34 lines
1.0 KiB
Python
34 lines
1.0 KiB
Python
import gradio as gr
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from transformers import AutoProcessor, CLIPSegForImageSegmentation
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def inference(img):
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model_path = "clipseg-rd64-refined"
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processor = AutoProcessor.from_pretrained(model_path)
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model = CLIPSegForImageSegmentation.from_pretrained(model_path)
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texts = ["a cat", "a remote", "a blanket"]
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inputs = processor(text=texts, images=[img] * len(texts), padding=True, return_tensors="pt")
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outputs = model(**inputs)
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logits = outputs.logits
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print(logits.shape)
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return logits.shape
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examples=[['example_cat.jpg']]
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with gr.Blocks() as demo:
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gr.Markdown(
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"""
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# Semantic segmentation:clipseg-rd64-refined
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Gradio Demo for clipseg-rd64-refined. To use it, simply upload your image, or click one of the examples to load them.
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""")
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with gr.Row():
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image_input = gr.Image(type="pil")
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text_output = gr.Textbox()
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image_button = gr.Button("上传")
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image_button.click(inference, inputs=image_input, outputs=text_output)
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gr.Examples(examples,inputs=image_input)
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demo.launch() |