Spaces:
Running
Running
| 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-combined-v1' | |
| pretrained_revision = 'main' | |
| # revision: '348ddad8e958d370b7e341acd6050330faa0500f' # Iou = 0.47 | |
| # revision: '41210d7c42a22e77711711ec45508a6b63ec380f' # : IoU=0.42 | |
| # use 'main' for latest revision | |
| print(f"Loading model checkpoint: {pretrained_repo_name}") | |
| processor = DonutProcessor.from_pretrained(pretrained_repo_name, revision=pretrained_revision) | |
| model = VisionEncoderDecoderModel.from_pretrained(pretrained_repo_name, revision=pretrained_revision) | |
| 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_target_bounding_box>" | |
| 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)}") | |
| seqjson = processor.token2json(sequence) | |
| # safeguard in case predicted sequence does not include a target_bounding_box token | |
| bbox = seqjson.get('target_bounding_box') | |
| if bbox is None: | |
| print( | |
| f"token2bbox seq has no predicted target_bounding_box, seq:{seq}") | |
| bbox = {"xmin": 0, "ymin": 0, "xmax": 0, "ymax": 0} | |
| return bbox | |
| print(f"predicted bounding box with text coordinates: {bbox}") | |
| # safeguard in case text prediction is missing some bounding box coordinates | |
| # or coordinates are not valid numeric values | |
| try: | |
| xmin = float(bbox.get("xmin", 0)) | |
| except ValueError: | |
| xmin = 0 | |
| try: | |
| ymin = float(bbox.get("ymin", 0)) | |
| except ValueError: | |
| ymin = 0 | |
| try: | |
| xmax = float(bbox.get("xmax", 1)) | |
| except ValueError: | |
| xmax = 1 | |
| try: | |
| ymax = float(bbox.get("ymax", 1)) | |
| except ValueError: | |
| ymax = 1 | |
| # replace str with float coords | |
| bbox = {"xmin": xmin, "ymin": ymin, "xmax": xmax, | |
| "ymax": ymax, "decoder output sequence": sequence} | |
| print(f"predicted bounding box with float coordinates: {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}") | |
| # safeguard in case text prediction is missing some bounding box coordinates | |
| xmin = math.floor(width*bbox["xmin"]) | |
| ymin = math.floor(height*bbox["ymin"]) | |
| xmax = math.floor(width*bbox["xmax"]) | |
| ymax = math.floor(height*bbox["ymax"]) | |
| print( | |
| f"to image pixel values: xmin, ymin, xmax, ymax: {xmin, ymin, xmax, ymax}") | |
| shape = [(xmin, ymin), (xmax, ymax)] | |
| # deaw bbox rectangle | |
| img1 = ImageDraw.Draw(image) | |
| img1.rectangle(shape, outline="green", width=5) | |
| img1.rectangle(shape, outline="white", width=2) | |
| return image, bbox | |
| title = "Demo: Donut 🍩 for UI RefExp (by GuardianUI)" | |
| description = "Gradio Demo for Donut RefExp task, an instance of `VisionEncoderDecoderModel` fine-tuned on [UIBert RefExp](https://huggingface.co/datasets/ivelin/ui_refexp_saved) Dataset (UI Referring Expression). To use it, simply upload your image and type a prompt and click 'submit', or click one of the examples to load them. See the model training <a href='https://colab.research.google.com/github/ivelin/donut_ui_refexp/blob/main/Fine_tune_Donut_on_UI_RefExp.ipynb' target='_parent'>Colab Notebook</a> for this space. 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_1.jpg", "click on down arrow beside the entertainment"], | |
| ["example_1.jpg", "select the down arrow button beside lifestyle"], | |
| ["example_1.jpg", "click on the image beside the option traffic"], | |
| ["example_3.jpg", "select the third row first image"], | |
| ["example_3.jpg", "click the tick mark on the first image"], | |
| ["example_3.jpg", "select the ninth image"], | |
| ["example_3.jpg", "select the add icon"], | |
| ["example_3.jpg", "click the first image"], | |
| ["val-image-4.jpg", 'select 4153365454'], | |
| ['val-image-4.jpg', 'go to cell'], | |
| ['val-image-4.jpg', 'select number above cell'], | |
| ["val-image-1.jpg", "select calendar option"], | |
| ["val-image-1.jpg", "select photos&videos option"], | |
| ["val-image-2.jpg", "click on change store"], | |
| ["val-image-2.jpg", "click on shop menu at the bottom"], | |
| ["val-image-3.jpg", "click on image above short meow"], | |
| ["val-image-3.jpg", "go to cat sounds"], | |
| ["example_2.jpg", "click on green color button"], | |
| ["example_2.jpg", "click on text which is beside call now"], | |
| ["example_2.jpg", "click on more button"], | |
| ["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, | |
| # caching examples inference takes too long to start space after app change commit | |
| cache_examples=False | |
| ) | |
| demo.launch() | |