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("==============================================================")
