- update predictor.py to remove unused methods - update model_loader.py & model_saver.py to support entire model saving & loading - update pos tagging training scripttags/v0.2.0
@@ -0,0 +1,44 @@ | |||||
import pickle | |||||
import numpy as np | |||||
from fastNLP.core.dataset import DataSet | |||||
from fastNLP.loader.model_loader import ModelLoader | |||||
from fastNLP.core.predictor import Predictor | |||||
class POS_tagger: | |||||
def __init__(self): | |||||
pass | |||||
def predict(self, query): | |||||
""" | |||||
:param query: List[str] | |||||
:return answer: List[str] | |||||
""" | |||||
# TODO: 根据query 构建DataSet | |||||
pos_dataset = DataSet() | |||||
pos_dataset["text_field"] = np.array(query) | |||||
# 加载pipeline和model | |||||
pipeline = self.load_pipeline("./xxxx") | |||||
# 将DataSet作为参数运行 pipeline | |||||
pos_dataset = pipeline(pos_dataset) | |||||
# 加载模型 | |||||
model = ModelLoader().load_pytorch("./xxx") | |||||
# 调 predictor | |||||
predictor = Predictor() | |||||
output = predictor.predict(model, pos_dataset) | |||||
# TODO: 转成最终输出 | |||||
return None | |||||
@staticmethod | |||||
def load_pipeline(path): | |||||
with open(path, "r") as fp: | |||||
pipeline = pickle.load(fp) | |||||
return pipeline |
@@ -2,9 +2,7 @@ import numpy as np | |||||
import torch | import torch | ||||
from fastNLP.core.batch import Batch | from fastNLP.core.batch import Batch | ||||
from fastNLP.core.preprocess import load_pickle | |||||
from fastNLP.core.sampler import SequentialSampler | from fastNLP.core.sampler import SequentialSampler | ||||
from fastNLP.loader.dataset_loader import convert_seq2seq_dataset, convert_seq2tag_dataset, convert_seq_dataset | |||||
class Predictor(object): | class Predictor(object): | ||||
@@ -16,19 +14,9 @@ class Predictor(object): | |||||
Currently, Predictor does not support GPU. | Currently, Predictor does not support GPU. | ||||
""" | """ | ||||
def __init__(self, pickle_path, post_processor): | |||||
""" | |||||
:param pickle_path: str, the path to the pickle files. | |||||
:param post_processor: a function or callable object, that takes list of batch outputs as input | |||||
""" | |||||
def __init__(self): | |||||
self.batch_size = 1 | self.batch_size = 1 | ||||
self.batch_output = [] | self.batch_output = [] | ||||
self.pickle_path = pickle_path | |||||
self._post_processor = post_processor | |||||
self.label_vocab = load_pickle(self.pickle_path, "label2id.pkl") | |||||
self.word_vocab = load_pickle(self.pickle_path, "word2id.pkl") | |||||
def predict(self, network, data): | def predict(self, network, data): | ||||
"""Perform inference using the trained model. | """Perform inference using the trained model. | ||||
@@ -37,9 +25,6 @@ class Predictor(object): | |||||
:param data: a DataSet object. | :param data: a DataSet object. | ||||
:return: list of list of strings, [num_examples, tag_seq_length] | :return: list of list of strings, [num_examples, tag_seq_length] | ||||
""" | """ | ||||
# transform strings into DataSet object | |||||
# data = self.prepare_input(data) | |||||
# turn on the testing mode; clean up the history | # turn on the testing mode; clean up the history | ||||
self.mode(network, test=True) | self.mode(network, test=True) | ||||
batch_output = [] | batch_output = [] | ||||
@@ -51,7 +36,7 @@ class Predictor(object): | |||||
prediction = self.data_forward(network, batch_x) | prediction = self.data_forward(network, batch_x) | ||||
batch_output.append(prediction) | batch_output.append(prediction) | ||||
return self._post_processor(batch_output, self.label_vocab) | |||||
return batch_output | |||||
def mode(self, network, test=True): | def mode(self, network, test=True): | ||||
if test: | if test: | ||||
@@ -64,37 +49,19 @@ class Predictor(object): | |||||
y = network(**x) | y = network(**x) | ||||
return y | return y | ||||
def prepare_input(self, data): | |||||
"""Transform two-level list of strings into an DataSet object. | |||||
In the training pipeline, this is done by Preprocessor. But in inference time, we do not call Preprocessor. | |||||
:param data: list of list of strings. | |||||
:: | |||||
[ | |||||
[word_11, word_12, ...], | |||||
[word_21, word_22, ...], | |||||
... | |||||
] | |||||
:return data_set: a DataSet instance. | |||||
""" | |||||
assert isinstance(data, list) | |||||
data = convert_seq_dataset(data) | |||||
data.index_field("word_seq", self.word_vocab) | |||||
class SeqLabelInfer(Predictor): | class SeqLabelInfer(Predictor): | ||||
def __init__(self, pickle_path): | def __init__(self, pickle_path): | ||||
print( | print( | ||||
"[FastNLP Warning] SeqLabelInfer will be deprecated. Please use Predictor directly.") | "[FastNLP Warning] SeqLabelInfer will be deprecated. Please use Predictor directly.") | ||||
super(SeqLabelInfer, self).__init__(pickle_path, seq_label_post_processor) | |||||
super(SeqLabelInfer, self).__init__() | |||||
class ClassificationInfer(Predictor): | class ClassificationInfer(Predictor): | ||||
