Initial commit

docbank.py

@@ -16,6 +16,8 @@
 
 import csv
 import os
+from glob import glob
+
 import numpy as np
 from PIL import Image
 from transformers import LayoutXLMTokenizerFast
@@ -49,7 +51,10 @@ _LICENSE = ""
 # 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",
-    "full": "",
+    "data": [
+        'http://hyperion.bbirke.de/data/geocite/train.zip',
+        'http://hyperion.bbirke.de/data/geocite/test.zip',
+    ],
 }
 
 _FEATURES = datasets.Features(
@@ -165,40 +170,44 @@ class Docbank(datasets.GeneratorBasedBuilder):
         # 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)
-        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)]
+        train_txts = glob(data_dir + '/train/txt/*.csv')
+        train_data = [(txt, data_dir + '/train/img/' + os.path.basename(txt)[:-4] + '.jpg') for txt in train_txts]
+        test_txts = glob(data_dir + '/test/txt/*.csv')
+        test_data = [(txt, data_dir + '/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": files_train,
+                    "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.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": files_test,
+                    "filepath": test_data,
                     "split": "test"
                 },
             ),
@@ -210,11 +219,11 @@ class Docbank(datasets.GeneratorBasedBuilder):
         # 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:
+        for f_fp_txt, f_fp_img in filepath:
             #print(f)
-            f_id = f['id']
-            f_fp_txt = f['filepath_txt']
-            f_fp_img = f['filepath_img']
+            f_id = key
+            #f_fp_txt = f['filepath_txt']
+            #f_fp_img = f['filepath_img']
             tokens = []
             bboxes = []
             # rgbs = []
@@ -227,14 +236,14 @@ class Docbank(datasets.GeneratorBasedBuilder):
 
             try:
                 with open(f_fp_txt, newline='', encoding='utf-8') as csvfile:
-                    reader = csv.reader(csvfile, delimiter='\t', quotechar=' ')
+                    reader = csv.reader(csvfile, delimiter=',')
                     for row in reader:
                         # normalized_bbox = normalize_bbox(row[1:5], size)
                         normalized_bbox = [int(x) for x in row[1:5]]
                         tokens.append(row[0])
                         bboxes.append(normalized_bbox)
                         #print(f'Before: {row[9]}')
-                        labels.append(row[9])
+                        labels.append(row[5])
                         #print(f'After: {row[9]}')
                         # tokenized_input = self.TOKENIZER(
                         #     row[0],