import torch.nn as nn

from .build import BACKBONE_REGISTRY
from ...config import cfg

__all__ = ['IBN_ResNet', 'ibn_resnet50', 'ibn_resnet101']


class IBN_BottleneckV1b(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
                 previous_dilation=1, norm_layer=nn.BatchNorm2d, ibn=None):
        super(IBN_BottleneckV1b, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
        self.bn1 = norm_layer(planes)
        self.conv2 = nn.Conv2d(planes, planes, 3, stride,
                               dilation, dilation, bias=False)
        self.bn2 = norm_layer(planes)
        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
        self.bn3 = norm_layer(planes * self.expansion)
        self.IN = nn.InstanceNorm2d(planes * 4, affine=True) if ibn == 'b' else None
        self.relu = nn.ReLU(True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = 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)

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

        out += identity
        if self.IN is not None:
            out = self.IN(out)
        out = self.relu(out)

        return out


class IBN_ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000, deep_stem=False,
                 norm_layer=nn.BatchNorm2d, ibn_cfg=('b', 'b', 'b', None)):
        output_stride = cfg.MODEL.OUTPUT_STRIDE
        scale = cfg.MODEL.BACKBONE_SCALE
        if output_stride == 32:
            dilations = [1, 1]
            strides = [2, 2]
        elif output_stride == 16:
            dilations = [1, 2]
            strides = [2, 1]
        elif output_stride == 8:
            dilations = [2, 4]
            strides = [1, 1]
        else:
            raise NotImplementedError
        self.inplanes = int((128 if deep_stem else 64) * scale)
        super(IBN_ResNet, self).__init__()
        
        self.conv1 = nn.Conv2d(3, self.inplanes, 7, 2, 3, bias=False)
        self.bn1 = nn.InstanceNorm2d(self.inplanes)
        self.relu = nn.ReLU(True)
        self.maxpool = nn.MaxPool2d(3, 2, 1)
        self.layer1 = self._make_layer(block, int(64 * scale), layers[0], norm_layer=norm_layer, ibn=ibn_cfg[0])
        self.layer2 = self._make_layer(block, int(128 * scale), layers[1], stride=2, norm_layer=norm_layer, ibn=ibn_cfg[1])

        self.layer3 = self._make_layer(block, int(256 * scale), layers[2], stride=strides[0], dilation=dilations[0],
                                       norm_layer=norm_layer, ibn=ibn_cfg[2])
        self.layer4 = self._make_layer(block, int(512 * scale), layers[3], stride=strides[1], dilation=dilations[1],
                                       norm_layer=norm_layer, ibn=ibn_cfg[3])

        self.last_inp_channels = int(512 * block.expansion * scale)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(int(512 * block.expansion * scale), num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)


    def _make_layer(self, block, planes, blocks, stride=1, dilation=1, norm_layer=nn.BatchNorm2d, ibn=None):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion, 1, stride, bias=False),
                norm_layer(planes * block.expansion),
            )

        layers = []
        if dilation in (1, 2):
            layers.append(block(self.inplanes, planes, stride, dilation=1, downsample=downsample,
                                previous_dilation=dilation, norm_layer=norm_layer, ibn=ibn))
        elif dilation == 4:
            layers.append(block(self.inplanes, planes, stride, dilation=2, downsample=downsample,
                                previous_dilation=dilation, norm_layer=norm_layer, ibn=ibn))
        else:
            raise RuntimeError("=> unknown dilation size: {}".format(dilation))
        
        self.inplanes = planes * block.expansion
        
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, dilation=dilation,
                                previous_dilation=dilation, norm_layer=norm_layer, ibn=ibn))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        c1 = self.layer1(x)
        c2 = self.layer2(c1)
        c3 = self.layer3(c2)
        c4 = self.layer4(c3)

        return c1, c2, c3, c4


@BACKBONE_REGISTRY.register()
def ibn_resnet50(norm_layer=nn.BatchNorm2d):
    num_block = [3, 4, 6, 3]
    return IBN_ResNet(IBN_BottleneckV1b, num_block, norm_layer=norm_layer)


@BACKBONE_REGISTRY.register()
def ibn_resnet101(norm_layer=nn.BatchNorm2d):
    num_block = [3, 4, 23, 3]
    return IBN_ResNet(IBN_BottleneckV1b, num_block, norm_layer=norm_layer)



if __name__ == '__main__':
    model = ibn_resnet50()
    import torch
    input = torch.rand((1,3,640,640))
    outs = model(input)
    for out in outs:
        print(out.shape)

