import torch
import torch.nn as nn
import math

'''https://github.com/blandocs/Tag2Pix/blob/master/model/pretrained.py'''

# Pretrained version
class Selayer(nn.Module):
    def __init__(self, inplanes):
        super(Selayer, self).__init__()
        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
        self.conv1 = nn.Conv2d(inplanes, inplanes // 16, kernel_size=1, stride=1)
        self.conv2 = nn.Conv2d(inplanes // 16, inplanes, kernel_size=1, stride=1)
        self.relu = nn.ReLU(inplace=True)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        out = self.global_avgpool(x)
        out = self.conv1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.sigmoid(out)

        return x * out


class BottleneckX_Origin(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, cardinality, stride=1, downsample=None):
        super(BottleneckX_Origin, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes * 2, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes * 2)

        self.conv2 = nn.Conv2d(planes * 2, planes * 2, kernel_size=3, stride=stride,
                               padding=1, groups=cardinality, bias=False)
        self.bn2 = nn.BatchNorm2d(planes * 2)

        self.conv3 = nn.Conv2d(planes * 2, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)

        self.selayer = Selayer(planes * 4)

        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)

        out = self.selayer(out)

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

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

        return out

class SEResNeXt_extractor(nn.Module):
    def __init__(self, block, layers, input_channels=3, cardinality=32):
        super(SEResNeXt_extractor, self).__init__()
        self.cardinality = cardinality
        self.inplanes = 64
        self.input_channels = input_channels

        self.conv1 = nn.Conv2d(input_channels, 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)

        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))
                if m.bias is not None:
                    m.bias.data.zero_()
            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, self.cardinality, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, self.cardinality))

        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)
        
        return x

def get_seresnext_extractor():
    return SEResNeXt_extractor(BottleneckX_Origin, [3, 4, 6, 3], 1)