File size: 3,847 Bytes
53ebf66 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | import torch
import numpy as np
import pandas as pd
from torch.utils.data.sampler import Sampler,BatchSampler,SubsetRandomSampler
class Basic_sampler(Sampler):
def __init__(self, data):
super().__init__(data)
self.data= data
def __len__(self):
return self.data.shape[0]
def __iter__(self):
return (i for i in range(self.data.shape[0]))
class sort_sampler(Sampler):
def __init__(self, data, sort_key="utr_len"):
super().__init__(data)
self.data = data
self.sort_key = sort_key
zip_ = [(i, seq_len) for i, seq_len in enumerate(data[sort_key].values)]
zip_ = sorted(zip_, key=lambda r: r[1])
self.sorted_indexes = [item[0] for item in zip_]
def __iter__(self):
return iter(self.sorted_indexes)
def __len__(self):
return len(self.data)
class Bucket_Sampler(BatchSampler):
""" `BucketBatchSampler` toggles between `sampler` batches and sorted batches.
Typically, the `sampler` will be a `RandomSampler` allowing the user to toggle between
random batches and sorted batches. A larger `bucket_size_multiplier` is more sorted and vice
versa.
Background:
``BucketBatchSampler`` is similar to a ``BucketIterator`` found in popular libraries like
``AllenNLP`` and ``torchtext``. A ``BucketIterator`` pools together examples with a similar
size length to reduce the padding required for each batch while maintaining some noise
through bucketing.
**AllenNLP Implementation:**
https://github.com/allenai/allennlp/blob/master/allennlp/data/iterators/bucket_iterator.py
**torchtext Implementation:**
https://github.com/pytorch/text/blob/master/torchtext/data/iterator.py#L225
Args:
sampler (torch.data.utils.sampler.Sampler):
batch_size (int): Size of mini-batch.
drop_last (bool): If `True` the sampler will drop the last batch if its size would be less
than `batch_size`.
sort_key (callable, optional): Callable to specify a comparison key for sorting.
bucket_size_multiplier (int, optional): Buckets are of size
`batch_size * bucket_size_multiplier`.
Example:
>>> from torchnlp.random import set_seed
>>> set_seed(123)
>>>
>>> from torch.utils.data.sampler import SequentialSampler
>>> sampler = SequentialSampler(list(range(10)))
>>> list(BucketBatchSampler(sampler, batch_size=3, drop_last=False))
[[6, 7, 8], [0, 1, 2], [3, 4, 5], [9]]
>>> list(BucketBatchSampler(sampler, batch_size=3, drop_last=True))
[[0, 1, 2], [3, 4, 5], [6, 7, 8]]
"""
def __init__(self,
data,
batch_size,
drop_last=False,
sort_key='utr_len',
bucket_size_multiplier=100):
self.data = data
self.sampler = Basic_sampler(data)
super().__init__(self.sampler, batch_size, drop_last)
self.sort_key = sort_key
_bucket_size = batch_size * bucket_size_multiplier
if hasattr(self.sampler, "__len__"):
_bucket_size = min(_bucket_size, len(self.sampler))
self.bucket_sampler = BatchSampler(self.sampler, _bucket_size, False)
def __iter__(self):
for bucket in self.bucket_sampler:
sorted_sampler = sort_sampler(self.data.iloc[bucket], self.sort_key)
for batch in SubsetRandomSampler(
list(BatchSampler(sorted_sampler, self.batch_size, self.drop_last))):
yield [bucket[i] for i in batch]
def __len__(self):
if self.drop_last:
return len(self.sampler) // self.batch_size
else:
return np.ceil(len(self.sampler) / self.batch_size)
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