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import os.path |
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import gdown |
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import gradio as gr |
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import torch |
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from Model import TRCaptionNet, clip_transform |
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model_ckpt = "./checkpoints/TRCaptionNet_L14_berturk_tasviret.pth" |
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device = "cpu" |
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preprocess = clip_transform(224) |
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model = TRCaptionNet({ |
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"max_length": 35, |
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"clip": "ViT-L/14", |
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"bert": "bert.json", |
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"proj": True, |
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"proj_num_head": 16 |
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}) |
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model.load_state_dict(torch.load(model_ckpt, map_location=device)["model"], strict=True) |
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model = model.to(device) |
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model.eval() |
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def inference(raw_image, min_length, repetition_penalty): |
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batch = preprocess(raw_image).unsqueeze(0).to(device) |
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caption = model.generate(batch, min_length=min_length, repetition_penalty=repetition_penalty)[0] |
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return caption |
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inputs = [gr.Image(type='pil', interactive=True,), |
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gr.Slider(minimum=6, maximum=22, value=11, label="MINIMUM CAPTION LENGTH", step=1), |
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gr.Slider(minimum=1, maximum=2, value=1.6, label="REPETITION PENALTY")] |
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outputs = gr.components.Textbox(label="Caption") |
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title = "TRCaptionNet" |
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paper_link = "" |
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github_link = "https://github.com/serdaryildiz/TRCaptionNet" |
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description = f"<p style='text-align: center'><a href='{github_link}' target='_blank'>TRCaptionNet</a> : A novel and accurate deep Turkish image captioning model with vision transformer based image encoders and deep linguistic text decoders" |
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examples = [ |
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["images/test1.jpg"], |
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["images/test2.jpg"], |
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["images/test3.jpg"], |
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["images/test4.jpg"] |
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] |
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article = f"<p style='text-align: center'><a href='{paper_link}' target='_blank'>Paper</a> | <a href='{github_link}' target='_blank'>Github Repo</a></p>" |
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css = ".output-image, .input-image, .image-preview {height: 600px !important}" |
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iface = gr.Interface(fn=inference, |
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inputs=inputs, |
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outputs=outputs, |
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title=title, |
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description=description, |
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examples=examples, |
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article=article, |
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css=css) |
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iface.launch() |
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