@@ -21,30 +21,24 @@ from fastNLP.core.dataset import DataSet as FDataSet | |||
class _JittorDataset(Dataset): | |||
""" | |||
对用户传的dataset进行封装,以便JittorDataLoader能够支持使用自定义的dataset使用jittor的dataset | |||
对用户传的dataset进行封装,以便JittorDataLoader能够支持使用自定义的dataset | |||
""" | |||
def __init__(self, dataset) -> None: | |||
super(_JittorDataset, self).__init__() | |||
self.dataset = dataset | |||
self.total_len = len(dataset) | |||
def __getitem__(self, item): | |||
return (item, self.dataset[item]) | |||
def __len__(self) -> int: | |||
return len(self.dataset) | |||
# def __getattr__(self, item): | |||
# # jittor的Dataset没有的方法而用户的dataset存在且实现了getattribute方法,此时用户可以调用 | |||
# try: | |||
# self.dataset.__getattribute__(item) | |||
# except Exception as e: | |||
# raise e | |||
class JittorDataLoader: | |||
""" | |||
提供给使用jittor框架的DataLoader函数,提供了auto_collate的功能, 支持实现了__getitem__和__len__的dataset | |||
提供给使用jittor框架的DataLoader函数,其能够自动检测数据的类型并判断是否能够pad,若能会自动pad数据,默认pad_val=0; | |||
用户可以调用set_pad方法来更改pad_val的值,也可以自定义针对某个field的callate_fn传入到set_field;若用户不想自动pad某个field, | |||
则可以调用set_ignore来忽略对某个field的检测和pad。值得注意的是JittorDataLoader输入dataset只要是实现了__getitem__和__len__方法即可。 | |||
""" | |||
def __init__(self, dataset, batch_size: int = 16, shuffle: bool = True, | |||
@@ -53,23 +47,36 @@ class JittorDataLoader: | |||
collate_fn: Union[None, str, Callable] = "auto") -> None: | |||
""" | |||
:param dataset: 实现__getitem__和__len__的dataset | |||
:param dataset: 实现``__getitem__``和``__len__``的dataset | |||
:param batch_size: 批次大小 | |||
:param shuffle: 是否打乱数据集 | |||
:param drop_last: 是否去掉最后一个不符合batch_size的数据 | |||
:param num_workers: 进程的数量,当num_workers=0时不开启多进程 | |||
:param buffer_size: | |||
:param drop_last: 是否去掉最后一个不符合``batch_size``的数据 | |||
:param num_workers: 进程的数量,当``num_workers=0``时不开启多进程 | |||
:param buffer_size: 每个进程占用的内存空间,默认为512M。主要是配合num_workers使用,用户可以自定义每个进程的内存大小。 | |||
:param stop_grad: | |||
:param keep_numpy_array: | |||
:param endless: | |||
:param collate_fn: 对取得到的数据进行打包的callable函数 | |||
:param as_numpy: 返回数据是否设置为numpy类型,否则为torch.tensor类型 | |||
:param keep_numpy_array: 返回的数据是``np.array`类`型而不是``jittor.array``类型,默认为``False`` | |||
:param endless: 是否让``JittorDataLoader``无限返回数据,也就是将dataset循环使用使得返回数据是没有限制的。默认为``False``. | |||
:param collate_fn: 用来对从dataset取到的数据进行打包处理成batch的callable函数,其值应该为一下三个:``[None, "auto", callable]``. | |||
* ``callate_fn=None``时,第一点值得注意的是此时传进来的datset不能为``fastNLP``的dataset,采用fastNLP的dataset时,``collate_fn``不能为``None``; | |||
第二点注意的是此时``JittorDataLoader``会调用默认的`callate_batch`函数对sampler到的数据进行简单打包,组成一个batch返回。` | |||
* ``callate_fn="auto"``时,``JittorDataLoader``会自动调用``fastNLP``自带的``Collator``,其会自动检测dataset的每个``field``, | |||
并判断是否能够pad处理,若能则会自动进行pad操作,默认``pad_val=0``。若想要更改其值,可调用``set_pad``方法;若不想自动pad某个field, | |||
可以调用``set_ignore``方法忽略某个field。 | |||
* ``callate_fn=callable``时,callable函数是用户自定义的callate_fn函数,此时``JittorDataLoader``会调用传进来的callable函数对 | |||
数据进行打包处理并返回。值得注意的是用户自定义的callable函数的输入为batch,batch为list类型数据,其中batch的每一条数据都为dataset的一条数据。 | |||
""" | |||
# TODO 验证支持replacesampler (以后完成) | |||
# TODO 验证支持replacesampler (以后完成) 增加Sampler | |||
# 将内部dataset批次设置为1 | |||
if isinstance(dataset, Dataset): | |||
dataset.set_attrs(batch_size=1) | |||
# FastNLP Datset, collate_fn not None | |||
if isinstance(dataset, FDataSet) and collate_fn is None: | |||
raise ValueError("When use FastNLP DataSet, collate_fn must be not None") | |||
# 将所有dataset转为jittor类型的dataset | |||
if not isinstance(dataset, _JittorDataset): | |||
self.dataset = _JittorDataset(dataset) | |||
@@ -85,17 +92,13 @@ class JittorDataLoader: | |||
else: | |||
raise ValueError(f"collate_fn: {collate_fn} must be 'auto'") | |||
elif isinstance(collate_fn, Callable): | |||
if collate_fn is not collate_batch: | |||
self.collate_fn = collate_fn | |||
self.collate_fn = collate_fn | |||
else: | |||
self.collate_fn = collate_batch | |||
self.dataset.set_attrs(batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, | |||
num_workers=num_workers, buffer_size=buffer_size, stop_grad=stop_grad, | |||
keep_numpy_array=keep_numpy_array, endless=endless) | |||
# 将内部dataset批次设置为1 | |||
if isinstance(self.dataset.dataset, Dataset): | |||
self.dataset.dataset.set_attrs(batch_size=1) | |||
self.cur_batch_indices = None | |||
@@ -108,12 +111,10 @@ class JittorDataLoader: | |||
yield data | |||
def __len__(self): | |||
if self.dataset.drop_last: | |||
return len(self.dataset) // self.dataset.batch_size | |||
return (len(self.dataset) - 1) // self.dataset.batch_size + 1 | |||
return len(self.dataset) | |||
def set_pad(self, field_name: Union[str, tuple], pad_val: Union[int, float, None] = 0, dtype=None, backend=None, | |||
pad_fn: Callable = None) -> "JittorDataLoader": | |||
pad_fn: Callable = None) -> Collator: | |||
""" | |||
如果需要对某个 field 的内容进行特殊的调整,请使用这个函数。 | |||
@@ -132,14 +133,27 @@ class JittorDataLoader: | |||
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 | |||
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 set_ignore(self, *field_names) -> "JittorDataLoader": | |||
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。 | |||
Example:: | |||
@@ -151,9 +165,10 @@ class JittorDataLoader: | |||
__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 | |||
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.") | |||
@@ -23,7 +23,6 @@ class _MixDataset: | |||
""" | |||
def __init__(self, datasets: list = None) -> None: | |||
""" | |||
:param datasets: 数据集的列表 | |||
""" | |||
self.datasets = datasets | |||
@@ -36,8 +35,9 @@ class _MixDataset: | |||
def __getitem__(self, idx: Union[int, List[int]]) -> Union[Tuple[Instance, int], Tuple[DataSet, int]]: | |||
""" | |||
根据index索引获取数据 | |||
:param idx: | |||
:param idx: 整数类型的index或者列表 | |||
:return: | |||
""" | |||
if isinstance(idx, int): | |||
@@ -1,3 +1,31 @@ | |||
""" | |||
``PaddleDataLoader``是专门提供给``paddle``框架的``DataLoader``,其集成了``fastNLP``的``Collator``并对``paddle``的``DataLoader``进行了 | |||
封装,使得其具备以下功能:1.``PaddleDataLoader``支持输入的dataset是无框架的,只要实现了``__getitem__``和``__len__``方法即可,当不使用``fastNLP``的 | |||
``DataSet``时候也能够自动检测数据的类型并进行padding,只需要将``collate_fn="auto"``即可,例如:: | |||
from fastNLP import PaddleDataLoader | |||
class MyDataset: | |||
def __init(self, data_lens=100): | |||
self.data_lens = 100 | |||
def __getitem__(self, item): | |||
if item % 2 == 0: | |||
return {'x':[101, 256, 453], 'y': 0} | |||
else: | |||
return {'x': [101, 200], 'y': 1} | |||
def __len__(self): | |||
return self.data_lens | |||
dataset = MyDataset() | |||
paddle_dl = PaddleDataLoader(dataset, collate_fn="auto") | |||
for batch in paddle_dl: | |||
... | |||
2.当设置``collate_fn="auto"``时,``PaddleDataLoader``会调用fastNLP的Collator对数据进行自动pad处理,此时可以调用``set_pad``和``set_ignore``方法 | |||
来设置field的pad_val或者忽略某个field的pad操作。 | |||
.. note:: | |||
当传入的dataset为fastNLP的DataSet时,collate_fn不能为None。默认可以是"auto"或者自定义callable函数。 | |||
""" | |||
__all__ = [ | |||
'PaddleDataLoader', | |||
'prepare_paddle_dataloader' | |||
@@ -23,7 +51,7 @@ from fastNLP.core.samplers import ReproducibleBatchSampler, RandomBatchSampler | |||
