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| | | import torch.nn.functional as F |
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| | | |
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| | | class Hopenet(nn.Module): |
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| | | # This is just Hopenet with 3 output layers for yaw, pitch and roll. |
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| | | def __init__(self, block, layers, num_bins, iter_ref): |
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| | | # Hopenet with 3 output layers for yaw, pitch and roll |
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| | | # Predicts Euler angles by binning and regression with the expected value |
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| | | def __init__(self, block, layers, num_bins): |
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| | | self.inplanes = 64 |
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| | | super(Hopenet, self).__init__() |
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| | | self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, |
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| | |
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| | | self.fc_pitch = nn.Linear(512 * block.expansion, num_bins) |
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| | | self.fc_roll = nn.Linear(512 * block.expansion, num_bins) |
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| | | |
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| | | self.softmax = nn.Softmax() |
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| | | self.fc_finetune = nn.Linear(512 * block.expansion + 3, 3) |
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| | | |
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| | | # Used to get the expected value of angle from bins |
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| | | self.softmax = nn.Softmax() |
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| | | self.idx_tensor = Variable(torch.FloatTensor(range(66))).cuda() |
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| | | |
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| | | self.iter_ref = iter_ref |
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| | | |
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| | | for m in self.modules(): |
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| | | if isinstance(m, nn.Conv2d): |
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| | |
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| | | yaw = yaw.view(yaw.size(0), 1) |
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| | | pitch = pitch.view(pitch.size(0), 1) |
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| | | roll = roll.view(roll.size(0), 1) |
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| | | angles = [] |
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| | | preangles = torch.cat([yaw, pitch, roll], 1) |
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| | | angles.append(preangles) |
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| | | |
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| | | # angles predicts the residual |
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| | | for idx in xrange(self.iter_ref): |
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| | | angles.append(self.fc_finetune(torch.cat((angles[idx], x), 1))) |
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| | | |
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| | | return pre_yaw, pre_pitch, pre_roll, angles |
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| | | return pre_yaw, pre_pitch, pre_roll, preangles |
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| | | |
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| | | class ResNet(nn.Module): |
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| | | |
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| | | # ResNet for regression of 3 Euler angles. |
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| | | def __init__(self, block, layers, num_classes=1000): |
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| | | self.inplanes = 64 |
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| | | super(ResNet, self).__init__() |
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| | |
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| | | x = self.avgpool(x) |
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| | | x = x.view(x.size(0), -1) |
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| | | x = self.fc_angles(x) |
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| | | |
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| | | return x |
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| | | |
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| | | class AlexNet(nn.Module): |
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| | | |
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| | | # AlexNet laid out as a Hopenet - classify Euler angles in bins and |
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| | | # regress the expected value. |
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| | | def __init__(self, num_bins): |
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| | | super(AlexNet, self).__init__() |
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| | | self.features = nn.Sequential( |
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