![]() Successfully created plugin: RnRes2FullFusion_TRT Searching for plugin: RnRes2FullFusion_TRT, plugin_version: 1, plugin_namespace: No importer registered for op: RnRes2FullFusion_TRT. Plugin SmallTileGEMM_TRT fused successful for res3/4/5 branch2c Fusing SmallTileGEMM_TRT_res5c_branch2c_conv_residual_relu with smallk. Fusing SmallTileGEMM_TRT_res5b_branch2c_conv_residual_relu with smallk. Fusing SmallTileGEMM_TRT_res5a_branch2c_conv_residual_relu with smallk. Fusing SmallTileGEMM_TRT_res4f_branch2c_conv_residual_relu with smallk. Fusing SmallTileGEMM_TRT_res4e_branch2c_conv_residual_relu with smallk. Fusing SmallTileGEMM_TRT_res4d_branch2c_conv_residual_relu with smallk. Fusing SmallTileGEMM_TRT_res4c_branch2c_conv_residual_relu with smallk. Fusing SmallTileGEMM_TRT_res4b_branch2c_conv_residual_relu with smallk. Fusing SmallTileGEMM_TRT_res4a_branch2c_conv_residual_relu with smallk. Fusing SmallTileGEMM_TRT_res3d_branch2c_conv_residual_relu with smallk. Fusing SmallTileGEMM_TRT_res3c_branch2c_conv_residual_relu with smallk. Fusing SmallTileGEMM_TRT_res3b_branch2c_conv_residual_relu with smallk. Fusing SmallTileGEMM_TRT_res3a_branch2c_conv_residual_relu with smallk. Replacing all branch2c beta=1 conv with smallk kernel. The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. Reformatting CopyNode for Input Tensor 0 to topk_layer Rebuild the engine with ProfilingVerbosity::kDETAILED to get more verbose layer information. The profiling verbosity was set to ProfilingVerbosity::kLAYER_NAMES_ONLY when the engine was built, so only the layer names will be returned. TensorRT-managed allocation in building engine: CPU +26, GPU +4, now: CPU 26, GPU 4 (MiB) Algorithm ShiftNTopDown took 0.012416ms to assign 3 blocks to 3 nodes requiring 65536 bytes. Peak memory usage of TRT CPU/GPU memory allocators: CPU 26 MiB, GPU 31 MiB Detected 1 inputs and 1 output network tensors. Profiling results in this builder pass will not be stored. TensorRT will generate a new calibration cache. To regenerate calibration cache, please delete the existing one. Make sure that calibration cache has latest scales. Generated calibration scales using calibration cache. Reading Calibration Cache for calibrator: Entrop圜alibration2 Switching this layer's device type to GPU. build/engines/Orin/resnet50/Offline/resnet50-Offline-dla-b16-int8.lwis_k_99_an Dynamic range should be symmetric for better accuracy. Unmarking output: topk_layer_output_value Init builder kernel library: CPU +351, GPU +333, now: CPU 627, GPU 6079 (MiB) Building engines for resnet50 benchmark in Offline scenario. Please have a look on my Error log below: make: Entering directory '/MLPerfv2.1/inference_results_v2.1/closed/NVIDIA' I have passed over OpenCV error by removing ScopedRestrictedImport() as you suggested to me. Perhaps if you could list me the command line for bare-metal only, would be great. So how I can run MLPerf and reproduce the benchmark results. I tried and change the submission-checker and I put submission because in the file here there is no submission-checker.py instead there is submission.py. ![]() Make: Leaving directory '/MLPerfv2.1/inference_results_v2.1/closed/NVIDIA' ModuleNotFoundError: No module named 'submission-checker' Return _bootstrap._gcd_import(name, package, level) Submission_checker = import_module("submission-checker")įile "/usr/lib/python3.8/importlib/_init_.py", line 127, in import_module Return _run_code(code, main_globals, None,įile "/usr/lib/python3.8/runpy.py", line 87, in _run_codeįile "/MLPerfv2.1/inference_results_v2.1/closed/NVIDIA/code/main_v2.py", line 39, in įile "/MLPerfv2.1/inference_results_v2.1/closed/NVIDIA/code/actionhandler/_init_.py", line 19, in įrom _harness import RunHarnessHandlerįile "/MLPerfv2.1/inference_results_v2.1/closed/NVIDIA/code/actionhandler/run_harness.py", line 31, in įrom _checker import check_accuracyįile "/MLPerfv2.1/inference_results_v2.1/closed/NVIDIA/code/common/accuracy_checker.py", line 38, in So I tried to run the benchmark Resnet50 and ssd-mobilenet but I am facing an error when runing for example : make run RUN_ARGS="-benchmarks=ssd-mobilenet -scenarios=Offline -test_mode=AccuracyOnly" make: Entering directory '/MLPerfv2.1/inference_results_v2.1/closed/NVIDIA'įile "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main Now to run the benchmark I was luittle bit confused since the command line are mixed between docker and bare-metal, which is not very clear. I am runniing the benchmark bare-metal on my Jetson AGX Orin. I am trying to reproduce the MLPerf results v2.1 here since v2 was only internal see here. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |