Function eval_one_epoch

main_code.py:355–398

(sess, ops, test_writer, epoch)

            code_loss_sum = 0.

def eval_one_epoch(sess, ops, test_writer, epoch):
    is_training = False
    total_batch = TEST_DATASET.shape[0]
    chamfer_loss_sum = 0.


    for i in range(total_batch):
        batch_input_data = TEST_DATASET[i]
        batch_data_gt = TEST_DATASET_GT[i]

        feed_dict = {
            ops['pointclouds_pl']: batch_input_data[:, :, 0:3],
            ops['pointclouds_gt']: batch_data_gt[:, :, 0:3],
            ops['pointclouds_Y']: batch_data_gt[:, :, 0:3],
            ops['is_training']: is_training
        }
        complete_CD, pred_val, pred_Y2X, pred_Y2X2Y, pred_X2Y2X = sess.run([ops['complete_CD'], ops['pointclouds_pred'], ops['incomplete_Y'], ops['pred_Y2X2Y'],ops['pred_X2Y2X']], feed_dict=feed_dict)
        chamfer_loss_sum += complete_CD
  
    mean_chamfer_loss = chamfer_loss_sum / total_batch

    log_string('eval  chamfer loss: %.3f' % \
               (mean_chamfer_loss/2048. * 10000.))
    LOG_RESULT_FOUT.write('%.3f\n' % (mean_chamfer_loss/2048. * 10000.))
    LOG_RESULT_FOUT.flush()

    os.makedirs(os.path.join(LOG_DIR,'vis/epoch_%d_%.2f'%(epoch, mean_chamfer_loss*4.883)))
    for i in range(pred_val.shape[0]):
        gt = batch_data_gt[i]
        pred = pred_val[i]
        res = batch_input_data[i]
        Y2X = pred_Y2X[i]
        Y2X2Y = pred_Y2X2Y[i]
        X2Y2X = pred_X2Y2X[i]


        io_util.write_ply(gt, os.path.join(LOG_DIR,'vis/epoch_%d_%.2f/gt_%d.ply'%(epoch, mean_chamfer_loss*4.883, i)))
        io_util.write_ply(pred, os.path.join(LOG_DIR,'vis/epoch_%d_%.2f/pred_%d.ply'%(epoch, mean_chamfer_loss*4.883, i)))
        io_util.write_ply(res, os.path.join(LOG_DIR,'vis/epoch_%d_%.2f/res_%d.ply'%(epoch, mean_chamfer_loss*4.883, i)))
        io_util.write_ply(Y2X, os.path.join(LOG_DIR,'vis/epoch_%d_%.2f/pred_Y2X_%d.ply'%(epoch, mean_chamfer_loss*4.883, i)))
        io_util.write_ply(Y2X2Y, os.path.join(LOG_DIR,'vis/epoch_%d_%.2f/pred_Y2X2Y_%d.ply'%(epoch, mean_chamfer_loss*4.883, i)))
        io_util.write_ply(X2Y2X, os.path.join(LOG_DIR,'vis/epoch_%d_%.2f/pred_X2Y2X_%d.ply'%(epoch, mean_chamfer_loss*4.883, i)))

    return mean_chamfer_loss*4.883


if __name__ == "__main__":