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| import torch | |
| import torch.nn as nn | |
| from torch.autograd import Variable | |
| import math | |
| import torch.nn.functional as F | |
| class Hopenet(nn.Module): | |
| # Hopenet with 3 output layers for yaw, pitch and roll | |
| # Predicts Euler angles by binning and regression with the expected value | |
| def __init__(self, block, layers, num_bins): | |
| self.inplanes = 64 | |
| super(Hopenet, self).__init__() | |
| self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, | |
| bias=False) | |
| self.bn1 = nn.BatchNorm2d(64) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
| self.layer1 = self._make_layer(block, 64, layers[0]) | |
| self.layer2 = self._make_layer(block, 128, layers[1], stride=2) | |
| self.layer3 = self._make_layer(block, 256, layers[2], stride=2) | |
| self.layer4 = self._make_layer(block, 512, layers[3], stride=2) | |
| self.avgpool = nn.AvgPool2d(7) | |
| self.fc_yaw = nn.Linear(512 * block.expansion, num_bins) | |
| self.fc_pitch = nn.Linear(512 * block.expansion, num_bins) | |
| self.fc_roll = nn.Linear(512 * block.expansion, num_bins) | |
| # Vestigial layer from previous experiments | |
| self.fc_finetune = nn.Linear(512 * block.expansion + 3, 3) | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
| m.weight.data.normal_(0, math.sqrt(2. / n)) | |
| elif isinstance(m, nn.BatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| def _make_layer(self, block, planes, blocks, stride=1): | |
| downsample = None | |
| if stride != 1 or self.inplanes != planes * block.expansion: | |
| downsample = nn.Sequential( | |
| nn.Conv2d(self.inplanes, planes * block.expansion, | |
| kernel_size=1, stride=stride, bias=False), | |
| nn.BatchNorm2d(planes * block.expansion), | |
| ) | |
| layers = [] | |
| layers.append(block(self.inplanes, planes, stride, downsample)) | |
| self.inplanes = planes * block.expansion | |
| for i in range(1, blocks): | |
| layers.append(block(self.inplanes, planes)) | |
| return nn.Sequential(*layers) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.bn1(x) | |
| x = self.relu(x) | |
| x = self.maxpool(x) | |
| x = self.layer1(x) | |
| x = self.layer2(x) | |
| x = self.layer3(x) | |
| x = self.layer4(x) | |
| x = self.avgpool(x) | |
| x = x.view(x.size(0), -1) | |
| pre_yaw = self.fc_yaw(x) | |
| pre_pitch = self.fc_pitch(x) | |
| pre_roll = self.fc_roll(x) | |
| return pre_yaw, pre_pitch, pre_roll | |
| class ResNet(nn.Module): | |
| # ResNet for regression of 3 Euler angles. | |
| def __init__(self, block, layers, num_classes=1000): | |
| self.inplanes = 64 | |
| super(ResNet, self).__init__() | |
| self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, | |
| bias=False) | |
| self.bn1 = nn.BatchNorm2d(64) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
| self.layer1 = self._make_layer(block, 64, layers[0]) | |
| self.layer2 = self._make_layer(block, 128, layers[1], stride=2) | |
| self.layer3 = self._make_layer(block, 256, layers[2], stride=2) | |
| self.layer4 = self._make_layer(block, 512, layers[3], stride=2) | |
| self.avgpool = nn.AvgPool2d(7) | |
| self.fc_angles = nn.Linear(512 * block.expansion, num_classes) | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
| m.weight.data.normal_(0, math.sqrt(2. / n)) | |
| elif isinstance(m, nn.BatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| def _make_layer(self, block, planes, blocks, stride=1): | |
| downsample = None | |
| if stride != 1 or self.inplanes != planes * block.expansion: | |
| downsample = nn.Sequential( | |
| nn.Conv2d(self.inplanes, planes * block.expansion, | |
| kernel_size=1, stride=stride, bias=False), | |
| nn.BatchNorm2d(planes * block.expansion), | |
| ) | |
| layers = [] | |
| layers.append(block(self.inplanes, planes, stride, downsample)) | |
| self.inplanes = planes * block.expansion | |
| for i in range(1, blocks): | |
| layers.append(block(self.inplanes, planes)) | |
| return nn.Sequential(*layers) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.bn1(x) | |
| x = self.relu(x) | |
| x = self.maxpool(x) | |
| x = self.layer1(x) | |
| x = self.layer2(x) | |
| x = self.layer3(x) | |
| x = self.layer4(x) | |
| x = self.avgpool(x) | |
| x = x.view(x.size(0), -1) | |
| x = self.fc_angles(x) | |
| return x | |
| class AlexNet(nn.Module): | |
| # AlexNet laid out as a Hopenet - classify Euler angles in bins and | |
| # regress the expected value. | |
| def __init__(self, num_bins): | |
| super(AlexNet, self).__init__() | |
| self.features = nn.Sequential( | |
| nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2), | |
| nn.ReLU(inplace=True), | |
| nn.MaxPool2d(kernel_size=3, stride=2), | |
| nn.Conv2d(64, 192, kernel_size=5, padding=2), | |
| nn.ReLU(inplace=True), | |
| nn.MaxPool2d(kernel_size=3, stride=2), | |
| nn.Conv2d(192, 384, kernel_size=3, padding=1), | |
| nn.ReLU(inplace=True), | |
| nn.Conv2d(384, 256, kernel_size=3, padding=1), | |
| nn.ReLU(inplace=True), | |
| nn.Conv2d(256, 256, kernel_size=3, padding=1), | |
| nn.ReLU(inplace=True), | |
| nn.MaxPool2d(kernel_size=3, stride=2), | |
| ) | |
| self.classifier = nn.Sequential( | |
| nn.Dropout(), | |
| nn.Linear(256 * 6 * 6, 4096), | |
| nn.ReLU(inplace=True), | |
| nn.Dropout(), | |
| nn.Linear(4096, 4096), | |
| nn.ReLU(inplace=True), | |
| ) | |
| self.fc_yaw = nn.Linear(4096, num_bins) | |
| self.fc_pitch = nn.Linear(4096, num_bins) | |
| self.fc_roll = nn.Linear(4096, num_bins) | |
| def forward(self, x): | |
| x = self.features(x) | |
| x = x.view(x.size(0), 256 * 6 * 6) | |
| x = self.classifier(x) | |
| yaw = self.fc_yaw(x) | |
| pitch = self.fc_pitch(x) | |
| roll = self.fc_roll(x) | |
| return yaw, pitch, roll | |