class _PaddleDataset(Dataset): | |||
""" | |||
对用户传的dataset进行封装,以便Fdataloader能够支持使用自定义的dataset使用paddle的dataloader | |||
对用户传的dataset进行封装,以便PaddleDataLoader能够支持使用自定义的dataset | |||
""" | |||
def __init__(self, dataset) -> None: | |||
@@ -44,6 +72,10 @@ class _PaddleDataset(Dataset): | |||
class PaddleDataLoader(DataLoader): | |||
""" | |||
提供给``paddle``框架使用的``DataLoader``函数,``PaddleDataLoader``提供了``Collator``的功能,用户可以通过设置``collate_fn="auto"``来 | |||
使用,并可以配套使用``set_pad``和``set_ignore``方法设置p``ad_val``和忽略某个field的pad操作。 | |||
""" | |||
def __init__(self, dataset, feed_list=None, places=None, | |||
return_list: bool = True, batch_sampler=None, | |||
@@ -52,6 +84,51 @@ class PaddleDataLoader(DataLoader): | |||
num_workers: int = 0, use_buffer_reader: bool = True, | |||
use_shared_memory: bool = True, timeout: int = 0, | |||
worker_init_fn: Callable = None, persistent_workers=False) -> None: | |||
""" | |||
:param dataset: 实现了__getitem__和__len__的数据容器 | |||
:param feed_list: (list(Tensor)|tuple(Tensor)): feed Tensor list. | |||
The Tensors should be created by :code:`paddle.static.data()`. | |||
:attr:`feed_list` must be set if :attr:`return_list` is | |||
False. Default None. | |||
:param places: (list(Place)|tuple(Place)|list(str)|optional): a list of Place, | |||
to put data onto, :attr:`places` can be None, if | |||
:attr:`places` is None, default place(CPUPlace or CUDAPlace(0)) | |||
will be used. Default None. If ``places`` is list of string, | |||
the string in the list can be ``cpu``, ``gpu:x`` and ``gpu_pinned``, | |||
where ``x`` is the index of the GPUs. | |||
:param return_list: whether the return value on each device is | |||
presented as a list. If :attr:`return_list=False`, the return | |||
value on each device would be a dict of str -> Tensor, where | |||
the key of the dict is the name of each fed Tensors. If | |||
:attr:`return_list=True`, the return value on each device would | |||
be a list(Tensor). :attr:`return_list` can only be True | |||
in dynamic graph mode. Default True. | |||
:param batch_sampler: 实现了``__iter__``和``__len__``方法的实例化对象,它的功能是根据dataset生成数据indices并组成一个batch数据。 | |||
:param batch_size: dataloader每次获得数据的批次大小 | |||
:param shuffle: 是否将数据打乱,若``shuffle=True``则会将dataset打乱;若否则什么也不做。 | |||
:param drop_last: 当``drop_last=True``时,``PaddleDataLoader``会扔掉最后一个不能组成``batch_size``大小的batch数据; | |||
若``drop_last=False``, 则什么也不做。 | |||
:param collate_fn:用来对从dataset取到的数据进行打包处理成batch的callable函数,其值应该为一下三个:``[None, "auto", callable]``. | |||
* ``callate_fn=None``时,第一点值得注意的是此时传进来的datset不能为``fastNLP``的dataset,采用fastNLP的dataset时,``collate_fn``不能为``None``; | |||
第二点注意的是此时``PaddleDataLoader``会调用默认的`default_collate_fn`函数对sampler到的数据进行简单打包,组成一个batch返回。` | |||
* ``callate_fn="auto"``时,``PaddleDataLoader``会自动调用``fastNLP``自带的``Collator``,其会自动检测dataset的每个``field``, | |||
并判断是否能够pad处理,若能则会自动进行pad操作,默认``pad_val=0``。若想要更改其值,可调用``set_pad``方法;若不想自动pad某个field, | |||
可以调用``set_ignore``方法忽略某个field。 | |||
* ``callate_fn=callable``时,callable函数是用户自定义的callate_fn函数,此时``PaddleDataLoader``会调用传进来的callable函数对 | |||
数据进行打包处理并返回。值得注意的是用户自定义的callable函数的输入为batch,batch为list类型数据,其中batch的每一条数据都为dataset的一条数据。 | |||
:param num_workers: 开启多进程的数量,当``num_workers=0``时不开启多进程 | |||
:param use_buffer_reader: 是否开启buffer_reader。如果``use_buffer_reader=True``,那么``PaddleDataLoader``将会异步的预取下一个batch的 | |||
数据,因此它将会加快数据传输的速度,但是将会占用更多的内存或者显存。默认值是``True``。如果``use_buffer_reader=False``,那么什么也不错 | |||
:param use_shared_memory: 是否使用共享内存。当``use_shared_memory=True``时,将采用共享内存来加快将数据放进进程队列。建议仅当计算机上的 | |||
共享空间足够大时。(例如Linux上的/dev/shm/空间足够大)共享内存仅在多进程模式(num_workers>0)下生效。 | |||
:param timeout: 从子进程的输出队列获取数据的超时值 | |||
:param worker_init_fn: init函数,如果不设置为None,则将会在每个子进程初始化时调用该函数。 | |||
:param persistent_workers: | |||
""" | |||
# FastNLP Datset, collate_fn not None | |||
if isinstance(dataset, FDataSet) and collate_fn is None: | |||
raise ValueError("When use FastNLP DataSet, collate_fn must be not None") | |||
@@ -173,7 +250,7 @@ def prepare_paddle_dataloader(ds_or_db, feed_list=None, places=None, | |||
return_list: bool = True, | |||
batch_sampler: Union["Sampler[Sequence[int]]", ReproducibleBatchSampler] = None, | |||
train_batch_size: int = 1, shuffle: bool = False, | |||
drop_last: bool = False, collate_fn: Union[Callable, str, None] = None, | |||
drop_last: bool = False, collate_fn: Union[Callable, str, None] = 'auto', | |||
num_workers: int = 0, use_buffer_reader: bool = True, | |||
use_shared_memory: bool = True, timeout: int = 0, | |||
worker_init_fn: Callable = None, persistent_workers=False, | |||
@@ -177,12 +177,13 @@ class TorchDataLoader(DataLoader): | |||
return self.cur_batch_indices | |||
def prepare_torch_dataloader(ds_or_db: Union[DataSet, Sequence[DataSet], Mapping[str, DataSet]], | |||
batch_size: int = 1, | |||
shuffle: bool = True, | |||
def prepare_torch_dataloader(ds_or_db, | |||
train_batch_size: int = 16, | |||
shuffle: bool = False, | |||
sampler: Union["Sampler[int]", ReproducibleSampler, UnrepeatedSampler] = None, | |||
batch_sampler: Union["Sampler[Sequence[int]]", ReproducibleBatchSampler] = None, | |||
num_workers: int = 0, collate_fn: Union[str, Callable, None] = 'auto', | |||
num_workers: int = 0, collate_fn: Union[Callable, str, None] = 'auto', | |||
pin_memory: bool = False, drop_last: bool = False, | |||
timeout: float = 0, worker_init_fn: Optional[Callable] = None, | |||
multiprocessing_context=None, generator=None, prefetch_factor: int = 2, | |||
@@ -214,7 +215,7 @@ def prepare_torch_dataloader(ds_or_db: Union[DataSet, Sequence[DataSet], Mapping | |||
from fastNLP.io import DataBundle | |||
if isinstance(ds_or_db, DataSet): | |||
dl = TorchDataLoader(dataset=ds_or_db, batch_size=batch_size, | |||
dl = TorchDataLoader(dataset=ds_or_db, batch_size=train_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, | |||
@@ -227,7 +228,7 @@ def prepare_torch_dataloader(ds_or_db: Union[DataSet, Sequence[DataSet], Mapping | |||
dl_bundle = {} | |||
for name, ds in ds_or_db.iter_datasets(): | |||
if 'train' in name: | |||
dl_bundle[name] = TorchDataLoader(dataset=ds, batch_size=batch_size, | |||
dl_bundle[name] = TorchDataLoader(dataset=ds, batch_size=train_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, | |||
@@ -236,7 +237,7 @@ def prepare_torch_dataloader(ds_or_db: Union[DataSet, Sequence[DataSet], Mapping | |||
persistent_workers=persistent_workers, | |||
) | |||
else: | |||
dl_bundle[name] = TorchDataLoader(dataset=ds, batch_size=non_train_batch_size if non_train_batch_size else batch_size, | |||
dl_bundle[name] = TorchDataLoader(dataset=ds, batch_size=non_train_batch_size if non_train_batch_size else train_batch_size, | |||
shuffle=shuffle, sampler=non_train_sampler if non_train_sampler else sampler, | |||
batch_sampler=batch_sampler, | |||
num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory, | |||
@@ -250,8 +251,11 @@ def prepare_torch_dataloader(ds_or_db: Union[DataSet, Sequence[DataSet], Mapping | |||
