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import re | |
import gradio as gr | |
from PIL import Image, ImageDraw | |
import math | |
import torch | |
import html | |
from transformers import DonutProcessor, VisionEncoderDecoderModel | |
pretrained_repo_name = "ivelin/donut-refexp-draft" | |
processor = DonutProcessor.from_pretrained(pretrained_repo_name) | |
model = VisionEncoderDecoderModel.from_pretrained(pretrained_repo_name) | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
model.to(device) | |
def process_refexp(image: Image, prompt: str): | |
print(f"(image, prompt): {image}, {prompt}") | |
# trim prompt to 80 characters and normalize to lowercase | |
prompt = prompt[:80].lower() | |
# prepare encoder inputs | |
pixel_values = processor(image, return_tensors="pt").pixel_values | |
# prepare decoder inputs | |
task_prompt = "<s_refexp><s_prompt>{user_input}</s_prompt><s_refexp>" | |
prompt = task_prompt.replace("{user_input}", prompt) | |
decoder_input_ids = processor.tokenizer( | |
prompt, add_special_tokens=False, return_tensors="pt").input_ids | |
# generate answer | |
outputs = model.generate( | |
pixel_values.to(device), | |
decoder_input_ids=decoder_input_ids.to(device), | |
max_length=model.decoder.config.max_position_embeddings, | |
early_stopping=True, | |
pad_token_id=processor.tokenizer.pad_token_id, | |
eos_token_id=processor.tokenizer.eos_token_id, | |
use_cache=True, | |
num_beams=1, | |
bad_words_ids=[[processor.tokenizer.unk_token_id]], | |
return_dict_in_generate=True, | |
) | |
# postprocess | |
sequence = processor.batch_decode(outputs.sequences)[0] | |
print(fr"predicted decoder sequence: {html.escape(sequence)}") | |
sequence = sequence.replace(processor.tokenizer.eos_token, "").replace( | |
processor.tokenizer.pad_token, "") | |
# remove first task start token | |
sequence = re.sub(r"<.*?>", "", sequence, count=1).strip() | |
print( | |
fr"predicted decoder sequence before token2json: {html.escape(sequence)}") | |
bbox = processor.token2json(sequence) | |
print(f"predicted bounding box: {bbox}") | |
print(f"image object: {image}") | |
print(f"image size: {image.size}") | |
width, height = image.size | |
print(f"image width, height: {width, height}") | |
print(f"processed prompt: {prompt}") | |
xmin = math.floor(width*float(bbox["xmin"])) if bbox.get("xmin") else 0 | |
ymin = math.floor(height*float(bbox["ymin"])) if bbox.get("ymin") else 0 | |
xmax = math.floor(width*float(bbox["xmax"])) if bbox.get("xmax") else 1 | |
ymax = math.floor(height*float(bbox["ymax"])) if bbox.get("ymax") else 1 | |
print( | |
f"to image pixel values: xmin, ymin, xmax, ymax: {xmin, ymin, xmax, ymax}") | |
shape = [(xmin, ymin), (xmax, ymax)] | |
# create rectangle image | |
img1 = ImageDraw.Draw(image) | |
img1.rectangle(shape, outline="green", width=5) | |
return image, bbox | |
title = "Demo: Donut π© for UI RefExp" | |
description = "Gradio Demo for Donut RefExp task, an instance of `VisionEncoderDecoderModel` fine-tuned on UIBert RefExp Dataset (UI Referring Expression). To use it, simply upload your image and type a question and click 'submit', or click one of the examples to load them. Read more at the links below." | |
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2111.15664' target='_blank'>Donut: OCR-free Document Understanding Transformer</a> | <a href='https://github.com/clovaai/donut' target='_blank'>Github Repo</a></p>" | |
examples = [["example_1.jpg", "select the setting icon from top right corner"], | |
["example_2.jpg", "enter the text field next to the name"]] | |
demo = gr.Interface(fn=process_refexp, | |
inputs=[gr.Image(type="pil"), "text"], | |
outputs=[gr.Image(type="pil"), "json"], | |
title=title, | |
description=description, | |
article=article, | |
examples=examples, | |
cache_examples=True | |
) | |
demo.launch() | |