| | 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) |
| |
|
| | 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=2) |
| |
|
| | |
| | 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): |
| |
|
| | |
| | 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.training and self.dropout is not None: |
| | x = self.dropout(x) |
| |
|
| | if is_list: |
| | x = torch.split(x, [batch_dim, batch_dim], dim=0) |
| |
|
| | return x |
| |
|
| |
|
| | class SmallEncoder(nn.Module): |
| | def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0): |
| | super(SmallEncoder, self).__init__() |
| | self.norm_fn = norm_fn |
| |
|
| | if self.norm_fn == 'group': |
| | self.norm1 = nn.GroupNorm(num_groups=8, num_channels=32) |
| | |
| | elif self.norm_fn == 'batch': |
| | self.norm1 = nn.BatchNorm2d(32) |
| |
|
| | elif self.norm_fn == 'instance': |
| | self.norm1 = nn.InstanceNorm2d(32) |
| |
|
| | elif self.norm_fn == 'none': |
| | self.norm1 = nn.Sequential() |
| |
|
| | self.conv1 = nn.Conv2d(3, 32, kernel_size=7, stride=2, padding=3) |
| | self.relu1 = nn.ReLU(inplace=True) |
| |
|
| | self.in_planes = 32 |
| | self.layer1 = self._make_layer(32, stride=1) |
| | self.layer2 = self._make_layer(64, stride=2) |
| | self.layer3 = self._make_layer(96, stride=2) |
| |
|
| | self.dropout = None |
| | if dropout > 0: |
| | self.dropout = nn.Dropout2d(p=dropout) |
| | |
| | self.conv2 = nn.Conv2d(96, output_dim, kernel_size=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.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 = BottleneckBlock(self.in_planes, dim, self.norm_fn, stride=stride) |
| | layer2 = BottleneckBlock(dim, dim, self.norm_fn, stride=1) |
| | layers = (layer1, layer2) |
| | |
| | self.in_planes = dim |
| | return nn.Sequential(*layers) |
| |
|
| |
|
| | def forward(self, x): |
| |
|
| | |
| | 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.training and self.dropout is not None: |
| | x = self.dropout(x) |
| |
|
| | if is_list: |
| | x = torch.split(x, [batch_dim, batch_dim], dim=0) |
| |
|
| | return x |
| |
|
