class Net(nn.Cell): def __init__(self): super(Net, self).__init__() self.value = Tensor([[1, 2, 3], [4, 5, 6]], dtype=mstype.float32) def construct(self): return self.value.reshape((1, 2, 3.5))
callback = MCC() elif assessment_method == "spearman_correlation": callback = Spearman_Correlation() else: raise ValueError("Assessment method not supported, support: [accuracy, f1, mcc, spearman_correlation]") file_name = os.listdir(args.label_dir) for f in file_name: if use_crf.lower() == "true": logits = () for j in range(bert_net_cfg.seq_length): f_name = f.split('.')[0] + '_' + str(j) + '.bin' data_tmp = np.fromfile(os.path.join(args.result_dir, f_name), np.int32) data_tmp = data_tmp.reshape(args.batch_size, num_class + 2) logits += ((Tensor(data_tmp),),) f_name = f.split('.')[0] + '_' + str(bert_net_cfg.seq_length) + '.bin' data_tmp = np.fromfile(os.path.join(args.result_dir, f_name), np.int32).tolist() data_tmp = Tensor(data_tmp) logits = (logits, data_tmp) else: f_name = os.path.join(args.result_dir, f.split('.')[0] + '_0.bin') logits = np.fromfile(f_name, np.float32).reshape(bert_net_cfg.seq_length * args.batch_size, num_class) logits = Tensor(logits) label_ids = np.fromfile(os.path.join(args.label_dir, f), np.int32) label_ids = Tensor(label_ids.reshape(args.batch_size, bert_net_cfg.seq_length)) callback.update(logits, label_ids) print("==============================================================") eval_result_print(assessment_method, callback) print("==============================================================")