elif isinstance(ds_or_db, Sequence): | |||
dl_bundle = [] | |||
for idx, ds in enumerate(ds_or_db): | |||
if idx > 0: | |||
train_batch_size = non_train_batch_size if non_train_batch_size else train_batch_size | |||
sampler = non_train_sampler if non_train_sampler else sampler | |||
dl_bundle.append( | |||
TorchDataLoader(dataset=ds, batch_size=batch_size, | |||
TorchDataLoader(dataset=ds, batch_size=train_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, | |||
@@ -265,7 +269,7 @@ def prepare_torch_dataloader(ds_or_db: Union[DataSet, Sequence[DataSet], Mapping | |||
dl_bundle = {} | |||
for name, ds in ds_or_db.items(): | |||
if 'train' in name: | |||
dl_bundle[name] = TorchDataLoader(dataset=ds, batch_size=batch_size, | |||
dl_bundle[name] = TorchDataLoader(dataset=ds, batch_size=train_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, | |||
@@ -274,8 +278,8 @@ def prepare_torch_dataloader(ds_or_db: Union[DataSet, Sequence[DataSet], Mapping | |||
persistent_workers=persistent_workers, | |||
) | |||
else: | |||
dl_bundle[name] = TorchDataLoader(dataset=ds, batch_size=non_train_batch_size, | |||
shuffle=shuffle, sampler=non_train_sampler, | |||
dl_bundle[name] = TorchDataLoader(dataset=ds, batch_size=non_train_batch_size if non_train_batch_size else train_batch_size, | |||
shuffle=shuffle, sampler=non_train_sampler if non_train_sampler else 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, | |||
@@ -32,7 +32,7 @@ def initialize_torch_driver(driver: str, device: Optional[Union[str, "torch.devi | |||
return TorchDDPDriver(model, torch.device(f"cuda:{os.environ['LOCAL_RANK']}"), True, **kwargs) | |||
if driver not in {"torch", "fairscale"}: | |||
raise ValueError("Parameter `driver` can only be one of these values: ['torch', 'torch_ddp', 'fairscale'].") | |||
raise ValueError("Parameter `driver` can only be one of these values: ['torch', 'fairscale'].") | |||
_could_use_device_num = torch.cuda.device_count() | |||
if isinstance(device, str): | |||
@@ -43,6 +43,7 @@ def initialize_torch_driver(driver: str, device: Optional[Union[str, "torch.devi | |||
raise ValueError("Parameter `device` can only be '-1' when it is smaller than 0.") | |||
device = [torch.device(f"cuda:{w}") for w in range(_could_use_device_num)] | |||
elif device >= _could_use_device_num: | |||
print(device, _could_use_device_num) | |||
raise ValueError("The gpu device that parameter `device` specifies is not existed.") | |||
else: | |||
device = torch.device(f"cuda:{device}") | |||
@@ -11,6 +11,7 @@ __all__ = [ | |||
from collections import Counter | |||
from functools import partial | |||
from functools import wraps | |||
from typing import List, Callable, Union | |||
from fastNLP.core.dataset import DataSet | |||
from fastNLP.core.utils.utils import Option | |||
@@ -20,6 +21,9 @@ import io | |||
class VocabularyOption(Option): | |||
""" | |||
""" | |||
def __init__(self, | |||
max_size=None, | |||
min_freq=None, | |||
@@ -33,8 +37,11 @@ class VocabularyOption(Option): | |||
) | |||
def _check_build_vocab(func): | |||
r"""A decorator to make sure the indexing is built before used. | |||
def _check_build_vocab(func: Callable): | |||
r""" | |||
A decorator to make sure the indexing is built before used. | |||
:param func: 传入的callable函数 | |||
""" | |||
@@ -48,7 +55,10 @@ def _check_build_vocab(func): | |||
def _check_build_status(func): | |||
r"""A decorator to check whether the vocabulary updates after the last build. | |||
r""" | |||
A decorator to check whether the vocabulary updates after the last build. | |||
:param func: 用户传入要修饰的callable函数 | |||
""" | |||
@@ -69,27 +79,30 @@ class Vocabulary(object): | |||
r""" | |||
用于构建, 存储和使用 `str` 到 `int` 的一一映射:: | |||
from fastNLP.core import Vocabulary | |||
vocab = Vocabulary() | |||
word_list = "this is a word list".split() | |||
# vocab更新自己的字典,输入为list列表 | |||
vocab.update(word_list) | |||
vocab["word"] # str to int | |||
vocab.to_word(5) # int to str | |||
""" | |||
def __init__(self, max_size=None, min_freq=None, padding='<pad>', unknown='<unk>'): | |||
def __init__(self, max_size:int=None, min_freq:int=None, padding:str='<pad>', unknown:str='<unk>'): | |||
r""" | |||
:param int max_size: `Vocabulary` 的最大大小, 即能存储词的最大数量 | |||
:param max_size: `Vocabulary` 的最大大小, 即能存储词的最大数量 | |||
若为 ``None`` , 则不限制大小. Default: ``None`` | |||
:param int min_freq: 能被记录下的词在文本中的最小出现频率, 应大于或等于 1. | |||
:param min_freq: 能被记录下的词在文本中的最小出现频率, 应大于或等于 1. | |||
若小于该频率, 词语将被视为 `unknown`. 若为 ``None`` , 所有文本中的词都被记录. Default: ``None`` | |||
:param str optional padding: padding的字符. 如果设置为 ``None`` , | |||
:param padding: padding的字符. 如果设置为 ``None`` , | |||
则vocabulary中不考虑padding, 也不计入词表大小,为 ``None`` 的情况多在为label建立Vocabulary的情况. | |||
Default: '<pad>' | |||
:param str optional unknown: unknown的字符,所有未被记录的词在转为 `int` 时将被视为unknown. | |||
:param unknown: unknown的字符,所有未被记录的词在转为 `int` 时将被视为unknown. | |||
如果设置为 ``None`` ,则vocabulary中不考虑unknow, 也不计入词表大小. | |||
为 ``None`` 的情况多在为label建立Vocabulary的情况. | |||
Default: '<unk>' | |||
""" | |||
self.max_size = max_size | |||
self.min_freq = min_freq | |||
@@ -121,45 +134,50 @@ class Vocabulary(object): | |||
self._word2idx = value | |||
@_check_build_status | |||
def update(self, word_lst, no_create_entry=False): | |||
r"""依次增加序列中词在词典中的出现频率 | |||
def update(self, word_lst: list, no_create_entry:bool=False): | |||
r""" | |||
依次增加序列中词在词典中的出现频率 | |||
:param list word_lst: a list of strings | |||
:param bool no_create_entry: 如果词语来自于非训练集建议设置为True。在使用fastNLP.TokenEmbedding加载预训练模型时,没有从预训练词表中找到这个词的处理方式。 | |||
:param word_lst: 列表形式的词语,如word_list=['I', 'am', 'a', 'Chinese'],列表中的每个词会计算出现频率并加入到词典中。 | |||
:param no_create_entry: 如果词语来自于非训练集建议设置为True。 | |||
如果为True,则不会有这个词语创建一个单独的entry,它将一直被指向unk的表示; 如果为False,则为这个词创建一个单独 | |||
的entry。如果这个word来自于dev或者test,一般设置为True,如果来自与train一般设置为False。以下两种情况: 如果新 | |||
加入一个word,且no_create_entry为True,但这个词之前已经在Vocabulary中且并不是no_create_entry的,则还是会为这 | |||
个词创建一个单独的vector; 如果no_create_entry为False,但这个词之前已经在Vocabulary中且并不是no_create_entry的, | |||
则这个词将认为是需要创建单独的vector的。 | |||
""" | |||
self._add_no_create_entry(word_lst, no_create_entry) | |||
self.word_count.update(word_lst) | |||
return self | |||
@_check_build_status | |||
def add(self, word, no_create_entry=False): | |||
def add(self, word:str, no_create_entry:bool=False): | |||
r""" | |||
增加一个新词在词典中的出现频率 | |||
:param str word: 新词 | |||
:param bool no_create_entry: 如果词语来自于非训练集建议设置为True。在使用fastNLP.TokenEmbedding加载预训练模型时,没有从预训练词表中找到这个词的处理方式。 | |||
:param word: 要添加进字典的新词, word为一个字符串 | |||
:param no_create_entry: 如果词语来自于非训练集建议设置为True。 | |||
如果为True,则不会有这个词语创建一个单独的entry,它将一直被指向unk的表示; 如果为False,则为这个词创建一个单独 | |||
的entry。如果这个word来自于dev或者test,一般设置为True,如果来自与train一般设置为False。以下两种情况: 如果新 | |||
加入一个word,且no_create_entry为True,但这个词之前已经在Vocabulary中且并不是no_create_entry的,则还是会为这 | |||
个词创建一个单独的vector; 如果no_create_entry为False,但这个词之前已经在Vocabulary中且并不是no_create_entry的, | |||
则这个词将认为是需要创建单独的vector的。 | |||
""" | |||
self._add_no_create_entry(word, no_create_entry) | |||
