| | |
|---|
| | | _, pitch_bpred = torch.max(pitch.data, 1) |
|---|
| | | _, roll_bpred = torch.max(roll.data, 1) |
|---|
| | | |
|---|
| | | yaw_predicted = F.softmax(yaw) |
|---|
| | | pitch_predicted = F.softmax(pitch) |
|---|
| | | roll_predicted = F.softmax(roll) |
|---|
| | | |
|---|
| | | # Continuous predictions |
|---|
| | | yaw_predicted = torch.sum(yaw_predicted.data * idx_tensor, 1) |
|---|
| | | pitch_predicted = torch.sum(pitch_predicted.data * idx_tensor, 1) |
|---|
| | | roll_predicted = torch.sum(roll_predicted.data * idx_tensor, 1) |
|---|
| | | 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 = yaw_predicted.cpu() |
|---|
| | | pitch_predicted = pitch_predicted.cpu() |
|---|
| | | roll_predicted = roll_predicted.cpu() |
|---|
| | | yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1).cpu() |
|---|
| | | pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1).cpu() |
|---|
| | | roll_predicted = torch.sum(roll_predicted * idx_tensor, 1).cpu() |
|---|
| | | |
|---|
| | | # Mean absolute error |
|---|
| | | yaw_error += torch.sum(torch.abs(yaw_predicted - label_yaw) * 3) |
|---|
