Delete dataset.py

dataset.py

@@ -1,155 +0,0 @@
-### Create file named dataset.py
-### Paste 
-# coding=utf-8
-import json
-import os
-from pathlib import Path
-import datasets
-from PIL import Image
-import pandas as pd
-
-logger = datasets.logging.get_logger(__name__)
-_CITATION = """{}"""
-_DESCRIPTION = """Discharge Summary"""
-
-
-def load_image(image_path):
-    image = Image.open(image_path)
-    w, h = image.size
-    return image, (w, h)
-
-def normalize_bbox(bbox, size):
-    return [
-        int(1000 * bbox[0] / size[0]),
-        int(1000 * bbox[1] / size[1]),
-        int(1000 * bbox[2] / size[0]),
-        int(1000 * bbox[3] / size[1]),
-    ]
-
-
-class SroieConfig(datasets.BuilderConfig):
-    """BuilderConfig for SROIE"""
-    def __init__(self, **kwargs):
-        """BuilderConfig for SROIE.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(SroieConfig, self).__init__(**kwargs)
-
-
-class Sroie(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        SroieConfig(name="discharge", version=datasets.Version("1.0.0"), description="Discharge summary dataset"),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "words": datasets.Sequence(datasets.Value("string")),
-                    "bboxes": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
-                    "ner_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=['others',
-                                    'produttore_key',
-                                    'produttore_value',
-                                    'cliente_key',
-                                    'cliente_value',
-                                    'unitloc_key',
-                                    'unitloc_value',
-                                    'operatore_key',
-                                    'operatore_value',
-                                    'referente_key',
-                                    'referente_value',
-                                    'cfproduttore_key',
-                                    'cfproduttore_value',
-                                    'telefono_key',
-                                    'telefono_value',
-                                    'emailcliente_key',
-                                    'emailcliente_value',
-                                    'datarichiesta_key',
-                                    'datarichiesta_value',
-                                    'orariorichiesta_key',
-                                    'orariorichiesta_value',
-                                    'emailproduttore_key',
-                                    'emailproduttore_value',
-                                    'mattina_key',
-                                    'mattina_value',
-                                    'pomeriggio_key',
-                                    'pomeriggio_value',
-                                    'cer_key',
-                                    'cer_value',
-                                    'descrizione_key',
-                                    'descrizione_value',
-                                    'sf_key',
-                                    'sf_value',
-                                    'classpericolo_key',
-                                    'classpericolo_value',
-                                    'destino_key',
-                                    'destino_value',
-                                    'confezionamento_key',
-                                    'confezionamento_value',
-                                    'destinazione_key',
-                                    'destinazione_value'
-                                    ]
-                            )
-                    ),
-                    #"image": datasets.Array3D(shape=(3, 224, 224), dtype="uint8"),
-                    "image_path": datasets.Value("string"),
-                }
-            ),
-            supervised_keys=None,
-            citation=_CITATION,
-            homepage="",
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        """Uses local files located with data_dir"""
-        #downloaded_file = dl_manager.download_and_extract(_URLS)
-        # move files from the second URL together with files from the first one.
-        dest = Path('dataset')
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": dest/"train"}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"filepath": dest/"test"}
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-
-        logger.info("⏳ Generating examples from = %s", filepath)
-        ann_dir = os.path.join(filepath, "annotation_dir")
-        img_dir = os.path.join(filepath, "img_dir")
-
-        for guid, fname in enumerate(sorted(os.listdir(img_dir))):
-
-            name, ext = os.path.splitext(fname)
-            file_path = os.path.join(ann_dir, name + ".csv")
-
-
-            df = pd.read_csv(file_path)
-
-            image_path = os.path.join(img_dir, fname)
-
-            image, size = load_image(image_path)
-
-            boxes = [[xmin, ymin, xmax, ymax] for xmin, ymin, xmax, ymax in zip(df['left'],df['top'],df['left']+df['width'],df['top']+df['height'])]
-            text = [i for i in df['text']]
-            label = [i for i in df['label']]
-
-            boxes = [normalize_bbox(box, size) for box in boxes]
-
-            print(image_path)
-            for i in boxes:
-              for j in i:
-                if j>1000:
-                  print(j)
-                  pass
-
-            yield guid, {"id": str(guid), "words": text, "bboxes": boxes, "ner_tags": label, "image_path": image_path}