| | |
|---|
| | | # Regression loss coefficient |
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| | | alpha = args.alpha |
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| | | |
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| | | softmax = nn.Softmax().cuda(gpu) |
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| | | idx_tensor = [idx for idx in xrange(66)] |
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| | | idx_tensor = Variable(torch.FloatTensor(idx_tensor)).cuda(gpu) |
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| | | |
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| | | optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': 0}, |
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| | | {'params': get_non_ignored_params(model), 'lr': args.lr}, |
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| | | {'params': get_fc_params(model), 'lr': args.lr * 5}], |
|---|
| | |
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| | | loss_roll = criterion(roll, label_roll) |
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| | | |
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| | | # MSE loss |
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| | | yaw_predicted = angles[:,0] |
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| | | pitch_predicted = angles[:,1] |
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| | | roll_predicted = angles[:,2] |
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| | | yaw_predicted = softmax(yaw) |
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| | | pitch_predicted = softmax(pitch) |
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| | | roll_predicted = softmax(roll) |
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| | | |
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| | | yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1) * 3 - 99 |
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| | | pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1) * 3 - 99 |
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| | | roll_predicted = torch.sum(roll_predicted * idx_tensor, 1) * 3 - 99 |
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| | | |
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| | | loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw_cont) |
|---|
| | | loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch_cont) |
|---|
