| | |
|---|
| | | _, roll_bpred = torch.max(roll.data, 1) |
|---|
| | | |
|---|
| | | # Continuous predictions |
|---|
| | | yaw_predicted = utils.softmax_temperature(yaw.data, 1) |
|---|
| | | pitch_predicted = utils.softmax_temperature(pitch.data, 1) |
|---|
| | | roll_predicted = utils.softmax_temperature(roll.data, 1) |
|---|
| | | |
|---|
| | | yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1).cpu() * 3 - 99 |
|---|
| | | pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1).cpu() * 3 - 99 |
|---|
| | | roll_predicted = torch.sum(roll_predicted * idx_tensor, 1).cpu() * 3 - 99 |
|---|
| | | yaw_predicted = angles[:,0].data.cpu() |
|---|
| | | pitch_predicted = angles[:,1].data.cpu() |
|---|
| | | roll_predicted = angles[:,2].data.cpu() |
|---|
| | | |
|---|
| | | # Mean absolute error |
|---|
| | | yaw_error += torch.sum(torch.abs(yaw_predicted - label_yaw)) |
|---|
| | | pitch_error += torch.sum(torch.abs(pitch_predicted - label_pitch)) |
|---|
| | | roll_error += torch.sum(torch.abs(roll_predicted - label_roll)) |
|---|
| | | |
|---|
| | | # Save images with pose cube. |
|---|
| | | # TODO: fix for larger batch size |
|---|
| | | # Save first image in batch with pose cube or axis. |
|---|
| | | if args.save_viz: |
|---|
| | | name = name[0] |
|---|
| | | if args.dataset == 'BIWI': |
|---|
