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Duplicate from kasun/blip-larg

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  1. .gitattributes +34 -0
  2. README.md +13 -0
  3. app.py +125 -0
  4. requirements.txt +5 -0
.gitattributes ADDED
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ title: Comparing Captioning Models
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+ emoji: 🔥
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+ colorFrom: yellow
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+ colorTo: pink
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+ sdk: gradio
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+ sdk_version: 3.15.0
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+ app_file: app.py
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+ pinned: false
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+ duplicated_from: kasun/blip-larg
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+ ---
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+
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
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+ import gradio as gr
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+ from transformers import AutoProcessor, BlipForConditionalGeneration, AutoModelForCausalLM, AutoImageProcessor, VisionEncoderDecoderModel, AutoTokenizer
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+
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+ # from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, Blip2ForConditionalGeneration, VisionEncoderDecoderModel
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+ import torch
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+ import open_clip
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+
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+ from huggingface_hub import hf_hub_download
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+
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+ torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg')
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+ torch.hub.download_url_to_file('https://huggingface.co/datasets/nielsr/textcaps-sample/resolve/main/stop_sign.png', 'stop_sign.png')
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+ torch.hub.download_url_to_file('https://cdn.openai.com/dall-e-2/demos/text2im/astronaut/horse/photo/0.jpg', 'astronaut.jpg')
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+
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+ git_processor_base = AutoProcessor.from_pretrained("microsoft/git-base-coco")
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+ git_model_base = AutoModelForCausalLM.from_pretrained("microsoft/git-base-coco")
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+
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+ # git_processor_large_coco = AutoProcessor.from_pretrained("microsoft/git-large-coco")
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+ # git_model_large_coco = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco")
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+
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+ # git_processor_large_textcaps = AutoProcessor.from_pretrained("microsoft/git-large-r-textcaps")
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+ # git_model_large_textcaps = AutoModelForCausalLM.from_pretrained("microsoft/git-large-r-textcaps")
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+
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+ blip_processor_base = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
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+ blip_model_base = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
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+
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+ # blip_processor_large = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
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+ # blip_model_large = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
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+
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+ # blip2_processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
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+ # blip2_model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16)
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+
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+ # blip2_processor_8_bit = AutoProcessor.from_pretrained("Salesforce/blip2-opt-6.7b")
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+ # blip2_model_8_bit = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-6.7b", device_map="auto", load_in_8bit=True)
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+
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+ # vitgpt_processor = AutoImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
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+ # vitgpt_model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
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+ # vitgpt_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
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+
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+ # coca_model, _, coca_transform = open_clip.create_model_and_transforms(
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+ # model_name="coca_ViT-L-14",
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+ # pretrained="mscoco_finetuned_laion2B-s13B-b90k"
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+ # )
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+
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+ device = "cuda" if torch.cuda.is_available() else "cpu"
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+
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+ git_model_base.to(device)
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+ blip_model_base.to(device)
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+ # git_model_large_coco.to(device)
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+ # git_model_large_textcaps.to(device)
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+ # blip_model_large.to(device)
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+ # vitgpt_model.to(device)
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+ # coca_model.to(device)
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+ # blip2_model.to(device)
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+
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+ def generate_caption(processor, model, image, tokenizer=None, use_float_16=False):
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+ inputs = processor(images=image, return_tensors="pt").to(device)
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+
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+ if use_float_16:
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+ inputs = inputs.to(torch.float16)
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+
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+ generated_ids = model.generate(pixel_values=inputs.pixel_values, max_length=50)
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+
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+ if tokenizer is not None:
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+ generated_caption = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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+ else:
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+ generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
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+
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+ return generated_caption
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+
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+
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+ def generate_caption_coca(model, transform, image):
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+ im = transform(image).unsqueeze(0).to(device)
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+ with torch.no_grad(), torch.cuda.amp.autocast():
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+ generated = model.generate(im, seq_len=20)
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+ return open_clip.decode(generated[0].detach()).split("<end_of_text>")[0].replace("<start_of_text>", "")
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+
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+
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+ def generate_captions(image):
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+ caption_git_base = generate_caption(git_processor_base, git_model_base, image)
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+
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+ # caption_git_large_coco = generate_caption(git_processor_large_coco, git_model_large_coco, image)
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+
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+ # caption_git_large_textcaps = generate_caption(git_processor_large_textcaps, git_model_large_textcaps, image)
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+
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+ caption_blip_base = generate_caption(blip_processor_base, blip_model_base, image)
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+
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+ # caption_blip_large = generate_caption(blip_processor_large, blip_model_large, image)
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+
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+ # caption_vitgpt = generate_caption(vitgpt_processor, vitgpt_model, image, vitgpt_tokenizer)
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+
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+ # caption_coca = generate_caption_coca(coca_model, coca_transform, image)
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+
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+ # caption_blip2 = generate_caption(blip2_processor, blip2_model, image, use_float_16=True).strip()
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+
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+ # caption_blip2_8_bit = generate_caption(blip2_processor_8_bit, blip2_model_8_bit, image, use_float_16=True).strip()
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+
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+ # return caption_git_large_coco, caption_git_large_textcaps, caption_blip_large, caption_coca, caption_blip2_8_bit
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+ return caption_git_base, caption_blip_base
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+
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+
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+
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+ examples = [["cats.jpg"], ["stop_sign.png"], ["astronaut.jpg"]]
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+ # outputs = [gr.outputs.Textbox(label="Caption generated by GIT-large fine-tuned on COCO"), gr.outputs.Textbox(label="Caption generated by GIT-large fine-tuned on TextCaps"), gr.outputs.Textbox(label="Caption generated by BLIP-large"), gr.outputs.Textbox(label="Caption generated by CoCa"), gr.outputs.Textbox(label="Caption generated by BLIP-2 OPT 6.7b")]
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+ outputs = [
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+ gr.outputs.Textbox(label="Caption generated by GIT-base fine-tuned on COCO"),
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+ # gr.outputs.Textbox(label="Caption generated by GIT-large fine-tuned on COCO"),
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+ # gr.outputs.Textbox(label="Caption generated by GIT-large fine-tuned on TextCaps"),
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+ gr.outputs.Textbox(label="Caption generated by BLIP-base"),
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+ # gr.outputs.Textbox(label="Caption generated by BLIP-large"),
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+ # gr.outputs.Textbox(label="Caption generated by vitgpt")
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+ ]
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+
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+ title = "Interactive demo: comparing image captioning models"
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+ description = "Gradio Demo to compare GIT, BLIP, CoCa, and BLIP-2, 4 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."
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+ 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>"
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+
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+ interface = gr.Interface(fn=generate_captions,
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+ inputs=gr.inputs.Image(type="pil"),
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+ outputs=outputs,
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+ examples=examples,
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+ title=title,
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+ description=description,
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+ article=article,
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+ enable_queue=True)
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+ interface.launch()
requirements.txt ADDED
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+ git+https://github.com/huggingface/transformers.git@main
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+ torch
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+ scipy
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+ open_clip_torch
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+