# This implementation is adopted from LoFTR, # made public available under the Apache License, Version 2.0, # at https://github.com/zju3dv/LoFTR import torch.nn as nn import torch.nn.functional as F def conv1x1(in_planes, out_planes, stride=1): """1x1 convolution without padding""" return nn.Conv2d( in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False) def conv3x3(in_planes, out_planes, stride=1): """3x3 convolution with padding""" return nn.Conv2d( in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) class BasicBlock(nn.Module): def __init__(self, in_planes, planes, stride=1): super().__init__() self.conv1 = conv3x3(in_planes, planes, stride) self.conv2 = conv3x3(planes, planes) self.bn1 = nn.BatchNorm2d(planes) self.bn2 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) if stride == 1: self.downsample = None else: self.downsample = nn.Sequential( conv1x1(in_planes, planes, stride=stride), nn.BatchNorm2d(planes)) def forward(self, x): y = x y = self.relu(self.bn1(self.conv1(y))) y = self.bn2(self.conv2(y)) if self.downsample is not None: x = self.downsample(x) return self.relu(x + y) class ResNetFPN_8_2(nn.Module): """ ResNet+FPN, output resolution are 1/8 and 1/2. Each block has 2 layers. """ def __init__(self, config): super().__init__() # Config block = BasicBlock initial_dim = config['initial_dim'] block_dims = config['block_dims'] # Class Variable self.in_planes = initial_dim # Networks self.conv1 = nn.Conv2d( 1, initial_dim, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(initial_dim) self.relu = nn.ReLU(inplace=True) self.layer1 = self._make_layer(block, block_dims[0], stride=1) # 1/2 self.layer2 = self._make_layer(block, block_dims[1], stride=2) # 1/4 self.layer3 = self._make_layer(block, block_dims[2], stride=2) # 1/8 # 3. FPN upsample self.layer3_outconv = conv1x1(block_dims[2], block_dims[2]) self.layer2_outconv = conv1x1(block_dims[1], block_dims[2]) self.layer2_outconv2 = nn.Sequential( conv3x3(block_dims[2], block_dims[2]), nn.BatchNorm2d(block_dims[2]), nn.LeakyReLU(), conv3x3(block_dims[2], block_dims[1]), ) self.layer1_outconv = conv1x1(block_dims[0], block_dims[1]) self.layer1_outconv2 = nn.Sequential( conv3x3(block_dims[1], block_dims[1]), nn.BatchNorm2d(block_dims[1]), nn.LeakyReLU(), conv3x3(block_dims[1], block_dims[0]), ) 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.GroupNorm)): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) def _make_layer(self, block, dim, stride=1): layer1 = block(self.in_planes, dim, stride=stride) layer2 = block(dim, dim, stride=1) layers = (layer1, layer2) self.in_planes = dim return nn.Sequential(*layers) def forward(self, x): # ResNet Backbone x0 = self.relu(self.bn1(self.conv1(x))) x1 = self.layer1(x0) # 1/2 x2 = self.layer2(x1) # 1/4 x3 = self.layer3(x2) # 1/8 # FPN x3_out = self.layer3_outconv(x3) x3_out_2x = F.interpolate( x3_out, scale_factor=2., mode='bilinear', align_corners=True) x2_out = self.layer2_outconv(x2) x2_out = self.layer2_outconv2(x2_out + x3_out_2x) x2_out_2x = F.interpolate( x2_out, scale_factor=2., mode='bilinear', align_corners=True) x1_out = self.layer1_outconv(x1) x1_out = self.layer1_outconv2(x1_out + x2_out_2x) return [x3_out, x1_out] class ResNetFPN_16_4(nn.Module): """ ResNet+FPN, output resolution are 1/16 and 1/4. Each block has 2 layers. """ def __init__(self, config): super().__init__() # Config block = BasicBlock initial_dim = config['initial_dim'] block_dims = config['block_dims'] # Class Variable self.in_planes = initial_dim # Networks self.conv1 = nn.Conv2d( 1, initial_dim, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(initial_dim) self.relu = nn.ReLU(inplace=True) self.layer1 = self._make_layer(block, block_dims[0], stride=1) # 1/2 self.layer2 = self._make_layer(block, block_dims[1], stride=2) # 1/4 self.layer3 = self._make_layer(block, block_dims[2], stride=2) # 1/8 self.layer4 = self._make_layer(block, block_dims[3], stride=2) # 1/16 # 3. FPN upsample self.layer4_outconv = conv1x1(block_dims[3], block_dims[3]) self.layer3_outconv = conv1x1(block_dims[2], block_dims[3]) self.layer3_outconv2 = nn.Sequential( conv3x3(block_dims[3], block_dims[3]), nn.BatchNorm2d(block_dims[3]), nn.LeakyReLU(), conv3x3(block_dims[3], block_dims[2]), ) self.layer2_outconv = conv1x1(block_dims[1], block_dims[2]) self.layer2_outconv2 = nn.Sequential( conv3x3(block_dims[2], block_dims[2]), nn.BatchNorm2d(block_dims[2]), nn.LeakyReLU(), conv3x3(block_dims[2], block_dims[1]), ) 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.GroupNorm)): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) def _make_layer(self, block, dim, stride=1): layer1 = block(self.in_planes, dim, stride=stride) layer2 = block(dim, dim, stride=1) layers = (layer1, layer2) self.in_planes = dim return nn.Sequential(*layers) def forward(self, x): # ResNet Backbone x0 = self.relu(self.bn1(self.conv1(x))) x1 = self.layer1(x0) # 1/2 x2 = self.layer2(x1) # 1/4 x3 = self.layer3(x2) # 1/8 x4 = self.layer4(x3) # 1/16 # FPN x4_out = self.layer4_outconv(x4) x4_out_2x = F.interpolate( x4_out, scale_factor=2., mode='bilinear', align_corners=True) x3_out = self.layer3_outconv(x3) x3_out = self.layer3_outconv2(x3_out + x4_out_2x) x3_out_2x = F.interpolate( x3_out, scale_factor=2., mode='bilinear', align_corners=True) x2_out = self.layer2_outconv(x2) x2_out = self.layer2_outconv2(x2_out + x3_out_2x) return [x4_out, x2_out]