# coding: utf8
from collections import Counter, OrderedDict
from torch.utils.data.dataloader import default_collate
from itertools import chain
import six
import torch
import numpy as np
import h5py
import os
import warnings
import shutil
from .dataset import Dataset
from .vocab import Vocab
from .utils import get_tokenizer
class RawField(object):
""" Defines a general datatype.
Every dataset consists of one or more types of data. For instance,
a machine translation dataset contains paired examples of text, while
an image captioning dataset contains images and texts.
Each of these types of data is represented by a RawField object.
An RawField object does not assume any property of the data type and
it holds parameters relating to how a datatype should be processed.
Attributes:
preprocessing: The Pipeline that will be applied to examples
using this field before creating an example.
Default: None.
postprocessing: A Pipeline that will be applied to a list of examples
using this field before assigning to a batch.
Function signature: (batch(list)) -> object
Default: None.
"""
def __init__(self, preprocessing=None, postprocessing=None):
self.preprocessing = preprocessing
self.postprocessing = postprocessing
def preprocess(self, x):
""" Preprocess an example if the `preprocessing` Pipeline is provided. """
if self.preprocessing is not None:
return self.preprocessing(x)
else:
return x
def process(self, batch, *args, **kwargs):
""" Process a list of examples to create a batch.
Postprocess the batch with user-provided Pipeline.
Args:
batch (list(object)): A list of object from a batch of examples.
Returns:
object: Processed object given the input and custom
postprocessing Pipeline.
"""
if self.postprocessing is not None:
batch = self.postprocessing(batch)
return default_collate(batch)
class Merge(RawField):
def __init__(self, *fields):
super(Merge, self).__init__()
self.fields = fields
def preprocess(self, x):
return tuple(f.preprocess(x) for f in self.fields)
def process(self, batch, *args, **kwargs):
if len(self.fields) == 1:
batch = [batch, ]
else:
batch = list(zip(*batch))
out = list(f.process(b, *args, **kwargs) for f, b in zip(self.fields, batch))
return out
class ImageDetectionsField(RawField):
def __init__(self, preprocessing=None, postprocessing=None, detections_path=None, max_detections=100,
sort_by_prob=False, load_in_tmp=True):
self.max_detections = max_detections
self.detections_path = detections_path
self.sort_by_prob = sort_by_prob
tmp_detections_path = os.path.join('/tmp', os.path.basename(detections_path))
if load_in_tmp:
if not os.path.isfile(tmp_detections_path):
if shutil.disk_usage("/tmp")[-1] < os.path.getsize(detections_path):
warnings.warn('Loading from %s, because /tmp has no enough space.' % detections_path)
else:
warnings.warn("Copying detection file to /tmp")
shutil.copyfile(detections_path, tmp_detections_path)
warnings.warn("Done.")
self.detections_path = tmp_detections_path
else:
self.detections_path = tmp_detections_path
super(ImageDetectionsField, self).__init__(preprocessing, postprocessing)
def preprocess(self, x, avoid_precomp=False):
image_id = int(x.split('_')[-1].split('.')[0])
try:
f = h5py.File(self.detections_path, 'r')
precomp_data = f['%d_features' % image_id][()]
if self.sort_by_prob:
precomp_data = precomp_data[np.argsort(np.max(f['%d_cls_prob' % image_id][()], -1))[::-1]]
except KeyError:
warnings.warn('Could not find detections for %d' % image_id)
precomp_data = np.random.rand(10,2048)
delta = self.max_detections - precomp_data.shape[0]
if delta > 0:
precomp_data = np.concatenate([precomp_data, np.zeros((delta, precomp_data.shape[1]))], axis=0)
elif delta < 0:
precomp_data = precomp_data[:self.max_detections]
return precomp_data.astype(np.float32)
class TextField(RawField):
vocab_cls = Vocab
# Dictionary mapping PyTorch tensor dtypes to the appropriate Python
# numeric type.
dtypes = {
torch.float32: float,
torch.float: float,
torch.float64: float,
torch.double: float,
torch.float16: float,
torch.half: float,
torch.uint8: int,
torch.int8: int,
torch.int16: int,
torch.short: int,
torch.int32: int,
torch.int: int,
torch.int64: int,
torch.long: int,
}
punctuations = ["''", "'", "``", "`", "-LRB-", "-RRB-", "-LCB-", "-RCB-", \
".", "?", "!", ",", ":", "-", "--", "...", ";"]
def __init__(self, use_vocab=True, init_token=None, eos_token=None, fix_length=None, dtype=torch.long,
preprocessing=None, postprocessing=None, lower=False, tokenize=(lambda s: s.split()),
remove_punctuation=False, include_lengths=False, batch_first=True, pad_token="<pad>",
unk_token="<unk>", pad_first=False, truncate_first=False, vectors=None, nopoints=True):
self.use_vocab = use_vocab
self.init_token = init_token
self.eos_token = eos_token
self.fix_length = fix_length
self.dtype = dtype
self.lower = lower
self.tokenize = get_tokenizer(tokenize)
self.remove_punctuation = remove_punctuation
self.include_lengths = include_lengths
self.batch_first = batch_first
self.pad_token = pad_token
self.unk_token = unk_token
self.pad_first = pad_first
self.truncate_first = truncate_first
self.vocab = None
self.vectors = vectors
if nopoints:
self.punctuations.append("..")
