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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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import numpy as np
import paddle
import paddle.nn.functional as F
[docs]class Perplexity(paddle.metric.Metric):
"""
Perplexity is a metric used to judge how good a language model is.
We can define perplexity as the inverse probability of the test set,
normalised by the number of the words in the test set.
Perplexity is calculated using cross entropy. It supports both padding data
and no padding data.
If data is not padded, users should provide `seq_len` for `Metric`
initialization. If data is padded, your label should contain `seq_mask`,
which indicates the actual length of samples.
This Perplexity requires that the output of your network is prediction,
label and sequence length (optional). If the Perplexity here doesn't meet
your needs, you could override the `compute` or `update` method for
calculating Perplexity.
Args:
seq_len(int): Sequence length of each sample, it must be provided while
data is not padded. Defaults to 20.
name(str): Name of `Metric` instance. Defaults to 'Perplexity'.
Example:
.. code-block::
import paddle
from paddlenlp.transformers import BertTokenizer
from paddlenlp.metrics import Perplexity
paddle.seed(2021)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
batch_size, seq_len, vocab_size = 1, 4, tokenizer.vocab_size
logits = paddle.rand([batch_size, seq_len, vocab_size])
labels= paddle.to_tensor([[1,0,1,1]])
perplexity = Perplexity()
correct = perplexity.compute(logits,labels)
perplexity.update(correct.numpy())
res = perplexity.accumulate()
print(res)
# 48263.528820122105
"""
def __init__(self, name="Perplexity", *args, **kwargs):
super(Perplexity, self).__init__(*args, **kwargs)
self._name = name
self.total_ce = 0
self.total_word_num = 0
[docs] def compute(self, pred, label, seq_mask=None):
"""
Computes cross entropy loss.
Args:
pred (Tensor):
Predictor tensor, and its dtype is float32 or float64, and has
a shape of [batch_size, sequence_length, vocab_size].
label(Tensor):
Label tensor, and its dtype is int64, and has a shape of
[batch_size, sequence_length, 1] or [batch_size, sequence_length].
seq_mask(Tensor, optional):
Sequence mask tensor, and its type could be float32, float64,
int32 or int64, and has a shape of [batch_size, sequence_length].
It's used to calculate loss. Defaults to None.
Returns:
tuple or Tensor: Returns tuple (`ce, word_num`) if `seq_mask` is not None. Otherwise, returns tensor `ce`.
`ce` it the cross entropy loss, its shape is [batch_size, sequence_length] and its data type should be float32.
"""
if label.dim() == 2:
label = paddle.unsqueeze(label, axis=2)
ce = F.cross_entropy(input=pred, label=label, reduction="none", soft_label=False)
ce = paddle.squeeze(ce, axis=[2])
if seq_mask is not None:
ce = ce * seq_mask
word_num = paddle.sum(seq_mask)
return ce, word_num
return ce
[docs] def update(self, ce, word_num=None):
"""
Updates metric states.
Args:
ce (numpy.ndarray):
Cross entropy loss, it's calculated by `compute` and converted
to `numpy.ndarray`.
word_num (numpy.ndarray):
The number of words of sequence, it's calculated by `compute`
and converted to `numpy.ndarray`. Defaults to None.
"""
batch_ce = np.sum(ce)
if word_num is None:
word_num = ce.shape[0] * ce.shape[1]
else:
word_num = word_num.item()
self.total_ce += batch_ce
self.total_word_num += word_num
[docs] def reset(self):
"""
Resets all metric states.
"""
self.total_ce = 0
self.total_word_num = 0
[docs] def accumulate(self):
"""
Calculates and returns the value of perplexity.
Returns:
float: Returns `perplexity`, the calculation results.
"""
return np.exp(self.total_ce / self.total_word_num)
[docs] def name(self):
"""
Returns name of the metric instance.
Returns:
str: The name of the metric instance.
"""
return self._name
