Index out of range in self while using the LILT Model
Hello,I am trying to fine-tune the LILT model to extract information from invoices. I annotated my documents using UBIAI, and then I ran this code to adjust the format to be the input of my model. This is the code:import pandas as pdimport numpy as npimport osimport argparsefrom datasets.features import ClassLabelfrom transformers import AutoProcessorfrom sklearn.model_selection import train_test_splitfrom datasets import Features, Sequence, ClassLabel, Value, Array2D, Array3D, Datasetfrom datasets import Image as Imgfrom PIL import Imageimport warningswarnings.filterwarnings(‘ignore’)def read_text_file(file_path):with open(file_path, ‘r’) as f:return (f.readlines())def prepare_examples(examples):images = examples[image_column_name]words = examples[text_column_name]boxes = examples[boxes_column_name]word_labels = examples[label_column_name]encoding = processor(images, words, boxes=boxes, word_labels=word_labels,truncation=True, padding=“max_length”,  max_length=512)return encodingdef get_zip_dir_name():try:os.chdir(‘/content/data’)dir_list = os.listdir()any_file_name = dir_list[0]zip_dir_name = any_file_name[:any_file_name.find(‘/’)]if all(list(map(lambda x: x.startswith(zip_dir_name), dir_list))):return zip_dir_namereturn Falsefinally:os.chdir(‘./…/’)def filter_out_unannotated(example):tags = example[‘ner_tags’]return not all([tag == label2id[‘O’] for tag in tags])ifname== ‘main’:parser = argparse.ArgumentParser()
parser.add_argument('--valid_size')
parser.add_argument('--output_path')
args = parser.parse_args()
TEST_SIZE = float(args.valid_size)
OUTPUT_PATH = args.output_path

os.makedirs(args.output_path, exist_ok=True)
files = {}
zip_dir_name = "/content/data"
zip_id = "34c8d6d4-5a0f-48be-bc8d-5085ae70ff89"
if zip_dir_name and zip_id:
    files['train_box'] = read_text_file(os.path.join(
        os.curdir, 'data', f'{zip_dir_name}/{zip_id}_box.txt'))
    files['train_image'] = read_text_file(os.path.join(
        os.curdir, 'data', f'{zip_dir_name}/{zip_id}_image.txt'))
    files['train'] = read_text_file(os.path.join(
        os.curdir, 'data', f'{zip_dir_name}/{zip_id}.txt'))
else:
    for f in os.listdir():
        if f.endswith('.txt') and f.find('box') != -1:
            files['train_box'] = read_text_file(os.path.join(os.curdir, f))
        elif f.endswith('.txt') and f.find('image') != -1:
            files['train_image'] = read_text_file(
                os.path.join(os.curdir, f))
        elif f.endswith('.txt') and f.find('labels') == -1:
            files['train'] = read_text_file(os.path.join(os.curdir, f))

assert(len(files['train']) == len(files['train_box']))
assert(len(files['train_box']) == len(files['train_image']))
assert(len(files['train_image']) == len(files['train']))

images = {}
for i, row in enumerate(files['train_image']):
    if row != '\n':
        image_name = row.split('\t')[-1]
        images.setdefault(image_name.replace('\n', ''), []).append(i)

words, bboxes, ner_tags, image_path = [], [], [], []
for image, rows in images.items():
    words.append([row.split('\t')[0].replace('\n', '')
                 for row in files['train'][rows[0]:rows[-1]+1] if len(row) > 1])
    ner_tags.append([row.split('\t')[1].replace('\n', '')
                    for row in files['train'][rows[0]:rows[-1]+1] if len(row) > 1])
    bboxes.append([box.split('\t')[1].replace('\n', '')
                  for box in files['train_box'][rows[0]:rows[-1]+1] if len(box) > 1])

    im = Image.open(str('data/'+image))
    width, height = im.size

    for i in range(len(bboxes)):

        list_box=bboxes[0][i].split()

        list_box[0]=str(round(1000 * (float(list_box[0]) / width)))
        list_box[1]=str(round(1000 * (float(list_box[1]) / height)))
        list_box[2]=str(round(1000 * (float(list_box[2]) / width)))
        list_box[3]=str(round(1000 * (float(list_box[3]) / height)))
        bboxes[0][i] = ' '.join(list_box)




    if zip_dir_name:
        image_path.append(f"/content/data/{image}")
    else:
        image_path.append(f"/content/data/{image}")

labels = list(set([tag for doc_tag in ner_tags for tag in doc_tag]))
id2label = {v: k for v, k in enumerate(labels)}
label2id = {k: v for v, k in enumerate(labels)}

dataset_dict = {
    'id': range(len(words)),
    'tokens': words,
    'bboxes': [[list(map(int, bbox.split())) for bbox in doc] for doc in bboxes],
    'ner_tags': [[label2id[tag] for tag in ner_tag] for ner_tag in ner_tags],
    'image': [Image.open(path).convert("RGB") for path in image_path]
}

