# Copyright (c) Facebook, Inc. and its affiliates.
"""
The processors exist in Pythia to make data processing pipelines in various
datasets as similar as possible while allowing code reuse.
The processors also help maintain proper abstractions to keep only what matters
inside the dataset's code. This allows us to keep the dataset ``get_item``
logic really clean and no need about maintaining opinions about data type.
Processors can work on both images and text due to their generic structure.
To create a new processor, follow these steps:
1. Inherit the ``BaseProcessor`` class.
2. Implement ``_call`` function which takes in a dict and returns a dict with
same keys preprocessed as well as any extra keys that need to be returned.
3. Register the processor using ``@registry.register_processor('name')`` to
registry where 'name' will be used to refer to your processor later.
In processor's config you can specify ``preprocessor`` option to specify
different kind of preprocessors you want in your dataset.
Let's break down processor's config inside a dataset (VQA2.0) a bit to understand
different moving parts.
Config::
task_attributes:
vqa:
datasets:
- vqa2
dataset_attributes:
vqa2:
processors:
text_processor:
type: vocab
params:
max_length: 14
vocab:
type: intersected
embedding_name: glove.6B.300d
vocab_file: vocabs/vocabulary_100k.txt
answer_processor:
type: vqa_answer
params:
num_answers: 10
vocab_file: vocabs/answers_vqa.txt
preprocessor:
type: simple_word
params: {}
``BaseDataset`` will init the processors and they will available inside your
dataset with same attribute name as the key name, for e.g. `text_processor` will
be available as `self.text_processor` inside your dataset. As is with every module
in Pythia, processor also accept a ``ConfigNode`` with a `type` and `params`
attributes. `params` defined the custom parameters for each of the processors.
By default, processor initialization process will also init `preprocessor` attribute
which can be a processor config in itself. `preprocessor` can be then be accessed
inside the processor's functions.
Example::
from pythia.common.registry import registry
from pythia.tasks.processors import BaseProcessor
class MyProcessor(BaseProcessor):
def __init__(self, config, *args, **kwargs):
return
def __call__(self, item, *args, **kwargs):
text = item['text']
text = [t.strip() for t in text.split(" ")]
return {"text": text}
"""
import multiprocessing
import os
import warnings
from collections import Counter
import torch
from pythia.common.registry import registry
from pythia.utils.configuration import ConfigNode
from pythia.utils.distributed_utils import is_main_process, synchronize
from pythia.utils.general import get_pythia_root
from pythia.utils.text_utils import VocabDict
from pythia.utils.vocab import Vocab, WordToVectorDict
[docs]class BaseProcessor:
"""Every processor in Pythia needs to inherit this class for compatability
with Pythia. End user mainly needs to implement ``__call__`` function.
Args:
config (ConfigNode): Config for this processor, containing `type` and
`params` attributes if available.
"""
def __init__(self, config, *args, **kwargs):
return
def __call__(self, item, *args, **kwargs):
"""Main function of the processor. Takes in a dict and returns back
a dict
Args:
item (Dict): Some item that needs to be processed.
Returns:
Dict: Processed dict.
"""
return item
[docs]class Processor:
"""Wrapper class used by Pythia to initialized processor based on their
``type`` as passed in configuration. It retrieves the processor class
registered in registry corresponding to the ``type`` key and initializes
with ``params`` passed in configuration. All functions and attributes of
the processor initialized are directly available via this class.
Args:
config (ConfigNode): ConfigNode containing ``type`` of the processor to
be initialized and ``params`` of that procesor.
