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use example about efficient_densenet_pytorch HOT 3 CLOSED

gpleiss avatar gpleiss commented on May 24, 2024
use example

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Comments (3)

taineleau-zz avatar taineleau-zz commented on May 24, 2024

Hi, maybe you could try to put the model file under the project killer framework to use a more user-friendly APIs, such as reloading model and inference.

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taineleau-zz avatar taineleau-zz commented on May 24, 2024

From the error message above, it seems that either the model is not converted to GPU mode (model = model.cuda()) or the images are not on GPU.

You must keep the model and the inputs on the same chip (i.e. all on CPUs or all on GPUs) to do the inference.

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chmaz avatar chmaz commented on May 24, 2024

Dear Tainelau,

Thanks a lot! Following your suggestion, this is how I corrected the problem:

outputs = model(Variable(image, volatile=True).cuda(async=True)).cpu()

Christophe

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