# LayoutLM fine-tuned on FUNSD for Document/Forms token classification ## Usage (WIP) ```python import torch import numpy as np from PIL import Image, ImageDraw, ImageFont import pytesseract from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer device = torch.device("cuda" if torch.cuda.is_available() else "cpu") tokenizer = LayoutLMTokenizer.from_pretrained("mrm8488/layoutlm-finetuned-funsd") model = LayoutLMForTokenClassification.from_pretrained("mrm8488/layoutlm-finetuned-funsd", num_labels=13) model.to(device) image = Image.open("/83443897.png") image = image.convert("RGB") # Display the image # Run Tesseract (OCR) on the image width, height = image.size w_scale = 1000/width h_scale = 1000/height ocr_df = pytesseract.image_to_data(image, output_type='data.frame') \\n ocr_df = ocr_df.dropna() \\n .assign(left_scaled = ocr_df.left*w_scale, width_scaled = ocr_df.width*w_scale, top_scaled = ocr_df.top*h_scale, height_scaled = ocr_df.height*h_scale, right_scaled = lambda x: x.left_scaled + x.width_scaled, bottom_scaled = lambda x: x.top_scaled + x.height_scaled) float_cols = ocr_df.select_dtypes('float').columns ocr_df[float_cols] = ocr_df[float_cols].round(0).astype(int) ocr_df = ocr_df.replace(r'^\s*$', np.nan, regex=True) ocr_df = ocr_df.dropna().reset_index(drop=True) ocr_df[:20] # create a list of words, actual bounding boxes, and normalized boxes words = list(ocr_df.text) coordinates = ocr_df[['left', 'top', 'width', 'height']] actual_boxes = [] for idx, row in coordinates.iterrows(): x, y, w, h = tuple(row) # the row comes in (left, top, width, height) format actual_box = [x, y, x+w, y+h] # we turn it into (left, top, left+widght, top+height) to get the actual box actual_boxes.append(actual_box) def normalize_box(box, width, height): return [ int(1000 * (box[0] / width)), int(1000 * (box[1] / height)), int(1000 * (box[2] / width)), int(1000 * (box[3] / height)), ] boxes = [] for box in actual_boxes: boxes.append(normalize_box(box, width, height)) # Display boxes def convert_example_to_features(image, words, boxes, actual_boxes, tokenizer, args, cls_token_box=[0, 0, 0, 0], sep_token_box=[1000, 1000, 1000, 1000], pad_token_box=[0, 0, 0, 0]): width, height = image.size tokens = [] token_boxes = [] actual_bboxes = [] # we use an extra b because actual_boxes is already used token_actual_boxes = [] for word, box, actual_bbox in zip(words, boxes, actual_boxes): word_tokens = tokenizer.tokenize(word) tokens.extend(word_tokens) token_boxes.extend([box] * len(word_tokens)) actual_bboxes.extend([actual_bbox] * len(word_tokens)) token_actual_boxes.extend([actual_bbox] * len(word_tokens)) # Truncation: account for [CLS] and [SEP] with "- 2". special_tokens_count = 2 if len(tokens) > args.max_seq_length - special_tokens_count: tokens = tokens[: (args.max_seq_length - special_tokens_count)] token_boxes = token_boxes[: (args.max_seq_length - special_tokens_count)] actual_bboxes = actual_bboxes[: (args.max_seq_length - special_tokens_count)] token_actual_boxes = token_actual_boxes[: (args.max_seq_length - special_tokens_count)] # add [SEP] token, with corresponding token boxes and actual boxes tokens += [tokenizer.sep_token] token_boxes += [sep_token_box] actual_bboxes += [[0, 0, width, height]] token_actual_boxes += [[0, 0, width, height]] segment_ids = [0] * len(tokens) # next: [CLS] token tokens = [tokenizer.cls_token] + tokens token_boxes = [cls_token_box] + token_boxes actual_bboxes = [[0, 0, width, height]] + actual_bboxes token_actual_boxes = [[0, 0, width, height]] + token_actual_boxes segment_ids = [1] + segment_ids input_ids = tokenizer.convert_tokens_to_ids(tokens) # The mask has 1 for real tokens and 0 for padding tokens. Only real # tokens are attended to. input_mask = [1] * len(input_ids) # Zero-pad up to the sequence length. padding_length = args.max_seq_length - len(input_ids) input_ids += [tokenizer.pad_token_id] * padding_length input_mask += [0] * padding_length segment_ids += [tokenizer.pad_token_id] * padding_length token_boxes += [pad_token_box] * padding_length token_actual_boxes += [pad_token_box] * padding_length assert len(input_ids) == args.max_seq_length assert len(input_mask) == args.max_seq_length assert len(segment_ids) == args.max_seq_length assert len(token_boxes) == args.max_seq_length assert len(token_actual_boxes) == args.max_seq_length return input_ids, input_mask, segment_ids, token_boxes, token_actual_boxes input_ids, input_mask, segment_ids, token_boxes, token_actual_boxes = convert_example_to_features(image=image, words=words, boxes=boxes, actual_boxes=actual_boxes, tokenizer=tokenizer, args=args) input_ids = torch.tensor(input_ids, device=device).unsqueeze(0) attention_mask = torch.tensor(input_mask, device=device).unsqueeze(0) token_type_ids = torch.tensor(segment_ids, device=device).unsqueeze(0) bbox = torch.tensor(token_boxes, device=device).unsqueeze(0) outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids) token_predictions = outputs.logits.argmax(-1).squeeze().tolist() # the predictions are at the token level word_level_predictions = [] # let's turn them into word level predictions final_boxes = [] for id, token_pred, box in zip(input_ids.squeeze().tolist(), token_predictions, token_actual_boxes): if (tokenizer.decode([id]).startswith("##")) or (id in [tokenizer.cls_token_id, tokenizer.sep_token_id, tokenizer.pad_token_id]): # skip prediction + bounding box continue else: word_level_predictions.append(token_pred) final_boxes.append(box) #print(word_level_predictions) draw = ImageDraw.Draw(image) font = ImageFont.load_default() def iob_to_label(label): if label != 'O': return label[2:] else: return "other" label2color = {'question':'blue', 'answer':'green', 'header':'orange', 'other':'violet'} for prediction, box in zip(word_level_predictions, final_boxes): predicted_label = iob_to_label(label_map[prediction]).lower() draw.rectangle(box, outline=label2color[predicted_label]) draw.text((box[0] + 10, box[1] - 10), text=predicted_label, fill=label2color[predicted_label], font=font) # Display the result (image) ```