Update README.md

README.md

@@ -1,3 +1,144 @@
 
+# LayoutLM fine-tuned on FUNSD for Document token classification
 
-# LayoutLM fine-tuned on FUNSD for Document token classification
+## Usage
+
+```python
+import torch
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+import pytesseract
+from transformers import LayoutLMForTokenClassification
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+model = LayoutLMForTokenClassification.from_pretrained("mrm8488/layoutlm-finetuned-funsd", num_labels=num_labels)
+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') \
+            
+ocr_df = ocr_df.dropna() \
+               .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)
+
+
+```