import torch.nn as nn

class ConvBnReLU3D(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, pad=1, norm_act=nn.BatchNorm3d):
        super(ConvBnReLU3D, self).__init__()
        self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride=stride, padding=pad, bias=False)
        self.bn = norm_act(out_channels)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        return self.relu(self.bn(self.conv(x)))

class CostRegNet(nn.Module):
    def __init__(self, in_channels, norm_act=nn.BatchNorm3d):
        super(CostRegNet, self).__init__()
        self.conv0 = ConvBnReLU3D(in_channels, 8, norm_act=norm_act)

        self.conv1 = ConvBnReLU3D(8, 16, stride=2, norm_act=norm_act)
        self.conv2 = ConvBnReLU3D(16, 16, norm_act=norm_act)

        self.conv3 = ConvBnReLU3D(16, 32, stride=2, norm_act=norm_act)
        self.conv4 = ConvBnReLU3D(32, 32, norm_act=norm_act)

        self.conv5 = ConvBnReLU3D(32, 64, stride=2, norm_act=norm_act)
        self.conv6 = ConvBnReLU3D(64, 64, norm_act=norm_act)

        self.conv7 = nn.Sequential(
            nn.ConvTranspose3d(64, 32, 3, padding=1, output_padding=1, stride=2, bias=False),
            norm_act(32)
        )

        self.conv9 = nn.Sequential(
            nn.ConvTranspose3d(32, 16, 3, padding=1, output_padding=1, stride=2, bias=False),
            norm_act(16)
        )

        self.conv11 = nn.Sequential(
            nn.ConvTranspose3d(16, 8, 3, padding=1, output_padding=1,stride=2, bias=False),
            norm_act(8)
        )
        self.depth_conv = nn.Sequential(nn.Conv3d(8, 1, 3, padding=1, bias=False))
        self.feat_conv = nn.Sequential(nn.Conv3d(8, 8, 3, padding=1, bias=False))

    def forward(self, x):
        conv0 = self.conv0(x)
        conv2 = self.conv2(self.conv1(conv0))
        conv4 = self.conv4(self.conv3(conv2))
        x = self.conv6(self.conv5(conv4))
        x = conv4 + self.conv7(x)
        del conv4
        x = conv2 + self.conv9(x)
        del conv2
        x = conv0 + self.conv11(x)
        del conv0
        feat = self.feat_conv(x)
        depth = self.depth_conv(x)
        return feat, depth


class MinCostRegNet(nn.Module):
    def __init__(self, in_channels, norm_act=nn.BatchNorm3d):
        super(MinCostRegNet, self).__init__()
        self.conv0 = ConvBnReLU3D(in_channels, 8, norm_act=norm_act)

        self.conv1 = ConvBnReLU3D(8, 16, stride=2, norm_act=norm_act)
        self.conv2 = ConvBnReLU3D(16, 16, norm_act=norm_act)

        self.conv3 = ConvBnReLU3D(16, 32, stride=2, norm_act=norm_act)
        self.conv4 = ConvBnReLU3D(32, 32, norm_act=norm_act)

        self.conv9 = nn.Sequential(
            nn.ConvTranspose3d(32, 16, 3, padding=1, output_padding=1,
                               stride=2, bias=False),
            norm_act(16))

        self.conv11 = nn.Sequential(
            nn.ConvTranspose3d(16, 8, 3, padding=1, output_padding=1,
                               stride=2, bias=False),
            norm_act(8))

        self.depth_conv = nn.Sequential(nn.Conv3d(8, 1, 3, padding=1, bias=False))
        self.feat_conv = nn.Sequential(nn.Conv3d(8, 8, 3, padding=1, bias=False))

    def forward(self, x):
        conv0 = self.conv0(x)
        conv2 = self.conv2(self.conv1(conv0))
        conv4 = self.conv4(self.conv3(conv2))
        x = conv4
        x = conv2 + self.conv9(x)
        del conv2
        x = conv0 + self.conv11(x)
        del conv0
        feat = self.feat_conv(x)
        depth = self.depth_conv(x)
        return feat, depth