| import torch.nn as nn | |
| import torch.nn.functional as F | |
| class LambdaLayer(nn.Module): | |
| def __init__(self, lambd): | |
| super(LambdaLayer, self).__init__() | |
| self.lambd = lambd | |
| def forward(self, x): | |
| return self.lambd(x) | |
| class Block(nn.Module): | |
| expansion = 1 | |
| def __init__(self, in_planes, planes, conv_layer, stride=1): | |
| super(Block, self).__init__() | |
| self.conv1 = conv_layer(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) | |
| self.bn1 = nn.BatchNorm2d(planes) | |
| self.conv2 = conv_layer(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) | |
| self.bn2 = nn.BatchNorm2d(planes) | |
| self.shortcut = nn.Sequential() | |
| if stride != 1 or in_planes != planes: | |
| diff = planes - in_planes | |
| self.shortcut = LambdaLayer( | |
| lambda x: F.pad(x[:, :, ::2, ::2], (0, 0, 0, 0, int(diff * 0.5), int((diff + 1) * 0.5)), "constant", 0)) | |
| def forward(self, x): | |
| out = F.relu(self.bn1(self.conv1(x))) | |
| out = self.bn2(self.conv2(out)) | |
| out += self.shortcut(x) | |
| out = F.relu(out) | |
| return out | |
| class Router(nn.Module): | |
| def __init__(self, block, num_blocks, num_experts=2): | |
| super(Router, self).__init__() | |
| self.in_planes = 16 | |
| self.conv_layer = nn.Conv2d | |
| self.conv1 = nn.Conv2d(3, self.in_planes, kernel_size=3, stride=1, padding=1, bias=False) | |
| self.bn1 = nn.BatchNorm2d(self.in_planes) | |
| self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1) | |
| self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2) | |
| self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2) | |
| self.fc = nn.Linear(64, num_experts) | |
| self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) | |
| def _make_layer(self, block, planes, num_blocks, stride): | |
| planes = planes | |
| strides = [stride] + [1] * (num_blocks - 1) | |
| layers = [] | |
| for stride in strides: | |
| layers.append(block(self.in_planes, planes, self.conv_layer, stride)) | |
| self.in_planes = planes * block.expansion | |
| return nn.Sequential(*layers) | |
| def forward(self, x): | |
| out = F.relu(self.bn1(self.conv1(x))) | |
| out = self.layer1(out) | |
| out = self.layer2(out) | |
| out = self.layer3(out) | |
| out = self.avgpool(out) | |
| out = out.view(out.size(0), -1) | |
| out = self.fc(out) | |
| return out | |
| def build_router(**kwargs): | |
| return Router(Block, [3, 3, 3], **kwargs) | |