"""
def __init__(self, config, *args, **kwargs):
self.writer = registry.get("writer")
if not hasattr(config, "type"):
raise AttributeError(
"Config must have 'type' attribute to specify type of processor"
)
processor_class = registry.get_processor_class(config.type)
params = {}
if not hasattr(config, "params"):
self.writer.write(
"Config doesn't have 'params' attribute to "
"specify parameters of the processor "
"of type {}. Setting to default \{\}".format(config.type)
)
else:
params = config.params
self.processor = processor_class(params, *args, **kwargs)
self._dir_representation = dir(self)
def __call__(self, item, *args, **kwargs):
return self.processor(item, *args, **kwargs)
def __getattr__(self, name):
if name in self._dir_representation:
return getattr(self, name)
elif hasattr(self.processor, name):
return getattr(self.processor, name)
else:
raise AttributeError(name)
[docs]@registry.register_processor("vocab")
class VocabProcessor(BaseProcessor):
"""Use VocabProcessor when you have vocab file and you want to process
words to indices. Expects UNK token as "<unk>" and pads sentences using
"<pad>" token. Config parameters can have ``preprocessor`` property which
is used to preprocess the item passed and ``max_length`` property which
points to maximum length of the sentence/tokens which can be convert to
indices. If the length is smaller, the sentence will be padded. Parameters
for "vocab" are necessary to be passed.
**Key**: vocab
Example Config::
task_attributes:
vqa:
vqa2:
processors:
text_processor:
type: vocab
params:
max_length: 14
vocab:
type: intersected
embedding_name: glove.6B.300d
vocab_file: vocabs/vocabulary_100k.txt
Args:
config (ConfigNode): node containing configuration parameters of
the processor
Attributes:
vocab (Vocab): Vocab class object which is abstraction over the vocab
file passed.
"""
MAX_LENGTH_DEFAULT = 50
PAD_TOKEN = "<pad>"
PAD_INDEX = 0
def __init__(self, config, *args, **kwargs):
if not hasattr(config, "vocab"):
raise AttributeError(
"config passed to the processor has no attribute vocab"
)
self.vocab = Vocab(*args, **config.vocab, **kwargs)
self._init_extras(config)
def _init_extras(self, config, *args, **kwargs):
self.writer = registry.get("writer")
self.preprocessor = None
if hasattr(config, "max_length"):
self.max_length = config.max_length
else:
warnings.warn(
"No 'max_length' parameter in Processor's "
"configuration. Setting to {}.".format(self.MAX_LENGTH_DEFAULT)
)
self.max_length = self.MAX_LENGTH_DEFAULT
if hasattr(config, "preprocessor"):
self.preprocessor = Processor(config.preprocessor, *args, **kwargs)
if self.preprocessor is None:
raise ValueError(
"No text processor named {} is defined.".format(config.preprocessor)
)
def __call__(self, item):
"""Call requires item to have either "tokens" attribute or either
"text" attribute. If "text" is present, it will tokenized using
the preprocessor.
Args:
item (Dict): Dict containing the "text" or "tokens".
Returns:
Dict: Dict containing indices in "text" key, "tokens" in "tokens"
key and "length" of the string in "length" key.
"""
indices = None
if not isinstance(item, dict):
raise TypeError(
"Argument passed to the processor must be "
"a dict with either 'text' or 'tokens' as "
"keys"
)
if "tokens" in item:
tokens = item["tokens"]
indices = self._map_strings_to_indices(item["tokens"])
elif "text" in item:
if self.preprocessor is None:
raise AssertionError(
"If tokens are not provided, a text "
"processor must be defined in the config"
)
tokens = self.preprocessor({"text": item["text"]})["text"]
indices = self._map_strings_to_indices(tokens)
else:
raise AssertionError(
"A dict with either 'text' or 'tokens' keys "
"must be passed to the processor"
)
tokens, length = self._pad_tokens(tokens)
return {"text": indices, "tokens": tokens, "length": length}
def _pad_tokens(self, tokens):
padded_tokens = [self.PAD_TOKEN] * self.max_length
token_length = min(len(tokens), self.max_length)
padded_tokens[:token_length] = tokens[:token_length]
token_length = torch.tensor(token_length, dtype=torch.long)
return padded_tokens, token_length
[docs] def get_pad_index(self):
"""Get index of padding <pad> token in vocabulary.
