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natanielruiz

2017-09-19 e624d2ace8296e130a4fa4d2d307041798c538e0

 code/test_preangles.py

@@ -116,14 +116,14 @@
        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()
        pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1).cpu()
        roll_predicted = torch.sum(roll_predicted * idx_tensor, 1).cpu()
        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 * 3 - 99 - label_yaw))
        pitch_error += torch.sum(torch.abs(pitch_predicted * 3 - 99 - label_pitch))
        roll_error += torch.sum(torch.abs(roll_predicted * 3 - 99 - label_roll))
        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
@@ -131,9 +131,9 @@
            name = name[0]
            cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg'))
            if args.batch_size == 1:
                error_string = 'y %.2f, p %.2f, r %.2f' % (torch.sum(torch.abs(yaw_predicted - label_yaw) * 3), torch.sum(torch.abs(pitch_predicted - label_pitch) * 3), torch.sum(torch.abs(roll_predicted - label_roll) * 3))
                error_string = 'y %.2f, p %.2f, r %.2f' % (torch.sum(torch.abs(yaw_predicted - label_yaw)), torch.sum(torch.abs(pitch_predicted - label_pitch)), torch.sum(torch.abs(roll_predicted - label_roll)))
                cv2_img = cv2.putText(cv2_img, error_string, (30, cv2_img.shape[0]- 30), fontFace=1, fontScale=1, color=(0,0,255), thickness=1)
            utils.plot_pose_cube(cv2_img, yaw_predicted[0] * 3 - 99, pitch_predicted[0] * 3 - 99, roll_predicted[0] * 3 - 99)
            utils.plot_pose_cube(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0])
            cv2.imwrite(os.path.join('output/images', name + '.jpg'), cv2_img)

    print('Test error in degrees of the model on the ' + str(total) +