From 61234e2cc1866758863719f2fa22cbe2122b6096 Mon Sep 17 00:00:00 2001 From: "Bruce, Henry" Date: Wed, 2 Dec 2020 13:21:29 -0800 Subject: [PATCH 01/50] LVA test image build script now correctly uses extension image --- samples/lva_ai_extension/tests/build.sh | 16 ++++++---------- 1 file changed, 6 insertions(+), 10 deletions(-) diff --git a/samples/lva_ai_extension/tests/build.sh b/samples/lva_ai_extension/tests/build.sh index ad8fd39..3bd9857 100755 --- a/samples/lva_ai_extension/tests/build.sh +++ b/samples/lva_ai_extension/tests/build.sh @@ -5,12 +5,6 @@ LVA_DIR=$(dirname $WORK_DIR) VAS_BASE_IMAGE="openvisualcloud/xeone3-ubuntu1804-analytics-gst:20.10" EXTENSION_IMAGE_TAG="video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension" TEST_IMAGE_TAG="video-analytics-serving-lva-tests" - -echo $WORK_DIR -echo $ROOT_DIR -echo $LVA_DIR -echo $VAS_BASE_IMAGE - SAMPLE_BUILD_ARGS=$(env | cut -f1 -d= | grep -E '_(proxy|REPO|VER)$' | sed 's/^/--build-arg / ' | tr '\n' ' ') function launch { $@ @@ -33,7 +27,7 @@ function get_options { --lva-image) if [ "$2" ]; then LVA_IMAGE=$2 - SAMPLE_BUILD_ARGS+=" --build-arg BASE $LVA_IMAGE" + SAMPLE_BUILD_ARGS+=" --build-arg BASE=$LVA_IMAGE" shift else error 'ERROR: "--base" requires an argument.' @@ -46,7 +40,6 @@ function get_options { else error 'ERROR: "--docker-cache" requires an argument.' fi - shift ;; # For backwards compatbility with scripts that took cache prefix as $1 *cache*) @@ -56,7 +49,7 @@ function get_options { break ;; esac - + shift done } @@ -66,11 +59,14 @@ function show_help { echo " [ --docker-cache : Docker cache prefix ] " } +get_options "$@" + # Build LVA image if not specified -if [ ! -z "$LVA_IMAGE" ]; then +if [ -z "$LVA_IMAGE" ]; then echo Building $EXTENSION_IMAGE_TAG launch "$LVA_DIR/docker/build.sh --remove-gstlibav --base ${CACHE_PREFIX}$VAS_BASE_IMAGE" fi # Add tests layer +echo "docker build -f $WORK_DIR/Dockerfile $SAMPLE_BUILD_ARGS -t $TEST_IMAGE_TAG $WORK_DIR" launch "docker build -f $WORK_DIR/Dockerfile $SAMPLE_BUILD_ARGS -t $TEST_IMAGE_TAG $WORK_DIR" From 1ce897445408239ab23e4bb5a55443369290b72b Mon Sep 17 00:00:00 2001 From: "Thakkalapelli, Thanaji Rao" Date: Wed, 2 Dec 2020 15:22:59 -0800 Subject: [PATCH 02/50] Pass arguments from `run_server.sh` to parent `run.sh` with proper handling of pipeline directory mounting. --- samples/lva_ai_extension/docker/run_server.sh | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/samples/lva_ai_extension/docker/run_server.sh b/samples/lva_ai_extension/docker/run_server.sh index c560fdd..c750573 100755 --- a/samples/lva_ai_extension/docker/run_server.sh +++ b/samples/lva_ai_extension/docker/run_server.sh @@ -6,7 +6,7 @@ LVA_DIR=$(dirname $CURRENT_DIR) IMAGE=video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension NAME=${IMAGE//[\:]/_} PORT=5001 -DEV_MODE= +PIPELINES= ENTRYPOINT_ARGS= #Get options passed into script @@ -38,7 +38,7 @@ function get_options { ENTRYPOINT_ARGS+="--entrypoint-args $1 " ;; --dev) - DEV_MODE="--dev --pipelines $LVA_DIR/pipelines" + PIPELINES="--pipelines $LVA_DIR/pipelines " ;; *) break @@ -83,4 +83,4 @@ if [ ! -z "$PARAMETERS" ]; then ENV+="-e PARAMETERS=$PARAMETERS " fi -"$ROOT_DIR/docker/run.sh" --image $IMAGE -v /tmp:/tmp -v /dev/shm:/dev/shm -p $PORT:$PORT $ENTRYPOINT_ARGS $DEV_MODE $ENV +"$ROOT_DIR/docker/run.sh" --image $IMAGE -v /tmp:/tmp -v /dev/shm:/dev/shm -p $PORT:$PORT $ENTRYPOINT_ARGS $PIPELINES $ENV $@ From c0042e2ad7f95b444c1348847f3a21a8990fe17d Mon Sep 17 00:00:00 2001 From: "Thakkalapelli, Thanaji Rao" Date: Thu, 3 Dec 2020 13:44:42 -0800 Subject: [PATCH 03/50] Added support for NCS2 --- docker/run.sh | 14 +++++++++++++- vaserving/model_manager.py | 1 + 2 files changed, 14 insertions(+), 1 deletion(-) diff --git a/docker/run.sh b/docker/run.sh index f98fd66..9931e37 100755 --- a/docker/run.sh +++ b/docker/run.sh @@ -22,6 +22,7 @@ PRIVILEGED= NETWORK= USER= INTERACTIVE=-it +DEVICE_CGROUP_RULE= SCRIPT_DIR=$(dirname "$(readlink -f "$0")") SOURCE_DIR=$(dirname $SCRIPT_DIR) @@ -66,6 +67,17 @@ get_options() { error 'ERROR: "--device" requires a non-empty option argument.' fi ;; + --privileged) + PRIVILEGED="--privileged " + ;; + --device-cgroup-rule) + if [ "$2" ]; then + DEVICE_CGROUP_RULE="--device-cgroup-rule=$2 " + shift + else + error 'ERROR: "--device-cgroup-rule" requires a non-empty option argument.' + fi + ;; --pipelines) if [ "$2" ]; then PIPELINES=$(realpath $2) @@ -265,5 +277,5 @@ fi show_options set -x -docker run $INTERACTIVE --rm $ENVIRONMENT $VOLUME_MOUNT $DEVICES $NETWORK $PORTS $ENTRYPOINT --name ${NAME} ${PRIVILEGED} ${USER} $IMAGE ${ENTRYPOINT_ARGS} +docker run $INTERACTIVE --rm $ENVIRONMENT $VOLUME_MOUNT $DEVICE_CGROUP_RULE $DEVICES $NETWORK $PORTS $ENTRYPOINT --name ${NAME} ${PRIVILEGED} ${USER} $IMAGE ${ENTRYPOINT_ARGS} { set +x; } 2>/dev/null diff --git a/vaserving/model_manager.py b/vaserving/model_manager.py index dc66887..d787ad8 100644 --- a/vaserving/model_manager.py +++ b/vaserving/model_manager.py @@ -52,6 +52,7 @@ def __init__(self, model_dir, network_preference=None, ignore_init_errors=False) 'HDDL': ["FP16"], 'GPU': ["FP16"], 'VPU': ["FP16"], + 'MYRIAD': ["FP16"], 'KMB': ["U8"]} success = self.load_models(self.model_dir, network_preference) From 5c24b07a1793452c8c228f5ab6213a61a23eaf8a Mon Sep 17 00:00:00 2001 From: "Thakkalapelli, Thanaji Rao" Date: Thu, 3 Dec 2020 15:12:31 -0800 Subject: [PATCH 04/50] Updates to LVA deployment files and instructions. --- samples/lva_ai_extension/README.md | 16 +- .../deployment.cpu.grpc.template.json | 166 ++++++++++++++++++ ...json => deployment.gpu.grpc.template.json} | 7 +- .../topologies/operations.json | 2 +- .../lva_ai_extension/topologies/topology.json | 137 +++++++++++++++ 5 files changed, 322 insertions(+), 6 deletions(-) create mode 100644 samples/lva_ai_extension/deployment/deployment.cpu.grpc.template.json rename samples/lva_ai_extension/deployment/{deployment.grpc.template.json => deployment.gpu.grpc.template.json} (96%) create mode 100644 samples/lva_ai_extension/topologies/topology.json diff --git a/samples/lva_ai_extension/README.md b/samples/lva_ai_extension/README.md index 2cedc4b..72fff4a 100644 --- a/samples/lva_ai_extension/README.md +++ b/samples/lva_ai_extension/README.md @@ -96,9 +96,19 @@ The following pipelines are included in the AI Extension: ## Configuring the AI Extension Module for Live Video Analytics -Update the [deployment manifest](/samples/lva_ai_extension/deployment/deployment.grpc.template.json) template located in the deployment folder: -* Make sure that the 'lvaExtension'->'image' property shows the URI of the OpenVINO DL Streamer - Edge AI Extension docker image. -* Update the 'lvaExtension' -> 'ENV' property to configure container behavior. Our example selects and `object_detection` pipeline and selects GPU inference +Based on HW target, choose and update the appropriate deployment manifest located in the [deployment directory](/samples/lva_ai_extension/deployment/). +* Make sure that the 'lvaExtension'->'image' property shows the Azure URI of VAS LVA Edge AI Extension docker image. +* Update the 'lvaExtension' -> 'ENV' property to configure container behavior. Our manifest file is configured for object detection by default. +* Update registryCredentials from .env +```json + "registryCredentials": { + "":{ + "username":"", + "password":"", + "address": ".azurecr.io" + } + } +``` You will also need to create a graph topology with gRPC extension and then create a graph instance based on that topology. Here is a sample [operations.json](/samples/lva_ai_extension/topologies/operations.json). diff --git a/samples/lva_ai_extension/deployment/deployment.cpu.grpc.template.json b/samples/lva_ai_extension/deployment/deployment.cpu.grpc.template.json new file mode 100644 index 0000000..ed22a9e --- /dev/null +++ b/samples/lva_ai_extension/deployment/deployment.cpu.grpc.template.json @@ -0,0 +1,166 @@ +{ + "$schema-template": "2.0.0", + "modulesContent": { + "$edgeAgent": { + "properties.desired": { + "schemaVersion": "1.0", + "runtime": { + "type": "docker", + "settings": { + "minDockerVersion": "v1.25", + "loggingOptions": "", + "registryCredentials": { + + } + } + }, + "systemModules": { + "edgeAgent": { + "type": "docker", + "settings": { + "image": "mcr.microsoft.com/azureiotedge-agent:1.0", + "createOptions": {} + } + }, + "edgeHub": { + "type": "docker", + "status": "running", + "restartPolicy": "always", + "settings": { + "image": "mcr.microsoft.com/azureiotedge-hub:1.0", + "createOptions": { + "HostConfig": { + "PortBindings": { + "5671/tcp": [ + { + "HostPort": "5671" + } + ], + "8883/tcp": [ + { + "HostPort": "8883" + } + ], + "443/tcp": [ + { + "HostPort": "443" + } + ] + } + } + } + } + } + }, + "modules": { + "lvaEdge": { + "version": "1.0", + "type": "docker", + "status": "running", + "restartPolicy": "always", + "settings": { + "image": "mcr.microsoft.com/media/live-video-analytics:1", + "createOptions": { + "HostConfig": { + "LogConfig": { + "Type": "", + "Config": { + "max-size": "10m", + "max-file": "10" + } + }, + "IpcMode" : "shareable" + } + } + } + }, + "rtspsim": { + "version": "1.0", + "type": "docker", + "status": "running", + "restartPolicy": "always", + "settings": { + "image": "mcr.microsoft.com/lva-utilities/rtspsim-live555:1.2", + "createOptions": { + "HostConfig": { + "Binds": [ + "$INPUT_VIDEO_FOLDER_ON_DEVICE:/live/mediaServer/media" + ] + } + } + } + }, + "lvaExtension" : { + "version": "1.0", + "type": "docker", + "status": "running", + "restartPolicy": "always", + "settings": { + "image": "< update the image url >", + "createOptions": { + "ExposedPorts": { + "80/tcp": {}, + "5001/tcp" : {} + }, + "HostConfig": { + "Binds": [ + "/tmp/:/tmp/" + ], + "PortBindings": { + "80/tcp": [ + { + "HostPort": "8080" + } + ], + "5001/tcp" : [ + { + "HostPort" : "5001" + } + ] + }, + "LogConfig": { + "Type": "", + "Config": { + "max-size": "10m", + "max-file": "10" + } + }, + "IpcMode": "container:lvaEdge" + } + } + } + } + } + } + }, + "$edgeHub": { + "properties.desired": { + "schemaVersion": "1.0", + "routes": { + "LVAToHub": "FROM /messages/modules/lvaEdge/outputs/* INTO $upstream" + }, + "storeAndForwardConfiguration": { + "timeToLiveSecs": 7200 + } + } + }, + "lvaEdge": { + "properties.desired": { + "applicationDataDirectory": "/var/lib/azuremediaservices", + "azureMediaServicesArmId": "/subscriptions/$SUBSCRIPTION_ID/resourceGroups/$RESOURCE_GROUP/providers/microsoft.media/mediaservices/$AMS_ACCOUNT", + "aadTenantId": "$AAD_TENANT_ID", + "aadServicePrincipalAppId": "$AAD_SERVICE_PRINCIPAL_ID", + "aadServicePrincipalSecret": "$AAD_SERVICE_PRINCIPAL_SECRET", + "aadEndpoint": "https://login.microsoftonline.com", + "aadResourceId": "https://management.core.windows.net/", + "armEndpoint": "https://management.azure.com/", + "diagnosticsEventsOutputName": "AmsDiagnostics", + "operationalEventsOutputName": "AmsOperational", + "logLevel": "Information", + "logCategories": "Application,Events", + "allowUnsecuredEndpoints": true, + "telemetryOptOut": false + } + } + } +} \ No newline at end of file diff --git a/samples/lva_ai_extension/deployment/deployment.grpc.template.json b/samples/lva_ai_extension/deployment/deployment.gpu.grpc.template.json similarity index 96% rename from samples/lva_ai_extension/deployment/deployment.grpc.template.json rename to samples/lva_ai_extension/deployment/deployment.gpu.grpc.template.json index c99d4ce..4b46f8d 100644 --- a/samples/lva_ai_extension/deployment/deployment.grpc.template.json +++ b/samples/lva_ai_extension/deployment/deployment.gpu.grpc.template.json @@ -83,6 +83,9 @@ "image": "mcr.microsoft.com/lva-utilities/rtspsim-live555:1.2", "createOptions": { "HostConfig": { + "Binds": [ + "$INPUT_VIDEO_FOLDER_ON_DEVICE:/live/mediaServer/media" + ] } } @@ -94,7 +97,7 @@ "status": "running", "restartPolicy": "always", "settings": { - "image": "lva_vas_server_instance", + "image": "< update the image url >", "createOptions": { "ExposedPorts": { "80/tcp": {}, @@ -114,7 +117,7 @@ "PathOnHost":"/dev/dri", "PathInContainer":"/dev/dri", "CgroupPermissions":"rwm" - }, + }], "PortBindings": { "80/tcp": [ { diff --git a/samples/lva_ai_extension/topologies/operations.json b/samples/lva_ai_extension/topologies/operations.json index 474500b..121d4cb 100644 --- a/samples/lva_ai_extension/topologies/operations.json +++ b/samples/lva_ai_extension/topologies/operations.json @@ -4,7 +4,7 @@ { "opName": "GraphTopologySet", "opParams": { - "topologyUrl": "https://raw.githubusercontent.com/Azure/live-video-analytics/master/MediaGraph/topologies/grpcExtension/topology.json" + "topologyFile": "" } }, { diff --git a/samples/lva_ai_extension/topologies/topology.json b/samples/lva_ai_extension/topologies/topology.json new file mode 100644 index 0000000..ca1e6e4 --- /dev/null +++ b/samples/lva_ai_extension/topologies/topology.json @@ -0,0 +1,137 @@ +{ + "@apiVersion": "1.0", + "name": "InferencingWithGrpcExtension", + "properties": { + "description": "Record on motion to AMS Asset and record events from external models through gRPC Extension", + "parameters": [ + { + "name": "rtspUrl", + "type": "String", + "description": "Rtsp source Url address" + }, + { + "name": "rtspUserName", + "type": "String", + "description": "Rtsp source user name.", + "default": "dummyUsername" + }, + { + "name": "rtspPassword", + "type": "String", + "description": "Rtsp source password.", + "default": "dummyPassword" + }, + { + "name": "motionSensitivity", + "type": "String", + "description": "Motion detection sensitivity", + "default": "medium" + }, + { + "name": "grpcExtensionAddress", + "type": "String", + "description": "grpc LVA Extension Address", + "default": "tcp://lvaextension:44000" + }, + { + "name": "grpcExtensionUserName", + "type": "String", + "description": "inferencing endpoint user name.", + "default": "dummyUserName" + }, + { + "name": "grpcExtensionPassword", + "type": "String", + "description": "inferencing endpoint password.", + "default": "dummyPassword" + }, + { + "name": "hubSinkOutputName", + "type": "String", + "description": "Hub sink output name", + "default": "iothubsinkoutput" + }, + { + "name": "imageScaleMode", + "type": "String", + "description": "image scaling mode", + "default": "pad" + }, + { + "name": "frameWidth", + "type": "String", + "description": "Width of the video frame to be received from LVA.", + "default": "416" + }, + { + "name": "frameHeight", + "type": "String", + "description": "Height of the video frame to be received from LVA.", + "default": "416" + } + ], + "sources": [ + { + "@type": "#Microsoft.Media.MediaGraphRtspSource", + "name": "rtspSource", + "endpoint": { + "@type": "#Microsoft.Media.MediaGraphUnsecuredEndpoint", + "url": "${rtspUrl}", + "credentials": { + "@type": "#Microsoft.Media.MediaGraphUsernamePasswordCredentials", + "username": "${rtspUserName}", + "password": "${rtspPassword}" + } + } + } + ], + "processors": [ + { + "@type": "#Microsoft.Media.MediaGraphGrpcExtension", + "name": "grpcExtension", + "endpoint": { + "@type": "#Microsoft.Media.MediaGraphUnsecuredEndpoint", + "url": "${grpcExtensionAddress}", + "credentials": { + "@type": "#Microsoft.Media.MediaGraphUsernamePasswordCredentials", + "username": "${grpcExtensionUserName}", + "password": "${grpcExtensionPassword}" + } + }, + "dataTransfer": { + "mode": "sharedMemory", + "SharedMemorySizeMiB": "5" + }, + "image": { + "scale": { + "mode": "${imageScaleMode}", + "width": "${frameWidth}", + "height": "${frameHeight}" + }, + "format": { + "@type": "#Microsoft.Media.MediaGraphImageFormatRaw", + "pixelFormat": "bgr24" + } + }, + "inputs": [ + { + "nodeName": "rtspSource" + } + ] + } + ], + "sinks": [ + { + "@type": "#Microsoft.Media.MediaGraphIoTHubMessageSink", + "name": "hubSink", + "hubOutputName": "${hubSinkOutputName}", + "inputs": [ + { + "nodeName": "grpcExtension" + } + ] + } + ] + } + } + \ No newline at end of file From 6368c80dd537830c172f426e7459386a9229a4f3 Mon Sep 17 00:00:00 2001 From: "Thakkalapelli, Thanaji Rao" Date: Thu, 3 Dec 2020 16:04:23 -0800 Subject: [PATCH 05/50] Script to stop and remove all containers and VA Serving images --- tests/stop.sh | 48 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 48 insertions(+) create mode 100755 tests/stop.sh diff --git a/tests/stop.sh b/tests/stop.sh new file mode 100755 index 0000000..31f79b2 --- /dev/null +++ b/tests/stop.sh @@ -0,0 +1,48 @@ +#!/bin/bash -e +# +# Copyright (C) 2019 Intel Corporation. +# +# SPDX-License-Identifier: BSD-3-Clause +# + +#!/bin/bash + +echo "Stopping all docker containers with name starts with video-analytics-serving" +docker stop $(docker ps -f name=video-analytics-serving) || true + +echo "Removing all docker containers with name starts with video-analytics-serving" +docker rm $(docker ps -f name=video-analytics-serving) || true + +#Get options passed into script +function get_options { + while :; do + case $1 in + -h | -\? | --help) + show_help + exit + ;; + --remove) + echo "Removing all docker images with name starts with video-analytics-serving" + docker rmi $(docker images | grep 'video-analytics-serving') || true + shift + ;; + *) + break + ;; + esac + + shift + done +} + +function show_help { + echo "usage: ./stop.sh" + echo " [ remove : removes all docker images starts with video-analytics-serving ]" +} + +function error { + printf '%s\n' "$1" >&2 + exit +} + +get_options "$@" \ No newline at end of file From 3b44f0eb53099fb30fba3be56171dc295b546b94 Mon Sep 17 00:00:00 2001 From: Henry Bruce Date: Thu, 3 Dec 2020 16:17:39 -0800 Subject: [PATCH 06/50] Performance test now selects correct %ge difference threshold --- tests/test_pipeline_performance.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_pipeline_performance.py b/tests/test_pipeline_performance.py index 2e90237..70f4d43 100644 --- a/tests/test_pipeline_performance.py +++ b/tests/test_pipeline_performance.py @@ -59,8 +59,8 @@ def test_pipeline_performance(VAServing, test_case, test_filename, generate): pytest.fail("Required parameter gst_launch_string missing") if "iterations" in _test_case: iterations = _test_case["iterations"] - if "avg_diff_pct_limit" in _test_case: - avg_diff_pct_limit = _test_case["avg_diff_pct_limit"] + if "avg_percentage_diff_limit" in _test_case: + avg_diff_pct_limit = _test_case["avg_percentage_diff_limit"] run_gst_pipeline(iterations, start_times_gst_launch, gst_launch_string, stop_times_gst_launch) From b840f873d1f9daba2f07e2cc4bd79528b2324819 Mon Sep 17 00:00:00 2001 From: "Thakkalapelli, Thanaji Rao" Date: Thu, 10 Dec 2020 12:52:06 -0800 Subject: [PATCH 07/50] LVA deployment templates: Updated cpu and gpu templates, added ncs2 and hddl templates --- samples/lva_ai_extension/README.md | 10 - samples/lva_ai_extension/deployment/.env | 9 +- .../deployment.cpu.grpc.template.json | 8 +- .../deployment.gpu.grpc.template.json | 8 +- .../deployment.hddl.grpc.template.json | 186 ++++++++++++++++++ .../deployment.movidius.grpc.template.json | 179 +++++++++++++++++ 6 files changed, 384 insertions(+), 16 deletions(-) mode change 100644 => 100755 samples/lva_ai_extension/deployment/.env mode change 100644 => 100755 samples/lva_ai_extension/deployment/deployment.cpu.grpc.template.json mode change 100644 => 100755 samples/lva_ai_extension/deployment/deployment.gpu.grpc.template.json create mode 100755 samples/lva_ai_extension/deployment/deployment.hddl.grpc.template.json create mode 100755 samples/lva_ai_extension/deployment/deployment.movidius.grpc.template.json diff --git a/samples/lva_ai_extension/README.md b/samples/lva_ai_extension/README.md index 72fff4a..e49d212 100644 --- a/samples/lva_ai_extension/README.md +++ b/samples/lva_ai_extension/README.md @@ -99,16 +99,6 @@ The following pipelines are included in the AI Extension: Based on HW target, choose and update the appropriate deployment manifest located in the [deployment directory](/samples/lva_ai_extension/deployment/). * Make sure that the 'lvaExtension'->'image' property shows the Azure URI of VAS LVA Edge AI Extension docker image. * Update the 'lvaExtension' -> 'ENV' property to configure container behavior. Our manifest file is configured for object detection by default. -* Update registryCredentials from .env -```json - "registryCredentials": { - "":{ - "username":"", - "password":"", - "address": ".azurecr.io" - } - } -``` You will also need to create a graph topology with gRPC extension and then create a graph instance based on that topology. Here is a sample [operations.json](/samples/lva_ai_extension/topologies/operations.json). diff --git a/samples/lva_ai_extension/deployment/.env b/samples/lva_ai_extension/deployment/.env old mode 100644 new mode 100755 index e85a38a..b1de79a --- a/samples/lva_ai_extension/deployment/.env +++ b/samples/lva_ai_extension/deployment/.env @@ -1,7 +1,12 @@ -SUBSCRIPTION_ID="xx" +SUBSCRIPTION_ID="" RESOURCE_GROUP="" AMS_ACCOUNT="" AAD_TENANT_ID="" AAD_SERVICE_PRINCIPAL_ID="" AAD_SERVICE_PRINCIPAL_SECRET="" - +IOTHUB_CONNECTION_STRING="" +INPUT_VIDEO_FOLDER_ON_DEVICE="" +OUTPUT_VIDEO_FOLDER_ON_DEVICE="" +APPDATA_FOLDER_ON_DEVICE="" +CONTAINER_REGISTRY_USERNAME_myacr= +CONTAINER_REGISTRY_PASSWORD_myacr= \ No newline at end of file diff --git a/samples/lva_ai_extension/deployment/deployment.cpu.grpc.template.json b/samples/lva_ai_extension/deployment/deployment.cpu.grpc.template.json old mode 100644 new mode 100755 index ed22a9e..0531356 --- a/samples/lva_ai_extension/deployment/deployment.cpu.grpc.template.json +++ b/samples/lva_ai_extension/deployment/deployment.cpu.grpc.template.json @@ -10,7 +10,11 @@ "minDockerVersion": "v1.25", "loggingOptions": "", "registryCredentials": { - + "$CONTAINER_REGISTRY_USERNAME_myacr":{ + "username":"$CONTAINER_REGISTRY_USERNAME_myacr", + "password":"$CONTAINER_REGISTRY_PASSWORD_myacr", + "address": "$CONTAINER_REGISTRY_USERNAME_myacr.azurecr.io" + } } } }, @@ -96,7 +100,7 @@ "status": "running", "restartPolicy": "always", "settings": { - "image": "< update the image url >", + "image": "", "createOptions": { "ExposedPorts": { "80/tcp": {}, diff --git a/samples/lva_ai_extension/deployment/deployment.gpu.grpc.template.json b/samples/lva_ai_extension/deployment/deployment.gpu.grpc.template.json old mode 100644 new mode 100755 index 4b46f8d..3e70a08 --- a/samples/lva_ai_extension/deployment/deployment.gpu.grpc.template.json +++ b/samples/lva_ai_extension/deployment/deployment.gpu.grpc.template.json @@ -10,7 +10,11 @@ "minDockerVersion": "v1.25", "loggingOptions": "", "registryCredentials": { - + "$CONTAINER_REGISTRY_USERNAME_myacr":{ + "username":"$CONTAINER_REGISTRY_USERNAME_myacr", + "password":"$CONTAINER_REGISTRY_PASSWORD_myacr", + "address": "$CONTAINER_REGISTRY_USERNAME_myacr.azurecr.io" + } } } }, @@ -97,7 +101,7 @@ "status": "running", "restartPolicy": "always", "settings": { - "image": "< update the image url >", + "image": "", "createOptions": { "ExposedPorts": { "80/tcp": {}, diff --git a/samples/lva_ai_extension/deployment/deployment.hddl.grpc.template.json b/samples/lva_ai_extension/deployment/deployment.hddl.grpc.template.json new file mode 100755 index 0000000..14486f8 --- /dev/null +++ b/samples/lva_ai_extension/deployment/deployment.hddl.grpc.template.json @@ -0,0 +1,186 @@ +{ + "$schema-template": "2.0.0", + "modulesContent": { + "$edgeAgent": { + "properties.desired": { + "schemaVersion": "1.0", + "runtime": { + "type": "docker", + "settings": { + "minDockerVersion": "v1.25", + "loggingOptions": "", + "registryCredentials": { + "$CONTAINER_REGISTRY_USERNAME_myacr":{ + "username":"$CONTAINER_REGISTRY_USERNAME_myacr", + "password":"$CONTAINER_REGISTRY_PASSWORD_myacr", + "address": "$CONTAINER_REGISTRY_USERNAME_myacr.azurecr.io" + } + } + } + }, + "systemModules": { + "edgeAgent": { + "type": "docker", + "settings": { + "image": "mcr.microsoft.com/azureiotedge-agent:1.0", + "createOptions": {} + } + }, + "edgeHub": { + "type": "docker", + "status": "running", + "restartPolicy": "always", + "settings": { + "image": "mcr.microsoft.com/azureiotedge-hub:1.0", + "createOptions": { + "HostConfig": { + "PortBindings": { + "5671/tcp": [ + { + "HostPort": "5671" + } + ], + "8883/tcp": [ + { + "HostPort": "8883" + } + ], + "443/tcp": [ + { + "HostPort": "443" + } + ] + } + } + } + } + } + }, + "modules": { + "lvaEdge": { + "version": "1.0", + "type": "docker", + "status": "running", + "restartPolicy": "always", + "settings": { + "image": "mcr.microsoft.com/media/live-video-analytics:1", + "createOptions": { + "HostConfig": { + "LogConfig": { + "Type": "", + "Config": { + "max-size": "10m", + "max-file": "10" + } + }, + "IpcMode" : "shareable" + } + } + } + }, + "rtspsim": { + "version": "1.0", + "type": "docker", + "status": "running", + "restartPolicy": "always", + "settings": { + "image": "mcr.microsoft.com/lva-utilities/rtspsim-live555:1.2", + "createOptions": { + "HostConfig": { + "$CONTAINER_REGISTRY_USERNAME_myacr":{ + "username":"$CONTAINER_REGISTRY_USERNAME_myacr", + "password":"$CONTAINER_REGISTRY_PASSWORD_myacr", + "address": "$CONTAINER_REGISTRY_USERNAME_myacr.azurecr.io" + } + } + } + } + }, + "lvaextension" : { + "version": "1.0", + "type": "docker", + "status": "running", + "restartPolicy": "always", + "settings": { + "image": "", + "createOptions": { + "ExposedPorts": { + "80/tcp": {}, + "5001/tcp" : {} + }, + "Env":[ + "PIPELINE_NAME=object_detection", + "PIPELINE_VERSION=person_vehicle_bike_detection", + "PARAMETERS= {\"device\":\"HDDL\"}" + ], + "HostConfig": { + "Binds": [ + "/var/tmp:/var/tmp", + "/tmp:/tmp", + "/dev/shm:/dev/shm" + ], + "Devices": [ + { + "PathOnHost":"/dev/ion", + "PathInContainer":"/dev/ion", + "CgroupPermissions":"rwm" + } + ], + "PortBindings": { + "80/tcp": [ + { + "HostPort": "8080" + } + ], + "5001/tcp" : [ + { + "HostPort" : "5001" + } + ] + }, + "LogConfig": { + "Type": "", + "Config": { + "max-size": "10m", + "max-file": "10" + } + }, + "IpcMode": "container:lvaEdge" + } + } + } + } + } + } + }, + "$edgeHub": { + "properties.desired": { + "schemaVersion": "1.0", + "routes": { + "LVAToHub": "FROM /messages/modules/lvaEdge/outputs/* INTO $upstream" + }, + "storeAndForwardConfiguration": { + "timeToLiveSecs": 7200 + } + } + }, + "lvaEdge": { + "properties.desired": { + "applicationDataDirectory": "/var/lib/azuremediaservices", + "azureMediaServicesArmId": "/subscriptions/$SUBSCRIPTION_ID/resourceGroups/$RESOURCE_GROUP/providers/microsoft.media/mediaservices/$AMS_ACCOUNT", + "aadTenantId": "$AAD_TENANT_ID", + "aadServicePrincipalAppId": "$AAD_SERVICE_PRINCIPAL_ID", + "aadServicePrincipalSecret": "$AAD_SERVICE_PRINCIPAL_SECRET", + "aadEndpoint": "https://login.microsoftonline.com", + "aadResourceId": "https://management.core.windows.net/", + "armEndpoint": "https://management.azure.com/", + "diagnosticsEventsOutputName": "AmsDiagnostics", + "operationalEventsOutputName": "AmsOperational", + "logLevel": "Information", + "logCategories": "Application,Events", + "allowUnsecuredEndpoints": true, + "telemetryOptOut": false + } + } + } +} diff --git a/samples/lva_ai_extension/deployment/deployment.movidius.grpc.template.json b/samples/lva_ai_extension/deployment/deployment.movidius.grpc.template.json new file mode 100755 index 0000000..0a6af89 --- /dev/null +++ b/samples/lva_ai_extension/deployment/deployment.movidius.grpc.template.json @@ -0,0 +1,179 @@ +{ + "$schema-template": "2.0.0", + "modulesContent": { + "$edgeAgent": { + "properties.desired": { + "schemaVersion": "1.0", + "runtime": { + "type": "docker", + "settings": { + "minDockerVersion": "v1.25", + "loggingOptions": "", + "registryCredentials": { + "$CONTAINER_REGISTRY_USERNAME_myacr":{ + "username":"$CONTAINER_REGISTRY_USERNAME_myacr", + "password":"$CONTAINER_REGISTRY_PASSWORD_myacr", + "address": "$CONTAINER_REGISTRY_USERNAME_myacr.azurecr.io" + } + } + } + }, + "systemModules": { + "edgeAgent": { + "type": "docker", + "settings": { + "image": "mcr.microsoft.com/azureiotedge-agent:1.0", + "createOptions": {} + } + }, + "edgeHub": { + "type": "docker", + "status": "running", + "restartPolicy": "always", + "settings": { + "image": "mcr.microsoft.com/azureiotedge-hub:1.0", + "createOptions": { + "HostConfig": { + "PortBindings": { + "5671/tcp": [ + { + "HostPort": "5671" + } + ], + "8883/tcp": [ + { + "HostPort": "8883" + } + ], + "443/tcp": [ + { + "HostPort": "443" + } + ] + } + } + } + } + } + }, + "modules": { + "lvaEdge": { + "version": "1.0", + "type": "docker", + "status": "running", + "restartPolicy": "always", + "settings": { + "image": "mcr.microsoft.com/media/live-video-analytics:1", + "createOptions": { + "HostConfig": { + "LogConfig": { + "Type": "", + "Config": { + "max-size": "10m", + "max-file": "10" + } + }, + "IpcMode" : "shareable" + } + } + } + }, + "rtspsim": { + "version": "1.0", + "type": "docker", + "status": "running", + "restartPolicy": "always", + "settings": { + "image": "mcr.microsoft.com/lva-utilities/rtspsim-live555:1.2", + "createOptions": { + "HostConfig": { + + } + } + } + }, + "lvaextension" : { + "version": "1.0", + "type": "docker", + "status": "running", + "restartPolicy": "always", + "settings": { + "image": "", + "createOptions": { + "ExposedPorts": { + "80/tcp": {}, + "5001/tcp" : {} + }, + "Env":[ + "PIPELINE_NAME=object_detection", + "PIPELINE_VERSION=person_vehicle_bike_detection", + "PARAMETERS= {\"device\":\"MYRIAD\"}" + ], + "HostConfig": { + "Binds": [ + "/var/tmp:/var/tmp", + "/tmp:/tmp", + "/dev/shm:/dev/shm", + "/dev/bus/usb:/dev/bus/usb" + ], + "DeviceCgroupRules":[ + "c 189:* rmw" + ], + "PortBindings": { + "80/tcp": [ + { + "HostPort": "8080" + } + ], + "5001/tcp" : [ + { + "HostPort" : "5001" + } + ] + }, + "LogConfig": { + "Type": "", + "Config": { + "max-size": "10m", + "max-file": "10" + } + }, + "IpcMode": "container:lvaEdge" + } + } + } + } + } + } + }, + "$edgeHub": { + "properties.desired": { + "schemaVersion": "1.0", + "routes": { + "LVAToHub": "FROM /messages/modules/lvaEdge/outputs/* INTO $upstream" + }, + "storeAndForwardConfiguration": { + "timeToLiveSecs": 7200 + } + } + }, + "lvaEdge": { + "properties.desired": { + "applicationDataDirectory": "/var/lib/azuremediaservices", + "azureMediaServicesArmId": "/subscriptions/$SUBSCRIPTION_ID/resourceGroups/$RESOURCE_GROUP/providers/microsoft.media/mediaservices/$AMS_ACCOUNT", + "aadTenantId": "$AAD_TENANT_ID", + "aadServicePrincipalAppId": "$AAD_SERVICE_PRINCIPAL_ID", + "aadServicePrincipalSecret": "$AAD_SERVICE_PRINCIPAL_SECRET", + "aadEndpoint": "https://login.microsoftonline.com", + "aadResourceId": "https://management.core.windows.net/", + "armEndpoint": "https://management.azure.com/", + "diagnosticsEventsOutputName": "AmsDiagnostics", + "operationalEventsOutputName": "AmsOperational", + "logLevel": "Information", + "logCategories": "Application,Events", + "allowUnsecuredEndpoints": true, + "telemetryOptOut": false + } + } + } +} From 0620e47374cb3e0961a6c8bd07d10d56ffd09efe Mon Sep 17 00:00:00 2001 From: "Khoshnevissan, Farid" Date: Fri, 11 Dec 2020 15:45:35 -0800 Subject: [PATCH 08/50] Use VideoSource for both video and images (as supported by OpenCV). Adding tests for all three pipelines with suitable media. Adding schema validation. --- samples/lva_ai_extension/client/__main__.py | 59 ++++++------ samples/lva_ai_extension/client/arguments.py | 4 +- samples/lva_ai_extension/tests/Dockerfile | 1 + .../tests/test_client_server.py | 65 +++++++------ .../tests/test_missing_file.py | 39 ++++++++ .../tests/test_multiple_clients.py | 61 ++++++------ .../tests/test_object_pipelines.py | 65 +++++++++++++ .../lva_ai_extension/tests/test_parameters.py | 69 +++++++------- .../tests/test_validate_schema.py | 95 +++++++++++-------- samples/lva_ai_extension/tests/test_video.py | 88 ++++++++++------- 10 files changed, 342 insertions(+), 204 deletions(-) create mode 100644 samples/lva_ai_extension/tests/test_missing_file.py create mode 100644 samples/lva_ai_extension/tests/test_object_pipelines.py diff --git a/samples/lva_ai_extension/client/__main__.py b/samples/lva_ai_extension/client/__main__.py index 0c10dde..bb59582 100644 --- a/samples/lva_ai_extension/client/__main__.py +++ b/samples/lva_ai_extension/client/__main__.py @@ -38,42 +38,35 @@ from arguments import parse_args from media_stream_processor import MediaStreamProcessor - -class ImageSource: - def __init__(self, filename, count): - self._image = cv2.imread(filename, cv2.IMREAD_COLOR) - self._count = count - - def dimensions(self): - height, width, _ = self._image.shape - return width, height - - def get_frame(self): - frame = self._image.tobytes() if self._count > 0 else None - self._count -= 1 - return frame - - def close(self): - pass - - class VideoSource: - def __init__(self, filename): + def __init__(self, filename, loop_count): + self._loop_count = loop_count self._vid_cap = cv2.VideoCapture(filename) + if self._vid_cap is None or not self._vid_cap.isOpened(): + print("{}: Error opening video source".format(filename)) + sys.exit(1) def dimensions(self): - height = int(self._vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) width = int(self._vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + height = int(self._vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) return width, height def get_frame(self): ret, frame = self._vid_cap.read() - return frame.tobytes() if ret else None + if ret: + return frame.tobytes() + else: + if self._loop_count > 0: + self._vid_cap.set(cv2.CAP_PROP_POS_FRAMES, 0) + self._loop_count -= 1 + ret, frame = self._vid_cap.read() + if ret: + return frame.tobytes() + return None def close(self): self._vid_cap.release() - def _log_options(args): heading = "Options for {}".format(os.path.basename(__file__)) banner = "="*len(heading) @@ -84,6 +77,12 @@ def _log_options(args): logging.info("{} == {}".format(arg, getattr(args, arg))) logging.info(banner) +def remove_empty_lists(d): + if not isinstance(d, (dict, list)): + return d + if isinstance(d, list): + return [v for v in (remove_empty_lists(v) for v in d) if v or v == 0] + return {k: v for k, v in ((k, remove_empty_lists(v)) for k, v in d.items()) if v or v == 0} def print_result(response, output): logging.info("Inference result {}".format(response.ack_sequence_number)) @@ -96,7 +95,10 @@ def print_result(response, output): atrributes.append(attribute_string) logging.info("- {} ({:.2f}) [{:.2f}, {:.2f}, {:.2f}, {:.2f}] {}"\ .format(tag.value, tag.confidence, box.l, box.t, box.w, box.h, atrributes)) - response_dict = MessageToDict(response.media_sample) + # default value field is used to avoid not including values set to 0, but it also causes empty lists to be included + # empty and none values are filtered out in response_dict + returned_dict = MessageToDict(response.media_sample, including_default_value_fields=True) + response_dict = remove_empty_lists(returned_dict) if response_dict.get("inferences"): for inference in response_dict["inferences"]: inference["type"] = inference["type"].lower() @@ -112,14 +114,7 @@ def main(): msp = MediaStreamProcessor(args.grpc_server_address, args.use_shared_memory) - _, extension = os.path.splitext(args.sample_file) - if extension in ['.png', '.jpg']: - frame_source = ImageSource(args.sample_file, args.loop_count) - elif extension in ['.mp4']: - frame_source = VideoSource(args.sample_file) - else: - print("{}: unsupported file type".format(args.sample_file)) - sys.exit(1) + frame_source = VideoSource(args.sample_file, args.loop_count) width, height = frame_source.dimensions() print("{} {}".format(width, height)) diff --git a/samples/lva_ai_extension/client/arguments.py b/samples/lva_ai_extension/client/arguments.py index ec0e46b..2de98da 100644 --- a/samples/lva_ai_extension/client/arguments.py +++ b/samples/lva_ai_extension/client/arguments.py @@ -43,9 +43,9 @@ def parse_args(args=None, program_name="AI Extension Sample Client"): default="/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/sample01.png") parser.add_argument('-l', metavar=('loop_count'), dest="loop_count", - help='How many times to send sample video frame.', + help='How many times to loop the source after it finishes.', type=int, - default=1) + default=0) parser.add_argument('-m', action='store_const', dest='use_shared_memory', const=True, diff --git a/samples/lva_ai_extension/tests/Dockerfile b/samples/lva_ai_extension/tests/Dockerfile index de53606..d7ef7fb 100644 --- a/samples/lva_ai_extension/tests/Dockerfile +++ b/samples/lva_ai_extension/tests/Dockerfile @@ -23,6 +23,7 @@ ENV PIPELINE_NAME 'object_detection' ENV PIPELINE_VERSION 'person_vehicle_bike_detection' RUN wget -O classroom.mp4 https://github.com/intel-iot-devkit/sample-videos/blob/master/classroom.mp4?raw=true +RUN wget -O person-bicycle-car-detection.mp4 https://github.com/intel-iot-devkit/sample-videos/blob/master/person-bicycle-car-detection.mp4?raw=true ARG USER=vaserving diff --git a/samples/lva_ai_extension/tests/test_client_server.py b/samples/lva_ai_extension/tests/test_client_server.py index 200031c..95931df 100644 --- a/samples/lva_ai_extension/tests/test_client_server.py +++ b/samples/lva_ai_extension/tests/test_client_server.py @@ -8,33 +8,44 @@ import time import os -def test_lva_client_server(): - port = 5001 - loops = 10 - sleep_period = 0.25 - server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", - "-p", str(port)] - client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", - "-s", "127.0.0.1:" + str(port), "-l", str(loops), - "-f", "/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/sample01.png"] - print(' '.join(server_args)) - server_process = subprocess.Popen(server_args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, - bufsize=1, universal_newlines=True) - time.sleep(sleep_period) - print(' '.join(client_args)) - client_process = subprocess.Popen(client_args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, - bufsize=1, universal_newlines=True) - client_process.poll() - elapsed_time = 0 - while client_process.returncode is None and elapsed_time < 5: - #print(client_process.stdout.readline()) +class TestLvaClientServer: + def teardown_method(self, test_method): + if self.server_process is not None: + self.server_process.kill() + + def run_server(self, sleep_period = 0.25, port = 5001): + server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", "-p", str(port)] + print(' '.join(server_args)) + self.server_process = subprocess.Popen(server_args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, + bufsize=1, universal_newlines=True) time.sleep(sleep_period) - elapsed_time += sleep_period + + def run_client(self, source, sleep_period = 0.25, port = 5001, output_location = None, shared_memory = True, timeout = 300): + client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", + "-s", "127.0.0.1:" + str(port), + "-f", source] + if shared_memory: + client_args.append("-m") + if output_location is not None: + client_args.append("-o") + client_args.append(output_location) + print(' '.join(client_args)) + client_process = subprocess.Popen(client_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) client_process.poll() - assert client_process.returncode is not None - assert client_process.returncode == 0 - print("Elapsed time = {}s".format(elapsed_time)) - server_process.kill() + elapsed_time = 0 + while client_process.returncode is None and elapsed_time < timeout: + time.sleep(sleep_period) + elapsed_time += sleep_period + client_process.poll() + return client_process.returncode + + def test_lva_client_server(self, sleep_period=0.25, port=5001): + self.run_server(sleep_period=sleep_period, port=port) + + returnCode = self.run_client(source="/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/sample01.png", + sleep_period=sleep_period, + port=port, + shared_memory=False) -if __name__ == "__main__": - test_lva_client_server() + assert returnCode is not None + assert returnCode == 0 diff --git a/samples/lva_ai_extension/tests/test_missing_file.py b/samples/lva_ai_extension/tests/test_missing_file.py new file mode 100644 index 0000000..3602f52 --- /dev/null +++ b/samples/lva_ai_extension/tests/test_missing_file.py @@ -0,0 +1,39 @@ +''' +* Copyright (C) 2019-2020 Intel Corporation. +* +* SPDX-License-Identifier: MIT License +''' + +import subprocess +import time +import os + +class TestLvaValidate: + + def run_client(self, source, sleep_period = 0.25, port = 5001, output_location = None, shared_memory = True, timeout = 300): + client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", + "-s", "127.0.0.1:" + str(port), + "-f", source] + if shared_memory: + client_args.append("-m") + if output_location is not None: + client_args.append("-o") + client_args.append(output_location) + print(' '.join(client_args)) + client_process = subprocess.Popen(client_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + client_process.poll() + elapsed_time = 0 + while client_process.returncode is None and elapsed_time < timeout: + time.sleep(sleep_period) + elapsed_time += sleep_period + client_process.poll() + return client_process.returncode + + def test_lva_missing_file(self, sleep_period=0.25, port=5001): + returnCode = self.run_client(source="/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/nonexistantimage.png", + sleep_period=sleep_period, + port=port, + shared_memory=False) + + assert returnCode is not None + assert returnCode == 1 diff --git a/samples/lva_ai_extension/tests/test_multiple_clients.py b/samples/lva_ai_extension/tests/test_multiple_clients.py index 4a1c83e..53bdd8a 100644 --- a/samples/lva_ai_extension/tests/test_multiple_clients.py +++ b/samples/lva_ai_extension/tests/test_multiple_clients.py @@ -8,36 +8,35 @@ import time import os -def test_multiple_clients(port=5001, loops=10, max_running_pipelines=10): - sleep_period = 0.25 - server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", - "-p", str(port), "--max-running-pipelines", str(max_running_pipelines)] - client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", - "-s", "127.0.0.1:" + str(port), "-l", str(loops), - "-f", "/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/sample01.png"] - print(' '.join(server_args)) - server_process = subprocess.Popen(server_args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, - bufsize=1, universal_newlines=True) - time.sleep(sleep_period) - print(' '.join(client_args)) - client_process_list = [] - for _ in range(max_running_pipelines): - client_process = subprocess.Popen(client_args, stdout=subprocess.PIPE, - stderr=subprocess.PIPE, bufsize=1, - universal_newlines=True) - client_process_list.append(client_process) - print(client_process_list) - client_process_list[-1].poll() - elapsed_time = 0 - while client_process_list[-1].returncode is None and elapsed_time < 60: - #print(client_process.stdout.readline()) +class TestLvaMultipleClients: + def teardown_method(self, test_method): + if self.server_process is not None: + self.server_process.kill() + + def test_multiple_clients(self, sleep_period=0.25, port=5001, max_running_pipelines=10): + server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", "-p", str(port)] + print(' '.join(server_args)) + self.server_process = subprocess.Popen(server_args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, + bufsize=1, universal_newlines=True) time.sleep(sleep_period) - elapsed_time += sleep_period - client_process_list[-1].poll() - assert client_process_list[-1].returncode is not None - assert client_process_list[-1].returncode == 0 - print("Elapsed time = {}s".format(elapsed_time)) - server_process.kill() -if __name__ == "__main__": - test_multiple_clients() + client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", + "-s", "127.0.0.1:" + str(port), + "-f", "/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/sample01.png"] + print(' '.join(client_args)) + client_process_list = [] + for _ in range(max_running_pipelines): + client_process = subprocess.Popen(client_args, stdout=subprocess.PIPE, + stderr=subprocess.PIPE, bufsize=1, + universal_newlines=True) + client_process_list.append(client_process) + print(client_process_list) + client_process_list[-1].poll() + elapsed_time = 0 + while client_process_list[-1].returncode is None and elapsed_time < 60: + time.sleep(sleep_period) + elapsed_time += sleep_period + client_process_list[-1].poll() + assert client_process_list[-1].returncode is not None + assert client_process_list[-1].returncode == 0 + print("Elapsed time = {}s".format(elapsed_time)) diff --git a/samples/lva_ai_extension/tests/test_object_pipelines.py b/samples/lva_ai_extension/tests/test_object_pipelines.py new file mode 100644 index 0000000..0ab6e4b --- /dev/null +++ b/samples/lva_ai_extension/tests/test_object_pipelines.py @@ -0,0 +1,65 @@ +import subprocess +import time +import os +import json +import tempfile +import pytest + +from jsonschema import validate + +class TestLvaObjectPipelines: + def teardown_method(self, test_method): + if self.server_process is not None: + self.server_process.kill() + + def run_client(self, source, sleep_period = 0.25, port = 5001, output_location = None, shared_memory = True, timeout = 300): + client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", + "-s", "127.0.0.1:" + str(port), + "-f", source] + if shared_memory: + client_args.append("-m") + if output_location is not None: + client_args.append("-o") + client_args.append(output_location) + print(' '.join(client_args)) + client_process = subprocess.Popen(client_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + client_process.poll() + elapsed_time = 0 + while client_process.returncode is None and elapsed_time < timeout: + time.sleep(sleep_period) + elapsed_time += sleep_period + client_process.poll() + assert client_process.returncode is not None + assert client_process.returncode == 0 + + def validate_output(self, output_location): + json_schema = None + json_schema_file = os.path.join(os.path.dirname(__file__), 'common/Extension_Data_Schema.json') + with open(json_schema_file, "r") as read_file: + json_schema = json.load(read_file) + + #Read each inference result and compare against the schema + with open(output_location, "r") as file: + for line in file: + if line and line != '': + validate(instance=json.loads(line),schema=json_schema) + + @pytest.mark.parametrize("pipeline_name,pipeline_version", [("object_detection", "person_vehicle_bike_detection"), ("object_classification", "vehicle_attributes_recognition"), ("object_tracking", "person_vehicle_bike_tracking")]) + def test_lva_object_pipeline(self, pipeline_name, pipeline_version, sleep_period=0.25, port=5001): + server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", "-p", str(port), + "--pipeline-name", "object_detection", "--pipeline-version", "person_vehicle_bike_detection"] + print(' '.join(server_args)) + self.server_process = subprocess.Popen(server_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + time.sleep(sleep_period) + + #Create