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DFN5B-CLIP-ViT-H-14-preprocessor_config

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DFN5B-CLIP-ViT-H-14-preprocessor_config
File size: 3,021 Bytes 
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import marimo

__generated_with = "0.17.0"
app = marimo.App()


@app.cell
def _():
    from open_clip import create_model_from_pretrained
    import torch
    from transformers import CLIPImageProcessor, AutoImageProcessor
    from urllib.request import urlopen
    from PIL import Image
    return (
        AutoImageProcessor,
        CLIPImageProcessor,
        Image,
        create_model_from_pretrained,
        torch,
        urlopen,
    )


@app.cell
def _(Image, urlopen):
    image = Image.open(urlopen(
        # 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
        'https://media.gettyimages.com/id/1309385864/photo/las-vegas-strip-skyline-landscaped-ultra-wide-shot-at-night.jpg?s=1024x1024&w=gi&k=20&c=bBxkjq8FoK2bPKYLtqbJMCgpsvjUm-vI7-yw04cq7AU='
    ))
    image.size
    return (image,)


@app.cell
def _():
    model = "apple/DFN5B-CLIP-ViT-H-14"
    return (model,)


@app.cell
def _(
    AutoImageProcessor,
    CLIPImageProcessor,
    create_model_from_pretrained,
    model,
):
    try:
        hf_pre = AutoImageProcessor.from_pretrained(model)
    except:
        print("Auto image processor not found")
        hf_pre = CLIPImageProcessor()
    hf_pre_fix = CLIPImageProcessor(
        do_center_crop=False,
        do_normalize=True,
        do_resize=True,
        feature_extractor_type="CLIPFeatureExtractor",
        image_mean=[
            0.48145466,
            0.4578275,
            0.40821073
        ],
        image_std=[
            0.26862954,
            0.26130258,
            0.27577711
        ],
        size={"width": 224, "height": 224},
        do_convert_rgb=True
    )
    _, preprocess = create_model_from_pretrained(f'hf-hub:{model}')
    preprocess
    return hf_pre, hf_pre_fix, preprocess


@app.cell
def _(hf_pre, hf_pre_fix, image, preprocess, torch):
    hf_res_fix = torch.tensor(hf_pre_fix(images=image)["pixel_values"]).squeeze()
    hf_res = torch.tensor(hf_pre(images=image)["pixel_values"]).squeeze()
    op_res = preprocess(image)
    op_res.shape, hf_res.shape
    return hf_res, hf_res_fix, op_res


@app.cell
def _(hf_res, op_res):
    (hf_res == op_res).all()
    return


@app.cell
def _(hf_res_fix, op_res):
    (hf_res_fix == op_res).all()
    return


@app.cell
def _(hf_res, hf_res_fix, image, op_res):
    # view the images
    import matplotlib.pyplot as plt
    fig, axs = plt.subplots(1, 4, figsize=(15, 5))
    axs[0].imshow(image)
    axs[0].set_title("Original Image")
    axs[1].imshow(op_res.permute(1, 2, 0).numpy())
    axs[1].set_title("OpenCLIP Preprocessed Image")
    axs[2].imshow(hf_res.permute(1, 2, 0).numpy())
    axs[2].set_title("HuggingFace Preprocessed Image")
    axs[3].imshow(hf_res_fix.permute(1, 2, 0).numpy())
    axs[3].set_title("HuggingFace FIXED Preprocessed Image")
    plt.show()
    return


@app.cell
def _(hf_pre_fix):
    hf_pre_fix.to_json_file("test.json")
    return


@app.cell
def _():
    return


if __name__ == "__main__":
    app.run()