import torch.nn as nn
import math

__all__ = [
    'ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet50c', 'resnet50d',
    'resnet101', 'resnet101d', 'resnet152', 'resnet152d'
]


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.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        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 * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

        bypass_bn_weight_list.append(self.bn3.weight)

    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,
                 block,
                 layers,
                 num_classes=1000,
                 deep_stem=False,
                 avg_down=False,
                 bypass_last_bn=False):

        global bypass_bn_weight_list

        bypass_bn_weight_list = []

        self.inplanes = 64
        super(ResNet, self).__init__()

        self.deep_stem = deep_stem
        self.avg_down = avg_down

        if self.deep_stem:
            self.conv1 = nn.Sequential(
                nn.Conv2d(3,
                          32,
                          kernel_size=3,
                          stride=2,
                          padding=1,
                          bias=False),
                nn.BatchNorm2d(32),
                nn.ReLU(inplace=True),
                nn.Conv2d(32,
                          32,
                          kernel_size=3,
                          stride=1,
                          padding=1,
                          bias=False),
                nn.BatchNorm2d(32),
                nn.ReLU(inplace=True),
                nn.Conv2d(32,
                          64,
                          kernel_size=3,
                          stride=1,
                          padding=1,
                          bias=False),
            )
        else:
            self.conv1 = nn.Conv2d(3,
                                   64,
                                   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(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AvgPool2d(7, stride=1)
        self.fc = nn.Linear(512 * block.expansion, num_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_()

        if bypass_last_bn:
            for param in bypass_bn_weight_list:
                param.data.zero_()
            print('bypass {} bn.weight in BottleneckBlocks'.format(
                len(bypass_bn_weight_list)))

    def _make_layer(self, block, planes, blocks, stride=1, avg_down=False):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            if self.avg_down:
                downsample = nn.Sequential(
                    nn.AvgPool2d(stride, stride=stride, ceil_mode=True),
                    nn.Conv2d(self.inplanes,
                              planes * block.expansion,
                              kernel_size=1,
                              stride=1,
                              bias=False),
                    nn.BatchNorm2d(planes * block.expansion),
                )
            else:
                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):
        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


def resnet18(pretrained=False, **kwargs):
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    return model


def resnet34(pretrained=False, **kwargs):
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    return model


def resnet50(pretrained=False, **kwargs):
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    return model


def resnet50c(pretrained=False, **kwargs):
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs, deep_stem=True)
    return model


def resnet50d(pretrained=False, **kwargs):
    model = ResNet(Bottleneck, [3, 4, 6, 3],
                   **kwargs,
                   deep_stem=True,
                   avg_down=True)
    return model


def resnet101(pretrained=False, **kwargs):
    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
    return model


def resnet101d(pretrained=False, **kwargs):
    model = ResNet(Bottleneck, [3, 4, 23, 3],
                   **kwargs,
                   deep_stem=True,
                   avg_down=True)
    return model


def resnet152(pretrained=False, **kwargs):
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    return model


def resnet152d(pretrained=False, **kwargs):
    model = ResNet(Bottleneck, [3, 8, 36, 3],
                   **kwargs,
                   deep_stem=True,
                   avg_down=True)
    return model
