import torch.nn as nn
import math
import torch
import numpy as np
import torch.nn.functional as F
affine_par = True

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, affine = affine_par)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes, affine = affine_par)
        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 Bottle2neck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1,  dilation_ = 1, downsample=None, baseWidth=26, scale = 4, stype='normal'):
        super(Bottle2neck, self).__init__()
        width = int(math.floor(planes * (baseWidth/64.0)))
        self.conv1 = nn.Conv2d(inplanes, width*scale, kernel_size=1, stride=1, bias=False) 
        self.bn1 = nn.BatchNorm2d(width*scale,affine = affine_par)
        for i in self.bn1.parameters():
            i.requires_grad = False
        if scale == 1:
          self.nums = 1
        else:
          self.nums = scale -1
        if stype == 'stage':
            self.pool = nn.AvgPool2d(kernel_size=3, stride = stride, padding=1)
        convs = []
        bns = []
        for i in range(self.nums):
          convs.append(nn.Conv2d(width, width, kernel_size=3, stride = stride, dilation = dilation_, padding=dilation_, bias=False))
          bns.append(nn.BatchNorm2d(width, affine = affine_par))
        self.convs = nn.ModuleList(convs)
        self.bns = nn.ModuleList(bns)
        for j in range(self.nums):
            for i in self.bns[j].parameters():
                i.requires_grad = False
        self.conv3 = nn.Conv2d(width*scale, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion, affine = affine_par)
        for i in self.bn3.parameters():
            i.requires_grad = False
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stype = stype
        self.scale = scale
        self.width  = width
        self.stride = stride

    def forward(self, x):
        residual = x

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

        spx = torch.split(out, self.width, 1)
        for i in range(self.nums):
          if i==0 or self.stype=='stage':
            sp = spx[i]
          else:
            sp = sp + spx[i]
          sp = self.convs[i](sp)
          sp = self.relu(self.bns[i](sp))
          if i==0:
            out = sp
          else:
            out = torch.cat((out, sp), 1)
        if self.scale != 1 and self.stype=='normal':
          out = torch.cat((out, spx[self.nums]),1)
        elif self.scale != 1 and self.stype=='stage':
          out = torch.cat((out, self.pool(spx[self.nums])),1)

        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 Res2Net(nn.Module):
    def __init__(self, block, layers, baseWidth = 26, scale = 4, num_classes=1000):
        self.inplanes = 64
        super(Res2Net, self).__init__()
        self.baseWidth = baseWidth
        self.scale = scale
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64,affine = affine_par)
        for i in self.bn1.parameters():
            i.requires_grad = False
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True) # change
        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=1, dilation__ = 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, 0.01)
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1,dilation__ = 1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion or dilation__ == 2 or dilation__ == 4:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion,affine = affine_par),
            )
        for i in downsample._modules['1'].parameters():
            i.requires_grad = False
        layers = []
        layers.append(block(self.inplanes, planes, stride,dilation_=dilation__, downsample = downsample,
                        stype='stage', baseWidth = self.baseWidth, scale=self.scale))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, dilation_=dilation__, baseWidth = self.baseWidth, scale=self.scale))

        return nn.Sequential(*layers)

    def forward(self, x):
        tmp_x = []
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        tmp_x.append(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        tmp_x.append(x)
        x = self.layer2(x)
        tmp_x.append(x)
        x = self.layer3(x)
        tmp_x.append(x)
        x = self.layer4(x)
        tmp_x.append(x)

        return tmp_x


class Res2Net_locate(nn.Module):
    def __init__(self, block, layers):
        super(Res2Net_locate,self).__init__()
        self.resnet = Res2Net(block, layers)
        self.in_planes = 512
        self.out_planes = [512, 256, 256, 128]

        self.ppms_pre = nn.Conv2d(2048, self.in_planes, 1, 1, bias=False)
        ppms, infos = [], []
        for ii in [1, 3, 5]:
            ppms.append(nn.Sequential(nn.AdaptiveAvgPool2d(ii), nn.Conv2d(self.in_planes, self.in_planes, 1, 1, bias=False), nn.ReLU(inplace=True)))
        self.ppms = nn.ModuleList(ppms)

        self.ppm_cat = nn.Sequential(nn.Conv2d(self.in_planes * 4, self.in_planes, 3, 1, 1, bias=False), nn.ReLU(inplace=True))
        for ii in self.out_planes:
            infos.append(nn.Sequential(nn.Conv2d(self.in_planes, ii, 3, 1, 1, bias=False), nn.ReLU(inplace=True)))
        self.infos = nn.ModuleList(infos)

        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, 0.01)
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def load_pretrained_model(self, model):
        self.resnet.load_state_dict(model, strict=False)

    def forward(self, x):
        x_size = x.size()[2:]
        xs = self.resnet(x)

        xs_1 = self.ppms_pre(xs[-1])
        xls = [xs_1]
        for k in range(len(self.ppms)):
            xls.append(F.interpolate(self.ppms[k](xs_1), xs_1.size()[2:], mode='bilinear', align_corners=True))
        xls = self.ppm_cat(torch.cat(xls, dim=1))

        infos = []
        for k in range(len(self.infos)):
            infos.append(self.infos[k](F.interpolate(xls, xs[len(self.infos) - 1 - k].size()[2:], mode='bilinear', align_corners=True)))

        return xs, infos

def res2net50_locate():
    model = Res2Net_locate(Bottle2neck, [3, 4, 6, 3])
    return model
