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| import gradio as gr | |
| from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, VisionEncoderDecoderModel | |
| import torch | |
| git_processor_base = AutoProcessor.from_pretrained("microsoft/git-base-coco") | |
| git_model_base = AutoModelForCausalLM.from_pretrained("microsoft/git-base-coco") | |
| git_processor_large = AutoProcessor.from_pretrained("microsoft/git-large-coco") | |
| git_model_large = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco") | |
| blip_processor_base = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") | |
| blip_model_base = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") | |
| blip_processor_large = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large") | |
| blip_model_large = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large") | |
| vitgpt_processor = AutoImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
| vitgpt_model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
| vitgpt_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
| 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) | |
| vitgpt_model.to(device) | |
| def generate_caption(processor, model, image, tokenizer=None): | |
| inputs = processor(images=image, return_tensors="pt").to(device) | |
| generated_ids = model.generate(pixel_values=inputs.pixel_values, max_length=50) | |
| if tokenizer is not None: | |
| generated_caption = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
| else: | |
| generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
| return generated_caption | |
| def generate_captions(image): | |
| caption_git_base = generate_caption(git_processor_base, git_model_base, image) | |
| caption_git_large = generate_caption(git_processor_large, git_model_large, image) | |
| caption_blip_base = generate_caption(blip_processor_base, blip_model_base, image) | |
| caption_blip_large = generate_caption(blip_processor_large, blip_model_large, image) | |
| caption_vitgpt = generate_caption(vitgpt_processor, vitgpt_model, image, vitgpt_tokenizer) | |
| return caption_git_base, caption_git_large, caption_blip_base, caption_blip_large, caption_vitgpt | |
| examples = [["test-1.jpeg"], ["test-2.jpeg"], ["test-3.jpeg"], ["test-4.jpeg"], ["test-5.jpeg"], ["test-6.jpg"]] | |
| outputs = [gr.outputs.Textbox(label="Caption generated by GIT-base"), gr.outputs.Textbox(label="Caption generated by GIT-large"), gr.outputs.Textbox(label="Caption generated by BLIP-base"), gr.outputs.Textbox(label="Caption generated by BLIP-large"), gr.outputs.Textbox(label="Caption generated by ViT+GPT-2")] | |
| interface = gr.Interface(fn=generate_captions, | |
| inputs=gr.inputs.Image(type="pil"), | |
| outputs=outputs, | |
| examples=examples, | |
| enable_queue=True) | |
| interface.launch(debug=True) |