn.Sequential(
                    nn.Conv2d(self.inplanes, planes * block.expansion,
                              kernel_size=1, stride=stride, bias=False),
                    nn.BatchNorm2d(planes * block.expansion),
                )

            layers = []
            layers.append(block(self.inplanes, planes, stride, downsample))
            self.inplanes = planes * block.expansion
            for i in range(1, blocks):
                layers.append(block(self.inplanes, planes))

            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.relu(x)

                x = self.layer1(x)
                x = self.layer2(x)
                x = self.layer3(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.avgpool(x)
                x = x.view(x.size(0), -1)
                x = self.fc(x)

            return x