temporary directory for saving output + workdir_path = tempfile.TemporaryDirectory() + output_file = "output.jsonl" + output_location = os.path.join(workdir_path.name, output_file) + + self.run_client(source="/home/video-analytics-serving/person-bicycle-car-detection.mp4", + sleep_period=sleep_period, + port=port, + output_location=output_location) + self.validate_output(output_location) + diff --git a/samples/lva_ai_extension/tests/test_parameters.py b/samples/lva_ai_extension/tests/test_parameters.py index 1903e90..6ab8d69 100644 --- a/samples/lva_ai_extension/tests/test_parameters.py +++ b/samples/lva_ai_extension/tests/test_parameters.py @@ -9,44 +9,41 @@ import os import tempfile -def parameter_run_client_server(port, loops, sleep_period, parameters): - server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", - "-p", str(port), "--parameters", parameters] - client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", - "-s", "127.0.0.1:" + str(port), "-l", str(loops), - "-f", "/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/sample01.png"] - print(' '.join(server_args)) - server_process = subprocess.Popen(server_args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, - bufsize=1, universal_newlines=True) - time.sleep(sleep_period) - print(' '.join(client_args)) - client_process = subprocess.Popen(client_args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, - bufsize=1, universal_newlines=True) - client_process.poll() - elapsed_time = 0 - while client_process.returncode is None and elapsed_time < 5: - #print(client_process.stdout.readline()) +class TestLvaParameters: + def teardown_method(self, test_method): + if self.server_process is not None: + self.server_process.kill() + + def parameter_run_client_server(self, sleep_period, port, parameters): + server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", "-p", str(port)] + print(' '.join(server_args)) + self.server_process = subprocess.Popen(server_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) time.sleep(sleep_period) - elapsed_time += sleep_period - client_process.poll() - assert client_process.returncode is not None - assert client_process.returncode == 0 - print("Elapsed time = {}s".format(elapsed_time)) - server_process.kill() -def test_lva_parameter_string_argument(port=5001, loops=10, sleep_period=0.25, - parameter_string="{\"device\":\"CPU\"}"): - parameter_run_client_server(port, loops, sleep_period, parameter_string) + client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", + "-s", "127.0.0.1:" + str(port), + "-f", "/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/sample01.png"] + print(' '.join(client_args)) + client_process = subprocess.Popen(client_args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, + bufsize=1, universal_newlines=True) + client_process.poll() + elapsed_time = 0 + while client_process.returncode is None and elapsed_time < 5: + time.sleep(sleep_period) + elapsed_time += sleep_period + client_process.poll() + assert client_process.returncode is not None + assert client_process.returncode == 0 -def test_lva_parameter_file_argument(port=5001, loops=10, sleep_period=0.25): - #Create temporary parameter file - workdir_path = tempfile.TemporaryDirectory() - parameter_path = os.path.join(workdir_path.name, "parameters.json") - with open(parameter_path, "w") as parameter_file: - parameter_file.write("{\"device\":\"CPU\"}") + def test_lva_parameter_string_argument(self, sleep_period=0.25, port=5001, + parameter_string="{\"device\":\"CPU\"}"): + self.parameter_run_client_server(sleep_period, port, parameter_string) - parameter_run_client_server(port, loops, sleep_period, parameter_path) + def test_lva_parameter_file_argument(self, sleep_period=0.25, port=5001): + #Create temporary parameter file + workdir_path = tempfile.TemporaryDirectory() + parameter_path = os.path.join(workdir_path.name, "parameters.json") + with open(parameter_path, "w") as parameter_file: + parameter_file.write("{\"device\":\"CPU\"}") -if __name__ == "__main__": - test_lva_parameter_string_argument() - test_lva_parameter_file_argument() + self.parameter_run_client_server(sleep_period, port, parameter_path) diff --git a/samples/lva_ai_extension/tests/test_validate_schema.py b/samples/lva_ai_extension/tests/test_validate_schema.py index 3c44702..b288ca9 100644 --- a/samples/lva_ai_extension/tests/test_validate_schema.py +++ b/samples/lva_ai_extension/tests/test_validate_schema.py @@ -6,47 +6,58 @@ from jsonschema import validate -def test_lva_validate_schema(port=5001, sleep_period=0.25): - #Create temporary parameter file - workdir_path = tempfile.TemporaryDirectory() - output_file = "output.json" - output_location = os.path.join(workdir_path.name, output_file) - - #Run server client to generate a results file - server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", "-p", str(port)] - client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", - "-s", "127.0.0.1:" + str(port), "-l", str(1), - "-f", "/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/sample01.png", - "-o", output_location] - print(' '.join(server_args)) - server_process = subprocess.Popen(server_args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, - bufsize=1, universal_newlines=True) - time.sleep(sleep_period) - print(' '.join(client_args)) - client_process = subprocess.Popen(client_args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, - bufsize=1, universal_newlines=True) - client_process.poll() - elapsed_time = 0 - while client_process.returncode is None and elapsed_time < 5: - time.sleep(sleep_period) - elapsed_time += sleep_period +class TestLvaValidate: + def teardown_method(self, test_method): + if self.server_process is not None: + self.server_process.kill() + + def run_client(self, source, sleep_period = 0.25, port = 5001, output_location = None, shared_memory = True, timeout = 300): + client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", + "-s", "127.0.0.1:" + str(port), + "-f", source] + if shared_memory: + client_args.append("-m") + if output_location is not None: + client_args.append("-o") + client_args.append(output_location) + print(' '.join(client_args)) + client_process = subprocess.Popen(client_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) client_process.poll() - assert client_process.returncode is not None - assert client_process.returncode == 0 - - #Validate the output file against the schema - - json_data = None - with open(output_location, "r") as read_file: - json_data = json.load(read_file) - - json_schema = None - json_schema_file = os.path.join(os.path.dirname(__file__), 'common/Extension_Data_Schema.json') - with open(json_schema_file, "r") as read_file: - json_schema = json.load(read_file) - validate(instance=json_data, schema=json_schema) - - server_process.kill() + elapsed_time = 0 + while client_process.returncode is None and elapsed_time < timeout: + time.sleep(sleep_period) + elapsed_time += sleep_period + client_process.poll() + assert client_process.returncode is not None + assert client_process.returncode == 0 + + def validate_output(self, output_location): + json_schema = None + json_schema_file = os.path.join(os.path.dirname(__file__), 'common/Extension_Data_Schema.json') + with open(json_schema_file, "r") as read_file: + json_schema = json.load(read_file) + + #Read each inference result and compare against the schema + with open(output_location, "r") as file: + for line in file: + if line and line != '': + validate(instance=json.loads(line),schema=json_schema) + + def test_lva_validate_schema(self, sleep_period=0.25, port=5001): + server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", "-p", str(port)] + print(' '.join(server_args)) + self.server_process = subprocess.Popen(server_args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, + bufsize=1, universal_newlines=True) + time.sleep(sleep_period) -if __name__ == "__main__": - test_lva_validate_schema() + #Create temporary parameter file + workdir_path = tempfile.TemporaryDirectory() + output_file = "output.jsonl" + output_location = os.path.join(workdir_path.name, output_file) + + self.run_client(source="/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/sample01.png", + sleep_period=sleep_period, + port=port, + output_location=output_location, + timeout=5) + self.validate_output(output_location) diff --git a/samples/lva_ai_extension/tests/test_video.py b/samples/lva_ai_extension/tests/test_video.py index 3097750..8f689dd 100644 --- a/samples/lva_ai_extension/tests/test_video.py +++ b/samples/lva_ai_extension/tests/test_video.py @@ -11,49 +11,70 @@ import json import tempfile +from jsonschema import validate + class TestLvaVideo: - def setup_method(self, test_method): - server_args = [ "python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", "-p", str(5001)] + def teardown_method(self, test_method): + if self.server_process is not None: + self.server_process.kill() + + def run_client(self, source, sleep_period = 0.25, port = 5001, output_location = None, shared_memory = True, timeout = 300): + client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", + "-s", "127.0.0.1:" + str(port), + "-f", source] + if shared_memory: + client_args.append("-m") + if output_location is not None: + client_args.append("-o") + client_args.append(output_location) + print(' '.join(client_args)) + client_process = subprocess.Popen(client_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + client_process.poll() + elapsed_time = 0 + while client_process.returncode is None and elapsed_time < timeout: + time.sleep(sleep_period) + elapsed_time += sleep_period + client_process.poll() + assert client_process.returncode is not None + assert client_process.returncode == 0 + + def validate_output(self, output_location): + json_schema = None + json_schema_file = os.path.join(os.path.dirname(__file__), 'common/Extension_Data_Schema.json') + with open(json_schema_file, "r") as read_file: + json_schema = json.load(read_file) + + #Read each inference result and compare against the schema + with open(output_location, "r") as file: + for line in file: + if line and line != '': + validate(instance=json.loads(line),schema=json_schema) + + def test_lva_video_consistent_results(self, sleep_period=0.25, port=5001): + server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", "-p", str(port)] print(' '.join(server_args)) self.server_process = subprocess.Popen(server_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) - time.sleep(0.25) - - def teardown_method(self, test_method): - self.server_process.kill() + time.sleep(sleep_period) - def test_lva_video(self, sleep_period=0.25): #Create temporary parameter file workdir_path = tempfile.TemporaryDirectory() - output_file1 = "output1.jsonl" + output_file = "output.jsonl" + output_location1 = os.path.join(workdir_path.name, output_file) output_file2 = "output2.jsonl" - output_location1 = os.path.join(workdir_path.name, output_file1) output_location2 = os.path.join(workdir_path.name, output_file2) - client1_args = [ "python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", "-s", "127.0.0.1:" + str(5001), "-f", "/home/video-analytics-serving/classroom.mp4", "-m", "-o", output_location1] - client2_args = [ "python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", "-s", "127.0.0.1:" + str(5001), "-f", "/home/video-analytics-serving/classroom.mp4", "-m", "-o", output_location2] - print(' '.join(client1_args)) - - client_process1 = subprocess.Popen(client1_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) - client_process1.poll() - elapsed_time = 0 - while client_process1.returncode is None and elapsed_time < 300: - time.sleep(sleep_period) - elapsed_time += sleep_period - client_process1.poll() - assert client_process1.returncode is not None - assert client_process1.returncode == 0 - - client_process2 = subprocess.Popen(client2_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) - client_process2.poll() - elapsed_time = 0 - while client_process2.returncode is None and elapsed_time < 300: - time.sleep(sleep_period) - elapsed_time += sleep_period - client_process2.poll() - assert client_process2.returncode is not None - assert client_process2.returncode == 0 + self.run_client(source="/home/video-analytics-serving/person-bicycle-car-detection.mp4", + sleep_period=sleep_period, + port=port, + output_location=output_location1) + self.run_client(source="/home/video-analytics-serving/person-bicycle-car-detection.mp4", + sleep_period=sleep_period, + port=port, + output_location=output_location2) + self.validate_output(output_location1) + self.validate_output(output_location2) - #Read the first inference result from the first client + #Compare the two outputs to see if they match json_data1 = None json_data2 = None with open(output_location1, "r") as file1: @@ -72,4 +93,3 @@ def test_lva_video(self, sleep_period=0.25): assert False if file1_s == '' and file2_s != '': assert False - From 0f387db20487fd4d0838a0184fb553f7e83cd52b Mon Sep 17 00:00:00 2001 From: "Mosby, Tobias" Date: Tue, 15 Dec 2020 11:16:49 -0800 Subject: [PATCH 09/50] Set the home environment correctly to /home/video-analytics-serving. --- docker/build.sh | 1 + 1 file changed, 1 insertion(+) diff --git a/docker/build.sh b/docker/build.sh index ea9a8b5..6d057ca 100755 --- a/docker/build.sh +++ b/docker/build.sh @@ -410,6 +410,7 @@ done if [ ! -z "$ENVIRONMENT_FILE_LIST" ]; then cat $ENVIRONMENT_FILE_LIST | grep -E '=' | tr '\n' ' ' | tr '\r' ' ' > $DOCKERFILE_DIR/final.env + echo " HOME=/home/video-analytics-serving " >> $DOCKERFILE_DIR/final.env echo "ENV " | cat - $DOCKERFILE_DIR/final.env | tr -d '\n' >> $DOCKERFILE_DIR/Dockerfile.env fi From 6a61571a638c0fdf2761ddd6a649e4f0497ca9b8 Mon Sep 17 00:00:00 2001 From: "Thakkalapelli, Thanaji Rao" Date: Tue, 15 Dec 2020 12:27:27 -0800 Subject: [PATCH 10/50] Tests Run Script reference --- tests/README.md | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) create mode 100644 tests/README.md diff --git a/tests/README.md b/tests/README.md new file mode 100644 index 0000000..27dacf1 --- /dev/null +++ b/tests/README.md @@ -0,0 +1,24 @@ +# Video Analytics Serving Tests and Scans + +## Tests Run Script Reference +The `run.sh` can be used to run tests or scans with option to select only one at a time, default selected as --pytest-gstreamer. It uses [docker/run.sh](../docker/run.sh) to pass common options to the underlying docker run command [Reference Doc](../docs/run_script_reference.md). +``` +$ tests/run.sh --help +usage: run.sh + [ --pytest-gstreamer : Run gstreamer tests ] + [ --pytest-ffmpeg: Run ffmpeg tests ] + [ --pylint : Run pylint scan ] + [ --pybandit: Run pybandit scan ] + [ --clamav : Run antivirus scan ] +``` + +### Architecture +* RESULTS_DIR : Results directory is created and volume mounted according to test or scan selected, Directory path is Set as environment varible used by entrypoint scripts to save results in the directory. +* ENTRYPOINT: selected in the script according to test or scan selected. +* Entrypoint Args can be changed through environment variable defined in respective entrypoint scripts, or through --entrypoint-args option. + +#### Entrypoint Scripts +Entrypoint directory used to maintain Docker entrypoint scripts to run tests and scans. Each entrypoint script contains below mentioned details if supported. +* Support to read RESULTS_DIR environment variable to save results in that path. +* Other environment args required and that can be updated from run.sh. +* Command to run respective tests/scans. \ No newline at end of file From f859ecb7f5523d687932cdb7e7bb3daf401246d3 Mon Sep 17 00:00:00 2001 From: "Lawrens, Liz" Date: Tue, 15 Dec 2020 16:57:42 -0800 Subject: [PATCH 11/50] Automatically set model-proc if exists but not specified and handle models without model-procs. --- tools/model_downloader/downloader.py | 4 ---- vaserving/ffmpeg_pipeline.py | 14 ++++++++++++++ vaserving/gstreamer_pipeline.py | 13 +++++++++++++ vaserving/model_manager.py | 9 +++++++-- 4 files changed, 34 insertions(+), 6 deletions(-) diff --git a/tools/model_downloader/downloader.py b/tools/model_downloader/downloader.py index 7750c4e..37e2cbc 100644 --- a/tools/model_downloader/downloader.py +++ b/tools/model_downloader/downloader.py @@ -40,10 +40,6 @@ def download_model_proc(target_model, model): print("Downloaded {0} model-proc file from gst-video-analytics repo".format(model)) else: print("Warning, model-proc not found in gst-video-analytics repo.") - print("Creating empty json file for {0} to allow model to load in "\ - "VA-Serving".format(model)) - print("Do not specify model-proc in pipeline that utilizes this model") - Path('{0}/{1}.json'.format(temp_dir.name, model)).touch() model_proc = os.path.abspath('{0}/{1}.json'.format(temp_dir.name, model)) shutil.move(model_proc, os.path.join(target_model, '{}.json'.format(model))) diff --git a/vaserving/ffmpeg_pipeline.py b/vaserving/ffmpeg_pipeline.py index 79b521a..99b4fad 100644 --- a/vaserving/ffmpeg_pipeline.py +++ b/vaserving/ffmpeg_pipeline.py @@ -265,6 +265,19 @@ def _set_default_models(self): self._logger.debug("Setting model to {} for filter {}".format( _filter.properties["model"], _filter_key)) + def _set_model_proc(self): + for video_filters in self._video_filters: + for _filter_key, _filter in video_filters.filters.items(): + if ((_filter_key[0] in FFmpegPipeline.GVA_INFERENCE_FILTER_TYPES)): + if "model_proc" not in _filter.properties: + model_proc = None + if _filter.properties["model"] in self.model_manager.model_procs: + model_proc = self.model_manager.model_procs[_filter.properties["model"]] + if model_proc is not None: + _filter.properties["model_proc"] = model_proc + self._logger.debug("Setting model proc to {} for filter {}".format( + model_proc, _filter_key)) + def _unescape_args(self, args): for i, arg in enumerate(args): args[i] = arg.replace( @@ -652,6 +665,7 @@ def start(self): self._parse_ffmpeg_launch_string(self._ffmpeg_launch_string) self._set_properties() self._set_default_models() + self._set_model_proc() self._initialize_segment_recording() self._generate_ffmpeg_launch_args() self._unescape_source() diff --git a/vaserving/gstreamer_pipeline.py b/vaserving/gstreamer_pipeline.py index a5a7926..a3c2492 100755 --- a/vaserving/gstreamer_pipeline.py +++ b/vaserving/gstreamer_pipeline.py @@ -255,6 +255,18 @@ def _get_elements_by_type(pipeline, type_strings): return [element for element in pipeline.iterate_elements() if element.__gtype__.name in type_strings] + def _set_model_proc(self): + gva_elements = [element for element in self.pipeline.iterate_elements() if ( + element.__gtype__.name in self.GVA_INFERENCE_ELEMENT_TYPES)] + for element in gva_elements: + if element.get_property("model-proc") is None: + proc = None + if element.get_property("model") in self.model_manager.model_procs: + proc = self.model_manager.model_procs[element.get_property("model")] + if proc is not None: + logger.debug("Setting model proc to {} for element {}".format( + proc, element.get_name())) + element.set_property("model-proc", proc) @staticmethod def validate_config(config): @@ -369,6 +381,7 @@ def start(self): self._set_properties() self._set_bus_messages_flag() self._set_default_models() + self._set_model_proc() self._cache_inference_elements() src = self._get_any_source() diff --git a/vaserving/model_manager.py b/vaserving/model_manager.py index d787ad8..6f33fc6 100644 --- a/vaserving/model_manager.py +++ b/vaserving/model_manager.py @@ -48,6 +48,7 @@ def __init__(self, model_dir, network_preference=None, ignore_init_errors=False) self.model_dir = model_dir self.network_preference = network_preference self.models = defaultdict(dict) + self.model_procs = {} self.network_preference = {'CPU': ["FP32"], 'HDDL': ["FP16"], 'GPU': ["FP16"], @@ -160,14 +161,18 @@ def load_models(self, model_dir, network_preference): if (os.path.isdir(version_path)): version = self.convert_version(version) proc = self._get_model_proc(version_path) + if proc is None: + self.logger.info("Model {model}/{ver} is missing Model-Proc".format( + model=model_name, ver=version)) networks = self._get_model_networks( version_path) - if (proc) and (networks): + if (networks): for key in networks: networks[key].update({"proc": proc, "version": version, "type": "IntelDLDT", "description": model_name}) + self.model_procs[networks.get(key).get("network")] = proc models[model_name][version] = ModelsDict(model_name, version, @@ -184,7 +189,7 @@ def load_models(self, model_dir, network_preference): "type: {} from {}".format( model_name, version, "IntelDLDT", network_paths)) else: - raise Exception("{model}/{ver} is missing Model-Proc or Network" + raise Exception("{model}/{ver} is missing Network" .format(model=model_name, ver=version)) except Exception as error: From bbae567f93c8007570510978e5edb8aa9be02c5f Mon Sep 17 00:00:00 2001 From: "Khoshnevissan, Farid" Date: Tue, 15 Dec 2020 17:15:07 -0800 Subject: [PATCH 12/50] Leverage VA Serving argument parsing in LVA service init --- samples/lva_ai_extension/README.md | 30 ++++++++++---- samples/lva_ai_extension/client/__main__.py | 12 +++--- .../deployment.gpu.grpc.template.json | 4 +- .../deployment.hddl.grpc.template.json | 2 +- .../deployment.movidius.grpc.template.json | 2 +- samples/lva_ai_extension/docker/run_server.sh | 14 +++++-- .../pipeline.json | 6 ++- .../pipeline.json | 6 ++- .../pipeline.json | 6 ++- samples/lva_ai_extension/server/__main__.py | 36 +++++++++++++--- .../server/media_graph_extension.py | 41 ++++++++----------- .../lva_ai_extension/tests/test_parameters.py | 3 +- vaserving/arguments.py | 9 +++- 13 files changed, 110 insertions(+), 61 deletions(-) diff --git a/samples/lva_ai_extension/README.md b/samples/lva_ai_extension/README.md index e49d212..4e9b34d 100644 --- a/samples/lva_ai_extension/README.md +++ b/samples/lva_ai_extension/README.md @@ -76,13 +76,13 @@ $ ./docker/run_client.sh The module can be configured using command line options or environment variables (command line options take precedence). -| Setting | Command line option | Environment variable | Default value | -|---------------------|---------------------|----------------------|------------------| -| gRPC port | -p | PORT | 5001 | -| Pipeline name | --pipeline-name | PIPELINE_NAME | object_detection | -| Pipeline version | --pipeline-version | PIPELINE_VERSION | person_vehicle_bike_detection | -| Pipeline parameters | --parameters | PARAMETERS | {} | -| Use debug pipeline | --debug | DEBUG_PIPELINE | | +| Setting | Command line option | Environment variable | Default value | +|---------------------|-----------------------|----------------------|------------------| +| gRPC port | -p | PORT | 5001 | +| Pipeline name | --pipeline-name | PIPELINE_NAME | object_detection | +| Pipeline version | --pipeline-version | PIPELINE_VERSION | person_vehicle_bike_detection | +| Pipeline parameters | --pipeline-parameters | PIPELINE_PARAMETERS | {} | +| Use debug pipeline | --debug | DEBUG_PIPELINE | | ## Video Analytics Pipelines @@ -104,6 +104,18 @@ You will also need to create a graph topology with gRPC extension and then creat # Additional Standalone Edge AI Extension Examples +### Specifying VAServing parameters for LVA Server + +The LVA Server application will filter command line arguments between the LVA layer and VAServing layer. +Command line arguments are first handled by run_server.sh; if not specifically handled by run_server.sh the argument +is passed into the LVA Server application. +Command line arguments that are not recognized by LVA Server are then passed to VAServing, if VAServing does not recognize +the arguments an error will be reported + +```bash +./docker/run_server.sh --log_level DEBUG +``` + ### Selecting and Configuring Pipelines Run with object classification pipeline specified on command line @@ -116,7 +128,7 @@ Run with classification pipeline with iGPU inference specified via environment v ``` $ export PIPELINE_NAME=object_classification $ export PIPELINE_VERSION=vehicle_attributes_recognition -$ export PARAMETERS={\"device\":\"GPU\"} +$ export PIPELINE_PARAMETERS={\"device\":\"GPU\"} $ ./docker/run_server.sh ``` @@ -144,7 +156,7 @@ Integrated Graphics. Run default pipeline with iGPU inference (be careful with escaping the JSON string) ```bash -$ ./docker/run_server.sh --parameters "{\"device\":\"GPU\"}" +$ ./docker/run_server.sh --pipeline-parameters "{\"device\":\"GPU\"}" ``` ### Logging diff --git a/samples/lva_ai_extension/client/__main__.py b/samples/lva_ai_extension/client/__main__.py index bb59582..5122970 100644 --- a/samples/lva_ai_extension/client/__main__.py +++ b/samples/lva_ai_extension/client/__main__.py @@ -77,12 +77,12 @@ def _log_options(args): logging.info("{} == {}".format(arg, getattr(args, arg))) logging.info(banner) -def remove_empty_lists(d): - if not isinstance(d, (dict, list)): - return d - if isinstance(d, list): - return [v for v in (remove_empty_lists(v) for v in d) if v or v == 0] - return {k: v for k, v in ((k, remove_empty_lists(v)) for k, v in d.items()) if v or v == 0} +def remove_empty_lists(dictionary): + if not isinstance(dictionary, (dict, list)): + return dictionary + if isinstance(dictionary, list): + return [v for v in (remove_empty_lists(v) for v in dictionary) if v or v == 0] + return {k: v for k, v in ((k, remove_empty_lists(v)) for k, v in dictionary.items()) if v or v == 0} def print_result(response, output): logging.info("Inference result {}".format(response.ack_sequence_number)) diff --git a/samples/lva_ai_extension/deployment/deployment.gpu.grpc.template.json b/samples/lva_ai_extension/deployment/deployment.gpu.grpc.template.json index 3e70a08..b5a049a 100755 --- a/samples/lva_ai_extension/deployment/deployment.gpu.grpc.template.json +++ b/samples/lva_ai_extension/deployment/deployment.gpu.grpc.template.json @@ -110,7 +110,7 @@ "Env":[ "PIPELINE_NAME=object_detection", "PIPELINE_VERSION=person_vehicle_bike_detection", - "PARAMETERS= \"{\"device\":\"GPU\"}\"" + "PIPELINE_PARAMETERS=\"{\"device\":\"GPU\"}\"" ], "HostConfig": { "Binds": [ @@ -179,4 +179,4 @@ } } } -} \ No newline at end of file +} diff --git a/samples/lva_ai_extension/deployment/deployment.hddl.grpc.template.json b/samples/lva_ai_extension/deployment/deployment.hddl.grpc.template.json index 14486f8..9dc8651 100755 --- a/samples/lva_ai_extension/deployment/deployment.hddl.grpc.template.json +++ b/samples/lva_ai_extension/deployment/deployment.hddl.grpc.template.json @@ -111,7 +111,7 @@ "Env":[ "PIPELINE_NAME=object_detection", "PIPELINE_VERSION=person_vehicle_bike_detection", - "PARAMETERS= {\"device\":\"HDDL\"}" + "PIPELINE_PARAMETERS= {\"device\":\"HDDL\"}" ], "HostConfig": { "Binds": [ diff --git a/samples/lva_ai_extension/deployment/deployment.movidius.grpc.template.json b/samples/lva_ai_extension/deployment/deployment.movidius.grpc.template.json index 0a6af89..5691cec 100755 --- a/samples/lva_ai_extension/deployment/deployment.movidius.grpc.template.json +++ b/samples/lva_ai_extension/deployment/deployment.movidius.grpc.template.json @@ -107,7 +107,7 @@ "Env":[ "PIPELINE_NAME=object_detection", "PIPELINE_VERSION=person_vehicle_bike_detection", - "PARAMETERS= {\"device\":\"MYRIAD\"}" + "PIPELINE_PARAMETERS= {\"device\":\"MYRIAD\"}" ], "HostConfig": { "Binds": [ diff --git a/samples/lva_ai_extension/docker/run_server.sh b/samples/lva_ai_extension/docker/run_server.sh index c750573..b86aba5 100755 --- a/samples/lva_ai_extension/docker/run_server.sh +++ b/samples/lva_ai_extension/docker/run_server.sh @@ -11,7 +11,7 @@ ENTRYPOINT_ARGS= #Get options passed into script function get_options { - while :; do + while (($#)); do case $1 in -h | -\? | --help) show_help @@ -25,7 +25,7 @@ function get_options { error "-p expects a value" fi ;; - --pipeline-name|--pipeline-version|--max-running-pipelines|--parameters) + --pipeline-name|--pipeline-version|--max-running-pipelines|--parameters|--pipeline-parameters) if [ "$2" ]; then ENTRYPOINT_ARGS+="--entrypoint-args $1 " ENTRYPOINT_ARGS+="--entrypoint-args $2 " @@ -41,7 +41,7 @@ function get_options { PIPELINES="--pipelines $LVA_DIR/pipelines " ;; *) - break + ENTRYPOINT_ARGS+="--entrypoint-args $1 " ;; esac @@ -56,6 +56,8 @@ function show_help { echo " [ --pipeline-version : Specify the pipeline version to use ] " echo " [ --debug : Use debug pipeline ] " echo " [ --max-running-pipelines : Specify the maximum number of concurrent pipelines, default is 10 ] " + echo " [ --parameters : Specify a json string or file for pipeline parameters *Deprecated* ] " + echo " [ --pipeline-parameters : Specify a json string or file for pipeline parameters ] " } function error { @@ -83,4 +85,8 @@ if [ ! -z "$PARAMETERS" ]; then ENV+="-e PARAMETERS=$PARAMETERS " fi -"$ROOT_DIR/docker/run.sh" --image $IMAGE -v /tmp:/tmp -v /dev/shm:/dev/shm -p $PORT:$PORT $ENTRYPOINT_ARGS $PIPELINES $ENV $@ +if [ ! -z "$PIPELINE_PARAMETERS" ]; then + ENV+="-e PIPELINE_PARAMETERS=$PIPELINE_PARAMETERS " +fi + +"$ROOT_DIR/docker/run.sh" --image $IMAGE -v /tmp:/tmp -v /dev/shm:/dev/shm -p $PORT:$PORT $ENTRYPOINT_ARGS $PIPELINES $ENV diff --git a/samples/lva_ai_extension/pipelines/object_classification/debug_vehicle_attributes_recognition/pipeline.json b/samples/lva_ai_extension/pipelines/object_classification/debug_vehicle_attributes_recognition/pipeline.json index cef8fed..048749c 100755 --- a/samples/lva_ai_extension/pipelines/object_classification/debug_vehicle_attributes_recognition/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_classification/debug_vehicle_attributes_recognition/pipeline.json @@ -146,11 +146,13 @@ }, "location": { "element": "filesink", - "type":"string" + "type":"string", + "default":"/tmp/frame_%07d.jpeg" }, "max-files": { "element": "filesink", - "type":"integer" + "type":"integer", + "default": 10 } } } diff --git a/samples/lva_ai_extension/pipelines/object_detection/debug_person_vehicle_bike_detection/pipeline.json b/samples/lva_ai_extension/pipelines/object_detection/debug_person_vehicle_bike_detection/pipeline.json index ec43f25..9da36b0 100755 --- a/samples/lva_ai_extension/pipelines/object_detection/debug_person_vehicle_bike_detection/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_detection/debug_person_vehicle_bike_detection/pipeline.json @@ -75,11 +75,13 @@ }, "location": { "element": "filesink", - "type":"string" + "type":"string", + "default":"/tmp/frame_%07d.jpeg" }, "max-files": { "element": "filesink", - "type":"integer" + "type":"integer", + "default": 10 } } } diff --git a/samples/lva_ai_extension/pipelines/object_tracking/debug_person_vehicle_bike_tracking/pipeline.json b/samples/lva_ai_extension/pipelines/object_tracking/debug_person_vehicle_bike_tracking/pipeline.json index de5a8f4..356cd61 100755 --- a/samples/lva_ai_extension/pipelines/object_tracking/debug_person_vehicle_bike_tracking/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_tracking/debug_person_vehicle_bike_tracking/pipeline.json @@ -167,11 +167,13 @@ }, "location": { "element": "filesink", - "type":"string" + "type":"string", + "default":"/tmp/frame_%07d.jpeg" }, "max-files": { "element": "filesink", - "type":"integer" + "type":"integer", + "default": 10 } } } diff --git a/samples/lva_ai_extension/server/__main__.py b/samples/lva_ai_extension/server/__main__.py index 7e9e50e..b9c13b6 100644 --- a/samples/lva_ai_extension/server/__main__.py +++ b/samples/lva_ai_extension/server/__main__.py @@ -31,10 +31,12 @@ import argparse import os import sys +import json from concurrent import futures import grpc import extension_pb2_grpc # pylint: disable=import-error from vaserving.vaserving import VAServing +from vaserving.common.utils.logging import get_logger from media_graph_extension import MediaGraphExtension @@ -68,26 +70,48 @@ def parse_args(args=None, program_name="VA Serving AI Extension"): type=int, default=int(os.getenv('MAX_RUNNING_PIPELINES', '10'))) parser.add_argument("--parameters", action="store", - dest="parameters", + dest="parameters_arg", type=str, default=os.getenv('PARAMETERS', '{}')) + parser.add_argument("--pipeline-parameters", action="store", + dest="pipeline_parameters_arg", + type=str, default=os.getenv('PIPELINE_PARAMETERS', '{}')) + if (isinstance(args, dict)): args = ["--{}={}".format(key, value) for key, value in args.items() if value] - return parser.parse_args(args) + return parser.parse_known_args(args) + +def append_default_server_args(va_serving_args, max_running_pipelines): + va_serving_args.append('--max_running_pipelines') + va_serving_args.append(str(max_running_pipelines)) + return va_serving_args if __name__ == "__main__": - args = parse_args() + args, va_serving_args = parse_args() + logger = get_logger("Main") try: - VAServing.start({'log_level': 'INFO', "ignore_init_errors":True, - 'max_running_pipelines': args.max_running_pipelines}) + server_args = append_default_server_args(va_serving_args, args.max_running_pipelines) + + try: + VAServing.start(server_args) + except Exception: + print("Exception encountered during VAServing start") + raise + + # For 0.4.1, if both parameters and pipeline_parameters are specified, pipeline_parameters takes precedence + if args.parameters_arg != '{}': + logger.warning("Warning, parameters argument is deprecated and will be removed in 0.5.") + logger.warning("If parameters and pipeline_parameters are defined pipeline_parameters takes precedence") + if args.pipeline_parameters_arg != '{}': + args.parameters_arg = args.pipeline_parameters_arg # create gRPC server and start running server = grpc.server(futures.ThreadPoolExecutor(max_workers=args.max_running_pipelines)) extension_pb2_grpc.add_MediaGraphExtensionServicer_to_server( - MediaGraphExtension(args.pipeline, args.version, args.debug, args.parameters), server) + MediaGraphExtension(args.pipeline, args.version, args.debug, args.parameters_arg), server) server.add_insecure_port(f'[::]:{args.port}') print("Starting Protocol Server Application on port", args.port) server.start() diff --git a/samples/lva_ai_extension/server/media_graph_extension.py b/samples/lva_ai_extension/server/media_graph_extension.py index 7f8f56b..5417d35 100644 --- a/samples/lva_ai_extension/server/media_graph_extension.py +++ b/samples/lva_ai_extension/server/media_graph_extension.py @@ -84,13 +84,13 @@ def __init__(self, media_stream_descriptor): raise class MediaGraphExtension(extension_pb2_grpc.MediaGraphExtensionServicer): - def __init__(self, pipeline, version, debug=False, parameters=None, input_queue_size=1): + def __init__(self, pipeline, version, debug=False, pipeline_parameter_arg=None, input_queue_size=1): self._pipeline = pipeline self._version = version self._input_queue_size = input_queue_size self._logger = get_logger("MediaGraphExtension") self._debug = debug - self._parameters = parameters + self._pipeline_parameter_arg = pipeline_parameter_arg def _generate_media_stream_message(self, gva_sample): message = json.loads(list(gva_sample.video_frame.messages())[0]) @@ -253,33 +253,28 @@ def ProcessMediaStream(self, requestIterator, context): if self._debug and (not self._version.startswith("debug")): self._version = "debug_" + self._version - parameters = {} - if self._parameters: - if os.path.isfile(self._parameters): - with open(self._parameters) as json_file: - try: - parameters = json.load(json_file) - except ValueError: - self._logger.error('Issue loading json parameters from file') - parameters = {} - else: - try: - parameters = json.loads(self._parameters) - except ValueError: - self._logger.error('Issue loading json parameters from string') - parameters = {} - + final_pipeline_parameters = {} if self._version.startswith("debug"): timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S') location = os.path.join(tempfile.gettempdir(), "vaserving", self._version, timestamp) os.makedirs(os.path.abspath(location)) - debug_parameters = {"location": os.path.join(location, "frame_%07d.jpeg"), - "max-files":10} - parameters.update(debug_parameters) + final_pipeline_parameters = {"location": os.path.join(location, "frame_%07d.jpeg")} + + try: + if self._pipeline_parameter_arg: + pipeline_parameters = {} + if os.path.isfile(self._pipeline_parameter_arg): + with open(self._pipeline_parameter_arg) as json_file: + pipeline_parameters = json.load(json_file) + else: + pipeline_parameters = json.loads(self._pipeline_parameter_arg) + final_pipeline_parameters.update(pipeline_parameters) + except ValueError: + self._logger.error('Issue loading json parameters') self._logger.info('Pipeline Name : {}'.format(self._pipeline)) self._logger.info('Pipeline Version : {}'.format(self._version)) - self._logger.info('Pipeline Parameters : {}'.format(parameters)) + self._logger.info('Pipeline Parameters : {}'.format(final_pipeline_parameters)) detect_input = Queue(maxsize=self._input_queue_size) detect_output = Queue() # Start object detection pipeline @@ -293,7 +288,7 @@ def ProcessMediaStream(self, requestIterator, context): "class": "GStreamerAppDestination", "output": detect_output, "mode": "frames"}, - parameters=parameters) + parameters=final_pipeline_parameters) # Process rest of the MediaStream message sequence for request in requestIterator: diff --git a/samples/lva_ai_extension/tests/test_parameters.py b/samples/lva_ai_extension/tests/test_parameters.py index 6ab8d69..260a9d2 100644 --- a/samples/lva_ai_extension/tests/test_parameters.py +++ b/samples/lva_ai_extension/tests/test_parameters.py @@ -15,7 +15,8 @@ def teardown_method(self, test_method): self.server_process.kill() def parameter_run_client_server(self, sleep_period, port, parameters): - server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", "-p", str(port)] + server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", "-p", str(port), + "--pipeline-parameters", parameters] print(' '.join(server_args)) self.server_process = subprocess.Popen(server_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) time.sleep(sleep_period) diff --git a/vaserving/arguments.py b/vaserving/arguments.py index e58b845..6e1a7c4 100644 --- a/vaserving/arguments.py +++ b/vaserving/arguments.py @@ -46,8 +46,13 @@ def parse_options(args=None): args = ["--{}={}".format(key, value) for key, value in args.items() if value] - result = parser.parse_args(args) - parse_network_preference(result) + try: + result = parser.parse_args(args) + parse_network_preference(result) + except Exception: + print("Unrecognized argument passed to VAServing") + parser.print_help() + raise return result From c67710430ad181b84660831d7f02c9ea324bb3b9 Mon Sep 17 00:00:00 2001 From: "Bruce, Henry" Date: Wed, 16 Dec 2020 16:09:09 -0800 Subject: [PATCH 13/50] Handle spaces in command line arguments --- docker/run.sh | 337 +++++++++--------- samples/lva_ai_extension/docker/run_client.sh | 109 +++--- samples/lva_ai_extension/docker/run_server.sh | 77 ++-- tests/run.sh | 90 +++-- tests/run_script_tests.sh | 17 + .../script_tests/spaces_in_test_run_arguments | 4 + 6 files changed, 318 insertions(+), 316 deletions(-) create mode 100755 tests/run_script_tests.sh create mode 100755 tests/script_tests/spaces_in_test_run_arguments diff --git a/docker/run.sh b/docker/run.sh index 9931e37..0eb25a8 100755 --- a/docker/run.sh +++ b/docker/run.sh @@ -5,7 +5,7 @@ # SPDX-License-Identifier: BSD-3-Clause # - +RUN_PREFIX= MODELS= PIPELINES= FRAMEWORK= @@ -28,167 +28,6 @@ SCRIPT_DIR=$(dirname "$(readlink -f "$0")") SOURCE_DIR=$(dirname $SCRIPT_DIR) ENVIRONMENT=$(env | cut -f1 -d= | grep -E '_(proxy)$' | sed 's/^/-e / ' | tr '\n' ' ') -get_options() { - while :; do - case $1 in - -h | -\? | --help) - show_help # Display a usage synopsis. - exit - ;; - --image) # Takes an option argument; ensure it has been specified. - if [ "$2" ]; then - IMAGE=$2 - shift - else - error 'ERROR: "--image" requires a non-empty option argument.' - fi - ;; - --models) - if [ "$2" ]; then - MODELS=$(realpath $2) - shift - else - error 'ERROR: "--models" requires a non-empty option argument.' - fi - ;; - --user) - if [ "$2" ]; then - USER="--user $2" - shift - else - error 'ERROR: "--models" requires a non-empty option argument.' - fi - ;; - --device) - if [ "$2" ]; then - DEVICES+="--device $2 " - shift - else - error 'ERROR: "--device" requires a non-empty option argument.' - fi - ;; - --privileged) - PRIVILEGED="--privileged " - ;; - --device-cgroup-rule) - if [ "$2" ]; then - DEVICE_CGROUP_RULE="--device-cgroup-rule=$2 " - shift - else - error 'ERROR: "--device-cgroup-rule" requires a non-empty option argument.' - fi - ;; - --pipelines) - if [ "$2" ]; then - PIPELINES=$(realpath $2) - shift - else - error 'ERROR: "--pipelines" requires a non-empty option argument.' - fi - ;; - --framework) - if [ "$2" ]; then - FRAMEWORK=$2 - shift - else - error 'ERROR: "--framework" requires a non-empty option argument.' - fi - ;; - -e) - if [ "$2" ]; then - ENVIRONMENT+="-e $2 " - shift - else - error 'ERROR: "-e" requires a non-empty option argument.' - fi - ;; - --entrypoint-args) - if [ "$2" ]; then - ENTRYPOINT_ARGS+="$2 " - shift - else - error 'ERROR: "--entrypoint-args" requires a non-empty option argument.' - fi - ;; - -p) - if [ "$2" ]; then - PORTS+="-p $2 " - shift - else - error 'ERROR: "-p" requires a non-empty option argument.' - fi - ;; - -v) - if [ "$2" ]; then - VOLUME_MOUNT+="-v $2 " - shift - else - error 'ERROR: "-v" requires a non-empty option argument.' - fi - ;; - --dev) - MODE=DEV - ;; - --name) - if [ "$2" ]; then - NAME=$2 - shift - else - error 'ERROR: "--name" requires a non-empty option argument.' - fi - ;; - --network) - if [ "$2" ]; then - NETWORK="--network $2" - shift - else - error 'ERROR: "--network" requires a non-empty option argument.' - fi - ;; - --entrypoint) - if [ "$2" ]; then - ENTRYPOINT="--entrypoint $2" - shift - else - error 'ERROR: "--entrypoint" requires a non-empty option argument.' - fi - ;; - --non-interactive) - unset INTERACTIVE - ;; - --) # End of all options. - shift - break - ;; - -?*) - printf 'WARN: Unknown option (ignored): %s\n' "$1" >&2 - ;; - *) # Default case: No more options, so break out of the loop. - break ;; - esac - - shift - done - - if [ -z "$FRAMEWORK" ]; then - FRAMEWORK="gstreamer" - elif [ $FRAMEWORK != 'gstreamer' ] && [ $FRAMEWORK != 'ffmpeg' ]; then - echo "Invalid framework" - show_help - fi - - if [ -z "$IMAGE" ]; then - IMAGE=DEFAULT_${FRAMEWORK^^}_IMAGE - IMAGE=${!IMAGE} - fi - - if [ -z "$NAME" ]; then - # Convert tag separator if exists - NAME=${IMAGE//[\:]/_} - fi - -} - show_options() { echo "" echo "Running Video Analytics Serving Image: '${IMAGE}'" @@ -202,17 +41,18 @@ show_options() { echo " Name: '${NAME}'" echo " Network: '${NETWORK}'" echo " Entrypoint: '${ENTRYPOINT}'" - echo " EntrypointArgs: '${ENTRYPOINT_ARGS}" + echo " EntrypointArgs: '${ENTRYPOINT_ARGS}'" echo " User: '${USER}'" echo " Devices: '${DEVICES}'" + echo " Device Group Rule: '${DEVICE_CGROUP_RULE}'" echo "" } show_help() { - echo "usage: run.sh" + echo "usage: run.sh" echo " [--image image]" echo " [--framework ffmpeg || gstreamer]" - echo " [--models path to models directory]" + echo " [--models path to models directory]" echo " [--pipelines path to pipelines directory]" echo " [-v additional volume mount to pass to docker run]" echo " [-e additional environment to pass to docker run]" @@ -222,7 +62,7 @@ show_help() { echo " [--user name of user to pass to docker run]" echo " [--name container name to pass to docker run]" echo " [--device device to pass to docker run]" - echo " [--dev run in developer mode]" + echo " [--dev run in developer mode]" exit 0 } @@ -231,7 +71,166 @@ error() { exit } -get_options "$@" +while [[ "$#" -gt 0 ]]; do + case $1 in + -h | -\? | --help) + show_help # Display a usage synopsis. + exit + ;; + --dry-run) + RUN_PREFIX=echo + ;; + --image) # Takes an option argument; ensure it has been specified. + if [ "$2" ]; then + IMAGE=$2 + shift + else + error 'ERROR: "--image" requires a non-empty option argument.' + fi + ;; + --models) + if [ "$2" ]; then + MODELS=$(realpath $2) + shift + else + error 'ERROR: "--models" requires a non-empty option argument.' + fi + ;; + --user) + if [ "$2" ]; then + USER="--user $2" + shift + else + error 'ERROR: "--models" requires a non-empty option argument.' + fi + ;; + --device) + if [ "$2" ]; then + DEVICES+="--device $2 " + shift + else + error 'ERROR: "--device" requires a non-empty option argument.' + fi + ;; + --privileged) + PRIVILEGED="--privileged " + ;; + --device-cgroup-rule) + if [ "$2" ]; then + DEVICE_CGROUP_RULE="--device-cgroup-rule=$2 " + shift + else + error 'ERROR: "--device-cgroup-rule" requires a non-empty option argument.' + fi + ;; + --pipelines) + if [ "$2" ]; then + PIPELINES=$(realpath $2) + shift + else + error 'ERROR: "--pipelines" requires a non-empty option argument.' + fi + ;; + --framework) + if [ "$2" ]; then + FRAMEWORK=$2 + shift + else + error 'ERROR: "--framework" requires a non-empty option argument.' + fi + ;; + -e) + if [ "$2" ]; then + ENVIRONMENT+="-e $2 " + shift + else + error 'ERROR: "-e" requires a non-empty option argument.' + fi + ;; + --entrypoint-args) + if [ "$2" ]; then + ENTRYPOINT_ARGS+="$2 " + shift + else + error 'ERROR: "--entrypoint-args" requires a non-empty option argument.' + fi + ;; + -p) + if [ "$2" ]; then + PORTS+="-p $2 " + shift + else + error 'ERROR: "-p" requires a non-empty option argument.' + fi + ;; + -v) + if [ "$2" ]; then + VOLUME_MOUNT+="-v $2 " + shift + else + error 'ERROR: "-v" requires a non-empty option argument.' + fi + ;; + --dev) + MODE=DEV + ;; + --name) + if [ "$2" ]; then + NAME=$2 + shift + else + error 'ERROR: "--name" requires a non-empty option argument.' + fi + ;; + --network) + if [ "$2" ]; then + NETWORK="--network $2" + shift + else + error 'ERROR: "--network" requires a non-empty option argument.' + fi + ;; + --entrypoint) + if [ "$2" ]; then + ENTRYPOINT="--entrypoint $2" + shift + else + error 'ERROR: "--entrypoint" requires a non-empty option argument.' + fi + ;; + --non-interactive) + unset INTERACTIVE + ;; + --) # End of all options. + shift + break + ;; + -?