| | |
|---|
| | | label_roll = labels[:,2].float() |
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| | | |
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| | | pre_yaw, pre_pitch, pre_roll, angles = model(images) |
|---|
| | | yaw = angles[args.iter_ref-1][:,0].cpu().data |
|---|
| | | pitch = angles[args.iter_ref-1][:,1].cpu().data |
|---|
| | | roll = angles[args.iter_ref-1][:,2].cpu().data |
|---|
| | | yaw = angles[args.iter_ref][:,0].cpu().data |
|---|
| | | pitch = angles[args.iter_ref][:,1].cpu().data |
|---|
| | | roll = angles[args.iter_ref][:,2].cpu().data |
|---|
| | | |
|---|
| | | # Mean absolute error |
|---|
| | | print yaw.numpy(), label_yaw.numpy() |
|---|
| | | yaw_error += torch.sum(torch.abs(yaw - label_yaw) * 3) |
|---|
| | | pitch_error += torch.sum(torch.abs(pitch - label_pitch) * 3) |
|---|
| | | roll_error += torch.sum(torch.abs(roll - label_roll) * 3) |
|---|
