LayoutLM fine-tuned on FUNSD for Document/Forms token classification
Usage (WIP)
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")
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*{{%htmlContent%}}#39;, 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]):
continue
else:
word_level_predictions.append(token_pred)
final_boxes.append(box)
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)
