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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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class ResidualBlock(nn.Module): |
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def __init__(self, in_planes, planes, norm_fn='group', stride=1): |
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super(ResidualBlock, self).__init__() |
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self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, stride=stride) |
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1) |
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self.relu = nn.ReLU(inplace=True) |
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num_groups = planes // 8 |
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if norm_fn == 'group': |
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self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) |
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self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) |
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if not (stride == 1 and in_planes == planes): |
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self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) |
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elif norm_fn == 'batch': |
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self.norm1 = nn.BatchNorm2d(planes) |
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self.norm2 = nn.BatchNorm2d(planes) |
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if not (stride == 1 and in_planes == planes): |
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self.norm3 = nn.BatchNorm2d(planes) |
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elif norm_fn == 'instance': |
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self.norm1 = nn.InstanceNorm2d(planes) |
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self.norm2 = nn.InstanceNorm2d(planes) |
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if not (stride == 1 and in_planes == planes): |
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self.norm3 = nn.InstanceNorm2d(planes) |
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elif norm_fn == 'none': |
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self.norm1 = nn.Sequential() |
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self.norm2 = nn.Sequential() |
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if not (stride == 1 and in_planes == planes): |
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self.norm3 = nn.Sequential() |
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if stride == 1 and in_planes == planes: |
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self.downsample = None |
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else: |
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self.downsample = nn.Sequential( |
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nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3) |
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def forward(self, x): |
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y = x |
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y = self.conv1(y) |
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y = self.norm1(y) |
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y = self.relu(y) |
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y = self.conv2(y) |
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y = self.norm2(y) |
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y = self.relu(y) |
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if self.downsample is not None: |
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x = self.downsample(x) |
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return self.relu(x+y) |
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class BottleneckBlock(nn.Module): |
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def __init__(self, in_planes, planes, norm_fn='group', stride=1): |
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super(BottleneckBlock, self).__init__() |
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self.conv1 = nn.Conv2d(in_planes, planes//4, kernel_size=1, padding=0) |
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self.conv2 = nn.Conv2d(planes//4, planes//4, kernel_size=3, padding=1, stride=stride) |
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self.conv3 = nn.Conv2d(planes//4, planes, kernel_size=1, padding=0) |
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self.relu = nn.ReLU(inplace=True) |
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num_groups = planes // 8 |
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if norm_fn == 'group': |
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self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes//4) |
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self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes//4) |
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self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) |
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if not stride == 1: |
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self.norm4 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) |
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elif norm_fn == 'batch': |
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self.norm1 = nn.BatchNorm2d(planes//4) |
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self.norm2 = nn.BatchNorm2d(planes//4) |
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self.norm3 = nn.BatchNorm2d(planes) |
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if not stride == 1: |
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self.norm4 = nn.BatchNorm2d(planes) |
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elif norm_fn == 'instance': |
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self.norm1 = nn.InstanceNorm2d(planes//4) |
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self.norm2 = nn.InstanceNorm2d(planes//4) |
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self.norm3 = nn.InstanceNorm2d(planes) |
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if not stride == 1: |
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self.norm4 = nn.InstanceNorm2d(planes) |
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elif norm_fn == 'none': |
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self.norm1 = nn.Sequential() |
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self.norm2 = nn.Sequential() |
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self.norm3 = nn.Sequential() |
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if not stride == 1: |
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self.norm4 = nn.Sequential() |
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if stride == 1: |
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self.downsample = None |
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else: |
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self.downsample = nn.Sequential( |
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nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm4) |
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def forward(self, x): |
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y = x |
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y = self.relu(self.norm1(self.conv1(y))) |
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y = self.relu(self.norm2(self.conv2(y))) |
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y = self.relu(self.norm3(self.conv3(y))) |
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if self.downsample is not None: |
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x = self.downsample(x) |
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return self.relu(x+y) |
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class BasicEncoder(nn.Module): |
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def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0, downsample=3): |
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super(BasicEncoder, self).__init__() |
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self.norm_fn = norm_fn |
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self.downsample = downsample |
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if self.norm_fn == 'group': |
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self.norm1 = nn.GroupNorm(num_groups=8, num_channels=64) |
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elif self.norm_fn == 'batch': |
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self.norm1 = nn.BatchNorm2d(64) |
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elif self.norm_fn == 'instance': |
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self.norm1 = nn.InstanceNorm2d(64) |
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elif self.norm_fn == 'none': |
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self.norm1 = nn.Sequential() |
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=1 + (downsample > 2), padding=3) |
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self.relu1 = nn.ReLU(inplace=True) |
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self.in_planes = 64 |
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self.layer1 = self._make_layer(64, stride=1) |
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self.layer2 = self._make_layer(96, stride=1 + (downsample > 1)) |
