| | 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
|
| |
|