*) + printf 'WARN: Unknown option (ignored): %s\n' "$1" >&2 + ;; + *) # Default case: No more options, so break out of the loop. + break ;; + esac + + shift +done + +if [ -z "$FRAMEWORK" ]; then + FRAMEWORK="gstreamer" +elif [ $FRAMEWORK != 'gstreamer' ] && [ $FRAMEWORK != 'ffmpeg' ]; then + echo "Invalid framework" + show_help +fi + +if [ -z "$IMAGE" ]; then + IMAGE=DEFAULT_${FRAMEWORK^^}_IMAGE + IMAGE=${!IMAGE} +fi + +if [ -z "$NAME" ]; then + # Convert tag separator if exists + NAME=${IMAGE//[\:]/_} +fi if [ "${MODE}" == "DEV" ]; then VOLUME_MOUNT+="-v $SOURCE_DIR:/home/video-analytics-serving/ " @@ -275,7 +274,5 @@ if [ ! -z "$PIPELINES" ]; then fi show_options +$RUN_PREFIX docker run $INTERACTIVE --rm $ENVIRONMENT $VOLUME_MOUNT $DEVICE_CGROUP_RULE $DEVICES $NETWORK $PORTS $ENTRYPOINT --name ${NAME} ${PRIVILEGED} ${USER} $IMAGE ${ENTRYPOINT_ARGS} -set -x -docker run $INTERACTIVE --rm $ENVIRONMENT $VOLUME_MOUNT $DEVICE_CGROUP_RULE $DEVICES $NETWORK $PORTS $ENTRYPOINT --name ${NAME} ${PRIVILEGED} ${USER} $IMAGE ${ENTRYPOINT_ARGS} -{ set +x; } 2>/dev/null diff --git a/samples/lva_ai_extension/docker/run_client.sh b/samples/lva_ai_extension/docker/run_client.sh index 38120c5..bd52c96 100755 --- a/samples/lva_ai_extension/docker/run_client.sh +++ b/samples/lva_ai_extension/docker/run_client.sh @@ -9,61 +9,6 @@ SHARED_MEMORY= INTERACTIVE= IMAGE=video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension -#Get options passed into script -function get_options { - while :; do - case $1 in - -h | -\? | --help) - show_help - exit - ;; - --server-ip) - if [ "$2" ]; then - SERVER_IP=$2 - shift - else - error "--server-ip expects a value" - fi - ;; - --server-port) - if [ "$2" ]; then - SERVER_PORT=$2 - shift - else - error "--server-port expects a value" - fi - ;; - --shared-memory) - SHARED_MEMORY="-m" - ;; - --sample-file-path) - if [ "$2" ]; then - SAMPLE_FILE_PATH=$2 - shift - else - error "--sample-file-path expects a value" - fi - ;; - -it) - INTERACTIVE="-it" - ;; - --output-file-path) - if [ "$2" ]; then - OUTPUT_FILE_PATH="-o $2" - shift - else - error "--output-file-path expects a value" - fi - ;; - *) - break - ;; - esac - - shift - done -} - function show_help { echo "usage: ./run_client.sh" echo " [ -it : Run client in interactive mode ] " @@ -79,7 +24,57 @@ function error { exit } -get_options "$@" -RUN_COMMAND="python3 $LVA_ROOT/client -s $SERVER_IP:$SERVER_PORT -l 1 $SHARED_MEMORY -f $SAMPLE_FILE_PATH $OUTPUT_FILE_PATH" +while [[ "$#" -gt 0 ]]; do + case $1 in + -h | -\? | --help) + show_help + exit + ;; + --server-ip) + if [ "$2" ]; then + SERVER_IP=$2 + shift + else + error "--server-ip expects a value" + fi + ;; + --server-port) + if [ "$2" ]; then + SERVER_PORT=$2 + shift + else + error "--server-port expects a value" + fi + ;; + --shared-memory) + SHARED_MEMORY="-m" + ;; + --sample-file-path) + if [ "$2" ]; then + SAMPLE_FILE_PATH=$2 + shift + else + error "--sample-file-path expects a value" + fi + ;; + -it) + INTERACTIVE="-it" + ;; + --output-file-path) + if [ "$2" ]; then + OUTPUT_FILE_PATH="-o $2" + shift + else + error "--output-file-path expects a value" + fi + ;; + *) + break + ;; + esac + + shift +done +RUN_COMMAND="python3 $LVA_ROOT/client -s $SERVER_IP:$SERVER_PORT -l 1 $SHARED_MEMORY -f $SAMPLE_FILE_PATH $OUTPUT_FILE_PATH" docker run $INTERACTIVE --rm --network=host -v /dev/shm:/dev/shm -v /tmp:/tmp --user "$UID" --entrypoint /bin/bash $IMAGE -c "$RUN_COMMAND" diff --git a/samples/lva_ai_extension/docker/run_server.sh b/samples/lva_ai_extension/docker/run_server.sh index b86aba5..9be0f50 100755 --- a/samples/lva_ai_extension/docker/run_server.sh +++ b/samples/lva_ai_extension/docker/run_server.sh @@ -9,46 +9,6 @@ PORT=5001 PIPELINES= ENTRYPOINT_ARGS= -#Get options passed into script -function get_options { - while (($#)); do - case $1 in - -h | -\? | --help) - show_help - exit - ;; - -p) - if [ "$2" ]; then - PORT=$2 - shift - else - error "-p expects a value" - fi - ;; - --pipeline-name|--pipeline-version|--max-running-pipelines|--parameters|--pipeline-parameters) - if [ "$2" ]; then - ENTRYPOINT_ARGS+="--entrypoint-args $1 " - ENTRYPOINT_ARGS+="--entrypoint-args $2 " - shift - else - error "$1 expects a value" - fi - ;; - --debug) - ENTRYPOINT_ARGS+="--entrypoint-args $1 " - ;; - --dev) - PIPELINES="--pipelines $LVA_DIR/pipelines " - ;; - *) - ENTRYPOINT_ARGS+="--entrypoint-args $1 " - ;; - esac - - shift - done -} - function show_help { echo "usage: ./run_server.sh" echo " [ -p : Specify the port to use ] " @@ -65,7 +25,42 @@ function error { exit } -get_options "$@" +while [[ "$#" -gt 0 ]]; do + case $1 in + -h | -\? | --help) + show_help + exit + ;; + -p) + if [ "$2" ]; then + PORT=$2 + shift + else + error "-p expects a value" + fi + ;; + --pipeline-name|--pipeline-version|--max-running-pipelines|--parameters|--pipeline-parameters) + if [ "$2" ]; then + ENTRYPOINT_ARGS+="--entrypoint-args $1 " + ENTRYPOINT_ARGS+="--entrypoint-args $2 " + shift + else + error "$1 expects a value" + fi + ;; + --debug) + ENTRYPOINT_ARGS+="--entrypoint-args $1 " + ;; + --dev) + PIPELINES="--pipelines $LVA_DIR/pipelines " + ;; + *) + ENTRYPOINT_ARGS+="--entrypoint-args $1 " + ;; + esac + + shift +done ENV= diff --git a/tests/run.sh b/tests/run.sh index db92706..cd8952c 100755 --- a/tests/run.sh +++ b/tests/run.sh @@ -1,5 +1,7 @@ #!/bin/bash +ENTRYPOINT_ARGS= +DOCKER_RUN_OPTIONS= TESTS_DIR=$(dirname "$(readlink -f "$0")") SOURCE_DIR=$(dirname $TESTS_DIR) INTERACTIVE=--non-interactive @@ -13,8 +15,8 @@ PYBANDIT_RESULTS_DIR="$RESULTS_DIR/pybandit" CLAMAV_RESULTS_DIR="$RESULTS_DIR/clamav" # Default selected as gstreamer pytests +FRAMEWORK=gstreamer OUTPUT_DIR="$PYTEST_GSTREAMER_RESULTS_DIR" -IMAGE=video-analytics-serving-gstreamer-tests:latest SELECTED="--pytest-gstreamer" ENTRYPOINT="--entrypoint ./tests/entrypoint/pytest.sh" @@ -22,52 +24,8 @@ ENTRYPOINT="--entrypoint ./tests/entrypoint/pytest.sh" # TODO: fix user permsion issue for generating reports USER="--user root" -#Get options passed into script -function get_options { - while :; do - case $1 in - -h | -\? | --help) - show_help - exit - ;; - --pytest-gstreamer) - shift - ;; - --pytest-ffmpeg) - OUTPUT_DIR="$PYTEST_FFMPEG_RESULTS_DIR" - IMAGE=video-analytics-serving-ffmpeg-tests:latest - SELECTED="$1" - shift - ;; - --pylint) - OUTPUT_DIR="$PYLINT_RESULTS_DIR" - ENTRYPOINT="--entrypoint ./tests/entrypoint/pylint.sh" - SELECTED="$1" - shift - ;; - --pybandit) - OUTPUT_DIR="$PYBANDIT_RESULTS_DIR" - ENTRYPOINT="--entrypoint ./tests/entrypoint/pybandit.sh" - SELECTED="$1" - shift - ;; - --clamav) - OUTPUT_DIR="$CLAMAV_RESULTS_DIR" - ENTRYPOINT="--entrypoint ./tests/entrypoint/clamav.sh" - SELECTED="$1" - shift - ;; - *) - break - ;; - esac - - shift - done -} - function show_help { - echo "usage: run.sh" + echo "usage: run.sh (options are exclusive)" echo " [ --pytest-gstreamer : Run gstreamer tests ]" echo " [ --pytest-ffmpeg: Run ffmpeg tests ] " echo " [ --pylint : Run pylint scan ] " @@ -80,7 +38,43 @@ function error { exit } -get_options "$@" +ARGS=$@ +while [[ "$#" -gt 0 ]]; do + case $1 in + -h | -\? | --help) + show_help + exit + ;; + --pytest-gstreamer) + ;; + --pytest-ffmpeg) + OUTPUT_DIR="$PYTEST_FFMPEG_RESULTS_DIR" + FRAMEWORK=ffmpeg + SELECTED="$1" + ;; + --pylint) + OUTPUT_DIR="$PYLINT_RESULTS_DIR" + ENTRYPOINT="--entrypoint ./tests/entrypoint/pylint.sh" + SELECTED="$1" + ;; + --pybandit) + OUTPUT_DIR="$PYBANDIT_RESULTS_DIR" + ENTRYPOINT="--entrypoint ./tests/entrypoint/pybandit.sh" + SELECTED="$1" + ;; + --clamav) + OUTPUT_DIR="$CLAMAV_RESULTS_DIR" + ENTRYPOINT="--entrypoint ./tests/entrypoint/clamav.sh" + SELECTED="$1" + ;; + *) + break + ;; + esac + shift +done + +IMAGE=video-analytics-serving-${FRAMEWORK}-tests:latest DOCKER_RESULTS_DIR="$DOCKER_TESTS_DIR/$OUTPUT_DIR" LOCAL_RESULTS_DIR="$TESTS_DIR/$OUTPUT_DIR" @@ -92,4 +86,4 @@ mkdir -p "$LOCAL_RESULTS_DIR" VOLUME_MOUNT="-v $LOCAL_RESULTS_DIR:$DOCKER_RESULTS_DIR " -$SOURCE_DIR/docker/run.sh --image $IMAGE $USER $VOLUME_MOUNT $ENVIRONMENT $INTERACTIVE $ENTRYPOINT $@ +$SOURCE_DIR/docker/run.sh --image $IMAGE --framework $FRAMEWORK $USER $VOLUME_MOUNT $ENVIRONMENT $INTERACTIVE $ENTRYPOINT "$@" diff --git a/tests/run_script_tests.sh b/tests/run_script_tests.sh new file mode 100755 index 0000000..dbad4ca --- /dev/null +++ b/tests/run_script_tests.sh @@ -0,0 +1,17 @@ +#!/bin/bash +set -e + +TEST_DIR=$(dirname "$(readlink -f "$0")") +TAG=##teamcity +echo " ${TAG}[testSuiteStarted name='script-tests'] " + +for TEST in $TEST_DIR/script_tests/* ; do + TEST_NAME=$(basename $TEST) + printf " %s[testStarted name='%s' captureStandardOutput='true']\n" "$TAG" "$TEST_NAME" + TEST_DIR=$TEST_DIR $TEST || { + printf " %s[testFailed name='%s']\n" "$TAG" "$TEST_NAME" + } + printf " %s[testFinished name='%s']\n" "$TAG" "$TEST_NAME" +done + +echo " ${TAG}[testSuiteFinished name='script-tests'] " diff --git a/tests/script_tests/spaces_in_test_run_arguments b/tests/script_tests/spaces_in_test_run_arguments new file mode 100755 index 0000000..6fb0bdc --- /dev/null +++ b/tests/script_tests/spaces_in_test_run_arguments @@ -0,0 +1,4 @@ +#!/bin/bash +NUM_REST_TESTS=9 +NUM_TESTS=$($TEST_DIR/run.sh --pytest-gstreamer --entrypoint-args "-k rest --collect-only" 2> /dev/null | grep -c Function) +[[ $NUM_TESTS == $NUM_REST_TESTS ]] From f329729767b5ff6cafed1d64348e0b101a92ea20 Mon Sep 17 00:00:00 2001 From: "Bruce, Henry" Date: Wed, 16 Dec 2020 18:13:14 -0800 Subject: [PATCH 14/50] Updates to run script to automatically enable HW access for known accelerators. --- docker/run.sh | 33 +++++++++++++++++--- docs/running_video_analytics_serving.md | 39 +++++++++++++++++++----- samples/lva_ai_extension/README.md | 40 ++++++++++++++----------- 3 files changed, 84 insertions(+), 28 deletions(-) diff --git a/docker/run.sh b/docker/run.sh index 0eb25a8..431cd9c 100755 --- a/docker/run.sh +++ b/docker/run.sh @@ -44,7 +44,7 @@ show_options() { echo " EntrypointArgs: '${ENTRYPOINT_ARGS}'" echo " User: '${USER}'" echo " Devices: '${DEVICES}'" - echo " Device Group Rule: '${DEVICE_CGROUP_RULE}'" + echo " Device CGroup Rule: '${DEVICE_CGROUP_RULE}'" echo "" } @@ -71,6 +71,28 @@ error() { exit } +enable_hardware_access() { + # GPU + if [ -d /dev/dri ]; then + echo "Found /dev/dri - enabling for GPU" + DEVICES+='--device /dev/dri ' + fi + + # NCS2 + if [ -d /dev/bus/usb ]; then + echo "Found /dev/bus/usb - enabling for NCS2" + DEVICE_CGROUP_RULE=--device-cgroup-rule=\'c\ 189:*\ rmw\' + VOLUME_MOUNT+="-v /dev/bus/usb:/dev/bus/usb " + fi + + # HDDL + if [ -e /dev/ion ]; then + echo "Found /dev/ion - enabling for HDDL-R" + DEVICES+="--device /dev/ion " + VOLUME_MOUNT+="-v /var/tmp:/var/tmp " + fi +} + while [[ "$#" -gt 0 ]]; do case $1 in -h | -\? | --help) @@ -253,8 +275,10 @@ if [ "${MODE}" == "DEV" ]; then USER="--user $UID" fi elif [ "${MODE}" == "SERVICE" ]; then - PORTS+="-p 8080:8080 " - DEVICES+='--device /dev/dri ' + if [ -z "$PORTS" ]; then + PORTS+="-p 8080:8080 " + fi + enable_hardware_access else echo "Invalid Mode" show_help @@ -274,5 +298,6 @@ if [ ! -z "$PIPELINES" ]; then fi show_options -$RUN_PREFIX docker run $INTERACTIVE --rm $ENVIRONMENT $VOLUME_MOUNT $DEVICE_CGROUP_RULE $DEVICES $NETWORK $PORTS $ENTRYPOINT --name ${NAME} ${PRIVILEGED} ${USER} $IMAGE ${ENTRYPOINT_ARGS} +# eval must be used to ensure the --device-cgroup-rule string is correctly parsed +eval "$RUN_PREFIX docker run $INTERACTIVE --rm $ENVIRONMENT $VOLUME_MOUNT $DEVICE_CGROUP_RULE $DEVICES $NETWORK $PORTS $ENTRYPOINT --name ${NAME} ${PRIVILEGED} ${USER} $IMAGE ${ENTRYPOINT_ARGS}" diff --git a/docs/running_video_analytics_serving.md b/docs/running_video_analytics_serving.md index 1f43de4..b87e590 100644 --- a/docs/running_video_analytics_serving.md +++ b/docs/running_video_analytics_serving.md @@ -1,5 +1,5 @@ # Running Video Analytics Serving -| [Video Analytics Serving Microservice](#video-analytics-serving-microservice) | [Interacting with the Microservice](#interacting-with-the-microservice) | [Selecting Pipelines and Models at Runtime](#selecting-pipelines-and-models-at-runtime) | [Developer Mode](#developer-mode) | +| [Video Analytics Serving Microservice](#video-analytics-serving-microservice) | [Interacting with the Microservice](#interacting-with-the-microservice) | [Selecting Pipelines and Models at Runtime](#selecting-pipelines-and-models-at-runtime) | [Developer Mode](#developer-mode) | Video Analytics Serving docker images can be started using standard `docker run` and `docker compose` commands. For convenience a simplified run script is provided to pass common options to `docker run` such as proxies, device mounts, and to expose the default microservice port (8080). @@ -18,7 +18,7 @@ that exposes a RESTful interface on port 8080. The microservice has endpoints to list, start, stop, and get the status of media analytics pipelines. -## Microservice Endpoints +## Microservice Endpoints | Path | Description | |----|------| @@ -35,7 +35,7 @@ the status of media analytics pipelines. | Command | Media Analytics Base Image | Image Name | Description | | --- | --- | --- | ---- | | `./docker/run.sh`|**DL Streamer** docker [file](https://github.com/opencv/gst-video-analytics/blob/preview/audio-detect/docker/Dockerfile) |`video-analytics-serving-gstreamer` | DL Streamer based microservice with default pipeline definitions and deep learning models. Exposes port 8080. Mounts the host system's graphics devices. | -| `./docker/run.sh --framework ffmpeg`| **FFmpeg Video Analytics** docker [file](https://github.com/VCDP/FFmpeg-patch/blob/ffmpeg4.2_va/docker/Dockerfile.source) |`video-analytics-serving-ffmpeg`| FFmpeg Video Analytics based microservice with default pipeline definitions and deep learning models. Mounts the graphics devices. | +| `./docker/run.sh --framework ffmpeg`| **FFmpeg Video Analytics** docker [file](https://github.com/VCDP/FFmpeg-patch/blob/ffmpeg4.2_va/docker/Dockerfile.source) |`video-analytics-serving-ffmpeg`| FFmpeg Video Analytics based microservice with default pipeline definitions and deep learning models. Mounts the graphics devices. | # Interacting with the Microservice @@ -80,7 +80,7 @@ From a new shell use curl to issue requests to the running microservice. ### Getting Loaded Pipelines -**Example:** +**Example:** ```bash $ curl localhost:8080/pipelines @@ -108,7 +108,7 @@ $ curl localhost:8080/pipelines ```bash curl localhost:8080/pipelines/object_detection/1 -X POST -H \ 'Content-Type: application/json' -d \ -'{ +'{ "source": { "uri": "https://github.com/intel-iot-devkit/sample-videos/blob/master/bottle-detection.mp4?raw=true", "type": "uri" @@ -164,12 +164,37 @@ appropriate directories when starting the container. $ ./docker/run.sh --framework gstreamer --pipelines /path/to/my-pipelines --models /path/to/my-models ``` +# Enabling Hardware Accelerators +The run script automatically gives docker access (i.e. device, volume mount and device cgroup rule) to the following accelerators +* iGPU +* Myriad NCS2 +* HDDL-R + +You also need to specify the inference device in the parameters section +of the VA Serving request. Example for GPU below +```json +"parameters": { + "device": "GPU" +} +``` +See [Customizing Pipeline Requests](customizing_pipeline_requests.md) for more information of forming requests. + +The following the table shows docker configuration and inference device name for all accelerators. +> **Note:** Open Visual Cloud base images only support the GPU accelerator. +> OpenVINO base images support all accelerators. + +|Accelerator|Docker Device|Volume Mount |CGroup Rule|Inference Device| +|-----------|-------------|--------------|-----------|----------------| +| GPU | /dev/dri | | | GPU | +| NCS2 | | /dev/bus/usb |c 189:* rmw| MYRIAD | +| HDDL-R | /dev/ion | /var/tmp | | HDDL | + # Developer Mode The run script includes a `--dev` flag which starts the container in "developer" mode. "Developer mode," sets `docker run` options to make development and modification of media analytics -pipelines easier. +pipelines easier. > **Note:** Pipeline and Model directories are only scanned once at > service start-up. When making modifications to the models, @@ -190,7 +215,7 @@ Developer mode: ```bash $ docker/run.sh --dev -vaserving@my-host:~$ python3 -m vaserving +vaserving@my-host:~$ python3 -m vaserving ``` --- diff --git a/samples/lva_ai_extension/README.md b/samples/lva_ai_extension/README.md index 4e9b34d..66b69c7 100644 --- a/samples/lva_ai_extension/README.md +++ b/samples/lva_ai_extension/README.md @@ -3,7 +3,7 @@ | [Getting Started](#getting-started) | [Edge AI Extension Module Options](#edge-ai-extension-module-options) | [Additional Examples](#additional-standalone-edge-ai-extension-examples) | [Test Client](#test-client) | [Changing Models](#updating-or-changing-detection-and-classification-models) -The OpenVINO™ DL Streamer - Edge AI Extension module is a microservice based on [Video Analytics Serving](/README.md) that provides video analytics pipelines built with OpenVINO™ DL Streamer. Developers can send decoded video frames to the AI Extension module which performs detection, classification, or tracking and returns the results. The AI Extension module exposes gRPC APIs that are compatible with [Live Video Analytics on IoT Edge](https://azure.microsoft.com/en-us/services/media-services/live-video-analytics/). Powered by OpenVINO™ toolkit, the AI Extension module enables developers to build, optimize and deploy deep learning inference workloads for maximum performance across Intel® architectures. +The OpenVINO™ DL Streamer - Edge AI Extension module is a microservice based on [Video Analytics Serving](/README.md) that provides video analytics pipelines built with OpenVINO™ DL Streamer. Developers can send decoded video frames to the AI Extension module which performs detection, classification, or tracking and returns the results. The AI Extension module exposes gRPC APIs that are compatible with [Live Video Analytics on IoT Edge](https://azure.microsoft.com/en-us/services/media-services/live-video-analytics/). Powered by OpenVINO™ toolkit, the AI Extension module enables developers to build, optimize and deploy deep learning inference workloads for maximum performance across Intel® architectures. ## Highlights: @@ -16,7 +16,7 @@ The OpenVINO™ DL Streamer - Edge AI Extension module is a microservice based o # Getting Started -The OpenVINO™ DL Streamer - Edge AI Extension module is available as a pre-built docker image. The image can run as a standalone microservice or as a module within an Live Video Analytics graph. For more information on deploying the module as part of a Live Video Analytics graph please see [Configuring the AI Extension Module for Live Video Analytics](#configuring-the-ai-extension-module-for-live-video-analytics) and refer to the [Live Video Analytics documentation](https://azure.microsoft.com/en-us/services/media-services/live-video-analytics/). The following instructions demonstrate running the microservice and test client outside of Live Video Analytics. +The OpenVINO™ DL Streamer - Edge AI Extension module is available as a pre-built docker image. The image can run as a standalone microservice or as a module within an Live Video Analytics graph. For more information on deploying the module as part of a Live Video Analytics graph please see [Configuring the AI Extension Module for Live Video Analytics](#configuring-the-ai-extension-module-for-live-video-analytics) and refer to the [Live Video Analytics documentation](https://azure.microsoft.com/en-us/services/media-services/live-video-analytics/). The following instructions demonstrate running the microservice and test client outside of Live Video Analytics. ## Pulling the Image from Docker Hub @@ -26,9 +26,9 @@ Pull the pre-built image using the following command. For instructions on buildi docker pull intel/video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension ``` -## Running the Edge AI Extension Module +## Running the Edge AI Extension Module -To run the module as a standalone microservice with an `object_detection` pipeline use the `run_server.sh` script with default options. For examples of additional options see [Additional Standalone Edge AI Extension Examples](#additional-standalone-edge-ai-extension-examples). +To run the module as a standalone microservice with an `object_detection` pipeline use the `run_server.sh` script with default options. For examples of additional options see [Additional Standalone Edge AI Extension Examples](#additional-standalone-edge-ai-extension-examples). ```bash $ ./docker/run_server.sh @@ -38,7 +38,7 @@ Starting Protocol Server Application on port 5001 ## Sending a Test Frame for Object Detection -To send a test frame to the microservice and receive `object_detection` results use the `run_client.sh` script. +To send a test frame to the microservice and receive `object_detection` results use the `run_client.sh` script. ```bash $ ./docker/run_client.sh @@ -72,9 +72,9 @@ $ ./docker/run_client.sh [AIXC] [2020-11-20 23:29:11,422] [MainThread ] [INFO]: Client finished execution ``` -# Edge AI Extension Module Options +# Edge AI Extension Module Options -The module can be configured using command line options or environment variables (command line options take precedence). +The module can be configured using command line options or environment variables (command line options take precedence). | Setting | Command line option | Environment variable | Default value | |---------------------|-----------------------|----------------------|------------------| @@ -133,7 +133,7 @@ $ ./docker/run_server.sh ``` Notes: -* Only one pipeline can be enabled per container instance. +* Only one pipeline can be enabled per container instance. * If selecting a pipeline both name and version must be specified * The `--debug` option selects debug pipelines that watermark inference results and saves images in `/tmp/vaserving/{--pipeline-version}/{timestamp}/` and can also be set using the environment variable DEBUG_PIPELINE * The `--parameters` option specifies pipeline parameters for the selected pipeline. It can be either a JSON string or the name of a file containing the JSON. See the parameters section of the [pipeline definition](/docs/defining_pipelines.md#pipeline-parameters) document for more details. The individual definition files for [object_detection](/samples/lva_ai_extension/pipelines/object_detection/person_vehicle_bike_detection/pipeline.json), [object_classification](/samples/lva_ai_extension/pipelines/object_classification/vehicle_attributes_recognition/pipeline.json), and [object_tracking](/samples/lva_ai_extension/pipelines/object_tracking/person_vehicle_bike_tracking/pipeline.json) contain the supported parameters for the pre-loaded pipelines. @@ -141,26 +141,32 @@ Notes: ### Debug Mode -Debug pipelines can be selected using the `--debug` command line parameter or setting the `DEBUG_PIPELINE` environment variable. Debug pipelines save watermarked frames to `/tmp/vaserving/{--pipeline-version}/{timestamp}/` as JPEG images. +Debug pipelines can be selected using the `--debug` command line parameter or setting the `DEBUG_PIPELINE` environment variable. Debug pipelines save watermarked frames to `/tmp/vaserving/{--pipeline-version}/{timestamp}/` as JPEG images. Run default pipeline in debug mode ```bash $ ./docker/run_server.sh --debug ``` -### Inference Using Intel iGPU +### Inference Accelerators -Pipelines can be configured to perform inference using Intel® -Integrated Graphics. +Pipelines can be configured to perform inference using a range of accelerators. +This is a two step process: +1. Give docker access to the accelerator's resources +2. Set the inference accelerator device name when starting the pipeline -Run default pipeline with iGPU inference (be careful with escaping the JSON string) +See [Enabling Hardware Accelerators](/docs/running_video_analytics_serving.md#enabling-hardware-accelerators) +for details on docker resources and inference device name for supported accelerators. +This will allow you to customize the deployment manifest for a given accelerator. +The run server script will automatically detect installed accelerators and provide access to their resources. +Here we run the default pipeline with inference running on Intel® Integrated Graphics (be careful with escaping the JSON string) ```bash -$ ./docker/run_server.sh --pipeline-parameters "{\"device\":\"GPU\"}" +$ ./docker/run_server.sh --pipeline-parameters '{\"device\":\"GPU\"}' ``` ### Logging -Run the following command to monitor the logs from the docker container +Run the following command to monitor the logs from the docker container ```bash $ docker logs video-analytics-serving_0.4.0-dlstreamer-edge-ai-extension -f ``` @@ -181,7 +187,7 @@ Starting Protocol Server Application on port 5001 The python application supports the same [options](#edge-ai-extension-module-options) as the `run_server.sh` script. # Test Client -A test client is provided to demonstrate the capabilities of the Edge AI Extension module. +A test client is provided to demonstrate the capabilities of the Edge AI Extension module. The test client script `run_client.sh` sends frames(s) to the extension module and prints inference results. Use the --help option to see how to use the script. All arguments are optional. @@ -222,7 +228,7 @@ Resulting image name is `video-analytics-serving:0.4.0-dlstreamer-edge-ai-extens # Updating or Changing Detection and Classification Models Before updating the models used by a pipeline please see the format of [pipeline definition files](/docs/defining_pipelines.md) and read the -tutorial on [changing object detection models](/docs/changing_object_detection_models.md). +tutorial on [changing object detection models](/docs/changing_object_detection_models.md). Most of the steps to changes models are the same, but the existing tutorial assumes you are working with the REST service and not the AI From 438f6424707f7783638580cf20026383634734c5 Mon Sep 17 00:00:00 2001 From: "Thakkalapelli, Thanaji Rao" Date: Wed, 6 Jan 2021 16:02:42 -0800 Subject: [PATCH 15/50] Added html report for LVA tests and updated Teamcity to attach artifacts --- .gitignore | 1 + samples/lva_ai_extension/tests/Dockerfile | 5 +++-- samples/lva_ai_extension/tests/requirements.tests.txt | 1 + samples/lva_ai_extension/tests/run.sh | 6 +++++- 4 files changed, 10 insertions(+), 3 deletions(-) diff --git a/.gitignore b/.gitignore index c06c39d..451d3c4 100644 --- a/.gitignore +++ b/.gitignore @@ -7,3 +7,4 @@ docker/Dockerfile.env docker/final.env models tests/results/**/* +samples/lva_ai_extension/tests/results/**/* \ No newline at end of file diff --git a/samples/lva_ai_extension/tests/Dockerfile b/samples/lva_ai_extension/tests/Dockerfile index d7ef7fb..c311fb2 100644 --- a/samples/lva_ai_extension/tests/Dockerfile +++ b/samples/lva_ai_extension/tests/Dockerfile @@ -29,5 +29,6 @@ ARG USER=vaserving USER ${USER} -ENTRYPOINT [ "pytest", "/home/video-analytics-serving/samples/lva_ai_extension" ] -#ENTRYPOINT [ "/bin/bash" ] +WORKDIR /home/video-analytics-serving/samples/lva_ai_extension + +ENTRYPOINT [ "pytest", "-s", "--html=tests/results/report.html", "--self-contained-html"] diff --git a/samples/lva_ai_extension/tests/requirements.tests.txt b/samples/lva_ai_extension/tests/requirements.tests.txt index f32ec89..ab3cce3 100644 --- a/samples/lva_ai_extension/tests/requirements.tests.txt +++ b/samples/lva_ai_extension/tests/requirements.tests.txt @@ -2,5 +2,6 @@ psutil == 5.7.0 pylint == 2.4.4 pytest == 5.4.1 pytest-cov == 2.8.1 +pytest-html == 2.1.1 requests == 2.23.0 teamcity-messages == 1.28 diff --git a/samples/lva_ai_extension/tests/run.sh b/samples/lva_ai_extension/tests/run.sh index cd3f088..77e206a 100755 --- a/samples/lva_ai_extension/tests/run.sh +++ b/samples/lva_ai_extension/tests/run.sh @@ -3,4 +3,8 @@ CURRENT_DIR=$(dirname $(readlink -f "$0")) ROOT_DIR=$(readlink -f "$CURRENT_DIR/../../..") LVA_DIR=$(dirname $CURRENT_DIR) docker stop $IMAGE -"$ROOT_DIR/docker/run.sh" --image $IMAGE -v /dev/shm:/dev/shm --pipelines "$LVA_DIR/pipelines" -p 5001:5001 --user $UID "$@" +LOCAL_RESULTS_DIR="$CURRENT_DIR/results" +DOCKER_RESULTS_DIR="/home/video-analytics-serving/samples/lva_ai_extension/tests/results" +mkdir -p "$LOCAL_RESULTS_DIR" +RESULTS_VOLUME_MOUNT="-v $LOCAL_RESULTS_DIR:$DOCKER_RESULTS_DIR " +"$ROOT_DIR/docker/run.sh" --image $IMAGE -v /dev/shm:/dev/shm $RESULTS_VOLUME_MOUNT --pipelines "$LVA_DIR/pipelines" -p 5001:5001 --user $UID "$@" From 1f7a530a705d4d56aef0b834aeb21edb34a36383 Mon Sep 17 00:00:00 2001 From: "Thakkalapelli, Thanaji Rao" Date: Thu, 7 Jan 2021 11:20:24 -0800 Subject: [PATCH 16/50] Model network preference updates --- vaserving/model_manager.py | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/vaserving/model_manager.py b/vaserving/model_manager.py index 6f33fc6..74a3206 100644 --- a/vaserving/model_manager.py +++ b/vaserving/model_manager.py @@ -49,14 +49,15 @@ def __init__(self, model_dir, network_preference=None, ignore_init_errors=False) self.network_preference = network_preference self.models = defaultdict(dict) self.model_procs = {} - self.network_preference = {'CPU': ["FP32"], - 'HDDL': ["FP16"], - 'GPU': ["FP16"], - 'VPU': ["FP16"], - 'MYRIAD': ["FP16"], - 'KMB': ["U8"]} - - success = self.load_models(self.model_dir, network_preference) + if not self.network_preference: + self.network_preference = {'CPU': ["FP32"], + 'HDDL': ["FP16"], + 'GPU': ["FP16"], + 'VPU': ["FP16"], + 'MYRIAD': ["FP16"], + 'KMB': ["U8"]} + + success = self.load_models(self.model_dir, self.network_preference) if (not ignore_init_errors) and (not success): raise Exception("Error Initializing Models") From 5363cb66fa2a1e8cb52f9741527a2d0b1ad0f5f8 Mon Sep 17 00:00:00 2001 From: "Khoshnevissan, Farid" Date: Fri, 8 Jan 2021 13:28:38 -0800 Subject: [PATCH 17/50] Updating all pipelines to have model-instance-id(s) as pipeline parameter(s). --- .../emotion_recognition/1/pipeline.json | 18 +- .../object_detection/1/pipeline.json | 9 +- .../object_detection/2/pipeline.json | 5 + .../app_src_dst/pipeline.json | 14 +- .../object_detection/edgex/pipeline.json | 7 +- .../pipeline.json | 29 +- .../pipeline.json | 29 +- .../pipeline.json | 7 +- .../pipeline.json | 7 +- .../pipeline.json | 29 +- .../pipeline.json | 29 +- ...t_detection_set_instance_id_gstreamer.json | 4023 +++++++++++++++++ .../get_object_detection_gstreamer.json | 13 +- .../rest_api/get_pipelines_gstreamer.json | 219 +- 14 files changed, 4293 insertions(+), 145 deletions(-) create mode 100644 tests/test_cases/pipeline_execution/object_detection_set_instance_id_gstreamer.json diff --git a/pipelines/gstreamer/emotion_recognition/1/pipeline.json b/pipelines/gstreamer/emotion_recognition/1/pipeline.json index e701e63..005b6ba 100644 --- a/pipelines/gstreamer/emotion_recognition/1/pipeline.json +++ b/pipelines/gstreamer/emotion_recognition/1/pipeline.json @@ -21,6 +21,22 @@ "VPU" ] }, + "model-instance-id-detection": { + "element": { + "name": "detection", + "property": "model-instance-id" + }, + "type": "string", + "default": "face_detection_retail0" + }, + "model-instance-id-classification": { + "element": { + "name": "classification", + "property": "model-instance-id" + }, + "type": "string", + "default": "" + }, "inference-interval": { "element": "detection", "type": "integer", @@ -46,4 +62,4 @@ } } } -} \ No newline at end of file +} diff --git a/pipelines/gstreamer/object_detection/1/pipeline.json b/pipelines/gstreamer/object_detection/1/pipeline.json index b95a47d..63bd9bd 100644 --- a/pipelines/gstreamer/object_detection/1/pipeline.json +++ b/pipelines/gstreamer/object_detection/1/pipeline.json @@ -1,7 +1,7 @@ { "type": "GStreamer", "template": ["urisourcebin name=source ! concat name=c ! decodebin ! video/x-raw ! videoconvert name=videoconvert", - " ! gvadetect model-instance-id=inf0 model={models[object_detection][1][network]} model-proc={models[object_detection][1][proc]} name=detection", + " ! gvadetect model={models[object_detection][1][network]} model-proc={models[object_detection][1][proc]} name=detection", " ! gvametaconvert name=metaconvert ! queue ! gvametapublish name=destination", " ! appsink name=appsink" ], @@ -13,6 +13,11 @@ "element": "detection", "type": "string" }, + "model-instance-id": { + "element": "detection", + "type": "string", + "default": "inf0" + }, "inference-interval": { "element": "detection", "type": "integer", @@ -40,4 +45,4 @@ } } } -} \ No newline at end of file +} diff --git a/pipelines/gstreamer/object_detection/2/pipeline.json b/pipelines/gstreamer/object_detection/2/pipeline.json index a178301..48c6aaf 100644 --- a/pipelines/gstreamer/object_detection/2/pipeline.json +++ b/pipelines/gstreamer/object_detection/2/pipeline.json @@ -15,6 +15,11 @@ "element": "detection", "type": "string" }, + "model-instance-id": { + "element": "detection", + "type": "string", + "default": "inf0" + }, "inference-interval": { "element": "detection", "type": "integer", diff --git a/pipelines/gstreamer/object_detection/app_src_dst/pipeline.json b/pipelines/gstreamer/object_detection/app_src_dst/pipeline.json index 665ac65..f761fab 100644 --- a/pipelines/gstreamer/object_detection/app_src_dst/pipeline.json +++ b/pipelines/gstreamer/object_detection/app_src_dst/pipeline.json @@ -1,7 +1,17 @@ { "type": "GStreamer", "template": ["appsrc name=source", - " ! gvadetect model={models[object_detection][1][network]} model-proc={models[object_detection][1][proc]}", + " ! gvadetect model={models[object_detection][1][network]} model-proc={models[object_detection][1][proc]} name=detection", " ! appsink name=destination"], - "description": "Object Detection Pipeline" + "description": "Object Detection Pipeline", + "parameters": { + "type": "object", + "properties": { + "model-instance-id": { + "element": "detection", + "type": "string", + "default": "" + } + } + } } diff --git a/pipelines/gstreamer/object_detection/edgex/pipeline.json b/pipelines/gstreamer/object_detection/edgex/pipeline.json index 10242b7..d9bcde4 100644 --- a/pipelines/gstreamer/object_detection/edgex/pipeline.json +++ b/pipelines/gstreamer/object_detection/edgex/pipeline.json @@ -1,7 +1,7 @@ { "type": "GStreamer", "template": ["urisourcebin name=source ! concat name=c ! decodebin ! video/x-raw ! videoconvert name=videoconvert", - " ! gvadetect model-instance-id=inf0 model={models[object_detection][1][network]} model-proc={models[object_detection][1][proc]} name=detection", + " ! gvadetect model={models[object_detection][1][network]} model-proc={models[object_detection][1][proc]} name=detection", " ! gvametaconvert name=metaconvert ", " ! gvapython name=edgexbridge module=/home/video-analytics-serving/extensions/edgex/edgex_transform.py class=Bridge", " ! queue ! gvametapublish name=destination", @@ -33,6 +33,11 @@ "element": "detection", "type": "string" }, + "model-instance-id": { + "element": "detection", + "type": "string", + "default": "inf0" + }, "inference-interval": { "element": "detection", "type": "integer", diff --git a/samples/lva_ai_extension/pipelines/object_classification/debug_vehicle_attributes_recognition/pipeline.json b/samples/lva_ai_extension/pipelines/object_classification/debug_vehicle_attributes_recognition/pipeline.json index 048749c..6e87e83 100755 --- a/samples/lva_ai_extension/pipelines/object_classification/debug_vehicle_attributes_recognition/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_classification/debug_vehicle_attributes_recognition/pipeline.json @@ -1,8 +1,8 @@ { "type": "GStreamer", "template": ["appsrc name=source", - " ! gvadetect model-instance-id=person_vehicle_bike_detection model={models[person_vehicle_bike_detection][1][network]} model-proc={models[person_vehicle_bike_detection][1][proc]} name=detection", - " ! gvaclassify model-instance-id=vehicle_attributes_recognition model={models[vehicle_attributes_recognition][1][network]} model-proc={models[vehicle_attributes_recognition][1][proc]} name=classification", + " ! gvadetect model={models[person_vehicle_bike_detection][1][network]} model-proc={models[person_vehicle_bike_detection][1][proc]} name=detection", + " ! gvaclassify model={models[vehicle_attributes_recognition][1][network]} model-proc={models[vehicle_attributes_recognition][1][proc]} name=classification", " ! tee name = t ! queue ! gvawatermark ! videoconvert ! jpegenc", " ! multifilesink name=filesink t. ! queue", " ! appsink name=destination"], @@ -79,15 +79,22 @@ ], "type": "string" }, - "model-instance-id": { - "element": - [ {"name":"detection", - "property":"model-instance-id"}, - {"name":"classification", - "property":"model-instance-id"} - ], - "type": "string" - }, + "model-instance-id-detection": { + "element": { + "name": "detection", + "property": "model-instance-id" + }, + "type": "string", + "default": "person_vehicle_bike_detection" + }, + "model-instance-id-classification": { + "element": { + "name": "classification", + "property": "model-instance-id" + }, + "type": "string", + "default": "vehicle_attributes_recognition" + }, "pre-process-backend": { "element": [ {"name":"detection", diff --git a/samples/lva_ai_extension/pipelines/object_classification/vehicle_attributes_recognition/pipeline.json b/samples/lva_ai_extension/pipelines/object_classification/vehicle_attributes_recognition/pipeline.json index 77517a6..d9fd118 100755 --- a/samples/lva_ai_extension/pipelines/object_classification/vehicle_attributes_recognition/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_classification/vehicle_attributes_recognition/pipeline.json @@ -1,8 +1,8 @@ { "type": "GStreamer", "template": ["appsrc name=source", - " ! gvadetect model-instance-id=person_vehicle_bike_detection model={models[person_vehicle_bike_detection][1][network]} model-proc={models[person_vehicle_bike_detection][1][proc]} name=detection", - " ! gvaclassify model-instance-id=vehicle_attributes_recognition model={models[vehicle_attributes_recognition][1][network]} model-proc={models[vehicle_attributes_recognition][1][proc]} name=classification", + " ! gvadetect model={models[person_vehicle_bike_detection][1][network]} model-proc={models[person_vehicle_bike_detection][1][proc]} name=detection", + " ! gvaclassify model={models[vehicle_attributes_recognition][1][network]} model-proc={models[vehicle_attributes_recognition][1][proc]} name=classification", " ! appsink name=destination"], "description": "Object Classification using gstreamer gvadetect & gvaclassify elements, check more about gvadetect at https://github.com/openvinotoolkit/dlstreamer_gst/wiki/gvadetect and gvaclassify at https://github.com/openvinotoolkit/dlstreamer_gst/wiki/gvaclassify", "parameters": { @@ -77,15 +77,22 @@ ], "type": "string" }, - "model-instance-id": { - "element": - [ {"name":"detection", - "property":"model-instance-id"}, - {"name":"classification", - "property":"model-instance-id"} - ], - "type": "string" - }, + "model-instance-id-detection": { + "element": { + "name": "detection", + "property": "model-instance-id" + }, + "type": "string", + "default": "person_vehicle_bike_detection" + }, + "model-instance-id-classification": { + "element": { + "name": "classification", + "property": "model-instance-id" + }, + "type": "string", + "default": "vehicle_attributes_recognition" + }, "pre-process-backend": { "element": [ {"name":"detection", diff --git a/samples/lva_ai_extension/pipelines/object_detection/debug_person_vehicle_bike_detection/pipeline.json b/samples/lva_ai_extension/pipelines/object_detection/debug_person_vehicle_bike_detection/pipeline.json index 9da36b0..d524429 100755 --- a/samples/lva_ai_extension/pipelines/object_detection/debug_person_vehicle_bike_detection/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_detection/debug_person_vehicle_bike_detection/pipeline.json @@ -1,7 +1,7 @@ { "type": "GStreamer", "template": ["appsrc name=source", - " ! gvadetect model-instance-id=person_vehicle_bike_detection model={models[person_vehicle_bike_detection][1][network]} model-proc={models[person_vehicle_bike_detection][1][proc]} name=detection", + " ! gvadetect model={models[person_vehicle_bike_detection][1][network]} model-proc={models[person_vehicle_bike_detection][1][proc]} name=detection", " ! tee name = t ! queue ! gvawatermark ! videoconvert ! jpegenc", " ! multifilesink name=filesink t. ! queue", " ! appsink name=destination"], @@ -13,6 +13,11 @@ "element": "detection", "type": "string" }, + "model-instance-id": { + "element": "detection", + "type": "string", + "default": "person_vehicle_bike_detection" + }, "batch-size": { "element": "detection", "type": "integer" diff --git a/samples/lva_ai_extension/pipelines/object_detection/person_vehicle_bike_detection/pipeline.json b/samples/lva_ai_extension/pipelines/object_detection/person_vehicle_bike_detection/pipeline.json index 860e9fe..59ae3c0 100755 --- a/samples/lva_ai_extension/pipelines/object_detection/person_vehicle_bike_detection/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_detection/person_vehicle_bike_detection/pipeline.json @@ -1,7 +1,7 @@ { "type": "GStreamer", "template": ["appsrc name=source", - " ! gvadetect model-instance-id=person_vehicle_bike_detection model={models[person_vehicle_bike_detection][1][network]} model-proc={models[person_vehicle_bike_detection][1][proc]} name=detection", + " ! gvadetect model={models[person_vehicle_bike_detection][1][network]} model-proc={models[person_vehicle_bike_detection][1][proc]} name=detection", " ! appsink name=destination"], "description": "Object Detection using gstreamer gvadetect element, check more about gvadetect at https://github.com/openvinotoolkit/dlstreamer_gst/wiki/gvadetect", "parameters": { @@ -11,6 +11,11 @@ "element": "detection", "type": "string" }, + "model-instance-id": { + "element": "detection", + "type": "string", + "default": "person_vehicle_bike_detection" + }, "batch-size": { "element": "detection", "type": "integer" diff --git a/samples/lva_ai_extension/pipelines/object_tracking/debug_person_vehicle_bike_tracking/pipeline.json b/samples/lva_ai_extension/pipelines/object_tracking/debug_person_vehicle_bike_tracking/pipeline.json index 356cd61..6aee515 100755 --- a/samples/lva_ai_extension/pipelines/object_tracking/debug_person_vehicle_bike_tracking/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_tracking/debug_person_vehicle_bike_tracking/pipeline.json @@ -1,9 +1,9 @@ { "type": "GStreamer", "template": ["appsrc name=source", - " ! gvadetect model-instance-id=person_vehicle_bike_detection model={models[person_vehicle_bike_detection][1][network]} model-proc={models[person_vehicle_bike_detection][1][proc]} name=detection", + " ! gvadetect model={models[person_vehicle_bike_detection][1][network]} model-proc={models[person_vehicle_bike_detection][1][proc]} name=detection", " ! queue ! gvatrack name=tracking", - " ! gvaclassify model-instance-id=vehicle_attributes_recognition model={models[vehicle_attributes_recognition][1][network]} model-proc={models[vehicle_attributes_recognition][1][proc]} name=classification", + " ! gvaclassify model={models[vehicle_attributes_recognition][1][network]} model-proc={models[vehicle_attributes_recognition][1][proc]} name=classification", " ! tee name = t ! queue ! gvawatermark ! videoconvert ! jpegenc", " ! multifilesink name=filesink t. ! queue", " ! appsink name=destination"], @@ -80,15 +80,22 @@ ], "type": "string" }, - "model-instance-id": { - "element": - [ {"name":"detection", - "property":"model-instance-id"}, - {"name":"classification", - "property":"model-instance-id"} - ], - "type": "string" - }, + "model-instance-id-detection": { + "element": { + "name": "detection", + "property": "model-instance-id" + }, + "type": "string", + "default": "person_vehicle_bike_detection" + }, + "model-instance-id-classification": { + "element": { + "name": "classification", + "property": "model-instance-id" + }, + "type": "string", + "default": "vehicle_attributes_recognition" + }, "pre-process-backend": { "element": [ {"name":"detection", diff --git a/samples/lva_ai_extension/pipelines/object_tracking/person_vehicle_bike_tracking/pipeline.json b/samples/lva_ai_extension/pipelines/object_tracking/person_vehicle_bike_tracking/pipeline.json index cae160d..fe24ce0 100755 --- a/samples/lva_ai_extension/pipelines/object_tracking/person_vehicle_bike_tracking/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_tracking/person_vehicle_bike_tracking/pipeline.json @@ -1,9 +1,9 @@ { "type": "GStreamer", "template": ["appsrc name=source", - " ! gvadetect model-instance-id=person_vehicle_bike_detection model={models[person_vehicle_bike_detection][1][network]} model-proc={models[person_vehicle_bike_detection][1][proc]} name=detection", + " ! gvadetect model={models[person_vehicle_bike_detection][1][network]} model-proc={models[person_vehicle_bike_detection][1][proc]} name=detection", " ! queue ! gvatrack name=tracking", - " ! gvaclassify model-instance-id=vehicle_attributes_recognition model={models[vehicle_attributes_recognition][1][network]} model-proc={models[vehicle_attributes_recognition][1][proc]} name=classification", + " ! gvaclassify model={models[vehicle_attributes_recognition][1][network]} model-proc={models[vehicle_attributes_recognition][1][proc]} name=classification", " ! appsink name=destination"], "description": "Object Tracking using gstreamer gvadetect, gvaclassify & gvatrack elements, check more about gvadetect at https://github.com/openvinotoolkit/dlstreamer_gst/wiki/gvadetect, gvaclassify at https://github.com/openvinotoolkit/dlstreamer_gst/wiki/gvaclassify and gvatrack at https://github.com/openvinotoolkit/dlstreamer_gst/wiki/gvatrack", "parameters": { @@ -78,15 +78,22 @@ ], "type": "string" }, - "model-instance-id": { - "element": - [ {"name":"detection", - "property":"model-instance-id"}, - {"name":"classification", - "property":"model-instance-id"} - ], - "type": "string" - }, + "model-instance-id-detection": { + "element": { + "name": "detection", + "property": "model-instance-id" + }, + "type": "string", + "default": "person_vehicle_bike_detection" + }, + "model-instance-id-classification": { + "element": { + "name": "classification", + "property": "model-instance-id" + }, + "type": "string", + "default": "vehicle_attributes_recognition" + }, "pre-process-backend": { "element": [ {"name":"detection", diff --git a/tests/test_cases/pipeline_execution/object_detection_set_instance_id_gstreamer.json 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"file:///home/video-analytics-serving/samples/pinwheel.ts", + "tags": {}, + "timestamp": 9760000000}, + {"objects": [{"detection": {"bounding_box": {"x_max": 0.9932312965393066, + "x_min": 0.005518049001693726, + "y_max": 0.9961479902267456, + "y_min": 0.004157662391662598}, + "confidence": 0.588447093963623, + "label": "pottedplant", + "label_id": 16}, + "h": 357, + "roi_type": "pottedplant", + "w": 632, + "x": 4, + "y": 1}], + "resolution": {"height": 360, "width": 640}, + "source": "file:///home/video-analytics-serving/samples/pinwheel.ts", + "tags": {}, + "timestamp": 9800000000}, + {"objects": [{"detection": {"bounding_box": {"x_max": 0.9932312965393066, + "x_min": 0.005518049001693726, + "y_max": 0.9961479902267456, + "y_min": 0.004157662391662598}, + "confidence": 0.588447093963623, + "label": "pottedplant", + "label_id": 16}, + "h": 357, + "roi_type": "pottedplant", + "w": 632, + "x": 4, + "y": 1}], + "resolution": {"height": 360, "width": 640}, + "source": "file:///home/video-analytics-serving/samples/pinwheel.ts", + "tags": {}, + "timestamp": 9840000000}, + {"objects": [{"detection": {"bounding_box": {"x_max": 