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self.layer3 = self._make_layer(128, stride=1 + (downsample > 0)) |
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self.conv2 = nn.Conv2d(128, output_dim, kernel_size=1) |
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self.dropout = None |
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if dropout > 0: |
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self.dropout = nn.Dropout2d(p=dropout) |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') |
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elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)): |
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if m.weight is not None: |
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nn.init.constant_(m.weight, 1) |
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if m.bias is not None: |
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nn.init.constant_(m.bias, 0) |
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def _make_layer(self, dim, stride=1): |
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layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride) |
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layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1) |
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layers = (layer1, layer2) |
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self.in_planes = dim |
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return nn.Sequential(*layers) |
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def forward(self, x, dual_inp=False): |
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is_list = isinstance(x, tuple) or isinstance(x, list) |
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if is_list: |
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batch_dim = x[0].shape[0] |
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x = torch.cat(x, dim=0) |
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x = self.conv1(x) |
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x = self.norm1(x) |
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x = self.relu1(x) |
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x = self.layer1(x) |
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x = self.layer2(x) |
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x = self.layer3(x) |
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x = self.conv2(x) |
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if self.training and self.dropout is not None: |
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x = self.dropout(x) |
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if is_list: |
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x = x.split(split_size=batch_dim, dim=0) |
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return x |
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class MultiBasicEncoder(nn.Module): |
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def __init__(self, output_dim=[128], norm_fn='batch', dropout=0.0, downsample=3): |
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super(MultiBasicEncoder, self).__init__() |
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self.norm_fn = norm_fn |
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self.downsample = downsample |
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if self.norm_fn == 'group': |
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self.norm1 = nn.GroupNorm(num_groups=8, num_channels=64) |
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elif self.norm_fn == 'batch': |
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self.norm1 = nn.BatchNorm2d(64) |
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elif self.norm_fn == 'instance': |
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self.norm1 = nn.InstanceNorm2d(64) |
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elif self.norm_fn == 'none': |
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self.norm1 = nn.Sequential() |
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=1 + (downsample > 2), padding=3) |
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self.relu1 = nn.ReLU(inplace=True) |
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self.in_planes = 64 |
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self.layer1 = self._make_layer(64, stride=1) |
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self.layer2 = self._make_layer(96, stride=1 + (downsample > 1)) |
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self.layer3 = self._make_layer(128, stride=1 + (downsample > 0)) |
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self.layer4 = self._make_layer(128, stride=2) |
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self.layer5 = self._make_layer(128, stride=2) |
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output_list = [] |
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for dim in output_dim: |
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conv_out = nn.Sequential( |
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ResidualBlock(128, 128, self.norm_fn, stride=1), |
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nn.Conv2d(128, dim[2], 3, padding=1)) |
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output_list.append(conv_out) |
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self.outputs08 = nn.ModuleList(output_list) |
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output_list = [] |
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for dim in output_dim: |
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conv_out = nn.Sequential( |
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ResidualBlock(128, 128, self.norm_fn, stride=1), |
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nn.Conv2d(128, dim[1], 3, padding=1)) |
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output_list.append(conv_out) |
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self.outputs16 = nn.ModuleList(output_list) |
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output_list = [] |
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for dim in output_dim: |
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conv_out = nn.Conv2d(128, dim[0], 3, padding=1) |
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output_list.append(conv_out) |
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self.outputs32 = nn.ModuleList(output_list) |
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if dropout > 0: |
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self.dropout = nn.Dropout2d(p=dropout) |
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else: |
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self.dropout = None |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') |
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elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)): |
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if m.weight is not None: |
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nn.init.constant_(m.weight, 1) |
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if m.bias is not None: |
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nn.init.constant_(m.bias, 0) |
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def _make_layer(self, dim, stride=1): |
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layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride) |
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layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1) |
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layers = (layer1, layer2) |
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self.in_planes = dim |
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return nn.Sequential(*layers) |
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def forward(self, x, dual_inp=False, num_layers=3): |
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x = self.conv1(x) |
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x = self.norm1(x) |
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x = self.relu1(x) |
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x = self.layer1(x) |
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x = self.layer2(x) |
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x = self.layer3(x) |
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if dual_inp: |
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v = x |
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x = x[:(x.shape[0]//2)] |
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outputs08 = [f(x) for f in self.outputs08] |
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if num_layers == 1: |
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return (outputs08, v) if dual_inp else (outputs08,) |
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y = self.layer4(x) |
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outputs16 = [f(y) for f in self.outputs16] |
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if num_layers == 2: |
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return (outputs08, outputs16, v) if dual_inp else (outputs08, outputs16) |
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z = self.layer5(y) |
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outputs32 = [f(z) for f in self.outputs32] |
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return (outputs08, outputs16, outputs32, v) if dual_inp else (outputs08, outputs16, outputs32) |
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