README.md

ResNet50v1.5 Inference

ResNet50v1.5 Inference best known configurations with Intel® Extension for PyTorch.

Model Information

Use Case	Framework	Model Repo	Branch/Commit/Tag	Optional Patch
Inference	Pytorch	-	-	-

Pre-Requisite

• Host has one of the following GPUs:

  • Arc Series - Intel® Arc™ A-Series Graphics
  • Flex Series - Intel® Data Center GPU Flex Series
  • Max Series - Intel® Data Center GPU Max Series

• Host has installed latest Intel® Data Center GPU Max & Flex Series Drivers https://dgpu-docs.intel.com/driver/installation.html

• The following Intel® oneAPI Base Toolkit components are required:

  • Intel® oneAPI DPC++ Compiler (Placeholder DPCPPROOT as its installation path)
  • Intel® oneAPI Math Kernel Library (oneMKL) (Placeholder MKLROOT as its installation path)
  • Intel® oneAPI MPI Library
  • Intel® oneAPI TBB Library

  Follow instructions at Intel® oneAPI Base Toolkit Download page to setup the package manager repository.

Prepare Dataset

Dataset: imagenet

Default is dummy dataset. ImageNet is recommended, the download link is https://image-net.org/challenges/LSVRC/2012/2012-downloads.php.

Inference

1. git clone https://github.com/IntelAI/models.git
2. cd models/models_v2/pytorch/resnet50v1_5/inference/gpu
3. Create virtual environment venv and activate it:

   python3 -m venv venv
   . ./venv/bin/activate
   

4. Run setup.sh

   ./setup.sh
   

5. Install the latest GPU versions of torch, torchvision and intel_extension_for_pytorch:

   python -m pip install torch==<torch_version> torchvision==<torchvvision_version> intel-extension-for-pytorch==<ipex_version> --extra-index-url https://pytorch-extension.intel.com/release-whl-aitools/
   

6. Set environment variables for Intel® oneAPI Base Toolkit: Default installation location {ONEAPI_ROOT} is /opt/intel/oneapi for root account, ${HOME}/intel/oneapi for other accounts

   source {ONEAPI_ROOT}/compiler/latest/env/vars.sh
   source {ONEAPI_ROOT}/mkl/latest/env/vars.sh
   source {ONEAPI_ROOT}/tbb/latest/env/vars.sh
   source {ONEAPI_ROOT}/mpi/latest/env/vars.sh
   source {ONEAPI_ROOT}/ccl/latest/env/vars.sh

7. Setup required environment paramaters

Parameter	export command
MULTI_TILE	export MULTI_TILE=True (True or False)
PLATFORM	export PLATFORM=Max (Max or Flex or Arc)
BATCH_SIZE (optional)	export BATCH_SIZE=1024
PRECISION (optional)	export PRECISION=INT8 (INT8,FP32, FP16 for all platform, BF16 and TF32 only for Max)
OUTPUT_DIR (optional)	export OUTPUT_DIR=$PWD
NUM_ITERATIONS (optional)	export NUM_ITERATIONS=500
DATASET_DIR (optional)	export DATASET_DIR=--dummy

8. Run run_model.sh

Output

Single-tile output will typically look like:

Test: [500/500] Time  0.039 ( 0.042)    Loss 8.4575e+00 (8.4625e+00)    Acc@1   0.20 (  0.10)   Acc@5   0.59 (  0.50)
Quantization Evalution performance: batch size:1024, throughput:26373.51 image/sec, Acc@1:0.10, Acc@5:0.50

Multi-tile output will typically looks like:

Test: [500/500] Time  0.040 ( 0.044)    Loss 8.4575e+00 (8.4625e+00)    Acc@1   0.20 (  0.10)   Acc@5   0.59 (  0.50)
Quantization Evalution performance: batch size:1024, throughput:25780.13 image/sec, Acc@1:0.10, Acc@5:0.50
Test: [500/500] Time  0.039 ( 0.044)    Loss 8.4575e+00 (8.4625e+00)    Acc@1   0.20 (  0.10)   Acc@5   0.59 (  0.50)
Quantization Evalution performance: batch size:1024, throughput:26216.49 image/sec, Acc@1:0.10, Acc@5:0.50

Final results of the inference run can be found in results.yaml file.

results:
 - key: throughput
   value: 26373.51
   unit: fps
 - key: latency
   value: 0.0388268379900893
   unit: s
 - key: accuracy
   value: 0.100
   unit: top1