import torch
import torch.nn as nn
import math
from collections import OrderedDict

################## AlexNet ##################
def bn_relu(inplanes):
    return nn.Sequential(nn.BatchNorm2d(inplanes), nn.ReLU(inplace=True))

def bn_relu_pool(inplanes, kernel_size=3, stride=2):
    return nn.Sequential(nn.BatchNorm2d(inplanes), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=kernel_size, stride=stride))

class AlexNet(nn.Module):
    def __init__(self, num_classes=1):
        super(AlexNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 96, kernel_size=11, stride=4, bias=False)
        self.relu_pool1 = bn_relu_pool(inplanes=96)
        self.conv2 = nn.Conv2d(96, 192, kernel_size=5, padding=2, groups=2, bias=False)
        self.relu_pool2 = bn_relu_pool(inplanes=192)
        self.conv3 = nn.Conv2d(192, 384, kernel_size=3, padding=1, groups=2, bias=False)
        self.relu3 = bn_relu(inplanes=384)
        self.conv4 = nn.Conv2d(384, 384, kernel_size=3, padding=1, groups=2, bias=False)
        self.relu4 = bn_relu(inplanes=384)
        self.conv5 = nn.Conv2d(384, 256, kernel_size=3, padding=1, groups=2, bias=False)
        self.relu_pool5 = bn_relu_pool(inplanes=256)
        # classifier
        self.conv6 = nn.Conv2d(256, 256, kernel_size=5, groups=2, bias=False)
        self.relu6 = bn_relu(inplanes=256)
        self.conv7 = nn.Conv2d(256, num_classes, kernel_size=1, bias=False)

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu_pool1(x)
        x = self.conv2(x)
        x = self.relu_pool2(x)
        x = self.conv3(x)
        x = self.relu3(x)
        x = self.conv4(x)
        x = self.relu4(x)
        x = self.conv5(x)
        x = self.relu_pool5(x)
        x = self.conv6(x)
        x = self.relu6(x)
        x = self.conv7(x)
        x = x.view(x.size(0), -1)
        return x



################## ResNet ##################
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__()
        m = OrderedDict()
        m['conv1'] = conv3x3(inplanes, planes, stride)
        m['bn1'] = nn.BatchNorm2d(planes)
        m['relu1'] = nn.ReLU(inplace=True)
        m['conv2'] = conv3x3(planes, planes)
        m['bn2'] = nn.BatchNorm2d(planes)
        self.group1 = nn.Sequential(m)
        self.relu = nn.Sequential(nn.ReLU(inplace=True))
        self.downsample = downsample

    def forward(self, x):
        if self.downsample is not None:
            residual = self.downsample(x)
        else:
            residual = x
        out = self.group1(x) + residual
        out = self.relu(out)

        return out

class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()

        m = OrderedDict()
        m['conv1'] = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        m['bn1'] = nn.BatchNorm2d(64)
        m['relu1'] = nn.ReLU(inplace=True)
        m['maxpool'] = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.group1= nn.Sequential(m)

        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.Sequential(nn.AvgPool2d(7))
        self.group2 = nn.Sequential(
            OrderedDict([
                ('fullyconnected', 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_()
            elif isinstance(m, nn.Linear):
                torch.nn.init.xavier_uniform_(m.weight.data)
                torch.nn.init.constant_(m.bias.data, 0)


    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.group1(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.group2(x)

        return x