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import math |
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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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from torch.autograd import Variable |
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class L2CS(nn.Module): |
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"""L2CS Gaze Detection Model. |
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This class is responsible for performing gaze detection using the L2CS-Net model. |
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Ref: https://github.com/Ahmednull/L2CS-Net |
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Methods: |
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forward: Performs inference on the given image. |
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""" |
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def __init__(self, block, layers, num_bins): |
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self.inplanes = 64 |
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super(L2CS, self).__init__() |
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) |
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self.bn1 = nn.BatchNorm2d(64) |
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self.relu = nn.ReLU(inplace=True) |
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
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self.layer1 = self._make_layer(block, 64, layers[0]) |
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2) |
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2) |
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2) |
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) |
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self.fc_yaw_gaze = nn.Linear(512 * block.expansion, num_bins) |
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self.fc_pitch_gaze = nn.Linear(512 * block.expansion, num_bins) |
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self.fc_finetune = nn.Linear(512 * block.expansion + 3, 3) |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels |
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m.weight.data.normal_(0, math.sqrt(2.0 / n)) |
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elif isinstance(m, nn.BatchNorm2d): |
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m.weight.data.fill_(1) |
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m.bias.data.zero_() |
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def _make_layer(self, block, planes, blocks, stride=1): |
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downsample = None |
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if stride != 1 or self.inplanes != planes * block.expansion: |
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downsample = nn.Sequential( |
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nn.Conv2d( |
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self.inplanes, |
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planes * block.expansion, |
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kernel_size=1, |
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stride=stride, |
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bias=False, |
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), |
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nn.BatchNorm2d(planes * block.expansion), |
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) |
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layers = [] |
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layers.append(block(self.inplanes, planes, stride, downsample)) |
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self.inplanes = planes * block.expansion |
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for i in range(1, blocks): |
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layers.append(block(self.inplanes, planes)) |
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return nn.Sequential(*layers) |
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def forward(self, x): |
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x = self.conv1(x) |
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x = self.bn1(x) |
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x = self.relu(x) |
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x = self.maxpool(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.layer4(x) |
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x = self.avgpool(x) |
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x = x.view(x.size(0), -1) |
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pre_yaw_gaze = self.fc_yaw_gaze(x) |
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pre_pitch_gaze = self.fc_pitch_gaze(x) |
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return pre_yaw_gaze, pre_pitch_gaze |
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