Returns:
int: index of the padding token.
"""
return self.vocab.get_pad_index()
[docs] def get_vocab_size(self):
"""Get size of the vocabulary.
Returns:
int: size of the vocabulary.
"""
return self.vocab.get_size()
def _map_strings_to_indices(self, tokens):
length = min(len(tokens), self.max_length)
tokens = tokens[:length]
output = torch.zeros(self.max_length, dtype=torch.long)
output.fill_(self.vocab.get_pad_index())
for idx, token in enumerate(tokens):
output[idx] = self.vocab.stoi[token]
return output
[docs]@registry.register_processor("glove")
class GloVeProcessor(VocabProcessor):
"""Inherits VocabProcessor, and returns GloVe vectors for each of the
words. Maps them to index using vocab processor, and then gets GloVe vectors
corresponding to those indices.
Args:
config (ConfigNode): Configuration parameters for GloVe same as
:func:`~VocabProcessor`.
"""
def __init__(self, config, *args, **kwargs):
if not hasattr(config, "vocab"):
raise AttributeError(
"Config passed to the processor has no attribute vocab"
)
vocab_processor_config = ConfigNode(config)
# GloVeProcessor needs vocab type to be "intersected"
vocab_processor_config.vocab.type = "intersected"
if "vocab_file" not in vocab_processor_config.vocab:
warnings.warn(
"'vocab_file' key is not present in the config."
" Switching to pretrained vocab."
)
vocab_processor_config.vocab.type = "pretrained"
super().__init__(vocab_processor_config, *args, **kwargs)
def __call__(self, item):
indices = super().__call__(item)["text"]
embeddings = torch.zeros(
(len(indices), self.vocab.get_embedding_dim()), dtype=torch.float
)
for idx, index in enumerate(indices):
embeddings[idx] = self.vocab.vectors[index]
return {"text": embeddings}
[docs]@registry.register_processor("fasttext")
class FastTextProcessor(VocabProcessor):
"""FastText processor, similar to GloVe processor but returns FastText vectors.
Args:
config (ConfigNode): Configuration values for the processor.
"""
def __init__(self, config, *args, **kwargs):
self._init_extras(config)
self.config = config
self._already_downloaded = False
def _try_download(self):
is_main_process = self._is_main_process()
if self._already_downloaded:
return
if is_main_process:
self.writer.write("Fetching fastText model for OCR processing")
needs_download = False
if not hasattr(self.config, "model_file"):
if is_main_process:
warnings.warn(
"'model_file' key is required but missing "
"from FastTextProcessor's config."
)
needs_download = True
model_file = self.config.model_file
model_file = os.path.join(get_pythia_root(), model_file)
if not os.path.exists(model_file):
if is_main_process:
warnings.warn("No model file present at {}.".format(model_file))
needs_download = True
if needs_download:
if is_main_process:
self.writer.write("Downloading FastText bin", "info")
model_file = self._download_model()
synchronize()
self._load_fasttext_model(model_file)
self._already_downloaded = True
def _download_model(self):
is_main_process = self._is_main_process()
model_file_path = os.path.join(
get_pythia_root(), ".vector_cache", "wiki.en.bin"
)
if not is_main_process:
return model_file_path
if os.path.exists(model_file_path):
if is_main_process:
self.writer.write(
"Vectors already present at {}.".format(model_file_path), "info"
)
return model_file_path
import requests
from pythia.common.constants import FASTTEXT_WIKI_URL
from tqdm import tqdm
os.makedirs(os.path.dirname(model_file_path), exist_ok=True)
response = requests.get(FASTTEXT_WIKI_URL, stream=True)
with open(model_file_path, "wb") as f:
pbar = tqdm(
total=int(response.headers["Content-Length"]) / 4096,
miniters=50,
disable=not is_main_process,
)
idx = 0
for data in response.iter_content(chunk_size=4096):
if data:
if idx % 50 == 0:
pbar.update(len(data))
f.write(data)
idx += 1
pbar.close()
