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Update app.py
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app.py
CHANGED
@@ -32,8 +32,7 @@ def process_image(image):
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# forward pass
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with torch.no_grad():
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outputs = model(**encoding)
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predicted_depth = outputs.predicted_depth
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# interpolate to original size
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prediction = torch.nn.functional.interpolate(
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@@ -55,16 +54,11 @@ title = "Interactive demo: DPT"
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description = "Demo for Intel's DPT, a Dense Prediction Transformer for state-of-the-art dense prediction tasks such as semantic segmentation and depth estimation."
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examples =[['cats.jpg']]
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css = ".output-image, .input-image {height: 40rem !important; width: 100% !important;}"
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#css = "@media screen and (max-width: 600px) { .output_image, .input_image {height:20rem !important; width: 100% !important;} }"
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# css = ".output_image, .input_image {height: 600px !important}"
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iface = gr.Interface(fn=process_image,
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inputs=gr.inputs.Image(type="pil"),
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outputs=gr.outputs.Image(type="pil", label="predicted depth"),
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title=title,
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description=description,
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examples=examples,
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css=css,
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enable_queue=True)
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iface.launch(debug=True)
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# forward pass
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with torch.no_grad():
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outputs = model(**encoding)
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predicted_depth = outputs.predicted_depth
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# interpolate to original size
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prediction = torch.nn.functional.interpolate(
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description = "Demo for Intel's DPT, a Dense Prediction Transformer for state-of-the-art dense prediction tasks such as semantic segmentation and depth estimation."
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examples =[['cats.jpg']]
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iface = gr.Interface(fn=process_image,
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inputs=gr.inputs.Image(type="pil"),
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outputs=gr.outputs.Image(type="pil", label="predicted depth"),
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title=title,
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description=description,
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examples=examples,
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enable_queue=True)
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iface.launch(debug=True)
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