def do_eval(dataset=None, network=None, num_class=2, assessment_method="accuracy", load_checkpoint_path=""):
""" do eval """
if load_checkpoint_path == "":
raise ValueError("Finetune model missed, evaluation task must load finetune model!")
net_for_pretraining = network(bert_net_cfg, False, num_class)
net_for_pretraining.set_train(False)
param_dict = load_checkpoint(load_checkpoint_path)
load_param_into_net(net_for_pretraining, param_dict)
model = Model(net_for_pretraining)
if assessment_method == "accuracy":
callback = Accuracy()
elif assessment_method == "f1":
callback = F1(False, num_class)
elif assessment_method == "mcc":
callback = MCC()
elif assessment_method == "spearman_correlation":
callback = Spearman_Correlation()
else:
raise ValueError("Assessment method not supported, support: [accuracy, f1, mcc, spearman_correlation]")
columns_list = ["input_ids", "input_mask", "segment_ids", "label_ids"]
for data in dataset.create_dict_iterator():
input_data = []
for i in columns_list:
input_data.append(Tensor(data[i]))
input_ids, input_mask, token_type_id, label_ids = input_data
logits = model.predict(input_ids, input_mask, token_type_id, label_ids)
callback.update(logits, label_ids)
print("==============================================================")
eval_result_print(assessment_method, callback)
print("==============================================================")
def do_eval(dataset=None,
network=None,
use_crf="",
num_class=2,
assessment_method="accuracy",
data_file="",
load_checkpoint_path="",
vocab_file="",
label2id_file="",
tag_to_index=None):
""" do eval """
if load_checkpoint_path == "":
raise ValueError(
"Finetune model missed, evaluation task must load finetune model!")
if assessment_method == "clue_benchmark":
bert_net_cfg.batch_size = 1
net_for_pretraining = network(bert_net_cfg,
False,
num_class,
use_crf=(use_crf.lower() == "true"),
tag_to_index=tag_to_index)
net_for_pretraining.set_train(False)
param_dict = load_checkpoint(load_checkpoint_path)
load_param_into_net(net_for_pretraining, param_dict)
model = Model(net_for_pretraining)
if assessment_method == "clue_benchmark":
from src.cluener_evaluation import submit
submit(model=model,
path=data_file,
vocab_file=vocab_file,
use_crf=use_crf,
label2id_file=label2id_file)
else:
if assessment_method == "accuracy":
callback = Accuracy()
elif assessment_method == "f1":
callback = F1((use_crf.lower() == "true"), num_class)
elif assessment_method == "mcc":
callback = MCC()
elif assessment_method == "spearman_correlation":
callback = Spearman_Correlation()
else:
raise ValueError(
"Assessment method not supported, support: [accuracy, f1, mcc, spearman_correlation]"
)
columns_list = ["input_ids", "input_mask", "segment_ids", "label_ids"]
for data in dataset.create_dict_iterator():
input_data = []
for i in columns_list:
input_data.append(Tensor(data[i]))
input_ids, input_mask, token_type_id, label_ids = input_data
logits = model.predict(input_ids, input_mask, token_type_id,
label_ids)
callback.update(logits, label_ids)
print("==============================================================")
eval_result_print(assessment_method, callback)
print("==============================================================")
args, _ = parser.parse_known_args()
if __name__ == "__main__":
num_class = 41
assessment_method = args.assessment_method.lower()
use_crf = args.use_crf
if assessment_method == "accuracy":
callback = Accuracy()
elif assessment_method == "bf1":
callback = F1((use_crf.lower() == "true"), num_class)
elif assessment_method == "mf1":
callback = F1((use_crf.lower() == "true"), num_labels=num_class, mode="MultiLabel")
elif assessment_method == "mcc":
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'