修复了部分bug

fastNLP/core/callbacks/callback_manager.py

@@ -50,20 +50,20 @@ def prepare_callbacks(callbacks, progress_bar):
                 raise TypeError(f"callbacks must be of Callback type, instead of `{type(_callback)}`")
         _callbacks += callbacks
 
-    has_no_progress = False
+    has_no_progress = True
     for _callback in _callbacks:
         if isinstance(_callback, ProgressCallback):
-            has_no_progress = True
-    if not has_no_progress:
+            has_no_progress = False
+    if has_no_progress and progress_bar is not None:
         callback = choose_progress_callback(progress_bar)
         if callback is not None:
             _callbacks.append(callback)
-    elif progress_bar is not None and progress_bar != 'auto':
-        logger.warning(f"Since you have passed in ProgressBar callback, progress_bar will be ignored.")
+            has_no_progress = False
+    elif has_no_progress is False and progress_bar not in ('auto', None):
+        logger.rank_zero_warning(f"Since you have passed in ProgressCallback, progress_bar={progress_bar} will be ignored.")
 
-    if has_no_progress and progress_bar is None:
-        rank_zero_call(logger.warning)("No progress bar is provided, there will have no information output "
-                                       "during training.")
+    if has_no_progress:
+        logger.rank_zero_warning("No progress bar is provided, there will have no progress output during training.")
 
     return _callbacks
 

fastNLP/core/callbacks/load_best_model_callback.py

@@ -87,17 +87,20 @@ class LoadBestModelCallback(HasMonitorCallback):
                     trainer.save_model(folder=self.buffer, only_state_dict=self.only_state_dict)
 
     def on_train_end(self, trainer):
-        logger.info(f"Loading best model with {self.monitor_name}: {self.monitor_value}...")
-        if self.real_save_folder:
-            trainer.load_model(folder=self.real_save_folder, only_state_dict=self.only_state_dict,
-                               model_load_fn=self.model_load_fn)
-        else:
-            self.buffer.seek(0)
-            trainer.load_model(folder=self.buffer, only_state_dict=self.only_state_dict)
-        if self.delete_after_after:
-            trainer.driver.barrier()
-            self._delete_folder()
-            trainer.driver.barrier()
+        if abs(self.monitor_value) != float('inf'):  # 如果是 inf 说明从来没有运行过。
+            if self.real_save_folder:
+                logger.info(f"Loading best model from {self.real_save_folder} with {self.monitor_name}: {self.monitor_value}...")
+                trainer.load_model(folder=self.real_save_folder, only_state_dict=self.only_state_dict,
+                                   model_load_fn=self.model_load_fn)
+            else:
+                logger.info(
+                    f"Loading best model from buffer with {self.monitor_name}: {self.monitor_value}...")
+                self.buffer.seek(0)
+                trainer.load_model(folder=self.buffer, only_state_dict=self.only_state_dict)
+            if self.delete_after_after:
+                trainer.driver.barrier()
+                self._delete_folder()
+                trainer.driver.barrier()
 
     def _delete_folder(self):
         if self.real_save_folder:

fastNLP/core/collators/padders/paddle_padder.py

@@ -138,8 +138,6 @@ class PaddleTensorPadder(Padder):
 
         shapes = [field.shape for field in batch_field]
         max_shape = [len(batch_field)] + [max(*_) for _ in zip(*shapes)]
-        if isinstance(dtype, np.dtype):
-            print(dtype)
         tensor = paddle.full(max_shape, fill_value=pad_val, dtype=dtype)
         for i, field in enumerate(batch_field):
             slices = (i, ) + tuple(slice(0, s) for s in shapes[i])

fastNLP/core/dataloaders/torch_dataloader/fdl.py

@@ -4,11 +4,11 @@ __all__ = [
 ]
 
 from typing import Optional, Callable, Sequence, Union, Tuple, Dict, Mapping, List
+import inspect
 
 from fastNLP.core.dataset import DataSet
 from fastNLP.core.collators import Collator
 from fastNLP.core.dataloaders.utils import indice_collate_wrapper
-from fastNLP.io.data_bundle import DataBundle
 from fastNLP.envs.imports import _NEED_IMPORT_TORCH
 from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleSampler, UnrepeatedSampler, RandomSampler
 