if is_main_process:
self.writer.write(
"fastText bin downloaded at {}.".format(model_file_path), "info"
)
return model_file_path
def _load_fasttext_model(self, model_file):
from fastText import load_model
is_main_process = self._is_main_process()
if is_main_process:
self.writer.write("Loading fasttext model now from %s" % model_file)
self.model = load_model(model_file)
# String to Vector
self.stov = WordToVectorDict(self.model)
if is_main_process:
self.writer.write("Finished loading fasttext model")
def _is_main_process(self):
return multiprocessing.current_process().name == "Process-1"
def _map_strings_to_indices(self, tokens):
length = min(len(tokens), self.max_length)
tokens = tokens[:length]
output = torch.full(
(self.max_length, self.model.get_dimension()),
fill_value=self.PAD_INDEX,
dtype=torch.float,
)
for idx, token in enumerate(tokens):
output[idx] = torch.from_numpy(self.stov[token])
return output
def __call__(self, item):
self._try_download()
return super().__call__(item)
[docs]@registry.register_processor("vqa_answer")
class VQAAnswerProcessor(BaseProcessor):
"""Processor for generating answer scores for answers passed using VQA
accuracy formula. Using VocabDict class to represent answer vocabulary,
so parameters must specify "vocab_file". "num_answers" in parameter config
specify the max number of answers possible. Takes in dict containing
"answers" or "answers_tokens". "answers" are preprocessed to generate
"answers_tokens" if passed.
Args:
config (ConfigNode): Configuration for the processor
Attributes:
answer_vocab (VocabDict): Class representing answer vocabulary
"""
DEFAULT_NUM_ANSWERS = 10
def __init__(self, config, *args, **kwargs):
self.writer = registry.get("writer")
if not hasattr(config, "vocab_file"):
raise AttributeError(
"'vocab_file' argument required, but not "
"present in AnswerProcessor's config"
)
self.answer_vocab = VocabDict(config.vocab_file, *args, **kwargs)
self.preprocessor = None
if hasattr(config, "preprocessor"):
self.preprocessor = Processor(config.preprocessor)
if self.preprocessor is None:
raise ValueError(
"No processor named {} is defined.".format(config.preprocessor)
)
if hasattr(config, "num_answers"):
self.num_answers = config.num_answers
else:
self.num_answers = self.DEFAULT_NUM_ANSWERS
warnings.warn(
"'num_answers' not defined in the config. "
"Setting to default of {}".format(self.DEFAULT_NUM_ANSWERS)
)
def __call__(self, item):
"""Takes in dict with answers or answers_tokens, and returns back
a dict with answers (processed), "answers_indices" which point to
indices of the answers if present and "answers_scores" which represent
VQA style scores for the answers.
Args:
item (Dict): Dict containing answers or answers_tokens
Returns:
Dict: Processed answers, indices and scores.
"""
tokens = None
if not isinstance(item, dict):
raise TypeError("'item' passed to processor must be a dict")
if "answer_tokens" in item:
tokens = item["answer_tokens"]
elif "answers" in item:
if self.preprocessor is None:
raise AssertionError(
"'preprocessor' must be defined if you "
"don't pass 'answer_tokens'"
)
tokens = [
self.preprocessor({"text": answer})["text"]
for answer in item["answers"]
]
else:
raise AssertionError(
"'answers' or 'answer_tokens' must be passed"
" to answer processor in a dict"
)
answers_indices = torch.zeros(self.num_answers, dtype=torch.long)
answers_indices.fill_(self.answer_vocab.get_unk_index())
for idx, token in enumerate(tokens):
answers_indices[idx] = self.answer_vocab.word2idx(token)
answers_scores = self.compute_answers_scores(answers_indices)
return {
"answers": tokens,
"answers_indices": answers_indices,
"answers_scores": answers_scores,
}
[docs] def get_vocab_size(self):
"""Get vocab size of the answer vocabulary. Can also include
soft copy dynamic answer space size.
