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from datasets import load_dataset
import numpy as np
from transformers import LayoutLMv3Processor, LayoutLMv3ForTokenClassification
from datasets import load_dataset
from PIL import Image, ImageDraw, ImageFont
import torch
tokenizer = LayoutLMv3Processor.from_pretrained("microsoft/layoutlmv3-base")
model = LayoutLMv3ForTokenClassification.from_pretrained(r"models")
"""device = torch.device("cuda")
model.cuda()
"""
labels = ['O', 'B-HEADER', 'I-HEADER', 'B-QUESTION', 'I-QUESTION', 'B-ANSWER', 'I-ANSWER']
id2label = {v: k for v, k in enumerate(labels)}
label2color = {
"question": "blue",
"answer": "green",
"header": "orange",
"other": "violet",
}
def unnormalize_box(bbox, width, height):
return [
width * (bbox[0] / 1000),
height * (bbox[1] / 1000),
width * (bbox[2] / 1000),
height * (bbox[3] / 1000),
]
def iob_to_label(label):
label = label[2:]
if not label:
return "other"
return label
def processor(image):
image = image.convert("RGB")
width, height = image.size
# encode
encoding = tokenizer(
image, truncation=True, return_offsets_mapping=True, return_tensors="pt"
)
offset_mapping = encoding.pop("offset_mapping")
encoding = encoding.to('cuda')
# forward pass
outputs = model(**encoding)
# get predictions
predictions = outputs.logits.argmax(-1).squeeze().tolist()
token_boxes = encoding.bbox.squeeze().tolist()
# only keep non-subword predictions
is_subword = np.array(offset_mapping.squeeze().tolist())[:, 0] != 0
true_predictions = [
id2label[pred] for idx, pred in enumerate(predictions) if not is_subword[idx]
]
true_boxes = [
unnormalize_box(box, width, height)
for idx, box in enumerate(token_boxes)
if not is_subword[idx]
]
draw = ImageDraw.Draw(image)
font = ImageFont.load_default()
for prediction, box in zip(true_predictions, true_boxes):
predicted_label = iob_to_label(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,
)
return image |