self.word_count[word] += 1 | |||
return self | |||
def _add_no_create_entry(self, word, no_create_entry): | |||
def _add_no_create_entry(self, word:Union[str, List[str]], no_create_entry:bool): | |||
r""" | |||
在新加入word时,检查_no_create_word的设置。 | |||
:param str List[str] word: | |||
:param bool no_create_entry: | |||
:param word: 要添加的新词或者是List类型的新词,如word='I'或者word=['I', 'am', 'a', 'Chinese']均可 | |||
:param no_create_entry: 如果词语来自于非训练集建议设置为True。如果为True,则不会有这个词语创建一个单独的entry, | |||
它将一直被指向unk的表示; 如果为False,则为这个词创建一个单独的entry | |||
:return: | |||
""" | |||
if isinstance(word, str) or not _is_iterable(word): | |||
word = [word] | |||
@@ -170,32 +188,32 @@ class Vocabulary(object): | |||
self._no_create_word.pop(w) | |||
@_check_build_status | |||
def add_word(self, word, no_create_entry=False): | |||
def add_word(self, word:str, no_create_entry:bool=False): | |||
r""" | |||
增加一个新词在词典中的出现频率 | |||
:param str word: 新词 | |||
:param bool no_create_entry: 如果词语来自于非训练集建议设置为True。在使用fastNLP.TokenEmbedding加载预训练模型时,没有从预训练词表中找到这个词的处理方式。 | |||
如果为True,则不会有这个词语创建一个单独的entry,它将一直被指向unk的表示; 如果为False,则为这个词创建一个单独 | |||
的entry。如果这个word来自于dev或者test,一般设置为True,如果来自与train一般设置为False。以下两种情况: 如果新 | |||
加入一个word,且no_create_entry为True,但这个词之前已经在Vocabulary中且并不是no_create_entry的,则还是会为这 | |||
个词创建一个单独的vector; 如果no_create_entry为False,但这个词之前已经在Vocabulary中且并不是no_create_entry的, | |||
则这个词将认为是需要创建单独的vector的。 | |||
:param word: 要添加进字典的新词, word为一个字符串 | |||
:param no_create_entry: 如果词语来自于非训练集建议设置为True。如果为True,则不会有这个词语创建一个单独的entry, | |||
它将一直被指向unk的表示; 如果为False,则为这个词创建一个单独的entry。如果这个word来自于dev或者test,一般设置为True, | |||
如果来自与train一般设置为False。以下两种情况: 如果新加入一个word,且no_create_entry为True,但这个词之前已经在Vocabulary | |||
中且并不是no_create_entry的,则还是会为这词创建一个单独的vector; 如果no_create_entry为False,但这个词之前已经在Vocabulary | |||
中且并不是no_create_entry的,则这个词将认为是需要创建单独的vector的。 | |||
""" | |||
self.add(word, no_create_entry=no_create_entry) | |||
@_check_build_status | |||
def add_word_lst(self, word_lst, no_create_entry=False): | |||
def add_word_lst(self, word_lst: List[str], no_create_entry:bool=False): | |||
r""" | |||
依次增加序列中词在词典中的出现频率 | |||
:param list[str] word_lst: 词的序列 | |||
:param bool no_create_entry: 如果词语来自于非训练集建议设置为True。在使用fastNLP.TokenEmbedding加载预训练模型时,没有从预训练词表中找到这个词的处理方式。 | |||
如果为True,则不会有这个词语创建一个单独的entry,它将一直被指向unk的表示; 如果为False,则为这个词创建一个单独 | |||
的entry。如果这个word来自于dev或者test,一般设置为True,如果来自与train一般设置为False。以下两种情况: 如果新 | |||
加入一个word,且no_create_entry为True,但这个词之前已经在Vocabulary中且并不是no_create_entry的,则还是会为这 | |||
个词创建一个单独的vector; 如果no_create_entry为False,但这个词之前已经在Vocabulary中且并不是no_create_entry的, | |||
则这个词将认为是需要创建单独的vector的。 | |||
:param word_lst: 需要添加的新词的list序列,如word_lst=['I', 'am', 'a', 'Chinese'] | |||
:param no_create_entry: 如果词语来自于非训练集建议设置为True。如果为True,则不会有这个词语创建一个单独的entry, | |||
它将一直被指向unk的表示; 如果为False,则为这个词创建一个单独的entry。如果这个word来自于dev或者test,一般设置为True, | |||
如果来自与train一般设置为False。以下两种情况: 如果新加入一个word,且no_create_entry为True,但这个词之前已经在Vocabulary | |||
中且并不是no_create_entry的,则还是会为这词创建一个单独的vector; 如果no_create_entry为False,但这个词之前已经在Vocabulary | |||
中且并不是no_create_entry的,则这个词将认为是需要创建单独的vector的。 | |||
""" | |||
self.update(word_lst, no_create_entry=no_create_entry) | |||
return self | |||
@@ -238,7 +256,7 @@ class Vocabulary(object): | |||
return len(self._word2idx) | |||
@_check_build_vocab | |||
def __contains__(self, item): | |||
def __contains__(self, item:str): | |||
r""" | |||
检查词是否被记录 | |||
@@ -247,7 +265,7 @@ class Vocabulary(object): | |||
""" | |||
return item in self._word2idx | |||
def has_word(self, w): | |||
def has_word(self, w:str): | |||
r""" | |||
检查词是否被记录:: | |||
@@ -255,7 +273,7 @@ class Vocabulary(object): | |||
# equals to | |||
has_abc = 'abc' in vocab | |||
:param item: the word | |||
:param item: 输入的str类型的词 | |||
:return: ``True`` or ``False`` | |||
""" | |||
return self.__contains__(w) | |||
@@ -263,7 +281,7 @@ class Vocabulary(object): | |||
@_check_build_vocab | |||
def __getitem__(self, w): | |||
r""" | |||
To support usage like:: | |||
支持从字典中直接得到词语的index,例如:: | |||
vocab[w] | |||
""" | |||
@@ -275,15 +293,15 @@ class Vocabulary(object): | |||
raise ValueError("word `{}` not in vocabulary".format(w)) | |||
@_check_build_vocab | |||
def index_dataset(self, *datasets, field_name, new_field_name=None): | |||
def index_dataset(self, *datasets, field_name:Union[List, str], new_field_name:Union[List, str, None]=None): | |||
r""" | |||
将DataSet中对应field的词转为数字,Example:: | |||
# remember to use `field_name` | |||
vocab.index_dataset(train_data, dev_data, test_data, field_name='words') | |||
:param ~fastNLP.DataSet,List[~fastNLP.DataSet] datasets: 需要转index的一个或多个数据集 | |||
:param list,str field_name: 需要转index的field, 若有多个 DataSet, 每个DataSet都必须有此 field. | |||
:param datasets: 其类型为:~fastNLP.core.Dataset或者List[~fastNLP.core.Dataset] 需要转index的一个或多个数据集 | |||
:param field_name: 需要转index的field, 若有多个 DataSet, 每个DataSet都必须有此 field. | |||
目前支持 ``str`` , ``List[str]`` | |||
:param list,str new_field_name: 保存结果的field_name. 若为 ``None`` , 将覆盖原field. | |||
Default: ``None``. | |||
@@ -334,17 +352,16 @@ class Vocabulary(object): | |||
def _no_create_word_length(self): | |||
return len(self._no_create_word) | |||
def from_dataset(self, *datasets, field_name, no_create_entry_dataset=None): | |||
def from_dataset(self, *datasets, field_name:Union[str,List[str]], no_create_entry_dataset=None): | |||
r""" | |||
使用dataset的对应field中词构建词典:: | |||
# remember to use `field_name` | |||
vocab.from_dataset(train_data1, train_data2, field_name='words') | |||
vocab.from_dataset(train_data1, train_data2, field_name='words', no_create_entry_dataset=[test_data1, test_data2]) | |||
:param ~fastNLP.DataSet,List[~fastNLP.DataSet] datasets: 需要转index的一个或多个数据集 | |||
:param str,List[str] field_name: 可为 ``str`` 或 ``List[str]`` . | |||
构建词典所使用的 field(s), 支持一个或多个field,若有多个 DataSet, 每个DataSet都必须有这些field. 目前支持的field结构 | |||
: ``str`` , ``List[str]`` | |||
:param 其类型为:~fastNLP.core.Dataset或者List[~fastNLP.core.Dataset] 需要转index的一个或多个数据集 | |||
:param field_name: 构建词典所使用的 field(s), 支持一个或多个field,若有多个 DataSet, 每个DataSet都必须有这些field. | |||
目前支持的field结构: ``str`` , ``List[str]`` | |||
:param no_create_entry_dataset: 可以传入DataSet, List[DataSet]或者None(默认), 建议直接将非训练数据都传入到这个参数。该选项用在接下来的模型会使用pretrain | |||
的embedding(包括glove, word2vec, elmo与bert)且会finetune的情况。如果仅使用来自于train的数据建立vocabulary,会导致test与dev | |||
中的数据无法充分利用到来自于预训练embedding的信息,所以在建立词表的时候将test与dev考虑进来会使得最终的结果更好。 | |||
@@ -352,7 +369,8 @@ class Vocabulary(object): | |||
finetune embedding的话,这个词在更新之后可能会有更好的表示; 而如果这个词仅出现在了dev或test中,那么就不能为它们单独建立vector, | |||
而应该让它指向unk这个vector的值。所以只位于no_create_entry_dataset中的token,将首先从预训练的词表中寻找它的表示, | |||
如果找到了,就使用该表示; 如果没有找到,则认为该词的表示应该为unk的表示。 | |||
:return self: | |||
:return Vocabulary自身 | |||
""" | |||
if isinstance(field_name, str): | |||
field_name = [field_name] | |||
@@ -396,15 +414,16 @@ class Vocabulary(object): | |||
dataset.apply(partial_construct_vocab, show_progress_bar=False) | |||
return self | |||
def _is_word_no_create_entry(self, word): | |||