Returns:
int: size of the answer vocabulary
"""
return self.answer_vocab.num_vocab
[docs] def get_true_vocab_size(self):
"""True vocab size can be different from normal vocab size in some cases
such as soft copy where dynamic answer space is added.
Returns:
int: True vocab size.
"""
return self.answer_vocab.num_vocab
[docs] def word2idx(self, word):
"""Convert a word to its index according to vocabulary
Args:
word (str): Word to be converted to index.
Returns:
int: Index of the word.
"""
return self.answer_vocab.word2idx(word)
[docs] def idx2word(self, idx):
"""Index to word according to the vocabulary.
Args:
idx (int): Index to be converted to the word.
Returns:
str: Word corresponding to the index.
"""
return self.answer_vocab.idx2word(idx)
[docs] def compute_answers_scores(self, answers_indices):
"""Generate VQA based answer scores for answers_indices.
Args:
answers_indices (torch.LongTensor): tensor containing indices of the answers
Returns:
torch.FloatTensor: tensor containing scores.
"""
scores = torch.zeros(self.get_vocab_size(), dtype=torch.float)
gt_answers = list(enumerate(answers_indices))
unique_answers = set(answers_indices.tolist())
for answer in unique_answers:
accs = []
for gt_answer in gt_answers:
other_answers = [item for item in gt_answers if item != gt_answer]
matching_answers = [item for item in other_answers if item[1] == answer]
acc = min(1, float(len(matching_answers)) / 3)
accs.append(acc)
avg_acc = sum(accs) / len(accs)
if answer != self.answer_vocab.UNK_INDEX:
scores[answer] = avg_acc
return scores
[docs]@registry.register_processor("multi_hot_answer_from_vocab")
class MultiHotAnswerFromVocabProcessor(VQAAnswerProcessor):
def __init__(self, config, *args, **kwargs):
super().__init__(config, *args, **kwargs)
[docs] def compute_answers_scores(self, answers_indices):
scores = torch.zeros(self.get_vocab_size(), dtype=torch.float)
scores[answers_indices] = 1
scores[self.answer_vocab.UNK_INDEX] = 0
return scores
[docs]@registry.register_processor("soft_copy_answer")
class SoftCopyAnswerProcessor(VQAAnswerProcessor):
"""Similar to Answer Processor but adds soft copy dynamic answer space to it.
Read https://arxiv.org/abs/1904.08920 for extra information on soft copy
and LoRRA.
Args:
config (ConfigNode): Configuration for soft copy processor.
"""
DEFAULT_MAX_LENGTH = 50
def __init__(self, config, *args, **kwargs):
super().__init__(config, *args, **kwargs)
if hasattr(config, "max_length"):
self.max_length = config.max_length
else:
self.max_length = self.DEFAULT_MAX_LENGTH
warnings.warn(
"'max_length' not defined in the config. "
"Setting to default of {}".format(self.DEFAULT_MAX_LENGTH)
)
self.context_preprocessor = None
if hasattr(config, "context_preprocessor"):
self.context_preprocessor = Processor(config.context_preprocessor)
[docs] def get_vocab_size(self):
"""Size of Vocab + Size of Dynamic soft-copy based answer space
Returns:
int: Size of vocab + size of dynamic soft-copy answer space.
"""
answer_vocab_nums = self.answer_vocab.num_vocab
answer_vocab_nums += self.max_length
return answer_vocab_nums
[docs] def get_true_vocab_size(self):
"""Actual vocab size which only include size of the vocabulary file.
Returns:
int: Actual size of vocabs.