super(TextField, self).__init__(preprocessing, postprocessing)
def preprocess(self, x):
if six.PY2 and isinstance(x, six.string_types) and not isinstance(x, six.text_type):
x = six.text_type(x, encoding='utf-8')
if self.lower:
x = six.text_type.lower(x)
x = self.tokenize(x.rstrip('\n'))
if self.remove_punctuation:
x = [w for w in x if w not in self.punctuations]
if self.preprocessing is not None:
return self.preprocessing(x)
else:
return x
def process(self, batch, device=None):
padded = self.pad(batch)
tensor = self.numericalize(padded, device=device)
return tensor
def build_vocab(self, *args, **kwargs):
counter = Counter()
sources = []
for arg in args:
if isinstance(arg, Dataset):
sources += [getattr(arg, name) for name, field in arg.fields.items() if field is self]
else:
sources.append(arg)
for data in sources:
for x in data:
x = self.preprocess(x)
try:
counter.update(x)
except TypeError:
counter.update(chain.from_iterable(x))
specials = list(OrderedDict.fromkeys([
tok for tok in [self.unk_token, self.pad_token, self.init_token,
self.eos_token]
if tok is not None]))
self.vocab = self.vocab_cls(counter, specials=specials, **kwargs)
def pad(self, minibatch):
"""Pad a batch of examples using this field.
Pads to self.fix_length if provided, otherwise pads to the length of
the longest example in the batch. Prepends self.init_token and appends
self.eos_token if those attributes are not None. Returns a tuple of the
padded list and a list containing lengths of each example if
`self.include_lengths` is `True`, else just
returns the padded list.
"""
minibatch = list(minibatch)
if self.fix_length is None:
max_len = max(len(x) for x in minibatch)
else:
max_len = self.fix_length + (
self.init_token, self.eos_token).count(None) - 2
padded, lengths = [], []
for x in minibatch:
if self.pad_first:
padded.append(
[self.pad_token] * max(0, max_len - len(x)) +
([] if self.init_token is None else [self.init_token]) +
list(x[-max_len:] if self.truncate_first else x[:max_len]) +
([] if self.eos_token is None else [self.eos_token]))
else:
padded.append(
([] if self.init_token is None else [self.init_token]) +
list(x[-max_len:] if self.truncate_first else x[:max_len]) +
([] if self.eos_token is None else [self.eos_token]) +
[self.pad_token] * max(0, max_len - len(x)))
lengths.append(len(padded[-1]) - max(0, max_len - len(x)))
if self.include_lengths:
return padded, lengths
return padded
def numericalize(self, arr, device=None):
"""Turn a batch of examples that use this field into a list of Variables.
If the field has include_lengths=True, a tensor of lengths will be
included in the return value.
Arguments:
arr (List[List[str]], or tuple of (List[List[str]], List[int])):
List of tokenized and padded examples, or tuple of List of
tokenized and padded examples and List of lengths of each
example if self.include_lengths is True.
device (str or torch.device): A string or instance of `torch.device`
specifying which device the Variables are going to be created on.
If left as default, the tensors will be created on cpu. Default: None.
"""
if self.include_lengths and not isinstance(arr, tuple):
raise ValueError("Field has include_lengths set to True, but "
"input data is not a tuple of "
"(data batch, batch lengths).")
if isinstance(arr, tuple):
arr, lengths = arr
lengths = torch.tensor(lengths, dtype=self.dtype, device=device)
if self.use_vocab:
arr = [[self.vocab.stoi[x] for x in ex] for ex in arr]
if self.postprocessing is not None:
arr = self.postprocessing(arr, self.vocab)
var = torch.tensor(arr, dtype=self.dtype, device=device)
else:
if self.vectors:
arr = [[self.vectors[x] for x in ex] for ex in arr]
if self.dtype not in self.dtypes:
raise ValueError(
"Specified Field dtype {} can not be used with "
"use_vocab=False because we do not know how to numericalize it. "
"Please raise an issue at "
"https://github.com/pytorch/text/issues".format(self.dtype))
numericalization_func = self.dtypes[self.dtype]
# It doesn't make sense to explictly coerce to a numeric type if
# the data is sequential, since it's unclear how to coerce padding tokens
# to a numeric type.
arr = [numericalization_func(x) if isinstance(x, six.string_types)
else x for x in arr]
if self.postprocessing is not None:
arr = self.postprocessing(arr, None)
var = torch.cat([torch.cat([a.unsqueeze(0) for a in ar]).unsqueeze(0) for ar in arr])
# var = torch.tensor(arr, dtype=self.dtype, device=device)
if not self.batch_first:
var.t_()
var = var.contiguous()
if self.include_lengths:
return var, lengths
return var
def decode(self, word_idxs, join_words=True):
if isinstance(word_idxs, list) and len(word_idxs) == 0:
return self.decode([word_idxs, ], join_words)[0]
if isinstance(word_idxs, list) and isinstance(word_idxs[0], int):
return self.decode([word_idxs, ], join_words)[0]
elif isinstance(word_idxs, np.ndarray) and word_idxs.ndim == 1:
return self.decode(word_idxs.reshape((1, -1)), join_words)[0]
elif isinstance(word_idxs, torch.Tensor) and word_idxs.ndimension() == 1:
return self.decode(word_idxs.unsqueeze(0), join_words)[0]
captions = []
for wis in word_idxs:
caption = []
for wi in wis:
word = self.vocab.itos[int(wi)]
if word == self.eos_token:
break
caption.append(word)
if join_words:
caption = ' '.join(caption)
captions.append(caption)
return captions
