r"""
losses 模块定义了 fastNLP 中所需的各种损失函数，一般做为 :class:`~fastNLP.Trainer` 的参数使用。
"""
__all__ = [
"LossBase",
"LossFunc",
"LossInForward",
"CrossEntropyLoss",
"BCELoss",
"BCEWithLogits",
"L1Loss",
"NLLLoss",
"MSELoss",
"CMRC2018Loss"
]
import inspect
from collections import defaultdict
import torch
import torch.nn.functional as F
from .utils import _CheckError
from .utils import _CheckRes
from .utils import _build_args
from .utils import _check_arg_dict_list
from .utils import _check_function_or_method
from .utils import _get_func_signature
from .utils import seq_len_to_mask
from ..core.const import Const
[文档]class LossBase(object):
r"""
所有loss的基类。如果需要结合到Trainer之中需要实现get_loss方法
"""
def __init__(self):
self._param_map = {} # key是fun的参数，value是以该值从传入的dict取出value
self._checked = False
@property
def param_map(self):
if len(self._param_map) == 0: # 如果为空说明还没有初始化
func_spect = inspect.getfullargspec(self.get_loss)
func_args = [arg for arg in func_spect.args if arg != 'self']
for arg in func_args:
self._param_map[arg] = arg
return self._param_map
[文档] def get_loss(self, *args, **kwargs):
"""
:param args:
:param kwargs:
:return: torch.Tensor
"""
raise NotImplementedError
def _init_param_map(self, key_map=None, **kwargs):
r"""检查key_map和其他参数map，并将这些映射关系添加到self._param_map
:param dict key_map: 表示key的映射关系
:param kwargs: key word args里面的每一个的键-值对都会被构造成映射关系
:return: None
"""
value_counter = defaultdict(set)
if key_map is not None:
if not isinstance(key_map, dict):
raise TypeError("key_map must be `dict`, got {}.".format(type(key_map)))
for key, value in key_map.items():
if value is None:
self._param_map[key] = key
continue
if not isinstance(key, str):
raise TypeError(f"key in key_map must be `str`, not `{type(key)}`.")
if not isinstance(value, str):
raise TypeError(f"value in key_map must be `str`, not `{type(value)}`.")
self._param_map[key] = value
value_counter[value].add(key)
for key, value in kwargs.items():
if value is None:
self._param_map[key] = key
continue
if not isinstance(value, str):
raise TypeError(f"in {key}={value}, value must be `str`, not `{type(value)}`.")
self._param_map[key] = value
value_counter[value].add(key)
for value, key_set in value_counter.items():
if len(key_set) > 1:
raise ValueError(f"Several parameters:{key_set} are provided with one output {value}.")
# check consistence between signature and _param_map
func_spect = inspect.getfullargspec(self.get_loss)
func_args = [arg for arg in func_spect.args if arg != 'self']
for func_param, input_param in self._param_map.items():
if func_param not in func_args:
raise NameError(
f"Parameter `{func_param}` is not in {_get_func_signature(self.get_loss)}. Please check the "
f"initialization parameters, or change its signature.")
# evaluate should not have varargs.
# if func_spect.varargs:
# raise NameError(f"Delete `*{func_spect.varargs}` in {get_func_signature(self.get_loss)}(Do not use "
# f"positional argument.).")
def __call__(self, pred_dict, target_dict, check=False):
r"""
:param dict pred_dict: 模型的forward函数返回的dict
:param dict target_dict: DataSet.batch_y里的键-值对所组成的dict
:param Boolean check: 每一次执行映射函数的时候是否检查映射表，默认为不检查
:return:
"""
if not self._checked:
# 1. check consistence between signature and _param_map
func_spect = inspect.getfullargspec(self.get_loss)
func_args = set([arg for arg in func_spect.args if arg != 'self'])
for func_arg, input_arg in self._param_map.items():
if func_arg not in func_args:
raise NameError(f"`{func_arg}` not in {_get_func_signature(self.get_loss)}.")