@@ -79,35 +79,30 @@ class TorchDataLoader(DataLoader):
         if sampler is None and batch_sampler is None:
             sampler = RandomSampler(dataset, shuffle=shuffle)
             shuffle=False
-
-        super().__init__(dataset=dataset, batch_size=batch_size, shuffle=shuffle, sampler=sampler,
-                         batch_sampler=batch_sampler, num_workers=num_workers, collate_fn=None,
-                         pin_memory=pin_memory, drop_last=drop_last, timeout=timeout, worker_init_fn=worker_init_fn,
-                         multiprocessing_context=multiprocessing_context, generator=generator,
-                         prefetch_factor=prefetch_factor,
-                         persistent_workers=persistent_workers)
         if isinstance(collate_fn, str):
             if collate_fn == 'auto':
                 if isinstance(dataset.dataset, DataSet):  # 使用了 fastnlp dataset
-                    self._collate_fn = dataset.dataset.collator
-                    self._collate_fn.set_backend(backend="torch")
+                    collate_fn = dataset.dataset.collator
+                    collate_fn.set_backend(backend="torch")
                 else:
-                    self._collate_fn = Collator(backend="torch")
+                    collate_fn = Collator(backend="torch")
             else:
                 raise ValueError(f"collate_fn: {collate_fn} must be 'auto'")
-        elif isinstance(collate_fn, Callable):
-            if collate_fn is not default_collate:
-                self._collate_fn = collate_fn
-        else:
-            self._collate_fn = default_collate
 
-        self.cur_indices_batch = None
+        super().__init__(dataset=dataset, batch_size=batch_size, shuffle=shuffle, sampler=sampler,
+                         batch_sampler=batch_sampler, num_workers=num_workers, collate_fn=collate_fn,
+                         pin_memory=pin_memory, drop_last=drop_last, timeout=timeout, worker_init_fn=worker_init_fn,
+                         multiprocessing_context=multiprocessing_context, generator=generator,
+                         prefetch_factor=prefetch_factor,
+                         persistent_workers=persistent_workers)
+
+        self.cur_batch_indices = None
 
     def __iter__(self):
         # 如果没有auto_collator 也没有自定义collate_fn， 那么此时采用dataloader自带的collate_fn， 将数据打包即可。
         # if len(self._collate_fn.get_collators()) == 0:
         #     self._collate_fn.add_collator(self.collate_fn)
-        self.collate_fn = indice_collate_wrapper(self._collate_fn)
+        self.collate_fn = indice_collate_wrapper(self.collate_fn)
         for indices, data in super().__iter__():
             self.cur_batch_indices = indices
             yield data
@@ -132,12 +127,26 @@ class TorchDataLoader(DataLoader):
             形式，输出将被直接作为结果输出。
         :return: 返回 Collator 自身
         """
-        if isinstance(self._collate_fn, Collator):
-            self._collate_fn.set_pad(field_name=field_name, pad_val=pad_val, dtype=dtype, pad_fn=pad_fn, backend=backend)
-            return self._collate_fn
+        collator = self._get_collator()
+        if isinstance(collator, Collator):
+            collator.set_pad(field_name=field_name, pad_val=pad_val, dtype=dtype, pad_fn=pad_fn, backend=backend)
+            return collator
         else:
             raise ValueError(f"Only when the collate_fn is a fastNLP Collator, set_pad() is allowed.")
 
+    def _get_collator(self):
+        """
+        如果 collate_fn 是 Collator 对象，得到该对象。如果没有的话，返回 None
+
+        :return:
+        """
+        collator = None
+        if hasattr(self.collate_fn, '__wrapped__') and isinstance(self.collate_fn.__wrapped__, Collator):
+            collator = self.collate_fn.__wrapped__
+        elif isinstance(self.collate_fn, Collator):
+            collator = self.collate_fn
+        return collator
+
     def set_ignore(self, *field_names) -> Collator:
         """
         如果有的内容不希望输出，可以在此处进行设置，被设置的 field 将在 batch 的输出中被忽略。
@@ -149,9 +158,10 @@ class TorchDataLoader(DataLoader):
             __getitem__ 返回的是 Sequence 类型的，则可以使用 '_0', '_1' 表示序列中第 0 或 1 个元素。
         :return: 返回 Collator 自身
         """
-        if isinstance(self._collate_fn, Collator):
-            self._collate_fn.set_ignore(*field_names)
-            return self._collate_fn
+        collator = self._get_collator()
+        if isinstance(collator, Collator):
+            collator.set_ignore(*field_names)
+            return collator
         else:
             raise ValueError(f"Only when the collate_fn is a fastNLP Collator, set_ignore() is allowed.")
 
