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1. 修复Trainer check_code中检查evaluate时使用train_data的bug
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@ -138,20 +138,30 @@ class Trainer(object):
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开始训练过程。主要有以下几个步骤::
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对于每次循环
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1. 使用Batch从DataSet中按批取出数据，并自动对DataSet中dtype为float, int的fields进行padding。并转换为Tensor。
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for epoch in range(num_epochs):
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# 使用Batch从DataSet中按批取出数据，并自动对DataSet中dtype为(float, int)的fields进行padding。并转换为Tensor。
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非float，int类型的参数将不会被转换为Tensor，且不进行padding。
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for batch_x, batch_y in Batch(DataSet)
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# batch_x中为设置为input的field
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# batch_y中为设置为target的field
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2. 将batch_x的数据送入到model.forward函数中，并获取结果
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3. 将batch_y与model.forward的结果一并送入loss中计算loss
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# batch_x是一个dict, 被设为input的field会出现在这个dict中，
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key为DataSet中的field_name, value为该field的value
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# batch_y也是一个dict，被设为target的field会出现在这个dict中，
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key为DataSet中的field_name, value为该field的value
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2. 将batch_x的数据送入到model.forward函数中，并获取结果。这里我们就是通过匹配batch_x中的key与forward函数的形
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参完成参数传递。例如，
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forward(self, x, seq_lens) # fastNLP会在batch_x中找到key为"x"的value传递给x，key为"seq_lens"的
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value传递给seq_lens。若在batch_x中没有找到所有必须要传递的参数，就会报错。如果forward存在默认参数
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而且默认参数这个key没有在batch_x中，则使用默认参数。
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3. 将batch_y与model.forward的结果一并送入loss中计算loss。loss计算时一般都涉及到pred与target。但是在不同情况
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中，可能pred称为output或prediction, target称为y或label。fastNLP通过初始化loss时传入的映射找到pred或
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target。比如在初始化Trainer时初始化loss为CrossEntropyLoss(pred='output', target='y'), 那么fastNLP计
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算loss时，就会使用"output"在batch_y与forward的结果中找到pred;使用"y"在batch_y与forward的结果中找target
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, 并完成loss的计算。
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4. 获取到loss之后，进行反向求导并更新梯度
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如果测试集不为空
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根据metrics进行evaluation，并根据是否提供了save_path判断是否存储模型
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根据需要适时进行验证机测试
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根据metrics进行evaluation，并根据是否提供了save_path判断是否存储模型
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:param bool load_best_model: 该参数只有在初始化提供了dev_data的情况下有效，如果True, trainer将在返回之前重新加载dev表现最好的
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模型参数。
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:param bool load_best_model: 该参数只有在初始化提供了dev_data的情况下有效，如果True, trainer将在返回之前重新加载dev表现
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最好的模型参数。
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:return results: 返回一个字典类型的数据, 内含以下内容::
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seconds: float, 表示训练时长
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@ -196,8 +206,11 @@ class Trainer(object):
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results['best_step'] = self.best_dev_step
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if load_best_model:
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model_name = "best_" + "_".join([self.model.__class__.__name__, self.metric_key, self.start_time])
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# self._load_model(self.model, model_name)
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print("Reloaded the best model.")
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load_succeed = self._load_model(self.model, model_name)
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if load_succeed:
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print("Reloaded the best model.")
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else:
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print("Fail to reload best model.")
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finally:
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self._summary_writer.close()
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del self._summary_writer
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@ -208,7 +221,7 @@ class Trainer(object):
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def _tqdm_train(self):
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self.step = 0
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data_iterator = Batch(self.train_data, batch_size=self.batch_size, sampler=self.sampler,
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as_numpy=False)
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as_numpy=False)
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total_steps = data_iterator.num_batches*self.n_epochs
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with tqdm(total=total_steps, postfix='loss:{0:<6.5f}', leave=False, dynamic_ncols=True) as pbar:
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avg_loss = 0
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@ -297,7 +310,8 @@ class Trainer(object):
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if self.save_path is not None:
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self._save_model(self.model,
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"best_" + "_".join([self.model.__class__.__name__, self.metric_key, self.start_time]))
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else:
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self._best_model_states = {name:param.cpu().clone() for name, param in self.model.named_parameters()}
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self.best_dev_perf = res
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self.best_dev_epoch = epoch
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self.best_dev_step = step
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@ -356,7 +370,7 @@ class Trainer(object):
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torch.save(model, model_name)
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def _load_model(self, model, model_name, only_param=False):
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# TODO: 这个是不是有问题？
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# 返回bool值指示是否成功reload模型
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if self.save_path is not None:
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model_path = os.path.join(self.save_path, model_name)
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if only_param:
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@ -364,6 +378,11 @@ class Trainer(object):
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else:
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states = torch.load(model_path).state_dict()
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model.load_state_dict(states)
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elif hasattr(self, "_best_model_states"):
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model.load_state_dict(self._best_model_states)
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else:
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return False
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return True
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def _better_eval_result(self, metrics):
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"""Check if the current epoch yields better validation results.
