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import gradio as gr |
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from PIL import Image |
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from torchvision import transforms |
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from gradcam import do_gradcam |
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from lrp import do_lrp, do_partial_lrp |
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from rollout import do_rollout |
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from tiba import do_tiba |
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normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) |
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TRANSFORM = transforms.Compose( |
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[ |
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transforms.Resize(256), |
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transforms.CenterCrop(224), |
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transforms.ToTensor(), |
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normalize, |
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] |
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) |
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METHOD_MAP = { |
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"tiba": do_tiba, |
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"gradcam": do_gradcam, |
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"lrp": do_lrp, |
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"partial_lrp": do_partial_lrp, |
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"rollout": do_rollout, |
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} |
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def generate_viz(image, method, class_index=None): |
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viz_method = METHOD_MAP[method] |
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viz = viz_method(TRANSFORM, image, class_index=class_index) |
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viz.savefig("visualization.png") |
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return Image.open("visualization.png").convert("RGB") |
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title = "Compare different methods of explaining ViTs 🤖" |
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article = "Different methods for explaining Vision Transformers as explored by Chefer et al. in [Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks](https://arxiv.org/abs/2012.09838)." |
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iface = gr.Interface( |
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generate_viz, |
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inputs=[ |
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gr.Image(type="pil", label="Input Image"), |
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gr.Dropdown( |
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list(METHOD_MAP.keys()), |
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label="Method", |
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info="Explainability method to investigate.", |
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), |
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gr.Number(label="Class Index", info="Class index to inspect"), |
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], |
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outputs=gr.Image(), |
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title=title, |
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article=article, |
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allow_flagging="never", |
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cache_examples=True, |
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examples=[ |
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["Transformer-Explainability/samples/catdog.png", "tiba", None], |
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["Transformer-Explainability/samples/catdog.png", "rollout", 243], |
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["Transformer-Explainability/samples/el2.png", "tiba", None], |
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["Transformer-Explainability/samples/dogbird.png", "lrp", 161], |
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], |
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) |
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iface.launch(debug=True) |
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