def _is_word_no_create_entry(self, word:str): | |||
r""" | |||
判断当前的word是否是不需要创建entry的,具体参见from_dataset的说明 | |||
:param word: str | |||
:return: bool | |||
:param word: 输入的str类型的词语 | |||
:return: bool值的判断结果 | |||
""" | |||
return word in self._no_create_word | |||
def to_index(self, w): | |||
def to_index(self, w:str): | |||
r""" | |||
将词转为数字. 若词不再词典中被记录, 将视为 unknown, 若 ``unknown=None`` , 将抛出 ``ValueError`` :: | |||
@@ -412,8 +431,8 @@ class Vocabulary(object): | |||
# equals to | |||
index = vocab['abc'] | |||
:param str w: a word | |||
:return int index: the number | |||
:param w: 需要输入的词语 | |||
:return 词语w对应的int类型的index | |||
""" | |||
return self.__getitem__(w) | |||
@@ -421,7 +440,7 @@ class Vocabulary(object): | |||
@_check_build_vocab | |||
def unknown_idx(self): | |||
r""" | |||
unknown 对应的数字. | |||
获得unknown 对应的数字. | |||
""" | |||
if self.unknown is None: | |||
return None | |||
@@ -431,14 +450,14 @@ class Vocabulary(object): | |||
@_check_build_vocab | |||
def padding_idx(self): | |||
r""" | |||
padding 对应的数字 | |||
获得padding 对应的数字 | |||
""" | |||
if self.padding is None: | |||
return None | |||
return self._word2idx[self.padding] | |||
@_check_build_vocab | |||
def to_word(self, idx): | |||
def to_word(self, idx: int): | |||
r""" | |||
给定一个数字, 将其转为对应的词. | |||
@@ -461,7 +480,8 @@ class Vocabulary(object): | |||
return self | |||
def __getstate__(self): | |||
r"""Use to prepare data for pickle. | |||
r""" | |||
用来从pickle中加载data | |||
""" | |||
len(self) # make sure vocab has been built | |||
@@ -471,7 +491,8 @@ class Vocabulary(object): | |||
return state | |||
def __setstate__(self, state): | |||
r"""Use to restore state from pickle. | |||
r""" | |||
支持pickle的保存,保存到pickle的data state | |||
""" | |||
self.__dict__.update(state) | |||
@@ -486,11 +507,11 @@ class Vocabulary(object): | |||
for index in range(len(self._word2idx)): | |||
yield self.to_word(index), index | |||
def save(self, filepath): | |||
def save(self, filepath: [str, io.StringIO]): | |||
r""" | |||
:param str,io.StringIO filepath: Vocabulary的储存路径 | |||
:param filepath: Vocabulary的储存路径 | |||
:return: | |||
""" | |||
if isinstance(filepath, io.IOBase): | |||
assert filepath.writable() | |||
@@ -522,10 +543,11 @@ class Vocabulary(object): | |||
f.close() | |||
@staticmethod | |||
def load(filepath): | |||
def load(filepath: Union[str,io.StringIO]): | |||
r""" | |||
从文件路径中加载数据 | |||
:param str,io.StringIO filepath: Vocabulary的读取路径 | |||
:param filepath: Vocabulary的读取路径 | |||
:return: Vocabulary | |||
""" | |||
if isinstance(filepath, io.IOBase): | |||
@@ -0,0 +1,5 @@ | |||
__all__ = [ | |||
"LSTM", | |||
] | |||
from .lstm import LSTM |
@@ -0,0 +1,82 @@ | |||
r"""undocumented | |||
轻量封装的 Pytorch LSTM 模块. | |||
可在 forward 时传入序列的长度, 自动对padding做合适的处理. | |||
""" | |||
__all__ = [ | |||
"LSTM" | |||
] | |||
import torch | |||
import torch.nn as nn | |||
import torch.nn.utils.rnn as rnn | |||
class LSTM(nn.Module): | |||
r""" | |||
LSTM 模块, 轻量封装的Pytorch LSTM. 在提供seq_len的情况下,将自动使用pack_padded_sequence; 同时默认将forget gate的bias初始化 | |||
为1; 且可以应对DataParallel中LSTM的使用问题。 | |||
""" | |||
def __init__(self, input_size, hidden_size=100, num_layers=1, dropout=0.0, batch_first=True, | |||
bidirectional=False, bias=True): | |||
r""" | |||
:param input_size: 输入 `x` 的特征维度 | |||
:param hidden_size: 隐状态 `h` 的特征维度. 如果bidirectional为True,则输出的维度会是hidde_size*2 | |||
:param num_layers: rnn的层数. Default: 1 | |||
:param dropout: 层间dropout概率. Default: 0 | |||
:param bidirectional: 若为 ``True``, 使用双向的RNN. Default: ``False`` | |||
:param batch_first: 若为 ``True``, 输入和输出 ``Tensor`` 形状为 | |||
:(batch, seq, feature). Default: ``False`` | |||
:param bias: 如果为 ``False``, 模型将不会使用bias. Default: ``True`` | |||
""" | |||
super(LSTM, self).__init__() | |||
self.batch_first = batch_first | |||
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, bias=bias, batch_first=batch_first, | |||
dropout=dropout, bidirectional=bidirectional) | |||
self.init_param() | |||
def init_param(self): | |||
for name, param in self.named_parameters(): | |||
if 'bias' in name: | |||
# based on https://github.com/pytorch/pytorch/issues/750#issuecomment-280671871 | |||
param.data.fill_(0) | |||
n = param.size(0) | |||
start, end = n // 4, n // 2 | |||
param.data[start:end].fill_(1) | |||
else: | |||
nn.init.xavier_uniform_(param) | |||
def forward(self, x, seq_len=None, h0=None, c0=None): | |||
r""" | |||
:param x: [batch, seq_len, input_size] 输入序列 | |||
:param seq_len: [batch, ] 序列长度, 若为 ``None``, 所有输入看做一样长. Default: ``None`` | |||
:param h0: [batch, hidden_size] 初始隐状态, 若为 ``None`` , 设为全0向量. Default: ``None`` | |||
:param c0: [batch, hidden_size] 初始Cell状态, 若为 ``None`` , 设为全0向量. Default: ``None`` | |||
:return (output, (ht, ct)): output: [batch, seq_len, hidden_size*num_direction] 输出序列 | |||
和 ht,ct: [num_layers*num_direction, batch, hidden_size] 最后时刻隐状态. | |||
""" | |||
batch_size, max_len, _ = x.size() | |||
if h0 is not None and c0 is not None: | |||
hx = (h0, c0) | |||
else: | |||
hx = None | |||
if seq_len is not None and not isinstance(x, rnn.PackedSequence): | |||
sort_lens, sort_idx = torch.sort(seq_len, dim=0, descending=True) | |||
if self.batch_first: | |||
x = x[sort_idx] | |||
else: | |||
x = x[:, sort_idx] | |||
x = rnn.pack_padded_sequence(x, sort_lens.cpu(), batch_first=self.batch_first) | |||
output, hx = self.lstm(x, hx) # -> [N,L,C] | |||
output, _ = rnn.pad_packed_sequence(output, batch_first=self.batch_first, total_length=max_len) | |||
_, unsort_idx = torch.sort(sort_idx, dim=0, descending=False) | |||
if self.batch_first: | |||
output = output[unsort_idx] | |||
else: | |||
output = output[:, unsort_idx] | |||
hx = hx[0][:, unsort_idx], hx[1][:, unsort_idx] | |||
else: | |||
output, hx = self.lstm(x, hx) | |||
return output, hx |
@@ -74,7 +74,7 @@ def model_and_optimizers(request): | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 1)]) # ("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 1) | |||
@pytest.mark.parametrize("driver,device", [("torch", [0, 1])]) # ("torch", "cpu"), ("torch", [0, 1]), ("torch", 1) | |||
@pytest.mark.parametrize("version", [0, 1]) | |||
@pytest.mark.parametrize("only_state_dict", [True, False]) | |||
@magic_argv_env_context(timeout=100) | |||
@@ -121,7 +121,7 @@ def test_model_checkpoint_callback_1( | |||
# 检查生成保存模型文件的数量是不是正确的; | |||
if version == 0: | |||
if driver == "torch": | |||
if not isinstance(device, list): | |||
assert "model-epoch_10" in all_saved_model_paths | |||
assert "model-epoch_4-batch_123" in all_saved_model_paths | |||
@@ -144,7 +144,7 @@ def test_model_checkpoint_callback_1( | |||
pattern = re.compile("model-epoch_[0-9]+-batch_[0-9]+-[a-zA-Z#]+_[0-9]*.?[0-9]*") | |||
if driver == "torch": | |||
if not isinstance(device, list): | |||
assert "model-epoch_9" in all_saved_model_paths | |||
assert "model-last" in all_saved_model_paths | |||
aLL_topk_folders = [] | |||
@@ -206,7 +206,7 @@ def test_model_checkpoint_callback_1( | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 1)]) # ("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 1) | |||