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@ -469,7 +488,7 @@ def _check_code(dataset, model, losser, metrics, batch_size=DEFAULT_CHECK_BATCH_
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break
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if dev_data is not None:
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tester = Tester(data=dataset[:batch_size * DEFAULT_CHECK_NUM_BATCH], model=model, metrics=metrics,
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tester = Tester(data=dev_data[:batch_size * DEFAULT_CHECK_NUM_BATCH], model=model, metrics=metrics,
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batch_size=batch_size, verbose=-1)
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evaluate_results = tester.test()
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_check_eval_results(metrics=evaluate_results, metric_key=metric_key, metric_list=metrics)
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@ -448,4 +448,33 @@ class BMES2OutputProcessor(Processor):
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words.append(''.join(chars[start_idx:idx+1]))
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start_idx = idx + 1
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return ' '.join(words)
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dataset.apply(func=inner_proc, new_field_name=self.new_added_field_name)
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dataset.apply(func=inner_proc, new_field_name=self.new_added_field_name)
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class InputTargetProcessor(Processor):
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def __init__(self, input_fields, target_fields):
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"""
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对DataSet操作，将input_fields中的field设置为input，target_fields的中field设置为target
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:param input_fields: List[str], 设置为input_field的field_name。如果为None，则不将任何field设置为target。
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:param target_fields: List[str], 设置为target_field的field_name。 如果为None，则不将任何field设置为target。
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"""
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super(InputTargetProcessor, self).__init__(None, None)
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if input_fields is not None and not isinstance(input_fields, list):
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raise TypeError("input_fields should be List[str], not {}.".format(type(input_fields)))
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else:
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self.input_fields = input_fields
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if target_fields is not None and not isinstance(target_fields, list):
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raise TypeError("target_fiels should be List[str], not{}.".format(type(target_fields)))
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else:
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self.target_fields = target_fields
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def process(self, dataset):
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assert isinstance(dataset, DataSet), "Only Dataset class is allowed, not {}.".format(type(dataset))
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if self.input_fields is not None:
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for field in self.input_fields:
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dataset.set_input(field)
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if self.target_fields is not None:
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for field in self.target_fields:
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dataset.set_target(field)
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@ -6,7 +6,7 @@ from reproduction.chinese_word_segment.process.cws_processor import CWSCharSegPr
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from reproduction.chinese_word_segment.process.cws_processor import CWSBMESTagProcessor
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from reproduction.chinese_word_segment.process.cws_processor import Pre2Post2BigramProcessor
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from reproduction.chinese_word_segment.process.cws_processor import VocabIndexerProcessor
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from reproduction.chinese_word_segment.process.cws_processor import InputTargetProcessor
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from reproduction.chinese_word_segment.cws_io.cws_reader import ConllCWSReader
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from reproduction.chinese_word_segment.models.cws_model import CWSBiLSTMCRF
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@ -39,6 +39,8 @@ bigram_vocab_proc = VocabIndexerProcessor('bigrams_lst', new_added_filed_name='b
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seq_len_proc = SeqLenProcessor('chars')
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input_target_proc = InputTargetProcessor(input_fields=['chars', 'bigrams', 'seq_lens', "target"],
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target_fields=['target', 'seq_lens'])
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# 2. 使用processor
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fs2hs_proc(tr_dataset)
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@ -61,14 +63,11 @@ char_vocab_proc(dev_dataset)
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bigram_vocab_proc(dev_dataset)
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seq_len_proc(dev_dataset)
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dev_dataset.set_input('chars', 'bigrams', 'target')
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tr_dataset.set_input('chars', 'bigrams', 'target')
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dev_dataset.set_target('seq_lens')
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tr_dataset.set_target('seq_lens')
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input_target_proc(tr_dataset)
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input_target_proc(dev_dataset)
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print("Finish preparing data.")
