This document has instructions for running ResNet50* FP32 inference using Intel® Extension for PyTorch*.
The ImageNet validation dataset is used when testing accuracy. The inference scripts use synthetic data, so no dataset is needed.
The accuracy script looks for a folder named
val, so after running the data prep script, your folder structure should look something like this:
imagenet └── val ├── ILSVRC2012_img_val.tar ├── n01440764 │ ├── ILSVRC2012_val_00000293.JPEG │ ├── ILSVRC2012_val_00002138.JPEG │ ├── ILSVRC2012_val_00003014.JPEG │ ├── ILSVRC2012_val_00006697.JPEG │ └── ... └── ...
The folder that contains the
val directory should be set as the
DATASET_DIR when running accuracy (for example:
Quick Start Scripts
|fp32_online_inference||Runs online inference using synthetic data (batch_size=1).|
|fp32_batch_inference||Runs batch inference using synthetic data (batch_size=128).|
|fp32_accuracy||Measures the model accuracy (batch_size=128).|
To run on bare metal, the following prerequisites must be installed in your environment:
Download and untar the model package and then run a quick start script.
# Optional: to run accuracy script export DATASET_DIR=<path to the preprocessed imagenet dataset> # Download and extract the model package wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_3_0/pytorch-resnet50-fp32-inference.tar.gz tar -xzf pytorch-resnet50-fp32-inference.tar.gz cd pytorch-resnet50-fp32-inference bash quickstart/<script name>.sh
Documentation and Sources
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