started from a .pt trained yolov5L 6.1 model
.onnx exported with python3 export_yoloV5.py -w model.pt --simplify
it works for batch size 1 but not for batch size 2
file .engine is generated : model_b2_gpu0_fp32.engine
environment is deepstream 6.1.1 on the Jetson Orin NX
the run fails with these messages
thank you!
0:00:07.082318833 29 0xffff7cd19490 INFO nvinfer gstnvinfer.cpp:646:gst_nvinfer_logger: NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::deserializeEngineAndBackend() [UID = 1]: deserialized trt engine from :/model_b2_gpu0_fp32.engine
WARNING: [TRT]: The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1.
INFO: [Implicit Engine Info]: layers num: 4
0 INPUT kFLOAT input 3x640x640
1 OUTPUT kFLOAT boxes 25200x4
2 OUTPUT kFLOAT scores 25200x1
3 OUTPUT kFLOAT classes 25200x1
0:00:07.232329468 29 0xffff7cd19490 WARN nvinfer gstnvinfer.cpp:643:gst_nvinfer_logger: NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::checkBackendParams() [UID = 1]: Backend has maxBatchSize 1 whereas 2 has been requested
0:00:07.234135304 29 0xffff7cd19490 WARN nvinfer gstnvinfer.cpp:643:gst_nvinfer_logger: NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::generateBackendContext() [UID = 1]: deserialized backend context :/model_b2_gpu0_fp32.engine failed to match config params, trying rebuild
0:00:07.287486636 29 0xffff7cd19490 INFO nvinfer gstnvinfer.cpp:646:gst_nvinfer_logger: NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() [UID = 1]: Trying to create engine from model files
WARNING: [TRT]: onnx2trt_utils.cpp:367: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: Tensor DataType is determined at build time for tensors not marked as input or output.
started from a .pt trained yolov5L 6.1 model .onnx exported with python3 export_yoloV5.py -w model.pt --simplify it works for batch size 1 but not for batch size 2 file .engine is generated : model_b2_gpu0_fp32.engine environment is deepstream 6.1.1 on the Jetson Orin NX the run fails with these messages
thank you!
0:00:07.082318833 29 0xffff7cd19490 INFO nvinfer gstnvinfer.cpp:646:gst_nvinfer_logger: NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::deserializeEngineAndBackend() [UID = 1]: deserialized trt engine from :/model_b2_gpu0_fp32.engine
WARNING: [TRT]: The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1.
INFO: [Implicit Engine Info]: layers num: 4
0 INPUT kFLOAT input 3x640x640
1 OUTPUT kFLOAT boxes 25200x4
2 OUTPUT kFLOAT scores 25200x1
3 OUTPUT kFLOAT classes 25200x1
0:00:07.232329468 29 0xffff7cd19490 WARN nvinfer gstnvinfer.cpp:643:gst_nvinfer_logger: NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::checkBackendParams() [UID = 1]: Backend has maxBatchSize 1 whereas 2 has been requested
0:00:07.234135304 29 0xffff7cd19490 WARN nvinfer gstnvinfer.cpp:643:gst_nvinfer_logger: NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::generateBackendContext() [UID = 1]: deserialized backend context :/model_b2_gpu0_fp32.engine failed to match config params, trying rebuild
0:00:07.287486636 29 0xffff7cd19490 INFO nvinfer gstnvinfer.cpp:646:gst_nvinfer_logger: NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() [UID = 1]: Trying to create engine from model files
WARNING: [TRT]: onnx2trt_utils.cpp:367: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
WARNING: [TRT]: onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
WARNING: [TRT]: Tensor DataType is determined at build time for tensors not marked as input or output.
Building the TensorRT Engine