# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # TODO: Address all TODOs and remove all explanatory comments """TODO: Add a description here.""" from glob import glob import os import numpy as np from PIL import Image from tokenizers import pre_tokenizers from tokenizers.pre_tokenizers import Digits, Split, Whitespace, Sequence import datasets from itertools import chain import pandas as pd # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @InProceedings{huggingface:dataset, title = {A great new dataset}, author={huggingface, Inc. }, year={2020} } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ This new dataset is designed to solve this great NLP task and is crafted with a lot of care. """ # TODO: Add a link to an official homepage for the dataset here _HOMEPAGE = "" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "" # TODO: Add link to the official dataset URLs here # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLS = { 'train': "http://hyperion.bbirke.de/data/ref_seg/ref_seg_ger_train.zip", 'test': "http://hyperion.bbirke.de/data/ref_seg/ref_seg_ger_test.zip", } _LABELS = [ 'publisher', 'source', 'url', 'other', 'author', 'editor', 'lpage', 'volume', 'year', 'issue', 'title', 'fpage', 'edition' ] _FEATURES = datasets.Features( { "id": datasets.Value("string"), "tokens": datasets.Sequence(datasets.Value("string")), # "attention_mask": datasets.Sequence(datasets.Value("int64")), # "bbox": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))), # "RGBs": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))), # "fonts": datasets.Sequence(datasets.Value("string")), # "image": datasets.Array3D(shape=(3, 224, 224), dtype="uint8"), # "original_image": datasets.features.Image(), "labels": datasets.Sequence(datasets.features.ClassLabel( names=list(chain.from_iterable([['B-' + x, 'I-' + x] for x in _LABELS])) + ['O'] )), "labels_ref": datasets.Sequence(datasets.features.ClassLabel( names=['B-ref', 'I-ref', ] )) # These are the features of your dataset like images, labels ... } ) # def load_image(image_path, size=None): # image = Image.open(image_path).convert("RGB") # w, h = image.size # if size is not None: # # resize image # image = image.resize((size, size)) # image = np.asarray(image) # image = image[:, :, ::-1] # flip color channels from RGB to BGR # image = image.transpose(2, 0, 1) # move channels to first dimension # return image, (w, h) # def normalize_bbox(bbox, size): # return [ # int(1000 * int(bbox[0]) / size[0]), # int(1000 * int(bbox[1]) / size[1]), # int(1000 * int(bbox[2]) / size[0]), # int(1000 * int(bbox[3]) / size[1]), # ] # # # def simplify_bbox(bbox): # return [ # min(bbox[0::2]), # min(bbox[1::2]), # max(bbox[2::2]), # max(bbox[3::2]), # ] # # # def merge_bbox(bbox_list): # x0, y0, x1, y1 = list(zip(*bbox_list)) # return [min(x0), min(y0), max(x1), max(y1)] # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case class RefSeg(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" CHUNK_SIZE = 256 VERSION = datasets.Version("1.0.0") # This is an example of a dataset with multiple configurations. # If you don't want/need to define several sub-sets in your dataset, # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. # If you need to make complex sub-parts in the datasets with configurable options # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig # BUILDER_CONFIG_CLASS = MyBuilderConfig # You will be able to load one or the other configurations in the following list with # data = datasets.load_dataset('my_dataset', 'first_domain') # data = datasets.load_dataset('my_dataset', 'second_domain') # BUILDER_CONFIGS = [ # datasets.BuilderConfig(name="sample", version=VERSION, # description="This part of my dataset covers a first domain"), # datasets.BuilderConfig(name="full", version=VERSION, # description="This part of my dataset covers a second domain"), # ] # DEFAULT_CONFIG_NAME = "small" # It's not mandatory to have a default configuration. Just use one if it make sense. split_tokens = [".", ":", ",", ";", "/", "-", "(", ")"] TOKENIZER = Sequence([ Whitespace(), Digits(), ] + [Split(x, behavior="isolated") for x in split_tokens]) #TOKENIZER = Whitespace() def _info(self): # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=_FEATURES, # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and # specify them. They'll be used if as_supervised=True in