| | |
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| | | |
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| | | print 'Second phase of training (finetuning layer).' |
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| | | for epoch in range(num_epochs_ft): |
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| | | for i, (images, labels, name) in enumerate(train_loader): |
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| | | for i, (images, labels, cont_labels, name) in enumerate(train_loader): |
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| | | images = Variable(images.cuda(gpu)) |
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| | | label_yaw = Variable(labels[:,0].cuda(gpu)) |
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| | | label_pitch = Variable(labels[:,1].cuda(gpu)) |
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| | | label_roll = Variable(labels[:,2].cuda(gpu)) |
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| | | label_angles = Variable(labels[:,:3].cuda(gpu)) |
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| | | |
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| | | label_angles = Variable(cont_labels[:,:3].cuda(gpu)) |
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| | | |
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| | | optimizer.zero_grad() |
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| | | model.zero_grad() |
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| | |
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| | | # Finetuning loss |
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| | | loss_seq = [] |
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| | | for idx in xrange(1,len(angles)): |
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| | | label_angles_residuals = label_angles.float() - angles[0] |
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| | | label_angles_residuals = label_angles - (angles[0] * 3 - 99) |
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| | | label_angles_residuals = label_angles_residuals.detach() |
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| | | loss_angles = reg_criterion(angles[idx], label_angles_residuals) |
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| | | loss_seq.append(loss_angles) |
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