def get_max_preds(self, heatmaps):
        '''get predictions from score maps
 
        Args:
            heatmaps: numpy.ndarray([batch_size, num_joints, height, width])
 
        Returns:
            preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
            maxvals: numpy.ndarray([batch_size, num_joints, 2]), the maximum confidence of the keypoints
        '''
        assert isinstance(heatmaps,
                          np.ndarray), 'heatmaps should be numpy.ndarray'
        assert heatmaps.ndim == 4, 'batch_images should be 4-ndim'

        batch_size = heatmaps.shape[0]
        num_joints = heatmaps.shape[1]
        width = heatmaps.shape[3]
        heatmaps_reshaped = heatmaps.reshape((batch_size, num_joints, -1))
        idx = np.argmax(heatmaps_reshaped, 2)
        maxvals = np.amax(heatmaps_reshaped, 2)

        maxvals = maxvals.reshape((batch_size, num_joints, 1))
        idx = idx.reshape((batch_size, num_joints, 1))

        preds = np.tile(idx, (1, 1, 2)).astype(np.float32)

        preds[:, :, 0] = (preds[:, :, 0]) % width
        preds[:, :, 1] = np.floor((preds[:, :, 1]) / width)

        pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2))
        pred_mask = pred_mask.astype(np.float32)

        preds *= pred_mask

        return preds, maxvals
get predictions from score maps
 
        Args:
            heatmaps: numpy.ndarray([batch_size, num_joints, height, width])
 
        Returns:
            preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
            maxvals: numpy.ndarray([batch_size, num_joints, 2]), the maximum confidence of the keypoints
        
ppdet/modeling/architectures/keypoint_hrnet.py