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ardianumam / Tensorflow-TensorRT

This repository is for my YT video series about optimizing a Tensorflow deep learning model using TensorRT. We demonstrate optimizing LeNet-like model and YOLOv3 model, and get 3.7x and 1.5x faster for the former and the latter, respectively, compared to the original models.
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Changing number of classes of YOLOv3 #12

Closed RogerAylagas closed 5 years ago

RogerAylagas commented 5 years ago

Hi, I wanna know how would the line your_outputs = ["Placeholder:0", "concat_9:0", "mul_9:0"] if instead of having 10 classes as output we only have 4 classes An error is appearing when I convert my graph to the trt graph

Cannot assign a device for operation 'unstack_9': Could not satisfy explicit device specification '/device:GPU:0' because no supported kernel for GPU devices is available. Registered kernels: device='CPU'; T in [DT_VARIANT] device='CPU'; T in [DT_RESOURCE] device='CPU'; T in [DT_STRING] device='CPU'; T in [DT_BOOL] device='CPU'; T in [DT_COMPLEX128] device='CPU'; T in [DT_COMPLEX64] device='CPU'; T in [DT_DOUBLE] device='CPU'; T in [DT_FLOAT] device='CPU'; T in [DT_BFLOAT16] device='CPU'; T in [DT_HALF] device='CPU'; T in [DT_INT8] device='CPU'; T in [DT_UINT8] device='CPU'; T in [DT_INT16] device='CPU'; T in [DT_UINT16] device='CPU'; T in [DT_INT32] device='CPU'; T in [DT_INT64] device='GPU'; T in [DT_INT64] device='GPU'; T in [DT_INT32] device='GPU'; T in [DT_BFLOAT16] device='GPU'; T in [DT_DOUBLE] device='GPU'; T in [DT_FLOAT] device='GPU'; T in [DT_HALF]

[[{{node unstack_9}} = UnpackT=DT_STRING, axis=0, num=24, _device="/device:GPU:0"]]

Thank you

ardianumam commented 5 years ago

Hi, Are you familiar with Tensorflow (TF)? It's exactly same with frozen model in tensorflow. When you are going to do inference, you need to specify which tensors are the input and outputs, so ["Placeholder:0", "concat_9:0", "mul_9:0"] are the in-out tensor used in this repo. You can specify the tensor names with yours.