@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch", [0, 1]), ("torch", 1)]) # ("torch", "cpu"), ("torch", [0, 1]), ("torch", 1) | |||
@pytest.mark.parametrize("only_state_dict", [True]) | |||
@magic_argv_env_context(timeout=100) | |||
def test_model_checkpoint_callback_2( | |||
@@ -259,7 +259,7 @@ def test_model_checkpoint_callback_2( | |||
# 检查生成保存模型文件的数量是不是正确的; | |||
all_saved_model_paths = {w.name: w for w in path.joinpath(os.environ[FASTNLP_LAUNCH_TIME]).iterdir()} | |||
if driver == "torch": | |||
if not isinstance(device, list): | |||
assert "model-epoch_4-batch_100-exception_NotImplementedError" in all_saved_model_paths | |||
exception_model_path = all_saved_model_paths["model-epoch_4-batch_100-exception_NotImplementedError"] | |||
# ddp 下的文件名不同,因为同样的数据,ddp 用了更少的步数跑完; | |||
@@ -299,7 +299,7 @@ def test_model_checkpoint_callback_2( | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 0)]) # ("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 1) | |||
@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch", [0, 1]), ("torch", 0)]) # ("torch", "cpu"), ("torch", [0, 1]), ("torch", 1) | |||
@pytest.mark.parametrize("version", [0, 1]) | |||
@pytest.mark.parametrize("only_state_dict", [True, False]) | |||
@magic_argv_env_context(timeout=100) | |||
@@ -347,7 +347,7 @@ def test_trainer_checkpoint_callback_1( | |||
# 检查生成保存模型文件的数量是不是正确的; | |||
if version == 0: | |||
if driver == "torch": | |||
if not isinstance(device, list): | |||
assert "trainer-epoch_7" in all_saved_model_paths | |||
assert "trainer-epoch_4-batch_123" in all_saved_model_paths | |||
@@ -371,7 +371,7 @@ def test_trainer_checkpoint_callback_1( | |||
pattern = re.compile("trainer-epoch_[0-9]+-batch_[0-9]+-[a-zA-Z#]+_[0-9]*.?[0-9]*") | |||
# all_saved_model_paths = {w.name: w for w in path.joinpath(os.environ[FASTNLP_LAUNCH_TIME]).iterdir()} | |||
if driver == "torch": | |||
if not isinstance(device, list): | |||
assert "trainer-last" in all_saved_model_paths | |||
aLL_topk_folders = [] | |||
for each_folder_name in all_saved_model_paths: | |||
@@ -417,7 +417,7 @@ def test_trainer_checkpoint_callback_1( | |||
n_epochs=13, | |||
output_from_new_proc="all" | |||
) | |||
trainer.load(folder, only_state_dict=only_state_dict) | |||
trainer.load_checkpoint(folder, only_state_dict=only_state_dict) | |||
trainer.run() | |||
trainer.driver.barrier() | |||
@@ -489,7 +489,7 @@ def test_load_state(model_and_optimizers): | |||
callbacks=callbacks, | |||
output_from_new_proc="all" | |||
) | |||
trainer.load(folder=epoch_2_path) | |||
trainer.load_checkpoint(folder=epoch_2_path) | |||
with Capturing() as output: | |||
trainer.run(num_eval_sanity_batch=0, num_train_batch_per_epoch=2) | |||
@@ -503,7 +503,7 @@ def test_load_state(model_and_optimizers): | |||
@pytest.mark.torch | |||
# 通过自己编写 model_save_fn 和 model_load_fn 来测试 huggingface 的 transformers 的模型的保存和加载; | |||
@pytest.mark.parametrize("driver,device", [("torch_ddp", [6, 7]), ("torch", 7)]) # ("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 1) | |||
@pytest.mark.parametrize("driver,device", [("torch", [6, 7]), ("torch", 7)]) # ("torch", "cpu"), ("torch", [0, 1]), ("torch", 1) | |||
@pytest.mark.parametrize("version", [0, 1]) | |||
@magic_argv_env_context | |||
@pytest.mark.skip("Skip transformers test for now.") | |||
@@ -675,7 +675,7 @@ def test_trainer_checkpoint_callback_2( | |||
# 检查生成保存模型文件的数量是不是正确的; | |||
if version == 0: | |||
if driver == "torch": | |||
if not isinstance(device, list): | |||
assert "trainer-epoch_1-batch_200" in all_saved_model_paths | |||
epoch_save_path = all_saved_model_paths["trainer-epoch_1-batch_200"] | |||
@@ -695,7 +695,7 @@ def test_trainer_checkpoint_callback_2( | |||
pattern = re.compile("trainer-epoch_[0-9]+-batch_[0-9]+-[a-zA-Z#]+_[0-9]*.?[0-9]*") | |||
# all_saved_model_paths = {w.name: w for w in path.joinpath(os.environ[FASTNLP_LAUNCH_TIME]).iterdir()} | |||
if driver == "torch": | |||
if not isinstance(device, list): | |||
assert "trainer-last" in all_saved_model_paths | |||
aLL_topk_folders = [] | |||
for each_folder_name in all_saved_model_paths: | |||
@@ -740,7 +740,7 @@ def test_trainer_checkpoint_callback_2( | |||
output_mapping=bert_output_mapping, | |||
metrics={"acc": acc}, | |||
) | |||
trainer.load(folder, model_load_fn=model_load_fn) | |||
trainer.load_checkpoint(folder, model_load_fn=model_load_fn) | |||
trainer.run() | |||
trainer.driver.barrier() | |||
@@ -72,7 +72,7 @@ def model_and_optimizers(request): | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("driver,device", [("torch_ddp", [4, 5]), ("torch", 1), ("torch", "cpu")]) # ("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 1) | |||
@pytest.mark.parametrize("driver,device", [("torch", [4, 5]), ("torch", 1), ("torch", "cpu")]) # ("torch", "cpu"), ("torch", [0, 1]), ("torch", 1) | |||
@pytest.mark.parametrize("save_folder", ['save_models', None]) | |||
@pytest.mark.parametrize("only_state_dict", [True, False]) | |||
@magic_argv_env_context | |||
@@ -98,7 +98,7 @@ def model_and_optimizers(request): | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 1)]) # ("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 1) | |||
@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch", [0, 1]), ("torch", 1)]) # ("torch", "cpu"), ("torch", [0, 1]), ("torch", 1) | |||
@pytest.mark.parametrize("version", [0, 1]) | |||
@pytest.mark.parametrize("only_state_dict", [True, False]) | |||
@magic_argv_env_context | |||
@@ -183,7 +183,7 @@ def test_model_more_evaluate_callback_1( | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 0)]) # ("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 1) | |||
@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch", [0, 1]), ("torch", 0)]) # ("torch", "cpu"), ("torch", [0, 1]), ("torch", 1) | |||
@pytest.mark.parametrize("version", [0, 1]) | |||
@pytest.mark.parametrize("only_state_dict", [True, False]) | |||
@magic_argv_env_context | |||
@@ -256,7 +256,7 @@ def test_trainer_checkpoint_callback_1( | |||
evaluate_fn='train_step' | |||
) | |||
folder = path.joinpath(os.environ[FASTNLP_LAUNCH_TIME]).joinpath(folder) | |||
trainer.load(folder, only_state_dict=only_state_dict) | |||
trainer.load_checkpoint(folder, only_state_dict=only_state_dict) | |||
trainer.run() | |||
trainer.driver.barrier() | |||
@@ -85,7 +85,7 @@ def _test_trainer_torch_with_evaluator_fp16_accumulation_steps( | |||
): | |||
trainer = Trainer( | |||
model=model, | |||
driver="torch_ddp", | |||
driver="torch", | |||
device=None, | |||
optimizers=optimizers, | |||
train_dataloader=train_dataloader, | |||
@@ -73,7 +73,7 @@ def _test_trainer_torch_with_evaluator_fp16_accumulation_steps( | |||
): | |||
trainer = Trainer( | |||
model=model, | |||
driver="torch_ddp", | |||
driver="torch", | |||
device=None, | |||
optimizers=optimizers, | |||
train_dataloader=train_dataloader, | |||
