initial commit

ref_seg_ger.py

@@ -0,0 +1,256 @@
+# 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
+import numpy as np
+from PIL import Image
+from transformers import AutoTokenizer
+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 = [
+    "http://hyperion.bbirke.de/data/ref_seg/ref_seg_ger_train.zip",
+    "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', 'identifier'
+]
+
+_FEATURES = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "input_ids": 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'
+        ))
+        # 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 = 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="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.
+    TOKENIZER = AutoTokenizer.from_pretrained("xlm-roberta-base")
+
+    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)
+        # 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)
+        key = 0
+        for f in filepath:
+            df = pd.read_csv(f)
+            input_ids = []
+            labels = []
+            for i, row in df.iterrows():
+                tokenized_input = self.TOKENIZER(
+                    row['token'],
+                    add_special_tokens=False,
+                    return_offsets_mapping=False,
+                    return_attention_mask=False,
+                )
+                if len(tokenized_input['input_ids']) > 1:
+                    if row['tag'] == 'B':
+                        input_ids.append(tokenized_input['input_ids'][0])
+                        labels.append(row['tag'] + '-' + row['label'])
+                        for input_id in tokenized_input['input_ids'][1:]:
+                            input_ids.append(input_id)
+                            labels.append('I-' + row['label'])
+                    elif row['tag'] == 'I':
+                        for input_id in tokenized_input['input_ids']:
+                            input_ids.append(input_id)
+                            labels.append('I-' + row['label'])
+                    else:
+                        for input_id in tokenized_input['input_ids']:
+                            input_ids.append(input_id)
+                            labels.append('O')
+                else:
+                    input_ids.append(tokenized_input['input_ids'])
+                    if row['tag'] == 'O':
+                        labels.append(row['tag'])
+                    else:
+                        labels.append(row['tag'] + '-' + row['label'])
+
+            for chunk_id, index in enumerate(range(0, len(input_ids), self.CHUNK_SIZE)):
+                split_ids = input_ids[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]
+
+                yield key, {
+                    "id": f"{os.path.basename(f)}_{chunk_id}",
+                    'input_ids': split_ids,
+                    #"bbox": split_bboxes,
+                    # "RGBs": split_rgbs,
+                    # "fonts": split_fonts,
+                    #"image": image,
+                    #"original_image": original_image,
+                    "labels": split_labels
+                }
+                key += 1