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| import re | |
| 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", | |
| } | |
| colour_map = { | |
| "N": "#f77189", | |
| "CARDINAL": "#f7764a", | |
| "DATE": "#d98a32", | |
| "EVENT": "#bf9632", | |
| "FAC": "#a99e31", | |
| "GPE": "#90a531", | |
| "LANGUAGE": "#68ad31", | |
| "LAW": "#32b25e", | |
| "LOC": "#34af86", | |
| "MONEY": "#35ae9c", | |
| "NORP": "#36acac", | |
| "ORDINAL": "#37aabd", | |
| "ORG": "#39a7d4", | |
| "PERCENT": "#539ff4", | |
| "PERSON": "#9890f4", | |
| "PRODUCT": "#c47ef4", | |
| "QUANTITY": "#ef5ff4", | |
| "TIME": "#f565d0", | |
| "WORK_OF_ART": "#f66baf", | |
| } | |
| 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: | |
| default_inputs = [ | |
| gr.components.Image(type='pil', label="Original Image"), | |
| gr.components.Textbox(label="Image description"), | |
| ] | |
| default_outputs = [ | |
| gr.components.Image(type='pil', label="Output Image"), | |
| gr.components.HighlightedText(label="Text importance"), | |
| ] | |
| description = """This demo is a copy of the demo CLIPGroundingExlainability built by Paul Hilders, Danilo de Goede and Piyush Bagad, as part of the course Interpretability and Explainability in AI (MSc AI, UvA, June 2022). | |
| <br> <br> | |
| This demo shows attributions scores on both the image and the text input when presenting CLIP with a | |
| <text,image> pair. Attributions are computed as Gradient-weighted Attention Rollout (Chefer et al., | |
| 2021), and can be thought of as an estimate of the effective attention CLIP pays to its input when | |
| computing a multimodal representation. <span style="color:red">Warning:</span> Note that attribution | |
| methods such as the one from this demo can only give an estimate of the real underlying behavior | |
| of the model.""" | |
| iface = gr.Interface(fn=run_demo, | |
| inputs=default_inputs, | |
| outputs=default_outputs, | |
| title="CLIP Grounding Explainability", | |
| description=description, | |
| 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, add_entity_label=True): | |
| img = ImageOps.expand(img, border=45, fill=(255,255,255)) | |
| draw = ImageDraw.Draw(img) | |
| font = ImageFont.truetype("arial.ttf", 24) | |
| m = re.match(r".*\((\w+)\)", label) | |
| if add_entity_label and m is not None: | |
| cat = m.group(1) | |
| colours = tuple(map(lambda l: int(''.join(l),16), zip(*[iter(colour_map[cat][1:])]*2))) | |
| draw.text((5,5), label , align="center", fill=colours, font=font) | |
| else: | |
| draw.text((5,5), label, align="center", fill=(0, 0, 0), font=font) | |
| return img | |
| def NER_demo(image, text): | |
| # As the default image, we run the default demo on the input image and text: | |
| overlapped, highlighted_text = run_demo(image, text) | |
| gallery_images = [add_label_to_img(overlapped, "Complete sentence", add_entity_label=False)] | |
| labeled_text = dict( | |
| text=text, | |
| entities=[], | |
| ) | |
| # Then, we run the demo for each of the noun chunks in the text: | |
| for chunk in nlp(text).noun_chunks: | |
| if len(chunk) == 1 and chunk[0].pos_ == "PRON": | |
| continue | |
| chunk_text = chunk.text | |
| chunk_label = None | |
| for t in chunk: | |
| if t.ent_type_ != '': | |
| chunk_label = t.ent_type_ | |
| break | |
| if chunk_label is None: | |
| chunk_label = "N" | |
| labeled_text['entities'].append({'entity': chunk_label, 'start': chunk.start_char, 'end': chunk.end_char}) | |
| overlapped, highlighted_text = run_demo(image, chunk_text) | |
| overlapped_labelled = add_label_to_img(overlapped, f"{chunk_text} ({chunk_label})") | |
| gallery_images.append(overlapped_labelled) | |
| return labeled_text, gallery_images | |
| inputs_NER = [ | |
| gr.Image(type='pil', label="Original Image"), | |
| gr.components.Textbox(label="Descriptive text"), | |
| ] | |
| #colours = highlighter._style["color_map"] | |
| outputs_NER = [ | |
| gr.components.HighlightedText(show_legend=True, color_map=colour_map, label="Noun chunks"), | |
| gr.components.Gallery(type='pil', label="NER Entity explanations") | |
| ] | |
| description_NER = """Automatically generated CLIP grounding explanations for | |
| named entities, retrieved from the spacy NER model. <span style="color:red">Warning:</span> Note | |
| that attribution methods such as the one from this demo can only give an estimate of the real | |
| underlying behavior of the model.""" | |
| iface_NER = gr.Interface(fn=NER_demo, | |
| inputs=inputs_NER, | |
| outputs=outputs_NER, | |
| title="Named Entity Grounding explainability using CLIP", | |
| description=description_NER, | |
| examples=[ | |
| ["example_images/London.png", "In this image we see Big Ben and the London Eye, on both sides of the river Thames."], | |
| ["example_images/harrypotter.png", "Hermione, Harry and Ron in their school uniform"], | |
| ], | |
| cache_examples=False) | |
| demo_tabs = gr.TabbedInterface([iface, iface_NER], ["Default", "NER"]) | |
| with demo_tabs: | |
| gr.Markdown(""" | |
| ### Acknowledgements | |
| This demo was developed for the Interpretability & Explainability in AI course at the University of | |
| Amsterdam. We would like to express our thanks to Jelle Zuidema, Jaap Jumelet, Tom Kersten, Christos | |
| Athanasiadis, Peter Heemskerk, Zhi Zhang, and all the other TAs who helped us during this course. | |
| --- | |
| ### References | |
| \[1\]: Chefer, H., Gur, S., & Wolf, L. (2021). Generic attention-model explainability for interpreting bi-modal and encoder-decoder transformers. <br> | |
| \[2\]: Abnar, S., & Zuidema, W. (2020). Quantifying attention flow in transformers. arXiv preprint arXiv:2005.00928. <br> | |
| \[3\]: [https://samiraabnar.github.io/articles/2020-04/attention_flow](https://samiraabnar.github.io/articles/2020-04/attention_flow) <br> | |
| """) | |
| demo_tabs.launch(show_error=True) | |