#raw features
features = Features({
    'id': Value(dtype='string', id=None),
    'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),
    'bboxes': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None),
    'ner_tags': Sequence(feature=ClassLabel(num_classes=len(labels), names=labels, names_file=None, id=None), length=-1, id=None),
    'image': Img(decode=True, id=None)
})

full_data_set = Dataset.from_dict(dataset_dict, features=features)
dataset = full_data_set.train_test_split(test_size=TEST_SIZE)
dataset["train"] = dataset["train"].filter(filter_out_unannotated)
processor = AutoProcessor.from_pretrained(
    "microsoft/layoutlmv3-base", apply_ocr=False)

features = dataset["train"].features
column_names = dataset["train"].column_names
image_column_name = "image"
text_column_name = "tokens"
boxes_column_name = "bboxes"
label_column_name = "ner_tags"

features = Features({
    'pixel_values': Array3D(dtype="float32", shape=(3, 224, 224)),
    'input_ids': Sequence(feature=Value(dtype='int64')),
    'attention_mask': Sequence(Value(dtype='int64')),
    'bbox': Array2D(dtype="int64", shape=(512, 4)),
    'labels': Sequence(ClassLabel(names=labels)),
})

train_dataset = dataset["train"].map(
    prepare_examples,
    batched=True,
    remove_columns=column_names,
    features=features,
)
eval_dataset = dataset["test"].map(
    prepare_examples,
    batched=True,
    remove_columns=column_names,
    features=features,
)
train_dataset.set_format("torch")
print(bboxes)
if not OUTPUT_PATH.endswith('/'):
    OUTPUT_PATH += '/'
train_dataset.save_to_disk(f'{OUTPUT_PATH}train_split')
eval_dataset.save_to_disk(f'{OUTPUT_PATH}eval_split')
dataset.save_to_disk(f'{OUTPUT_PATH}raw_data')Then I used Trainer.train() to train the model:from transformers import Trainer, TrainingArgumentsNUM_TRAIN_EPOCHS = 50PER_DEVICE_TRAIN_BATCH_SIZE = 8PER_DEVICE_EVAL_BATCH_SIZE = 8LEARNING_RATE = 4e-5training_args = TrainingArguments(output_dir=“LiLT_INVOICE”,# max_steps=1500,num_train_epochs=NUM_TRAIN_EPOCHS,logging_strategy=“epoch”,save_total_limit=1,per_device_train_batch_size=PER_DEVICE_TRAIN_BATCH_SIZE,per_device_eval_batch_size=PER_DEVICE_EVAL_BATCH_SIZE,learning_rate=LEARNING_RATE,evaluation_strategy=“no”,save_strategy=“no”,# eval_steps=100,load_best_model_at_end=True,metric_for_best_model=“f1”)Initialize our Trainertrainer = Trainer(model=model,args=training_args,train_dataset=train_dataset,eval_dataset=eval_dataset,data_collator=default_data_collator,compute_metrics=compute_metrics,)trainer.train()but I got this error:IndexError Traceback (most recent call last) Cell In[12],line 1---->1trainer.train() File [c:\Users\heha\AppData\Local\Programs\Python\Python310\lib\site-packages\transformers\trainer.py:1624](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/transformers/trainer.py:1624), in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs) [1622](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/transformers/trainer.py:1622) REDACTED_TOKEN_utils.enable_progress_bars() [1623](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/transformers/trainer.py:1623) else: → [1624](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/transformers/trainer.py:1624) return inner_training_loop( [1625](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/transformers/trainer.py:1625) args=args, [1626](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/transformers/trainer.py:1626) resume_from_checkpoint=resume_from_checkpoint, [1627](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/transformers/trainer.py:1627) trial=trial, [1628](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/transformers/trainer.py:1628) ignore_keys_for_eval=ignore_keys_for_eval, [1629](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/transformers/trainer.py:1629) ) File [c:\Users\heha\AppData\Local\Programs\Python\Python310\lib\site-packages\transformers\trainer.py:1961](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/transformers/trainer.py:1961), in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval) [1958](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/transformers/trainer.py:1958) self.control = self.callback_handler.on_step_begin(args, self.state, self.control) [1960](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/transformers/trainer.py:1960) with self.accelerator.accumulate(model): → [1961](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/transformers/trainer.py:1961) tr_loss_step = self.training_step(model, inputs) [1963](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/transformers/trainer.py:1963) if ( [1964](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/transformers/trainer.py:1964) args.logging_nan_inf_filter [1965](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/transformers/trainer.py:1965) and not is_torch_tpu_available() [1966](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/transformers/trainer.py:1966) and (torch.isnan(tr_loss_step) or torch.isinf(tr_loss_step)) [1967](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/transformers/trainer.py:1967) ): [1968](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/transformers/trainer.py:1968) # if loss is nan or inf simply add the average of previous logged losses…[2235](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/torch/nn/functional.py:2235) # remove once script supports set_grad_enabled [2236](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/torch/nn/functional.py:2236)no_grad_embedding_renorm(weight, input, max_norm, norm_type) → [2237](file:///C:/Users/heha/AppData/Local/Programs/Python/Python310/lib/site-packages/torch/nn/functional.py:2237) return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
2024-02-25T20:25:39Z
Answered here:Error while using LILT model "index out of range in self" - #3 by nielsr