Update app.py

update

init gradio space

app.py

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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) 

debiased_openclip.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:275df1f6c23201b78f9cce5a4e319182a403364772a0ce6c9be5895a04070186
+size 1815703758

open_clip_pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1bd3c7172de5b207ceac554f5ab5266166f3b9baccc9af5989bc801016d080ad
+size 605219813

requirements.txt

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+open_clip_torch
+gradio