EfficientNet is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth, width, and resolution for image classification. This directory contains a sample implementation of image classification with EfficientNet. It is targeted to run on Intel Discrete Graphics platforms (XPUs) by leveraging Intel® Extension for Pytorch.
The input to the model is a tensor representing a batch of input images that is fed to the EfficientNet model for inference. The output is a tensor representing the inputs classification results for all possible classes.
The sample supports two modes of execution:
- A performance benchmarking mode where the sample executes EfficientNet inference based on a dummy tensor of the requested batch size over a specified number of input frames. This dummy tensor dataset is itself looped over repeatedly until a minimum test duration has been reached. Average throughput and latency values is reported.
- A accuracy-check mode which takes in frames from the ImageNet 2012 validation dataset and measures the accuracy of the resulting classification against references contained in the dataset. A percentage score - both top 1 accuracy and top 5 accuracy values - of passing frames along with average throughput and latency values is reported.
The rest of this document covers more details about the model, dataset, and the control knobs for each mode of execution. Further, instructions are provided on how to use the scripts in this directory for execution in bare-metal and docker container environments.
The sample uses EfficientNet model implementations from torchvision:
Model | Documentation | Weights |
---|---|---|
efficientnet_b0 | torchvision.models.efficientnet_b0 | EfficientNet_B0_Weights.IMAGENET1K_V1 |
efficientnet_b1 | torchvision.models.efficientnet_b1 | EfficientNet_B1_Weights.IMAGENET1K_V1 |
efficientnet_b2 | torchvision.models.efficientnet_b2 | EfficientNet_B2_Weights.IMAGENET1K_V1 |
efficientnet_b3 | torchvision.models.efficientnet_b3 | EfficientNet_B3_Weights.IMAGENET1K_V1 |
efficientnet_b4 | torchvision.models.efficientnet_b4 | EfficientNet_B4_Weights.IMAGENET1K_V1 |
efficientnet_b5 | torchvision.models.efficientnet_b5 | EfficientNet_B5_Weights.IMAGENET1K_V1 |
efficientnet_b6 | torchvision.models.efficientnet_b6 | EfficientNet_B6_Weights.IMAGENET1K_V1 |
efficientnet_b7 | torchvision.models.efficientnet_b7 | EfficientNet_B7_Weights.IMAGENET1K_V1 |
Note
Throughput and latency benchmarking can be done with dummy data (./run_model.sh --dummy
). In such a case dataset setup can be skipped. As a downside expect to see low accuracy on the dummy data.
Note
~13.3 GB of free disk space is required to download and extract ImageNet dataset.
ImageNet validation dataset is required to measure accuracy during inference. Visit ImageNet site and download the following files:
Note
Both dataset components must be downloaded to the same folder. This folder must be the $DATASET_DIR
referenced in the following sections.
get_dataset.sh script can be used to download these files. There is no need to extract and format these files before running this sample. On the first run sample script will extract the archive with torchvision.datasets.ImageNet. Consequent runs will skip extraction.
Hardware:
Software:
- Intel® Data Center GPU Flex Series Driver
- Intel® Extension for PyTorch
Note
Sample requires network connection to download model from the network via HTTPS. Make sure to set https_proxy
under running container if you work behind the proxy.
Pull pre-built image with the sample:
docker pull intel/image-recognition:pytorch-flex-gpu-efficientnet-inference
or build it locally:
docker build \
$(env | grep -E '(_proxy=|_PROXY)' | sed 's/^/--build-arg /') \
-f docker/flex-gpu/pytorch-efficientnet-inference/pytorch-flex-series-efficientnet-inference.Dockerfile \
-t intel/image-recognition:pytorch-flex-gpu-efficientnet-inference .