# 2. only part of the _param_map are passed, left are not
for arg in func_args:
if arg not in self._param_map:
self._param_map[arg] = arg # This param does not need mapping.
self._evaluate_args = func_args
self._reverse_param_map = {input_arg: func_arg for func_arg, input_arg in self._param_map.items()}
mapped_pred_dict = {}
mapped_target_dict = {}
for input_arg, mapped_arg in self._reverse_param_map.items():
if input_arg in pred_dict:
mapped_pred_dict[mapped_arg] = pred_dict[input_arg]
if input_arg in target_dict:
mapped_target_dict[mapped_arg] = target_dict[input_arg]
# missing
if not self._checked:
duplicated = []
for input_arg, mapped_arg in self._reverse_param_map.items():
if input_arg in pred_dict and input_arg in target_dict:
duplicated.append(input_arg)
check_res = _check_arg_dict_list(self.get_loss, [mapped_pred_dict, mapped_target_dict])
# replace missing.
missing = check_res.missing
replaced_missing = list(missing)
for idx, func_arg in enumerate(missing):
# Don't delete `` in this information, nor add ``
replaced_missing[idx] = f"{self._param_map[func_arg]}" + f"(assign to `{func_arg}` " \
f"in `{self.__class__.__name__}`)"
check_res = _CheckRes(missing=replaced_missing,
unused=check_res.unused,
duplicated=duplicated,
required=check_res.required,
all_needed=check_res.all_needed,
varargs=check_res.varargs)
if check_res.missing or check_res.duplicated:
raise _CheckError(check_res=check_res,
func_signature=_get_func_signature(self.get_loss))
self._checked = True
refined_args = _build_args(self.get_loss, **mapped_pred_dict, **mapped_target_dict)
loss = self.get_loss(**refined_args)
self._checked = True
return loss
[文档]class LossFunc(LossBase):
r"""
提供给用户使用自定义损失函数的类
:param func: 用户自行定义的损失函数，应当为一个函数。
:param dict key_map: 参数映射表。键为Model/DataSet参数名，值为损失函数参数名。
fastNLP的trainer将在训练时从模型返回值或者训练数据DataSet的target=True的field中
找到相对应的参数名为value的参数，并传入func中作为参数名为key的参数
:param kwargs: 除了参数映射表以外可以用key word args的方式设置参数映射关系
使用方法::
import torch.nn.functional as F
loss_func = LossFunc(F.cross_entropy, input="pred", target="label")
# 这表示构建了一个损失函数类，由func计算损失函数，其中将从模型返回值或者DataSet的target=True的field
# 当中找到一个参数名为`pred`的参数传入func一个参数名为`input`的参数；找到一个参数名为`label`的参数
# 传入func作为一个名为`target`的参数
"""
def __init__(self, func, key_map=None, **kwargs):
super(LossFunc, self).__init__()
_check_function_or_method(func)
self.get_loss = func
if key_map is not None:
if not isinstance(key_map, dict):
raise RuntimeError(f"Loss error: key_map except a {type({})} but got a {type(key_map)}")
self._init_param_map(key_map, **kwargs)
[文档]class CrossEntropyLoss(LossBase):
r"""
交叉熵损失函数
:param pred: 参数映射表中 `pred` 的映射关系，None表示映射关系为 `pred` -> `pred`
:param target: 参数映射表中 `target` 的映射关系，None表示映射关系为 `target` -> `target`
:param seq_len: 句子的长度, 长度之外的token不会计算loss。
:param int class_in_dim: 在序列标注的场景中，pred可能的shape为(batch_size, max_len, num_classes)
或(batch_size, num_classes, max_len)， CrossEntropyLoss需要知道哪一维是class的维度以计算loss。如果为-1，就根据pred的第
二维是否等于target的第二维来判断是否需要交换pred的第二维和第三维，因为target的第二维是length的维度，如果这一维度上和pred相等，
那么pred可能第二维也是长度维(存在误判的可能，如果有误判的情况，请显示设置该值)。其它大于0的值则认为该维度是class的维度。
:param ignore_idx: padding的index，在计算loss时将忽略target中标号为padding_idx的内容, 可以通过该值代替
传入seq_len.
:param str reduction: 支持 `mean` ，`sum` 和 `none` .
Example::
loss = CrossEntropyLoss(pred='pred', target='label', padding_idx=0)
"""
def __init__(self, pred=None, target=None, seq_len=None, class_in_dim=-1, ignore_idx=-100, reduction='mean', **kwargs):
super(CrossEntropyLoss, self).__init__()
self._init_param_map(pred=pred, target=target, seq_len=seq_len)
ignore_idx = kwargs.pop('padding_idx', ignore_idx)
self.ignore_idx = ignore_idx
assert reduction in ('mean', 'sum', 'none')
self.reduction = reduction
self.class_in_dim = class_in_dim
def get_loss(self, pred, target, seq_len=None):
if seq_len is not None and target.dim()>1:
mask = seq_len_to_mask(seq_len, max_len=target.size(1)).eq(False)
target = target.masked_fill(mask, self.ignore_idx)
if pred.dim() > 2:
if self.class_in_dim == -1:
if pred.size(1) != target.size(1): # 有可能顺序替换了
pred = pred.transpose(1, 2)
else:
pred = pred.transpose(-1, self.class_in_dim)
pred = pred.reshape(-1, pred.size(-1))
target = target.reshape(-1)
return F.cross_entropy(input=pred, target=target,
ignore_index=self.ignore_idx, reduction=self.reduction)
[文档]class L1Loss(LossBase):
r"""
L1损失函数
:param pred: 参数映射表中 `pred` 的映射关系，None表示映射关系为 `pred` -> `pred`
:param target: 参数映射表中 `target` 的映射关系，None表示映射关系为 `target` >`target`
:param str reduction: 支持'mean'，'sum'和'none'.