builder.as_dataset. # supervised_keys=("sentence", "label"), # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive data_dir = dl_manager.download_and_extract(_URLS) # print(data_dir) # with open(os.path.join(data_dir, "train.csv")) as f: # files_train = [{'id': row['id'], 'filepath_txt': os.path.join(data_dir, row['filepath_txt']), # 'filepath_img': os.path.join(data_dir, row['filepath_img'])} for row in # csv.DictReader(f, skipinitialspace=True)] # with open(os.path.join(data_dir, "test.csv")) as f: # files_test = [{'id': row['id'], 'filepath_txt': os.path.join(data_dir, row['filepath_txt']), # 'filepath_img': os.path.join(data_dir, row['filepath_img'])} for row in # csv.DictReader(f, skipinitialspace=True)] # with open(os.path.join(data_dir, "validation.csv")) as f: # files_validation = [{'id': row['id'], 'filepath_txt': os.path.join(data_dir, row['filepath_txt']), # 'filepath_img': os.path.join(data_dir, row['filepath_img'])} for row in # csv.DictReader(f, skipinitialspace=True)] return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir['train'], "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir['test'], "split": "test" }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath, split): # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. # print(filepath) # print(split) paths = glob(filepath + '/' + split + '/*.csv') key = 0 for f in paths: df = pd.read_csv(f, keep_default_na=False) input_ids = [] labels = [] refs = [] for i, row in df.iterrows(): # tokenized_input = row['token'].split(' ') tkn = self.TOKENIZER.pre_tokenize_str(row['token']) if row['label'] == 'identifier': row['label'] = 'other' if not tkn: continue tokenized_input, offsets = zip(*tkn) tokenized_input = list(tokenized_input) for t in range(len(tokenized_input)): if t == 0: refs.append(row['ref'] + '-ref') else: refs.append('I-ref') if len(tokenized_input) > 1: if row['tag'] == 'B': if tokenized_input[0] == '': continue input_ids.append(tokenized_input[0]) labels.append(row['tag'] + '-' + row['label']) for input_id in tokenized_input[1:]: if input_id == '': continue input_ids.append(input_id) labels.append('I-' + row['label']) elif row['tag'] == 'I': for input_id in tokenized_input: input_ids.append(input_id) labels.append('I-' + row['label']) else: if tokenized_input[0] == '': continue for input_id in tokenized_input: input_ids.append(input_id) labels.append('O') elif len(tokenized_input) == 1: if tokenized_input[0] == '': continue input_ids.append(tokenized_input[0]) if row['tag'] == 'O': labels.append(row['tag']) else: labels.append(row['tag'] + '-' + row['label']) else: continue clean_input_ids = [] clean_labels = [] clean_refs = [] for i, input in enumerate(input_ids): if input != '': clean_input_ids.append(input) clean_labels.append(labels[i]) clean_refs.append(refs[i]) # n_chunks = int(len(clean_input_ids) / self.CHUNK_SIZE) if len(clean_input_ids) % self.CHUNK_SIZE == 0 \ # else int(len(clean_input_ids) / self.CHUNK_SIZE) + 1 # split_ids = np.array_split(clean_input_ids, n_chunks) # split_labels = np.array_split(clean_labels, n_chunks) # split_refs = np.array_split(clean_refs, n_chunks) # print(clean_input_ids) # for chunk_ids, chunk_labels, chunk_refs in zip(clean_input_ids, clean_labels, clean_refs): # for chunk_id, index in enumerate(range(0, len(clean_input_ids), self.CHUNK_SIZE)): # split_ids = clean_input_ids[index:max(len(clean_input_ids), index + self.CHUNK_SIZE)] # split_bboxes = bboxes[index:index + self.CHUNK_SIZE] # split_rgbs = rgbs[index:index + self.CHUNK_SIZE] # split_fonts = fonts[index:index + self.CHUNK_SIZE] # split_labels = clean_labels[index:max(len(clean_input_ids), index + self.CHUNK_SIZE)] # split_labels_post = [item for sublist in split_labels for item in sublist] # if(len(split_ids) != len(split_labels)): # print(f) # print(len(input_ids), input_ids) # print(len(split_labels), split_labels) # for s in split_labels: # if type(s) is not str: # print(f) # print(len(input_ids), input_ids) # print(len(split_labels), split_labels) # print(len(split_labels_post), split_labels_post) # print(split_labels, len(split_labels)) # print(split_ids, len(split_ids)) yield key, { "id": f"{os.path.basename(f)}", 'tokens': clean_input_ids, # 'attention_mask': [1] * len(chunk_ids), # "bbox": split_bboxes, # "RGBs": split_rgbs, # "fonts": split_fonts, # "image": image, # "original_image": original_image, "labels": clean_labels, "labels_ref": clean_refs } key += 1