import torch import torch.nn as nn import torch.nn.functional as F # Ref: https://github.com/princeton-vl/RAFT/blob/master/core/extractor.py class ResidualBlock(nn.Module): def __init__(self, in_planes, planes, norm_fn='group', stride=1): super(ResidualBlock, self).__init__() self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, stride=stride) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1) self.relu = nn.ReLU(inplace=True) num_groups = planes // 8 if norm_fn == 'group': self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) elif norm_fn == 'batch': self.norm1 = nn.BatchNorm2d(planes) self.norm2 = nn.BatchNorm2d(planes) self.norm3 = nn.BatchNorm2d(planes) elif norm_fn == 'instance': self.norm1 = nn.InstanceNorm2d(planes) self.norm2 = nn.InstanceNorm2d(planes) self.norm3 = nn.InstanceNorm2d(planes) elif norm_fn == 'none': self.norm1 = nn.Sequential() self.norm2 = nn.Sequential() self.norm3 = nn.Sequential() self.downsample = nn.Sequential( nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3) def forward(self, x): y = x y = self.relu(self.norm1(self.conv1(y))) y = self.relu(self.norm2(self.conv2(y))) x = self.downsample(x) return self.relu(x+y) class BasicEncoder(nn.Module): def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0): super(BasicEncoder, self).__init__() self.norm_fn = norm_fn if self.norm_fn == 'group': self.norm1 = nn.GroupNorm(num_groups=8, num_channels=64) elif self.norm_fn == 'batch': self.norm1 = nn.BatchNorm2d(64) elif self.norm_fn == 'instance': self.norm1 = nn.InstanceNorm2d(64) elif self.norm_fn == 'none': self.norm1 = nn.Sequential() self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3) self.relu1 = nn.ReLU(inplace=True) self.in_planes = 64 self.layer1 = self._make_layer(64, stride=1) self.layer2 = self._make_layer(96, stride=2) self.layer3 = self._make_layer(128, stride=1) # output convolution self.conv2 = nn.Conv2d(128, output_dim, kernel_size=1) self.dropout = None if dropout > 0: self.dropout = nn.Dropout2d(p=dropout) 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.InstanceNorm2d, nn.GroupNorm)): if m.weight is not None: nn.init.constant_(m.weight, 1) if m.bias is not None: nn.init.constant_(m.bias, 0) def _make_layer(self, dim, stride=1): layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride) layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1) layers = (layer1, layer2) self.in_planes = dim return nn.Sequential(*layers) def forward(self, x): # if input is list, combine batch dimension is_list = isinstance(x, tuple) or isinstance(x, list) if is_list: batch_dim = x[0].shape[0] x = torch.cat(x, dim=0) x = self.conv1(x) x = self.norm1(x) x = self.relu1(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.conv2(x) if self.dropout is not None: x = self.dropout(x) if is_list: x = torch.split(x, x.shape[0]//2, dim=0) return x