Spaces:
Running
on
Zero
Running
on
Zero
| import math | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torch.autograd import Variable | |
| class L2CS(nn.Module): | |
| """L2CS Gaze Detection Model. | |
| This class is responsible for performing gaze detection using the L2CS-Net model. | |
| Ref: https://github.com/Ahmednull/L2CS-Net | |
| Methods: | |
| forward: Performs inference on the given image. | |
| """ | |
| def __init__(self, block, layers, num_bins): | |
| self.inplanes = 64 | |
| super(L2CS, 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.AdaptiveAvgPool2d((1, 1)) | |
| self.fc_yaw_gaze = nn.Linear(512 * block.expansion, num_bins) | |
| self.fc_pitch_gaze = 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(2.0 / 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) | |
| # gaze | |
| pre_yaw_gaze = self.fc_yaw_gaze(x) | |
| pre_pitch_gaze = self.fc_pitch_gaze(x) | |
| return pre_yaw_gaze, pre_pitch_gaze | |