"""
return self.answer_vocab.num_vocab
def __call__(self, item):
answers = item["answers"]
scores = super().__call__({"answers": answers})
indices = scores["answers_indices"]
answers = scores["answers"]
scores = scores["answers_scores"]
tokens_scores = scores.new_zeros(self.max_length)
tokens = item["tokens"]
length = min(len(tokens), self.max_length)
gt_answers = list(enumerate(answers))
unique_answers = set(answers)
if self.context_preprocessor is not None:
tokens = [
self.context_preprocessor({"text": token})["text"] for token in tokens
]
answer_counter = Counter(answers)
for idx, token in enumerate(tokens[:length]):
if answer_counter[token] == 0:
continue
accs = []
for gt_answer in gt_answers:
other_answers = [item for item in gt_answers if item != gt_answer]
matching_answers = [item for item in other_answers if item[1] == token]
acc = min(1, float(len(matching_answers)) / 3)
accs.append(acc)
tokens_scores[idx] = sum(accs) / len(accs)
# Scores are already proper size, see L314. Now,
# fix scores for soft copy candidates
scores[-len(tokens_scores) :] = tokens_scores
return {
"answers": answers,
"answers_indices": indices,
"answers_scores": scores,
}
[docs]@registry.register_processor("simple_word")
class SimpleWordProcessor(BaseProcessor):
"""Tokenizes a word and processes it.
Attributes:
tokenizer (function): Type of tokenizer to be used.
"""
def __init__(self, *args, **kwargs):
from pythia.utils.text_utils import word_tokenize
self.tokenizer = word_tokenize
def __call__(self, item, *args, **kwargs):
return {"text": self.tokenizer(item["text"], *args, **kwargs)}
[docs]@registry.register_processor("simple_sentence")
class SimpleSentenceProcessor(BaseProcessor):
"""Tokenizes a sentence and processes it.
Attributes:
tokenizer (function): Type of tokenizer to be used.
"""
def __init__(self, *args, **kwargs):
from pythia.utils.text_utils import tokenize
self.tokenizer = tokenize
def __call__(self, item, *args, **kwargs):
return {"text": self.tokenizer(item["text"], *args, **kwargs)}
[docs]@registry.register_processor("bbox")
class BBoxProcessor(VocabProcessor):
"""Generates bboxes in proper format.
Takes in a dict which contains "info" key which is a list of dicts
containing following for each of the the bounding box
Example bbox input::
{
"info": [
{
"bounding_box": {
"top_left_x": 100,
"top_left_y": 100,
"width": 200,
"height": 300
}
},
...
]
}
This will further return a Sample in a dict with key "bbox" with last
dimension of 4 corresponding to "xyxy". So sample will look like following:
Example Sample::
Sample({
"coordinates": torch.Size(n, 4),
"width": List[number], # size n
"height": List[number], # size n
"bbox_types": List[str] # size n, either xyxy or xywh.
# currently only supports xyxy.
})
"""
def __init__(self, config, *args, **kwargs):
from pythia.utils.dataset_utils import build_bbox_tensors
self.lambda_fn = build_bbox_tensors
self._init_extras(config)
def __call__(self, item):
info = item["info"]
if self.preprocessor is not None:
info = self.preprocessor(info)
return {"bbox": self.lambda_fn(info, self.max_length)}
[docs]@registry.register_processor("caption")
class CaptionProcessor(BaseProcessor):
"""Processes a caption with start, end and pad tokens and returns raw string.
Args:
config (ConfigNode): Configuration for caption processor.
"""
def __init__(self, config, *args, **kwargs):
if not hasattr(config, "vocab"):
raise AttributeError(
"config passed to the processor has no " "attribute vocab"
)
self.vocab = Vocab(*args, **config.vocab, **kwargs)
def __call__(self, item):
for idx, v in enumerate(item):
if v == self.vocab.EOS_INDEX:
item = item[:idx]
break
tokens = [
self.vocab.get_itos()[w]
for w in item
if w
not in {self.vocab.SOS_INDEX, self.vocab.EOS_INDEX, self.vocab.PAD_INDEX}
]
caption = " ".join(tokens)
return {"tokens": tokens, "caption": caption}