inital

clipseg_rd64_refined/app.py

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

clipseg_rd64_refined/clipseg-rd64-refined

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+Subproject commit 583b388deb98a04feb3e1f816dcdb8f3062ee205