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| 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 | |