1. Tester has a parameter "print_every_step" to control printing. print_every_step == 0 means NO print. 2. Tester's evaluate return (list of) floats, rather than torch.cuda.tensor 3. Trainer also has a parameter "print_every_step". The same usage. 4. In training, validation steps are not shown. 5. Updates to code comments. 6. fastnlp.py is ready for CWS. test_fastNLP.py works.tags/v0.1.0
@@ -19,13 +19,13 @@ DEFAULT_WORD_TO_INDEX = {DEFAULT_PADDING_LABEL: 0, DEFAULT_UNKNOWN_LABEL: 1, | |||||
def save_pickle(obj, pickle_path, file_name): | def save_pickle(obj, pickle_path, file_name): | ||||
with open(os.path.join(pickle_path, file_name), "wb") as f: | with open(os.path.join(pickle_path, file_name), "wb") as f: | ||||
_pickle.dump(obj, f) | _pickle.dump(obj, f) | ||||
print("{} saved in {}.".format(file_name, pickle_path)) | |||||
print("{} saved in {}".format(file_name, pickle_path)) | |||||
def load_pickle(pickle_path, file_name): | def load_pickle(pickle_path, file_name): | ||||
with open(os.path.join(pickle_path, file_name), "rb") as f: | with open(os.path.join(pickle_path, file_name), "rb") as f: | ||||
obj = _pickle.load(f) | obj = _pickle.load(f) | ||||
print("{} loaded from {}.".format(file_name, pickle_path)) | |||||
print("{} loaded from {}".format(file_name, pickle_path)) | |||||
return obj | return obj | ||||
@@ -98,7 +98,7 @@ class BaseTester(object): | |||||
print_output = "[test step {}] {}".format(step, eval_results) | print_output = "[test step {}] {}".format(step, eval_results) | ||||
logger.info(print_output) | logger.info(print_output) | ||||
if step % self.print_every_step == 0: | |||||
if self.print_every_step > 0 and step % self.print_every_step == 0: | |||||
print(print_output) | print(print_output) | ||||
step += 1 | step += 1 | ||||
@@ -187,7 +187,7 @@ class SeqLabelTester(BaseTester): | |||||
# make sure "results" is in the same device as "truth" | # make sure "results" is in the same device as "truth" | ||||
results = results.to(truth) | results = results.to(truth) | ||||
accuracy = torch.sum(results == truth.view((-1,))).to(torch.float) / results.shape[0] | accuracy = torch.sum(results == truth.view((-1,))).to(torch.float) / results.shape[0] | ||||
return [loss.data, accuracy.data] | |||||
return [float(loss), float(accuracy)] | |||||
def metrics(self): | def metrics(self): | ||||
batch_loss = np.mean([x[0] for x in self.eval_history]) | batch_loss = np.mean([x[0] for x in self.eval_history]) | ||||
@@ -4,7 +4,6 @@ import os | |||||
import time | import time | ||||
from datetime import timedelta | from datetime import timedelta | ||||
import numpy as np | |||||
import torch | import torch | ||||
from fastNLP.core.action import Action | from fastNLP.core.action import Action | ||||
@@ -47,7 +46,7 @@ class BaseTrainer(object): | |||||
Otherwise, error will raise. | Otherwise, error will raise. | ||||
""" | """ | ||||
default_args = {"epochs": 3, "batch_size": 8, "validate": True, "use_cuda": True, "pickle_path": "./save/", | default_args = {"epochs": 3, "batch_size": 8, "validate": True, "use_cuda": True, "pickle_path": "./save/", | ||||
"save_best_dev": True, "model_name": "default_model_name.pkl", | |||||
"save_best_dev": True, "model_name": "default_model_name.pkl", "print_every_step": 1, | |||||
"loss": Loss(None), | "loss": Loss(None), | ||||
"optimizer": Optimizer("Adam", lr=0.001, weight_decay=0) | "optimizer": Optimizer("Adam", lr=0.001, weight_decay=0) | ||||
} | } | ||||
@@ -86,6 +85,7 @@ class BaseTrainer(object): | |||||
self.save_best_dev = default_args["save_best_dev"] | self.save_best_dev = default_args["save_best_dev"] | ||||
self.use_cuda = default_args["use_cuda"] | self.use_cuda = default_args["use_cuda"] | ||||
self.model_name = default_args["model_name"] | self.model_name = default_args["model_name"] | ||||
self.print_every_step = default_args["print_every_step"] | |||||
self._model = None | self._model = None | ||||