0.9932312965393066, + "x_min": 0.005518049001693726, + "y_max": 0.9961479902267456, + "y_min": 0.004157662391662598}, + "confidence": 0.588447093963623, + "label": "pottedplant", + "label_id": 16}, + "h": 357, + "roi_type": "pottedplant", + "w": 632, + "x": 4, + "y": 1}], + "resolution": {"height": 360, "width": 640}, + "source": "file:///home/video-analytics-serving/samples/pinwheel.ts", + "tags": {}, + "timestamp": 9880000000}, + {"objects": [{"detection": {"bounding_box": {"x_max": 0.9932311177253723, + "x_min": 0.0055182576179504395, + "y_max": 0.9961481094360352, + "y_min": 0.004157900810241699}, + "confidence": 0.5885038375854492, + "label": "pottedplant", + "label_id": 16}, + "h": 357, + "roi_type": "pottedplant", + "w": 632, + "x": 4, + "y": 1}], + "resolution": {"height": 360, "width": 640}, + "source": "file:///home/video-analytics-serving/samples/pinwheel.ts", + "tags": {}, + "timestamp": 9920000000}, + {"objects": [{"detection": {"bounding_box": {"x_max": 0.9932311177253723, + "x_min": 0.0055182576179504395, + "y_max": 0.9961481094360352, + "y_min": 0.004157900810241699}, + "confidence": 0.5885038375854492, + "label": "pottedplant", + "label_id": 16}, + "h": 357, + "roi_type": "pottedplant", + "w": 632, + "x": 4, + "y": 1}], + "resolution": {"height": 360, "width": 640}, + "source": "file:///home/video-analytics-serving/samples/pinwheel.ts", + "tags": {}, + "timestamp": 9960000000}] + +} diff --git a/tests/test_cases/rest_api/get_object_detection_gstreamer.json b/tests/test_cases/rest_api/get_object_detection_gstreamer.json index 68b9ae5..69aa039 100644 --- a/tests/test_cases/rest_api/get_object_detection_gstreamer.json +++ b/tests/test_cases/rest_api/get_object_detection_gstreamer.json @@ -6,16 +6,16 @@ "name": "object_detection", "parameters": { "properties": { - "device": { - "element": "detection", - "type": "string" - }, "cpu-throughput-streams": { "element": "detection", "maximum": 4294967295, "minimum": 0, "type": "integer" }, + "device": { + "element": "detection", + "type": "string" + }, "inference-interval": { "default": 1, "element": "detection", @@ -23,6 +23,11 @@ "minimum": 1, "type": "integer" }, + "model-instance-id": { + "default": "inf0", + "element": "detection", + "type": "string" + }, "n-threads": { "default": 1, "element": "videoconvert", diff --git a/tests/test_cases/rest_api/get_pipelines_gstreamer.json b/tests/test_cases/rest_api/get_pipelines_gstreamer.json index 63fdfa0..7697d27 100644 --- a/tests/test_cases/rest_api/get_pipelines_gstreamer.json +++ b/tests/test_cases/rest_api/get_pipelines_gstreamer.json @@ -3,24 +3,34 @@ "path": "pipelines", "result": [ { - "description": "Emotion Recognition Pipeline", - "name": "emotion_recognition", + "description": "Decode Pipeline", + "name": "video_decode", + "type": "GStreamer", + "version": "app_dst" + }, + { + "description": "Object Detection Pipeline", + "name": "object_detection", "parameters": { "properties": { "cpu-throughput-streams": { "element": "detection", + "maximum": 4294967295, + "minimum": 0, "type": "integer" }, "device": { "element": "detection", - "enum": [ - "CPU", - "HDDL", - "GPU", - "VPU" - ], "type": "string" }, + "edgexbridge": { + "element": { + "format": "json", + "name": "edgexbridge", + "property": "kwarg" + }, + "type": "object" + }, "inference-interval": { "default": 1, "element": "detection", @@ -28,6 +38,11 @@ "minimum": 1, "type": "integer" }, + "model-instance-id": { + "default": "inf0", + "element": "detection", + "type": "string" + }, "n-threads": { "default": 1, "element": "videoconvert", @@ -36,53 +51,43 @@ "nireq": { "default": 2, "element": "detection", - "maximum": 1024, + "maximum": 64, "minimum": 1, "type": "integer" + }, + "properties": { + "mqtt_devicename": { + "default": "objectDetect-mqtt", + "element": "edgexbridge", + "type": "string" + }, + "topic": { + "default": "edgex", + "element": "edgexbridge", + "type": "string" + } } }, "type": "object" }, "type": "GStreamer", - "version": "1" + "version": "edgex" }, { - "description": "Audio Detection Pipeline", - "name": "audio_detection", + "description": "Object Detection Pipeline", + "name": "object_detection", "parameters": { "properties": { - "bus-messages": { - "default": false, - "description": "Prints GstBus messages as logger info", - "type": "boolean" - }, - "device": { + "model-instance-id": { + "default": "", "element": "detection", "type": "string" - }, - "output-buffer-duration": { - "default": 100000000, - "element": "audiomixer", - "type": "integer" - }, - "post-messages": { - "element": "level", - "type": "boolean" - }, - "sliding-window": { - "default": 0.2, - "element": "detection", - "type": "number" - }, - "threshold": { - "element": "detection", - "type": "number" } }, "type": "object" }, "type": "GStreamer", - "version": "1" + "version": "app_src_dst" }, { "description": "Object Detection Pipeline", @@ -106,6 +111,18 @@ "minimum": 1, "type": "integer" }, + "max-size-time": { + "default": 2000000000, + "element": "splitmuxsink", + "maximum": 200000000000, + "minimum": 1, + "type": "integer" + }, + "model-instance-id": { + "default": "inf0", + "element": "detection", + "type": "string" + }, "n-threads": { "default": 1, "element": "videoconvert", @@ -117,12 +134,20 @@ "maximum": 64, "minimum": 1, "type": "integer" + }, + "recording_prefix": { + "default": "/tmp/vaserving", + "element": { + "name": "splitmuxsink", + "property": "location" + }, + "type": "string" } }, "type": "object" }, "type": "GStreamer", - "version": "1" + "version": "2" }, { "description": "Object Detection Pipeline", @@ -146,12 +171,10 @@ "minimum": 1, "type": "integer" }, - "max-size-time": { - "default": 2000000000, - "element": "splitmuxsink", - "maximum": 200000000000, - "minimum": 1, - "type": "integer" + "model-instance-id": { + "default": "inf0", + "element": "detection", + "type": "string" }, "n-threads": { "default": 1, @@ -164,44 +187,70 @@ "maximum": 64, "minimum": 1, "type": "integer" + } + }, + "type": "object" + }, + "type": "GStreamer", + "version": "1" + }, + { + "description": "Audio Detection Pipeline", + "name": "audio_detection", + "parameters": { + "properties": { + "bus-messages": { + "default": false, + "description": "Prints GstBus messages as logger info", + "type": "boolean" }, - "recording_prefix": { - "default": "/tmp/vaserving", - "element": { - "name": "splitmuxsink", - "property": "location" - }, + "device": { + "element": "detection", "type": "string" + }, + "output-buffer-duration": { + "default": 100000000, + "element": "audiomixer", + "type": "integer" + }, + "post-messages": { + "element": "level", + "type": "boolean" + }, + "sliding-window": { + "default": 0.2, + "element": "detection", + "type": "number" + }, + "threshold": { + "element": "detection", + "type": "number" } }, "type": "object" }, "type": "GStreamer", - "version": "2" + "version": "1" }, { - "description": "Object Detection Pipeline", - "name": "object_detection", + "description": "Emotion Recognition Pipeline", + "name": "emotion_recognition", "parameters": { "properties": { "cpu-throughput-streams": { "element": "detection", - "maximum": 4294967295, - "minimum": 0, "type": "integer" }, "device": { "element": "detection", + "enum": [ + "CPU", + "HDDL", + "GPU", + "VPU" + ], "type": "string" }, - "edgexbridge": { - "element": { - "format": "json", - "name": "edgexbridge", - "property": "kwarg" - }, - "type": "object" - }, "inference-interval": { "default": 1, "element": "detection", @@ -209,6 +258,22 @@ "minimum": 1, "type": "integer" }, + "model-instance-id-classification": { + "default": "", + "element": { + "name": "classification", + "property": "model-instance-id" + }, + "type": "string" + }, + "model-instance-id-detection": { + "default": "face_detection_retail0", + "element": { + "name": "detection", + "property": "model-instance-id" + }, + "type": "string" + }, "n-threads": { "default": 1, "element": "videoconvert", @@ -217,39 +282,15 @@ "nireq": { "default": 2, "element": "detection", - "maximum": 64, + "maximum": 1024, "minimum": 1, "type": "integer" - }, - "properties": { - "mqtt_devicename": { - "default": "objectDetect-mqtt", - "element": "edgexbridge", - "type": "string" - }, - "topic": { - "default": "edgex", - "element": "edgexbridge", - "type": "string" - } } }, "type": "object" }, "type": "GStreamer", - "version": "edgex" - }, - { - "description": "Object Detection Pipeline", - "name": "object_detection", - "type": "GStreamer", - "version": "app_src_dst" - }, - { - "description": "Decode Pipeline", - "name": "video_decode", - "type": "GStreamer", - "version": "app_dst" + "version": "1" } ], "status_code": 200, From 33b5fe5abfca3eb23b1275fc31ce4b3a5aa60ae5 Mon Sep 17 00:00:00 2001 From: Tobias Mosby Date: Fri, 8 Jan 2021 13:52:19 -0800 Subject: [PATCH 18/50] Revert dynamic removal of gstlibav --- samples/lva_ai_extension/docker/Dockerfile | 6 ------ samples/lva_ai_extension/docker/build.sh | 8 +------- samples/lva_ai_extension/tests/build.sh | 2 +- 3 files changed, 2 insertions(+), 14 deletions(-) diff --git a/samples/lva_ai_extension/docker/Dockerfile b/samples/lva_ai_extension/docker/Dockerfile index fd7816d..48855ef 100644 --- a/samples/lva_ai_extension/docker/Dockerfile +++ b/samples/lva_ai_extension/docker/Dockerfile @@ -3,12 +3,6 @@ FROM $BASE USER root -ARG INCLUDE_GSTLIBAV=true -RUN if [ $INCLUDE_GSTLIBAV = "false" ]; then \ - rm /usr/local/lib/x86_64-linux-gnu/gstreamer-1.0/libgstlibav.so /usr/local/lib/x86_64-linux-gnu/gstreamer-1.0/libgstlibav.la; \ - fi - - RUN DEBIAN_FRONTEND=noninteractive apt-get update && apt-get install -y python3-opencv # Dependencies installed via pip diff --git a/samples/lva_ai_extension/docker/build.sh b/samples/lva_ai_extension/docker/build.sh index c6d9e43..14713c7 100755 --- a/samples/lva_ai_extension/docker/build.sh +++ b/samples/lva_ai_extension/docker/build.sh @@ -3,7 +3,6 @@ WORK_DIR=$(dirname $(readlink -f "$0")) SAMPLE_DIR=$(dirname $WORK_DIR) SAMPLE_BUILD_ARGS=$(env | cut -f1 -d= | grep -E '_(proxy|REPO|VER)$' | sed 's/^/--build-arg / ' | tr '\n' ' ') -REMOVE_GSTLIBAV= BASE_IMAGE="openvisualcloud/xeone3-ubuntu1804-analytics-gst:20.10" OMZ_VERSION="2021.1" TAG="video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension" @@ -24,10 +23,6 @@ function get_options { error 'ERROR: "--base" requires an argument.' fi ;; - --remove-gstlibav) - REMOVE_GSTLIBAV="--build-arg INCLUDE_GSTLIBAV=false" - shift - ;; *) break ;; @@ -38,7 +33,6 @@ function get_options { function show_help { echo "usage: ./run_server.sh" - echo " [ --remove-gstlibav : Remove gstlibav package from build ] " echo " [ --base : Base image for VA Serving build ] " } @@ -59,4 +53,4 @@ launch "$SAMPLE_DIR/../../docker/build.sh --framework gstreamer --create-service # Build AI Extention echo $SAMPLE_DIR/.. -launch "docker build -f $WORK_DIR/Dockerfile $SAMPLE_BUILD_ARGS $REMOVE_GSTLIBAV -t $TAG $SAMPLE_DIR" +launch "docker build -f $WORK_DIR/Dockerfile $SAMPLE_BUILD_ARGS -t $TAG $SAMPLE_DIR" diff --git a/samples/lva_ai_extension/tests/build.sh b/samples/lva_ai_extension/tests/build.sh index 3bd9857..c0e88ae 100755 --- a/samples/lva_ai_extension/tests/build.sh +++ b/samples/lva_ai_extension/tests/build.sh @@ -64,7 +64,7 @@ get_options "$@" # Build LVA image if not specified if [ -z "$LVA_IMAGE" ]; then echo Building $EXTENSION_IMAGE_TAG - launch "$LVA_DIR/docker/build.sh --remove-gstlibav --base ${CACHE_PREFIX}$VAS_BASE_IMAGE" + launch "$LVA_DIR/docker/build.sh --base ${CACHE_PREFIX}$VAS_BASE_IMAGE" fi # Add tests layer From cd0db2c570ab90462b2e65c0c41ddd168644f12f Mon Sep 17 00:00:00 2001 From: nnshah1 Date: Fri, 8 Jan 2021 16:20:53 -0800 Subject: [PATCH 19/50] Removing test for now as it won't be able to test if the parameters are actually in the pipeline (it succeeds either way) --- ...t_detection_set_instance_id_gstreamer.json | 4023 ----------------- 1 file changed, 4023 deletions(-) delete mode 100644 tests/test_cases/pipeline_execution/object_detection_set_instance_id_gstreamer.json diff --git a/tests/test_cases/pipeline_execution/object_detection_set_instance_id_gstreamer.json b/tests/test_cases/pipeline_execution/object_detection_set_instance_id_gstreamer.json deleted file mode 100644 index 297989b..0000000 --- a/tests/test_cases/pipeline_execution/object_detection_set_instance_id_gstreamer.json +++ /dev/null @@ -1,4023 +0,0 @@ -{ - "options": {}, - "pipeline": { - "name": "object_detection", - "version": 1 - }, - "request": { - "source": { - "type": "uri", - "uri": "file:///home/video-analytics-serving/samples/pinwheel.ts" - }, - "destination": { - "type": "file", - "path": "/tmp/object_detection_results.json", - "format": "json" - }, - "parameters": { - "model-instance-id": "objectdetection1" - } - }, - "result": - [{"objects": 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"file:///home/video-analytics-serving/samples/pinwheel.ts", - "tags": {}, - "timestamp": 9920000000}, - {"objects": [{"detection": {"bounding_box": {"x_max": 0.9932311177253723, - "x_min": 0.0055182576179504395, - "y_max": 0.9961481094360352, - "y_min": 0.004157900810241699}, - "confidence": 0.5885038375854492, - "label": "pottedplant", - "label_id": 16}, - "h": 357, - "roi_type": "pottedplant", - "w": 632, - "x": 4, - "y": 1}], - "resolution": {"height": 360, "width": 640}, - "source": "file:///home/video-analytics-serving/samples/pinwheel.ts", - "tags": {}, - "timestamp": 9960000000}] - -} From 67299a75628187b237421ae4ac9cd803fd436d2b Mon Sep 17 00:00:00 2001 From: "Thakkalapelli, Thanaji Rao" Date: Tue, 12 Jan 2021 16:46:30 -0800 Subject: [PATCH 20/50] GPU & MYRIAD tests --- .../emotion_recognition/1/pipeline.json | 3 +- tests/Dockerfile | 4 +- tests/common/results_processing.py | 14 +- tests/conftest.py | 34 +- tests/test_cases/initialization/.gitignore | 0 .../test_cases/pipeline_execution/.gitignore | 0 .../{ => cpu}/audio_detection_gstreamer.json | 0 .../{ => cpu}/emotion_recognition_ffmpeg.json | 0 .../emotion_recognition_fifo_ffmpeg.json | 0 .../emotion_recognition_fifo_gstreamer.json | 0 .../emotion_recognition_gstreamer.json | 0 ...n_recognition_stringver_cpu_gstreamer.json | 0 .../stringified_version/pipeline.json | 3 +- .../object_detection_2_fifo_gstreamer.json | 0 .../object_detection_app_gstreamer.json | 0 .../object_detection_cpu_gstreamer.json | 0 .../{ => cpu}/object_detection_ffmpeg.desc | 0 .../{ => cpu}/object_detection_ffmpeg.json | 0 .../object_detection_fifo_ffmpeg.json | 0 .../object_detection_fifo_gstreamer.json | 0 .../{ => cpu}/object_detection_gstreamer.json | 0 .../emotion_recognition_gpu_gstreamer.json | 53693 ++++++++++++++++ .../object_detection_gpu_FP16_gstreamer.json | 7274 +++ .../object_detection/1/pipeline.json | 43 + .../object_detection_gpu_FP32_gstreamer.json | 7274 +++ .../object_detection/1/pipeline.json | 43 + .../gpu/object_detection_gpu_gstreamer.json | 7274 +++ .../object_detection_myriad_gstreamer.json | 7275 +++ ...erformance_object_detection_gstreamer.json | 0 .../stability_audio_detection_gstreamer.json | 0 .../1/pipeline.json | 0 ...bility_audio_detection_many_gstreamer.json | 0 ...ability_emotion_recognition_gstreamer.json | 0 .../1/pipeline.json | 3 +- ...ility_emotion_recognition_many_ffmpeg.json | 0 ...ty_emotion_recognition_many_gstreamer.json | 0 .../stability_object_detection_gstreamer.json | 0 .../1/pipeline.json | 0 ...object_detection_many2_fifo_gstreamer.json | 0 ...tability_object_detection_many_ffmpeg.json | 0 ...ility_object_detection_many_gstreamer.json | 0 ...bility_object_detection_two_gstreamer.json | 0 .../1/pipeline.json | 0 .../rest_api/get_pipelines_gstreamer.json | 5 +- .../rest_audio_detection_gstreamer.json | 0 .../{ => cpu}/rest_emotion_recognition.json | 0 .../{ => cpu}/rest_object_detection.json | 0 ..._object_detection_bad_param_gstreamer.json | 0 .../rest_audio_detection_gpu_gstreamer.json | 28 + ...est_emotion_recognition_gpu_gstreamer.json | 28 + .../rest_object_detection_gpu_gstreamer.json | 28 + ...est_object_detection_myriad_gstreamer.json | 26 + 52 files changed, 83040 insertions(+), 12 deletions(-) delete mode 100644 tests/test_cases/initialization/.gitignore delete mode 100644 tests/test_cases/pipeline_execution/.gitignore rename tests/test_cases/pipeline_execution/{ => cpu}/audio_detection_gstreamer.json (100%) rename tests/test_cases/pipeline_execution/{ => cpu}/emotion_recognition_ffmpeg.json (100%) rename tests/test_cases/pipeline_execution/{ => cpu}/emotion_recognition_fifo_ffmpeg.json (100%) rename tests/test_cases/pipeline_execution/{ => cpu}/emotion_recognition_fifo_gstreamer.json (100%) rename tests/test_cases/pipeline_execution/{ => cpu}/emotion_recognition_gstreamer.json (100%) rename tests/test_cases/pipeline_execution/{ => cpu}/emotion_recognition_stringver_cpu_gstreamer.json (100%) rename tests/test_cases/pipeline_execution/{ => cpu}/emotion_recognition_stringver_cpu_gstreamer_pipelines/emotion_recognition/stringified_version/pipeline.json (98%) rename tests/test_cases/pipeline_execution/{ => cpu}/object_detection_2_fifo_gstreamer.json (100%) rename tests/test_cases/pipeline_execution/{ => cpu}/object_detection_app_gstreamer.json (100%) rename tests/test_cases/pipeline_execution/{ => cpu}/object_detection_cpu_gstreamer.json (100%) rename tests/test_cases/pipeline_execution/{ => cpu}/object_detection_ffmpeg.desc (100%) rename tests/test_cases/pipeline_execution/{ => cpu}/object_detection_ffmpeg.json (100%) rename tests/test_cases/pipeline_execution/{ => cpu}/object_detection_fifo_ffmpeg.json (100%) rename tests/test_cases/pipeline_execution/{ => cpu}/object_detection_fifo_gstreamer.json (100%) rename tests/test_cases/pipeline_execution/{ => cpu}/object_detection_gstreamer.json (100%) create mode 100644 tests/test_cases/pipeline_execution/gpu/emotion_recognition_gpu_gstreamer.json create mode 100644 tests/test_cases/pipeline_execution/gpu/object_detection_gpu_FP16_gstreamer.json create mode 100644 tests/test_cases/pipeline_execution/gpu/object_detection_gpu_FP16_gstreamer_pipelines/object_detection/1/pipeline.json create mode 100644 tests/test_cases/pipeline_execution/gpu/object_detection_gpu_FP32_gstreamer.json create mode 100644 tests/test_cases/pipeline_execution/gpu/object_detection_gpu_FP32_gstreamer_pipelines/object_detection/1/pipeline.json create mode 100644 tests/test_cases/pipeline_execution/gpu/object_detection_gpu_gstreamer.json create mode 100644 tests/test_cases/pipeline_execution/myriad/object_detection_myriad_gstreamer.json rename tests/test_cases/pipeline_performance/{ => cpu}/performance_object_detection_gstreamer.json (100%) rename tests/test_cases/pipeline_stability/{ => cpu}/stability_audio_detection_gstreamer.json (100%) rename tests/test_cases/pipeline_stability/{ => cpu}/stability_audio_detection_gstreamer_pipelines/1/pipeline.json (100%) rename tests/test_cases/pipeline_stability/{ => cpu}/stability_audio_detection_many_gstreamer.json (100%) rename tests/test_cases/pipeline_stability/{ => cpu}/stability_emotion_recognition_gstreamer.json (100%) rename tests/test_cases/pipeline_stability/{ => cpu}/stability_emotion_recognition_gstreamer_pipelines/1/pipeline.json (98%) rename tests/test_cases/pipeline_stability/{ => cpu}/stability_emotion_recognition_many_ffmpeg.json (100%) rename tests/test_cases/pipeline_stability/{ => cpu}/stability_emotion_recognition_many_gstreamer.json (100%) rename tests/test_cases/pipeline_stability/{ => cpu}/stability_object_detection_gstreamer.json (100%) rename tests/test_cases/pipeline_stability/{ => cpu}/stability_object_detection_gstreamer_pipelines/1/pipeline.json (100%) rename tests/test_cases/pipeline_stability/{ => cpu}/stability_object_detection_many2_fifo_gstreamer.json (100%) rename tests/test_cases/pipeline_stability/{ => cpu}/stability_object_detection_many_ffmpeg.json (100%) rename tests/test_cases/pipeline_stability/{ => cpu}/stability_object_detection_many_gstreamer.json (100%) rename tests/test_cases/pipeline_stability/{ => cpu}/stability_object_detection_two_gstreamer.json (100%) rename tests/test_cases/pipeline_stability/{ => cpu}/stability_object_detection_two_gstreamer_pipelines/1/pipeline.json (100%) rename tests/test_cases/rest_execution/{ => cpu}/rest_audio_detection_gstreamer.json (100%) rename tests/test_cases/rest_execution/{ => cpu}/rest_emotion_recognition.json (100%) rename tests/test_cases/rest_execution/{ => cpu}/rest_object_detection.json (100%) rename tests/test_cases/rest_execution/{ => cpu}/rest_object_detection_bad_param_gstreamer.json (100%) create mode 100644 tests/test_cases/rest_execution/gpu/rest_audio_detection_gpu_gstreamer.json create mode 100644 tests/test_cases/rest_execution/gpu/rest_emotion_recognition_gpu_gstreamer.json create mode 100644 tests/test_cases/rest_execution/gpu/rest_object_detection_gpu_gstreamer.json create mode 100644 tests/test_cases/rest_execution/myriad/rest_object_detection_myriad_gstreamer.json diff --git a/pipelines/gstreamer/emotion_recognition/1/pipeline.json b/pipelines/gstreamer/emotion_recognition/1/pipeline.json index 005b6ba..6be4a27 100644 --- a/pipelines/gstreamer/emotion_recognition/1/pipeline.json +++ b/pipelines/gstreamer/emotion_recognition/1/pipeline.json @@ -18,7 +18,8 @@ "CPU", "HDDL", "GPU", - "VPU" + "VPU", + "MYRIAD" ] }, "model-instance-id-detection": { diff --git a/tests/Dockerfile b/tests/Dockerfile index 01730a9..0d782ab 100644 --- a/tests/Dockerfile +++ b/tests/Dockerfile @@ -28,9 +28,9 @@ COPY models_list/models.list.yml /home/video-analytics-serving/models/models.lis # Copy and rename for stringified version -RUN mkdir -p /home/video-analytics-serving/tests/test_cases/pipeline_execution/emotion_recognition_stringver_cpu_gstreamer_models/emotion_recognition/stringified_version; cp -rf /home/video-analytics-serving/models/emotion_recognition/1/* /home/video-analytics-serving/tests/test_cases/pipeline_execution/emotion_recognition_stringver_cpu_gstreamer_models/emotion_recognition/stringified_version +RUN mkdir -p /home/video-analytics-serving/tests/test_cases/pipeline_execution/cpu/emotion_recognition_stringver_cpu_gstreamer_models/emotion_recognition/stringified_version; cp -rf /home/video-analytics-serving/models/emotion_recognition/1/* /home/video-analytics-serving/tests/test_cases/pipeline_execution/cpu/emotion_recognition_stringver_cpu_gstreamer_models/emotion_recognition/stringified_version -RUN mkdir -p /home/video-analytics-serving/tests/test_cases/pipeline_execution/emotion_recognition_stringver_cpu_gstreamer_models/face_detection_retail/1; cp -rf /home/video-analytics-serving/models/face_detection_retail/1/* /home/video-analytics-serving/tests/test_cases/pipeline_execution/emotion_recognition_stringver_cpu_gstreamer_models/face_detection_retail/1 +RUN mkdir -p /home/video-analytics-serving/tests/test_cases/pipeline_execution/cpu/emotion_recognition_stringver_cpu_gstreamer_models/face_detection_retail/1; cp -rf /home/video-analytics-serving/models/face_detection_retail/1/* /home/video-analytics-serving/tests/test_cases/pipeline_execution/cpu/emotion_recognition_stringver_cpu_gstreamer_models/face_detection_retail/1 ARG USER=vaserving diff --git a/tests/common/results_processing.py b/tests/common/results_processing.py index 2f755e3..f13e968 100644 --- a/tests/common/results_processing.py +++ b/tests/common/results_processing.py @@ -5,6 +5,7 @@ ''' import os +import math import json from threading import Thread from vaserving.common.utils import logging @@ -92,7 +93,18 @@ def cmp_results(measured, expected, tolerance): assert type(measured) == type(expected), "Type Comparison Mismatch" - if isinstance(measured, int) or isinstance(measured, float): + if isinstance(measured, int): + if expected != 0: + msg = "Measured Value {} not within tolerance ({}) of Expected Value {}" + assert (abs(measured-expected) / abs(expected) <= math.ceil(tolerance)), \ + msg.format(measured, math.ceil(tolerance), expected) + else: + msg = "Measured Value {} not within tolerance ({}) of Expected Value {}" + assert tolerance > 1, \ + msg.format(measured, tolerance, expected) + return True + + if isinstance(measured, float): if expected != 0: msg = "Measured Value {} not within tolerance ({}) of Expected Value {}" assert (abs(measured-expected) / abs(expected)) < tolerance, \ diff --git a/tests/conftest.py b/tests/conftest.py index f5c422a..b5b2406 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -105,6 +105,13 @@ def pytest_addoption(parser): default=False) parser.addoption("--stability_duration", type=int, help="duration to run stability tests", action="store", default=None) + parser.addoption("--cpu", action="store_true", help="Run CPU tests", + default=True) + parser.addoption("--no-cpu", action="store_false", dest='cpu', help="Disable CPU tests") + parser.addoption("--gpu", action="store_true", help="Run GPU tests", + default=False) + parser.addoption("--myriad", action="store_true", help="Run MYRIAD tests", + default=False) def pytest_configure(config): config.addinivalue_line("markers", "stability: run stability tests") @@ -128,10 +135,29 @@ def numerical_tolerance(request): def load_test_cases(metafunc, directory): known_frameworks = ['ffmpeg', 'gstreamer'] dir_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_cases", directory) - filenames = [(os.path.abspath(os.path.join(dir_path, fn)), - os.path.splitext(fn)[0]) for fn in os.listdir(dir_path) - if os.path.isfile(os.path.join(dir_path, fn)) and - os.path.splitext(fn)[1] == '.json'] + list_of_dir_paths = [dir_path] + + filenames = [] + if metafunc.config.getoption("cpu"): + cpu_path = os.path.join(dir_path, "cpu") + if os.path.isdir(cpu_path): + list_of_dir_paths.append(cpu_path) + if metafunc.config.getoption("gpu"): + gpu_path = os.path.join(dir_path, "gpu") + if os.path.isdir(gpu_path): + list_of_dir_paths.append(gpu_path) + + if metafunc.config.getoption("myriad"): + gpu_path = os.path.join(dir_path, "myriad") + if os.path.isdir(gpu_path): + list_of_dir_paths.append(gpu_path) + + for path in list_of_dir_paths: + dir_filenames = [(os.path.abspath(os.path.join(path, fn)), + os.path.splitext(fn)[0]) for fn in os.listdir(path) + if os.path.isfile(os.path.join(path, fn)) and + os.path.splitext(fn)[1] == '.json'] + filenames.extend(dir_filenames) framework = metafunc.config.getoption("framework") filenames = [filename for filename in filenames if filename[1].split('_')[-1] == framework or diff --git a/tests/test_cases/initialization/.gitignore b/tests/test_cases/initialization/.gitignore deleted file mode 100644 index e69de29..0000000 diff --git a/tests/test_cases/pipeline_execution/.gitignore b/tests/test_cases/pipeline_execution/.gitignore deleted file mode 100644 index e69de29..0000000 diff --git a/tests/test_cases/pipeline_execution/audio_detection_gstreamer.json b/tests/test_cases/pipeline_execution/cpu/audio_detection_gstreamer.json similarity index 100% rename from tests/test_cases/pipeline_execution/audio_detection_gstreamer.json rename to tests/test_cases/pipeline_execution/cpu/audio_detection_gstreamer.json diff --git a/tests/test_cases/pipeline_execution/emotion_recognition_ffmpeg.json b/tests/test_cases/pipeline_execution/cpu/emotion_recognition_ffmpeg.json similarity index 100% rename from tests/test_cases/pipeline_execution/emotion_recognition_ffmpeg.json rename to tests/test_cases/pipeline_execution/cpu/emotion_recognition_ffmpeg.json diff --git a/tests/test_cases/pipeline_execution/emotion_recognition_fifo_ffmpeg.json b/tests/test_cases/pipeline_execution/cpu/emotion_recognition_fifo_ffmpeg.json similarity index 100% rename from tests/test_cases/pipeline_execution/emotion_recognition_fifo_ffmpeg.json rename to tests/test_cases/pipeline_execution/cpu/emotion_recognition_fifo_ffmpeg.json diff --git a/tests/test_cases/pipeline_execution/emotion_recognition_fifo_gstreamer.json b/tests/test_cases/pipeline_execution/cpu/emotion_recognition_fifo_gstreamer.json similarity index 100% rename from tests/test_cases/pipeline_execution/emotion_recognition_fifo_gstreamer.json rename to tests/test_cases/pipeline_execution/cpu/emotion_recognition_fifo_gstreamer.json diff --git a/tests/test_cases/pipeline_execution/emotion_recognition_gstreamer.json b/tests/test_cases/pipeline_execution/cpu/emotion_recognition_gstreamer.json similarity index 100% rename from tests/test_cases/pipeline_execution/emotion_recognition_gstreamer.json rename to tests/test_cases/pipeline_execution/cpu/emotion_recognition_gstreamer.json diff --git a/tests/test_cases/pipeline_execution/emotion_recognition_stringver_cpu_gstreamer.json b/tests/test_cases/pipeline_execution/cpu/emotion_recognition_stringver_cpu_gstreamer.json similarity index 100% rename from tests/test_cases/pipeline_execution/emotion_recognition_stringver_cpu_gstreamer.json rename to tests/test_cases/pipeline_execution/cpu/emotion_recognition_stringver_cpu_gstreamer.json diff --git a/tests/test_cases/pipeline_execution/emotion_recognition_stringver_cpu_gstreamer_pipelines/emotion_recognition/stringified_version/pipeline.json b/tests/test_cases/pipeline_execution/cpu/emotion_recognition_stringver_cpu_gstreamer_pipelines/emotion_recognition/stringified_version/pipeline.json similarity index 98% rename from tests/test_cases/pipeline_execution/emotion_recognition_stringver_cpu_gstreamer_pipelines/emotion_recognition/stringified_version/pipeline.json rename to tests/test_cases/pipeline_execution/cpu/emotion_recognition_stringver_cpu_gstreamer_pipelines/emotion_recognition/stringified_version/pipeline.json index e2eade1..993f593 100644 --- a/tests/test_cases/pipeline_execution/emotion_recognition_stringver_cpu_gstreamer_pipelines/emotion_recognition/stringified_version/pipeline.json +++ b/tests/test_cases/pipeline_execution/cpu/emotion_recognition_stringver_cpu_gstreamer_pipelines/emotion_recognition/stringified_version/pipeline.json @@ -18,7 +18,8 @@ "CPU", "HDDL", "GPU", - "VPU" + "VPU", + "MYRIAD" ] }, "inference-interval": { diff --git a/tests/test_cases/pipeline_execution/object_detection_2_fifo_gstreamer.json b/tests/test_cases/pipeline_execution/cpu/object_detection_2_fifo_gstreamer.json similarity index 100% rename from tests/test_cases/pipeline_execution/object_detection_2_fifo_gstreamer.json rename to tests/test_cases/pipeline_execution/cpu/object_detection_2_fifo_gstreamer.json diff --git a/tests/test_cases/pipeline_execution/object_detection_app_gstreamer.json b/tests/test_cases/pipeline_execution/cpu/object_detection_app_gstreamer.json similarity index 100% rename from tests/test_cases/pipeline_execution/object_detection_app_gstreamer.json rename to tests/test_cases/pipeline_execution/cpu/object_detection_app_gstreamer.json diff --git a/tests/test_cases/pipeline_execution/object_detection_cpu_gstreamer.json b/tests/test_cases/pipeline_execution/cpu/object_detection_cpu_gstreamer.json similarity index 100% rename from tests/test_cases/pipeline_execution/object_detection_cpu_gstreamer.json rename to tests/test_cases/pipeline_execution/cpu/object_detection_cpu_gstreamer.json diff --git a/tests/test_cases/pipeline_execution/object_detection_ffmpeg.desc b/tests/test_cases/pipeline_execution/cpu/object_detection_ffmpeg.desc similarity index 100% rename from tests/test_cases/pipeline_execution/object_detection_ffmpeg.desc rename to tests/test_cases/pipeline_execution/cpu/object_detection_ffmpeg.desc diff --git a/tests/test_cases/pipeline_execution/object_detection_ffmpeg.json b/tests/test_cases/pipeline_execution/cpu/object_detection_ffmpeg.json similarity index 100% rename from tests/test_cases/pipeline_execution/object_detection_ffmpeg.json rename to tests/test_cases/pipeline_execution/cpu/object_detection_ffmpeg.json diff --git a/tests/test_cases/pipeline_execution/object_detection_fifo_ffmpeg.json b/tests/test_cases/pipeline_execution/cpu/object_detection_fifo_ffmpeg.json similarity index 100% rename from tests/test_cases/pipeline_execution/object_detection_fifo_ffmpeg.json rename to tests/test_cases/pipeline_execution/cpu/object_detection_fifo_ffmpeg.json diff --git a/tests/test_cases/pipeline_execution/object_detection_fifo_gstreamer.json b/tests/test_cases/pipeline_execution/cpu/object_detection_fifo_gstreamer.json similarity index 100% rename from tests/test_cases/pipeline_execution/object_detection_fifo_gstreamer.json rename to tests/test_cases/pipeline_execution/cpu/object_detection_fifo_gstreamer.json diff --git a/tests/test_cases/pipeline_execution/object_detection_gstreamer.json b/tests/test_cases/pipeline_execution/cpu/object_detection_gstreamer.json similarity index 100% rename from tests/test_cases/pipeline_execution/object_detection_gstreamer.json rename to tests/test_cases/pipeline_execution/cpu/object_detection_gstreamer.json diff --git a/tests/test_cases/pipeline_execution/gpu/emotion_recognition_gpu_gstreamer.json b/tests/test_cases/pipeline_execution/gpu/emotion_recognition_gpu_gstreamer.json new file mode 100644 index 0000000..f646e23 --- /dev/null +++ b/tests/test_cases/pipeline_execution/gpu/emotion_recognition_gpu_gstreamer.json @@ -0,0 +1,53693 @@ +{ + "options": {}, + "pipeline": { + "name": "emotion_recognition", + "version": 1 + }, + "request": { + "source": { + "type": "uri", + "uri": 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"resolution": { + "height": 360, + "width": 640 + }, + "source": "file:///home/video-analytics-serving/samples/pinwheel.ts", + "tags": {}, + "timestamp": 9920000000 + }, + { + "objects": [ + { + "detection": { + "bounding_box": { + "x_max": 0.993230938911438, + "x_min": 0.005518496036529541, + "y_max": 0.996147871017456, + "y_min": 0.0041577816009521484 + }, + "confidence": 0.5885467529296875, + "label": "pottedplant", + "label_id": 16 + }, + "h": 357, + "roi_type": "pottedplant", + "w": 632, + "x": 4, + "y": 1 + } + ], + "resolution": { + "height": 360, + "width": 640 + }, + "source": "file:///home/video-analytics-serving/samples/pinwheel.ts", + "tags": {}, + "timestamp": 9960000000 + } + ] +} \ No newline at end of file diff --git a/tests/test_cases/pipeline_performance/performance_object_detection_gstreamer.json b/tests/test_cases/pipeline_performance/cpu/performance_object_detection_gstreamer.json similarity index 100% rename from tests/test_cases/pipeline_performance/performance_object_detection_gstreamer.json rename to tests/test_cases/pipeline_performance/cpu/performance_object_detection_gstreamer.json diff --git a/tests/test_cases/pipeline_stability/stability_audio_detection_gstreamer.json b/tests/test_cases/pipeline_stability/cpu/stability_audio_detection_gstreamer.json similarity index 100% rename from tests/test_cases/pipeline_stability/stability_audio_detection_gstreamer.json rename to tests/test_cases/pipeline_stability/cpu/stability_audio_detection_gstreamer.json diff --git a/tests/test_cases/pipeline_stability/stability_audio_detection_gstreamer_pipelines/1/pipeline.json b/tests/test_cases/pipeline_stability/cpu/stability_audio_detection_gstreamer_pipelines/1/pipeline.json similarity index 100% rename from tests/test_cases/pipeline_stability/stability_audio_detection_gstreamer_pipelines/1/pipeline.json rename to tests/test_cases/pipeline_stability/cpu/stability_audio_detection_gstreamer_pipelines/1/pipeline.json diff --git a/tests/test_cases/pipeline_stability/stability_audio_detection_many_gstreamer.json b/tests/test_cases/pipeline_stability/cpu/stability_audio_detection_many_gstreamer.json similarity index 100% rename from tests/test_cases/pipeline_stability/stability_audio_detection_many_gstreamer.json rename to tests/test_cases/pipeline_stability/cpu/stability_audio_detection_many_gstreamer.json diff --git a/tests/test_cases/pipeline_stability/stability_emotion_recognition_gstreamer.json b/tests/test_cases/pipeline_stability/cpu/stability_emotion_recognition_gstreamer.json similarity index 100% rename from tests/test_cases/pipeline_stability/stability_emotion_recognition_gstreamer.json rename to tests/test_cases/pipeline_stability/cpu/stability_emotion_recognition_gstreamer.json diff --git a/tests/test_cases/pipeline_stability/stability_emotion_recognition_gstreamer_pipelines/1/pipeline.json b/tests/test_cases/pipeline_stability/cpu/stability_emotion_recognition_gstreamer_pipelines/1/pipeline.json similarity index 98% rename from tests/test_cases/pipeline_stability/stability_emotion_recognition_gstreamer_pipelines/1/pipeline.json rename to tests/test_cases/pipeline_stability/cpu/stability_emotion_recognition_gstreamer_pipelines/1/pipeline.json index 5062942..ae12777 100644 --- a/tests/test_cases/pipeline_stability/stability_emotion_recognition_gstreamer_pipelines/1/pipeline.json +++ b/tests/test_cases/pipeline_stability/cpu/stability_emotion_recognition_gstreamer_pipelines/1/pipeline.json @@ -18,7 +18,8 @@ "CPU", "HDDL", "GPU", - "VPU" + "VPU", + "MYRIAD" ] }, "inference-interval": { diff --git a/tests/test_cases/pipeline_stability/stability_emotion_recognition_many_ffmpeg.json b/tests/test_cases/pipeline_stability/cpu/stability_emotion_recognition_many_ffmpeg.json similarity index 100% rename from tests/test_cases/pipeline_stability/stability_emotion_recognition_many_ffmpeg.json rename to tests/test_cases/pipeline_stability/cpu/stability_emotion_recognition_many_ffmpeg.json diff --git a/tests/test_cases/pipeline_stability/stability_emotion_recognition_many_gstreamer.json b/tests/test_cases/pipeline_stability/cpu/stability_emotion_recognition_many_gstreamer.json similarity index 100% rename from tests/test_cases/pipeline_stability/stability_emotion_recognition_many_gstreamer.json rename to tests/test_cases/pipeline_stability/cpu/stability_emotion_recognition_many_gstreamer.json diff --git a/tests/test_cases/pipeline_stability/stability_object_detection_gstreamer.json b/tests/test_cases/pipeline_stability/cpu/stability_object_detection_gstreamer.json similarity index 100% rename from tests/test_cases/pipeline_stability/stability_object_detection_gstreamer.json rename to tests/test_cases/pipeline_stability/cpu/stability_object_detection_gstreamer.json diff --git a/tests/test_cases/pipeline_stability/stability_object_detection_gstreamer_pipelines/1/pipeline.json b/tests/test_cases/pipeline_stability/cpu/stability_object_detection_gstreamer_pipelines/1/pipeline.json similarity index 100% rename from tests/test_cases/pipeline_stability/stability_object_detection_gstreamer_pipelines/1/pipeline.json rename to tests/test_cases/pipeline_stability/cpu/stability_object_detection_gstreamer_pipelines/1/pipeline.json diff --git a/tests/test_cases/pipeline_stability/stability_object_detection_many2_fifo_gstreamer.json b/tests/test_cases/pipeline_stability/cpu/stability_object_detection_many2_fifo_gstreamer.json similarity index 100% rename from tests/test_cases/pipeline_stability/stability_object_detection_many2_fifo_gstreamer.json rename to tests/test_cases/pipeline_stability/cpu/stability_object_detection_many2_fifo_gstreamer.json diff --git a/tests/test_cases/pipeline_stability/stability_object_detection_many_ffmpeg.json b/tests/test_cases/pipeline_stability/cpu/stability_object_detection_many_ffmpeg.json similarity index 100% rename from tests/test_cases/pipeline_stability/stability_object_detection_many_ffmpeg.json rename to tests/test_cases/pipeline_stability/cpu/stability_object_detection_many_ffmpeg.json diff --git a/tests/test_cases/pipeline_stability/stability_object_detection_many_gstreamer.json b/tests/test_cases/pipeline_stability/cpu/stability_object_detection_many_gstreamer.json similarity index 100% rename from tests/test_cases/pipeline_stability/stability_object_detection_many_gstreamer.json rename to tests/test_cases/pipeline_stability/cpu/stability_object_detection_many_gstreamer.json diff --git a/tests/test_cases/pipeline_stability/stability_object_detection_two_gstreamer.json b/tests/test_cases/pipeline_stability/cpu/stability_object_detection_two_gstreamer.json similarity index 100% rename from tests/test_cases/pipeline_stability/stability_object_detection_two_gstreamer.json rename to tests/test_cases/pipeline_stability/cpu/stability_object_detection_two_gstreamer.json diff --git a/tests/test_cases/pipeline_stability/stability_object_detection_two_gstreamer_pipelines/1/pipeline.json b/tests/test_cases/pipeline_stability/cpu/stability_object_detection_two_gstreamer_pipelines/1/pipeline.json similarity index 100% rename from tests/test_cases/pipeline_stability/stability_object_detection_two_gstreamer_pipelines/1/pipeline.json rename to tests/test_cases/pipeline_stability/cpu/stability_object_detection_two_gstreamer_pipelines/1/pipeline.json diff --git a/tests/test_cases/rest_api/get_pipelines_gstreamer.json b/tests/test_cases/rest_api/get_pipelines_gstreamer.json index 7697d27..29e1c66 100644 --- a/tests/test_cases/rest_api/get_pipelines_gstreamer.json +++ b/tests/test_cases/rest_api/get_pipelines_gstreamer.json @@ -247,7 +247,8 @@ "CPU", "HDDL", "GPU", - "VPU" + "VPU", + "MYRIAD" ], "type": "string" }, @@ -295,4 +296,4 @@ ], "status_code": 200, "verb": "get" -} \ No newline at end of file +} diff --git a/tests/test_cases/rest_execution/rest_audio_detection_gstreamer.json b/tests/test_cases/rest_execution/cpu/rest_audio_detection_gstreamer.json similarity index 100% rename from tests/test_cases/rest_execution/rest_audio_detection_gstreamer.json rename to tests/test_cases/rest_execution/cpu/rest_audio_detection_gstreamer.json diff --git a/tests/test_cases/rest_execution/rest_emotion_recognition.json b/tests/test_cases/rest_execution/cpu/rest_emotion_recognition.json similarity index 100% rename from tests/test_cases/rest_execution/rest_emotion_recognition.json rename to tests/test_cases/rest_execution/cpu/rest_emotion_recognition.json diff --git a/tests/test_cases/rest_execution/rest_object_detection.json b/tests/test_cases/rest_execution/cpu/rest_object_detection.json similarity index 100% rename from tests/test_cases/rest_execution/rest_object_detection.json rename to tests/test_cases/rest_execution/cpu/rest_object_detection.json diff --git a/tests/test_cases/rest_execution/rest_object_detection_bad_param_gstreamer.json b/tests/test_cases/rest_execution/cpu/rest_object_detection_bad_param_gstreamer.json similarity index 100% rename from tests/test_cases/rest_execution/rest_object_detection_bad_param_gstreamer.json rename to tests/test_cases/rest_execution/cpu/rest_object_detection_bad_param_gstreamer.json diff --git a/tests/test_cases/rest_execution/gpu/rest_audio_detection_gpu_gstreamer.json b/tests/test_cases/rest_execution/gpu/rest_audio_detection_gpu_gstreamer.json new file mode 100644 index 0000000..a51514b --- /dev/null +++ b/tests/test_cases/rest_execution/gpu/rest_audio_detection_gpu_gstreamer.json @@ -0,0 +1,28 @@ +{ + "path": "pipelines/audio_detection/1", + "start" : { + "body": { + "source": { + "uri": "file:///home/video-analytics-serving/tests/media/how_are_you_doing.wav", + "type": "uri" + }, + "destination": { + "type": "file", + "path": "/tmp/rest_audio_detection_gstreamer", + "format": "json-lines" + }, + "parameters": { + "device": "GPU" + } + } + }, + "check_running" : { + "timeout": 30.0 + }, + "abort" : { + "delay" : 0 + }, + "check_stopped" : { + "timeout": 0.5 + } +} diff --git a/tests/test_cases/rest_execution/gpu/rest_emotion_recognition_gpu_gstreamer.json b/tests/test_cases/rest_execution/gpu/rest_emotion_recognition_gpu_gstreamer.json new file mode 100644 index 0000000..0e26318 --- /dev/null +++ b/tests/test_cases/rest_execution/gpu/rest_emotion_recognition_gpu_gstreamer.json @@ -0,0 +1,28 @@ +{ + "path": "pipelines/emotion_recognition/1", + "start" : { + "body": { + "source": { + "uri": "file:///home/video-analytics-serving/samples/classroom.mp4", + "type": "uri" + }, + "destination": { + "type": "file", + "path": "/tmp/rest_emotion_recognition", + "format": "json-lines" + }, + "parameters": { + "device": "GPU" + } + } + }, + "check_running" : { + "timeout": 30.0 + }, + "abort" : { + "delay" : 0.5 + }, + "check_stopped" : { + "timeout": 0.5 + } +} diff --git a/tests/test_cases/rest_execution/gpu/rest_object_detection_gpu_gstreamer.json b/tests/test_cases/rest_execution/gpu/rest_object_detection_gpu_gstreamer.json new file mode 100644 index 0000000..861c6a4 --- /dev/null +++ b/tests/test_cases/rest_execution/gpu/rest_object_detection_gpu_gstreamer.json @@ -0,0 +1,28 @@ +{ + "path": "pipelines/object_detection/1", + "start" : { + "body": { + "source": { + "uri": "file:///home/video-analytics-serving/samples/pinwheel.ts", + "type": "uri" + }, + "destination": { + "type": "file", + "path": "/tmp/rest_object_detection", + "format": "json-lines" + }, + "parameters": { + "device": "GPU" + } + } + }, + "check_running" : { + "timeout": 30.0 + }, + "abort" : { + "delay" : 0.5 + }, + "check_stopped" : { + "timeout": 0.5 + } +} diff --git a/tests/test_cases/rest_execution/myriad/rest_object_detection_myriad_gstreamer.json b/tests/test_cases/rest_execution/myriad/rest_object_detection_myriad_gstreamer.json new file mode 100644 index 0000000..f07a905 --- /dev/null +++ b/tests/test_cases/rest_execution/myriad/rest_object_detection_myriad_gstreamer.json @@ -0,0 +1,26 @@ +{ + "path": "pipelines/object_detection/1", + "start" : { + "body": { + "source": { + "uri": "file:///home/video-analytics-serving/samples/pinwheel.ts", + "type": "uri" + }, + "destination": { + "type": "file", + "path": "/tmp/rest_object_detection", + "format": "json-lines" + }, + "parameters": { + "device": "MYRIAD", + "model-instance-id":"" + } + } + }, + "check_running" : { + "timeout": 5.0 + }, + "check_stopped" : { + "timeout": 100 + } +} From 2fc3cc8ce012fa7402279da8d05c38d68013913b Mon Sep 17 00:00:00 2001 From: "Mosby, Tobias" Date: Wed, 13 Jan 2021 09:28:37 -0800 Subject: [PATCH 21/50] Update LVA Build to use default base image --- docker/build.sh | 2 +- samples/lva_ai_extension/docker/Dockerfile | 2 -- samples/lva_ai_extension/docker/build.sh | 19 ++++++++++++++----- samples/lva_ai_extension/tests/build.sh | 7 ++++--- 4 files changed, 19 insertions(+), 11 deletions(-) diff --git a/docker/build.sh b/docker/build.sh index 6d057ca..878d4b6 100755 --- a/docker/build.sh +++ b/docker/build.sh @@ -367,7 +367,7 @@ if [ "$BASE" == "BUILD" ]; then BASE_IMAGE=$BASE_BUILD_TAG else #Ensure image is latest from Docker Hub - launch "$RUN_PREFIX docker pull $BASE_IMAGE" + launch "$RUN_PREFIX docker pull ${CACHE_PREFIX}$BASE_IMAGE" fi # BUILD IMAGE diff --git a/samples/lva_ai_extension/docker/Dockerfile b/samples/lva_ai_extension/docker/Dockerfile index 48855ef..25dbf67 100644 --- a/samples/lva_ai_extension/docker/Dockerfile +++ b/samples/lva_ai_extension/docker/Dockerfile @@ -3,8 +3,6 @@ FROM $BASE USER root -RUN DEBIAN_FRONTEND=noninteractive apt-get update && apt-get install -y python3-opencv - # Dependencies installed via pip COPY ./requirements.txt / RUN pip3 install --no-cache-dir -r /requirements.txt diff --git a/samples/lva_ai_extension/docker/build.sh b/samples/lva_ai_extension/docker/build.sh index 14713c7..e1b5a49 100755 --- a/samples/lva_ai_extension/docker/build.sh +++ b/samples/lva_ai_extension/docker/build.sh @@ -3,8 +3,8 @@ WORK_DIR=$(dirname $(readlink -f "$0")) SAMPLE_DIR=$(dirname $WORK_DIR) SAMPLE_BUILD_ARGS=$(env | cut -f1 -d= | grep -E '_(proxy|REPO|VER)$' | sed 's/^/--build-arg / ' | tr '\n' ' ') -BASE_IMAGE="openvisualcloud/xeone3-ubuntu1804-analytics-gst:20.10" -OMZ_VERSION="2021.1" +BASE_IMAGE= +OMZ_VERSION= TAG="video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension" #Get options passed into script @@ -17,12 +17,20 @@ function get_options { ;; --base) if [ "$2" ]; then - BASE_IMAGE=$2 + BASE_IMAGE="--base $2" shift else error 'ERROR: "--base" requires an argument.' fi ;; + --open-model-zoo-version) + if [ "$2" ]; then + OMZ_VERSION="--open-model-zoo-version $2" + shift + else + error 'ERROR: "--open-model-zoo-version" requires an argument.' + fi + ;; *) break ;; @@ -33,7 +41,8 @@ function get_options { function show_help { echo "usage: ./run_server.sh" - echo " [ --base : Base image for VA Serving build ] " + echo " [ --base : Base image override for VA Serving build ] " + echo " [ --open-model-zoo-version : Open Model Zoo version override for VA Serving build ] " } function launch { echo $@ @@ -49,7 +58,7 @@ function launch { echo $@ get_options "$@" # Build VA Serving -launch "$SAMPLE_DIR/../../docker/build.sh --framework gstreamer --create-service false --base $BASE_IMAGE --open-model-zoo-version $OMZ_VERSION --pipelines samples/lva_ai_extension/pipelines --models $SAMPLE_DIR/models/models.list.yml" +launch "$SAMPLE_DIR/../../docker/build.sh --framework gstreamer --create-service false $BASE_IMAGE $OMZ_VERSION --pipelines samples/lva_ai_extension/pipelines --models $SAMPLE_DIR/models/models.list.yml" # Build AI Extention echo $SAMPLE_DIR/.. diff --git a/samples/lva_ai_extension/tests/build.sh b/samples/lva_ai_extension/tests/build.sh index c0e88ae..a3082cc 100755 --- a/samples/lva_ai_extension/tests/build.sh +++ b/samples/lva_ai_extension/tests/build.sh @@ -2,7 +2,6 @@ WORK_DIR=$(dirname $(readlink -f "$0")) ROOT_DIR=$(readlink -f "$WORK_DIR/../../..") LVA_DIR=$(dirname $WORK_DIR) -VAS_BASE_IMAGE="openvisualcloud/xeone3-ubuntu1804-analytics-gst:20.10" EXTENSION_IMAGE_TAG="video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension" TEST_IMAGE_TAG="video-analytics-serving-lva-tests" SAMPLE_BUILD_ARGS=$(env | cut -f1 -d= | grep -E '_(proxy|REPO|VER)$' | sed 's/^/--build-arg / ' | tr '\n' ' ') @@ -30,7 +29,7 @@ function get_options { SAMPLE_BUILD_ARGS+=" --build-arg BASE=$LVA_IMAGE" shift else - error 'ERROR: "--base" requires an argument.' + error 'ERROR: "--lva-image" requires an argument.' fi ;; --docker-cache) @@ -64,7 +63,9 @@ get_options "$@" # Build LVA image if not specified if [ -z "$LVA_IMAGE" ]; then echo Building $EXTENSION_IMAGE_TAG - launch "$LVA_DIR/docker/build.sh --base ${CACHE_PREFIX}$VAS_BASE_IMAGE" + # Export to allow nested scripts to digest CACHE_PREFIX + export CACHE_PREFIX=${CACHE_PREFIX} + launch "$LVA_DIR/docker/build.sh" fi # Add tests layer From 1b0165905617912673071fbce3b8a843d385ea40 Mon Sep 17 00:00:00 2001 From: "Shah, Neelay N" Date: Wed, 13 Jan 2021 21:53:17 -0800 Subject: [PATCH 22/50] Updates to LVA Client: explicitly use cv2.CAP_GSTREAMER backend for opencv Video Capture Updates to LVA tests: added targetted test for opencv Video Capture Updates to LVA Tests: added explicit check for results (647) --- samples/lva_ai_extension/client/__main__.py | 2 +- samples/lva_ai_extension/tests/Dockerfile | 1 + samples/lva_ai_extension/tests/test_video.py | 43 ++++++++++++++++++-- 3 files changed, 42 insertions(+), 4 deletions(-) diff --git a/samples/lva_ai_extension/client/__main__.py b/samples/lva_ai_extension/client/__main__.py index 5122970..de0720f 100644 --- a/samples/lva_ai_extension/client/__main__.py +++ b/samples/lva_ai_extension/client/__main__.py @@ -41,7 +41,7 @@ class VideoSource: def __init__(self, filename, loop_count): self._loop_count = loop_count - self._vid_cap = cv2.VideoCapture(filename) + self._vid_cap = cv2.VideoCapture(filename, cv2.CAP_GSTREAMER) if self._vid_cap is None or not self._vid_cap.isOpened(): print("{}: Error opening video source".format(filename)) sys.exit(1) diff --git a/samples/lva_ai_extension/tests/Dockerfile b/samples/lva_ai_extension/tests/Dockerfile index c311fb2..462bfb0 100644 --- a/samples/lva_ai_extension/tests/Dockerfile +++ b/samples/lva_ai_extension/tests/Dockerfile @@ -24,6 +24,7 @@ ENV PIPELINE_VERSION 'person_vehicle_bike_detection' RUN wget -O classroom.mp4 https://github.com/intel-iot-devkit/sample-videos/blob/master/classroom.mp4?raw=true RUN wget -O person-bicycle-car-detection.mp4 https://github.com/intel-iot-devkit/sample-videos/blob/master/person-bicycle-car-detection.mp4?raw=true +RUN wget --no-proxy -O person-bicycle-car-detection_1920_1080_2min.mp4 https://gitlab.devtools.intel.com/media-analytics-pipeline-zoo/media/-/raw/main/video/person-bicycle-car-detection/person-bicycle-car-detection_1920_1080_2min.mp4?inline=false ARG USER=vaserving diff --git a/samples/lva_ai_extension/tests/test_video.py b/samples/lva_ai_extension/tests/test_video.py index 8f689dd..e5e13c8 100644 --- a/samples/lva_ai_extension/tests/test_video.py +++ b/samples/lva_ai_extension/tests/test_video.py @@ -10,13 +10,15 @@ import os import json import tempfile +import pytest from jsonschema import validate class TestLvaVideo: def teardown_method(self, test_method): - if self.server_process is not None: - self.server_process.kill() + server_process = getattr(self, "server_process", None) + if server_process is not None: + server_process.kill() def run_client(self, source, sleep_period = 0.25, port = 5001, output_location = None, shared_memory = True, timeout = 300): client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", @@ -50,6 +52,37 @@ def validate_output(self, output_location): if line and line != '': validate(instance=json.loads(line),schema=json_schema) + + def _download_video(self, uri, target, no_proxy=""): + if not os.path.isfile(target): + arguments = ["wget",no_proxy,"-O",target,uri] + subprocess.run(arguments) + return os.path.isfile(target) + + @pytest.mark.parametrize("filename", ["classroom.mp4","person-bicycle-car-detection.mp4","person-bicycle-car-detection_1920_1080_2min.mp4"]) + @pytest.mark.parametrize("backend",["CAP_GSTREAMER"]) + def test_opencv(self, filename, backend): + os.environ["OPENCV_VIDEOIO_DEBUG"] = "1" + os.environ["OPENCV_VIDEOCAPTURE_DEBUG"] = "1" + import cv2 + + print("OpenCV Version: {}".format(cv2.