def infer():
# Config Loader
test_args = ConfigSection()
ConfigLoader().load_config(cfgfile, {"POS_test": test_args})
# fetch dictionary size and number of labels from pickle files
word2index = load_pickle(pickle_path, "word2id.pkl")
test_args["vocab_size"] = len(word2index)
index2label = load_pickle(pickle_path, "label2id.pkl")
test_args["num_classes"] = len(index2label)
# Define the same model
model = AdvSeqLabel(test_args)
try:
ModelLoader.load_pytorch(model, "./save/trained_model.pkl")
print('model loaded!')
except Exception as e:
print('cannot load model!')
raise
# Data Loader
infer_data = SeqLabelDataSet(load_func=BaseLoader.load_lines)
infer_data.load(data_infer_path,
vocabs={"word_vocab": word2index},
infer=True)
print('data loaded')
# Inference interface
infer = SeqLabelInfer(pickle_path)
results = infer.predict(model, infer_data)
print(results)
print("Inference finished!")
def predict():
# Config Loader
test_args = ConfigSection()
ConfigLoader().load_config(cfgfile, {"POS_test": test_args})
# fetch dictionary size and number of labels from pickle files
word2index = load_pickle(pickle_path, "word2id.pkl")
test_args["vocab_size"] = len(word2index)
index2label = load_pickle(pickle_path, "label2id.pkl")
test_args["num_classes"] = len(index2label)
# load dev data
dev_data = load_pickle(pickle_path, "data_dev.pkl")
# Define the same model
model = AdvSeqLabel(test_args)
# Dump trained parameters into the model
ModelLoader.load_pytorch(model, "./save/trained_model.pkl")
print("model loaded!")
# Tester
test_args["evaluator"] = SeqLabelEvaluator()
tester = SeqLabelTester(**test_args.data)
# Start testing
tester.test(model, dev_data)
import torch
from fastNLP import Trainer
from fastNLP import Tester
from fastNLP import CrossEntropyLoss
from fastNLP import Adam
from fastNLP import AccuracyMetric
from fastNLP.io.config_io import ConfigSection, ConfigLoader
import fastNLP.core.utils as util
from model import myESIM
args = ConfigSection()
ConfigLoader().load_config("../data/config.json", {"train": args})
# 加载训练、验证数据集和词向量
print("\t* Loading train data...")
train_data = util.load_pickle(os.path.normpath(args["data_dir"]),
args["train_file"])
print("\t* Loading dev data...")
dev_data = util.load_pickle(os.path.normpath(args["data_dir"]),
args["dev_file"])
print("\t* Loading word embeddings...")
embeddings = util.load_pickle(os.path.normpath(args["data_dir"]),
args["embeddings_file"])
embeddings = torch.Tensor(embeddings)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = myESIM(embeddings.shape[0],
embeddings.shape[1],
300,
embeddings=embeddings,
dropout=0.5,
num_classes=3,
device=device).to(device)