import torch.nn as nn
import torch.nn.functional as F


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes,
                     out_planes,
                     kernel_size=3,
                     stride=stride,
                     padding=dilation,
                     groups=groups,
                     bias=False,
                     dilation=dilation)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self,
                 inplanes,
                 planes,
                 stride=1,
                 downsample=None,
                 groups=1,
                 base_width=64,
                 dilation=1,
                 norm_layer=None,
                 dcn=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError(
                'BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError(
                "Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        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)

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

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

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self,
                 inplanes,
                 planes,
                 stride=1,
                 downsample=None,
                 norm_layer=nn.BatchNorm2d,
                 dcn=None):
        super(Bottleneck, self).__init__()
        self.dcn = dcn
        self.with_dcn = dcn is not None

        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = norm_layer(planes, momentum=0.1)
        self.conv2 = nn.Conv2d(planes,
                               planes,
                               kernel_size=3,
                               stride=stride,
                               padding=1,
                               bias=False)

        self.bn2 = norm_layer(planes, momentum=0.1)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = norm_layer(planes * 4, momentum=0.1)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = F.relu(self.bn1(self.conv1(x)), inplace=True)
        if not self.with_dcn:
            out = F.relu(self.bn2(self.conv2(out)), inplace=True)
        elif self.with_modulated_dcn:
            offset_mask = self.conv2_offset(out)
            offset = offset_mask[:, :18 * self.deformable_groups, :, :]
            mask = offset_mask[:, -9 * self.deformable_groups:, :, :]
            mask = mask.sigmoid()
            out = F.relu(self.bn2(self.conv2(out, offset, mask)))
        else:
            offset = self.conv2_offset(out)
            out = F.relu(self.bn2(self.conv2(out, offset)), inplace=True)

        out = self.conv3(out)
        out = self.bn3(out)

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

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

        return out


class ResNet(nn.Module):
    """ ResNet """

    def __init__(self,
                 architecture,
                 norm_layer=nn.BatchNorm2d,
                 dcn=None,
                 stage_with_dcn=(False, False, False, False)):
        super(ResNet, self).__init__()
        self._norm_layer = norm_layer
        assert architecture in [
            "resnet18", "resnet34", "resnet50", "resnet101", 'resnet152'
        ]
        layers = {
            'resnet18': [2, 2, 2, 2],
            'resnet34': [3, 4, 6, 3],
            'resnet50': [3, 4, 6, 3],
            'resnet101': [3, 4, 23, 3],
            'resnet152': [3, 8, 36, 3],
        }
        self.inplanes = 64
        if architecture == "resnet18" or architecture == 'resnet34':
            self.block = BasicBlock
        else:
            self.block = Bottleneck
        self.layers = layers[architecture]

        self.conv1 = nn.Conv2d(3,
                               64,
                               kernel_size=7,
                               stride=2,
                               padding=3,
                               bias=False)
        self.bn1 = norm_layer(64, eps=1e-5, momentum=0.1, affine=True)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        stage_dcn = [dcn if with_dcn else None for with_dcn in stage_with_dcn]

        self.layer1 = self.make_layer(self.block,
                                      64,
                                      self.layers[0],
                                      dcn=stage_dcn[0])
        self.layer2 = self.make_layer(self.block,
                                      128,
                                      self.layers[1],
                                      stride=2,
                                      dcn=stage_dcn[1])
        self.layer3 = self.make_layer(self.block,
                                      256,
                                      self.layers[2],
                                      stride=2,
                                      dcn=stage_dcn[2])

        self.layer4 = self.make_layer(self.block,
                                      512,
                                      self.layers[3],
                                      stride=2,
                                      dcn=stage_dcn[3])

    def forward(self, x):
        x = self.maxpool(self.relu(self.bn1(self.conv1(x))))  # 64 * h/4 * w/4
        x = self.layer1(x)  # 256 * h/4 * w/4
        x = self.layer2(x)  # 512 * h/8 * w/8
        x = self.layer3(x)  # 1024 * h/16 * w/16
        x = self.layer4(x)  # 2048 * h/32 * w/32
        return x

    def stages(self):
        return [self.layer1, self.layer2, self.layer3, self.layer4]

    def make_layer(self, block, planes, blocks, stride=1, dcn=None):
        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),
                self._norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(
            block(self.inplanes,
                  planes,
                  stride,
                  downsample,
                  norm_layer=self._norm_layer,
                  dcn=dcn))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(
                block(self.inplanes,
                      planes,
                      norm_layer=self._norm_layer,
                      dcn=dcn))

        return nn.Sequential(*layers)