Delete layoutlmv3.py

layoutlmv3.py

@@ -1,136 +0,0 @@
-import json
-import os
-import ast
-from pathlib import Path
-import datasets
-from PIL import Image
-import pandas as pd
-
-logger = datasets.logging.get_logger(__name__)
-_CITATION = """\
-@article{,
-  title={},
-  author={},
-  journal={},
-  year={},
-  volume={}
-}
-"""
-_DESCRIPTION = """\
-This is a sample dataset for training layoutlmv3 model on custom annotated data.
-"""
-
-def load_image(image_path):
-    image = Image.open(image_path).convert("RGB")
-    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]),
-    ]
-
-
-_URLS = []
-
-'''Edit your working directory folder path here if required. 
-If this file is in the same folder as the "layoutlmv3" folder keep it as it is.
-'''
-data_path = r'./' 
-
-class DatasetConfig(datasets.BuilderConfig):
-    """BuilderConfig for InvoiceExtraction Dataset"""
-    def __init__(self, **kwargs):
-        """BuilderConfig for InvoiceExtraction Dataset.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(DatasetConfig, self).__init__(**kwargs)
-
-
-class InvoiceExtraction(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        DatasetConfig(name="InvoiceExtraction", version=datasets.Version("1.0.0"), description="InvoiceExtraction dataset"),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "tokens": datasets.Sequence(datasets.Value("string")),
-                    "bboxes": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
-                    "ner_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names = ['num_facture','date_facture','fournisseur','client','mat_client','mat_fournisseur','tva','pourcentage_tva','remise','pourcentage_remise','timbre','fodec','ttc','devise','net_ht'] #Enter the list of labels that you have here.
-                            )
-                    ),
-                    "image_path": datasets.Value("string"),
-                    "image": datasets.features.Image()
-                }
-            ),
-            supervised_keys=None,
-            citation=_CITATION,
-            homepage="",
-        )
-
-
-
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        """Uses local files located with data_dir"""
-        dest = os.path.join(data_path, 'layoutlmv3')
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(dest, "train.txt"), "dest": dest}
-            ),            
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(dest, "test.txt"), "dest": dest}
-            ),
-        ]
-
-    def _generate_examples(self, filepath, dest):
-
-        df = pd.read_csv(os.path.join(dest, 'class_list.txt'), delimiter=',', header=None)
-        id2labels = dict(zip(df[0].tolist(), df[1].tolist()))
-
-
-        logger.info("⏳ Generating examples from = %s", filepath)
-
-        item_list = []
-        with open(filepath, 'r', encoding='utf-8', errors='ignore') as f:
-            for line in f:
-                item_list.append(line.rstrip('\n\r'))
-        print(item_list)
-        for guid, fname in enumerate(item_list):
-            print(fname)
-            data = ast.literal_eval(fname)
-            image_path = os.path.join(dest, data['file_name'])
-            image, size = load_image(image_path)
-            boxes = data['bboxes']
-
-            text = data['tokens']
-            label = data['ner_tags']
-            
-            #print(boxes)
-            #for i in boxes:
-            #  print(i)
-            boxes = [normalize_bbox(box, size) for box in boxes]
-            flag=0
-            #print(image_path)
-            for i in boxes:
-              #print(i)
-              for j in i:
-                if j>1000:
-                  flag+=1
-                  #print(j)
-                  pass
-            if flag>0: print(image_path)
- 
-            yield guid, {"id": str(guid), "tokens": text, "bboxes": boxes, "ner_tags": label, "image_path": image_path, "image": image}