self._loss_func = default_args["loss"].get() # return a pytorch loss function or None | self._loss_func = default_args["loss"].get() # return a pytorch loss function or None | ||||
@@ -93,48 +93,35 @@ class BaseTrainer(object): | |||||
self._optimizer_proto = default_args["optimizer"] | self._optimizer_proto = default_args["optimizer"] | ||||
def train(self, network, train_data, dev_data=None): | def train(self, network, train_data, dev_data=None): | ||||
"""General Training Steps | |||||
"""General Training Procedure | |||||
:param network: a model | :param network: a model | ||||
:param train_data: three-level list, the training set. | :param train_data: three-level list, the training set. | ||||
:param dev_data: three-level list, the validation data (optional) | :param dev_data: three-level list, the validation data (optional) | ||||
The method is framework independent. | |||||
Work by calling the following methods: | |||||
- prepare_input | |||||
- mode | |||||
- define_optimizer | |||||
- data_forward | |||||
- get_loss | |||||
- grad_backward | |||||
- update | |||||
Subclasses must implement these methods with a specific framework. | |||||
""" | """ | ||||
# prepare model and data, transfer model to gpu if available | |||||
# transfer model to gpu if available | |||||
if torch.cuda.is_available() and self.use_cuda: | if torch.cuda.is_available() and self.use_cuda: | ||||
self._model = network.cuda() | self._model = network.cuda() | ||||
# self._model is used to access model-specific loss | |||||
else: | else: | ||||
self._model = network | self._model = network | ||||
# define tester over dev data | |||||
# define Tester over dev data | |||||
if self.validate: | if self.validate: | ||||
default_valid_args = {"save_output": True, "validate_in_training": True, "save_dev_input": True, | default_valid_args = {"save_output": True, "validate_in_training": True, "save_dev_input": True, | ||||
"save_loss": True, "batch_size": self.batch_size, "pickle_path": self.pickle_path, | "save_loss": True, "batch_size": self.batch_size, "pickle_path": self.pickle_path, | ||||
"use_cuda": self.use_cuda} | |||||
"use_cuda": self.use_cuda, "print_every_step": 0} | |||||
validator = self._create_validator(default_valid_args) | validator = self._create_validator(default_valid_args) | ||||
logger.info("validator defined as {}".format(str(validator))) | logger.info("validator defined as {}".format(str(validator))) | ||||
# optimizer and loss | |||||
self.define_optimizer() | self.define_optimizer() | ||||
logger.info("optimizer defined as {}".format(str(self._optimizer))) | logger.info("optimizer defined as {}".format(str(self._optimizer))) | ||||
self.define_loss() | self.define_loss() | ||||
logger.info("loss function defined as {}".format(str(self._loss_func))) | logger.info("loss function defined as {}".format(str(self._loss_func))) | ||||
# main training epochs | |||||
n_samples = len(train_data) | |||||
n_batches = n_samples // self.batch_size | |||||
n_print = 1 | |||||
# main training procedure | |||||
start = time.time() | start = time.time() | ||||
logger.info("training epochs started") | logger.info("training epochs started") | ||||
for epoch in range(1, self.n_epochs + 1): | for epoch in range(1, self.n_epochs + 1): | ||||
logger.info("training epoch {}".format(epoch)) | logger.info("training epoch {}".format(epoch)) | ||||
@@ -144,23 +131,30 @@ class BaseTrainer(object): | |||||
data_iterator = iter(Batchifier(RandomSampler(train_data), self.batch_size, drop_last=False)) | data_iterator = iter(Batchifier(RandomSampler(train_data), self.batch_size, drop_last=False)) | ||||
logger.info("prepared data iterator") | logger.info("prepared data iterator") | ||||
self._train_step(data_iterator, network, start=start, n_print=n_print, epoch=epoch) | |||||
# one forward and backward pass | |||||
self._train_step(data_iterator, network, start=start, n_print=self.print_every_step, epoch=epoch) | |||||
# validation | |||||
if self.validate: | if self.validate: | ||||