__version__), flush=True) + + if ("2min" in filename): + assert(self._download_video("https://gitlab.devtools.intel.com/media-analytics-pipeline-zoo/media/-/raw/main/video/person-bicycle-car-detection/{}?inline=false".format(filename), + "/home/video-analytics-serving/{}".format(filename), + "--no-proxy")) + else: + assert(self._download_video("https://github.com/intel-iot-devkit/sample-videos/blob/master/{}?raw=true".format(filename), + "/home/video-analytics-serving/{}".format(filename))) + + print("Opening: {}\n".format(filename),flush=True) + capture = cv2.VideoCapture("/home/video-analytics-serving/{}".format(filename), getattr(cv2,backend)) + assert(capture) + ret, frame = capture.read() + assert(ret and frame is not None) + + def test_lva_video_consistent_results(self, sleep_period=0.25, port=5001): server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", "-p", str(port)] print(' '.join(server_args)) @@ -77,14 +110,16 @@ def test_lva_video_consistent_results(self, sleep_period=0.25, port=5001): #Compare the two outputs to see if they match json_data1 = None json_data2 = None + count = 0 with open(output_location1, "r") as file1: with open(output_location2, "r") as file2: file1_s = file1.readline() file2_s = file2.readline() while file1_s != '' and file2_s != '': + count += 1 json_data1 = json.loads(file1_s) json_data2 = json.loads(file2_s) - + assert json_data1 == json_data2 file1_s = file1.readline() @@ -93,3 +128,5 @@ def test_lva_video_consistent_results(self, sleep_period=0.25, port=5001): assert False if file1_s == '' and file2_s != '': assert False + print("Got {} results".format(count), flush=True) + assert (count == 647) From 72ff498f0226fe51512f010b67bf3f7d255050f1 Mon Sep 17 00:00:00 2001 From: "Lawrens, Liz" Date: Wed, 13 Jan 2021 23:05:56 -0800 Subject: [PATCH 23/50] Model Download Tool refactor --- docker/build.sh | 11 +- docker/run.sh | 2 +- tests/run_script_tests.sh | 3 +- tests/script_tests/model_downloader | 69 +++++ tests/test_mdt_with_default_models_yml.py | 61 ---- tools/model_downloader/README.md | 101 +++++-- tools/model_downloader/__main__.py | 36 ++- tools/model_downloader/arguments.py | 12 +- tools/model_downloader/config.py | 21 -- tools/model_downloader/downloader.py | 306 ++++++++++++++------- tools/model_downloader/model_downloader.sh | 111 ++++++++ tools/model_downloader/requirements.txt | 1 + tools/model_downloader/schema.py | 33 +++ tools/model_downloader/utils.py | 77 ------ 14 files changed, 528 insertions(+), 316 deletions(-) create mode 100755 tests/script_tests/model_downloader delete mode 100644 tests/test_mdt_with_default_models_yml.py delete mode 100644 tools/model_downloader/config.py create mode 100755 tools/model_downloader/model_downloader.sh create mode 100644 tools/model_downloader/requirements.txt create mode 100644 tools/model_downloader/schema.py delete mode 100644 tools/model_downloader/utils.py diff --git a/docker/build.sh b/docker/build.sh index 878d4b6..e19af8e 100755 --- a/docker/build.sh +++ b/docker/build.sh @@ -22,6 +22,7 @@ PIPELINES= FRAMEWORK="gstreamer" TAG= RUN_PREFIX= +DRY_RUN= CREATE_SERVICE=TRUE ENVIRONMENT_FILES=() DOCKER_RUN_ENVIRONMENT=$(env | cut -f1 -d= | grep -E '_(proxy)$' | sed 's/^/-e / ' | tr '\n' ' ') @@ -192,6 +193,7 @@ get_options() { ;; --dry-run) RUN_PREFIX="echo" + DRY_RUN="--dry-run" echo "" echo "==============================" echo "DRY RUN: COMMANDS PRINTED ONLY" @@ -234,9 +236,6 @@ get_options() { fi if [ -f "$MODELS" ]; then - YML_DIR=$(dirname "${MODELS}") - YML_FILE_NAME=$(basename "${MODELS}") - VOLUME_MOUNT+="-v $SOURCE_DIR:/home/video-analytics-serving -v $YML_DIR:/models_yml" if [[ ! " ${SUPPORTED_IMAGES[@]} " =~ " ${BASE_IMAGE} " ]]; then if [ -z "$OPEN_MODEL_ZOO_VERSION" ]; then @@ -245,9 +244,9 @@ get_options() { else OPEN_MODEL_ZOO_VERSION=2021.1 fi - - $RUN_PREFIX docker run -t --rm $DOCKER_RUN_ENVIRONMENT --user "$UID" --entrypoint /bin/bash $VOLUME_MOUNT openvino/ubuntu18_data_dev:$OPEN_MODEL_ZOO_VERSION "-i" "-c" "pip3 install jsonschema==3.2.0; python3 /home/video-analytics-serving/tools/model_downloader --model-list /models_yml/$YML_FILE_NAME --output-dir /home/video-analytics-serving/ $FORCE_MODEL_DOWNLOAD" - + + $SOURCE_DIR/tools/model_downloader/model_downloader.sh --model-list $MODELS --output $SOURCE_DIR $FORCE_MODEL_DOWNLOAD --open-model-zoo-version $OPEN_MODEL_ZOO_VERSION $DRY_RUN + elif [ -d "$MODELS" ]; then if [ ! -d "$SOURCE_DIR/models" ]; then mkdir $SOURCE_DIR/models diff --git a/docker/run.sh b/docker/run.sh index 431cd9c..6dfeb00 100755 --- a/docker/run.sh +++ b/docker/run.sh @@ -251,7 +251,7 @@ fi if [ -z "$NAME" ]; then # Convert tag separator if exists - NAME=${IMAGE//[\:]/_} + NAME=${IMAGE//[\:\/]/_} fi if [ "${MODE}" == "DEV" ]; then diff --git a/tests/run_script_tests.sh b/tests/run_script_tests.sh index dbad4ca..751e9cc 100755 --- a/tests/run_script_tests.sh +++ b/tests/run_script_tests.sh @@ -2,13 +2,14 @@ set -e TEST_DIR=$(dirname "$(readlink -f "$0")") +SOURCE_DIR=$(dirname $TEST_DIR) TAG=##teamcity echo " ${TAG}[testSuiteStarted name='script-tests'] " for TEST in $TEST_DIR/script_tests/* ; do TEST_NAME=$(basename $TEST) printf " %s[testStarted name='%s' captureStandardOutput='true']\n" "$TAG" "$TEST_NAME" - TEST_DIR=$TEST_DIR $TEST || { + TEST_DIR=$TEST_DIR SOURCE_DIR=$SOURCE_DIR $TEST || { printf " %s[testFailed name='%s']\n" "$TAG" "$TEST_NAME" } printf " %s[testFinished name='%s']\n" "$TAG" "$TEST_NAME" diff --git a/tests/script_tests/model_downloader b/tests/script_tests/model_downloader new file mode 100755 index 0000000..a882695 --- /dev/null +++ b/tests/script_tests/model_downloader @@ -0,0 +1,69 @@ +#!/bin/bash +OUTPUT= +RETURN_CODE= + +MODEL_NAMES=(mobilenet-ssd) +MODEL_NAMES+=(emotions-recognition-retail-0003) +MODEL_NAMES+=(landmarks-regression-retail-0009) +MODEL_NAMES+=(face-detection-retail-0004) + +ALIAS_NAMES=(object_detection) +ALIAS_NAMES+=(emotion_recognition) +ALIAS_NAMES+=(landmarks_regression) +ALIAS_NAMES+=(face_detection_retail) + +tmp_dir=$(mktemp -d -t model-downloader-XXXXXXXX) + +error() { + echo "=======================" + echo "Test Failed" + echo "Return Code: $RETURN_CODE" + echo "Detail: $1" + echo "=======================" + echo "Output Begin" + echo "=======================" + echo "$OUTPUT" + echo "=======================" + echo "Output End" + echo "=======================" + exit 1 +} + +check_directory() { + model_path=$tmp_dir/models/$2/1 + if [ ! -d "$model_path" ]; then + error "Missing Model Path: $model_path" + fi + model_proc=$model_path/$1.json + if [ ! -f "$model_proc" ]; then + error "Missing Model Proc: $model_proc" + fi + model_precision=$model_path/FP32 + if [ ! -d "$model_precision" ]; then + error "Missing Model Precision: $model_precision" + fi + model_xml=$model_precision/$1.xml + if [ ! -f "$model_xml" ]; then + error "Missing Model XML: $model_xml" + fi + model_bin=$model_precision/$1.bin + if [ ! -f "$model_bin" ]; then + error "Missing Model BIN: $model_bin" + fi +} + +OUTPUT=$($SOURCE_DIR/tools/model_downloader/model_downloader.sh --model-list $SOURCE_DIR/models_list/models.list.yml --output $tmp_dir 2>&1) +RETURN_CODE=$? + +if [[ $RETURN_CODE != 0 ]]; then + rm -rf $tmp_dir + error +fi + +i=0 +for model_name in ${MODEL_NAMES[@]}; do + check_directory $model_name ${ALIAS_NAMES[$i]} + i=$i+1 +done + +rm -rf $tmp_dir diff --git a/tests/test_mdt_with_default_models_yml.py b/tests/test_mdt_with_default_models_yml.py deleted file mode 100644 index eee18ae..0000000 --- a/tests/test_mdt_with_default_models_yml.py +++ /dev/null @@ -1,61 +0,0 @@ -import os -import subprocess -import pytest -import tempfile - -# function to check if the model directory was created properly -def check_directory(path, name, alias, version=1, precision="FP32"): - # Check to see if folder was created in alias/version path - model_path = os.path.join(os.path.abspath(path), 'models', alias, str(version)) - assert os.path.isdir(model_path) - - # Check to see if model proc exists - model_proc_name = name + '.json' - model_proc_file = os.path.join(model_path, model_proc_name) - assert os.path.isfile(model_proc_file) - - # Check to see if precision folder exists - model_precision_path = os.path.join(model_path, precision) - assert os.path.isdir(model_precision_path) - - # Check to see if xml exists - model_xml_name = name + '.xml' - model_xml_file = os.path.join(model_precision_path, model_xml_name) - assert os.path.isfile(model_xml_file) - - # Check to see if bin exists - model_bin_name = name + '.bin' - model_bin_file = os.path.join(model_precision_path, model_bin_name) - assert os.path.isfile(model_bin_file) - -# Test the default models from models.list.yml, check to see if in IR and model-proc format -def test_mdt_with_default_models_yml(capfd): - workdir_path = tempfile.TemporaryDirectory() - model_download_tool_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), - '../tools/model_downloader') - model_yml_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), - '../models/models.list.yml') - # Run the tool - results = subprocess.run(['python3', model_download_tool_path, '--model-list', model_yml_path, - '--output-dir', workdir_path.name, '--force'], check=False) - - # Set the path of expected output based on the yml file - model_names = ["mobilenet-ssd", - "emotions-recognition-retail-0003", - "landmarks-regression-retail-0009", - "face-detection-retail-0004"] - alias_names = ["object_detection", - "emotion_recognition", - "landmarks_regression", - "face_detection_retail"] - version = 1 - if results.returncode != 0: - captured = capfd.readouterr() - assert 'Necessary tools needed from Intel(R) distribution of '\ - 'OpenVINO(TM) Toolkit not found' in captured.out - else: - for num, name in enumerate(model_names): - current_alias = alias_names[num] - current_version = version - current_model = name - check_directory(workdir_path.name, current_model, current_alias, current_version) diff --git a/tools/model_downloader/README.md b/tools/model_downloader/README.md index 929493c..82abe69 100644 --- a/tools/model_downloader/README.md +++ b/tools/model_downloader/README.md @@ -1,50 +1,97 @@ -# Overview - Model Download Tool +# Model Downloader +| [Specifying Models](#specifying-models) | [Downloading Models](#downloading-models) | [Reference](#command-line-reference) | -VA Serving pipelines exclusively use models in the OpenVINO Intermediate Representation (IR) format. -Intel maintains a set of ready to use models in the OpenVINO Open Model Zoo (OMZ). -The Model Download Tool improves developer experience by automatically downloading and converting specified models from OMZ. The tool also downloads model-proc files available from DL Streamer repo and lays out files in correct directory structure compatible with VA Serving. +The model downloader downloads and prepares models from the +OpenVINO Toolkit [Open Model +Zoo](https://github.com/openvinotoolkit/open_model_zoo) for use with +Video Analytics Serving. It can be run as a standalone tool or as +part of the Video Analytics Serving build process. For more +information on model file formats and the directory structure used by +Video Analytics Serving see [defining_pipelines](/docs/defining_pipelines.md#deep-learning-models). + +# Specifying Models -## How to use -The tool assumes that OpenVINO is installed and environment is set up. If your host is set up, you can run the tool from the host. Otherwise you can run -the tool while in developer mode in Docker (`./docker/run.sh --dev`). +Models are specified using a yaml file containing a list of model +entries. Model entries can either be strings (model names) or objects +(model names plus additional properties) and a single yaml file can +contain both forms of entries. An [example file](/models_list/models.list.yml) is used as part of the +default build process. -The tool will download models from Open Model Zoo(OMZ), so it will need network access. +## String Entries +String entries specify the model to download from the [Open Model +Zoo](https://github.com/openvinotoolkit/open_model_zoo). The model and +model-proc files will be downloaded and stored locally using [default +values](#default-values). -The tool accepts as input argument a yaml file that will have the model info in it. The yaml file can provide model name and the downloader will get all the model precisions from OMZ. -If no input file given, by default the tool looks for the `models.list.yml` file inside the models folder. This file will list a model name, alias, version and precision. Alias, version and precision are optional. If none provided, the default values for these would be: -* alias = model name -* version = 1 -* precision = all available precisions +Example: -A simple yaml input file will look like: ```yaml - mobilenet-ssd - emotions-recognition-retail-0003 ``` -An example of a detailed yaml entry would be: + +## Object Entries +Object entries specfiy the model to download from the [Open Model +Zoo](https://github.com/openvinotoolkit/open_model_zoo) and one or +more optional properties (alias, version, precision, local +model-proc). If an optional property is not specified the downloader +will use [default values](#default-values). + +Example: + ```yaml - model: yolo-v3-tf alias: object_detection version: 2 precision: [FP32,INT8] + model-proc: object_detection.json ``` -The yaml input file can contain a combination of simple as well as detailed entries. -The tool also downloads the model-proc file from DL streamer repo. The model-proc file must have the same name as the model being downloaded. +## Default Values + +* alias = model_name +* version = 1 +* precision = all available precisions +* model-proc = model_name.json + +If a local model-proc is not specified, the tool will download the +corresponding model-proc file from the DL streamer repository (if one +exists). + -## How to run -Run the tool without any arguments: -This will get the model names by default from the input yaml file `models.list.yml` in the model_downloader folder, create a `models` folder and download the models into it. +# Downloading Models + +The model downloader can be run either as a standalone tool or as part +of the Video Analytics Serving build process. + +## Downloading Models as part of Video Analytics Serving Build + +The Video Analytics Serving build script downloads models listed in a +yaml file that can be specified via the `--models` argument. + +Example: ```bash -$ python3 model_downloader +./docker/build.sh --models models_list/models.list.yml ``` -This `models` directory can then be moved over to the root folder to use with VA Serving. -### Options -Some of the options you can provide while running the tool include: +## Downloading Models with the standalone tool -[ --model-list : Specify a different models.list.yml file than the default ] +When run as a standalone tool, the model downloader will run within an +openvino/ubuntu18_data_dev docker image and download models listed in +a yaml file that can be specified via the `--model-list` argument. -[ --output-dir : Specify directory to create model folder ] +Example: +```bash +./tools/model_downloader/model_downloader.sh --model-list models_list/models.list.yml --output standalone_models +``` + +### Command Line Reference -[ --force : Specify if download needs to be forced ] \ No newline at end of file +```bash +usage: model_downloader.sh + [--output path where to save models] + [--model-list input file with model names and properties] + [--force force download and conversion of existing models] + [--open-model-zoo-version specify the version of openvino image to be used for downloading models from Open Model Zoo] + [--dry-run print commands without executing] +``` diff --git a/tools/model_downloader/__main__.py b/tools/model_downloader/__main__.py index cc3bc2e..4bb40c7 100644 --- a/tools/model_downloader/__main__.py +++ b/tools/model_downloader/__main__.py @@ -7,15 +7,14 @@ import sys import os -import config as cfg from arguments import parse_args -from downloader import download +import downloader -package_name = os.path.basename(os.path.dirname(__file__)) -def print_args(args): +def _print_args(args): + package_name = os.path.basename(os.path.dirname(__file__)) heading = "Arguments for {}".format(package_name) - banner = "="*len(heading) + banner = "=" * len(heading) print(banner) print(heading) print(banner) @@ -24,16 +23,25 @@ def print_args(args): print() -if __name__ == '__main__': - +def main(): args = parse_args() - print_args(args) + _print_args(args) - if (os.path.isfile(cfg.model_downloader) and - os.path.isfile(cfg.model_converter) and - os.path.isfile(cfg.model_optimizer)): - download(args.model_list, args.output_dir, args.force) + if ( + os.path.isfile(downloader.MODEL_DOWNLOADER_PATH) + and os.path.isfile(downloader.MODEL_CONVERTER_PATH) + and os.path.isfile(downloader.MODEL_OPTIMIZER_PATH) + ): + downloader.download_and_convert_models( + args.model_list, args.output_dir, args.force, args.dl_streamer_version + ) else: - print("Necessary tools needed from Intel(R) distribution of OpenVINO(TM) Toolkit not "\ - "found. Please check if all dependant tools are installed and try again.") + print( + "Intel(R) distribution of OpenVINO(TM) Toolkit tools not " + "found. Please check if all dependent tools are installed and try again." + ) sys.exit(1) + + +if __name__ == "__main__": + main() diff --git a/tools/model_downloader/arguments.py b/tools/model_downloader/arguments.py index 26a0109..c046e17 100644 --- a/tools/model_downloader/arguments.py +++ b/tools/model_downloader/arguments.py @@ -8,12 +8,16 @@ def parse_args(args=None): parser = argparse.ArgumentParser(description="Download & Convert models from Open Model Zoo.") - parser.add_argument('--output-dir', required=False, default=".", + parser.add_argument('--output', '--output-dir', required=False, default=".", + dest="output_dir", help='path where to save models') - parser.add_argument('--model-list', default="models/models.list.yml", - help='input file with model names') + parser.add_argument('--model-list', default="models_list/models.list.yml", + help='input file with model names and properties') + parser.add_argument('--model-proc-version', default="v1.2.1", + dest="dl_streamer_version", + help='DL Streamer version for model proc files') parser.add_argument("--force", required=False, dest="force", action="store_true", - default=False, help='force the download') + default=False, help='force download and conversion of existing models') args = parser.parse_args(args) return args diff --git a/tools/model_downloader/config.py b/tools/model_downloader/config.py deleted file mode 100644 index 5f95589..0000000 --- a/tools/model_downloader/config.py +++ /dev/null @@ -1,21 +0,0 @@ -''' -* Copyright (C) 2019-2020 Intel Corporation. -* -* SPDX-License-Identifier: BSD-3-Clause -''' - -import os - -mo_root = "/opt/intel/dldt" if os.path.isdir("/opt/intel/dldt") else "/opt/intel/openvino/deployment_tools" -omz_root = os.path.join(mo_root, "open_model_zoo") - -model_downloader = os.path.join(omz_root, "tools/downloader/downloader.py") -model_converter = os.path.join(omz_root, "tools/downloader/converter.py") -model_optimizer = os.path.join(mo_root, "model-optimizer/mo.py") \ - if mo_root == "/opt/intel/dldt" else os.path.join(mo_root, "model_optimizer/mo.py") -model_proc_root = "/opt/intel/dl_streamer/samples/model_proc" - -base_gst_video_analytics_repo_root = "https://raw.githubusercontent.com/openvinotoolkit/dlstreamer_gst" -base_gst_video_analytics_version = "v1.2.1" -base_gst_video_analytics_repo_url = "{root}/{version}/samples/model_proc/"\ - .format(root=base_gst_video_analytics_repo_root, version=base_gst_video_analytics_version) diff --git a/tools/model_downloader/downloader.py b/tools/model_downloader/downloader.py index 37e2cbc..bcf93f0 100644 --- a/tools/model_downloader/downloader.py +++ b/tools/model_downloader/downloader.py @@ -9,125 +9,223 @@ import shutil import subprocess import tempfile -from pathlib import Path +import shlex import requests +import yaml from jsonschema import Draft7Validator, FormatChecker -import config as cfg -from utils import create_convert_command -from utils import create_download_command -from utils import create_directory -from utils import load_document - -def find_model_root(model, output_dir): - for root, directories, _ in os.walk(output_dir): - if model in directories: - return os.path.abspath(os.path.join(root, model)) +from schema import model_list_schema + +MODEL_OPTIMIZER_ROOT = ( + "/opt/intel/dldt" + if os.path.isdir("/opt/intel/dldt") + else "/opt/intel/openvino/deployment_tools" +) +OPEN_MODEL_ZOO_ROOT = os.path.join(MODEL_OPTIMIZER_ROOT, "open_model_zoo") +MODEL_DOWNLOADER_PATH = os.path.join( + OPEN_MODEL_ZOO_ROOT, "tools/downloader/downloader.py" +) +MODEL_CONVERTER_PATH = os.path.join( + OPEN_MODEL_ZOO_ROOT, "tools/downloader/converter.py" +) +MODEL_OPTIMIZER_PATH = ( + os.path.join(MODEL_OPTIMIZER_ROOT, "model-optimizer/mo.py") + if MODEL_OPTIMIZER_ROOT == "/opt/intel/dldt" + else os.path.join(MODEL_OPTIMIZER_ROOT, "model_optimizer/mo.py") +) + +MODEL_PROC_ROOT = "/opt/intel/dl_streamer/samples/model_proc" +DL_STREAMER_REPO_ROOT = ( + "https://raw.githubusercontent.com/openvinotoolkit/dlstreamer_gst" +) + + +def _validate_schema(model_list): + try: + validator = Draft7Validator(model_list_schema, format_checker=FormatChecker()) + validator.validate(model_list) + except Exception as err: + print("Yaml input schema validation error.") + print(err) + sys.exit(1) + + +def _load_model_list(model_list_path): + model_list = None + try: + with open(model_list_path) as model_list_file: + model_list = yaml.safe_load(model_list_file) + except Exception: + print("Exception while loading yaml file. File could be malformed.") + print("Please make sure model list file is in correct yml file format.") + print("Expected Schema: ") + print("- model(Required): mobilenet-ssd") + print(" alias(Optional): object_detection") + print(" version(Optional): 1") + print(" precision(Optional): [FP16,FP32]") + + if model_list is None: + sys.exit(1) + else: + _validate_schema(model_list) + return model_list + + +def _find_downloaded_model(model_name, download_dir): + for root, directories, _ in os.walk(download_dir): + if model_name in directories: + return os.path.abspath(os.path.join(root, model_name)) return None -def download_model_proc(target_model, model): - if os.path.isdir(cfg.model_proc_root): - for root, _, files in os.walk(cfg.model_proc_root): + +def _download_model_proc(target_dir, model_name, dl_streamer_version): + + if os.path.isdir(MODEL_PROC_ROOT): + for root, _, files in os.walk(MODEL_PROC_ROOT): for filepath in files: - if os.path.splitext(filepath)[0] == model: + if os.path.splitext(filepath)[0] == model_name: model_proc = os.path.join(root, filepath) else: - url = cfg.base_gst_video_analytics_repo_url + '{0}.json'.format(model) - gva_response = requests.get(url) + url = "{0}/{1}/samples/model_proc/{2}.json".format( + DL_STREAMER_REPO_ROOT, dl_streamer_version, model_name + ) + response = requests.get(url) temp_dir = tempfile.TemporaryDirectory() - if gva_response.status_code == 200: - with open('{0}/{1}.json'.format(temp_dir.name, model), 'wb') as out_file: - out_file.write(gva_response.content) - print("Downloaded {0} model-proc file from gst-video-analytics repo".format(model)) + if response.status_code == 200: + with open( + "{0}/{1}.json".format(temp_dir.name, model_name), "wb" + ) as out_file: + out_file.write(response.content) + print( + "Downloaded {0} model-proc file from gst-video-analytics repo".format( + model_name + ) + ) + model_proc = os.path.abspath( + "{0}/{1}.json".format(temp_dir.name, model_name) + ) else: print("Warning, model-proc not found in gst-video-analytics repo.") - model_proc = os.path.abspath('{0}/{1}.json'.format(temp_dir.name, model)) - shutil.move(model_proc, os.path.join(target_model, '{}.json'.format(model))) - -def validate_schema(model_list): - schema = { - "type" : "array", - "items" : { - "oneOf": [ - { - "type" : "object", - "properties" : { - "model" : {"type" : "string"}, - "alias" : {"type" : "string"}, - "version" : {"type" : ["string", "integer"]}, - "precision" : { - "type" : "array", - "items" : {"enum" : ["FP32", "FP16", "INT8", - "FP16-INT8", "FP32-INT8", - "FP32-INT1", "FP16-INT1", "INT1"]} - } - }, - "required" : ["model"], - "additionalProperties": False - }, - { - "type" : "string" - } - ] - } - } - try: - validator = Draft7Validator(schema, format_checker=FormatChecker()) - validator.validate(model_list) - except Exception as err: - print("Yaml input schema validation error.") - print(err) + if model_proc: + shutil.move(model_proc, os.path.join(target_dir, "{}.json".format(model_name))) + + +def _create_convert_command(model_name, output_dir, precisions): + if precisions: + cmd = "python3 {0} -d {3} --name {1} --precisions {2} -o {3} --mo {4}" + return shlex.split( + cmd.format( + MODEL_CONVERTER_PATH, + model_name, + ",".join(map(str, precisions)), + output_dir, + MODEL_OPTIMIZER_PATH, + ) + ) + cmd = "python3 {0} -d {2} --name {1} -o {2} --mo {3}" + return shlex.split( + cmd.format(MODEL_CONVERTER_PATH, model_name, output_dir, MODEL_OPTIMIZER_PATH) + ) + + +def _create_download_command(model_name, output_dir, precisions): + if precisions: + cmd = "python3 {0} --name {1} --precisions {2} -o {3}" + return shlex.split( + cmd.format( + MODEL_DOWNLOADER_PATH, + model_name, + ",".join(map(str, precisions)), + output_dir, + ) + ) + cmd = "python3 {0} --name {1} -o {2}" + return shlex.split(cmd.format(MODEL_DOWNLOADER_PATH, model_name, output_dir)) + + +def _run_command(command, model_name, step): + print(" ".join(command)) + result = subprocess.run(command, check=False) + if result.returncode != 0: + print("Error occured while {0} {1} model.".format(step, model_name)) + print("Please remove from input yml file and try again.") sys.exit(1) -def download_and_convert_model(target_root, model, force): - precisions = None - if isinstance(model, dict): - model_name = model.get('model') - alias = model.get('alias', None) - precisions = model.get('precision', None) - if alias is not None: - target_model = os.path.join(target_root, alias) - else: - target_model = os.path.join(target_root, model_name) - model_version = model.get('version', None) - if model_version is not None: - target_model = os.path.join(target_model, str(model_version)) - else: - target_model = os.path.join(target_model, "1") - else: - model_name = model - target_model = os.path.join(os.path.join(target_root, model_name), "1") - if (not force) and (os.path.isdir(target_model)): - print("Model Directory {0} Exists - Skipping".format(target_model)) +def _download_model(model_name, output_dir, precisions): + command = _create_download_command(model_name, output_dir, precisions) + _run_command(command, model_name, "downloading") + + +def _convert_model(model_name, output_dir, precisions): + command = _create_convert_command(model_name, output_dir, precisions) + _run_command(command, model_name, "converting") + + +def _get_model_properties(model, model_list_path, target_root): + result = model + if not isinstance(model, dict): + result = {} + result["model"] = model + result.setdefault("alias", result["model"]) + result.setdefault("version", 1) + result.setdefault("precision", None) + result.setdefault("model-proc", None) + if result["model-proc"]: + result["model-proc"] = os.path.abspath( + os.path.join(os.path.dirname(model_list_path), result["model-proc"]) + ) + result["target-dir"] = os.path.join(target_root, + result["alias"], + str(result["version"])) + return result + + +def _download_and_convert_model( + target_root, model, force, model_list_path, dl_streamer_version +): + model_properties = _get_model_properties(model, model_list_path, target_root) + model_name = model_properties["model"] + precision = model_properties["precision"] + model_proc = model_properties["model-proc"] + target_dir = model_properties["target-dir"] + + if (not force) and (os.path.isdir(target_dir)): + print("Model Directory {0} Exists - Skipping".format(target_dir)) return + if os.path.isdir(target_dir): + shutil.rmtree(target_dir) + with tempfile.TemporaryDirectory() as output_dir: - command = create_download_command(model_name, output_dir, precisions) - print(' '.join(command)) - result = subprocess.run(command, check=False) - if result.returncode != 0: - print("Error occured while downloading {0} model.".format(model_name)) - print("Please remove from input yml file and try again.") - sys.exit(1) - command = create_convert_command(model_name, output_dir, precisions) - print(' '.join(command)) - subprocess.run(command, check=False) - model_path = find_model_root(model_name, output_dir) - if os.path.isdir(target_model): - #print("Directory {0} already exists, overwriting it.".format(target_model)) - shutil.rmtree(target_model) - for filename in os.listdir(model_path): - if os.path.isdir(os.path.join(model_path, filename)): - shutil.move(os.path.join(model_path, filename), - os.path.join(target_model, filename)) - download_model_proc(target_model, model_name) - -def download(model_list_path, output_dir, force): - model_list = load_document(model_list_path) - if model_list is None: - sys.exit(1) - validate_schema(model_list) + _download_model(model_name, output_dir, precision) + _convert_model(model_name, output_dir, precision) + downloaded_model_path = _find_downloaded_model(model_name, output_dir) + for path in os.listdir(downloaded_model_path): + source = os.path.join(downloaded_model_path, path) + target = os.path.join(target_dir, path) + if os.path.isdir(source): + shutil.move(source, target) + + if model_proc: + if os.path.isfile(model_proc): + shutil.copy(model_proc, target_dir) + else: + print("Error, model-proc {} specified but not found", model_proc) + sys.exit(1) + else: + _download_model_proc( + target_dir, model_name, dl_streamer_version + ) + + +def download_and_convert_models( + model_list_path, output_dir, force, dl_streamer_version +): + model_list = _load_model_list(model_list_path) target_root = os.path.join(output_dir, "models") - create_directory(target_root, False) + os.makedirs(target_root, exist_ok=True) + for model in model_list: - download_and_convert_model(target_root, model, force) + _download_and_convert_model( + target_root, model, force, model_list_path, dl_streamer_version + ) diff --git a/tools/model_downloader/model_downloader.sh b/tools/model_downloader/model_downloader.sh new file mode 100755 index 0000000..506714d --- /dev/null +++ b/tools/model_downloader/model_downloader.sh @@ -0,0 +1,111 @@ +#!/bin/bash -e +# +# Copyright (C) 2019-2020 Intel Corporation. +# +# SPDX-License-Identifier: BSD-3-Clause +# + +SCRIPT_DIR=$(dirname "$(readlink -f "$0")") +TOOLS_DIR=$(dirname "$SCRIPT_DIR") +SOURCE_DIR=$(dirname "$TOOLS_DIR") +OUTPUT_DIR=$(realpath $( pwd )) +FORCE= +RUN_PREFIX= +OPEN_MODEL_ZOO_VERSION="2021.1" +DL_STREAMER_VERSION="v1.2.1" +MODE= +MODEL_LIST=$SOURCE_DIR/"models_list/models.list.yml" +DRY_RUN= +NON_INTERACTIVE= + +show_help() { + echo "usage: model_downloader.sh" + echo " [--output path where to save models]" + echo " [--model-list input file with model names and properties]" + echo " [--force force download and conversion of existing models]" + echo " [--open-model-zoo-version specify the version of openvino image to be used for downloading models from Open Model Zoo]" + echo " [--dry-run print commands without executing]" + exit 0 +} + +error() { + printf '%s %s\n' "$1" "$2" >&2 + exit 1 +} + +while [[ "$#" -gt 0 ]]; do + case $1 in + -h | -\? | --help) + show_help # Display a usage synopsis. + exit + ;; + --dry-run) + DRY_RUN="--dry-run" + ;; + --force) + FORCE="--force" + ;; + --open-model-zoo-version) + if [ "$2" ]; then + OPEN_MODEL_ZOO_VERSION=$2 + shift + else + error 'ERROR: "--open-model-zoo-version" requires an argument.' + fi + ;; + --output) # Takes an option argument; ensure it has been specified. + if [ "$2" ]; then + OUTPUT_DIR=$2 + shift + else + error 'ERROR: "--output" requires a non-empty option argument.' + fi + ;; + --model-list) + if [ "$2" ]; then + MODEL_LIST=$(realpath $2) + shift + else + error 'ERROR: "--model-list" requires a non-empty option argument.' + fi + ;; + --) + shift + break + ;; + -?*) + error 'ERROR: Unknown option: ' $1 + ;; + ?*) + error 'ERROR: Unknown option: ' $1 + ;; + *) + break + ;; + esac + shift +done + +YML_DIR=$(dirname "${MODEL_LIST}") +YML_FILE_NAME=$(basename "${MODEL_LIST}") +VOLUME_MOUNT+="-v $TOOLS_DIR:/home/video-analytics-serving/tools -v $YML_DIR:/models_yml -v $OUTPUT_DIR:/output" + +case $OPEN_MODEL_ZOO_VERSION in + 2021.1) + DL_STREAMER_VERSION="v1.2.1" + ;; + 2021.2) + DL_STREAMER_VERSION="v1.3" + ;; + 2020.4) + DL_STREAMER_VERSION="v1.1.0" + ;; + *) + error 'ERROR: Unknown Open Model Zoo version: ' $OPEN_MODEL_ZOO_VERSION +esac + +if [ ! -z "$TEAMCITY_VERSION" ]; then + NON_INTERACTIVE=--non-interactive +fi + +$SOURCE_DIR/docker/run.sh --user "$UID" $NON_INTERACTIVE --image openvino/ubuntu18_data_dev:$OPEN_MODEL_ZOO_VERSION $VOLUME_MOUNT $DRY_RUN --entrypoint /bin/bash --entrypoint-args "\"-i\" \"-c\" \"pip3 install -r /home/video-analytics-serving/tools/model_downloader/requirements.txt ; python3 -u /home/video-analytics-serving/tools/model_downloader --model-list /models_yml/$YML_FILE_NAME --output /output $FORCE\"" diff --git a/tools/model_downloader/requirements.txt b/tools/model_downloader/requirements.txt new file mode 100644 index 0000000..ad75579 --- /dev/null +++ b/tools/model_downloader/requirements.txt @@ -0,0 +1 @@ +jsonschema==3.2.0 diff --git a/tools/model_downloader/schema.py b/tools/model_downloader/schema.py new file mode 100644 index 0000000..55513d1 --- /dev/null +++ b/tools/model_downloader/schema.py @@ -0,0 +1,33 @@ +''' +* Copyright (C) 2019-2020 Intel Corporation. +* +* SPDX-License-Identifier: BSD-3-Clause +''' + +model_list_schema = { + "type" : "array", + "items" : { + "oneOf": [ + { + "type" : "object", + "properties" : { + "model" : {"type" : "string"}, + "alias" : {"type" : "string"}, + "version" : {"type" : ["string", "integer"]}, + "precision" : { + "type" : "array", + "items" : {"enum" : ["FP32", "FP16", "INT8", + "FP16-INT8", "FP32-INT8", + "FP32-INT1", "FP16-INT1", "INT1"]} + }, + "model-proc":{"type": "string"} + }, + "required" : ["model"], + "additionalProperties": False + }, + { + "type" : "string" + } + ] + } + } diff --git a/tools/model_downloader/utils.py b/tools/model_downloader/utils.py deleted file mode 100644 index 053a8c3..0000000 --- a/tools/model_downloader/utils.py +++ /dev/null @@ -1,77 +0,0 @@ -''' -* Copyright (C) 2019-2020 Intel Corporation. -* -* SPDX-License-Identifier: BSD-3-Clause -''' - -import os -import shutil -import shlex -import yaml -import config as cfg - -def load_document(document_path): - document = None - try: - with open(document_path) as document_file: - if document_path.endswith('.yml'): - document = yaml.safe_load(document_file) - else: - print("Please make sure model list file is in correct yml file format.") - print("Expected Schema: ") - print("- model(Required): mobilenet-ssd") - print(" alias(Optional): object_detection") - print(" version(Optional): 1") - print(" precision(Optional): [FP16,FP32]") - except Exception as error: - print("Exception while loading yaml file. File could be malformed. "\ - "Please check the format and retry.") - print(error) - return document - -def print_action(action, details=None): - details = [] if details is None else details - banner = "="*len(action) - print(banner) - print(action) - print(banner) - for detail in details: - print("\t{}".format(detail)) - print() - -def create_directory(directory, remove=True): - if remove: - try: - print_action("Removing: {}".format(directory)) - shutil.rmtree(directory, ignore_errors=True) - except Exception as error: - print(error) - - print_action("Creating: {}".format(directory)) - os.makedirs(directory, exist_ok=True) - -def create_download_command(model_name, output_dir, precisions): - if precisions is not None: - cmd = "python3 {0} --name {1} --precisions {2} -o {3}" - return shlex.split(cmd.format(cfg.model_downloader, - model_name, - ','.join(map(str, precisions)), - output_dir)) - cmd = "python3 {0} --name {1} -o {2}" - return shlex.split(cmd.format(cfg.model_downloader, - model_name, - output_dir)) - -def create_convert_command(model_name, output_dir, precisions): - if precisions is not None: - cmd = "python3 {0} -d {3} --name {1} --precisions {2} -o {3} --mo {4}" - return shlex.split(cmd.format(cfg.model_converter, - model_name, - ','.join(map(str, precisions)), - output_dir, - cfg.model_optimizer)) - cmd = "python3 {0} -d {2} --name {1} -o {2} --mo {3}" - return shlex.split(cmd.format(cfg.model_converter, - model_name, - output_dir, - cfg.model_optimizer)) From 6e47d09c8704fa83f74e68ca4c6fbfc4d3a1816b Mon Sep 17 00:00:00 2001 From: "Shah, Neelay N" Date: Thu, 14 Jan 2021 12:26:38 -0800 Subject: [PATCH 24/50] Fix for build with no models and no pipelines Also updated script test framework --- docker/build.sh | 12 ++++-- tests/run_script_tests.sh | 42 ++++++++++++++++--- tests/script_tests/model_downloader | 18 -------- .../script_tests/no_models_no_pipelines_build | 24 +++++++++++ 4 files changed, 69 insertions(+), 27 deletions(-) create mode 100755 tests/script_tests/no_models_no_pipelines_build diff --git a/docker/build.sh b/docker/build.sh index e19af8e..10943e3 100755 --- a/docker/build.sh +++ b/docker/build.sh @@ -110,7 +110,7 @@ get_options() { ;; --models) if [ "$2" ]; then - MODELS=$(realpath $2) + MODELS=$2 shift else error 'ERROR: "--models" requires an argument.' @@ -220,6 +220,8 @@ get_options() { if [ "${MODELS^^}" == "NONE" ]; then MODELS= + else + MODELS=$(realpath $MODELS) fi if [ $FRAMEWORK != 'gstreamer' ] && [ $FRAMEWORK != 'ffmpeg' ]; then @@ -249,11 +251,13 @@ get_options() { elif [ -d "$MODELS" ]; then if [ ! -d "$SOURCE_DIR/models" ]; then - mkdir $SOURCE_DIR/models + $RUN_PREFIX mkdir $SOURCE_DIR/models fi - cp -R $MODELS/. $SOURCE_DIR/models + $RUN_PREFIX cp -R $MODELS/. $SOURCE_DIR/models else - error 'ERROR: "'$MODELS'" does not exist.' + if [ -n "$MODELS" ]; then + error 'ERROR: "'$MODELS'" does not exist.' + fi fi if [ -z "$PIPELINES" ]; then diff --git a/tests/run_script_tests.sh b/tests/run_script_tests.sh index 751e9cc..575fa13 100755 --- a/tests/run_script_tests.sh +++ b/tests/run_script_tests.sh @@ -6,13 +6,45 @@ SOURCE_DIR=$(dirname $TEST_DIR) TAG=##teamcity echo " ${TAG}[testSuiteStarted name='script-tests'] " -for TEST in $TEST_DIR/script_tests/* ; do - TEST_NAME=$(basename $TEST) + +error() { + + # Return an error message from test script + # OUTPUT and RETURN_CODE are taken from environment + + echo "=======================" + echo "Test Failed" + echo "Return Code: $RETURN_CODE" + echo "Detail: $1" + echo "=======================" + echo "Output Begin" + echo "=======================" + echo "$OUTPUT" + echo "=======================" + echo "Output End" + echo "=======================" + exit 1 +} + +# export function for use in test scripts +typeset -fx error + +run_test() { + TEST_NAME=$(basename $1) printf " %s[testStarted name='%s' captureStandardOutput='true']\n" "$TAG" "$TEST_NAME" - TEST_DIR=$TEST_DIR SOURCE_DIR=$SOURCE_DIR $TEST || { + (TEST_DIR=$TEST_DIR SOURCE_DIR=$SOURCE_DIR $1) || { printf " %s[testFailed name='%s']\n" "$TAG" "$TEST_NAME" } - printf " %s[testFinished name='%s']\n" "$TAG" "$TEST_NAME" -done + printf " %s[testFinished name='%s']\n" "$TAG" "$TEST_NAME" +} + +if [ ! -z "$1" ]; then + TEST=$TEST_DIR/script_tests/$(basename $1) + run_test $TEST +else + for TEST in $TEST_DIR/script_tests/* ; do + run_test $TEST + done +fi echo " ${TAG}[testSuiteFinished name='script-tests'] " diff --git a/tests/script_tests/model_downloader b/tests/script_tests/model_downloader index a882695..fffccd2 100755 --- a/tests/script_tests/model_downloader +++ b/tests/script_tests/model_downloader @@ -1,7 +1,4 @@ #!