To summarie: predict() api is intended to take numpy images and return numpy boxes. predict() was never designed to be the fast inference option, rather an easy one. For best performance it is suggested to export models to ONNX and run in TRTBlood...
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I implement the official Parseq with pytorch 1.10 and then converted it to onnx using author code. I used onnx-simplify 0.4.13 to simplify the onnx and used TRT >=8.2.1.8 to convert into TRT. Everything works well. But after checking the inference, I realized the inference on TRT is...
TensorRT sample: Jetson/L4T/TRT Customized Example - eLinux.org 4. Report issue If these suggestions don’t help and you want to report an issue to us, please attach the model, command/step, and the customized app (if any) with us to reproduce locally. Thanks!kcs...
trt.Runtime(TRT_LOGGER) as runtime: model = runtime.deserialize_cuda_engine(f.read()) ##data load stage with model.create_execution_context() as context: context.set_binding_shape(model.get_binding_index("input"), (1, 3, 8, 256, 256))##Set INPUT SHAPE to input using key context...