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| import gradio as gr | |
| import open_clip | |
| import numpy as np | |
| import torch | |
| import pandas as pd | |
| import os | |
| open_clip_model, _, preprocess = open_clip.create_model_and_transforms( | |
| 'ViT-B-32', | |
| pretrained='./open_clip_pytorch_model.bin') | |
| debiased_model, _, _ = open_clip.create_model_and_transforms( | |
| 'ViT-B-32', | |
| pretrained='./debiased_openclip.pt') | |
| open_clip_model.eval() | |
| debiased_model.eval() | |
| tokenizer = open_clip.get_tokenizer('ViT-B-32') | |
| def get_clip_scores(images, candidates, w=1): | |
| images = images / np.sqrt(np.sum(images**2, axis=1, keepdims=True)) | |
| candidates = candidates / np.sqrt(np.sum(candidates**2, axis=1, keepdims=True)) | |
| per = w*np.clip(np.sum(images * candidates, axis=1), 0, None) | |
| return per | |
| def predict(text1, text2, input_img): | |
| with torch.no_grad(): | |
| image = preprocess(input_img) | |
| image= image.unsqueeze(0) | |
| image_features = open_clip_model.encode_image(image) | |
| debiased_image_features = debiased_model.encode_image(image) | |
| texts = tokenizer([text1]) | |
| texts2 = tokenizer([text2]) | |
| text_features = open_clip_model.encode_text(texts) | |
| debiased_text_features = debiased_model.encode_text(texts) | |
| # print(image_features.size(), text_features.size()) | |
| # print(debiased_image_features.size(), debiased_text_features.size()) | |
| score = get_clip_scores(image_features.numpy(), text_features.numpy()) | |
| debiased_score = get_clip_scores(debiased_image_features.numpy(), debiased_text_features.numpy()) | |
| text_features2 = open_clip_model.encode_text(texts2) | |
| debiased_text_features2 = debiased_model.encode_text(texts2) | |
| score2 = get_clip_scores(image_features.numpy(), text_features2.numpy()) | |
| debiased_score2 = get_clip_scores(debiased_image_features.numpy(), debiased_text_features2.numpy()) | |
| print(score, score2) | |
| data = {'label': ["OpenCLIP for text1", "Debiased CLIP for text1", | |
| "OpenCLIP for text2", "Debiased CLIP for text2" | |
| ], | |
| 'score': [score[0], debiased_score[0], score2[0], debiased_score2[0]] | |
| } | |
| print(pd.DataFrame.from_dict(data)) | |
| return pd.DataFrame.from_dict(data) | |
| # gradio_app = gr.Interface( | |
| # predict, | |
| # inputs=["text", "text", | |
| # gr.Image(label="Select Image", sources=['upload', 'webcam'], type="pil"), | |
| # ], | |
| # outputs=gr.BarPlot(x="label", | |
| # y="score", | |
| # title="CLIP Score and Debiased Score", | |
| # vertical=False, | |
| # x_title=None | |
| # ), | |
| # title="Parrot Bias in CLIP!! (Both CLIP models are ViT-B-32)", | |
| # ) | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Parrot Bias in CLIP!! (Both CLIP models are ViT-B-32)") | |
| with gr.Row(): | |
| im = gr.Image(label="Select Image", | |
| sources=['upload', 'webcam'], | |
| type="pil", | |
| height=450) | |
| with gr.Row(): | |
| txt_1 = gr.Textbox(label="Input Text") | |
| txt_2 = gr.Textbox(label="Input Text 2") | |
| bar = gr.BarPlot(x="label", y="score", | |
| title="CLIP Score and Debiased Score", | |
| vertical=False, x_title=None) | |
| btn = gr.Button(value="Submit") | |
| btn.click(predict, inputs=[txt_1, txt_2, im], outputs=[bar]) | |
| gr.Markdown("## Examples (from https://joaanna.github.io/disentangling_spelling_in_clip/)") | |
| gr.Examples( | |
| [["A mug cup", "An iPad",os.path.join(os.path.dirname(__file__), "examples/IMG_2938.jpg")], | |
| ["A hat", "bad",os.path.join(os.path.dirname(__file__), "examples/IMG_3066.jpg")]], | |
| [txt_1, txt_2, im], | |
| fn=predict, | |
| outputs=bar, | |
| cache_examples=True, | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(show_api=False,share=True) |