# 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.""" import csv import os from glob import glob import numpy as np from PIL import Image from transformers import LayoutXLMTokenizerFast import datasets # 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 = { "sample": "http://hyperion.bbirke.de/data/docbank/sample_resized.zip", "data": { 'train': 'http://hyperion.bbirke.de/data/geocite/train.zip', 'test': 'http://hyperion.bbirke.de/data/geocite/test.zip', }, } _FEATURES = datasets.Features( { "id": datasets.Value("string"), "words": datasets.Sequence(datasets.Value("string")), "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(), "dataset": datasets.Value("string"), #"labels": datasets.Sequence(feature=datasets.Value(dtype='int64')) "labels": datasets.Sequence(datasets.features.ClassLabel( names=['abstract', 'author', 'caption', 'equation', 'figure', 'footer', 'paragraph', 'reference', 'section', 'table', 'title'] # names=['abstract', 'author', 'caption', 'date', 'equation', 'figure', 'footer', 'list', 'paragraph', # 'reference', 'section', 'table', 'title'] )) # 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 Docbank(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" CHUNK_SIZE = 512 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="data", 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. TOKENIZER = LayoutXLMTokenizerFast.from_pretrained("microsoft/layoutxlm-base", only_label_first_subword=False) LABELS = ['abstract', 'author', 'caption', 'date', 'equation', 'figure', 'footer', 'list', 'paragraph', 'reference', 'section', 'table', 'title'] ID2LABEL = {k: v for k, v in enumerate(LABELS)} LABEL2ID = {v: k for k, v in enumerate(LABELS)} 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 urls = _URLS[self.config.name] data_dir = dl_manager.download_and_extract(urls) #print(data_dir) train_txts = glob(data_dir['train'] + '/train/txt/*.csv') #print(train_txts[0]) train_data = [(txt, data_dir['train'] + '/train/img/' + os.path.basename(txt)[:-4] + '.jpg') for txt in train_txts] test_txts = glob(data_dir['test'] + '/test/txt/*.csv') test_data = [(txt, data_dir['test'] + '/test/img/' + os.path.basename(txt)[:-4] + '.jpg') for txt in test_txts] # 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": train_data, "split": "train", }, ), # datasets.SplitGenerator( # name=datasets.Split.VALIDATION, # # These kwargs will be passed to _generate_examples # gen_kwargs={ # "filepath": files_validation, # "split": "validation", # }, # ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": test_data, "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) key = 0 for f_fp_txt, f_fp_img in filepath: dataset = f_fp_txt.split(".")[-2].split("_")[-1] #print(f_fp_txt) f_id = key #f_fp_txt = f['filepath_txt'] #f_fp_img = f['filepath_img'] tokens = [] bboxes = [] # rgbs = [] # fonts = [] labels = [] #image, size = load_image(f_fp_img, size=224) original_image, _ = load_image(f_fp_img) try: with open(f_fp_txt, encoding='utf-8') as csvfile: reader = csv.DictReader(csvfile, delimiter=',') for row in reader: #print(row) # normalized_bbox = normalize_bbox(row[1:5], size) normalized_bbox = [int(row['x0']), int(row['y0']), int(row['x1']), int(row['y1'])] tokens.append(row['token']) bboxes.append(normalized_bbox) #print(f'Before: {row[9]}') label = row['label'] if (label == "list") or (label == "date"): label = "paragraph" labels.append(label) #print(f'After: {row[9]}') # tokenized_input = self.TOKENIZER( # row[0], # add_special_tokens=False, # return_offsets_mapping=False, # return_attention_mask=False, # max_length=512, truncation=True # ) # # for tkn in tokenized_input['input_ids']: # tokens.append(tkn) # bboxes.append(normalized_bbox) # # rgbs.append(row[5:8]) # # fonts.append(row[8]) # labels.append(row[9]) except: continue #print('Processing...') # processed = self.TOKENIZER( # tokens, # boxes=bboxes, # word_labels=labels, # add_special_tokens=False, # return_offsets_mapping=False, # return_attention_mask=False, # ) #print(processed) # for chunk_id, index in enumerate(range(0, len(tokens), self.CHUNK_SIZE)): # split_tokens = tokens[index: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 = labels[index:index + self.CHUNK_SIZE] #tokenized = self.TOKENIZER(processed['words'], boxes=processed['boxes']) yield key, { "id": f"file_{f_id}", 'words': tokens, "bbox": bboxes, # "RGBs": split_rgbs, # "fonts": split_fonts, #"image": image, "original_image": original_image, "dataset": dataset, "labels": labels } key += 1