resnet50

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ResNet50 Demo

Introduction

ResNet50 is a deep convolutional neural network architecture with 50 layers, designed to enable training of very deep networks by using residual learning to mitigate the vanishing gradient problem.

Details

• The entry point to the 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.

Demo

• To run the demo use:

pytest --disable-warnings models/demos/grayskull/resnet50/demo/demo.py::test_demo_sample

• where 20 is the batch size, and models/demos/ttnn_resnet/demo/images/ is where the images are located.
• Our model supports batch size of 2 and 1 as well, however the demo focuses on batch size 20 which has the highest throughput among the three options.
• This demo includes preprocessing, postprocessing and inference time for batch size 20.
• The demo will run the images through the inference thrice. First, discover the optimal shard scheme. Second to capture the compile time, and cache all the ops. Third, to capture the best inference time on TT hardware.

• Our second demo is designed to run ImageNet dataset, run this with

pytest --disable-warnings models/demos/grayskull/demo/demo.py::test_demo_imagenet

• The 20 refer to batch size here and 100 is number of iterations(batches), hence the model will process 100 batch of size 20, total of 2000 images.

• Note that the first time the model is run, ImageNet images must be downloaded from huggingface and stored in models/demos/ttnn_resnet/demo/images/; therefore you need to login to huggingface using your token: huggingface-cli login or by setting the token with the command export HF_TOKEN=<token>

• To obtain a huggingface token visit: https://huggingface.co/docs/hub/security-tokens

Performance

Single Device

Grayskull Device Performance

• To obtain device performance, run

pytest models/demos/grayskull/resnet50/tests/test_perf_device_resnet50.py::test_perf_device

• This will run the model for 4 times and generate CSV reports under <this repo dir>/generated/profiler/reports/ops/<report name>.
• The report file name is logged in the run output.
• It will also show a sumary of the device throughput in the run output.

Grayskull End-to-End Performance

• For end-to-end performance, run

pytest models/demos/grayskull/resnet50/tests/test_perf_e2e_resnet50.py::test_perf_trace_2cqs

• This will generate a CSV with the timings and throughputs.
• Expected end-to-end perf: For batch = 20, it is about 5,100 fps currently. This may vary machine to machine.