logger.info("validation started") | logger.info("validation started") | ||||
validator.test(network, dev_data) | validator.test(network, dev_data) | ||||
if self.save_best_dev and self.best_eval_result(validator): | if self.save_best_dev and self.best_eval_result(validator): | ||||
self.save_model(network, self.model_name) | self.save_model(network, self.model_name) | ||||
print("saved better model selected by dev") | |||||
logger.info("saved better model selected by dev") | |||||
print("Saved better model selected by validation.") | |||||
logger.info("Saved better model selected by validation.") | |||||
valid_results = validator.show_matrices() | valid_results = validator.show_matrices() | ||||
print("[epoch {}] {}".format(epoch, valid_results)) | print("[epoch {}] {}".format(epoch, valid_results)) | ||||
logger.info("[epoch {}] {}".format(epoch, valid_results)) | logger.info("[epoch {}] {}".format(epoch, valid_results)) | ||||
def _train_step(self, data_iterator, network, **kwargs): | def _train_step(self, data_iterator, network, **kwargs): | ||||
"""Training process in one epoch.""" | |||||
"""Training process in one epoch. | |||||
kwargs should contain: | |||||
- n_print: int, print training information every n steps. | |||||
- start: time.time(), the starting time of this step. | |||||
- epoch: int, | |||||
""" | |||||
step = 0 | step = 0 | ||||
for batch_x, batch_y in self.make_batch(data_iterator): | for batch_x, batch_y in self.make_batch(data_iterator): | ||||
@@ -287,10 +281,11 @@ class BaseTrainer(object): | |||||
raise NotImplementedError | raise NotImplementedError | ||||
def save_model(self, network, model_name): | def save_model(self, network, model_name): | ||||
""" | |||||
"""Save this model with such a name. | |||||
This method may be called multiple times by Trainer to overwritten a better model. | |||||
:param network: the PyTorch model | :param network: the PyTorch model | ||||
:param model_name: str | :param model_name: str | ||||
model_best_dev.pkl may be overwritten by a better model in future epochs. | |||||
""" | """ | ||||
if model_name[-4:] != ".pkl": | if model_name[-4:] != ".pkl": | ||||
model_name += ".pkl" | model_name += ".pkl" | ||||
@@ -300,33 +295,9 @@ class BaseTrainer(object): | |||||
raise NotImplementedError | raise NotImplementedError | ||||
class ToyTrainer(BaseTrainer): | |||||
""" | |||||
An example to show the definition of Trainer. | |||||
""" | |||||
def __init__(self, training_args): | |||||
super(ToyTrainer, self).__init__(training_args) | |||||
def load_train_data(self, data_path): | |||||
data_train = _pickle.load(open(data_path + "/data_train.pkl", "rb")) | |||||
data_dev = _pickle.load(open(data_path + "/data_train.pkl", "rb")) | |||||
return data_train, data_dev, 0, 1 | |||||
def data_forward(self, network, x): | |||||
return network(x) | |||||
def grad_backward(self, loss): | |||||
self._model.zero_grad() | |||||
loss.backward() | |||||
def get_loss(self, pred, truth): | |||||
return np.mean(np.square(pred - truth)) | |||||
class SeqLabelTrainer(BaseTrainer): | class SeqLabelTrainer(BaseTrainer): | ||||
""" | """ | ||||
Trainer for Sequence Modeling | |||||
Trainer for Sequence Labeling | |||||
""" | """ | ||||
@@ -384,7 +355,7 @@ class SeqLabelTrainer(BaseTrainer): | |||||
class ClassificationTrainer(BaseTrainer): | class ClassificationTrainer(BaseTrainer): | ||||
"""Trainer for classification.""" | |||||
"""Trainer for text classification.""" | |||||
def __init__(self, **train_args): | def __init__(self, **train_args): | ||||
super(ClassificationTrainer, self).__init__(**train_args) | super(ClassificationTrainer, self).__init__(**train_args) | ||||
@@ -1,4 +1,4 @@ | |||||
# from fastNLP.core.predictor import SeqLabelInfer, ClassificationInfer | |||||
from fastNLP.core.predictor import SeqLabelInfer, ClassificationInfer | |||||
from fastNLP.core.preprocess import load_pickle | from fastNLP.core.preprocess import load_pickle | ||||
