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

class ConvBnReLU(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, pad=1, norm_act=nn.BatchNorm2d):
        super(ConvBnReLU, self).__init__()
        self.conv = nn.Conv2d(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 FeatureNet(nn.Module):
    def __init__(self, norm_act=nn.BatchNorm2d):
        super(FeatureNet, self).__init__()
        self.conv0 = nn.Sequential(ConvBnReLU(3, 8, 3, 1, 1, norm_act=norm_act), ConvBnReLU(8, 8, 3, 1, 1, norm_act=norm_act))
        self.conv1 = nn.Sequential(ConvBnReLU(8, 16, 5, 2, 2, norm_act=norm_act), ConvBnReLU(16, 16, 3, 1, 1, norm_act=norm_act))
        self.conv2 = nn.Sequential(ConvBnReLU(16, 32, 5, 2, 2, norm_act=norm_act), ConvBnReLU(32, 32, 3, 1, 1, norm_act=norm_act))

        self.toplayer = nn.Conv2d(32, 32, 1)
        self.lat1 = nn.Conv2d(16, 32, 1)
        self.lat0 = nn.Conv2d(8, 32, 1)

        self.smooth1 = nn.Conv2d(32, 16, 3, padding=1)
        self.smooth0 = nn.Conv2d(32, 8, 3, padding=1)

    def _upsample_add(self, x, y):
        return F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True) + y

    def forward(self, x):
        conv0 = self.conv0(x)
        conv1 = self.conv1(conv0)
        conv2 = self.conv2(conv1)
        feat2 = self.toplayer(conv2)
        feat1 = self._upsample_add(feat2, self.lat1(conv1))
        feat0 = self._upsample_add(feat1, self.lat0(conv0))
        feat1 = self.smooth1(feat1)
        feat0 = self.smooth0(feat0)
        return feat2, feat1, feat0