changed to input ids

ref_seg_ger.py

@@ -62,14 +62,14 @@ _LABELS = [
 
 _FEATURES = datasets.Features(
     {
-        #"id": datasets.Value("string"),
+        # "id": datasets.Value("string"),
         "input_ids": datasets.Sequence(datasets.Value("string")),
         "attention_mask": datasets.Sequence(datasets.Value("int64")),
-        #"bbox": datasets.Sequence(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(),
+        # "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']
         )),
@@ -80,6 +80,7 @@ _FEATURES = datasets.Features(
     }
 )
 
+
 def load_image(image_path, size=None):
     image = Image.open(image_path).convert("RGB")
     w, h = image.size
@@ -170,7 +171,7 @@ class RefSeg(datasets.GeneratorBasedBuilder):
         # 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)
-        #print(data_dir)
+        # print(data_dir)
         # 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
@@ -207,8 +208,8 @@ class RefSeg(datasets.GeneratorBasedBuilder):
     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)
-        #print(split)
+        # print(filepath)
+        # print(split)
         paths = glob(filepath + '/' + split + '/*.csv')
         key = 0
         for f in paths:
@@ -218,14 +219,17 @@ class RefSeg(datasets.GeneratorBasedBuilder):
             refs = []
             for i, row in df.iterrows():
 
-                #tokenized_input = row['token'].split(' ')
+                # tokenized_input = row['token'].split(' ')
                 tkn = self.TOKENIZER.pre_tokenize_str(row['token'])
                 if not tkn:
                     continue
                 tokenized_input, offsets = zip(*tkn)
                 tokenized_input = list(tokenized_input)
                 for t in range(len(tokenized_input)):
-                    refs.append(row['ref'] + '-ref')
+                    if t == 0:
+                        refs.append(row['ref'] + '-ref')
+                    else:
+                        refs.append('I-ref')
                 if len(tokenized_input) > 1:
                     if row['tag'] == 'B':
                         if tokenized_input[0] == '':
@@ -266,20 +270,19 @@ class RefSeg(datasets.GeneratorBasedBuilder):
                     clean_input_ids.append(input)
                     clean_labels.append(labels[i])
                     clean_refs.append(refs[i])
-            n_chunks = int(len(clean_input_ids)/self.CHUNK_SIZE) if len(clean_input_ids)%self.CHUNK_SIZE == 0 \
-                else int(len(clean_input_ids)/self.CHUNK_SIZE) + 1
+            n_chunks = int(len(clean_input_ids) / self.CHUNK_SIZE) if len(clean_input_ids) % self.CHUNK_SIZE == 0 \
+                else int(len(clean_input_ids) / self.CHUNK_SIZE) + 1
             split_ids = np.array_split(clean_input_ids, n_chunks)
             split_labels = np.array_split(clean_labels, n_chunks)
             split_refs = np.array_split(clean_refs, n_chunks)
             for chunk_ids, chunk_labels, chunk_refs in zip(split_ids, split_labels, split_refs):
-
-            #for chunk_id, index in enumerate(range(0, len(clean_input_ids), self.CHUNK_SIZE)):
-                #split_ids = clean_input_ids[index:max(len(clean_input_ids), index + self.CHUNK_SIZE)]
-                #split_bboxes = bboxes[index:index + self.CHUNK_SIZE]
+                # for chunk_id, index in enumerate(range(0, len(clean_input_ids), self.CHUNK_SIZE)):
+                # split_ids = clean_input_ids[index:max(len(clean_input_ids), 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 = clean_labels[index:max(len(clean_input_ids), index + self.CHUNK_SIZE)]
-                #split_labels_post = [item for sublist in split_labels for item in sublist]
+                # split_labels = clean_labels[index:max(len(clean_input_ids), index + self.CHUNK_SIZE)]
+                # split_labels_post = [item for sublist in split_labels for item in sublist]
                 # if(len(split_ids) != len(split_labels)):
                 #     print(f)
                 #     print(len(input_ids), input_ids)
@@ -289,20 +292,19 @@ class RefSeg(datasets.GeneratorBasedBuilder):
                 #         print(f)
                 #         print(len(input_ids), input_ids)
                 #         print(len(split_labels), split_labels)
-                    #print(len(split_labels_post), split_labels_post)
-                #print(split_labels, len(split_labels))
-                #print(split_ids, len(split_ids))
-
+                # print(len(split_labels_post), split_labels_post)
+                # print(split_labels, len(split_labels))
+                # print(split_ids, len(split_ids))
 
                 yield key, {
-                    #"id": f"{os.path.basename(f)}_{chunk_id}",
+                    # "id": f"{os.path.basename(f)}_{chunk_id}",
                     'input_ids': chunk_ids,
                     'attention_mask': [1] * len(chunk_ids),
-                    #"bbox": split_bboxes,
+                    # "bbox": split_bboxes,
                     # "RGBs": split_rgbs,
                     # "fonts": split_fonts,
-                    #"image": image,
-                    #"original_image": original_image,
+                    # "image": image,
+                    # "original_image": original_image,
                     "labels": chunk_labels,
                     "labels_ref": chunk_refs
                 }