from fastNLP.loader.config_loader import ConfigLoader, ConfigSection | from fastNLP.loader.config_loader import ConfigLoader, ConfigSection | ||||
from fastNLP.loader.model_loader import ModelLoader | from fastNLP.loader.model_loader import ModelLoader | ||||
@@ -74,9 +74,11 @@ class FastNLP(object): | |||||
self._download(model_name, FastNLP_MODEL_COLLECTION[model_name]["url"]) | self._download(model_name, FastNLP_MODEL_COLLECTION[model_name]["url"]) | ||||
model_class = self._get_model_class(FastNLP_MODEL_COLLECTION[model_name]["class"]) | model_class = self._get_model_class(FastNLP_MODEL_COLLECTION[model_name]["class"]) | ||||
print("Restore model class {}".format(str(model_class))) | |||||
model_args = ConfigSection() | model_args = ConfigSection() | ||||
ConfigLoader.load_config(self.model_dir + config_file, {section_name: model_args}) | ConfigLoader.load_config(self.model_dir + config_file, {section_name: model_args}) | ||||
print("Restore model hyper-parameters {}".format(str(model_args.data))) | |||||
# fetch dictionary size and number of labels from pickle files | # fetch dictionary size and number of labels from pickle files | ||||
word2index = load_pickle(self.model_dir, "word2id.pkl") | word2index = load_pickle(self.model_dir, "word2id.pkl") | ||||
@@ -86,14 +88,16 @@ class FastNLP(object): | |||||
# Construct the model | # Construct the model | ||||
model = model_class(model_args) | model = model_class(model_args) | ||||
print("Model constructed.") | |||||
# To do: framework independent | # To do: framework independent | ||||
ModelLoader.load_pytorch(model, self.model_dir + FastNLP_MODEL_COLLECTION[model_name]["pickle"]) | ModelLoader.load_pytorch(model, self.model_dir + FastNLP_MODEL_COLLECTION[model_name]["pickle"]) | ||||
print("Model weights loaded.") | |||||
self.model = model | self.model = model | ||||
self.infer_type = FastNLP_MODEL_COLLECTION[model_name]["type"] | self.infer_type = FastNLP_MODEL_COLLECTION[model_name]["type"] | ||||
print("Model loaded. ") | |||||
print("Inference ready.") | |||||
def run(self, raw_input): | def run(self, raw_input): | ||||
""" | """ | ||||
@@ -168,10 +172,15 @@ class FastNLP(object): | |||||
:param language: str, one of ('zh', 'en'), Chinese or English. | :param language: str, one of ('zh', 'en'), Chinese or English. | ||||
:return data: list of list of string, each string is a token. | :return data: list of list of string, each string is a token. | ||||
""" | """ | ||||
assert language in ("zh", "en") | |||||
data = [] | data = [] | ||||
delimiter = " " if language is "en" else "" | |||||
for sent in text: | for sent in text: | ||||
tokens = sent.strip().split(delimiter) | |||||
if language == "en": | |||||
tokens = sent.strip().split() | |||||
elif language == "zh": | |||||
tokens = [char for char in sent] | |||||
else: | |||||
raise RuntimeError("Unknown language {}".format(language)) | |||||
data.append(tokens) | data.append(tokens) | ||||
return data | return data | ||||
@@ -6,21 +6,21 @@ sys.path.append(os.path.join(os.path.dirname(__file__), '../..')) | |||||
from fastNLP.loader.config_loader import ConfigLoader, ConfigSection | from fastNLP.loader.config_loader import ConfigLoader, ConfigSection | ||||
from fastNLP.core.trainer import SeqLabelTrainer | from fastNLP.core.trainer import SeqLabelTrainer | ||||
from fastNLP.loader.dataset_loader import TokenizeDatasetLoader, BaseLoader | from fastNLP.loader.dataset_loader import TokenizeDatasetLoader, BaseLoader | ||||
from fastNLP.loader.preprocess import POSPreprocess, load_pickle | |||||
from fastNLP.core.preprocess import SeqLabelPreprocess, load_pickle | |||||
from fastNLP.saver.model_saver import ModelSaver | from fastNLP.saver.model_saver import ModelSaver | ||||
from fastNLP.loader.model_loader import ModelLoader | from fastNLP.loader.model_loader import ModelLoader | ||||
from fastNLP.core.tester import SeqLabelTester | from fastNLP.core.tester import SeqLabelTester | ||||
from fastNLP.models.sequence_modeling import AdvSeqLabel | from fastNLP.models.sequence_modeling import AdvSeqLabel | ||||
