#!/usr/bin/env python3
# coding: utf-8

import torch.nn as nn

__all__ = ['ResNet', 'resnet22']


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 ResNet(nn.Module):
    """Another Strucutre used in caffe-resnet25"""

    def __init__(self, block, layers, num_classes=62, num_landmarks=136, input_channel=3, fc_flg=False):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(input_channel, 32, kernel_size=5, stride=2, padding=2, bias=False)
        self.bn1 = nn.BatchNorm2d(32)  # 32 is input channels number
        self.relu1 = nn.ReLU(inplace=True)

        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(64)
        self.relu2 = nn.ReLU(inplace=True)

        # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._make_layer(block, 128, layers[0], stride=2)
        self.layer2 = self._make_layer(block, 256, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 512, layers[2], stride=2)

        self.conv_param = nn.Conv2d(512, num_classes, 1)
        # self.conv_lm = nn.Conv2d(512, num_landmarks, 1)
        self.avgpool = nn.AdaptiveAvgPool2d(1)
        # self.fc = nn.Linear(512 * block.expansion, num_classes)
        self.fc_flg = fc_flg

        # parameter initialization
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                # 1.
                # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                # m.weight.data.normal_(0, math.sqrt(2. / n))

                # 2. kaiming normal
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            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):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)

        # x = self.maxpool(x)

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

        # if self.fc_flg:
        #     x = self.avgpool(x)
        #     x = x.view(x.size(0), -1)
        #     x = self.fc(x)
        # else:
        xp = self.conv_param(x)
        xp = self.avgpool(xp)
        xp = xp.view(xp.size(0), -1)

        # xl = self.conv_lm(x)
        # xl = self.avgpool(xl)
        # xl = xl.view(xl.size(0), -1)

        return xp  # , xl


def resnet22(**kwargs):
    model = ResNet(
        BasicBlock,
        [3, 4, 3],
        num_landmarks=kwargs.get('num_landmarks', 136),
        input_channel=kwargs.get('input_channel', 3),
        fc_flg=False
    )
    return model


def main():
    pass


if __name__ == '__main__':
    main()