| | |
|---|
| | | model.eval() # Change model to 'eval' mode (BN uses moving mean/var). |
|---|
| | | total = 0 |
|---|
| | | |
|---|
| | | idx_tensor = [idx for idx in xrange(66)] |
|---|
| | | idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu) |
|---|
| | | |
|---|
| | | yaw_error = .0 |
|---|
| | | pitch_error = .0 |
|---|
| | | roll_error = .0 |
|---|
| | | |
|---|
| | | l1loss = torch.nn.L1Loss(size_average=False) |
|---|
| | | |
|---|
| | | |
|---|
| | | |
|---|
| | | for i, (images, labels, cont_labels, name) in enumerate(test_loader): |
|---|
| | | images = Variable(images).cuda(gpu) |
|---|
| | |
|---|
| | | _, roll_bpred = torch.max(roll.data, 1) |
|---|
| | | |
|---|
| | | # Continuous predictions |
|---|
| | | yaw_predicted = angles[:,0].data.cpu() |
|---|
| | | pitch_predicted = angles[:,1].data.cpu() |
|---|
| | | roll_predicted = angles[:,2].data.cpu() |
|---|
| | | 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 |
|---|
| | | |
|---|
| | | # Mean absolute error |
|---|
| | | yaw_error += torch.sum(torch.abs(yaw_predicted - label_yaw)) |
|---|
