| | |
| | | print 'Ready to train network.' |
| | | print 'First phase of training.' |
| | | for epoch in range(num_epochs): |
| | | start = time.time() |
| | | # start = time.time() |
| | | for i, (images, labels, cont_labels, name) in enumerate(train_loader): |
| | | print i |
| | | print 'start: ', time.time() - start |
| | | # print i |
| | | # print 'start: ', time.time() - start |
| | | images = Variable(images).cuda(gpu) |
| | | label_yaw = Variable(labels[:,0]).cuda(gpu) |
| | | label_pitch = Variable(labels[:,1]).cuda(gpu) |
| | |
| | | torch.autograd.backward(loss_seq, grad_seq) |
| | | optimizer.step() |
| | | |
| | | print 'end: ', time.time() - start |
| | | # print 'end: ', time.time() - start |
| | | |
| | | if (i+1) % 100 == 0: |
| | | print ('Epoch [%d/%d], Iter [%d/%d] Losses: Yaw %.4f, Pitch %.4f, Roll %.4f' |