| 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) |
|
|
| |
| 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) |
|
|
| |
| pre_yaw_gaze = self.fc_yaw_gaze(x) |
| pre_pitch_gaze = self.fc_pitch_gaze(x) |
| return pre_yaw_gaze, pre_pitch_gaze |
|
|