/bin/bash -OUTPUT= -RETURN_CODE= - MODEL_NAMES=(mobilenet-ssd) MODEL_NAMES+=(emotions-recognition-retail-0003) MODEL_NAMES+=(landmarks-regression-retail-0009) @@ -14,21 +11,6 @@ ALIAS_NAMES+=(face_detection_retail) tmp_dir=$(mktemp -d -t model-downloader-XXXXXXXX) -error() { - echo "=======================" - echo "Test Failed" - echo "Return Code: $RETURN_CODE" - echo "Detail: $1" - echo "=======================" - echo "Output Begin" - echo "=======================" - echo "$OUTPUT" - echo "=======================" - echo "Output End" - echo "=======================" - exit 1 -} - check_directory() { model_path=$tmp_dir/models/$2/1 if [ ! -d "$model_path" ]; then diff --git a/tests/script_tests/no_models_no_pipelines_build b/tests/script_tests/no_models_no_pipelines_build new file mode 100755 index 0000000..9dc69b0 --- /dev/null +++ b/tests/script_tests/no_models_no_pipelines_build @@ -0,0 +1,24 @@ +#!/bin/bash + +# Test for building container without models and pipelines +# Verifies correct build argument passed to docker build +# Does not verify end to end functionality + +MODELS_COMMAND="--build-arg MODELS_COMMAND=do_not_copy_models " +PIPELINES_COMMAND="--build-arg PIPELINES_COMMAND=do_not_copy_pipelines " + +OUTPUT=$($SOURCE_DIR/docker/build.sh --models NONE --pipelines NONE --dry-run 2>&1) +RETURN_CODE=$? + +if [[ $RETURN_CODE != 0 ]]; then + error +fi + +if [[ ! "$OUTPUT" == *"$MODELS_COMMAND"* ]]; then + error "Invalid MODELS_COMMAND build-arg" +fi + +if [[ ! "$OUTPUT" == *"$PIPELINES_COMMAND"* ]]; then + error "Invalid PIPELINES_COMMAND build-arg" +fi + From d2eed8abedf7d11afb241a1bd9dded1b9a940fcc Mon Sep 17 00:00:00 2001 From: Farid Khoshnevissan Date: Thu, 14 Jan 2021 14:46:59 -0800 Subject: [PATCH 25/50] Have user id picked up by run_server --- samples/lva_ai_extension/docker/run_server.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/samples/lva_ai_extension/docker/run_server.sh b/samples/lva_ai_extension/docker/run_server.sh index 9be0f50..6a3f92e 100755 --- a/samples/lva_ai_extension/docker/run_server.sh +++ b/samples/lva_ai_extension/docker/run_server.sh @@ -84,4 +84,4 @@ if [ ! -z "$PIPELINE_PARAMETERS" ]; then ENV+="-e PIPELINE_PARAMETERS=$PIPELINE_PARAMETERS " fi -"$ROOT_DIR/docker/run.sh" --image $IMAGE -v /tmp:/tmp -v /dev/shm:/dev/shm -p $PORT:$PORT $ENTRYPOINT_ARGS $PIPELINES $ENV +"$ROOT_DIR/docker/run.sh" --image $IMAGE -v /tmp:/tmp --user "$UID" -v /dev/shm:/dev/shm -p $PORT:$PORT $ENTRYPOINT_ARGS $PIPELINES $ENV From 859595c2d244612804cbecfb1aa9236f4771a50d Mon Sep 17 00:00:00 2001 From: Thanaji Rao Thakkalapelli Date: Thu, 17 Dec 2020 14:31:15 -0800 Subject: [PATCH 26/50] Updated tests stop script to remove all versions of tagged images --- tests/stop.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/stop.sh b/tests/stop.sh index 31f79b2..9450cd5 100755 --- a/tests/stop.sh +++ b/tests/stop.sh @@ -23,7 +23,7 @@ function get_options { ;; --remove) echo "Removing all docker images with name starts with video-analytics-serving" - docker rmi $(docker images | grep 'video-analytics-serving') || true + docker rmi $(docker images --format '{{.Repository}}:{{.Tag}}' | grep 'video-analytics-serving') || true shift ;; *) From 60cddc8958d4807b2ed7ef580048e5b9ebd365ba Mon Sep 17 00:00:00 2001 From: "Thakkalapelli, Thanaji Rao" Date: Thu, 14 Jan 2021 17:50:21 -0800 Subject: [PATCH 27/50] Update to use latest LVA gRPC and JSON contracts --- samples/lva_ai_extension/client/__main__.py | 9 +- .../common/grpc_autogen/extension_pb2.py | 175 ++++------ .../common/grpc_autogen/inferencing_pb2.py | 330 +++++++++--------- .../common/grpc_autogen/media_pb2.py | 178 ++++------ .../contracts/extension.proto | 2 +- .../contracts/inferencing.proto | 3 +- .../server/media_graph_extension.py | 9 +- .../tests/common/Extension_Data_Schema.json | 7 +- 8 files changed, 328 insertions(+), 385 deletions(-) diff --git a/samples/lva_ai_extension/client/__main__.py b/samples/lva_ai_extension/client/__main__.py index de0720f..1502c00 100644 --- a/samples/lva_ai_extension/client/__main__.py +++ b/samples/lva_ai_extension/client/__main__.py @@ -89,12 +89,17 @@ def print_result(response, output): for inference in response.media_sample.inferences: tag = inference.entity.tag box = inference.entity.box + log_message = "- {} ({:.2f}) [{:.2f}, {:.2f}, {:.2f}, {:.2f}]"\ + .format(tag.value, tag.confidence, box.l, box.t, box.w, box.h) + if inference.entity.id: + log_message += " id:{} ".format(inference.entity.id) atrributes = [] for attribute in inference.entity.attributes: attribute_string = "{}: {}".format(attribute.name, attribute.value) atrributes.append(attribute_string) - logging.info("- {} ({:.2f}) [{:.2f}, {:.2f}, {:.2f}, {:.2f}] {}"\ - .format(tag.value, tag.confidence, box.l, box.t, box.w, box.h, atrributes)) + if len(atrributes) > 0: + log_message += " {}".format(atrributes) + logging.info(log_message) # default value field is used to avoid not including values set to 0, but it also causes empty lists to be included # empty and none values are filtered out in response_dict returned_dict = MessageToDict(response.media_sample, including_default_value_fields=True) diff --git a/samples/lva_ai_extension/common/grpc_autogen/extension_pb2.py b/samples/lva_ai_extension/common/grpc_autogen/extension_pb2.py index 86e7cbe..52a25f9 100644 --- a/samples/lva_ai_extension/common/grpc_autogen/extension_pb2.py +++ b/samples/lva_ai_extension/common/grpc_autogen/extension_pb2.py @@ -26,10 +26,13 @@ # Generated by the protocol buffer compiler. DO NOT EDIT! # source: extension.proto +import sys +_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database +from google.protobuf import descriptor_pb2 # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() @@ -43,11 +46,10 @@ name='extension.proto', package='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1', syntax='proto3', - serialized_options=None, - create_key=_descriptor._internal_create_key, - serialized_pb=b'\n\x0f\x65xtension.proto\x12@microsoft.azure.media.live_video_analytics.extensibility.grpc.v1\x1a\x11inferencing.proto\x1a\x0bmedia.proto\"\xb8\x02\n\x12MediaStreamMessage\x12\x17\n\x0fsequence_number\x18\x01 \x01(\x04\x12\x1b\n\x13\x61\x63k_sequence_number\x18\x02 \x01(\x04\x12z\n\x17media_stream_descriptor\x18\x05 \x01(\x0b\x32W.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamDescriptorH\x00\x12\x65\n\x0cmedia_sample\x18\x06 \x01(\x0b\x32M.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaSampleH\x00\x42\t\n\x07payload\"\xe1\x03\n\x15MediaStreamDescriptor\x12m\n\x10graph_identifier\x18\x01 \x01(\x0b\x32Q.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.GraphIdentifierH\x00\x12\x1e\n\x14\x65xtension_identifier\x18\x02 \x01(\tH\x00\x12k\n\x10media_descriptor\x18\x05 \x01(\x0b\x32Q.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaDescriptor\x12\x9a\x01\n(shared_memory_buffer_transfer_properties\x18\n \x01(\x0b\x32\x66.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.SharedMemoryBufferTransferPropertiesH\x01\x42\x13\n\x11stream_identifierB\x1a\n\x18\x64\x61ta_transfer_properties\"\xe6\x02\n\x0bMediaSample\x12\x11\n\ttimestamp\x18\x01 \x01(\x04\x12g\n\rcontent_bytes\x18\x05 \x01(\x0b\x32N.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.ContentBytesH\x00\x12o\n\x11\x63ontent_reference\x18\x06 \x01(\x0b\x32R.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.ContentReferenceH\x00\x12_\n\ninferences\x18\n \x03(\x0b\x32K.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.InferenceB\t\n\x07\x63ontent\"f\n\x0fGraphIdentifier\x12\x1d\n\x15media_services_arm_id\x18\x01 \x01(\t\x12\x1b\n\x13graph_instance_name\x18\x02 \x01(\t\x12\x17\n\x0fgraph_node_name\x18\x03 \x01(\t\"Q\n$SharedMemoryBufferTransferProperties\x12\x13\n\x0bhandle_name\x18\x01 \x01(\t\x12\x14\n\x0clength_bytes\x18\x02 \x01(\x04\x32\xdc\x01\n\x13MediaGraphExtension\x12\xc4\x01\n\x12ProcessMediaStream\x12T.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamMessage\x1aT.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamMessage(\x01\x30\x01\x62\x06proto3' + serialized_pb=_b('\n\x0f\x65xtension.proto\x12@microsoft.azure.media.live_video_analytics.extensibility.grpc.v1\x1a\x11inferencing.proto\x1a\x0bmedia.proto\"\xb8\x02\n\x12MediaStreamMessage\x12\x17\n\x0fsequence_number\x18\x01 \x01(\x04\x12\x1b\n\x13\x61\x63k_sequence_number\x18\x02 \x01(\x04\x12z\n\x17media_stream_descriptor\x18\x05 \x01(\x0b\x32W.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamDescriptorH\x00\x12\x65\n\x0cmedia_sample\x18\x06 \x01(\x0b\x32M.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaSampleH\x00\x42\t\n\x07payload\"\x82\x04\n\x15MediaStreamDescriptor\x12m\n\x10graph_identifier\x18\x01 \x01(\x0b\x32Q.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.GraphIdentifierH\x00\x12\x1e\n\x14\x65xtension_identifier\x18\x02 \x01(\tH\x00\x12\x1f\n\x17\x65xtension_configuration\x18\x03 \x01(\t\x12k\n\x10media_descriptor\x18\x05 \x01(\x0b\x32Q.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaDescriptor\x12\x9a\x01\n(shared_memory_buffer_transfer_properties\x18\n \x01(\x0b\x32\x66.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.SharedMemoryBufferTransferPropertiesH\x01\x42\x13\n\x11stream_identifierB\x1a\n\x18\x64\x61ta_transfer_properties\"\xe6\x02\n\x0bMediaSample\x12\x11\n\ttimestamp\x18\x01 \x01(\x04\x12g\n\rcontent_bytes\x18\x05 \x01(\x0b\x32N.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.ContentBytesH\x00\x12o\n\x11\x63ontent_reference\x18\x06 \x01(\x0b\x32R.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.ContentReferenceH\x00\x12_\n\ninferences\x18\n \x03(\x0b\x32K.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.InferenceB\t\n\x07\x63ontent\"f\n\x0fGraphIdentifier\x12\x1d\n\x15media_services_arm_id\x18\x01 \x01(\t\x12\x1b\n\x13graph_instance_name\x18\x02 \x01(\t\x12\x17\n\x0fgraph_node_name\x18\x03 \x01(\t\"Q\n$SharedMemoryBufferTransferProperties\x12\x13\n\x0bhandle_name\x18\x01 \x01(\t\x12\x14\n\x0clength_bytes\x18\x02 \x01(\x04\x32\xdc\x01\n\x13MediaGraphExtension\x12\xc4\x01\n\x12ProcessMediaStream\x12T.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamMessage\x1aT.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamMessage(\x01\x30\x01\x62\x06proto3') , dependencies=[inferencing__pb2.DESCRIPTOR,media__pb2.DESCRIPTOR,]) +_sym_db.RegisterFileDescriptor(DESCRIPTOR) @@ -58,7 +60,6 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='sequence_number', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamMessage.sequence_number', index=0, @@ -66,44 +67,42 @@ has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='ack_sequence_number', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamMessage.ack_sequence_number', index=1, number=2, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='media_stream_descriptor', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamMessage.media_stream_descriptor', index=2, number=5, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='media_sample', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamMessage.media_sample', index=3, number=6, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], - serialized_options=None, + options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name='payload', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamMessage.payload', - index=0, containing_type=None, - create_key=_descriptor._internal_create_key, - fields=[]), + index=0, containing_type=None, fields=[]), ], serialized_start=118, serialized_end=430, @@ -116,7 +115,6 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='graph_identifier', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamDescriptor.graph_identifier', index=0, @@ -124,52 +122,55 @@ has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='extension_identifier', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamDescriptor.extension_identifier', index=1, number=2, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), + has_default_value=False, default_value=_b("").decode('utf-8'), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + options=None), + _descriptor.FieldDescriptor( + name='extension_configuration', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamDescriptor.extension_configuration', index=2, + number=3, type=9, cpp_type=9, label=1, + has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( - name='media_descriptor', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamDescriptor.media_descriptor', index=2, + name='media_descriptor', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamDescriptor.media_descriptor', index=3, number=5, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( - name='shared_memory_buffer_transfer_properties', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamDescriptor.shared_memory_buffer_transfer_properties', index=3, + name='shared_memory_buffer_transfer_properties', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamDescriptor.shared_memory_buffer_transfer_properties', index=4, number=10, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], - serialized_options=None, + options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name='stream_identifier', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamDescriptor.stream_identifier', - index=0, containing_type=None, - create_key=_descriptor._internal_create_key, - fields=[]), + index=0, containing_type=None, fields=[]), _descriptor.OneofDescriptor( name='data_transfer_properties', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamDescriptor.data_transfer_properties', - index=1, containing_type=None, - create_key=_descriptor._internal_create_key, - fields=[]), + index=1, containing_type=None, fields=[]), ], serialized_start=433, - serialized_end=914, + serialized_end=947, ) @@ -179,7 +180,6 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='timestamp', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaSample.timestamp', index=0, @@ -187,47 +187,45 @@ has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='content_bytes', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaSample.content_bytes', index=1, number=5, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='content_reference', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaSample.content_reference', index=2, number=6, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='inferences', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaSample.inferences', index=3, number=10, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], - serialized_options=None, + options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name='content', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaSample.content', - index=0, containing_type=None, - create_key=_descriptor._internal_create_key, - fields=[]), + index=0, containing_type=None, fields=[]), ], - serialized_start=917, - serialized_end=1275, + serialized_start=950, + serialized_end=1308, ) @@ -237,43 +235,42 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='media_services_arm_id', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.GraphIdentifier.media_services_arm_id', index=0, number=1, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), + has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='graph_instance_name', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.GraphIdentifier.graph_instance_name', index=1, number=2, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), + has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='graph_node_name', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.GraphIdentifier.graph_node_name', index=2, number=3, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), + has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], - serialized_options=None, + options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], - serialized_start=1277, - serialized_end=1379, + serialized_start=1310, + serialized_end=1412, ) @@ -283,36 +280,35 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='handle_name', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.SharedMemoryBufferTransferProperties.handle_name', index=0, number=1, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), + has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='length_bytes', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.SharedMemoryBufferTransferProperties.length_bytes', index=1, number=2, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], - serialized_options=None, + options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], - serialized_start=1381, - serialized_end=1462, + serialized_start=1414, + serialized_end=1495, ) _MEDIASTREAMMESSAGE.fields_by_name['media_stream_descriptor'].message_type = _MEDIASTREAMDESCRIPTOR @@ -349,68 +345,41 @@ DESCRIPTOR.message_types_by_name['MediaSample'] = _MEDIASAMPLE DESCRIPTOR.message_types_by_name['GraphIdentifier'] = _GRAPHIDENTIFIER DESCRIPTOR.message_types_by_name['SharedMemoryBufferTransferProperties'] = _SHAREDMEMORYBUFFERTRANSFERPROPERTIES -_sym_db.RegisterFileDescriptor(DESCRIPTOR) -MediaStreamMessage = _reflection.GeneratedProtocolMessageType('MediaStreamMessage', (_message.Message,), { - 'DESCRIPTOR' : _MEDIASTREAMMESSAGE, - '__module__' : 'extension_pb2' +MediaStreamMessage = _reflection.GeneratedProtocolMessageType('MediaStreamMessage', (_message.Message,), dict( + DESCRIPTOR = _MEDIASTREAMMESSAGE, + __module__ = 'extension_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamMessage) - }) + )) _sym_db.RegisterMessage(MediaStreamMessage) -MediaStreamDescriptor = _reflection.GeneratedProtocolMessageType('MediaStreamDescriptor', (_message.Message,), { - 'DESCRIPTOR' : _MEDIASTREAMDESCRIPTOR, - '__module__' : 'extension_pb2' +MediaStreamDescriptor = _reflection.GeneratedProtocolMessageType('MediaStreamDescriptor', (_message.Message,), dict( + DESCRIPTOR = _MEDIASTREAMDESCRIPTOR, + __module__ = 'extension_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaStreamDescriptor) - }) + )) _sym_db.RegisterMessage(MediaStreamDescriptor) -MediaSample = _reflection.GeneratedProtocolMessageType('MediaSample', (_message.Message,), { - 'DESCRIPTOR' : _MEDIASAMPLE, - '__module__' : 'extension_pb2' +MediaSample = _reflection.GeneratedProtocolMessageType('MediaSample', (_message.Message,), dict( + DESCRIPTOR = _MEDIASAMPLE, + __module__ = 'extension_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaSample) - }) + )) _sym_db.RegisterMessage(MediaSample) -GraphIdentifier = _reflection.GeneratedProtocolMessageType('GraphIdentifier', (_message.Message,), { - 'DESCRIPTOR' : _GRAPHIDENTIFIER, - '__module__' : 'extension_pb2' +GraphIdentifier = _reflection.GeneratedProtocolMessageType('GraphIdentifier', (_message.Message,), dict( + DESCRIPTOR = _GRAPHIDENTIFIER, + __module__ = 'extension_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.GraphIdentifier) - }) + )) _sym_db.RegisterMessage(GraphIdentifier) -SharedMemoryBufferTransferProperties = _reflection.GeneratedProtocolMessageType('SharedMemoryBufferTransferProperties', (_message.Message,), { - 'DESCRIPTOR' : _SHAREDMEMORYBUFFERTRANSFERPROPERTIES, - '__module__' : 'extension_pb2' +SharedMemoryBufferTransferProperties = _reflection.GeneratedProtocolMessageType('SharedMemoryBufferTransferProperties', (_message.Message,), dict( + DESCRIPTOR = _SHAREDMEMORYBUFFERTRANSFERPROPERTIES, + __module__ = 'extension_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.SharedMemoryBufferTransferProperties) - }) + )) _sym_db.RegisterMessage(SharedMemoryBufferTransferProperties) - -_MEDIAGRAPHEXTENSION = _descriptor.ServiceDescriptor( - name='MediaGraphExtension', - full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaGraphExtension', - file=DESCRIPTOR, - index=0, - serialized_options=None, - create_key=_descriptor._internal_create_key, - serialized_start=1465, - serialized_end=1685, - methods=[ - _descriptor.MethodDescriptor( - name='ProcessMediaStream', - full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaGraphExtension.ProcessMediaStream', - index=0, - containing_service=None, - input_type=_MEDIASTREAMMESSAGE, - output_type=_MEDIASTREAMMESSAGE, - serialized_options=None, - create_key=_descriptor._internal_create_key, - ), -]) -_sym_db.RegisterServiceDescriptor(_MEDIAGRAPHEXTENSION) - -DESCRIPTOR.services_by_name['MediaGraphExtension'] = _MEDIAGRAPHEXTENSION - # @@protoc_insertion_point(module_scope) diff --git a/samples/lva_ai_extension/common/grpc_autogen/inferencing_pb2.py b/samples/lva_ai_extension/common/grpc_autogen/inferencing_pb2.py index fd30243..3835a33 100644 --- a/samples/lva_ai_extension/common/grpc_autogen/inferencing_pb2.py +++ b/samples/lva_ai_extension/common/grpc_autogen/inferencing_pb2.py @@ -26,10 +26,13 @@ # Generated by the protocol buffer compiler. DO NOT EDIT! # source: inferencing.proto +import sys +_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database +from google.protobuf import descriptor_pb2 # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() @@ -41,10 +44,9 @@ name='inferencing.proto', package='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1', syntax='proto3', - serialized_options=None, - create_key=_descriptor._internal_create_key, - serialized_pb=b'\n\x11inferencing.proto\x12@microsoft.azure.media.live_video_analytics.extensibility.grpc.v1\"\x84\x08\n\tInference\x12g\n\x04type\x18\x01 \x01(\x0e\x32Y.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Inference.InferenceType\x12\x0f\n\x07subtype\x18\x02 \x01(\t\x12\x14\n\x0cinference_id\x18\x03 \x01(\t\x12\x1a\n\x12related_inferences\x18\x04 \x03(\t\x12j\n\x0e\x63lassification\x18\x05 \x01(\x0b\x32P.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.ClassificationH\x00\x12Z\n\x06motion\x18\x06 \x01(\x0b\x32H.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MotionH\x00\x12Z\n\x06\x65ntity\x18\x07 \x01(\x0b\x32H.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.EntityH\x00\x12V\n\x04text\x18\x08 \x01(\x0b\x32\x46.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.TextH\x00\x12X\n\x05\x65vent\x18\t \x01(\x0b\x32G.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.EventH\x00\x12\x61\n\x05other\x18\r \x01(\x0b\x32P.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.InferenceOtherH\x00\x12o\n\nextensions\x18\x0f \x03(\x0b\x32[.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Inference.ExtensionsEntry\x1a\x31\n\x0f\x45xtensionsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\"e\n\rInferenceType\x12\x08\n\x04\x41UTO\x10\x00\x12\x12\n\x0e\x43LASSIFICATION\x10\x01\x12\n\n\x06MOTION\x10\x02\x12\n\n\x06\x45NTITY\x10\x03\x12\x08\n\x04TEXT\x10\x04\x12\t\n\x05\x45VENT\x10\x05\x12\t\n\x05OTHER\x10\x0f\x42\x07\n\x05value\"\xc5\x01\n\x0e\x43lassification\x12R\n\x03tag\x18\x01 \x01(\x0b\x32\x45.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Tag\x12_\n\nattributes\x18\x02 \x03(\x0b\x32K.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Attribute\"b\n\x06Motion\x12X\n\x03\x62ox\x18\x01 \x01(\x0b\x32K.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Rectangle\"\x97\x02\n\x06\x45ntity\x12R\n\x03tag\x18\x01 \x01(\x0b\x32\x45.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Tag\x12_\n\nattributes\x18\x02 \x03(\x0b\x32K.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Attribute\x12X\n\x03\x62ox\x18\x03 \x01(\x0b\x32K.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Rectangle\"W\n\x04Text\x12\r\n\x05value\x18\x01 \x01(\t\x12\x10\n\x08language\x18\x02 \x01(\t\x12\x17\n\x0fstart_timestamp\x18\x05 \x01(\x04\x12\x15\n\rend_timestamp\x18\x06 \x01(\x04\"\xb5\x01\n\x05\x45vent\x12\x0c\n\x04name\x18\x01 \x01(\t\x12k\n\nproperties\x18\x07 \x03(\x0b\x32W.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Event.PropertiesEntry\x1a\x31\n\x0fPropertiesEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\"=\n\x0eInferenceOther\x12\x14\n\x0c\x63ontent_type\x18\x01 \x01(\t\x12\x15\n\rcontent_bytes\x18\x02 \x01(\x0c\"<\n\tAttribute\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t\x12\x12\n\nconfidence\x18\x03 \x01(\x02\"(\n\x03Tag\x12\r\n\x05value\x18\x02 \x01(\t\x12\x12\n\nconfidence\x18\x03 \x01(\x02\"7\n\tRectangle\x12\t\n\x01l\x18\x01 \x01(\x02\x12\t\n\x01t\x18\x02 \x01(\x02\x12\t\n\x01w\x18\x03 \x01(\x02\x12\t\n\x01h\x18\x04 \x01(\x02\x62\x06proto3' + serialized_pb=_b('\n\x11inferencing.proto\x12@microsoft.azure.media.live_video_analytics.extensibility.grpc.v1\"\x84\x08\n\tInference\x12g\n\x04type\x18\x01 \x01(\x0e\x32Y.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Inference.InferenceType\x12\x0f\n\x07subtype\x18\x02 \x01(\t\x12\x14\n\x0cinference_id\x18\x03 \x01(\t\x12\x1a\n\x12related_inferences\x18\x04 \x03(\t\x12j\n\x0e\x63lassification\x18\x05 \x01(\x0b\x32P.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.ClassificationH\x00\x12Z\n\x06motion\x18\x06 \x01(\x0b\x32H.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MotionH\x00\x12Z\n\x06\x65ntity\x18\x07 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\x01(\x0b\x32\x45.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Tag\x12_\n\nattributes\x18\x02 \x03(\x0b\x32K.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Attribute\"b\n\x06Motion\x12X\n\x03\x62ox\x18\x01 \x01(\x0b\x32K.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Rectangle\"\xa3\x02\n\x06\x45ntity\x12R\n\x03tag\x18\x01 \x01(\x0b\x32\x45.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Tag\x12_\n\nattributes\x18\x02 \x03(\x0b\x32K.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Attribute\x12X\n\x03\x62ox\x18\x03 \x01(\x0b\x32K.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Rectangle\x12\n\n\x02id\x18\x04 \x01(\t\"W\n\x04Text\x12\r\n\x05value\x18\x01 \x01(\t\x12\x10\n\x08language\x18\x02 \x01(\t\x12\x17\n\x0fstart_timestamp\x18\x05 \x01(\x04\x12\x15\n\rend_timestamp\x18\x06 \x01(\x04\"\xb5\x01\n\x05\x45vent\x12\x0c\n\x04name\x18\x01 \x01(\t\x12k\n\nproperties\x18\x07 \x03(\x0b\x32W.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Event.PropertiesEntry\x1a\x31\n\x0fPropertiesEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\"=\n\x0eInferenceOther\x12\x14\n\x0c\x63ontent_type\x18\x01 \x01(\t\x12\x15\n\rcontent_bytes\x18\x02 \x01(\x0c\"<\n\tAttribute\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t\x12\x12\n\nconfidence\x18\x03 \x01(\x02\"(\n\x03Tag\x12\r\n\x05value\x18\x02 \x01(\t\x12\x12\n\nconfidence\x18\x03 \x01(\x02\"7\n\tRectangle\x12\t\n\x01l\x18\x01 \x01(\x02\x12\t\n\x01t\x18\x02 \x01(\x02\x12\t\n\x01w\x18\x03 \x01(\x02\x12\t\n\x01h\x18\x04 \x01(\x02\x62\x06proto3') ) +_sym_db.RegisterFileDescriptor(DESCRIPTOR) @@ -53,46 +55,38 @@ full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Inference.InferenceType', filename=None, file=DESCRIPTOR, - create_key=_descriptor._internal_create_key, values=[ _descriptor.EnumValueDescriptor( name='AUTO', index=0, number=0, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), _descriptor.EnumValueDescriptor( name='CLASSIFICATION', index=1, number=1, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), _descriptor.EnumValueDescriptor( name='MOTION', index=2, number=2, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), _descriptor.EnumValueDescriptor( name='ENTITY', index=3, number=3, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), _descriptor.EnumValueDescriptor( name='TEXT', index=4, number=4, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), _descriptor.EnumValueDescriptor( name='EVENT', index=5, number=5, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), _descriptor.EnumValueDescriptor( name='OTHER', index=6, number=15, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), ], containing_type=None, - serialized_options=None, + options=None, serialized_start=1006, serialized_end=1107, ) @@ -105,29 +99,28 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='key', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Inference.ExtensionsEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), + has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='value', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Inference.ExtensionsEntry.value', index=1, number=2, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), + has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], - serialized_options=b'8\001', + options=_descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001')), is_extendable=False, syntax='proto3', extension_ranges=[], @@ -143,7 +136,6 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='type', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Inference.type', index=0, @@ -151,77 +143,77 @@ has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='subtype', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Inference.subtype', index=1, number=2, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), + has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='inference_id', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Inference.inference_id', index=2, number=3, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), + has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='related_inferences', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Inference.related_inferences', index=3, number=4, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='classification', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Inference.classification', index=4, number=5, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='motion', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Inference.motion', index=5, number=6, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='entity', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Inference.entity', index=6, number=7, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='text', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Inference.text', index=7, number=8, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='event', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Inference.event', index=8, number=9, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='other', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Inference.other', index=9, number=13, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='extensions', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Inference.extensions', index=10, number=15, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], @@ -229,16 +221,14 @@ enum_types=[ _INFERENCE_INFERENCETYPE, ], - serialized_options=None, + options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name='value', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Inference.value', - index=0, containing_type=None, - create_key=_descriptor._internal_create_key, - fields=[]), + index=0, containing_type=None, fields=[]), ], serialized_start=88, serialized_end=1116, @@ -251,7 +241,6 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='tag', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Classification.tag', index=0, @@ -259,21 +248,21 @@ has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='attributes', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Classification.attributes', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], - serialized_options=None, + options=None, is_extendable=False, syntax='proto3', extension_ranges=[], @@ -290,7 +279,6 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='box', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Motion.box', index=0, @@ -298,14 +286,14 @@ has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], - serialized_options=None, + options=None, is_extendable=False, syntax='proto3', extension_ranges=[], @@ -322,7 +310,6 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='tag', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Entity.tag', index=0, @@ -330,35 +317,42 @@ has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='attributes', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Entity.attributes', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='box', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Entity.box', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), + _descriptor.FieldDescriptor( + name='id', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Entity.id', index=3, + number=4, type=9, cpp_type=9, label=1, + has_default_value=False, default_value=_b("").decode('utf-8'), + message_type=None, enum_type=None, containing_type=None, + is_extension=False, extension_scope=None, + options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], - serialized_options=None, + options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1419, - serialized_end=1698, + serialized_end=1710, ) @@ -368,50 +362,49 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='value', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Text.value', index=0, number=1, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), + has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='language', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Text.language', index=1, number=2, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), + has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='start_timestamp', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Text.start_timestamp', index=2, number=5, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='end_timestamp', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Text.end_timestamp', index=3, number=6, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], - serialized_options=None, + options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], - serialized_start=1700, - serialized_end=1787, + serialized_start=1712, + serialized_end=1799, ) @@ -421,36 +414,35 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='key', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Event.PropertiesEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), + has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='value', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Event.PropertiesEntry.value', index=1, number=2, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), + has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], - serialized_options=b'8\001', + options=_descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001')), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], - serialized_start=1922, - serialized_end=1971, + serialized_start=1934, + serialized_end=1983, ) _EVENT = _descriptor.Descriptor( @@ -459,36 +451,35 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='name', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Event.name', index=0, number=1, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), + has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='properties', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Event.properties', index=1, number=7, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], nested_types=[_EVENT_PROPERTIESENTRY, ], enum_types=[ ], - serialized_options=None, + options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], - serialized_start=1790, - serialized_end=1971, + serialized_start=1802, + serialized_end=1983, ) @@ -498,36 +489,35 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='content_type', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.InferenceOther.content_type', index=0, number=1, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), + has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='content_bytes', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.InferenceOther.content_bytes', index=1, number=2, type=12, cpp_type=9, label=1, - has_default_value=False, default_value=b"", + has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], - serialized_options=None, + options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], - serialized_start=1973, - serialized_end=2034, + serialized_start=1985, + serialized_end=2046, ) @@ -537,43 +527,42 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='name', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Attribute.name', index=0, number=1, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), + has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='value', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Attribute.value', index=1, number=2, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), + has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='confidence', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Attribute.confidence', index=2, number=3, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], - serialized_options=None, + options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], - serialized_start=2036, - serialized_end=2096, + serialized_start=2048, + serialized_end=2108, ) @@ -583,36 +572,35 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='value', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Tag.value', index=0, number=2, type=9, cpp_type=9, label=1, - has_default_value=False, default_value=b"".decode('utf-8'), + has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='confidence', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Tag.confidence', index=1, number=3, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], - serialized_options=None, + options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], - serialized_start=2098, - serialized_end=2138, + serialized_start=2110, + serialized_end=2150, ) @@ -622,7 +610,6 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='l', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Rectangle.l', index=0, @@ -630,42 +617,42 @@ has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='t', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Rectangle.t', index=1, number=2, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='w', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Rectangle.w', index=2, number=3, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='h', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Rectangle.h', index=3, number=4, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], - serialized_options=None, + options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], - serialized_start=2140, - serialized_end=2195, + serialized_start=2152, + serialized_end=2207, ) _INFERENCE_EXTENSIONSENTRY.containing_type = _INFERENCE @@ -714,95 +701,96 @@ DESCRIPTOR.message_types_by_name['Attribute'] = _ATTRIBUTE DESCRIPTOR.message_types_by_name['Tag'] = _TAG DESCRIPTOR.message_types_by_name['Rectangle'] = _RECTANGLE -_sym_db.RegisterFileDescriptor(DESCRIPTOR) -Inference = _reflection.GeneratedProtocolMessageType('Inference', (_message.Message,), { +Inference = _reflection.GeneratedProtocolMessageType('Inference', (_message.Message,), dict( - 'ExtensionsEntry' : _reflection.GeneratedProtocolMessageType('ExtensionsEntry', (_message.Message,), { - 'DESCRIPTOR' : _INFERENCE_EXTENSIONSENTRY, - '__module__' : 'inferencing_pb2' + ExtensionsEntry = _reflection.GeneratedProtocolMessageType('ExtensionsEntry', (_message.Message,), dict( + DESCRIPTOR = _INFERENCE_EXTENSIONSENTRY, + __module__ = 'inferencing_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Inference.ExtensionsEntry) - }) + )) , - 'DESCRIPTOR' : _INFERENCE, - '__module__' : 'inferencing_pb2' + DESCRIPTOR = _INFERENCE, + __module__ = 'inferencing_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Inference) - }) + )) _sym_db.RegisterMessage(Inference) _sym_db.RegisterMessage(Inference.ExtensionsEntry) -Classification = _reflection.GeneratedProtocolMessageType('Classification', (_message.Message,), { - 'DESCRIPTOR' : _CLASSIFICATION, - '__module__' : 'inferencing_pb2' +Classification = _reflection.GeneratedProtocolMessageType('Classification', (_message.Message,), dict( + DESCRIPTOR = _CLASSIFICATION, + __module__ = 'inferencing_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Classification) - }) + )) _sym_db.RegisterMessage(Classification) -Motion = _reflection.GeneratedProtocolMessageType('Motion', (_message.Message,), { - 'DESCRIPTOR' : _MOTION, - '__module__' : 'inferencing_pb2' +Motion = _reflection.GeneratedProtocolMessageType('Motion', (_message.Message,), dict( + DESCRIPTOR = _MOTION, + __module__ = 'inferencing_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Motion) - }) + )) _sym_db.RegisterMessage(Motion) -Entity = _reflection.GeneratedProtocolMessageType('Entity', (_message.Message,), { - 'DESCRIPTOR' : _ENTITY, - '__module__' : 'inferencing_pb2' +Entity = _reflection.GeneratedProtocolMessageType('Entity', (_message.Message,), dict( + DESCRIPTOR = _ENTITY, + __module__ = 'inferencing_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Entity) - }) + )) _sym_db.RegisterMessage(Entity) -Text = _reflection.GeneratedProtocolMessageType('Text', (_message.Message,), { - 'DESCRIPTOR' : _TEXT, - '__module__' : 'inferencing_pb2' +Text = _reflection.GeneratedProtocolMessageType('Text', (_message.Message,), dict( + DESCRIPTOR = _TEXT, + __module__ = 'inferencing_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Text) - }) + )) _sym_db.RegisterMessage(Text) -Event = _reflection.GeneratedProtocolMessageType('Event', (_message.Message,), { +Event = _reflection.GeneratedProtocolMessageType('Event', (_message.Message,), dict( - 'PropertiesEntry' : _reflection.GeneratedProtocolMessageType('PropertiesEntry', (_message.Message,), { - 'DESCRIPTOR' : _EVENT_PROPERTIESENTRY, - '__module__' : 'inferencing_pb2' + PropertiesEntry = _reflection.GeneratedProtocolMessageType('PropertiesEntry', (_message.Message,), dict( + DESCRIPTOR = _EVENT_PROPERTIESENTRY, + __module__ = 'inferencing_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Event.PropertiesEntry) - }) + )) , - 'DESCRIPTOR' : _EVENT, - '__module__' : 'inferencing_pb2' + DESCRIPTOR = _EVENT, + __module__ = 'inferencing_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Event) - }) + )) _sym_db.RegisterMessage(Event) _sym_db.RegisterMessage(Event.PropertiesEntry) -InferenceOther = _reflection.GeneratedProtocolMessageType('InferenceOther', (_message.Message,), { - 'DESCRIPTOR' : _INFERENCEOTHER, - '__module__' : 'inferencing_pb2' +InferenceOther = _reflection.GeneratedProtocolMessageType('InferenceOther', (_message.Message,), dict( + DESCRIPTOR = _INFERENCEOTHER, + __module__ = 'inferencing_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.InferenceOther) - }) + )) _sym_db.RegisterMessage(InferenceOther) -Attribute = _reflection.GeneratedProtocolMessageType('Attribute', (_message.Message,), { - 'DESCRIPTOR' : _ATTRIBUTE, - '__module__' : 'inferencing_pb2' +Attribute = _reflection.GeneratedProtocolMessageType('Attribute', (_message.Message,), dict( + DESCRIPTOR = _ATTRIBUTE, + __module__ = 'inferencing_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Attribute) - }) + )) _sym_db.RegisterMessage(Attribute) -Tag = _reflection.GeneratedProtocolMessageType('Tag', (_message.Message,), { - 'DESCRIPTOR' : _TAG, - '__module__' : 'inferencing_pb2' +Tag = _reflection.GeneratedProtocolMessageType('Tag', (_message.Message,), dict( + DESCRIPTOR = _TAG, + __module__ = 'inferencing_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Tag) - }) + )) _sym_db.RegisterMessage(Tag) -Rectangle = _reflection.GeneratedProtocolMessageType('Rectangle', (_message.Message,), { - 'DESCRIPTOR' : _RECTANGLE, - '__module__' : 'inferencing_pb2' +Rectangle = _reflection.GeneratedProtocolMessageType('Rectangle', (_message.Message,), dict( + DESCRIPTOR = _RECTANGLE, + __module__ = 'inferencing_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Rectangle) - }) + )) _sym_db.RegisterMessage(Rectangle) -_INFERENCE_EXTENSIONSENTRY._options = None -_EVENT_PROPERTIESENTRY._options = None +_INFERENCE_EXTENSIONSENTRY.has_options = True +_INFERENCE_EXTENSIONSENTRY._options = _descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001')) +_EVENT_PROPERTIESENTRY.has_options = True +_EVENT_PROPERTIESENTRY._options = _descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001')) # @@protoc_insertion_point(module_scope) diff --git a/samples/lva_ai_extension/common/grpc_autogen/media_pb2.py b/samples/lva_ai_extension/common/grpc_autogen/media_pb2.py index ec7094f..85231ff 100644 --- a/samples/lva_ai_extension/common/grpc_autogen/media_pb2.py +++ b/samples/lva_ai_extension/common/grpc_autogen/media_pb2.py @@ -26,10 +26,13 @@ # Generated by the protocol buffer compiler. DO NOT EDIT! # source: media.proto +import sys +_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database +from google.protobuf import descriptor_pb2 # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() @@ -41,10 +44,9 @@ name='media.proto', package='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1', syntax='proto3', - serialized_options=None, - create_key=_descriptor._internal_create_key, - serialized_pb=b'\n\x0bmedia.proto\x12@microsoft.azure.media.live_video_analytics.extensibility.grpc.v1\"\xba\x01\n\x0fMediaDescriptor\x12\x11\n\ttimescale\x18\x01 \x01(\r\x12}\n\x19video_frame_sample_format\x18\x05 \x01(\x0b\x32X.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.VideoFrameSampleFormatH\x00\x42\x15\n\x13media_sample_format\"\xce\x04\n\x16VideoFrameSampleFormat\x12s\n\x08\x65ncoding\x18\x01 \x01(\x0e\x32\x61.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.VideoFrameSampleFormat.Encoding\x12z\n\x0cpixel_format\x18\x02 \x01(\x0e\x32\x64.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.VideoFrameSampleFormat.PixelFormat\x12`\n\ndimensions\x18\x03 \x01(\x0b\x32L.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Dimensions\x12\x14\n\x0cstride_bytes\x18\x04 \x01(\r\".\n\x08\x45ncoding\x12\x07\n\x03\x42MP\x10\x00\x12\x07\n\x03JPG\x10\x01\x12\x07\n\x03PNG\x10\x02\x12\x07\n\x03RAW\x10\x03\"\x9a\x01\n\x0bPixelFormat\x12\x08\n\x04NONE\x10\x00\x12\x0b\n\x07YUV420P\x10\x14\x12\x0c\n\x08RGB565BE\x10(\x12\x0c\n\x08RGB565LE\x10)\x12\x0c\n\x08RGB555BE\x10*\x12\x0c\n\x08RGB555LE\x10+\x12\t\n\x05RGB24\x10<\x12\t\n\x05\x42GR24\x10=\x12\x08\n\x04\x41RGB\x10P\x12\x08\n\x04RGBA\x10Q\x12\x08\n\x04\x41\x42GR\x10R\x12\x08\n\x04\x42GRA\x10S\"+\n\nDimensions\x12\r\n\x05width\x18\x01 \x01(\r\x12\x0e\n\x06height\x18\x02 \x01(\r\"\x1d\n\x0c\x43ontentBytes\x12\r\n\x05\x62ytes\x18\x01 \x01(\x0c\"@\n\x10\x43ontentReference\x12\x16\n\x0e\x61\x64\x64ress_offset\x18\x01 \x01(\x04\x12\x14\n\x0clength_bytes\x18\x02 \x01(\x04\x62\x06proto3' + serialized_pb=_b('\n\x0bmedia.proto\x12@microsoft.azure.media.live_video_analytics.extensibility.grpc.v1\"\xba\x01\n\x0fMediaDescriptor\x12\x11\n\ttimescale\x18\x01 \x01(\r\x12}\n\x19video_frame_sample_format\x18\x05 \x01(\x0b\x32X.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.VideoFrameSampleFormatH\x00\x42\x15\n\x13media_sample_format\"\xce\x04\n\x16VideoFrameSampleFormat\x12s\n\x08\x65ncoding\x18\x01 \x01(\x0e\x32\x61.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.VideoFrameSampleFormat.Encoding\x12z\n\x0cpixel_format\x18\x02 \x01(\x0e\x32\x64.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.VideoFrameSampleFormat.PixelFormat\x12`\n\ndimensions\x18\x03 \x01(\x0b\x32L.microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Dimensions\x12\x14\n\x0cstride_bytes\x18\x04 \x01(\r\".