from fastNLP.core.inference import SeqLabelInfer | |||||
from fastNLP.core.predictor import SeqLabelInfer | |||||
# not in the file's dir | # not in the file's dir | ||||
if len(os.path.dirname(__file__)) != 0: | if len(os.path.dirname(__file__)) != 0: | ||||
os.chdir(os.path.dirname(__file__)) | os.chdir(os.path.dirname(__file__)) | ||||
datadir = 'icwb2-data' | |||||
cfgfile = 'cws.cfg' | |||||
datadir = "/home/zyfeng/data/" | |||||
cfgfile = './cws.cfg' | |||||
data_name = "pku_training.utf8" | data_name = "pku_training.utf8" | ||||
cws_data_path = os.path.join(datadir, "training/pku_training.utf8") | |||||
cws_data_path = os.path.join(datadir, "pku_training.utf8") | |||||
pickle_path = "save" | pickle_path = "save" | ||||
data_infer_path = os.path.join(datadir, "infer.utf8") | data_infer_path = os.path.join(datadir, "infer.utf8") | ||||
@@ -70,9 +70,10 @@ def train(): | |||||
train_data = loader.load_pku() | train_data = loader.load_pku() | ||||
# Preprocessor | # Preprocessor | ||||
p = POSPreprocess(train_data, pickle_path, train_dev_split=0.3) | |||||
train_args["vocab_size"] = p.vocab_size | |||||
train_args["num_classes"] = p.num_classes | |||||
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 | |||||
# Trainer | # Trainer | ||||
trainer = SeqLabelTrainer(**train_args.data) | trainer = SeqLabelTrainer(**train_args.data) | ||||
@@ -83,10 +84,11 @@ def train(): | |||||
ModelLoader.load_pytorch(model, "./save/saved_model.pkl") | ModelLoader.load_pytorch(model, "./save/saved_model.pkl") | ||||
print('model parameter loaded!') | print('model parameter loaded!') | ||||
except Exception as e: | except Exception as e: | ||||
print("No saved model. Continue.") | |||||
pass | pass | ||||
# Start training | # Start training | ||||
trainer.train(model) | |||||
trainer.train(model, data_train, data_dev) | |||||
print("Training finished!") | print("Training finished!") | ||||
# Saver | # Saver | ||||
@@ -106,6 +108,9 @@ def test(): | |||||
index2label = load_pickle(pickle_path, "id2class.pkl") | index2label = load_pickle(pickle_path, "id2class.pkl") | ||||
test_args["num_classes"] = len(index2label) | test_args["num_classes"] = len(index2label) | ||||
# load dev data | |||||
dev_data = load_pickle(pickle_path, "data_dev.pkl") | |||||
# Define the same model | # Define the same model | ||||
model = AdvSeqLabel(test_args) | model = AdvSeqLabel(test_args) | ||||
@@ -114,10 +119,10 @@ def test(): | |||||
print("model loaded!") | print("model loaded!") | ||||
# Tester | # Tester | ||||
tester = SeqLabelTester(test_args) | |||||
tester = SeqLabelTester(**test_args.data) | |||||
# Start testing | # Start testing | ||||
tester.test(model) | |||||
tester.test(model, dev_data) | |||||
# print test results | # print test results | ||||
print(tester.show_matrices()) | print(tester.show_matrices()) | ||||
@@ -123,7 +123,7 @@ def train_and_test(): | |||||
tester = SeqLabelTester(save_output=False, | tester = SeqLabelTester(save_output=False, | ||||
save_loss=False, | save_loss=False, | ||||
save_best_dev=False, | save_best_dev=False, | ||||
batch_size=8, | |||||
batch_size=4, | |||||
use_cuda=False, | use_cuda=False, | ||||
pickle_path=pickle_path, | pickle_path=pickle_path, | ||||
model_name="seq_label_in_test.pkl", | model_name="seq_label_in_test.pkl", | ||||
@@ -140,4 +140,4 @@ def train_and_test(): | |||||
if __name__ == "__main__": | if __name__ == "__main__": | ||||
train_and_test() | train_and_test() | ||||
infer() | |||||
# infer() |
@@ -3,7 +3,7 @@ import sys | |||||
sys.path.append("..") | sys.path.append("..") | ||||
from fastNLP.fastnlp import FastNLP | from fastNLP.fastnlp import FastNLP | ||||
PATH_TO_CWS_PICKLE_FILES = "/home/zyfeng/data/save/" | |||||
PATH_TO_CWS_PICKLE_FILES = "/home/zyfeng/fastNLP/reproduction/chinese_word_segment/save/" | |||||
def word_seg(): | def word_seg(): | ||||
nlp = FastNLP(model_dir=PATH_TO_CWS_PICKLE_FILES) | nlp = FastNLP(model_dir=PATH_TO_CWS_PICKLE_FILES) | ||||