detectron2/data/clip_datasets/oscar_tsv.py

import logging
import random

class InputExample(object):
    """A single training/test example for the language model."""

    def __init__(self, guid, tokens_a, tokens_b=None, is_next=None,
                 lm_labels=None, img_id=None, is_img_match=None,
                 img_label=None):
        """Constructs a InputExample.

        Args:
            guid: Unique id for the example.
            tokens_a: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
            tokens_b: (Optional) string. The untokenized text of the second sequence.
            Only must be specified for sequence pair tasks.
        """
        self.guid = guid
        self.tokens_a = tokens_a
        self.tokens_b = tokens_b
        self.is_next = is_next  # nextSentence
        self.lm_labels = lm_labels  # masked words for language model

        self.img_id = img_id
        self.is_img_match = is_img_match
        self.img_label = img_label

class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, input_ids, input_mask, segment_ids, is_next,
                 lm_label_ids, img_feat_len, is_img_match):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.is_next = is_next
        self.lm_label_ids = lm_label_ids

        self.img_feat_len = img_feat_len
        self.is_img_match = is_img_match


def random_word(tokens, tokenizer):
    """
    Masking some random tokens for Language Model task with probabilities as in the original BERT paper.
    :param tokens: list of str, tokenized sentence.
    :param tokenizer: Tokenizer, object used for tokenization (we need it's vocab here)
    :return: (list of str, list of int), masked tokens and related labels for LM prediction
    """
    output_label = []

    for i, token in enumerate(tokens):
        prob = random.random()
        # mask token with 15% probability
        if prob < 0.15:
            prob /= 0.15

            # 80% randomly change token to mask token
            if prob < 0.8:
                tokens[i] = "[MASK]"

            # 10% randomly change token to random token
            elif prob < 0.9:
                tokens[i] = random.choice(list(tokenizer.vocab.items()))[0]

            # -> rest 10% randomly keep current token

            # append current token to output (we will predict these later)
            try:
                output_label.append(tokenizer.vocab[token])
            except KeyError:
                # For unknown words (should not occur with BPE vocab)
                output_label.append(tokenizer.vocab["[UNK]"])
                logging.warning(
                    "Cannot find token '{}' in vocab. Using [UNK] insetad".format(
                        token))
        else:
            # no masking token (will be ignored by loss function later)
            output_label.append(-1)

    return tokens, output_label


def convert_example_to_features(args, example, max_seq_length, tokenizer,
                                img_feat_len):
    """
    Convert a raw sample (pair of sentences as tokenized strings) into a proper training sample with
    IDs, LM labels, input_mask, CLS and SEP tokens etc.
    :param args: parameter settings
    :param img_feat_len: lens of actual img features
    :param example: InputExample, containing sentence input as strings and is_next label
    :param max_seq_length: int, maximum length of sequence.
    :param tokenizer: Tokenizer
    :return: InputFeatures, containing all inputs and labels of one sample as IDs (as used for model training)
    """

    tokens_a = example.tokens_a
    tokens_b = None
    if example.tokens_b:
        tokens_b = example.tokens_b
        # Modifies `tokens_a` and `tokens_b` in place so that the total
        # length is less than the specified length.
        # Account for [CLS], [SEP], [SEP] with "- 3"
        _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
    else:
        if len(tokens_a) > max_seq_length - 2:
            tokens_a = tokens_a[:(max_seq_length - 2)]

    tokens_a, t1_label = random_word(tokens_a, tokenizer)
    if tokens_b:
        tokens_b, t2_label = random_word(tokens_b, tokenizer)

    # concatenate lm labels and account for CLS, SEP, SEP
    if tokens_b:
        lm_label_ids = ([-1] + t1_label + [-1] + t2_label + [-1])
    else:
        lm_label_ids = ([-1] + t1_label + [-1])

    # The convention in BERT is:
    # (a) For sequence pairs:
    #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
    #  type_ids: 0   0  0    0    0     0       0 0    1  1  1  1   1 1
    # (b) For single sequences:
    #  tokens:   [CLS] the dog is hairy . [SEP]
    #  type_ids: 0   0   0   0  0     0 0
    #
    # Where "type_ids" are used to indicate whether this is the first
    # sequence or the second sequence. The embedding vectors for `type=0` and
    # `type=1` were learned during pre-training and are added to the wordpiece
    # embedding vector (and position vector). This is not *strictly* necessary
    # since the [SEP] token unambigiously separates the sequences, but it makes
    # it easier for the model to learn the concept of sequences.
    #
    # For classification tasks, the first vector (corresponding to [CLS]) is
    # used as as the "sentence vector". Note that this only makes sense because
    # the entire model is fine-tuned.
    tokens = []
    segment_ids = []
    tokens.append("[CLS]")
    segment_ids.append(0)
    for token in tokens_a:
        tokens.append(token)
        segment_ids.append(0)
    tokens.append("[SEP]")
    segment_ids.append(0)

    if tokens_b:
        assert len(tokens_b) > 0
        for token in tokens_b:
            tokens.append(token)
            segment_ids.append(1)
        tokens.append("[SEP]")
        segment_ids.append(1)

    input_ids = tokenizer.convert_tokens_to_ids(tokens)

    # The mask has 1 for real tokens and 0 for padding tokens. Only real tokens are attended to.
    input_mask = [1] * len(input_ids)

    # Zero-pad up to the sequence length.
    while len(input_ids) < max_seq_length:
        input_ids.append(0)
        input_mask.append(0)
        segment_ids.append(0)
        lm_label_ids.append(-1)

    assert len(input_ids) == max_seq_length
    assert len(input_mask) == max_seq_length
    assert len(segment_ids) == max_seq_length
    assert len(lm_label_ids) == max_seq_length

    # image features
    if args.max_img_seq_length > 0:
        if img_feat_len > args.max_img_seq_length:
            input_mask = input_mask + [1] * img_feat_len
        else:
            input_mask = input_mask + [1] * img_feat_len
            pad_img_feat_len = args.max_img_seq_length - img_feat_len
            input_mask = input_mask + ([0] * pad_img_feat_len)

    lm_label_ids = lm_label_ids + [-1] * args.max_img_seq_length

    if example.guid < 1:
        logging.info("*** Example ***")
        logging.info("guid: %s" % example.guid)
        logging.info("tokens: %s" % " ".join([str(x) for x in tokens]))
        logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
        logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
        logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
        logging.info("LM label: %s " % lm_label_ids)
        logging.info("Is next sentence label: %s " % example.is_next)

    features = InputFeatures(input_ids=input_ids,
                             input_mask=input_mask,
                             segment_ids=segment_ids,
                             lm_label_ids=lm_label_ids,
                             is_next=example.is_next,
                             img_feat_len=img_feat_len,
                             is_img_match=example.is_img_match)
    return features


def _truncate_seq_pair(tokens_a, tokens_b, max_length):
    """Truncates a sequence pair in place to the maximum length."""

    # This is a simple heuristic which will always truncate the longer sequence
    # one token at a time. This makes more sense than truncating an equal percent
    # of tokens from each, since if one sequence is very short then each token
    # that's truncated likely contains more information than a longer sequence.
    while True:
        total_length = len(tokens_a) + len(tokens_b)
        if total_length <= max_length:
            break
        if len(tokens_a) > len(tokens_b):
            tokens_a.pop()
        else:
            tokens_b.pop()