41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
import os
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import clip
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import torch
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from torchvision.datasets import CIFAR100
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import gradio as gr
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# Load the model
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model, transform = clip.load("ViT-B/32", device=device)
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# Download the dataset
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cifar100 = CIFAR100(root=os.path.expanduser("~/.cache"), download=True, train=False)
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def classify(img):
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image = transform(img).unsqueeze(0).to(device)
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text_inputs = torch.cat([clip.tokenize(f"a photo of a {c}") for c in cifar100.classes]).to(device)
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# Calculate features
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with torch.no_grad():
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image_features = model.encode_image(image)
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text_features = model.encode_text(text_inputs)
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# Pick the top 5 most similar labels for the image
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image_features /= image_features.norm(dim=-1, keepdim=True)
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text_features /= text_features.norm(dim=-1, keepdim=True)
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similarity = (100.0 * image_features @ text_features.T).softmax(dim=-1)
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values, indices = similarity[0].topk(5)
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text=""
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# Print the result
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for value, index in zip(values, indices):
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text+=f"{cifar100.classes[index]:>16s}: {100 * value.item():.2f}%\n"
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return text
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inputs = gr.inputs.Image(type='pil', label="Original Image")
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outputs = gr.outputs.Textbox(type="str", label="Text Output")
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title = "CLIP"
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description = "CLIP demo"
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gr.Interface(classify, inputs, outputs, title=title, description=description).launch(debug=True) |