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| import sys | |
| import gradio as gr | |
| # sys.path.append("../") | |
| sys.path.append("CLIP_explainability/Transformer-MM-Explainability/") | |
| import torch | |
| import CLIP.clip as clip | |
| from clip_grounding.utils.image import pad_to_square | |
| from clip_grounding.datasets.png import ( | |
| overlay_relevance_map_on_image, | |
| ) | |
| from CLIP_explainability.utils import interpret, show_img_heatmap, show_heatmap_on_text | |
| clip.clip._MODELS = { | |
| "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt", | |
| "ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt", | |
| } | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model, preprocess = clip.load("ViT-B/32", device=device, jit=False) | |
| # Gradio Section: | |
| def run_demo(image, text): | |
| orig_image = pad_to_square(image) | |
| img = preprocess(orig_image).unsqueeze(0).to(device) | |
| text_input = clip.tokenize([text]).to(device) | |
| R_text, R_image = interpret(model=model, image=img, texts=text_input, device=device) | |
| image_relevance = show_img_heatmap(R_image[0], img, orig_image=orig_image, device=device, show=False) | |
| overlapped = overlay_relevance_map_on_image(image, image_relevance) | |
| text_scores, text_tokens_decoded = show_heatmap_on_text(text, text_input, R_text[0], show=False) | |
| highlighted_text = [] | |
| for i, token in enumerate(text_tokens_decoded): | |
| highlighted_text.append((str(token), float(text_scores[i]))) | |
| return overlapped, highlighted_text | |
| input_img = gr.inputs.Image(type='pil', label="Original Image") | |
| input_txt = "text" | |
| inputs = [input_img, input_txt] | |
| outputs = [gr.inputs.Image(type='pil', label="Output Image"), "highlight"] | |
| iface = gr.Interface(fn=run_demo, | |
| inputs=inputs, | |
| outputs=outputs, | |
| title="CLIP Grounding Explainability", | |
| description="A demonstration based on the Generic Attention-model Explainability method for Interpreting Bi-Modal Transformers by Chefer et al. (2021): https://github.com/hila-chefer/Transformer-MM-Explainability.", | |
| examples=[["example_images/London.png", "London Eye"], | |
| ["example_images/London.png", "Big Ben"], | |
| ["example_images/harrypotter.png", "Harry"], | |
| ["example_images/harrypotter.png", "Hermione"], | |
| ["example_images/harrypotter.png", "Ron"], | |
| ["example_images/Amsterdam.png", "Amsterdam canal"], | |
| ["example_images/Amsterdam.png", "Old buildings"], | |
| ["example_images/Amsterdam.png", "Pink flowers"], | |
| ["example_images/dogs_on_bed.png", "Two dogs"], | |
| ["example_images/dogs_on_bed.png", "Book"], | |
| ["example_images/dogs_on_bed.png", "Cat"]]) | |
| iface.launch(debug=True) |