Image Classification Demo (C++)

This demo provides 2 clients:

  • classification_client_sync - simple client using synchronous gRPC API, testing accuracy of classification models

  • classification_client_async_benchmark - client using asynchronous gRPC API, testing accuracy and performance with real image data

To build the clients, run make command in this directory. It will build docker image named ovms_cpp_image_classification with all dependencies. The example clients image also contains test images required for accuracy measurements. It is also possible to use custom images.

git clone https://github.com/openvinotoolkit/model_server.git
cd model_server/demos/image_classification/cpp
make

> Note : In this directory you can only see the code specific to the benchmark client. The code shared with other C++ demos as well as all building utilities are placed in the common C++ directory.

Prepare classification model

Start OVMS with resnet50-binary model:

curl -L --create-dir https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.1/models_bin/2/resnet50-binary-0001/FP32-INT1/resnet50-binary-0001.bin -o resnet50-binary/1/model.bin https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.1/models_bin/2/resnet50-binary-0001/FP32-INT1/resnet50-binary-0001.xml -o resnet50-binary/1/model.xml

Client requesting prediction synchronously

The client sends requests synchronously and displays latency for each request. You can specify number of iterations and layout: nchw, nhwc or binary. Each request contains image in selected format. The client also tests server responses for accuracy.

Prepare the server

docker run -d -u $(id -u):$(id -g) -v $(pwd)/resnet50-binary:/model -p 9001:9001 openvino/model_server:latest \
--model_path /model --model_name resnet --port 9001 --layout NHWC:NCHW

Start the client:

docker run --rm --network host -e "no_proxy=localhost" -v ${PWD}/input_images.txt:/clients/input_images.txt ovms_cpp_image_classification ./classification_client_sync --grpc_port=9001 --iterations=10 --layout="binary"

Address: localhost
Images list path: input_images.txt
Layout: binary
call predict ok
call predict time: 6ms
outputs size is 1
call predict ok
call predict time: 5ms
outputs size is 1
call predict ok
call predict time: 4ms
outputs size is 1
call predict ok
call predict time: 4ms
outputs size is 1
call predict ok
call predict time: 4ms
outputs size is 1
call predict ok
call predict time: 4ms
outputs size is 1
call predict ok
call predict time: 5ms
outputs size is 1
call predict ok
call predict time: 4ms
outputs size is 1
call predict ok
call predict time: 4ms
outputs size is 1
call predict ok
call predict time: 5ms
outputs size is 1
Overall accuracy: 100%
Total time divided by number of requests: 5ms

Clients requesting predictions asynchronously

The client sends requests asynchronously to mimic parallel clients scenario. There are plenty of parameters to configure those clients.

name

description

default

available with synthetic data

grpc_address

url to grpc service

localhost

yes

grpc_port

port to grpc service

9000

yes

model_name

model name to request

resnet

yes

input_name

input tensor name with image

0

no, deduced automatically

output_name

output tensor name with classification result

1463

no

iterations

number of requests to be send by each producer thread

10

yes

batch_size

batch size of each iteration

1

no, deduced automatically

images_list

path to a file with a list of labeled images

input_images.txt

no

layout

binary, nhwc or nchw

nchw

no, deduced automatically

producers

number of threads asynchronously scheduling prediction

1

yes

consumers

number of threads receiving responses

8

yes

max_parallel_requests

maximum number of parallel inference requests; 0=no limit

100

yes

benchmark_mode

1 removes pre/post-processing and logging; 0 enables accuracy measurement

0

no

Async client with real image data

Prepare the server

docker run -d -u $(id -u):$(id -g) -v $(pwd)/resnet50-binary:/model -p 9001:9001 openvino/model_server:latest \
--model_path /model --model_name resnet --port 9001 --layout NCHW

Start the client:

docker run --rm --network host -e "no_proxy=localhost"  -v ${PWD}/input_images.txt:/clients/input_images.txt ovms_cpp_image_classification ./classification_client_async_benchmark --grpc_port=9001 --layout="nchw" --iterations=2000 --batch_size=1 --max_parallel_requests=100 --consumers=8 --producers=1 --benchmark_mode=1

Address: localhost:9001
Model name: resnet
Images list path: input_images.txt

Running the workload...
========================
        Summary
========================
Benchmark mode: True
Accuracy: N/A
Total time: 1976ms
Total iterations: 2000
Layout: nchw
Batch size: 1
Producer threads: 1
Consumer threads: 8
Max parallel requests: 100
Avg FPS: 1012.15