Run sample as follows:
-
With dummy data:
- Running with dummy data is recommended for performance benchmarking (throughput and latency measurements)
- Use higher
NUM_INPUTS
values for more precise peak performance results.NUM_INPUTS
will be rounded to a multiple ofBATCH_SIZE
. - NOTE: Accuracy will be zero when using dummy data
mkdir -p /tmp/output && rm -f /tmp/output/* && chmod -R 777 /tmp/output export BATCH_SIZE=1 docker run -it --rm --ipc=host \ $(env | grep -E '(_proxy=|_PROXY)' | sed 's/^/-e /') \ --cap-add SYS_NICE \ --device /dev/dri/ \ -e MODEL_NAME=efficientnet_b0 \ -e PLATFORM=Flex \ -e MAX_TEST_DURATION=60 \ -e MIN_TEST_DURATION=60 \ -e NUM_INPUTS=1000 \ -e BATCH_SIZE=${BATCH_SIZE} \ -e OUTPUT_DIR=/tmp/output \ -v /tmp/output:/tmp/output \ intel/image-recognition:pytorch-flex-gpu-efficientnet-inference \ /bin/bash -c "./run_model.sh --dummy"
-
With ImageNet dataset (assumes that dataset was downloaded to the
$DATASET_DIR
folder):- Running with dataset images is recommended for accuracy measurements
- In this mode, the test duration can be controlled by using the
NUM_INPUTS
parameter. The app tests a number of batches equal tomax(1, NUM_INPUTS // BATCH_SIZE)
- NOTE: Performance results (throughput and latency measurements) may be impacted due to data handling overhead
mkdir -p /tmp/output && rm -f /tmp/output/* && chmod -R 777 /tmp/output export BATCH_SIZE=1 docker run -it --rm --ipc=host \ $(env | grep -E '(_proxy=|_PROXY)' | sed 's/^/-e /') \ --cap-add SYS_NICE \ --device /dev/dri/ \ -e MODEL_NAME=efficientnet_b0 \ -e PLATFORM=Flex \ -e NUM_INPUTS=50000 \ -e BATCH_SIZE=${BATCH_SIZE} \ -e OUTPUT_DIR=/tmp/output \ -v /tmp/output:/tmp/output \ -e DATASET_DIR=/dataset \ -v $DATASET_DIR:/dataset \ intel/image-recognition:pytorch-flex-gpu-efficientnet-inference \ /bin/bash -c "./run_model.sh"
Mind the following docker run
arguments:
- HTTPS proxy is required to download model over network (
-e https_proxy=<...>
) --cap-add SYS_NICE
is required fornumactl
--device /dev/dri
is required to expose GPU device to running container--ipc=host
is required for multi-stream benchmarking (./run_model.sh --dummy --streams 2
) or large dataset cases-v $DATASET_DIR:/dataset
in case where dataset is used.$DATASET_DIR
should be replaced with the actual path to the ImageNet dataset.
Note
Sample requires network connection to download model from the network via HTTPS. Make sure to set https_proxy
before running run_model.sh
if you work behind proxy.
-
Download the sample:
git clone https://github.com/IntelAI/models.git cd models/models_v2/pytorch/efficientnet/inference/gpu
-
Create virtual environment
venv
and activate it:python3 -m venv venv . ./venv/bin/activate
-
Install sample python dependencies:
python3 -m pip install -r requirements.txt
-
Install Intel® Extension for PyTorch
-
Add path to common python modules in the repo:
export PYTHONPATH=$(pwd)/../../../../common
-
Setup required environment variables and run the sample with
./run_model.sh
:-
With dummy data:
- Running with dummy data is recommended for performance benchmarking (throughput and latency measurements)
- Use higher
NUM_INPUTS
values for more precise peak performance results.NUM_INPUTS
will be rounded to a multiple ofBATCH_SIZE
. - NOTE: Accuracy will be zero when using dummy data
export MODEL_NAME=efficientnet_b0 export PLATFORM=Flex export BATCH_SIZE=1 export MAX_TEST_DURATION=60 export MIN_TEST_DURATION=60 export NUM_INPUTS=1000 export OUTPUT_DIR=/tmp/output ./run_model.sh --dummy
-
-
With ImageNet dataset (assumes that dataset was downloaded to the
$DATASET_DIR
folder):- Running with dataset images is recommended for accuracy measurements
- In this mode, the test duration can be controlled by using the
NUM_INPUTS
parameter. The app tests a number of batches equal tomax(1, NUM_INPUTS // BATCH_SIZE)
- NOTE: Performance results (throughput and latency measurements) may be impacted due to data handling overhead
export MODEL_NAME=efficientnet_b0 export PLATFORM=Flex export BATCH_SIZE=1 export NUM_INPUTS=50000 export OUTPUT_DIR=/tmp/output export DATASET_DIR=$DATASET_DIR ./run_model.sh
run_model.sh
accepts a number of arguments to tune behavior. run_model.sh
supports the use of environment variables as well as command line arguments for specifying these arguments (see the table below for details).
Before running run_model.sh
script, user is required to:
- Set
OUTPUT_DIR
environment variable (or use--output-dir
) where script should write logs. - Use
--dummy
data or setDATASET_DIR
environment variable (or use--data
) pointing to ImageNet dataset.
Other arguments and/or environment variables are optional and should be used according to the actual needs (see examples above).
