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· i am using trt5 with the python sample uff_resnet50.py and here are the metrics i need to extract Conv_0/conv2d at max batch size. Latency in milliseconds power efficiency:.

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· hi, i implemented a custom layer in trt4 before, using the iplugin and pluginfactory interface · hi all, i wanted to give a quick try to tensorrt and ran into the following errors when building the engine from an uff graph [tensorrt] error But i found trt5 uses the ipluginv2 and iplugincreator interface to add custom layers

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May i know any migration/upgrade planning for supporting trt5 in nvidia drive platform or.

· hi, can you provide a minimal repro package with code that reproduces the error · hi all, using tensor rt 5.0, i was able to improve the run time performances However, there is still something that i am not quite sure to get regarding the execution for multiple batches. · i used the pytorch model, turned it into onnx, and got the test result in trt4

When i used trt5, it didn’t get the output I output the result of tensorrt reasoning, which is completely different from. · got it working by provding input in nchw format and converting output back to nhwc format It’s a bit confusing though, i thought specifying the format in the parser.register_input was.