def __init__(self, pickle_path): | def __init__(self, pickle_path): | ||||
print( | print( | ||||
"[FastNLP Warning] ClassificationInfer will be deprecated. Please use Predictor directly.") | "[FastNLP Warning] ClassificationInfer will be deprecated. Please use Predictor directly.") | ||||
super(ClassificationInfer, self).__init__(pickle_path, text_classify_post_processor) | |||||
super(ClassificationInfer, self).__init__() | |||||
def seq_label_post_processor(batch_outputs, label_vocab): | def seq_label_post_processor(batch_outputs, label_vocab): | ||||
@@ -8,8 +8,8 @@ class ModelLoader(BaseLoader): | |||||
Loader for models. | Loader for models. | ||||
""" | """ | ||||
def __init__(self, data_path): | |||||
super(ModelLoader, self).__init__(data_path) | |||||
def __init__(self): | |||||
super(ModelLoader, self).__init__() | |||||
@staticmethod | @staticmethod | ||||
def load_pytorch(empty_model, model_path): | def load_pytorch(empty_model, model_path): | ||||
@@ -19,3 +19,10 @@ class ModelLoader(BaseLoader): | |||||
:param model_path: str, the path to the saved model. | :param model_path: str, the path to the saved model. | ||||
""" | """ | ||||
empty_model.load_state_dict(torch.load(model_path)) | empty_model.load_state_dict(torch.load(model_path)) | ||||
@staticmethod | |||||
def load_pytorch(model_path): | |||||
"""Load the entire model. | |||||
""" | |||||
return torch.load(model_path) |
@@ -127,7 +127,8 @@ class AdvSeqLabel(SeqLabeling): | |||||
:param word_seq: LongTensor, [batch_size, mex_len] | :param word_seq: LongTensor, [batch_size, mex_len] | ||||
:param word_seq_origin_len: list of int. | :param word_seq_origin_len: list of int. | ||||
:param truth: LongTensor, [batch_size, max_len] | :param truth: LongTensor, [batch_size, max_len] | ||||
:return y: | |||||
:return y: If truth is None, return list of [decode path(list)]. Used in testing and predicting. | |||||
If truth is not None, return loss, a scalar. Used in training. | |||||
""" | """ | ||||
self.mask = self.make_mask(word_seq, word_seq_origin_len) | self.mask = self.make_mask(word_seq, word_seq_origin_len) | ||||
@@ -15,10 +15,14 @@ class ModelSaver(object): | |||||
""" | """ | ||||
self.save_path = save_path | self.save_path = save_path | ||||
def save_pytorch(self, model): | |||||
def save_pytorch(self, model, param_only=True): | |||||
"""Save a pytorch model into .pkl file. | """Save a pytorch model into .pkl file. | ||||
:param model: a PyTorch model | :param model: a PyTorch model | ||||
:param param_only: bool, whether only to save the model parameters or the entire model. | |||||
""" | """ | ||||
torch.save(model.state_dict(), self.save_path) | |||||
if param_only is True: | |||||
torch.save(model.state_dict(), self.save_path) | |||||
else: | |||||
torch.save(model, self.save_path) |
@@ -59,42 +59,37 @@ def infer(): | |||||
print("Inference finished!") | print("Inference finished!") | ||||
def train(): | |||||
# Config Loader | |||||
train_args = ConfigSection() | |||||
test_args = ConfigSection() | |||||
ConfigLoader("good_name").load_config(cfgfile, {"train": train_args, "test": test_args}) | |||||
def train(): | |||||
# load config | |||||
trainer_args = ConfigSection() | |||||
model_args = ConfigSection() | |||||
ConfigLoader().load_config(cfgfile, {"train": train_args, "test": test_args}) | |||||
# Data Loader | # Data Loader | ||||
loader = PeopleDailyCorpusLoader() | loader = PeopleDailyCorpusLoader() | ||||
train_data, _ = loader.load() | train_data, _ = loader.load() | ||||
# Preprocessor | |||||
preprocessor = SeqLabelPreprocess() | |||||
data_train, data_dev = preprocessor.run(train_data, pickle_path=pickle_path, train_dev_split=0.3) | |||||
train_args["vocab_size"] = preprocessor.vocab_size | |||||
train_args["num_classes"] = preprocessor.num_classes | |||||
# TODO: define processors | |||||
# define pipeline | |||||
pp = Pipeline() | |||||
# TODO: pp.add_processor() | |||||
# Trainer | |||||
trainer = SeqLabelTrainer(**train_args.data) | |||||
# run the pipeline, get data_set | |||||
train_data = pp(train_data) | |||||
# Model | |||||
# define a model | |||||
model = AdvSeqLabel(train_args) | model = AdvSeqLabel(train_args) | ||||
try: | |||||
ModelLoader.load_pytorch(model, "./save/saved_model.pkl") | |||||
print('model parameter loaded!') | |||||
except Exception as e: | |||||
print("No saved model. Continue.") | |||||
pass | |||||
# Start training | |||||
# call trainer to train | |||||
trainer = SeqLabelTrainer(train_args) | |||||
trainer.train(model, data_train, data_dev) | trainer.train(model, data_train, data_dev) | ||||
print("Training finished!") | |||||
# Saver | |||||
saver = ModelSaver("./save/saved_model.pkl") | |||||
saver.save_pytorch(model) | |||||
print("Model saved!") | |||||
# save model | |||||
ModelSaver("./saved_model.pkl").save_pytorch(model, param_only=False) | |||||
# TODO:save pipeline | |||||
def test(): | def test(): | ||||