Argument | Environment variable | Valid Values | Purpose |
---|---|---|---|
--ipex |
IPEX |
yes |
Use Intel® Extension for Pytorch for XPU support (default: yes ) |
no |
Use PyTorch XPU backend instead of Intel® Extension for Pytorch. Requires PyTorch version 2.4.0a or later. | ||
--amp |
AMP |
yes |
Use AMP on model conversion to the desired precision (default: yes ) |
no |
|||
--arch |
MODEL_NAME |
efficientnet_b0 |
Torchvision model to run (default: efficientnet_b0 ) |
efficientnet_b1 |
|||
efficientnet_b2 |
|||
efficientnet_b3 |
|||
efficientnet_b4 |
|||
efficientnet_b5 |
|||
efficientnet_b6 |
|||
efficientnet_b7 |
|||
--batch-size |
BATCH_SIZE |
>=1 | Batch size to use (default: 1 ) |
--data |
DATASET_DIR |
String | Location to load images from |
--dummy |
DUMMY |
Use randomly generated dummy dataset in place of --data argument |
|
--jit |
JIT |
none |
JIT method to use (default: trace ) |
trace |
|||
script |
|||
--load |
LOAD_PATH |
Local path to load model from (default: disabled) | |
--max-test-duration |
MAX_TEST_DURATION |
>=0 | Maximum duration in seconds to run benchmark. Testing will be truncated once maximum test duration has been reached. (default: disabled) |
--min-test-duration |
MIN_TEST_DURATION |
>=0 | Minimum duration in seconds to run benchmark. Images will be repeated until minimum test duration has been reached. (default: disabled) |
--num-inputs |
NUM_INPUTS |
>=1 | Number of images to load (default: 1 ) |
--output-dir |
OUTPUT_DIR |
String | Location to write output |
--proxy |
https_proxy |
String | System proxy |
--precision |
PRECISION |
bp16 |
Precision to use for the model (default: fp32 ) |
fp16 |
|||
fp32 |
|||
--save |
SAVE_PATH |
Local path to save model to (default: disabled) | |
--streams |
STREAMS |
>=1 | Number of parallel streams to do inference on (default: 1 ) |
--socket |
SOCKET |
String | Socket to control telemetry capture (default: disabled) |
Note
- If
--dummy
is not specified (i.e. Quality Check mode),--min/max-test-duration
settings are ignored. Test length is limited by minimum of num-inputs and the size of the dataset.
For more details, check help with run_model.sh --help
Script output is written to the console as well as to the output directory in the file output.log
.
For multi-stream cases per-stream results can be found in the results_[STREAM_INSTANCE].json
files.
Final results of the inference run can be found in results.yaml
file. More verbose results summaries are in results.json
file.
The yaml file contents will look like:
results:
- key: throughput
value: 9199.48
unit: img/s
- key: latency
value: 31.394199
unit: ms
- key: accuracy
value: 76.06
unit: percents
benchmark.sh script can be used to benchmark EfficientNet performance for the predefined use cases. The benchmark.sh script is a tiny EfficientNet specific wrapper on top of benchmark.py script. The workflow for running a benchmark is as follows:
-
(optional) Specify path to svr-info:
export PATH_TO_SVR_INFO=/path/to/svrinfo
-
Specify path to output benchmark results (folder must be creatable/writable under
root
):export OUTPUT_DIR=/opt/output
-
Run the benchmark script (assumes
intel/image-recognition:pytorch-flex-gpu-efficientnet-inference
has already been pulled or built locally):sudo \ PATH=$PATH_TO_SVR_INFO:$PATH \ SOCKET=/tmp/telemetry.s \ IMAGE=intel/image-recognition:pytorch-flex-gpu-efficientnet-inference \ OUTPUT_DIR=$OUTPUT_DIR \ PROFILE=$(pwd)/models_v2/pytorch/efficientnet/inference/gpu/profiles/b0.bf16.csv \ PYTHONPATH=$(pwd)/models_v2/common \ $(env | grep -E '(_proxy=|_PROXY)' | sed 's/^//') \ $(pwd)/models_v2/pytorch/efficientnet/inference/gpu/benchmark.sh
-
Final output will be written to
$OUTPUT_DIR
.
Note
Additonal arguments that arent specified in the benchmark profile (b0.bf16.csv
in the example above) can be specified through environment variables as described in previous sections.
Scripts have a matching degree of functionality for usage on CUDA GPU's. However, this is significantly less validated and so may not work as smoothly. The primary difference for using these scripts with CUDA is building the associated docker image. We will not cover CUDA on baremetal here. In addition Intel does not provide pre-built dockers for CUDA. These must be built locally.
docker build \
$(env | grep -E '(_proxy=|_PROXY)' | sed 's/^/--build-arg /') \
-f docker/cuda-gpu/pytorch-efficientnet-inference/pytorch-cuda-series-efficientnet-inference.Dockerfile \
-t intel/image-recognition:pytorch-cuda-gpu-efficientnet-inference .
All other usage outlined in this README should be identical, with the exception of referencing this CUDA docker image in place of the for Intel GPU when running docker run
as well as needing to add the --gpus all
argument.
Example usage with dummy data is shown below:
mkdir -p /tmp/output && rm -f /tmp/output/* && chmod -R 777 /tmp/output
export BATCH_SIZE=1
docker run -it --rm --gpus all --ipc=host \
$(env | grep -E '(_proxy=|_PROXY)' | sed 's/^/-e /') \
--cap-add SYS_NICE \
--device /dev/dri/ \
-e MODEL_NAME=efficientnet_b0 \
-e PLATFORM=CUDA \
-e NUM_ITERATIONS=32 \
-e NUM_IMAGES=${BATCH_SIZE} \
-e BATCH_SIZE=${BATCH_SIZE} \
-e OUTPUT_DIR=/tmp/output \
-v /tmp/output:/tmp/output \
intel/image-recognition:pytorch-cuda-gpu-efficientnet-inference \
/bin/bash -c "./run_model.sh --dummy"