@@ -164,7 +174,7 @@ class TorchDataLoader(DataLoader):
         return self.cur_batch_indices
 
 
-def prepare_torch_dataloader(ds_or_db: Union[DataSet, DataBundle, Sequence[DataSet], Mapping[str, DataSet]],
+def prepare_torch_dataloader(ds_or_db: Union[DataSet, Sequence[DataSet], Mapping[str, DataSet]],
                              batch_size: int = 16,
                              shuffle: bool = True, sampler: Union["Sampler[int]", ReproducibleSampler, UnrepeatedSampler] = None,
                              batch_sampler: Union["Sampler[Sequence[int]]", ReproducibleBatchSampler] = None,
@@ -197,7 +207,7 @@ def prepare_torch_dataloader(ds_or_db: Union[DataSet, DataBundle, Sequence[DataS
     :param non_train_sampler: 非 'train' 数据使用的 Sampler, 以及Sequence的第二个以上的ds使用的 Sampler
     :param non_train_batch_size:
     """
-
+    from fastNLP.io import DataBundle
     if isinstance(ds_or_db, DataSet):
         dl = TorchDataLoader(dataset=ds_or_db, batch_size=batch_size,
                              shuffle=shuffle, sampler=sampler, batch_sampler=batch_sampler,
@@ -208,7 +218,7 @@ def prepare_torch_dataloader(ds_or_db: Union[DataSet, DataBundle, Sequence[DataS
                              )
         return dl
 
-    elif isinstance(ds_or_db, DataBundle):
+    elif type(ds_or_db, DataBundle):
         dl_bundle = {}
         for name, ds in ds_or_db.iter_datasets():
             if 'train' in name:

fastNLP/core/dataloaders/utils.py

@@ -5,12 +5,25 @@ def indice_collate_wrapper(func):
     :param func: 需要修饰的函数
     :return:
     """
+    if func.__name__ == '_indice_collate_wrapper':  # 如果已经被包裹过了
+       return func
 
-    def wrapper(tuple_data):
+    def _indice_collate_wrapper(tuple_data):  # 这里不能使用 functools.wraps ，否则会检测不到
         indice, ins_list = [], []
         for idx, ins in tuple_data:
             indice.append(idx)
             ins_list.append(ins)
         return indice, func(ins_list)
+    _indice_collate_wrapper.__wrapped__ = func  # 记录对应的
 
-    return wrapper
+    return _indice_collate_wrapper
+
+
+if __name__ == '__main__':
+    def demo(*args, **kwargs):
+        pass
+
+    d = indice_collate_wrapper(demo)
+
+    print(d.__name__)
+    print(d.__wrapped__)

fastNLP/core/dataset/field.py

@@ -8,6 +8,7 @@ __all__ = [
 
 from collections import Counter
 from typing import Any, Union, List, Callable
+from ..log import logger
 
 import numpy as np
 
@@ -21,7 +22,7 @@ class FieldArray:
         try:
             _content = list(_content)
         except BaseException as e:
-            print(f"Cannot convert content(of type:{type(content)}) into list.")
+            logger.error(f"Cannot convert content(of type:{type(content)}) into list.")
             raise e
         self.name = name
         self.content = _content
@@ -87,7 +88,7 @@ class FieldArray:
             try:
                 new_contents.append(cell.split(sep))
             except Exception as e:
-                print(f"Exception happens when process value in index {index}.")
+                logger.error(f"Exception happens when process value in index {index}.")
                 raise e
         return self._after_process(new_contents, inplace=inplace)
 

fastNLP/core/log/logger.py

@@ -111,7 +111,7 @@ class FastNLPLogger(logging.Logger, metaclass=LoggerSingleton):
 
     def warning_once(self, msg, *args, **kwargs):
         """
-        通过 warning 内容只会 warning 一次
+        相同的 warning 内容只会 warning 一次
 
         :param msg:
         :param args:
@@ -124,6 +124,22 @@ class FastNLPLogger(logging.Logger, metaclass=LoggerSingleton):
                 self._log(WARNING, msg, args, **kwargs)
             self._warning_msgs.add(msg)
 
+    def rank_zero_warning(self, msg, *args, **kwargs):
+        """
+        只在 rank 0 上 warning 。
+
+        :param msg:
+        :param args:
+        :param kwargs:
+        :return:
+        """
+        if os.environ.get(FASTNLP_GLOBAL_RANK, '0') == '0':
+            if msg not in self._warning_msgs:
+                if self.isEnabledFor(WARNING):
+                    # kwargs = self._add_rank_info(kwargs)
+                    self._log(WARNING, msg, args, **kwargs)
+                self._warning_msgs.add(msg)
+
     def warn(self, msg, *args, **kwargs):
         if self.isEnabledFor(WARNING):
             kwargs = self._add_rank_info(kwargs)

