from .se_module import SELayer
import torch.nn as nn
import torch
import math
from collections import OrderedDict
import torch.utils.checkpoint as cp
__all__ = ['se_resnet50_ibn_a', 'se_resnet101_ibn_a', 'se_resnet152_ibn_a']

def conv3x3(in_planes, out_planes, stride=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)


class IBN(nn.Module):
    def __init__(self, planes):
        super(IBN, self).__init__()
        half1 = int(planes/2)
        self.half = half1
        half2 = planes - half1
        self.IN = nn.InstanceNorm2d(half1, affine=True)
        self.BN = nn.BatchNorm2d(half2)
    
    def forward(self, x):
        split = torch.split(x, self.half, 1)
        out1 = self.IN(split[0].contiguous())
        out2 = self.BN(split[1].contiguous())
        out = torch.cat((out1, out2), 1)
        return out


class SEBasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=16):
        super(SEBasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes, 1)
        self.bn2 = nn.BatchNorm2d(planes)
        self.se = SELayer(planes, reduction)
        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.se(out)

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

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

        return out


class SEBottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, ibn=False, reduction=16):
        super(SEBottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        if ibn:
            self.bn1 = IBN(planes)
        else:
            self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.se = SELayer(planes * 4, reduction)
        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.se(out)

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

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

        return out

class ResNet(nn.Module):

    def __init__(self, last_stride,block, layers, frozen_stages=-1, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 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.frozen_stages = frozen_stages
        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=last_stride)
        self.avgpool = nn.AvgPool2d(7)
        self.fc = nn.Linear(512 * block.expansion, num_classes)
        
        self.conv1.weight.data.normal_(0, math.sqrt(2. / (7 * 7 * 64)))
        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.InstanceNorm2d):
                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 = []
        ibn = True
        if planes == 512:
            ibn = False
        layers.append(block(self.inplanes, planes, stride, downsample, ibn=ibn))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, 1, None, ibn=ibn))

        return nn.Sequential(*layers)

    def _freeze_stages(self):
        if self.frozen_stages >= 0:
            self.bn1.eval()
            for m in [self.conv1, self.bn1]:
                for param in m.parameters():
                    param.requires_grad = False

        for i in range(1, self.frozen_stages + 1):
            m = getattr(self, 'layer{}'.format(i))
            print('layer{}'.format(i))
            m.eval()
            for param in m.parameters():
                param.requires_grad = False

    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)
        x = self.layer3(x)
        x = self.layer4(x)

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

        return x


    def load_param(self, model_path):
        param_dict = torch.load(model_path)
        if 'state_dict' in param_dict:
            param_dict = param_dict['state_dict']
        for i in param_dict:
            if 'fc' in i:
                continue
            self.state_dict()[i.replace('module.','')].copy_(param_dict[i])

def se_resnet50_ibn_a(last_stride,num_classes=1000,**kwargs):
    """Constructs a ResNet-50 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(last_stride,SEBottleneck, [3, 4, 6, 3], num_classes=num_classes,**kwargs)
    model.avgpool = nn.AdaptiveAvgPool2d(1)
    return model


def se_resnet101_ibn_a(last_stride,num_classes=1000,**kwargs):
    """Constructs a ResNet-101 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(last_stride,SEBottleneck, [3, 4, 23, 3], num_classes=num_classes,**kwargs)
    model.avgpool = nn.AdaptiveAvgPool2d(1)
    return model


def se_resnet152_ibn_a(last_stride,num_classes):
    """Constructs a ResNet-152 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(last_stride,SEBottleneck, [3, 8, 36, 3], num_classes=num_classes)
    model.avgpool = nn.AdaptiveAvgPool2d(1)
    return model