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# 3. 得到数据集可以用于训练了
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# TODO pretrain的embedding是怎么解决的？
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@ -86,80 +85,18 @@ cws_model = CWSBiLSTMCRF(char_vocab_proc.get_vocab_size(), embed_dim=100,
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cws_model.cuda()
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num_epochs = 5
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optimizer = optim.Adagrad(cws_model.parameters(), lr=0.02)
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optimizer = optim.Adagrad(cws_model.parameters(), lr=0.005)
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from fastNLP.core.trainer import Trainer
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from fastNLP.core.sampler import BucketSampler
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from fastNLP.core.metrics import BMESF1PreRecMetric
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metric = BMESF1PreRecMetric(target='tags')
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trainer = Trainer(train_data=tr_dataset, model=cws_model, loss=None, metrics=metric, n_epochs=3,
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trainer = Trainer(train_data=tr_dataset, model=cws_model, loss=None, metrics=metric, n_epochs=num_epochs,
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batch_size=32, print_every=50, validate_every=-1, dev_data=dev_dataset, save_path=None,
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optimizer=optimizer, check_code_level=0, metric_key='f', sampler=BucketSampler(), use_tqdm=True)
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trainer.train()
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exit(0)
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#
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# print_every = 50
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# batch_size = 32
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# tr_batcher = Batch(tr_dataset, batch_size, BucketSampler(batch_size=batch_size), use_cuda=False)
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# dev_batcher = Batch(dev_dataset, batch_size, SequentialSampler(), use_cuda=False)
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# num_batch_per_epoch = len(tr_dataset) // batch_size
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# best_f1 = 0
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# best_epoch = 0
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# for num_epoch in range(num_epochs):
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# print('X' * 10 + ' Epoch: {}/{} '.format(num_epoch + 1, num_epochs) + 'X' * 10)
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# sys.stdout.flush()
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# avg_loss = 0
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# with tqdm(total=num_batch_per_epoch, leave=True) as pbar:
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# pbar.set_description_str('Epoch:%d' % (num_epoch + 1))
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# cws_model.train()
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# for batch_idx, (batch_x, batch_y) in enumerate(tr_batcher, 1):
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# optimizer.zero_grad()
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#
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# tags = batch_y['tags'].long()
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# pred_dict = cws_model(**batch_x, tags=tags) # B x L x tag_size
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#
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# seq_lens = pred_dict['seq_lens']
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# masks = seq_lens_to_mask(seq_lens).float()
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# tags = tags.to(seq_lens.device)
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#
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# loss = pred_dict['loss']
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#
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# # loss = torch.sum(loss_fn(pred_dict['pred_probs'].view(-1, tag_size),
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# # tags.view(-1)) * masks.view(-1)) / torch.sum(masks)
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# # loss = torch.mean(F.cross_entropy(probs.view(-1, 2), tags.view(-1)) * masks.float())
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#
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# avg_loss += loss.item()
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#
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# loss.backward()
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# for group in optimizer.param_groups:
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# for param in group['params']:
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# param.grad.clamp_(-5, 5)
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#
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# optimizer.step()
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#
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# if batch_idx % print_every == 0:
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# pbar.set_postfix_str('batch=%d, avg_loss=%.5f' % (batch_idx, avg_loss / print_every))
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# avg_loss = 0
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# pbar.update(print_every)
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# tr_batcher = Batch(tr_dataset, batch_size, BucketSampler(batch_size=batch_size), use_cuda=False)
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# # 验证集
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# pre, rec, f1 = calculate_pre_rec_f1(cws_model, dev_batcher, type='bmes')
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# print("f1:{:.2f}, pre:{:.2f}, rec:{:.2f}".format(f1*100,
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# pre*100,
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# rec*100))
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# if best_f1<f1:
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# best_f1 = f1
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# # 缓存最佳的parameter，可能之后会用于保存
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# best_state_dict = {
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# key:value.clone() for key, value in
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# cws_model.state_dict().items()
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# }
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# best_epoch = num_epoch
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#
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# cws_model.load_state_dict(best_state_dict)
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# 4. 组装需要存下的内容
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pp = Pipeline()
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@ -171,6 +108,7 @@ pp.add_processor(bigram_proc)
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pp.add_processor(char_vocab_proc)
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pp.add_processor(bigram_vocab_proc)
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pp.add_processor(seq_len_proc)
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pp.add_processor(input_target_proc)
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# te_filename = '/hdd/fudanNLP/CWS/CWS_semiCRF/all_data/{}/middle_files/{}_test.txt'.format(ds_name, ds_name)
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te_filename = '/home/hyan/ctb3/test.conllx'
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@ -181,6 +119,7 @@ from fastNLP.core.tester import Tester
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tester = Tester(data=te_dataset, model=cws_model, metrics=metric, batch_size=64, use_cuda=False,
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verbose=1)
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tester.test()
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#
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# batch_size = 64
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# te_batcher = Batch(te_dataset, batch_size, SequentialSampler(), use_cuda=False)
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@ -193,7 +132,7 @@ tester = Tester(data=te_dataset, model=cws_model, metrics=metric, batch_size=64,
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test_context_dict = {'pipeline': pp,
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'model': cws_model}
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torch.save(test_context_dict, 'models/test_context_crf.pkl')
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# torch.save(test_context_dict, 'models/test_context_crf.pkl')
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# 5. dev的pp
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