@@ -318,7 +318,7 @@ def test_torch_distributed_launch_2(version): | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("driver,device", [("torch", 0), ("torch_ddp", [0, 1])]) | |||
@pytest.mark.parametrize("driver,device", [("torch", 0), ("torch", [0, 1])]) | |||
@magic_argv_env_context | |||
def test_torch_wo_auto_param_call( | |||
driver, | |||
@@ -4,6 +4,7 @@ from datasets import Dataset as HfDataset | |||
from fastNLP.core.dataloaders.jittor_dataloader import JittorDataLoader | |||
from fastNLP.core.dataset import DataSet as Fdataset | |||
from fastNLP.core.collators import Collator | |||
from fastNLP.envs.imports import _NEED_IMPORT_JITTOR | |||
if _NEED_IMPORT_JITTOR: | |||
from jittor.dataset import Dataset | |||
@@ -53,9 +54,9 @@ class TestJittor: | |||
jtl.set_ignore("y") | |||
for batch in jtl: | |||
assert batch['x'].size() == (16, 4) | |||
jtl = JittorDataLoader(dataset, batch_size=16, drop_last=True, num_workers=2) | |||
jtl1 = JittorDataLoader(dataset, batch_size=16, drop_last=True, num_workers=2) | |||
for batch in jtl1: | |||
print(batch) | |||
def test_huggingface_datasets(self): | |||
@@ -79,4 +80,11 @@ class TestJittor: | |||
for idx, batch in enumerate(dataset): | |||
print(idx, batch.shape) | |||
for idx, batch in enumerate(dataset): | |||
print(idx, batch.shape) | |||
print(idx, batch.shape) | |||
def test_jittor_get_backend(self): | |||
collate_bacth = Collator(backend='auto') | |||
dl = MyDataset() | |||
dl = dl.set_attrs(collate_batch=collate_bacth, batch_size=256) | |||
for batch in dl: | |||
print(batch) |
@@ -4,11 +4,12 @@ import numpy as np | |||
from fastNLP.core.dataloaders.paddle_dataloader.fdl import PaddleDataLoader | |||
from fastNLP.core.dataset import DataSet | |||
from fastNLP.core.log import logger | |||
from fastNLP.core.collators import Collator | |||
from fastNLP.envs.imports import _NEED_IMPORT_PADDLE | |||
if _NEED_IMPORT_PADDLE: | |||
from paddle.io import Dataset | |||
from paddle.io import Dataset, DataLoader | |||
import paddle | |||
else: | |||
from fastNLP.core.utils.dummy_class import DummyClass as Dataset | |||
@@ -61,3 +62,32 @@ class TestPaddle: | |||
fdl1.set_ignore('label') | |||
for batch in fdl1: | |||
assert batch['image'].shape == [4, 10, 5] | |||
def test_get_backend(self): | |||
ds = RandomDataset() | |||
collate_fn = Collator(backend='auto') | |||
paddle_dl = DataLoader(ds, collate_fn=collate_fn) | |||
for batch in paddle_dl: | |||
print(batch) | |||
def test_v4(self): | |||
from paddle.io import DataLoader | |||
from fastNLP import Collator | |||
from paddle.io import Dataset | |||
import paddle | |||
class PaddleRandomMaxDataset(Dataset): | |||
def __init__(self, num_samples, num_features): | |||
self.x = paddle.randn((num_samples, num_features)) | |||
self.y = self.x.argmax(axis=-1) | |||
def __len__(self): | |||
return len(self.x) | |||
def __getitem__(self, item): | |||
return {"x": self.x[item], "y": self.y[item]} | |||
ds = PaddleRandomMaxDataset(100, 2) | |||
dl = DataLoader(ds, places=None, collate_fn=Collator(), batch_size=4) | |||
for batch in dl: | |||
print(batch) |
@@ -112,3 +112,19 @@ class TestFdl: | |||
seq_ds = prepare_torch_dataloader(sequence) | |||
assert isinstance(seq_ds[0], TorchDataLoader) | |||
assert isinstance(seq_ds[1], TorchDataLoader) | |||
def test_get_backend(self): | |||
from fastNLP.core.collators import Collator | |||
from torch.utils.data import DataLoader, Dataset | |||
class MyDatset(DataSet): | |||
def __len__(self): | |||
return 1000 | |||
def __getitem__(self, item): | |||
return [[1, 0], [1], [1, 2, 4]], [1, 0] | |||
collate_batch = Collator(backend='auto') | |||
dl = DataLoader(MyDatset(), collate_fn=collate_batch) | |||
for batch in dl: | |||
print(batch) |
@@ -626,9 +626,9 @@ class TestSaveLoad: | |||
sampler_states = dataloader.batch_sampler.state_dict() | |||
save_states = {"num_consumed_batches": num_consumed_batches} | |||
if only_state_dict: | |||
self.driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True) | |||
self.driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True) | |||
else: | |||
self.driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[paddle.ones((16, 10))]) | |||
self.driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[paddle.ones((16, 10))]) | |||
# 加载 | |||
# 更改 batch_size | |||
dataloader = DataLoader( | |||
@@ -644,7 +644,7 @@ class TestSaveLoad: | |||
rank=self.driver2.global_rank, | |||
pad=True | |||
) | |||
load_states = self.driver2.load(Path(path), dataloader, only_state_dict, should_load_model=True) | |||
load_states = self.driver2.load_checkpoint(Path(path), dataloader, only_state_dict, should_load_model=True) | |||
replaced_loader = load_states.pop("dataloader") | |||
# 1. 检查 optimizer 的状态 | |||
# TODO optimizer 的 state_dict 总是为空 | |||
@@ -736,9 +736,9 @@ class TestSaveLoad: | |||
sampler_states = dataloader.batch_sampler.sampler.state_dict() | |||
save_states = {"num_consumed_batches": num_consumed_batches} | |||
if only_state_dict: | |||
self.driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True) | |||
self.driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True) | |||
else: | |||
self.driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[paddle.ones((16, 10))]) | |||
self.driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[paddle.ones((16, 10))]) | |||
# 加载 | |||
# 更改 batch_size | |||
batch_sampler = BatchSampler(dataset=self.dataset, batch_size=2) | |||
@@ -752,7 +752,7 @@ class TestSaveLoad: | |||
self.dataset, | |||
batch_sampler=batch_sampler | |||
) | |||
load_states = self.driver2.load(Path(path), dataloader, only_state_dict, should_load_model=True) | |||
load_states = self.driver2.load_checkpoint(Path(path), dataloader, only_state_dict, should_load_model=True) | |||
replaced_loader = load_states.pop("dataloader") | |||
# 1. 检查 optimizer 的状态 | |||
@@ -615,16 +615,16 @@ def test_save_and_load_with_randombatchsampler(only_state_dict, fp16): | |||
sampler_states = dataloader.batch_sampler.state_dict() | |||
save_states = {"num_consumed_batches": num_consumed_batches} | |||
if only_state_dict: | |||
driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True) | |||
driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True) | |||
else: | |||
driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[paddle.ones((16, 10))]) | |||
driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[paddle.ones((16, 10))]) | |||
# 加载 | |||
# 更改 batch_size | |||
dataloader = DataLoader( | |||
dataset=dataset, | |||
batch_sampler=ReproduceBatchSampler(BatchSampler(dataset, batch_size=2, shuffle=True), 2, False) | |||
) | |||
load_states = driver2.load(Path(path), dataloader, only_state_dict, should_load_model=True) | |||
load_states = driver2.load_checkpoint(Path(path), dataloader, only_state_dict, should_load_model=True) | |||
replaced_loader = load_states.pop("dataloader") | |||
# 1. 检查 optimizer 的状态 | |||
# TODO optimizer 的 state_dict 总是为空 | |||
@@ -697,9 +697,9 @@ def test_save_and_load_with_randomsampler(only_state_dict, fp16): | |||
