| | |
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| | | |
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| | | l1loss = torch.nn.L1Loss(size_average=False) |
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| | | |
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| | | for i, (images, labels, name) in enumerate(test_loader): |
|---|
| | | for i, (images, labels, cont_labels, name) in enumerate(test_loader): |
|---|
| | | images = Variable(images).cuda(gpu) |
|---|
| | | total += labels.size(0) |
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| | | label_yaw = labels[:,0].float() |
|---|
| | | label_pitch = labels[:,1].float() |
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| | | label_roll = labels[:,2].float() |
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| | | total += cont_labels.size(0) |
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| | | label_yaw = cont_labels[:,0].float() |
|---|
| | | label_pitch = cont_labels[:,1].float() |
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| | | label_roll = cont_labels[:,2].float() |
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| | | |
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| | | pre_yaw, pre_pitch, pre_roll, angles = model(images) |
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| | | yaw = angles[args.iter_ref-1][:,0].cpu().data |
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| | | pitch = angles[args.iter_ref-1][:,1].cpu().data |
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| | | roll = angles[args.iter_ref-1][:,2].cpu().data |
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| | | yaw = angles[0][:,0].cpu().data * 3 - 99 |
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| | | pitch = angles[0][:,1].cpu().data * 3 - 99 |
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| | | roll = angles[0][:,2].cpu().data * 3 - 99 |
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| | | |
|---|
| | | for idx in xrange(1,args.iter_ref+1): |
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| | | yaw += angles[idx][:,0].cpu().data |
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| | | pitch += angles[idx][:,1].cpu().data |
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| | | roll += angles[idx][:,2].cpu().data |
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| | | |
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| | | # Mean absolute error |
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| | | yaw_error += torch.sum(torch.abs(yaw - label_yaw) * 3) |
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| | | pitch_error += torch.sum(torch.abs(pitch - label_pitch) * 3) |
|---|
| | | roll_error += torch.sum(torch.abs(roll - label_roll) * 3) |
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| | | yaw_error += torch.sum(torch.abs(yaw - label_yaw)) |
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| | | pitch_error += torch.sum(torch.abs(pitch - label_pitch)) |
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| | | roll_error += torch.sum(torch.abs(roll - label_roll)) |
|---|
| | | if args.save_viz: |
|---|
| | | name = name[0] |
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| | | cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg')) |
|---|
