def local_eval(model, test_loader=None, test_label_file=None): prediction_file = 'pred_train.txt' feed_infer( prediction_file, lambda root_path: _infer( model, root_path, test_loader=test_loader, local_val=True)) metric_result = evaluation_metrics(prediction_file, test_label_file) print('Eval result: {:.4f}'.format(metric_result)) return metric_result
def local_eval(model, data_loader=None): prediction_file = 'test_pred' feed_infer( prediction_file, lambda root_path: _infer(model, root_path, data_loader=data_loader)) test_label_file = '/home/data/nipa_faces_sr_tmp2/test/test_label' # local datapath metric_result = evaluation_metrics(prediction_file, test_label_file) print('Eval result: {:.4f}'.format(metric_result)) return metric_result
def local_eval(model, test_loader=None, test_label_file=None): model.eval() prediction_file = 'pred_train.txt' feed_infer(prediction_file, lambda root_path: _infer(model, root_path, test_loader=test_loader)) if not test_label_file: test_label_file = os.path.join(VAL_DATASET_PATH, 'test_label') metric_result = evaluation_metrics( prediction_file, test_label_file) return metric_result
def local_eval(model, test_loader=None, test_label_file=None): prediction_file = 'pred_train.txt' feed_infer( prediction_file, lambda root_path: _infer(model, root_path, test_loader=test_loader)) if not test_label_file: test_label_file = os.path.join(VAL_DATASET_PATH, 'test_label') metric_result = evaluation_metrics(prediction_file, test_label_file) logger.info('Eval result: {:.4f} mIoU'.format(metric_result)) return metric_result
def local_eval(model, test_loader=None, test_label_file="../food_img/food_label.txt"): prediction_file = "pred_train.txt" feed_infer( prediction_file, lambda root_path: _infer( model, root_path, test_loader=test_loader, local_val=True), ) metric_result = evaluation_metrics(prediction_file, test_label_file) print("Eval result: {:.4f}".format(metric_result)) return metric_result
def local_eval(model, test_loader=None, test_label_file=None): """Local debugging function. You can use this function for debugging. """ prediction_file = 'pred_train.txt' feed_infer( prediction_file, lambda root_path: _infer(model, root_path, test_loader=test_loader)) if not test_label_file: test_label_file = os.path.join(VAL_DATASET_PATH, 'test_label') metric_result = evaluation_metrics(prediction_file, test_label_file) print('[Eval result] recall: {:.2f}'.format(metric_result)) return metric_result
def local_eval(model, loader, gt_path): """Local debugging function. You can use this function for debugging. You may need dummy gt file. Args: model: instance. test_loader: instance. gt_path: string. Returns: metric_result: float. Performance of your method. """ pred_path = 'pred.txt' feed_infer( pred_path, lambda root_path: _infer( model=model, root_path=root_path, loader=loader)) metric_result = evaluation_metrics(pred_path, gt_path) return metric_result