"""
def __init__(self, pred=None, target=None, reduction='mean'):
super(L1Loss, self).__init__()
self._init_param_map(pred=pred, target=target)
assert reduction in ('mean', 'sum', 'none')
self.reduction = reduction
def get_loss(self, pred, target):
return F.l1_loss(input=pred, target=target, reduction=self.reduction)
[文档]class MSELoss(LossBase):
r"""
MSE损失函数
:param pred: 参数映射表中 `pred` 的映射关系，None表示映射关系为 `pred` -> `pred`
:param target: 参数映射表中 `target` 的映射关系，None表示映射关系为 `target` >`target`
:param str reduction: 支持'mean'，'sum'和'none'.
"""
def __init__(self, pred=None, target=None, reduction='mean'):
super(MSELoss, self).__init__()
self._init_param_map(pred=pred, target=target)
assert reduction in ('mean', 'sum', 'none')
self.reduction = reduction
def get_loss(self, pred, target):
return F.mse_loss(input=pred, target=target, reduction=self.reduction)
[文档]class BCELoss(LossBase):
r"""
二分类交叉熵损失函数
:param pred: 参数映射表中 `pred` 的映射关系，None表示映射关系为 `pred` -> `pred`
:param target: 参数映射表中 `target` 的映射关系，None表示映射关系为 `target` -> `target`
:param str reduction: 支持 `mean` ，`sum` 和 `none` .
"""
def __init__(self, pred=None, target=None, reduction='mean'):
super(BCELoss, self).__init__()
self._init_param_map(pred=pred, target=target)
assert reduction in ('mean', 'sum', 'none')
self.reduction = reduction
def get_loss(self, pred, target):
return F.binary_cross_entropy(input=pred, target=target, reduction=self.reduction)
[文档]class BCEWithLogits(LossBase):
r"""
二分类交叉熵损失函数, 传入数据之前不需要做sigmoid操作
:param pred: 参数映射表中 `pred` 的映射关系，None表示映射关系为 `pred` -> `pred`
:param target: 参数映射表中 `target` 的映射关系，None表示映射关系为 `target` -> `target`
:param int class_in_dim: 在序列标注的场景中，pred可能的shape为(batch_size, max_len, num_classes)
或(batch_size, num_classes, max_len)， BCEWithLogits需要知道哪一维是class的维度以计算loss。如果为-1，就根据pred的第
二维是否等于target的第二维来判断是否需要交换pred的第二维和第三维，因为target的第二维是length的维度，如果这一维度上和pred相等，
那么pred可能第二维也是长度维(存在误判的可能，如果有误判的情况，请显示设置该值)。其它大于0的值则认为该维度是class的维度。
:param str reduction: 支持 `mean` ，`sum` 和 `none` .
"""
def __init__(self, pred=None, target=None, class_in_dim=-1, reduction='mean'):
super(BCEWithLogits, self).__init__()
self._init_param_map(pred=pred, target=target)
assert reduction in ('mean', 'sum', 'none')
self.reduction = reduction
self.class_in_dim = class_in_dim
def get_loss(self, pred, target):
if pred.dim() > 2:
if self.class_in_dim == -1:
if pred.size(1) != target.size(1): # 有可能顺序替换了
pred = pred.transpose(1, 2)
else:
pred = pred.transpose(-1, self.class_in_dim)
pred = pred.reshape(-1)
target = target.reshape(-1)
return F.binary_cross_entropy_with_logits(input=pred, target=target, reduction=self.reduction)
[文档]class NLLLoss(LossBase):
r"""
负对数似然损失函数
"""
[文档] def __init__(self, pred=None, target=None, seq_len=None, class_in_dim=-1, ignore_idx=-100, reduction='mean'):
r"""
:param pred: 参数映射表中 `pred` 的映射关系，None表示映射关系为 `pred` -> `pred`
:param target: 参数映射表中 `target` 的映射关系，None表示映射关系为 `target` -> `target`
:param seq_len: 句子的长度, 长度之外的token不会计算loss。仅在输出为3d时需要
:param int class_in_dim: 在序列标注的场景中，pred可能的shape为(batch_size, max_len, num_classes)
或(batch_size, num_classes, max_len)， CrossEntropyLoss需要知道哪一维是class的维度以计算loss。如果为-1，就根据pred的第
二维是否等于target的第二维来判断是否需要交换pred的第二维和第三维，因为target的第二维是length的维度，如果这一维度上和pred相等，
那么pred可能第二维也是长度维(存在误判的可能，如果有误判的情况，请显示设置该值)。其它大于0的值则认为该维度是class的维度。
:param ignore_idx: ignore的index，在计算loss时将忽略target中标号为ignore_idx的内容, 可以通过该值代替
传入seq_len.