fastNLP/core/utils/rich_progress.py

@@ -156,8 +156,9 @@ class FRichProgress(Progress, metaclass=Singleton):
             super().stop_task(task_id)
             super().remove_task(task_id)
             self.refresh()  # 使得bar不残留
-        if len(self._tasks) == 0:
-            super().stop()
+        # 这里需要注释掉的原因是由于，在dataset多次apply的过程中会出现自动换行的问题。以前保留这个的原因应该是由于evaluate结束bar不消失。
+        # if len(self._tasks) == 0:
+        #     self.live.stop()
 
     def start(self) -> None:
         super().start()

fastNLP/core/vocabulary.py

@@ -15,6 +15,7 @@ from functools import wraps
 from fastNLP.core.dataset import DataSet
 from fastNLP.core.utils.utils import Option
 from fastNLP.core.utils.utils import _is_iterable
+from .log import logger
 import io
 
 
@@ -56,7 +57,7 @@ def _check_build_status(func):
         if self.rebuild is False:
             self.rebuild = True
             if self.max_size is not None and len(self.word_count) >= self.max_size:
-                print("[Warning] Vocabulary has reached the max size {} when calling {} method. "
+                logger.warning("Vocabulary has reached the max size {} when calling {} method. "
                             "Adding more words may cause unexpected behaviour of Vocabulary. ".format(
                     self.max_size, func.__name__))
         return func(self, *args, **kwargs)
@@ -322,7 +323,7 @@ class Vocabulary(object):
                     for f_n, n_f_n in zip(field_name, new_field_name):
                         dataset.apply_field(index_instance, field_name=f_n, new_field_name=n_f_n)
                 except Exception as e:
-                    print("When processing the `{}` dataset, the following error occurred.".format(idx))
+                    logger.error("When processing the `{}` dataset, the following error occurred.".format(idx))
                     raise e
             else:
                 raise RuntimeError("Only DataSet type is allowed.")
@@ -378,7 +379,7 @@ class Vocabulary(object):
                 try:
                     dataset.apply(construct_vocab)
                 except BaseException as e:
-                    print("When processing the `{}` dataset, the following error occurred:".format(idx))
+                    logger.error("When processing the `{}` dataset, the following error occurred:".format(idx))
                     raise e
             else:
                 raise TypeError("Only DataSet type is allowed.")

fastNLP/io/data_bundle.py

@@ -10,7 +10,7 @@ from typing import Union, List, Callable
 
 from ..core.dataset import DataSet
 from fastNLP.core.vocabulary import Vocabulary
-# from ..core._logger import _logger
+from fastNLP.core import logger
 
 
 class DataBundle:
@@ -72,7 +72,7 @@ class DataBundle:
         else:
             error_msg = f'DataBundle do NOT have DataSet named {name}. ' \
                         f'It should be one of {self.datasets.keys()}.'
-            print(error_msg)
+            logger.error(error_msg)
             raise KeyError(error_msg)
 
     def delete_dataset(self, name: str):
@@ -97,7 +97,7 @@ class DataBundle:
         else:
             error_msg = f'DataBundle do NOT have Vocabulary named {field_name}. ' \
                         f'It should be one of {self.vocabs.keys()}.'
-            print(error_msg)
+            logger.error(error_msg)
             raise KeyError(error_msg)
 
     def delete_vocab(self, field_name: str):

fastNLP/io/file_reader.py

@@ -117,7 +117,7 @@ def _read_conll(path, encoding='utf-8',sep=None, indexes=None, dropna=True):
                         yield line_idx, res
                     except Exception as e:
                         if dropna:
-                            print('Invalid instance which ends at line: {} has been dropped.'.format(line_idx))
+                            logger.error('Invalid instance which ends at line: {} has been dropped.'.format(line_idx))
                             sample = []
                             continue
                         raise ValueError('Invalid instance which ends at line: {}'.format(line_idx))
@@ -132,5 +132,5 @@ def _read_conll(path, encoding='utf-8',sep=None, indexes=None, dropna=True):
             except Exception as e:
                 if dropna:
                     return
-                print('invalid instance ends at line: {}'.format(line_idx))
+                logger.error('invalid instance ends at line: {}'.format(line_idx))
                 raise e

fastNLP/io/loader/classification.py

@@ -29,6 +29,7 @@ import warnings
 
 from .loader import Loader
 from fastNLP.core.dataset import Instance, DataSet
+from ...core import logger
 
 
 # from ...core._logger import log
@@ -86,7 +87,8 @@ class CLSBaseLoader(Loader):
                     if raw_words:
                         ds.append(Instance(raw_words=raw_words, target=target))
         except Exception as e:
-            print(f'Load file `{path}` failed for `{e}`')
+            logger.error(f'Fail to load `{path}`.')
+            raise e
         return ds