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| import gradio as gr | |
| from transformers import AutoProcessor, AutoModelForCausalLM, BlipForQuestionAnswering, ViltForQuestionAnswering | |
| import torch | |
| import math | |
| torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg') | |
| torch.hub.download_url_to_file('https://huggingface.co/datasets/nielsr/textcaps-sample/resolve/main/stop_sign.png', 'stop_sign.png') | |
| torch.hub.download_url_to_file('https://cdn.openai.com/dall-e-2/demos/text2im/astronaut/horse/photo/0.jpg', 'astronaut.jpg') | |
| git_processor_base = AutoProcessor.from_pretrained("microsoft/git-base-vqav2") | |
| git_model_base = AutoModelForCausalLM.from_pretrained("microsoft/git-base-vqav2") | |
| # git_processor_large = AutoProcessor.from_pretrained("microsoft/git-large-vqav2") | |
| # git_model_large = AutoModelForCausalLM.from_pretrained("microsoft/git-large-vqav2") | |
| blip_processor_base = AutoProcessor.from_pretrained("Salesforce/blip-vqa-base") | |
| blip_model_base = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base") | |
| # blip_processor_large = AutoProcessor.from_pretrained("Salesforce/blip-vqa-capfilt-large") | |
| # blip_model_large = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-capfilt-large") | |
| vilt_processor = AutoProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa") | |
| vilt_model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa") | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| git_model_base.to(device) | |
| # blip_model_base.to(device) | |
| #git_model_large.to(device) | |
| #blip_model_large.to(device) | |
| # vilt_model.to(device) | |
| def generate_answer_git(processor, model, image, question): | |
| # prepare image | |
| pixel_values = processor(images=image, return_tensors="pt").pixel_values | |
| # prepare question | |
| input_ids = processor(text=question, add_special_tokens=False).input_ids | |
| input_ids = [processor.tokenizer.cls_token_id] + input_ids | |
| input_ids = torch.tensor(input_ids).unsqueeze(0) | |
| generated_ids = model.generate(pixel_values=pixel_values, input_ids=input_ids, max_length=50, return_dict_in_generate=True, output_scores=True) | |
| print('scores:') | |
| print(generated_ids.scores) | |
| idx = generated_ids.scores[0].argmax(-1).item() | |
| idx1 = generated_ids.scores[1].argmax(-1).item() | |
| print(idx, idx1) | |
| print(model.config.id2label) | |
| print('sequences:') | |
| print(generated_ids.sequences) | |
| print(generated_ids) | |
| generated_answer = processor.batch_decode(generated_ids.sequences, skip_special_tokens=True) | |
| print(generated_answer) | |
| return 'haha' | |
| def generate_answer_blip(processor, model, image, question): | |
| # prepare image + question | |
| inputs = processor(images=image, text=question, return_tensors="pt") | |
| print('blip') | |
| inputs = processor(image, question, return_tensors="pt") | |
| out = model.generate(**inputs) | |
| print(processor.decode(out[0], skip_special_tokens=True)) | |
| print(out) | |
| generated_ids = model.generate(**inputs, max_length=50, output_scores=True) | |
| print(generated_ids) | |
| generated_answer = processor.batch_decode(generated_ids, skip_special_tokens=True) | |
| return generated_answer | |
| def generate_answer_vilt(processor, model, image, question): | |
| # prepare image + question | |
| encoding = processor(images=image, text=question, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = model(**encoding) | |
| predicted_class_idx = outputs.logits.argmax(-1).item() | |
| return model.config.id2label[predicted_class_idx] | |
| def generate_answers(image, question): | |
| answer_git_base = generate_answer_git(git_processor_base, git_model_base, image, question) | |
| # answer_git_large = generate_answer_git(git_processor_large, git_model_large, image, question) | |
| answer_blip_base = generate_answer_blip(blip_processor_base, blip_model_base, image, question) | |
| # answer_blip_large = generate_answer_blip(blip_processor_large, blip_model_large, image, question) | |
| # answer_vilt = generate_answer_vilt(vilt_processor, vilt_model, image, question) | |
| return answer_git_base, answer_blip_base | |
| examples = [["cats.jpg", "How many cats are there?"], ["stop_sign.png", "What's behind the stop sign?"], ["astronaut.jpg", "What's the astronaut riding on?"]] | |
| outputs = [gr.outputs.Textbox(label="Answer generated by GIT-base"), gr.outputs.Textbox(label="Answer generated by BLIP-base"), gr.outputs.Textbox(label="Answer generated by ViLT")] | |
| title = "Interactive demo: comparing visual question answering (VQA) models" | |
| description = "Gradio Demo to compare GIT, BLIP and ViLT, 3 state-of-the-art vision+language models. To use it, simply upload your image 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://huggingface.co/docs/transformers/main/model_doc/blip' target='_blank'>BLIP docs</a> | <a href='https://huggingface.co/docs/transformers/main/model_doc/git' target='_blank'>GIT docs</a></p>" | |
| interface = gr.Interface(fn=generate_answers, | |
| inputs=[gr.inputs.Image(type="pil"), gr.inputs.Textbox(label="Question")], | |
| outputs=outputs, | |
| examples=examples, | |
| title=title, | |
| description=description, | |
| article=article, | |
| enable_queue=True) | |
| interface.launch() |