import torch import torch.nn as nn import torch.nn.functional as F 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) if not stride == 1: 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) if not stride == 1: self.norm3 = nn.BatchNorm2d(planes) elif norm_fn == 'instance': self.norm1 = nn.InstanceNorm2d(planes) self.norm2 = nn.InstanceNorm2d(planes) if not stride == 1: self.norm3 = nn.InstanceNorm2d(planes) elif norm_fn == 'none': self.norm1 = nn.Sequential() self.norm2 = nn.Sequential() if not stride == 1: self.norm3 = nn.Sequential() if stride == 1: self.downsample = None else: 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))) if self.downsample is not None: x = self.downsample(x) return self.relu(x+y) class BottleneckBlock(nn.Module): def __init__(self, in_planes, planes, norm_fn='group', stride=1): super(BottleneckBlock, self).__init__() self.conv1 = nn.Conv2d(in_planes, planes//4, kernel_size=1, padding=0) self.conv2 = nn.Conv2d(planes//4, planes//4, kernel_size=3, padding=1, stride=stride) self.conv3 = nn.Conv2d(planes//4, planes, kernel_size=1, padding=0) 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//4) self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes//4) self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) if not stride == 1: self.norm4 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) elif norm_fn == 'batch': self.norm1 = nn.BatchNorm2d(planes//4) self.norm2 = nn.BatchNorm2d(planes//4) self.norm3 = nn.BatchNorm2d(planes) if not stride == 1: self.norm4 = nn.BatchNorm2d(planes) elif norm_fn == 'instance': self.norm1 = nn.InstanceNorm2d(planes//4) self.norm2 = nn.InstanceNorm2d(planes//4) self.norm3 = nn.InstanceNorm2d(planes) if not stride == 1: self.norm4 = nn.InstanceNorm2d(planes) elif norm_fn == 'none': self.norm1 = nn.Sequential() self.norm2 = nn.Sequential() self.norm3 = nn.Sequential() if not stride == 1: self.norm4 = nn.Sequential() if stride == 1: self.downsample = None else: self.downsample = nn.Sequential( nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm4) def forward(self, x): y = x y = self.relu(self.norm1(self.conv1(y))) y = self.relu(self.norm2(self.conv2(y))) y = self.relu(self.norm3(self.conv3(y))) if self.downsample is not None: x = self.downsample(x) return self.relu(x+y) DIM=32 class BasicEncoder(nn.Module): def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0, multidim=False): super(BasicEncoder, self).__init__() self.norm_fn = norm_fn self.multidim = multidim if self.norm_fn == 'group': self.norm1 = nn.GroupNorm(num_groups=8, num_channels=DIM) elif self.norm_fn == 'batch': self.norm1 = nn.BatchNorm2d(DIM) elif self.norm_fn == 'instance': self.norm1 = nn.InstanceNorm2d(DIM) elif self.norm_fn == 'none': self.norm1 = nn.Sequential() self.conv1 = nn.Conv2d(3, DIM, kernel_size=7, stride=2, padding=3) self.relu1 = nn.ReLU(inplace=True) self.in_planes = DIM self.layer1 = self._make_layer(DIM, stride=1) self.layer2 = self._make_layer(2*DIM, stride=2) self.layer3 = self._make_layer(4*DIM, stride=2) # output convolution self.conv2 = nn.Conv2d(4*DIM, output_dim, kernel_size=1) if self.multidim: self.layer4 = self._make_layer(256, stride=2) self.layer5 = self._make_layer(512, stride=2) self.in_planes = 256 self.layer6 = self._make_layer(256, stride=1) self.in_planes = 128 self.layer7 = self._make_layer(128, stride=1) self.up1 = nn.Conv2d(512, 256, 1) self.up2 = nn.Conv2d(256, 128, 1) self.conv3 = nn.Conv2d(128, output_dim, kernel_size=1) if dropout > 0: self.dropout = nn.Dropout2d(p=dropout) else: self.dropout = None 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): b, n, c1, h1, w1 = x.shape x = x.view(b*n, c1, h1, w1) 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) _, c2, h2, w2 = x.shape return x.view(b, n, c2, h2, w2) class BasicEncoder16(nn.Module): def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0, multidim=False): super(BasicEncoder16, self).__init__() self.norm_fn = norm_fn self.multidim = multidim if self.norm_fn == 'group': self.norm1 = nn.GroupNorm(num_groups=8, num_channels=DIM) elif self.norm_fn == 'batch': self.norm1 = nn.BatchNorm2d(DIM) elif self.norm_fn == 'instance': self.norm1 = nn.InstanceNorm2d(DIM) elif self.norm_fn == 'none': self.norm1 = nn.Sequential() self.conv1 = nn.Conv2d(3, DIM, kernel_size=7, stride=2, padding=3) self.relu1 = nn.ReLU(inplace=True) self.in_planes = DIM self.layer1 = self._make_layer(DIM, stride=1) self.layer2 = self._make_layer(2*DIM, stride=2) self.layer3 = self._make_layer(4*DIM, stride=2) self.layer4 = self._make_layer(4*DIM, stride=2) # output convolution self.conv2 = nn.Conv2d(4*DIM, output_dim, kernel_size=1) if dropout > 0: self.dropout = nn.Dropout2d(p=dropout) else: self.dropout = None 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): b, n, c1, h1, w1 = x.shape x = x.view(b*n, c1, h1, w1) 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.layer4(x) x = self.conv2(x) _, c2, h2, w2 = x.shape return x.view(b, n, c2, h2, w2)