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[GH-ISSUE #430] Multi-thread usage of open_clip #369

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opened 2026-10-31 21:14:25 +00:00 by bsakaguchi · 0 comments
bsakaguchi commented 2026-10-31 21:14:25 +00:00
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Originally created by @devparty1996 on GitHub (Mar 9, 2024).
Original GitHub issue: https://github.com/openai/CLIP/issues/430

hi I am using open_clip with a pretrained model in my project to compare images similarity but i only can run it in one thread i did not find any solution to use it as a multithread task is there any solution for it ?

i think the preprocess variable that create with the below code produce the problem :

preprocess_val = image_transform_v2(
        pp_cfg,
        is_train=False,
    )

the whole code :

device = "cuda" if torch.cuda.is_available() else "cpu"
model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-16-plus-240', pretrained="laion400m_e32")
model.to(device)


def imageEncoder(img):
    img1 = Image.fromarray(img).convert('RGB')
    img1 = preprocess(img1).unsqueeze(0).to(device)
    img1 = model.encode_image(img1)
    return img1


def generateScore(image1, image2):
    test_img = numpy.array(image1)
    data_img = numpy.array(image2)
    img1 = imageEncoder(test_img)
    img2 = imageEncoder(data_img)
    cos_scores = util.pytorch_cos_sim(img1, img2)
    score = round(float(cos_scores[0][0]) * 100, 2)
    return score

score = generateScore(pil_image1, pil_image2)
Originally created by @devparty1996 on GitHub (Mar 9, 2024). Original GitHub issue: https://github.com/openai/CLIP/issues/430 hi I am using open_clip with a pretrained model in my project to compare images similarity but i only can run it in one thread i did not find any solution to use it as a multithread task is there any solution for it ? i think the preprocess variable that create with the below code produce the problem : ``` preprocess_val = image_transform_v2( pp_cfg, is_train=False, ) ``` the whole code : ``` device = "cuda" if torch.cuda.is_available() else "cpu" model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-16-plus-240', pretrained="laion400m_e32") model.to(device) def imageEncoder(img): img1 = Image.fromarray(img).convert('RGB') img1 = preprocess(img1).unsqueeze(0).to(device) img1 = model.encode_image(img1) return img1 def generateScore(image1, image2): test_img = numpy.array(image1) data_img = numpy.array(image2) img1 = imageEncoder(test_img) img2 = imageEncoder(data_img) cos_scores = util.pytorch_cos_sim(img1, img2) score = round(float(cos_scores[0][0]) * 100, 2) return score score = generateScore(pil_image1, pil_image2) ```
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