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



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['elu'] = nn.ReLU(inplace=True)
        m['conv2'] = conv3x3(planes, planes)
        m['bn2'] = nn.BatchNorm2d(planes)
        self.group1 = nn.Sequential(m)
        
        self.elu = 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.elu(out)
        
        return out


class Bottleneck(nn.Module):
    expansion = 4
    
    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        m = OrderedDict()
        m['conv1'] = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        m['bn1'] = nn.BatchNorm2d(planes)
        m['elu1'] = nn.ReLU(inplace=True)
        m['conv2'] = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        m['bn2'] = nn.BatchNorm2d(planes)
        m['elu2'] = nn.ReLU(inplace=True)
        m['conv3'] = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        m['bn3'] = nn.BatchNorm2d(planes * 4)
        self.group1 = nn.Sequential(m)
        
        self.elu = 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.elu(out)
        
        return out


class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=10):
        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['elu1'] = 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))
        # Top layer
        self.toplayer = nn.Conv2d(2048, 256, kernel_size=1, stride=1, padding=0)  # Reduce channels
        
        self.group2 = nn.Sequential(
            OrderedDict([
                ('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_()
    
    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 _upsample_add(self, x, y):
        '''Upsample and add two feature maps.
        Args:
          x: (Variable) top feature map to be upsampled.
          y: (Variable) lateral feature map.
        Returns:
          (Variable) added feature map.
        Note in PyTorch, when input size is odd, the upsampled feature map
        with `F.upsample(..., scale_factor=2, mode='nearest')`
        maybe not equal to the lateral feature map size.
        e.g.
        original input size: [N,_,15,15] ->
        conv2d feature map size: [N,_,8,8] ->
        upsampled feature map size: [N,_,16,16]
        So we choose bilinear upsample which supports arbitrary output sizes.
        '''
        _, _, H, W = y.size()
        return F.upsample(x, size=(H, W), mode='bilinear') + y
    
    def forward(self, x):
        x1 = self.group1(x)
        
        c2 = self.layer1(x1)
        
        c3 = self.layer2(c2)
        c4 = self.layer3(c3)
        c5 = self.layer4(c4)
        
        x2 = self.avgpool(c5)

        x2 = x2.view(x2.size(0), -1)

        class_pre = self.group2(x2)
        
        return class_pre


def resnet18(pretrained=False, model_root=None, **kwargs):
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    
    return model


def resnet34(pretrained=False, model_root=None, **kwargs):
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    
    return model


def resnet50(pretrained=False, model_root=None, **kwargs):
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    
    return model


def resnet101(pretrained=False, model_root=None, **kwargs):
    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
    
    return model


def resnet152(pretrained=False, model_root=None, **kwargs):
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    
    return model

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



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['elu'] = nn.ReLU(inplace=True)
        m['conv2'] = conv3x3(planes, planes)
        m['bn2'] = nn.BatchNorm2d(planes)
        self.group1 = nn.Sequential(m)
        
        self.elu = 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.elu(out)
        
        return out


class Bottleneck(nn.Module):
    expansion = 4
    
    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        m = OrderedDict()
        m['conv1'] = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        m['bn1'] = nn.BatchNorm2d(planes)
        m['elu1'] = nn.ReLU(inplace=True)
        m['conv2'] = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        m['bn2'] = nn.BatchNorm2d(planes)
        m['elu2'] = nn.ReLU(inplace=True)
        m['conv3'] = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        m['bn3'] = nn.BatchNorm2d(planes * 4)
        self.group1 = nn.Sequential(m)
        
        self.elu = 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.elu(out)
        
        return out


class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=10):
        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['elu1'] = 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))
        # Top layer
        self.toplayer = nn.Conv2d(2048, 256, kernel_size=1, stride=1, padding=0)  # Reduce channels
        
        self.group2 = nn.Sequential(
            OrderedDict([
                ('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_()
    
    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 _upsample_add(self, x, y):
        '''Upsample and add two feature maps.
        Args:
          x: (Variable) top feature map to be upsampled.
          y: (Variable) lateral feature map.
        Returns:
          (Variable) added feature map.
        Note in PyTorch, when input size is odd, the upsampled feature map
        with `F.upsample(..., scale_factor=2, mode='nearest')`
        maybe not equal to the lateral feature map size.
        e.g.
        original input size: [N,_,15,15] ->
        conv2d feature map size: [N,_,8,8] ->
        upsampled feature map size: [N,_,16,16]
        So we choose bilinear upsample which supports arbitrary output sizes.
        '''
        _, _, H, W = y.size()
        return F.upsample(x, size=(H, W), mode='bilinear') + y
    
    def forward(self, x):
        x1 = self.group1(x)
        
        c2 = self.layer1(x1)
        
        c3 = self.layer2(c2)
        c4 = self.layer3(c3)
        c5 = self.layer4(c4)
        
        x2 = self.avgpool(c5)

        x2 = x2.view(x2.size(0), -1)

        class_pre = self.group2(x2)
        
        return class_pre


def resnet18(pretrained=False, model_root=None, **kwargs):
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    
    return model


def resnet34(pretrained=False, model_root=None, **kwargs):
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    
    return model


def resnet50(pretrained=False, model_root=None, **kwargs):
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    
    return model


def resnet101(pretrained=False, model_root=None, **kwargs):
    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
    
    return model


def resnet152(pretrained=False, model_root=None, **kwargs):
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    
    return model

# "3x3 convolution with padding" # Top layer # Reduce channels Upsample and add two feature maps.         Args:           x: (Variable) top feature map to be upsampled.           y: (Variable) lateral feature map.         Returns:           (Variable) added feature map.         Note in PyTorch, when input size is odd, the upsampled feature map         with `F.upsample(..., scale_factor=2, mode='nearest')`         maybe not equal to the lateral feature map size.         e.g.         original input size: [N,_,15,15] ->         conv2d feature map size: [N,_,8,8] ->         upsampled feature map size: [N,_,16,16]         So we choose bilinear upsample which supports arbitrary output sizes.