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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 | |
| import spacy | |
| from PIL import Image, ImageFont, ImageDraw, ImageOps | |
| import os | |
| os.system('python -m spacy download en_core_web_sm') | |
| 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) | |
| # nlp = spacy.load("en_core_web_sm") | |
| import en_core_web_sm | |
| nlp = en_core_web_sm.load() | |
| # 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 | |
| # Default demo: | |
| 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"]]) | |
| # NER demo: | |
| def add_label_to_img(img, label): | |
| img = ImageOps.expand(img, border=45, fill=(255,255,255)) | |
| draw = ImageDraw.Draw(img) | |
| font = ImageFont.truetype("arial.ttf", 36) | |
| draw.text((0,0), label, align="center", fill=(0, 0, 0), font=font) | |
| return img | |
| def NER_demo(image, text): | |
| # Apply NER to extract named entities, and run the explainability method | |
| # for each named entity. | |
| highlighed_entities = [] | |
| for ent in nlp(text).ents: | |
| ent_text = ent.text | |
| ent_label = ent.label_ | |
| highlighed_entities.append((ent_text, ent_label)) | |
| # As the default image, we run the default demo on the input image and text: | |
| overlapped, highlighted_text = run_demo(image, text) | |
| # Then, we run the demo for each of the named entities: | |
| gallery_images = [overlapped] | |
| for ent_text, ent_label in highlighed_entities: | |
| overlapped_ent, highlighted_text_ent = run_demo(image, ent_text) | |
| overlapped_ent_labelled = add_label_to_img(overlapped_ent, ent_text) | |
| gallery_images.append(overlapped_ent_labelled) | |
| return highlighed_entities, gallery_images | |
| input_img_NER = gr.inputs.Image(type='pil', label="Original Image") | |
| input_txt_NER = "text" | |
| inputs_NER = [input_img_NER, input_txt_NER] | |
| outputs_NER = ["highlight", gr.Gallery(type='pil', label="NER Entity explanations")] | |
| iface_NER = gr.Interface(fn=NER_demo, | |
| inputs=inputs_NER, | |
| outputs=outputs_NER, | |
| 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"]], | |
| cache_examples=False) | |
| demo_tabs = gr.TabbedInterface([iface, iface_NER], ["Default", "NER"]) | |
| demo_tabs.launch(debug=True) |