:param str reduction: 支持 `mean` ，`sum` 和 `none` .
"""
super(NLLLoss, self).__init__()
self._init_param_map(pred=pred, target=target, seq_len=seq_len)
assert reduction in ('mean', 'sum', 'none')
self.reduction = reduction
self.ignore_idx = ignore_idx
self.class_in_dim = class_in_dim
def get_loss(self, pred, target, seq_len=None):
if seq_len is not None and target.dim()>1:
mask = seq_len_to_mask(seq_len, max_len=target.size(1)).eq(False)
target = target.masked_fill(mask, self.ignore_idx)
if pred.dim() > 2:
if self.class_in_dim == -1:
if pred.size(1) != target.size(1): # 有可能顺序替换了
pred = pred.transpose(1, 2)
else:
pred = pred.transpose(-1, self.class_in_dim)
pred = pred.reshape(-1, pred.size(-1))
target = target.reshape(-1)
return F.nll_loss(input=pred, target=target, ignore_index=self.ignore_idx, reduction=self.reduction)
[文档]class LossInForward(LossBase):
r"""
从forward()函数返回结果中获取loss
"""
[文档] def __init__(self, loss_key=Const.LOSS):
r"""
:param str loss_key: 在forward函数中loss的键名，默认为loss
"""
super().__init__()
if not isinstance(loss_key, str):
raise TypeError(f"Only str allowed for loss_key, got {type(loss_key)}.")
self.loss_key = loss_key
def get_loss(self, **kwargs):
if self.loss_key not in kwargs:
check_res = _CheckRes(
missing=[self.loss_key + f"(assign to `{self.loss_key}` in `{self.__class__.__name__}`"],
unused=[],
duplicated=[],
required=[],
all_needed=[],
varargs=[])
raise _CheckError(check_res=check_res, func_signature=_get_func_signature(self.get_loss))
return kwargs[self.loss_key]
def __call__(self, pred_dict, target_dict, check=False):
loss = self.get_loss(**pred_dict)
if not (isinstance(loss, torch.Tensor) and len(loss.size()) == 0):
if not isinstance(loss, torch.Tensor):
raise TypeError(f"Loss excepted to be a torch.Tensor, got {type(loss)}")
loss = torch.sum(loss) / (loss.view(-1)).size(0)
# raise RuntimeError(f"The size of loss excepts to be torch.Size([]), got {loss.size()}")
return loss
[文档]class CMRC2018Loss(LossBase):
r"""
用于计算CMRC2018中文问答任务。
"""
def __init__(self, target_start=None, target_end=None, context_len=None, pred_start=None, pred_end=None,
reduction='mean'):
super().__init__()
assert reduction in ('mean', 'sum')
self._init_param_map(target_start=target_start, target_end=target_end, context_len=context_len,
pred_start=pred_start, pred_end=pred_end)
self.reduction = reduction
[文档] def get_loss(self, target_start, target_end, context_len, pred_start, pred_end):
r"""
:param target_start: batch_size
:param target_end: batch_size
:param context_len: batch_size
:param pred_start: batch_size x max_len
:param pred_end: batch_size x max_len
:return:
"""
batch_size, max_len = pred_end.size()
mask = seq_len_to_mask(context_len, max_len).eq(False)
pred_start = pred_start.masked_fill(mask, float('-inf'))
pred_end = pred_end.masked_fill(mask, float('-inf'))
start_loss = F.cross_entropy(pred_start, target_start, reduction='sum')
end_loss = F.cross_entropy(pred_end, target_end, reduction='sum')
loss = start_loss + end_loss
if self.reduction == 'mean':
loss = loss / batch_size
return loss/2
def _prepare_losser(losser):
if losser is None:
losser = LossInForward()
return losser
elif isinstance(losser, LossBase):
return losser
else:
raise TypeError(f"Type of loss should be `fastNLP.LossBase`, got {type(losser)}")