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How to Run

  • Demo is in progress

Details

  • The entry point to metal resnet model is ResNet in ttnn_functional_resnet50_new_conv_api.py. The model picks up certain configs and weights from TorchVision pretrained model. We have used torchvision.models.ResNet50_Weights.IMAGENET1K_V1 version from TorchVision as our reference. Our ImageProcessor on the other hand is based on microsoft/resnet-50 from huggingface.

Performance

  • To obtain device performance, run WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml ./tt_metal/tools/profiler/profile_this.py -c "pytest models/demos/ttnn_resnet/tests/test_ttnn_resnet50_performant.py::test_run_resnet50_inference[16-act_dtype0-weight_dtype0-math_fidelity0-device_params0]" This will generate a CSV report under <this repo dir>/generated/profiler/reports/ops/<report name>. The report file name is logged in the run output.

  • For end-to-end performance, run WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest models/demos/ttnn_resnet/tests/test_perf_ttnn_resnet.py::test_perf_trace_2cqs_bare_metal[16-0.004-25-device_params0]. This will generate a CSV with the timings and throughputs. Expected end-to-end perf: For batch = 16, it is about 4300 fps currently. This may vary machine to machine.