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| 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) | |
| # 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.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): | |
| # 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.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 | |