sampler_states = dataloader.batch_sampler.sampler.state_dict() | |||
save_states = {"num_consumed_batches": num_consumed_batches} | |||
if only_state_dict: | |||
driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True) | |||
driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True) | |||
else: | |||
driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[paddle.ones((16, 10))]) | |||
driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[paddle.ones((16, 10))]) | |||
# 加载 | |||
# 更改 batch_size | |||
@@ -709,7 +709,7 @@ def test_save_and_load_with_randomsampler(only_state_dict, fp16): | |||
dataset, | |||
batch_sampler=batch_sampler | |||
) | |||
load_states = driver2.load(Path(path), dataloader, only_state_dict, should_load_model=True) | |||
load_states = driver2.load_checkpoint(Path(path), dataloader, only_state_dict, should_load_model=True) | |||
replaced_loader = load_states.pop("dataloader") | |||
# 1. 检查 optimizer 的状态 | |||
@@ -648,7 +648,7 @@ class TestSaveLoad: | |||
# 保存状态 | |||
sampler_states = dataloader.batch_sampler.state_dict() | |||
save_states = {"num_consumed_batches": num_consumed_batches} | |||
driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True) | |||
driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True) | |||
# 加载 | |||
# 更改 batch_size | |||
dataloader = dataloader_with_bucketedbatchsampler( | |||
@@ -663,7 +663,7 @@ class TestSaveLoad: | |||
rank=driver2.global_rank, | |||
pad=True | |||
) | |||
load_states = driver2.load(Path(path), dataloader, only_state_dict, should_load_model=True) | |||
load_states = driver2.load_checkpoint(Path(path), dataloader, only_state_dict, should_load_model=True) | |||
replaced_loader = load_states.pop("dataloader") | |||
# 1. 检查 optimizer 的状态 | |||
# TODO optimizer 的 state_dict 总是为空 | |||
@@ -754,9 +754,9 @@ class TestSaveLoad: | |||
sampler_states = dataloader.batch_sampler.sampler.state_dict() | |||
save_states = {"num_consumed_batches": num_consumed_batches} | |||
if only_state_dict: | |||
driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True) | |||
driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True) | |||
else: | |||
driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[torch.ones((16, 10))]) | |||
driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[torch.ones((16, 10))]) | |||
# 加载 | |||
# 更改 batch_size | |||
dataloader = dataloader_with_randomsampler(self.dataset, 2, True, False, unrepeated=False) | |||
@@ -765,7 +765,7 @@ class TestSaveLoad: | |||
rank=driver2.global_rank, | |||
pad=True | |||
) | |||
load_states = driver2.load(Path(path), dataloader, only_state_dict, should_load_model=True) | |||
load_states = driver2.load_checkpoint(Path(path), dataloader, only_state_dict, should_load_model=True) | |||
replaced_loader = load_states.pop("dataloader") | |||
# 1. 检查 optimizer 的状态 | |||
@@ -37,28 +37,6 @@ def test_get_single_device(driver, device): | |||
driver = initialize_torch_driver(driver, device, model) | |||
assert isinstance(driver, TorchSingleDriver) | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize( | |||
"device", | |||
[0, 1] | |||
) | |||
@pytest.mark.parametrize( | |||
"driver", | |||
["torch_ddp"] | |||
) | |||
@magic_argv_env_context | |||
def test_get_ddp_2(driver, device): | |||
""" | |||
测试 ddp 多卡的初始化情况,但传入了单个 gpu | |||
""" | |||
model = TorchNormalModel_Classification_1(64, 10) | |||
driver = initialize_torch_driver(driver, device, model) | |||
assert isinstance(driver, TorchDDPDriver) | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize( | |||
"device", | |||
@@ -66,7 +44,7 @@ def test_get_ddp_2(driver, device): | |||
) | |||
@pytest.mark.parametrize( | |||
"driver", | |||
["torch", "torch_ddp"] | |||
["torch"] | |||
) | |||
@magic_argv_env_context | |||
def test_get_ddp(driver, device): | |||
@@ -79,21 +57,6 @@ def test_get_ddp(driver, device): | |||
assert isinstance(driver, TorchDDPDriver) | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize( | |||
("driver", "device"), | |||
[("torch_ddp", "cpu")] | |||
) | |||
def test_get_ddp_cpu(driver, device): | |||
""" | |||
测试试图在 cpu 上初始化分布式训练的情况 | |||
""" | |||
model = TorchNormalModel_Classification_1(64, 10) | |||
with pytest.raises(ValueError): | |||
driver = initialize_torch_driver(driver, device, model) | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize( | |||
"device", | |||
@@ -101,7 +64,7 @@ def test_get_ddp_cpu(driver, device): | |||
) | |||
@pytest.mark.parametrize( | |||
"driver", | |||
["torch", "torch_ddp"] | |||
["torch"] | |||
) | |||
def test_device_out_of_range(driver, device): | |||
""" | |||
@@ -595,12 +595,12 @@ def test_save_and_load_with_randombatchsampler(only_state_dict, fp16): | |||
sampler_states = dataloader.batch_sampler.state_dict() | |||
save_states = {"num_consumed_batches": num_consumed_batches} | |||
driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True) | |||
driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True) | |||
# 加载 | |||
# 更改 batch_size | |||
dataloader = dataloader_with_randombatchsampler(dataset, 2, True, False) | |||
load_states = driver2.load(Path(path), dataloader, only_state_dict, should_load_model=True) | |||
load_states = driver2.load_checkpoint(Path(path), dataloader, only_state_dict, should_load_model=True) | |||
replaced_loader = load_states.pop("dataloader") | |||
# 1. 检查 optimizer 的状态 | |||
# TODO optimizer 的 state_dict 总是为空 | |||
@@ -664,12 +664,12 @@ def test_save_and_load_with_randomsampler(only_state_dict, fp16): | |||
sampler_states = dataloader.batch_sampler.sampler.state_dict() | |||
save_states = {"num_consumed_batches": num_consumed_batches} | |||
driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True) | |||
driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True) | |||
# 加载 | |||
# 更改 batch_size | |||
dataloader = dataloader_with_randomsampler(dataset, 2, True, False) | |||
load_states = driver2.load(Path(path), dataloader, only_state_dict, should_load_model=True) | |||
load_states = driver2.load_checkpoint(Path(path), dataloader, only_state_dict, should_load_model=True) | |||
replaced_loader = load_states.pop("dataloader") | |||
# 1. 检查 optimizer 的状态 | |||
@@ -7,8 +7,9 @@ from fastNLP import Vocabulary, DataSet, Instance | |||
from fastNLP.embeddings.torch.char_embedding import LSTMCharEmbedding, CNNCharEmbedding | |||
@pytest.mark.torch | |||
class TestCharEmbed: | |||
@pytest.mark.test | |||
# @pytest.mark.test | |||
def test_case_1(self): | |||
ds = DataSet([Instance(words=['hello', 'world']), Instance(words=['Jack'])]) | |||
vocab = Vocabulary().from_dataset(ds, field_name='words') | |||
@@ -18,7 +19,7 @@ class TestCharEmbed: | |||
y = embed(x) | |||
assert tuple(y.size()) == (2, 3, 3) | |||
@pytest.mark.test | |||
# @pytest.mark.test | |||
def test_case_2(self): | |||
ds = DataSet([Instance(words=['hello', 'world']), Instance(words=['Jack'])]) | |||
vocab = Vocabulary().from_dataset(ds, field_name='words') | |||