next

natanielruiz

2017-10-30 4b67b5c8ed5566ec3030d537536282e830d87e40

 code/test_preangles.py

@@ -63,6 +63,8 @@
        pose_dataset = datasets.Pose_300W_LP_random_ds(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'AFLW2000':
        pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'AFLW2000_ds':
        pose_dataset = datasets.AFLW2000_ds(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'BIWI':
        pose_dataset = datasets.BIWI(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'AFLW':
@@ -86,6 +88,9 @@
    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
@@ -100,7 +105,7 @@
        label_pitch = cont_labels[:,1].float()
        label_roll = cont_labels[:,2].float()

        yaw, pitch, roll, angles = model(images)
        yaw, pitch, roll = model(images)

        # Binned predictions
        _, yaw_bpred = torch.max(yaw.data, 1)
@@ -108,9 +113,13 @@
        _, 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))