\n\x08\x45ncoding\x12\x07\n\x03\x42MP\x10\x00\x12\x07\n\x03JPG\x10\x01\x12\x07\n\x03PNG\x10\x02\x12\x07\n\x03RAW\x10\x03\"\x9a\x01\n\x0bPixelFormat\x12\x08\n\x04NONE\x10\x00\x12\x0b\n\x07YUV420P\x10\x14\x12\x0c\n\x08RGB565BE\x10(\x12\x0c\n\x08RGB565LE\x10)\x12\x0c\n\x08RGB555BE\x10*\x12\x0c\n\x08RGB555LE\x10+\x12\t\n\x05RGB24\x10<\x12\t\n\x05\x42GR24\x10=\x12\x08\n\x04\x41RGB\x10P\x12\x08\n\x04RGBA\x10Q\x12\x08\n\x04\x41\x42GR\x10R\x12\x08\n\x04\x42GRA\x10S\"+\n\nDimensions\x12\r\n\x05width\x18\x01 \x01(\r\x12\x0e\n\x06height\x18\x02 \x01(\r\"\x1d\n\x0c\x43ontentBytes\x12\r\n\x05\x62ytes\x18\x01 \x01(\x0c\"@\n\x10\x43ontentReference\x12\x16\n\x0e\x61\x64\x64ress_offset\x18\x01 \x01(\x04\x12\x14\n\x0clength_bytes\x18\x02 \x01(\x04\x62\x06proto3') ) +_sym_db.RegisterFileDescriptor(DESCRIPTOR) @@ -53,31 +55,26 @@ full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.VideoFrameSampleFormat.Encoding', filename=None, file=DESCRIPTOR, - create_key=_descriptor._internal_create_key, values=[ _descriptor.EnumValueDescriptor( name='BMP', index=0, number=0, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), _descriptor.EnumValueDescriptor( name='JPG', index=1, number=1, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), _descriptor.EnumValueDescriptor( name='PNG', index=2, number=2, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), _descriptor.EnumValueDescriptor( name='RAW', index=3, number=3, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), ], containing_type=None, - serialized_options=None, + options=None, serialized_start=658, serialized_end=704, ) @@ -88,71 +85,58 @@ full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.VideoFrameSampleFormat.PixelFormat', filename=None, file=DESCRIPTOR, - create_key=_descriptor._internal_create_key, values=[ _descriptor.EnumValueDescriptor( name='NONE', index=0, number=0, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), _descriptor.EnumValueDescriptor( name='YUV420P', index=1, number=20, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), _descriptor.EnumValueDescriptor( name='RGB565BE', index=2, number=40, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), _descriptor.EnumValueDescriptor( name='RGB565LE', index=3, number=41, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), _descriptor.EnumValueDescriptor( name='RGB555BE', index=4, number=42, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), _descriptor.EnumValueDescriptor( name='RGB555LE', index=5, number=43, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), _descriptor.EnumValueDescriptor( name='RGB24', index=6, number=60, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), _descriptor.EnumValueDescriptor( name='BGR24', index=7, number=61, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), _descriptor.EnumValueDescriptor( name='ARGB', index=8, number=80, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), _descriptor.EnumValueDescriptor( name='RGBA', index=9, number=81, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), _descriptor.EnumValueDescriptor( name='ABGR', index=10, number=82, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), _descriptor.EnumValueDescriptor( name='BGRA', index=11, number=83, - serialized_options=None, - type=None, - create_key=_descriptor._internal_create_key), + options=None, + type=None), ], containing_type=None, - serialized_options=None, + options=None, serialized_start=707, serialized_end=861, ) @@ -165,7 +149,6 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='timescale', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaDescriptor.timescale', index=0, @@ -173,30 +156,28 @@ has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='video_frame_sample_format', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaDescriptor.video_frame_sample_format', index=1, number=5, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], - serialized_options=None, + options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name='media_sample_format', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaDescriptor.media_sample_format', - index=0, containing_type=None, - create_key=_descriptor._internal_create_key, - fields=[]), + index=0, containing_type=None, fields=[]), ], serialized_start=82, serialized_end=268, @@ -209,7 +190,6 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='encoding', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.VideoFrameSampleFormat.encoding', index=0, @@ -217,28 +197,28 @@ has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='pixel_format', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.VideoFrameSampleFormat.pixel_format', index=1, number=2, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='dimensions', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.VideoFrameSampleFormat.dimensions', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='stride_bytes', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.VideoFrameSampleFormat.stride_bytes', index=3, number=4, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], @@ -247,7 +227,7 @@ _VIDEOFRAMESAMPLEFORMAT_ENCODING, _VIDEOFRAMESAMPLEFORMAT_PIXELFORMAT, ], - serialized_options=None, + options=None, is_extendable=False, syntax='proto3', extension_ranges=[], @@ -264,7 +244,6 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='width', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Dimensions.width', index=0, @@ -272,21 +251,21 @@ has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='height', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Dimensions.height', index=1, number=2, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], - serialized_options=None, + options=None, is_extendable=False, syntax='proto3', extension_ranges=[], @@ -303,22 +282,21 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='bytes', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.ContentBytes.bytes', index=0, number=1, type=12, cpp_type=9, label=1, - has_default_value=False, default_value=b"", + has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], - serialized_options=None, + options=None, is_extendable=False, syntax='proto3', extension_ranges=[], @@ -335,7 +313,6 @@ filename=None, file=DESCRIPTOR, containing_type=None, - create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='address_offset', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.ContentReference.address_offset', index=0, @@ -343,21 +320,21 @@ has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), _descriptor.FieldDescriptor( name='length_bytes', full_name='microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.ContentReference.length_bytes', index=1, number=2, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), + options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], - serialized_options=None, + options=None, is_extendable=False, syntax='proto3', extension_ranges=[], @@ -381,41 +358,40 @@ DESCRIPTOR.message_types_by_name['Dimensions'] = _DIMENSIONS DESCRIPTOR.message_types_by_name['ContentBytes'] = _CONTENTBYTES DESCRIPTOR.message_types_by_name['ContentReference'] = _CONTENTREFERENCE -_sym_db.RegisterFileDescriptor(DESCRIPTOR) -MediaDescriptor = _reflection.GeneratedProtocolMessageType('MediaDescriptor', (_message.Message,), { - 'DESCRIPTOR' : _MEDIADESCRIPTOR, - '__module__' : 'media_pb2' +MediaDescriptor = _reflection.GeneratedProtocolMessageType('MediaDescriptor', (_message.Message,), dict( + DESCRIPTOR = _MEDIADESCRIPTOR, + __module__ = 'media_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.MediaDescriptor) - }) + )) _sym_db.RegisterMessage(MediaDescriptor) -VideoFrameSampleFormat = _reflection.GeneratedProtocolMessageType('VideoFrameSampleFormat', (_message.Message,), { - 'DESCRIPTOR' : _VIDEOFRAMESAMPLEFORMAT, - '__module__' : 'media_pb2' +VideoFrameSampleFormat = _reflection.GeneratedProtocolMessageType('VideoFrameSampleFormat', (_message.Message,), dict( + DESCRIPTOR = _VIDEOFRAMESAMPLEFORMAT, + __module__ = 'media_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.VideoFrameSampleFormat) - }) + )) _sym_db.RegisterMessage(VideoFrameSampleFormat) -Dimensions = _reflection.GeneratedProtocolMessageType('Dimensions', (_message.Message,), { - 'DESCRIPTOR' : _DIMENSIONS, - '__module__' : 'media_pb2' +Dimensions = _reflection.GeneratedProtocolMessageType('Dimensions', (_message.Message,), dict( + DESCRIPTOR = _DIMENSIONS, + __module__ = 'media_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.Dimensions) - }) + )) _sym_db.RegisterMessage(Dimensions) -ContentBytes = _reflection.GeneratedProtocolMessageType('ContentBytes', (_message.Message,), { - 'DESCRIPTOR' : _CONTENTBYTES, - '__module__' : 'media_pb2' +ContentBytes = _reflection.GeneratedProtocolMessageType('ContentBytes', (_message.Message,), dict( + DESCRIPTOR = _CONTENTBYTES, + __module__ = 'media_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.ContentBytes) - }) + )) _sym_db.RegisterMessage(ContentBytes) -ContentReference = _reflection.GeneratedProtocolMessageType('ContentReference', (_message.Message,), { - 'DESCRIPTOR' : _CONTENTREFERENCE, - '__module__' : 'media_pb2' +ContentReference = _reflection.GeneratedProtocolMessageType('ContentReference', (_message.Message,), dict( + DESCRIPTOR = _CONTENTREFERENCE, + __module__ = 'media_pb2' # @@protoc_insertion_point(class_scope:microsoft.azure.media.live_video_analytics.extensibility.grpc.v1.ContentReference) - }) + )) _sym_db.RegisterMessage(ContentReference) diff --git a/samples/lva_ai_extension/contracts/extension.proto b/samples/lva_ai_extension/contracts/extension.proto index 087d596..471f72c 100644 --- a/samples/lva_ai_extension/contracts/extension.proto +++ b/samples/lva_ai_extension/contracts/extension.proto @@ -66,6 +66,7 @@ message MediaStreamDescriptor { GraphIdentifier graph_identifier = 1; // Media Stream graph identifier string extension_identifier = 2; // Media Stream extension identifier } + string extension_configuration = 3; // Optional extension configuration string provided by the user MediaDescriptor media_descriptor = 5; // Session media information. @@ -113,4 +114,3 @@ message SharedMemoryBufferTransferProperties{ string handle_name = 1; uint64 length_bytes = 2; } - diff --git a/samples/lva_ai_extension/contracts/inferencing.proto b/samples/lva_ai_extension/contracts/inferencing.proto index 89d7232..4d6c70c 100644 --- a/samples/lva_ai_extension/contracts/inferencing.proto +++ b/samples/lva_ai_extension/contracts/inferencing.proto @@ -64,6 +64,7 @@ message Entity { Tag tag = 1; // Entity tag. Examples: person, bicycle, car, ... repeated Attribute attributes = 2; // Additional entity attributes. Examples: color=red, body=sedan, etc. Rectangle box = 3; // Entity bounding box + string id = 4; // Optional entity id for identification and/or tracking } // @@ -124,4 +125,4 @@ message Rectangle { float t = 2; // Top: distance from the image's top edge to the rectangle's top edge float w = 3; // Width: rectangle width float h = 4; // Height: rectangle height -} +} \ No newline at end of file diff --git a/samples/lva_ai_extension/server/media_graph_extension.py b/samples/lva_ai_extension/server/media_graph_extension.py index 5417d35..fcc5253 100644 --- a/samples/lva_ai_extension/server/media_graph_extension.py +++ b/samples/lva_ai_extension/server/media_graph_extension.py @@ -103,6 +103,7 @@ def _generate_media_stream_message(self, gva_sample): inference = msg.media_sample.inferences.add() attributes = [] + object_id = None obj_label = None obj_confidence = 0 obj_left = 0 @@ -121,14 +122,12 @@ def _generate_media_stream_message(self, gva_sample): inference.type = inferencing_pb2.Inference.InferenceType.ENTITY # pylint: disable=no-member if region.object_id(): #Tracking - obj_id = region.object_id() - attributes.append(["object_id", str(obj_id), 0]) + object_id = str(region.object_id()) elif tensor["label"]: #Classification attr_name = name attr_label = tensor["label"] attr_confidence = region.confidence() attributes.append([attr_name, attr_label, attr_confidence]) - if obj_label is not None: try: entity = inferencing_pb2.Entity( @@ -143,7 +142,6 @@ def _generate_media_stream_message(self, gva_sample): h=obj_height ) ) - for attr in attributes: attribute = inferencing_pb2.Attribute( name=attr[0], @@ -151,7 +149,8 @@ def _generate_media_stream_message(self, gva_sample): confidence=attr[2] ) entity.attributes.append(attribute) - + if object_id: + entity.id = object_id except: log_exception(self._logger) raise diff --git a/samples/lva_ai_extension/tests/common/Extension_Data_Schema.json b/samples/lva_ai_extension/tests/common/Extension_Data_Schema.json index cd81e06..7192056 100644 --- a/samples/lva_ai_extension/tests/common/Extension_Data_Schema.json +++ b/samples/lva_ai_extension/tests/common/Extension_Data_Schema.json @@ -152,6 +152,11 @@ "title": "Entity", "description": "Entity detected or identified on the sample.", "properties": { + "id": { + "type": "string", + "title": "Id", + "description": "Optional entity identification (persistent or transient)." + }, "tag": { "description": "Entity identifier", "$ref": "#/definitions/tag" @@ -358,4 +363,4 @@ "additionalProperties": false } } -} +} \ No newline at end of file From c8c1c168ad6311882ca0c0468e537a912496fd97 Mon Sep 17 00:00:00 2001 From: "Dunham, Darrin" Date: Fri, 15 Jan 2021 09:59:42 -0800 Subject: [PATCH 28/50] Updated spaces_in_test_run_arguments script test to check output log --- tests/script_tests/spaces_in_test_run_arguments | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/tests/script_tests/spaces_in_test_run_arguments b/tests/script_tests/spaces_in_test_run_arguments index 6fb0bdc..871deb6 100755 --- a/tests/script_tests/spaces_in_test_run_arguments +++ b/tests/script_tests/spaces_in_test_run_arguments @@ -1,4 +1,15 @@ #!/bin/bash -NUM_REST_TESTS=9 -NUM_TESTS=$($TEST_DIR/run.sh --pytest-gstreamer --entrypoint-args "-k rest --collect-only" 2> /dev/null | grep -c Function) -[[ $NUM_TESTS == $NUM_REST_TESTS ]] + +ENTRYPOINT_ARGS="-c echo arg1 arg2" +EXPECTED="EntrypointArgs: '$ENTRYPOINT_ARGS '" + +OUTPUT=$($SOURCE_DIR/docker/run.sh --entrypoint /bin/bash --entrypoint-args "$ENTRYPOINT_ARGS" --dry-run) +RETURN_CODE=$? + +if [[ $RETURN_CODE != 0 ]]; then + error "docker/run.sh has failed" +fi + +if [[ ! "$OUTPUT" == *"$EXPECTED"* ]]; then + error "Invalid Entrypoint Args" +fi From 9628c3e9fa77e2a43f6ea17f13cb196a8f693df0 Mon Sep 17 00:00:00 2001 From: "Shah, Neelay N" Date: Fri, 15 Jan 2021 12:25:02 -0800 Subject: [PATCH 29/50] Several updates for Client and Server stability and testing: Fixes: 1. Shared memory off by 1 error 1. Loop for individual frame 1. Error handling in case pipeline with cached model fails (vaserving core change) 1. Error handling in client 1. Unbounded memory growth in client due to faster input than response Features: 1. Support multiple streams in client 1. Remove redundant argument handling 1. Add help from parent script / application 1. Support frame-rate 1. Support max input queue size 1. Support fps logging in client (end to end) --- samples/lva_ai_extension/client/__main__.py | 148 ++++++++--- samples/lva_ai_extension/client/arguments.py | 125 ++++++--- .../client/media_stream_processor.py | 121 +++++---- .../lva_ai_extension/common/shared_memory.py | 3 +- samples/lva_ai_extension/docker/run_client.sh | 99 ++++--- samples/lva_ai_extension/docker/run_server.sh | 53 ++-- .../pipeline.json | 4 - samples/lva_ai_extension/server/__main__.py | 171 ++++++++---- .../server/media_graph_extension.py | 249 +++++++++++------- vaserving/gstreamer_pipeline.py | 18 +- 10 files changed, 662 insertions(+), 329 deletions(-) diff --git a/samples/lva_ai_extension/client/__main__.py b/samples/lva_ai_extension/client/__main__.py index 1502c00..2e61c7d 100644 --- a/samples/lva_ai_extension/client/__main__.py +++ b/samples/lva_ai_extension/client/__main__.py @@ -1,4 +1,4 @@ -''' +""" * Copyright (C) 2019-2020 Intel Corporation. * * SPDX-License-Identifier: MIT License @@ -26,24 +26,31 @@ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE -''' +""" import logging import os import sys import queue import json +import time import cv2 + from google.protobuf.json_format import MessageToDict from samples.lva_ai_extension.common.exception_handler import log_exception from arguments import parse_args from media_stream_processor import MediaStreamProcessor + class VideoSource: def __init__(self, filename, loop_count): self._loop_count = loop_count - self._vid_cap = cv2.VideoCapture(filename, cv2.CAP_GSTREAMER) + self._filename = filename + self._open_video_source() + + def _open_video_source(self): + self._vid_cap = cv2.VideoCapture(self._filename, cv2.CAP_GSTREAMER) if self._vid_cap is None or not self._vid_cap.isOpened(): - print("{}: Error opening video source".format(filename)) + logging.error("Error opening video source: {}".format(self._filename)) sys.exit(1) def dimensions(self): @@ -56,9 +63,9 @@ def get_frame(self): if ret: return frame.tobytes() else: + self._loop_count -= 1 if self._loop_count > 0: - self._vid_cap.set(cv2.CAP_PROP_POS_FRAMES, 0) - self._loop_count -= 1 + self._open_video_source() ret, frame = self._vid_cap.read() if ret: return frame.tobytes() @@ -67,9 +74,10 @@ def get_frame(self): def close(self): self._vid_cap.release() + def _log_options(args): heading = "Options for {}".format(os.path.basename(__file__)) - banner = "="*len(heading) + banner = "=" * len(heading) logging.info(banner) logging.info(heading) logging.info(banner) @@ -77,14 +85,27 @@ def _log_options(args): logging.info("{} == {}".format(arg, getattr(args, arg))) logging.info(banner) -def remove_empty_lists(dictionary): + +def _remove_empty_lists(dictionary): if not isinstance(dictionary, (dict, list)): return dictionary if isinstance(dictionary, list): - return [v for v in (remove_empty_lists(v) for v in dictionary) if v or v == 0] - return {k: v for k, v in ((k, remove_empty_lists(v)) for k, v in dictionary.items()) if v or v == 0} - -def print_result(response, output): + return [v for v in (_remove_empty_lists(v) for v in dictionary) if v or v == 0] + return { + k: v + for k, v in ((k, _remove_empty_lists(v)) for k, v in dictionary.items()) + if v or v == 0 + } + + +def _log_result(response, output, log_result=True): + if not log_result: + return + if not response: + return + if isinstance(response, Exception): + logging.error(response) + return logging.info("Inference result {}".format(response.ack_sequence_number)) for inference in response.media_sample.inferences: tag = inference.entity.tag @@ -93,71 +114,124 @@ def print_result(response, output): .format(tag.value, tag.confidence, box.l, box.t, box.w, box.h) if inference.entity.id: log_message += " id:{} ".format(inference.entity.id) - atrributes = [] + attributes = [] for attribute in inference.entity.attributes: attribute_string = "{}: {}".format(attribute.name, attribute.value) - atrributes.append(attribute_string) - if len(atrributes) > 0: - log_message += " {}".format(atrributes) - logging.info(log_message) - # default value field is used to avoid not including values set to 0, but it also causes empty lists to be included + attributes.append(attribute_string) + logging.info( + "- {} ({:.2f}) [{:.2f}, {:.2f}, {:.2f}, {:.2f}] {}".format( + tag.value, tag.confidence, box.l, box.t, box.w, box.h, attributes + ) + ) + # default value field is used to avoid not including values set to 0, + # but it also causes empty lists to be included # empty and none values are filtered out in response_dict - returned_dict = MessageToDict(response.media_sample, including_default_value_fields=True) - response_dict = remove_empty_lists(returned_dict) + returned_dict = MessageToDict( + response.media_sample, including_default_value_fields=True + ) + response_dict = _remove_empty_lists(returned_dict) if response_dict.get("inferences"): for inference in response_dict["inferences"]: inference["type"] = inference["type"].lower() output.write("{}\n".format(json.dumps(response_dict))) + +def _log_fps(start_time, frames_received, prev_fps_delta, fps_interval): + delta = int(time.time() - start_time) + if (fps_interval > 0) and (delta != prev_fps_delta) and (delta % fps_interval == 0): + logging.info( + "FPS: {} Frames Recieved: {}".format( + (frames_received / delta), frames_received + ) + ) + return delta + return prev_fps_delta + + def main(): try: args = parse_args() _log_options(args) + frame_delay = 1 / args.frame_rate if args.frame_rate > 0 else 0 frame_source = None - frame_queue = queue.Queue() + frame_queue = queue.Queue(args.frame_queue_size) result_queue = queue.Queue() - msp = MediaStreamProcessor(args.grpc_server_address, - args.use_shared_memory) - + frames_received = 0 + prev_fps_delta = 0 + start_time = None frame_source = VideoSource(args.sample_file, args.loop_count) - width, height = frame_source.dimensions() - print("{} {}".format(width, height)) + image = frame_source.get_frame() + + if not image: + logging.error( + "Error getting frame from video source: {}".format(args.sample_file) + ) + sys.exit(1) + + msp = MediaStreamProcessor( + args.grpc_server_address, + args.use_shared_memory, + args.frame_queue_size, + len(image), + ) + msp.start(width, height, frame_queue, result_queue) with open(args.output_file, "w") as output: - image = frame_source.get_frame() - while image: + start_time = time.time() + result = True + while image and result: frame_queue.put(image) while not result_queue.empty(): result = result_queue.get() - print_result(result, output) + frames_received += 1 + prev_fps_delta = _log_fps( + start_time, frames_received, prev_fps_delta, args.fps_interval + ) + _log_result(result, output) image = frame_source.get_frame() - frame_queue.put(None) + time.sleep(frame_delay) - result = result_queue.get() + if result: + frame_queue.put(None) + result = result_queue.get() while result: - print_result(result, output) + frames_received += 1 + prev_fps_delta = _log_fps( + start_time, frames_received, prev_fps_delta, args.fps_interval + ) + _log_result(result, output) result = result_queue.get() frame_source.close() + delta = time.time() - start_time + logging.info( + "Start Time: {} End Time: {} Frames Recieved: {} FPS: {}".format( + start_time, + start_time + delta, + frames_received, + (frames_received / delta) if delta > 0 else None, + ) + ) except Exception: log_exception() sys.exit(-1) + if __name__ == "__main__": # Set logging parameters logging.basicConfig( level=logging.INFO, - format='[AIXC] [%(asctime)-15s] [%(threadName)-12.12s] [%(levelname)s]: %(message)s', + format="[AIXC] [%(asctime)-15s] [%(threadName)-12.12s] [%(levelname)s]: %(message)s", handlers=[ - #logging.FileHandler(LOG_FILE_NAME), # write in a log file - logging.StreamHandler(sys.stdout) # write in stdout - ] + # logging.FileHandler(LOG_FILE_NAME), # write in a log file + logging.StreamHandler(sys.stdout) # write in stdout + ], ) # Call Main logic main() - logging.info('Client finished execution') + logging.info("Client finished execution") diff --git a/samples/lva_ai_extension/client/arguments.py b/samples/lva_ai_extension/client/arguments.py index 2de98da..1b18af5 100644 --- a/samples/lva_ai_extension/client/arguments.py +++ b/samples/lva_ai_extension/client/arguments.py @@ -30,35 +30,98 @@ import argparse -def parse_args(args=None, program_name="AI Extension Sample Client"): - parser = argparse.ArgumentParser(prog=program_name, fromfile_prefix_chars='@', - formatter_class=argparse.ArgumentDefaultsHelpFormatter) - parser.add_argument('-s', metavar=('grpc_server_address'), - dest="grpc_server_address", - help='gRPC server address.', - default="localhost:5001") - parser.add_argument('-f', metavar=('sample_file'), - dest="sample_file", - help='Name of the sample video frame.', - default="/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/sample01.png") - parser.add_argument('-l', metavar=('loop_count'), - dest="loop_count", - help='How many times to loop the source after it finishes.', - type=int, - default=0) - parser.add_argument('-m', action='store_const', - dest='use_shared_memory', - const=True, - default=False, - help='set to use shared memory') - #nosec skips pybandit hits - parser.add_argument('-o', metavar=('output_file'), - dest="output_file", - help='Output file path', - default="/tmp/results.jsonl") # nosec - parser.add_argument('--version', action='version', version='%(prog)s 1.0') - if (isinstance(args, dict)): - args = ["--{}={}".format(key, value) - for key, value in args.items() if value] - return parser.parse_args(args) +def parse_args(args=None, program_name="DL Streamer Edge AI Extension Client"): + parser = argparse.ArgumentParser( + prog=program_name, + fromfile_prefix_chars="@", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "-s", + metavar=("grpc_server_address"), + dest="grpc_server_address", + help="gRPC server address.", + default=None, + ) + parser.add_argument( + "--server-ip", + dest="grpc_server_ip", + help="gRPC server ip.", + default="localhost", + ) + + parser.add_argument( + "--server-port", + dest="grpc_server_port", + help="gRPC server port.", + type=int, + default=5001, + ) + + parser.add_argument( + "-f", + "--sample-file-path", + metavar=("sample_file"), + dest="sample_file", + help="Name of the sample video frame.", + default="/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/sample01.png", + ) + parser.add_argument( + "-l", + "--loop-count", + metavar=("loop_count"), + dest="loop_count", + help="How many times to loop the source after it finishes.", + type=int, + default=0, + ) + parser.add_argument( + "--fps-interval", + dest="fps_interval", + help="How often to report FPS (every N seconds)", + type=int, + default=2, + ) + parser.add_argument( + "--frame-rate", + dest="frame_rate", + help="How many frames to send per second (-1 is no limit)", + type=int, + default=-1, + ) + parser.add_argument( + "--frame-queue-size", + dest="frame_queue_size", + help="Max number of frames to buffer in client (0 is no limit)", + type=int, + default=200, + ) + parser.add_argument( + "-m", + "--shared-memory", + action="store_const", + dest="use_shared_memory", + const=True, + default=False, + help="set to use shared memory", + ) + # nosec skips pybandit hits + parser.add_argument( + "-o", + "--output-file-path", + metavar=("output_file"), + dest="output_file", + help="Output file path", + default="/tmp/results.jsonl", + ) # nosec + + parser.add_argument("--version", action="version", version="%(prog)s 1.0") + if isinstance(args, dict): + args = ["--{}={}".format(key, value) for key, value in args.items() if value] + result = parser.parse_args(args) + if not result.grpc_server_address: + result.grpc_server_address = "{}:{}".format( + result.grpc_server_ip, result.grpc_server_port + ) + return result diff --git a/samples/lva_ai_extension/client/media_stream_processor.py b/samples/lva_ai_extension/client/media_stream_processor.py index 0c77147..df25e77 100644 --- a/samples/lva_ai_extension/client/media_stream_processor.py +++ b/samples/lva_ai_extension/client/media_stream_processor.py @@ -39,10 +39,8 @@ import samples.lva_ai_extension.common.grpc_autogen.extension_pb2_grpc as extension_pb2_grpc - class MediaStreamProcessor: - - class RequestGenerator(): + class RequestGenerator: def __init__(self, descriptor, shared_memory_manager, queue): try: self._request_seq_num = 1 @@ -58,28 +56,28 @@ def __iter__(self): def __next__(self): if self._request_seq_num == 1: - logging.info("MediaStreamDescriptor request #{}".format(self._request_seq_num)) + logging.info( + "MediaStreamDescriptor request #{}".format(self._request_seq_num) + ) request = extension_pb2.MediaStreamMessage( sequence_number=self._request_seq_num, ack_sequence_number=0, - media_stream_descriptor=self._descriptor) + media_stream_descriptor=self._descriptor, + ) else: logging.info("MediaSample request #{}".format(self._request_seq_num)) image = self._queue.get() if image is None: raise StopIteration media_sample = extension_pb2.MediaSample( - timestamp=0, - content_bytes=media_pb2.ContentBytes( - bytes=image - ) + timestamp=0, content_bytes=media_pb2.ContentBytes(bytes=image) ) if self._shared_memory_manager: media_sample = self.get_shared_memory_request(media_sample) request = extension_pb2.MediaStreamMessage( sequence_number=self._request_seq_num, ack_sequence_number=0, - media_sample=media_sample + media_sample=media_sample, ) self._request_seq_num += 1 @@ -87,8 +85,9 @@ def __next__(self): def get_memory_slot(self, sequence_number, content_bytes): try: - memory_slot = self._shared_memory_manager.get_empty_slot(sequence_number, - len(content_bytes)) + memory_slot = self._shared_memory_manager.get_empty_slot( + sequence_number, len(content_bytes) + ) if memory_slot is None: return None @@ -100,75 +99,85 @@ def get_memory_slot(self, sequence_number, content_bytes): return memory_slot def get_shared_memory_request(self, media_sample): - memory_slot = self.get_memory_slot(self._request_seq_num, media_sample.content_bytes.bytes) + memory_slot = self.get_memory_slot( + self._request_seq_num, media_sample.content_bytes.bytes + ) while memory_slot is None: - logging.info('***************** Shared Memory Full *****************') + logging.info("***************** Shared Memory Full *****************") time.sleep(1) - memory_slot = self.get_memory_slot(self._request_seq_num, media_sample.content_bytes.bytes) + memory_slot = self.get_memory_slot( + self._request_seq_num, media_sample.content_bytes.bytes + ) memory_slot_offset = memory_slot[0] memory_slot_length = (memory_slot[1] - memory_slot[0]) + 1 content_reference = media_pb2.ContentReference( - address_offset=memory_slot_offset, - length_bytes=memory_slot_length + address_offset=memory_slot_offset, length_bytes=memory_slot_length ) media_sample = extension_pb2.MediaSample( - timestamp=0, - content_reference=content_reference + timestamp=0, content_reference=content_reference ) return media_sample - - def __init__(self, grpc_server_address, - use_shared_memory): + def __init__( + self, grpc_server_address, use_shared_memory, frame_queue_size, frame_size + ): try: - self._grpc_server_address = grpc_server_address # Full address including port number i.e. "localhost:44001" + # Full address including port number i.e. "localhost:44001" + self._grpc_server_address = grpc_server_address self._shared_memory_manager = None if use_shared_memory: - self._shared_memory_manager = SharedMemoryManager(os.O_RDWR | os.O_SYNC | os.O_CREAT, - name=None, - size=100*1024*1024) + shared_memory_size = ( + frame_queue_size * frame_size + if frame_queue_size + else 100 * frame_size + ) + self._shared_memory_manager = SharedMemoryManager( + os.O_RDWR | os.O_SYNC | os.O_CREAT, + name=None, + size=shared_memory_size, + ) self._grpc_channel = grpc.insecure_channel(self._grpc_server_address) self._grpc_stub = extension_pb2_grpc.MediaGraphExtensionStub( - self._grpc_channel) + self._grpc_channel + ) except Exception: log_exception() raise - def get_media_stream_descriptor(self, width, height): try: smbtp = None if self._shared_memory_manager: smbtp = extension_pb2.SharedMemoryBufferTransferProperties( handle_name=self._shared_memory_manager.shm_file_name, - length_bytes=self._shared_memory_manager.shm_file_size + length_bytes=self._shared_memory_manager.shm_file_size, ) media_stream_descriptor = extension_pb2.MediaStreamDescriptor( graph_identifier=extension_pb2.GraphIdentifier( - media_services_arm_id='', - graph_instance_name='SampleGraph1', - graph_node_name='SampleGraph1' + media_services_arm_id="", + graph_instance_name="SampleGraph1", + graph_node_name="SampleGraph1", ), - media_descriptor=media_pb2.MediaDescriptor( timescale=90000, # pylint: disable=no-member # E1101: Class 'VideoFrameSampleFormat' has no 'Encoding' member (no-member) # E1101: Class 'VideoFrameSampleFormat' has no 'PixelFormat' member (no-member) video_frame_sample_format=media_pb2.VideoFrameSampleFormat( - encoding=media_pb2.VideoFrameSampleFormat.Encoding.Value('RAW'), - pixel_format=media_pb2.VideoFrameSampleFormat.PixelFormat.Value('BGR24'), - + encoding=media_pb2.VideoFrameSampleFormat.Encoding.Value("RAW"), + pixel_format=media_pb2.VideoFrameSampleFormat.PixelFormat.Value( + "BGR24" + ), dimensions=media_pb2.Dimensions( width=width, height=height, - ) - ) + ), + ), ), - shared_memory_buffer_transfer_properties=smbtp + shared_memory_buffer_transfer_properties=smbtp, ) except Exception: log_exception() @@ -176,26 +185,34 @@ def get_media_stream_descriptor(self, width, height): return media_stream_descriptor - def start(self, width, height, frame_queue, result_queue): descriptor = self.get_media_stream_descriptor(width, height) - request_generator = self.RequestGenerator(descriptor, self._shared_memory_manager, frame_queue) - # Use "wait_for_ready" (still in grpc preview...) to handle failure in case server not ready yet - sequence_iterator = self._grpc_stub.ProcessMediaStream(request_generator, - wait_for_ready=True) + request_generator = self.RequestGenerator( + descriptor, self._shared_memory_manager, frame_queue + ) + # Use "wait_for_ready" (still in grpc preview...) + # to handle failure in case server not ready yet + sequence_iterator = self._grpc_stub.ProcessMediaStream( + request_generator, wait_for_ready=True + ) response = next(sequence_iterator) ack_seq_no = response.ack_sequence_number - logging.info('[Received] AckNum: {0}'.format(ack_seq_no)) - thread = threading.Thread(target=self.run, args=(sequence_iterator, result_queue)) + logging.info("[Received] AckNum: {0}".format(ack_seq_no)) + thread = threading.Thread( + target=self.run, args=(sequence_iterator, result_queue) + ) thread.start() def run(self, sequence_iterator, result_queue): - for response in sequence_iterator: - ack_seq_no = response.ack_sequence_number - logging.info('[Received] AckNum: {0}'.format(ack_seq_no)) - result_queue.put(response) - if self._shared_memory_manager: - self._shared_memory_manager.delete_slot(ack_seq_no) + try: + for response in sequence_iterator: + ack_seq_no = response.ack_sequence_number + logging.info("[Received] AckNum: {0}".format(ack_seq_no)) + result_queue.put(response) + if self._shared_memory_manager: + self._shared_memory_manager.delete_slot(ack_seq_no) + except Exception as error: + result_queue.put(error) # Signal end of stream result_queue.put(None) diff --git a/samples/lva_ai_extension/common/shared_memory.py b/samples/lva_ai_extension/common/shared_memory.py index a487ceb..f02df09 100644 --- a/samples/lva_ai_extension/common/shared_memory.py +++ b/samples/lva_ai_extension/common/shared_memory.py @@ -126,7 +126,7 @@ def get_empty_slot(self, seq_no, size_needed): # no gap in between, check last possible gap if address is None: if (self.shm_file_size - prev_slot_end + 1) >= size_needed: - address = (prev_slot_end + 1, prev_slot_end + size_needed) + address = (prev_slot_end, prev_slot_end + size_needed) self._mem_slots[seq_no] = (address[0], address[1]) # interval [Begin, End] @@ -145,6 +145,7 @@ def __del__(self): self._shm_file.close() else: os.close(self._shm_file) + os.remove(self._shm_file_full_path) except: log_exception() raise diff --git a/samples/lva_ai_extension/docker/run_client.sh b/samples/lva_ai_extension/docker/run_client.sh index bd52c96..982cf3f 100755 --- a/samples/lva_ai_extension/docker/run_client.sh +++ b/samples/lva_ai_extension/docker/run_client.sh @@ -4,19 +4,34 @@ SERVER_IP=127.0.0.1 SERVER_PORT=5001 LVA_ROOT=/home/video-analytics-serving/samples/lva_ai_extension SAMPLE_FILE_PATH=$LVA_ROOT/sampleframes/sample01.png -OUTPUT_FILE_PATH= +OUTPUT_FILE_PATH=/tmp/result SHARED_MEMORY= INTERACTIVE= IMAGE=video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension +NUMBER_OF_STREAMS=1 +SCRIPT_DIR=$(dirname $(readlink -f "$0")) +SAMPLE_DIR=$(dirname $SCRIPT_DIR) +MODE= +VOLUME_MOUNT= +ENTRYPOINT_ARGS= function show_help { + echo "" + echo "**Run Script**" + echo "" echo "usage: ./run_client.sh" echo " [ -it : Run client in interactive mode ] " - echo " [ --server-ip : Specify the server ip to connect to ]" - echo " [ --server-port : Specify the server port to connect to ] " - echo " [ --shared-memory : Enables and uses shared memory between client and server ] " - echo " [ --sample-file-path : Specify the sample file path to run(file must be inside container or in volume mounted path)] " - echo " [ --output-file-path : Specify the output file path to save inference results to (file must be inside container or in volume mounted path)] " + echo " [ --output-file-path : Specify the output file path without extension to save inference results to (file must be inside container or in volume mounted path)] " + echo " [ --number-of-streams : Specify number of streams (one client process per stream)] " + echo " [ --dev : Mount local source code] " + echo "" + echo "**Application**" + echo "" + if [ "${MODE}" == "DEV" ]; then + VOLUME_MOUNT+="-v $SAMPLE_DIR:$LVA_ROOT " + fi + + docker run --rm $VOLUME_MOUNT --entrypoint /bin/bash $IMAGE -c "python3 ${LVA_ROOT}/client --help" } function error { @@ -30,51 +45,65 @@ while [[ "$#" -gt 0 ]]; do show_help exit ;; - --server-ip) - if [ "$2" ]; then - SERVER_IP=$2 - shift - else - error "--server-ip expects a value" - fi + -it) + INTERACTIVE="-it" ;; - --server-port) + --output-file-path) if [ "$2" ]; then - SERVER_PORT=$2 + OUTPUT_FILE_PATH=$2 shift else - error "--server-port expects a value" + error "--output-file-path expects a value" fi ;; - --shared-memory) - SHARED_MEMORY="-m" - ;; - --sample-file-path) + --number-of-streams) if [ "$2" ]; then - SAMPLE_FILE_PATH=$2 + NUMBER_OF_STREAMS=$2 shift else - error "--sample-file-path expects a value" + error "--number-of-streams expects a value" fi ;; - -it) - INTERACTIVE="-it" - ;; - --output-file-path) - if [ "$2" ]; then - OUTPUT_FILE_PATH="-o $2" - shift - else - error "--output-file-path expects a value" - fi + --dev) + MODE=DEV ;; *) - break + ENTRYPOINT_ARGS+=" $1 " ;; esac shift done -RUN_COMMAND="python3 $LVA_ROOT/client -s $SERVER_IP:$SERVER_PORT -l 1 $SHARED_MEMORY -f $SAMPLE_FILE_PATH $OUTPUT_FILE_PATH" -docker run $INTERACTIVE --rm --network=host -v /dev/shm:/dev/shm -v /tmp:/tmp --user "$UID" --entrypoint /bin/bash $IMAGE -c "$RUN_COMMAND" +VOLUME_MOUNT+="-v /tmp:/tmp " +VOLUME_MOUNT+="-v /dev/shm:/dev/shm " +PIDS= +CONTAINERS= + +if [ "${MODE}" == "DEV" ]; then + VOLUME_MOUNT+="-v $SAMPLE_DIR:$LVA_ROOT " +fi + +function clean_up { + kill -9 $PIDS + docker kill $CONTAINERS + exit +} + +if [ "$NUMBER_OF_STREAMS" -gt "1" ]; then + trap clean_up SIGHUP SIGINT SIGTERM + for i in $(seq "$NUMBER_OF_STREAMS") + do + echo "Starting Client $i Results to ${OUTPUT_FILE_PATH}_client_$i.jsonl, Output to: client_${i}.stdout.txt" + RUN_COMMAND="python3 $LVA_ROOT/client $ENTRYPOINT_ARGS -o ${OUTPUT_FILE_PATH}_client_$i.jsonl " + docker run $INTERACTIVE --name lva_ai_extension_client_$i --rm --network=host $VOLUME_MOUNT --user "$UID" --entrypoint /bin/bash $IMAGE -c "$RUN_COMMAND" >client_${i}.stdout.txt 2>&1 & + PIDS+=" $!" + CONTAINERS+=" lva_ai_extension_client_${i}" + sleep 1 + done + echo "waiting for clients to finish" + wait +else + RUN_COMMAND="python3 $LVA_ROOT/client $ENTRYPOINT_ARGS -o ${OUTPUT_FILE_PATH}.jsonl $@" + docker run $INTERACTIVE --rm --network=host $VOLUME_MOUNT --user "$UID" --entrypoint /bin/bash $IMAGE -c "$RUN_COMMAND" +fi diff --git a/samples/lva_ai_extension/docker/run_server.sh b/samples/lva_ai_extension/docker/run_server.sh index 6a3f92e..ecdfb39 100755 --- a/samples/lva_ai_extension/docker/run_server.sh +++ b/samples/lva_ai_extension/docker/run_server.sh @@ -3,21 +3,33 @@ CURRENT_DIR=$(dirname $(readlink -f "$0")) ROOT_DIR=$(readlink -f "$CURRENT_DIR/../../..") LVA_DIR=$(dirname $CURRENT_DIR) +LVA_ROOT=/home/video-analytics-serving/samples/lva_ai_extension IMAGE=video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension +VASERVING_ROOT=/home/video-analytics-serving NAME=${IMAGE//[\:]/_} PORT=5001 PIPELINES= ENTRYPOINT_ARGS= +MODE= +VOLUME_MOUNT= function show_help { + echo "" + echo "**Run Script**" + echo "" echo "usage: ./run_server.sh" echo " [ -p : Specify the port to use ] " - echo " [ --pipeline-name : Specify the pipeline name to use ] " - echo " [ --pipeline-version : Specify the pipeline version to use ] " - echo " [ --debug : Use debug pipeline ] " - echo " [ --max-running-pipelines : Specify the maximum number of concurrent pipelines, default is 10 ] " - echo " [ --parameters : Specify a json string or file for pipeline parameters *Deprecated* ] " - echo " [ --pipeline-parameters : Specify a json string or file for pipeline parameters ] " + echo " [ --dev : Mount local source code] " + echo "" + echo "**Application**" + echo "" + if [ "${MODE}" == "DEV" ]; then + VOLUME_MOUNT+="-v $LVA_DIR:$LVA_ROOT " + VOLUME_MOUNT+="-v $ROOT_DIR:$VASERVING_ROOT " + PIPELINES="--pipelines $LVA_DIR/pipelines " + fi + ENTRYPOINT_ARGS+="--entrypoint-args --help " + "$ROOT_DIR/docker/run.sh" --user "$UID" -p $PORT:$PORT --image $IMAGE $VOLUME_MOUNT $ENTRYPOINT_ARGS $PIPELINES } function error { @@ -39,20 +51,9 @@ while [[ "$#" -gt 0 ]]; do error "-p expects a value" fi ;; - --pipeline-name|--pipeline-version|--max-running-pipelines|--parameters|--pipeline-parameters) - if [ "$2" ]; then - ENTRYPOINT_ARGS+="--entrypoint-args $1 " - ENTRYPOINT_ARGS+="--entrypoint-args $2 " - shift - else - error "$1 expects a value" - fi - ;; - --debug) - ENTRYPOINT_ARGS+="--entrypoint-args $1 " - ;; --dev) - PIPELINES="--pipelines $LVA_DIR/pipelines " + PIPELINES="--pipelines $LVA_DIR/pipelines " + MODE="DEV" ;; *) ENTRYPOINT_ARGS+="--entrypoint-args $1 " @@ -84,4 +85,16 @@ if [ ! -z "$PIPELINE_PARAMETERS" ]; then ENV+="-e PIPELINE_PARAMETERS=$PIPELINE_PARAMETERS " fi -"$ROOT_DIR/docker/run.sh" --image $IMAGE -v /tmp:/tmp --user "$UID" -v /dev/shm:/dev/shm -p $PORT:$PORT $ENTRYPOINT_ARGS $PIPELINES $ENV +if [ ! -z "$GST_DEBUG" ]; then + ENV+="-e GST_DEBUG=$GST_DEBUG " +fi + +VOLUME_MOUNT+="-v /tmp:/tmp " +VOLUME_MOUNT+="-v /dev/shm:/dev/shm " + +if [ "${MODE}" == "DEV" ]; then + VOLUME_MOUNT+="-v $LVA_DIR:$LVA_ROOT " + VOLUME_MOUNT+="-v $ROOT_DIR:$VASERVING_ROOT " +fi + +"$ROOT_DIR/docker/run.sh" --user "$UID" --image $IMAGE $VOLUME_MOUNT -p $PORT:$PORT $ENTRYPOINT_ARGS $PIPELINES $ENV diff --git a/samples/lva_ai_extension/pipelines/object_detection/debug_person_vehicle_bike_detection/pipeline.json b/samples/lva_ai_extension/pipelines/object_detection/debug_person_vehicle_bike_detection/pipeline.json index d524429..7972bab 100755 --- a/samples/lva_ai_extension/pipelines/object_detection/debug_person_vehicle_bike_detection/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_detection/debug_person_vehicle_bike_detection/pipeline.json @@ -48,10 +48,6 @@ "element": "detection", "type": "string" }, - "model-instance-id": { - "element": "detection", - "type": "string" - }, "pre-process-backend": { "element": "detection", "type": "string" diff --git a/samples/lva_ai_extension/server/__main__.py b/samples/lva_ai_extension/server/__main__.py index b9c13b6..0aa70f8 100644 --- a/samples/lva_ai_extension/server/__main__.py +++ b/samples/lva_ai_extension/server/__main__.py @@ -31,93 +31,152 @@ import argparse import os import sys -import json from concurrent import futures +from collections import defaultdict import grpc import extension_pb2_grpc # pylint: disable=import-error from vaserving.vaserving import VAServing from vaserving.common.utils.logging import get_logger from media_graph_extension import MediaGraphExtension - -def parse_args(args=None, program_name="VA Serving AI Extension"): - - parser = argparse.ArgumentParser(prog=program_name, fromfile_prefix_chars='@', - formatter_class=argparse.ArgumentDefaultsHelpFormatter) - - parser.add_argument("-p", action="store", dest="port", - help='Port number to serve gRPC server', - type=int, default=int(os.getenv('PORT', "5001"))) - - parser.add_argument("--pipeline-name", action="store", - dest="pipeline", - help='name of the pipeline to run', - type=str, default=os.getenv('PIPELINE_NAME', - 'object_detection')) - - parser.add_argument("--pipeline-version", action="store", - dest="version", - help='name of the pipeline to run', - type=str, default=os.getenv('PIPELINE_VERSION', - 'person_vehicle_bike_detection')) - parser.add_argument("--debug", action="store_true", - dest="debug", - help='Use debug pipeline', - default=(os.getenv('DEBUG_PIPELINE', None) is not None)) - - parser.add_argument("--max-running-pipelines", action="store", - dest="max_running_pipelines", - type=int, default=int(os.getenv('MAX_RUNNING_PIPELINES', '10'))) - - parser.add_argument("--parameters", action="store", - dest="parameters_arg", - type=str, default=os.getenv('PARAMETERS', '{}')) - - parser.add_argument("--pipeline-parameters", action="store", - dest="pipeline_parameters_arg", - type=str, default=os.getenv('PIPELINE_PARAMETERS', '{}')) - - if (isinstance(args, dict)): - args = ["--{}={}".format(key, value) - for key, value in args.items() if value] +PROGRAM_NAME = "DL Streamer Edge AI Extension" + +def parse_args(args=None, program_name=PROGRAM_NAME): + + parser = argparse.ArgumentParser( + prog=program_name, + fromfile_prefix_chars="@", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + + parser.add_argument( + "-p", + action="store", + dest="port", + help="Port number to serve gRPC server", + type=int, + default=int(os.getenv("PORT", "5001")), + ) + + parser.add_argument( + "--pipeline-name", + action="store", + dest="pipeline_name", + help="name of the pipeline to run", + type=str, + default=os.getenv("PIPELINE_NAME", "object_detection"), + ) + + parser.add_argument( + "--pipeline-version", + action="store", + dest="pipeline_version", + help="name of the pipeline to run", + type=str, + default=os.getenv("PIPELINE_VERSION", "person_vehicle_bike_detection"), + ) + parser.add_argument( + "--debug", + action="store_true", + dest="debug", + help="Use debug pipeline", + default=(os.getenv("DEBUG_PIPELINE", None) is not None), + ) + + parser.add_argument( + "--max-running-pipelines", + action="store", + dest="max_running_pipelines", + type=int, + default=int(os.getenv("MAX_RUNNING_PIPELINES", "10")), + ) + + parser.add_argument( + "--parameters", + action="store", + dest="parameters_arg", + type=str, + default=os.getenv("PARAMETERS", "{}"), + ) + + parser.add_argument( + "--pipeline-parameters", + action="store", + dest="pipeline_parameters_arg", + type=str, + default=os.getenv("PIPELINE_PARAMETERS", "{}"), + ) + + if isinstance(args, dict): + args = ["--{}={}".format(key, value) for key, value in args.items() if value] return parser.parse_known_args(args) + def append_default_server_args(va_serving_args, max_running_pipelines): - va_serving_args.append('--max_running_pipelines') + va_serving_args.append("--max_running_pipelines") va_serving_args.append(str(max_running_pipelines)) return va_serving_args + if __name__ == "__main__": args, va_serving_args = parse_args() logger = get_logger("Main") try: - server_args = append_default_server_args(va_serving_args, args.max_running_pipelines) + server_args = append_default_server_args( + va_serving_args, args.max_running_pipelines + ) try: VAServing.start(server_args) - except Exception: - print("Exception encountered during VAServing start") + pipelines = VAServing.pipelines() + pipeline_versions = defaultdict(list) + for pipeline in pipelines: + pipeline_versions[pipeline.name()].append(pipeline.version()) + if args.pipeline_name not in pipeline_versions: + raise Exception("Unknown Pipeline: {}".format(args.pipeline_name)) + if (args.debug) and (not args.pipeline_version.startswith("debug")): + args.pipeline_version = "debug_{}".format(args.pipeline_version) + if args.pipeline_version not in pipeline_versions[args.pipeline_name]: + raise Exception( + "Unknown Pipeline Version: {}".format(args.pipeline_version) + ) + except Exception as error: + logger.error(error) + logger.error("Exception encountered during VAServing start") raise - # For 0.4.1, if both parameters and pipeline_parameters are specified, pipeline_parameters takes precedence - if args.parameters_arg != '{}': - logger.warning("Warning, parameters argument is deprecated and will be removed in 0.5.") - logger.warning("If parameters and pipeline_parameters are defined pipeline_parameters takes precedence") - if args.pipeline_parameters_arg != '{}': + if args.parameters_arg != "{}": + logger.warning( + "Warning, parameters argument is deprecated and will be removed in 0.5." + ) + logger.warning( + "If parameters and pipeline_parameters are defined pipeline_parameters takes precedence" + ) + if args.pipeline_parameters_arg != "{}": args.parameters_arg = args.pipeline_parameters_arg # create gRPC server and start running - server = grpc.server(futures.ThreadPoolExecutor(max_workers=args.max_running_pipelines)) + server = grpc.server( + futures.ThreadPoolExecutor(max_workers=args.max_running_pipelines) + ) extension_pb2_grpc.add_MediaGraphExtensionServicer_to_server( - MediaGraphExtension(args.pipeline, args.version, args.debug, args.parameters_arg), server) - server.add_insecure_port(f'[::]:{args.port}') - print("Starting Protocol Server Application on port", args.port) + MediaGraphExtension( + args.pipeline_name, + args.pipeline_version, + args.debug, + args.parameters_arg, + ), + server, + ) + server.add_insecure_port(f"[::]:{args.port}") + logger.info("Starting %s on port: %d", PROGRAM_NAME, args.port) server.start() server.wait_for_termination() VAServing.stop() - except Exception: + except Exception as error: + logger.error(error) VAServing.stop() sys.exit(-1) diff --git a/samples/lva_ai_extension/server/media_graph_extension.py b/samples/lva_ai_extension/server/media_graph_extension.py index fcc5253..5be1fa1 100644 --- a/samples/lva_ai_extension/server/media_graph_extension.py +++ b/samples/lva_ai_extension/server/media_graph_extension.py @@ -49,9 +49,10 @@ class TransferType(Enum): - BYTES = 1 # Embedded Content - REFERENCE = 2 # Shared Memory - HANDLE = 3 # Reserverd... + BYTES = 1 # Embedded Content + REFERENCE = 2 # Shared Memory + HANDLE = 3 # Reserved + class State: def __init__(self, media_stream_descriptor): @@ -60,14 +61,18 @@ def __init__(self, media_stream_descriptor): self.media_stream_descriptor = media_stream_descriptor # Get how data will be transferred - if self.media_stream_descriptor.WhichOneof( - "data_transfer_properties") is None: + if ( + self.media_stream_descriptor.WhichOneof("data_transfer_properties") + is None + ): self.content_transfer_type = TransferType.BYTES elif self.media_stream_descriptor.HasField( - "shared_memory_buffer_transfer_properties"): + "shared_memory_buffer_transfer_properties" + ): self.content_transfer_type = TransferType.REFERENCE elif self.media_stream_descriptor.HasField( - "shared_memory_segments_transfer_properties"): + "shared_memory_segments_transfer_properties" + ): self.content_transfer_type = TransferType.HANDLE # Setup if shared mem used @@ -75,7 +80,8 @@ def __init__(self, media_stream_descriptor): # Create shared memory accessor specific to the client self.shared_memory_manager = SharedMemoryManager( name=self.media_stream_descriptor.shared_memory_buffer_transfer_properties.handle_name, - size=self.media_stream_descriptor.shared_memory_buffer_transfer_properties.length_bytes) + size=self.media_stream_descriptor.shared_memory_buffer_transfer_properties.length_bytes, + ) else: self.shared_memory_manager = None @@ -83,8 +89,16 @@ def __init__(self, media_stream_descriptor): log_exception(get_logger("State")) raise + class MediaGraphExtension(extension_pb2_grpc.MediaGraphExtensionServicer): - def __init__(self, pipeline, version, debug=False, pipeline_parameter_arg=None, input_queue_size=1): + def __init__( + self, + pipeline, + version, + debug=False, + pipeline_parameter_arg=None, + input_queue_size=1, + ): self._pipeline = pipeline self._version = version self._input_queue_size = input_queue_size @@ -103,7 +117,7 @@ def _generate_media_stream_message(self, gva_sample): inference = msg.media_sample.inferences.add() attributes = [] - object_id = None + obj_id = None obj_label = None obj_confidence = 0 obj_left = 0 @@ -114,16 +128,19 @@ def _generate_media_stream_message(self, gva_sample): for tensor in region.tensors(): name = tensor.name() - if (name == 'detection'): + if name == "detection": obj_confidence = region.confidence() obj_label = region.label() obj_left, obj_top, obj_width, obj_height = region.normalized_rect() - inference.type = inferencing_pb2.Inference.InferenceType.ENTITY # pylint: disable=no-member - if region.object_id(): #Tracking - object_id = str(region.object_id()) - elif tensor["label"]: #Classification + inference.type = ( + # pylint: disable=no-member + inferencing_pb2.Inference.InferenceType.ENTITY + ) + if region.object_id(): # Tracking + obj_id = str(region.object_id()) + elif tensor["label"]: # Classification attr_name = name attr_label = tensor["label"] attr_confidence = region.confidence() @@ -132,25 +149,19 @@ def _generate_media_stream_message(self, gva_sample): try: entity = inferencing_pb2.Entity( tag=inferencing_pb2.Tag( - value=obj_label, - confidence=obj_confidence + value=obj_label, confidence=obj_confidence ), box=inferencing_pb2.Rectangle( - l=obj_left, - t=obj_top, - w=obj_width, - h=obj_height - ) + l=obj_left, t=obj_top, w=obj_width, h=obj_height + ), ) for attr in attributes: attribute = inferencing_pb2.Attribute( - name=attr[0], - value=attr[1], - confidence=attr[2] + name=attr[0], value=attr[1], confidence=attr[2] ) entity.attributes.append(attribute) - if object_id: - entity.id = object_id + if obj_id: + entity.id = obj_id except: log_exception(self._logger) raise @@ -159,7 +170,6 @@ def _generate_media_stream_message(self, gva_sample): return msg - def _generate_gva_sample(self, client_state, request): new_sample = None @@ -174,39 +184,58 @@ def _generate_gva_sample(self, client_state, request): length_bytes = request.media_sample.content_reference.length_bytes # Get memory reference to (in readonly mode) data sent over shared memory - raw_bytes = client_state.shared_memory_manager.read_bytes(address_offset, - length_bytes) + raw_bytes = client_state.shared_memory_manager.read_bytes( + address_offset, length_bytes + ) # Get encoding details of the media sent by client - encoding = client_state.media_stream_descriptor.media_descriptor.video_frame_sample_format.encoding + encoding = ( + client_state.media_stream_descriptor.media_descriptor.video_frame_sample_format.encoding + ) # Handle RAW content (Just place holder for the user to handle each variation...) - if encoding == client_state.media_stream_descriptor.media_descriptor.video_frame_sample_format.Encoding.RAW: - pixel_format = client_state.media_stream_descriptor.media_descriptor\ - .video_frame_sample_format.pixel_format + if ( + encoding + == client_state.media_stream_descriptor.media_descriptor.video_frame_sample_format.Encoding.RAW + ): + pixel_format = ( + client_state.media_stream_descriptor.media_descriptor.video_frame_sample_format.pixel_format + ) caps_format = None if pixel_format == media_pb2.VideoFrameSampleFormat.PixelFormat.RGBA: - caps_format = 'RGBA' + caps_format = "RGBA" elif pixel_format == media_pb2.VideoFrameSampleFormat.PixelFormat.RGB24: - caps_format = 'RGB' + caps_format = "RGB" elif pixel_format == media_pb2.VideoFrameSampleFormat.PixelFormat.BGR24: - caps_format = 'BGR' + caps_format = "BGR" if caps_format is not None: - caps = ''.join(("video/x-raw,format=", - caps_format, - ",width=", - str(client_state.media_stream_descriptor.media_descriptor\ - .video_frame_sample_format.dimensions.width), - ",height=", - str(client_state.media_stream_descriptor.media_descriptor\ - .video_frame_sample_format.dimensions.height))) - new_sample = GvaFrameData(bytes(raw_bytes), - caps, - message={'sequence_number':request.sequence_number, - 'timestamp':request.media_sample.timestamp}) + caps = "".join( + ( + "video/x-raw,format=", + caps_format, + ",width=", + str( + client_state.media_stream_descriptor. + media_descriptor.video_frame_sample_format.dimensions.width + ), + ",height=", + str( + client_state.media_stream_descriptor. + media_descriptor.video_frame_sample_format.dimensions.height + ), + ) + ) + new_sample = GvaFrameData( + bytes(raw_bytes), + caps, + message={ + "sequence_number": request.sequence_number, + "timestamp": request.media_sample.timestamp, + }, + ) else: - self._logger.info('Sample format is not RAW') + self._logger.info("Sample format is not RAW") except: log_exception(self._logger) raise @@ -214,9 +243,9 @@ def _generate_gva_sample(self, client_state, request): def _get_queued_samples(self, queue, block=False): samples = [] - if (block): + if block: samples.append(queue.get()) - while (not queue.empty()): + while not queue.empty(): samples.append(queue.get()) return samples @@ -233,31 +262,37 @@ def ProcessMediaStream(self, requestIterator, context): request_ack_seq_num = request.ack_sequence_number # State object per client client_state = State(request.media_stream_descriptor) - self._logger.info('[Received] SeqNum: {0:07d} | '\ - 'AckNum: {1}\nMediaStreamDescriptor:\n{2}'.format( + self._logger.info( + "[Received] SeqNum: {0:07d} | " + "AckNum: {1}\nMediaStreamDescriptor:\n{2}".format( request_seq_num, request_ack_seq_num, - client_state.media_stream_descriptor)) + client_state.media_stream_descriptor, + ) + ) # First message response ... media_stream_message = extension_pb2.MediaStreamMessage( sequence_number=1, ack_sequence_number=request_seq_num, media_stream_descriptor=extension_pb2.MediaStreamDescriptor( media_descriptor=media_pb2.MediaDescriptor( - timescale=client_state.media_stream_descriptor.media_descriptor.timescale)) + timescale=client_state.media_stream_descriptor.media_descriptor.timescale + ) + ), ) responses_sent += 1 yield media_stream_message - if self._debug and (not self._version.startswith("debug")): - self._version = "debug_" + self._version - final_pipeline_parameters = {} if self._version.startswith("debug"): - timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S') - location = os.path.join(tempfile.gettempdir(), "vaserving", self._version, timestamp) + timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + location = os.path.join( + tempfile.gettempdir(), "vaserving", self._version, timestamp + ) os.makedirs(os.path.abspath(location)) - final_pipeline_parameters = {"location": os.path.join(location, "frame_%07d.jpeg")} + final_pipeline_parameters = { + "location": os.path.join(location, "frame_%07d.jpeg") + } try: if self._pipeline_parameter_arg: @@ -269,69 +304,99 @@ def ProcessMediaStream(self, requestIterator, context): pipeline_parameters = json.loads(self._pipeline_parameter_arg) final_pipeline_parameters.update(pipeline_parameters) except ValueError: - self._logger.error('Issue loading json parameters') + self._logger.error("Issue loading json parameters") - self._logger.info('Pipeline Name : {}'.format(self._pipeline)) - self._logger.info('Pipeline Version : {}'.format(self._version)) - self._logger.info('Pipeline Parameters : {}'.format(final_pipeline_parameters)) + self._logger.info("Pipeline Name : {}".format(self._pipeline)) + self._logger.info("Pipeline Version : {}".format(self._version)) + self._logger.info("Pipeline Parameters : {}".format(final_pipeline_parameters)) detect_input = Queue(maxsize=self._input_queue_size) detect_output = Queue() # Start object detection pipeline # It will wait until it receives frames via the detect_input queue detect_pipeline = VAServing.pipeline(self._pipeline, self._version) - detect_pipeline.start(source={"type": "application", - "class": "GStreamerAppSource", - "input": detect_input, - "mode": "push"}, - destination={"type": "application", - "class": "GStreamerAppDestination", - "output": detect_output, - "mode": "frames"}, - parameters=final_pipeline_parameters) + detect_pipeline.start( + source={ + "type": "application", + "class": "GStreamerAppSource", + "input": detect_input, + "mode": "push", + }, + destination={ + "type": "application", + "class": "GStreamerAppDestination", + "output": detect_output, + "mode": "frames", + }, + parameters=final_pipeline_parameters, + ) # Process rest of the MediaStream message sequence for request in requestIterator: try: - if (requests_received - responses_sent >= self._input_queue_size): + if requests_received - responses_sent >= self._input_queue_size: queued_samples = self._get_queued_samples(detect_output, block=True) else: queued_samples = [] # Read request id, sent by client request_seq_num = request.sequence_number - self._logger.info('[Received] SeqNum: {0:07d}'.format(request_seq_num)) + self._logger.info("[Received] SeqNum: {0:07d}".format(request_seq_num)) requests_received += 1 gva_sample = self._generate_gva_sample(client_state, request) detect_input.put(gva_sample) queued_samples.extend(self._get_queued_samples(detect_output)) if context.is_active(): for gva_sample in queued_samples: - if (gva_sample): - media_stream_message = self._generate_media_stream_message(gva_sample) + if gva_sample: + media_stream_message = self._generate_media_stream_message( + gva_sample + ) responses_sent += 1 - self._logger.info('[Sent] AckSeqNum: {0:07d}'.format( - media_stream_message.ack_sequence_number)) + self._logger.info( + "[Sent] AckSeqNum: {0:07d}".format( + media_stream_message.ack_sequence_number + ) + ) yield media_stream_message else: break + if detect_pipeline.status().state.stopped(): + break + except: + log_exception(self._logger) + raise + + if detect_pipeline.status().state.stopped(): + try: + raise Exception(detect_pipeline.status().state) except: - log_exception() + log_exception(self._logger) raise # After the server has finished processing all the request iterator objects # Push a None object into the input queue. # When the None object comes out of the output queue, we know we've finished # processing all requests - detect_input.put(None) - gva_sample = detect_output.get() - while (gva_sample): + gva_sample = None + if not detect_pipeline.status().state.stopped(): + detect_input.put(None) + gva_sample = detect_output.get() + while gva_sample: media_stream_message = self._generate_media_stream_message(gva_sample) responses_sent += 1 - self._logger.info('[Sent] AckSeqNum: {0:07d}'.format( - media_stream_message.ack_sequence_number)) - yield media_stream_message + self._logger.info( + "[Sent] AckSeqNum: {0:07d}".format( + media_stream_message.ack_sequence_number + ) + ) + if context.is_active(): + yield media_stream_message gva_sample = detect_output.get() - self._logger.info('Done processing messages: Received: {}, Sent: {}'.format( - requests_received, responses_sent)) - self._logger.info('MediaStreamDescriptor:\n{0}'.format( - client_state.media_stream_descriptor)) + self._logger.info( + "Done processing messages: Received: {}, Sent: {}".format( + requests_received, responses_sent + ) + ) + self._logger.info( + "MediaStreamDescriptor:\n{0}".format(client_state.media_stream_descriptor) + ) diff --git a/vaserving/gstreamer_pipeline.py b/vaserving/gstreamer_pipeline.py index a3c2492..db6ff80 100755 --- a/vaserving/gstreamer_pipeline.py +++ b/vaserving/gstreamer_pipeline.py @@ -9,6 +9,7 @@ import string import time from threading import Lock, Thread +from collections import namedtuple import gi gi.require_version('Gst', '1.0') @@ -34,6 +35,7 @@ class GStreamerPipeline(Pipeline): _inference_element_cache = {} _mainloop = None _mainloop_thread = None + CachedElement = namedtuple("CachedElement",["element","pipelines"]) @staticmethod def gobject_mainloop(): @@ -78,6 +80,7 @@ def __init__(self, identifier, config, model_manager, request, finished_callback self._app_source = None self.appsink_element = None self._app_destination = None + self._cached_element_keys = [] if (not GStreamerPipeline._mainloop): GStreamerPipeline._mainloop_thread = Thread( target=GStreamerPipeline.gobject_mainloop) @@ -107,10 +110,20 @@ def _delete_pipeline(self, new_state): self.pipeline.set_state(Gst.State.NULL) del self.pipeline self.pipeline = None + if self._app_source: self._app_source.finish() + if self._app_destination: self._app_destination.finish() + + if (new_state == Pipeline.State.ERROR): + for key in self._cached_element_keys: + for pipeline in GStreamerPipeline._inference_element_cache[key].pipelines: + if (self != pipeline): + pipeline.stop() + del GStreamerPipeline._inference_element_cache[key] + self._finished_callback() def _delete_pipeline_with_lock(self, new_state): @@ -237,7 +250,10 @@ def _cache_inference_elements(self): and element.get_property(model_instance_id))] for element, key in gva_elements: if key not in GStreamerPipeline._inference_element_cache: - GStreamerPipeline._inference_element_cache[key] = element + GStreamerPipeline._inference_element_cache[key] = GStreamerPipeline.CachedElement( + element, []) + self._cached_element_keys.append(key) + GStreamerPipeline._inference_element_cache[key].pipelines.append(self) def _set_default_models(self): gva_elements = [element for element in self.pipeline.iterate_elements() if ( From 04ded22d6991068db6a822f6d853feb9cd9e9f7a Mon Sep 17 00:00:00 2001 From: "Bruce, Henry" Date: Fri, 15 Jan 2021 13:50:27 -0800 Subject: [PATCH 30/50] Update object detection model change tutorial --- docs/changing_object_detection_models.md | 325 ++++++++++------------- samples/lva_ai_extension/README.md | 105 ++++++-- samples/lva_ai_extension/docker/build.sh | 14 +- 3 files changed, 248 insertions(+), 196 deletions(-) diff --git a/docs/changing_object_detection_models.md b/docs/changing_object_detection_models.md index c332904..f052300 100644 --- a/docs/changing_object_detection_models.md +++ b/docs/changing_object_detection_models.md @@ -1,37 +1,58 @@ # Changing Object Detection Models -| [Tutorial Overview](#tutorial-overview) | [Analyze Existing VAS Object Detection Pipeline](#1-run-existing-vas-object-detection-pipeline) | [Download New Model](#2-download-new-model) | [Update Pipeline Definition File to Use New Model](#3-update-pipeline-definition-file-to-use-new-model) | [Run Pipeline with New Model](#4-run-pipeline-with-new-model) | +| [Tutorial Overview](#tutorial-overview) | [Understand Model Formats](#1-understand-model-formats) | [Run Existing Object Detection Pipeline](#2-run-existing-object-detection-pipeline) | [Add New Model](#3-add-new-model) | [Re-build the Image](#4-re-build-the-image) | [Run Pipeline with New Model](#5-run-pipeline-with-new-model) | -VA Serving Object Detection pipelines are customizable. A simple pipeline customization is to use a different model. +VA Serving Object Detection pipelines are customizable. A simple pipeline customization is to use a different model. -The following `object_detection` models and pipelines are included in the Video Analytics Serving library. -| Pipeline Endpoint | Model Name | Implementation | OMZ Model Name | -| ------------- | --------------------------- | -----------------------------------| -------------- | -| `pipelines/object_detection/1` | SSD with MobileNet | Caffe | [mobilenet-ssd](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mobilenet-ssd) | ## Tutorial Overview +It is assumed that you are familiar with VA Serving [docker operation](building_video_analytics_serving.md) and [pipeline definition files](defining_pipelines.md). It is also assumed that you have python3 installed on your Linux host. + This tutorial consists of the following sections: -1. Analyze the existing VAS Object Detection Model -2. Download new model -3. Create/Update the pipeline definition to use the new model -4. Run the pipeline with the new model +1. Understand model formats +2. Run existing object detection pipeline +3. Add a new model and update the pipeline definition to use it +4. Re-build image to include new model and pipeline +5. Run the pipeline with the new model -Once you complete this tutorial you should be able to quickly obtain, convert and add a new model to the VAS Object Detection Pipeline. +Once you complete this tutorial you should be able to quickly obtain and use a new model in a VA Serving pipeline. -## 1. Run Existing VAS Object Detection Pipeline -With the service running, you can use curl command line program to start the `object_detection` version `1` pipeline included in the VA Serving library as follows: -```bash -$ curl localhost:8080/pipelines/object_detection/1 -X POST -H 'Content-Type: application/json' -d '{ "source": { "uri": "https://github.com/intel-iot-devkit/sample-videos/blob/master/bottle-detection.mp4?raw=true", "type": "uri" }, "destination": { "type": "file", "path": "/tmp/results.txt", "format":"json-lines"}}' -``` +> **Note:** The tutorial assumes use of a GStreamer base image. + +## 1. Understand Model Formats +OpenVINO* provides a set of ready to use models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) (OMZ). +OMZ hosts optimized deep learning models to expedite development of inference applications by using free pre-trained models instead of training your own models. + +This tutorial assumes you are using a model from OMZ that has a `model-proc`, which is a file that describes the input and output formats supported by the model along with inference output labels. For more information see [DL Streamer Model Preparation](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Model-preparation). A list of supported models can be found in the [DL Streamer repo](https://github.com/openvinotoolkit/dlstreamer_gst/tree/v1.2.1/samples/model_proc). + +> **Note:** Model-proc files are optional but they ensure better inference by checking the input video format +and advertising labels that can be used by downstream elements. -As the video is being analyzed and as objects appear and disappear you will see detection results in the output file that can be viewed using: +VA Serving has a built in tool `model_downloader` that obtains a model from OMZ and converts it to the [Intermediate Representation](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_IR_and_opsets.html) (IR) format required by the OpenVINO* inference engine. The tool also gets the associated `model-proc` file. +The following `object_detection` models and pipelines are included in the Video Analytics Serving library. + +| Pipeline Endpoint | Model Name | Implementation | OMZ Model Name | +| ------------- | --------------------------- | -----------------------------------| -------------- | +| `pipelines/object_detection/1` | SSD with MobileNet | Caffe | [mobilenet-ssd](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mobilenet-ssd) | + + +## 2. Run Existing Object Detection Pipeline +With the service running, you can start the `object_detection` version `1` pipeline as follows: ```bash -$ tail -f /tmp/results.txt -``` -Pretty printed output below: -```json +$ samples/sample.py --pipeline object_detection --version 1 + +Pipeline Status: + +{ + "avg_fps": 51.38943397312732, + "elapsed_time": 13.621442317962646, + "id": 1, + "start_time": 1609889043.9670591, + "state": "RUNNING" +} + { "objects": [{ "detection": { @@ -60,76 +81,27 @@ Pretty printed output below: } ``` -> **Note:** Currently customization tasks assume use of a GStreamer base image. - -## 2. Download New Model -This tutorial is aimed at pipeline customization, not model selection or evaluation. We assume the model is already trained and is known to meet your needs. -This section will show how to obtain a model and update a pipeline description file to use it. -It is assumed that you are familiar with VA Serving [docker operation](building_video_analytics_serving.md) and [pipeline definition files](defining_pipelines.md). - -This example will show how to update the object detection pipeline to use the [yolo-v2-tiny-tf](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/yolo-v2-tiny-tf/yolo-v2-tiny-tf.md) model. - -### Start Developer Mode -As this is a development workflow, start the VA Serving container in [developer mode](run_script_reference.md#developer-mode---dev). All subsequent commands will be run in this container. -``` -$ docker/run.sh --dev -vaserving@host:~$ -``` - -### Download yolo-v2-tiny-tf - -OpenVINO* provides a set of ready to use models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) (OMZ). -OMZ hosts optimized deep learning models to expedite development of inference applications by using free pre-trained models instead of training your own models. -This tutorial assumes you are using a model from OMZ. +## 3. Add New Model -VA Serving has a built in tool `model_downloader` that obtains a model from OMZ and converts it to the [Intermediate Representation](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_IR_and_opsets.html) (IR) format required by the OpenVINO* inference engine. The tool also looks for an associated `model-proc` file that describes the input and output formats supported by the model along with inference output labels. Model-proc files are optional but they ensure better inference by checking the input video format -and advertising labels that can be used by downstream elements. For more information see [DL Streamer Model Preparation](https://github.com/opencv/gst-video-analytics/wiki/Model-preparation). +This example will show how to update the object detection pipeline to use the [yolo-v2-tiny-tf](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/yolo-v2-tiny-tf/yolo-v2-tiny-tf.md) object detection model. -Follow these steps to get the IR and model-proc files: -1. Select the model, for this example we are using `yolo-v2-tiny-tf`. We'll use the VA Serving model `alias` feature to make this `object_detection` version `yolo-v2-tiny-tf`. -2. Create a model list file, say `yolo.yml` that contains the `model` which is the model name `yolo-v2-tiny-tf` in this case, `alias` as `object_detection` and `version` as `yolo-v2-tiny-tf` in yaml file format. +### Select the model +For this example we are using `yolo-v2-tiny-tf`. We'll use the VA Serving model `alias` feature to make this `object_detection` version `yolo-v2-tiny-tf`. -```bash -vaserving@host:~$ cat yolo.yml +### Add model to model list +Create a copy of the existing model list file `models_list/models.list.yml`, say `models_list/yolo.yml`, then add an entry for the new object detection model `yolo-v2-tiny-tf`. +```yml - model: yolo-v2-tiny-tf alias: object_detection version: yolo-v2-tiny-tf -``` -You can also specify the `precision` you want to download in the yaml file but if none provided it will download all available precisions. - -3. Run the model download tool with `yolo.yml` as the input model list file. -``` -vaserving@host:~$ python3 tools/model_downloader --model-list yolo.yml + precision: [FP16,FP32] ``` -If the tool outputs the below message, it means it has created an empty model-proc file which cannot be referenced by a pipeline element. -``` -Warning, model-proc not found in gst-video-analytics repo. -Creating empty json file for to allow model to load in VA-Serving -Do not specify model-proc in pipeline that utilizes this model -``` -We'll need to know if an empty model-proc file was created when we get to the [Update/Create Pipeline Definition](#3-update-pipeline-definition-file-to-use-new-model) section. For this example a model-proc file is available from OMZ and will be downloaded. -The model will now be in `models` folder: -``` -models -└── object_detection - └── yolo-v2-tiny-tf - ├── FP16 - │   ├── yolo-v2-tiny-tf.bin - │   ├── yolo-v2-tiny-tf.mapping - │   └── yolo-v2-tiny-tf.xml - ├── FP32 - │   ├── yolo-v2-tiny-tf.bin - │   ├── yolo-v2-tiny-tf.mapping - │   └── yolo-v2-tiny-tf.xml - └── yolo-v2-tiny-tf.json -``` - -## 3. Update Pipeline Definition File to Use New Model +### Update Pipeline Definition File to Use New Model Make a copy of the `object_detection` version `1` pipeline definition file and change the version to `yolo-v2-tiny-tf`. > **Note:** You can also update the existing version `1` to point to the new model instead of creating a new version. ``` -vaserving@host:~$ cp -r pipelines/object_detection/1 pipelines/object_detection/yolo-v2-tiny-tf +$ cp -r pipelines/gstreamer/object_detection/1 pipelines/gstreamer/object_detection/yolo-v2-tiny-tf ``` The pipeline template is shown below: ``` @@ -139,11 +111,11 @@ The pipeline template is shown below: " ! appsink name=appsink" ] ``` -We need to change the model references used by the `gvadetect` element to use version `yolo-v2-tiny-tf`. +We need to change the model references used by the `gvadetect` element to use version `yolo-v2-tiny-tf`. You can use your favorite editor to do this. The below command makes this change using the `sed` utility program. ``` -vaserving@host:~$ sed -i -e s/\\[1\\]/\\[yolo-v2-tiny-tf\\]/g pipelines/object_detection/yolo-v2-tiny-tf/pipeline.json -``` +$ sed -i -e s/\\[1\\]/\\[yolo-v2-tiny-tf\\]/g pipelines/gstreamer/object_detection/yolo-v2-tiny-tf/pipeline.json +``` Now the template will look like this: ``` "template": ["urisourcebin name=source ! concat name=c ! decodebin ! video/x-raw ! videoconvert name=videoconvert", @@ -152,114 +124,111 @@ Now the template will look like this: " ! appsink name=appsink" ] ``` -> **Note:** If the download tool reported that it could not find a `model-proc` and created an empty file, we would have to remove the `model-proc` property from the `gvadetect` element. -## 4. Run Pipeline with New Model -1. Now test the pipeline using the curl commands. We'll run in developer mode so we don't need to re-build the container. -This means the container will have access to the new model we created but we will have to manually start the service. +## 4. Re-build the Image +Build the image to include the new model and pipeline that references it. ``` -vaserving@host:~$ python3 -m vaserving +$ docker/build.sh --models models_list/yolo.yml ``` -2. Check whether the new model is loaded - -Execute below command to get all model info and search for `yolo` +The model will now be in `models` folder in the root of the project: +``` +models +└── object_detection + └── yolo-v2-tiny-tf + ├── FP16 + │   ├── yolo-v2-tiny-tf.bin + │   ├── yolo-v2-tiny-tf.mapping + │   └── yolo-v2-tiny-tf.xml + ├── FP32 + │   ├── yolo-v2-tiny-tf.bin + │   ├── yolo-v2-tiny-tf.mapping + │   └── yolo-v2-tiny-tf.xml + └── yolo-v2-tiny-tf.json +``` + +## 5. Run Pipeline with New Model +1. Restart the service to ensure we are using the image with the yolo-v2-tiny-tf model +``` +$ docker stop video-analytics-serving-gstreamer +$ docker/run.sh -v /tmp:/tmp +``` +2. Check whether the new model and pipeline are loaded ``` $ curl localhost:8080/models | grep yolo - "description": "object_detection", - "name": "object_detection", "model-proc": "/home/video-analytics-serving/models/object_detection/yolo-v2-tiny-tf/yolo-v2-tiny-tf.json", "FP16": "/home/video-analytics-serving/models/object_detection/yolo-v2-tiny-tf/FP16/yolo-v2-tiny-tf.xml", "FP32": "/home/video-analytics-serving/models/object_detection/yolo-v2-tiny-tf/FP32/yolo-v2-tiny-tf.xml" + "version": "yolo-v2-tiny-tf" +$ curl localhost:8080/pipelines | grep yolo + "version": "yolo-v2-tiny-tf" ``` -3. Start pipeline - -Start the `object_detection` version `yolo-v2-tiny-tf` pipeline using the below curl command. -```bash -curl localhost:8080/pipelines/object_detection/yolo-v2-tiny-tf -X POST -H 'Content-Type: application/json' -d '{ "source": { "uri": "https://github.com/intel-iot-devkit/sample-videos/blob/master/bottle-detection.mp4?raw=true", "type": "uri" }, "destination": { "type": "file", "path": "/tmp/results2.txt", "format":"json-lines"}}' -``` -4. View results in /tmp/results2.txt. +3. Start pipeline +Start the `object_detection` version `yolo-v2-tiny-tf` pipeline. +> **Note:** The lower fps is due to a higher complexity model. + ```bash -$ tail -f /tmp/results2.txt -``` -Pretty printed output below: -```json +$ samples/sample.py --pipeline object_detection --version yolo-v2-tiny-tf + +Pipeline Status: + { - "objects": [{ - "detection": { - "bounding_box": { - "x_max": 0.5506289452314377, - "x_min": 0.4401100277900696, - "y_max": 0.8389964699745178, - "y_min": 0.3123674988746643 - }, - "confidence": 0.7932981252670288, - "label": "bottle", - "label_id": 39 - }, - "h": 190, - "roi_type": "bottle", - "w": 71, - "x": 282, - "y": 112 - }, { - "detection": { - "bounding_box": { - "x_max": 0.9281446486711502, - "x_min": 0.8032000064849854, - "y_max": 0.8659530878067017, - "y_min": 0.28817808628082275 - }, - "confidence": 0.6495864987373352, - "label": "bottle", - "label_id": 39 - }, - "h": 208, - "roi_type": "bottle", - "w": 80, - "x": 514, - "y": 104 - }, { - "detection": { - "bounding_box": { - "x_max": 0.18862692266702652, - "x_min": 0.05343208461999893, - "y_max": 0.8482187986373901, - "y_min": 0.2969546318054199 - }, - "confidence": 0.6058924198150635, - "label": "bottle", - "label_id": 39 - }, - "h": 198, - "roi_type": "bottle", - "w": 87, - "x": 34, - "y": 107 - }, { - "detection": { - "bounding_box": { - "x_max": 0.176092691719532, - "x_min": 0.06645423918962479, - "y_max": 0.6428740918636322, - "y_min": 0.3436809778213501 - }, - "confidence": 0.5761112570762634, - "label": "bottle", - "label_id": 39 - }, - "h": 108, - "roi_type": "bottle", - "w": 70, - "x": 43, - "y": 124 - }], - "resolution": { - "height": 360, - "width": 640 - }, - "source": "https://github.com/intel-iot-devkit/sample-videos/blob/master/bottle-detection.mp4?raw=true", - "timestamp": 39821229050 + "avg_fps": 31.230446318262235, + "elapsed_time": 11.111731052398682, + "id": 1, + "start_time": 1609896673.4552672, + "state": "RUNNING" +} + + +{ + "objects": [ + { + "detection": { + "bounding_box": { + "x_max": 0.19180697202682495, + "x_min": 0.09733753651380539, + "y_max": 0.8905727863311768, + "y_min": 0.3062259256839752 + }, + "confidence": 0.6085537075996399, + "label": "bottle", + "label_id": 5 + }, + "h": 210, + "roi_type": "bottle", + "w": 60, + "x": 62, + "y": 110 + }, + { + "detection": { + "bounding_box": { + "x_max": 0.8830730319023132, + "x_min": 0.7777565121650696, + "y_max": 0.890160083770752, + "y_min": 0.3054335117340088 + }, + "confidence": 0.5028868913650513, + "label": "bottle", + "label_id": 5 + }, + "h": 211, + "roi_type": "bottle", + "w": 67, + "x": 498, + "y": 110 + } + ], + "resolution": { + "height": 360, + "width": 640 + }, + "source": "https://github.com/intel-iot-devkit/sample-videos/blob/master/bottl +e-detection.mp4?raw=true", + "timestamp": 0 } ``` -## Related Links +## Related Links 1. For more information on the [Model Download Tool](../tools/model_downloader/README.md) 2. [Converting models to IR format](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_Converting_Model.html) diff --git a/samples/lva_ai_extension/README.md b/samples/lva_ai_extension/README.md index 66b69c7..538b5e3 100644 --- a/samples/lva_ai_extension/README.md +++ b/samples/lva_ai_extension/README.md @@ -230,20 +230,93 @@ Before updating the models used by a pipeline please see the format of [pipeline definition files](/docs/defining_pipelines.md) and read the tutorial on [changing object detection models](/docs/changing_object_detection_models.md). -Most of the steps to changes models are the same, but the existing -tutorial assumes you are working with the REST service and not the AI -Extension module. Use the developer mode to download new models and -add them to an existing pipeline as described. Note that the AI -Extension has different pipeline and model versions from the tutorial. -This table should help you adapt the steps in the tutorial to work for -the AI Extension. - -| | Pipeline Name | Pipeline Version | Model Name | Model Version | -|------------| ------------- | ------------- | ------------- | ------------- | -|Tutorial | object_detection | 1 | object_detection | 1 | -|AI Extension| object_detection | person_vehicle_bike_detection | person_vehicle_bike_detection | 1 | - -Once you've made your changes, use the [test client](#test-client) to -start the pipeline and view results. Note: the original tutorial uses -a curl command that is not compatible with the AI Extension module. +Most of the steps to changes models used by LVA extension are the same as for the above tutorial, but it assumes you are working with the REST service and not the AI +Extension module. The LVA specific steps are called out in the following sections. +## Run Existing Object Detection Pipeline +Get baseline results for existing object_detection model `person-vehicle-bike-detection-crossroad-0078` +``` +$ ./docker/run_client.sh + +[AIXC] [2020-11-20 23:29:11,417] [MainThread ] [INFO]: - person (1.00) [0.30, 0.47, 0.09, 0.39] +[AIXC] [2020-11-20 23:29:11,417] [MainThread ] [INFO]: - person (0.97) [0.36, 0.40, 0.05, 0.24] +[AIXC] [2020-11-20 23:29:11,417] [MainThread ] [INFO]: - person (0.94) [0.44, 0.42, 0.08, 0.43] +[AIXC] [2020-11-20 23:29:11,418] [MainThread ] [INFO]: - person (0.92) [0.57, 0.38, 0.05, 0.25] +[AIXC] [2020-11-20 23:29:11,418] [MainThread ] [INFO]: - person (0.91) [0.69, 0.56, 0.12, 0.43] +[AIXC] [2020-11-20 23:29:11,418] [MainThread ] [INFO]: - person (0.90) [0.68, 0.42, 0.04, 0.24] +[AIXC] [2020-11-20 23:29:11,418] [MainThread ] [INFO]: - person (0.82) [0.64, 0.36, 0.05, 0.27] +[AIXC] [2020-11-20 23:29:11,418] [MainThread ] [INFO]: - person (0.60) [0.84, 0.44, 0.05, 0.29] + +``` +## Add New Model +### Add Model to Models List +As per tutorial, we will add the model `yolo-v2-tiny-tf`. Copy existing model list models/models.list.yml to models/yolo.yml +then add the following entry +```yml +- model: yolo-v2-tiny-tf + alias: yolo + version: 1 + precision: [FP16,FP32] +``` + +### Update Pipeline Definition File to Use New Model +Follow [instructions in tutorial](/docs/changing_object_detection_models.md#3-add-new-model), but base new pipeline on `pipelines/object_detection/person_vehicle_bike_detection`. The tutorial has all the details, but the changes can be made with the following commands +```bash +$ cp -r pipelines/object_detection/person_vehicle_bike_detection pipelines/object_detection/yolo +$ sed -i -e s/person_vehicle_bike_detection/yolo/g pipelines/object_detection/yolo/pipeline.json +``` + +Then re-build the image with `yolo.yml` as the input model list file. +``` +$ ./docker/build.sh --models models/yolo.yml +``` +The model will now be in `models` folder in the root of the project: +``` +models +└── yolo + └── 1 + ├── FP16 + │   ├── yolo-v2-tiny-tf.bin + │   ├── yolo-v2-tiny-tf.mapping + │   └── yolo-v2-tiny-tf.xml + ├── FP32 + │   ├── yolo-v2-tiny-tf.bin + │   ├── yolo-v2-tiny-tf.mapping + │   └── yolo-v2-tiny-tf.xml + └── yolo-v2-tiny-tf.json +``` + +## 5. Run Pipeline with New Model +### Check that images contains the new model and pipeline +As the LVA API does not support model or pipeline queries, start the container with an interactive shell and check that expected model and pipeline are present. +```bash +$ docker run -it --entrypoint /bin/bash video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension +vaserving@82dd59743ca3:~$ ls models +person_vehicle_bike_detection vehicle_attributes_recognition yolo +vaserving@82dd59743ca3:~$ ls pipelines/object_detection/ +debug_person_vehicle_bike_detection person_vehicle_bike_detection yolo +``` + +### Re-start service +Restart the service to ensure we are using the image with the yolo-v2-tiny-tf model +``` +$ docker stop video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension +$ docker/run_server.sh --pipeline-name object_detection --pipeline-version yolo +``` +### Run the client +Note different results due to different model +``` +$ docker/run_client.sh + +[AIXC] [2021-01-07 06:51:13,081] [MainThread ] [INFO]: - person (0.82) [0.63, 0.36, 0.06, 0.24] [] +[AIXC] [2021-01-07 06:51:13,081] [MainThread ] [INFO]: - person (0.78) [0.56, 0.37, 0.06, 0.23] [] +[AIXC] [2021-01-07 06:51:13,081] [MainThread ] [INFO]: - person (0.63) [0.44, 0.43, 0.11, 0.43] [] +[AIXC] [2021-01-07 06:51:13,081] [MainThread ] [INFO]: - person (0.63) [0.31, 0.45, 0.09, 0.23] [] +[AIXC] [2021-01-07 06:51:13,081] [MainThread ] [INFO]: - person (0.62) [0.69, 0.38, 0.06, 0.23] [] +[AIXC] [2021-01-07 06:51:13,081] [MainThread ] [INFO]: - person (0.60) [0.40, 0.44, 0.07, 0.27] [] +[AIXC] [2021-01-07 06:51:13,081] [MainThread ] [INFO]: - person (0.59) [0.45, 0.43, 0.08, 0.29] [] +[AIXC] [2021-01-07 06:51:13,082] [MainThread ] [INFO]: - person (0.57) [0.33, 0.40, 0.07, 0.20] [] +[AIXC] [2021-01-07 06:51:13,082] [MainThread ] [INFO]: - person (0.57) [0.76, 0.46, 0.13, 0.23] [] +[AIXC] [2021-01-07 06:51:13,082] [MainThread ] [INFO]: - person (0.55) [0.41, 0.44, 0.03, 0.10] [] + +``` \ No newline at end of file diff --git a/samples/lva_ai_extension/docker/build.sh b/samples/lva_ai_extension/docker/build.sh index e1b5a49..dd2f2ad 100755 --- a/samples/lva_ai_extension/docker/build.sh +++ b/samples/lva_ai_extension/docker/build.sh @@ -3,6 +3,7 @@ WORK_DIR=$(dirname $(readlink -f "$0")) SAMPLE_DIR=$(dirname $WORK_DIR) SAMPLE_BUILD_ARGS=$(env | cut -f1 -d= | grep -E '_(proxy|REPO|VER)$' | sed 's/^/--build-arg / ' | tr '\n' ' ') +MODELS="models/models.list.yml" BASE_IMAGE= OMZ_VERSION= TAG="video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension" @@ -31,6 +32,14 @@ function get_options { error 'ERROR: "--open-model-zoo-version" requires an argument.' fi ;; + --models) + if [ "$2" ]; then + MODELS=$2 + shift + else + error 'ERROR: "--models" requires an argument.' + fi + ;; *) break ;; @@ -41,8 +50,9 @@ function get_options { function show_help { echo "usage: ./run_server.sh" - echo " [ --base : Base image override for VA Serving build ] " + echo " [ --base : Base image for VA Serving build ] " echo " [ --open-model-zoo-version : Open Model Zoo version override for VA Serving build ] " + echo " [ --models : Model list, must be a relative path ] " } function launch { echo $@ @@ -58,7 +68,7 @@ function launch { echo $@ get_options "$@" # Build VA Serving -launch "$SAMPLE_DIR/../../docker/build.sh --framework gstreamer --create-service false $BASE_IMAGE $OMZ_VERSION --pipelines samples/lva_ai_extension/pipelines --models $SAMPLE_DIR/models/models.list.yml" +launch "$SAMPLE_DIR/../../docker/build.sh --framework gstreamer --create-service false $BASE_IMAGE $OMZ_VERSION --pipelines samples/lva_ai_extension/pipelines --models $SAMPLE_DIR/$MODELS" # Build AI Extention echo $SAMPLE_DIR/.. From 76e8314223e7ffe648c285a18b93355d9332c871 Mon Sep 17 00:00:00 2001 From: "Khoshnevissan, Farid" Date: Fri, 15 Jan 2021 14:42:40 -0800 Subject: [PATCH 31/50] Re-designed LVA tests to follow test case driven pattern of main tests. --- samples/lva_ai_extension/client/__main__.py | 19 +- samples/lva_ai_extension/tests/README.md | 11 + samples/lva_ai_extension/tests/conftest.py | 179 + samples/lva_ai_extension/tests/run.sh | 2 +- ...on_vehicle_attributes_recognition_cpu.json | 24 + ...es_recognition_cpu_video_golden_truth.json | 10084 ++++++++++++ ...e_bike_detection_cpu_multiple_clients.json | 24 + ...rson_vehicle_bike_detection_cpu_video.json | 24 + ...bike_detection_cpu_video_golden_truth.json | 8368 ++++++++++ ...king_person_vehicle_bike_tracking_cpu.json | 24 + ..._bike_tracking_cpu_video_golden_truth.json | 12748 ++++++++++++++++ ...rson_vehicle_bike_detection_gpu_video.json | 24 + ...n_vehicle_bike_detection_myriad_video.json | 24 + .../tests/test_client_server.py | 51 - .../tests/test_missing_file.py | 39 - .../tests/test_multiple_clients.py | 42 - .../tests/test_nonexistant_source.py | 16 + .../tests/test_object_pipelines.py | 65 - .../lva_ai_extension/tests/test_parameters.py | 64 +- .../tests/test_pipeline_execution_positive.py | 46 + .../tests/test_validate_schema.py | 63 - samples/lva_ai_extension/tests/test_video.py | 132 - 22 files changed, 31624 insertions(+), 449 deletions(-) create mode 100644 samples/lva_ai_extension/tests/README.md create mode 100644 samples/lva_ai_extension/tests/conftest.py create mode 100644 samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_classification_vehicle_attributes_recognition_cpu.json create mode 100644 samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_classification_vehicle_attributes_recognition_cpu_video_golden_truth.json create mode 100644 samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_detection_person_vehicle_bike_detection_cpu_multiple_clients.json create mode 100644 samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_detection_person_vehicle_bike_detection_cpu_video.json create mode 100644 samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_detection_person_vehicle_bike_detection_cpu_video_golden_truth.json create mode 100644 samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_tracking_person_vehicle_bike_tracking_cpu.json create mode 100644 samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_tracking_person_vehicle_bike_tracking_cpu_video_golden_truth.json create mode 100644 samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/gpu/object_detection_person_vehicle_bike_detection_gpu_video.json create mode 100644 samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/myriad/object_detection_person_vehicle_bike_detection_myriad_video.json delete mode 100644 samples/lva_ai_extension/tests/test_client_server.py delete mode 100644 samples/lva_ai_extension/tests/test_missing_file.py delete mode 100644 samples/lva_ai_extension/tests/test_multiple_clients.py create mode 100644 samples/lva_ai_extension/tests/test_nonexistant_source.py delete mode 100644 samples/lva_ai_extension/tests/test_object_pipelines.py create mode 100644 samples/lva_ai_extension/tests/test_pipeline_execution_positive.py delete mode 100644 samples/lva_ai_extension/tests/test_validate_schema.py delete mode 100644 samples/lva_ai_extension/tests/test_video.py diff --git a/samples/lva_ai_extension/client/__main__.py b/samples/lva_ai_extension/client/__main__.py index 2e61c7d..16bd2a1 100644 --- a/samples/lva_ai_extension/client/__main__.py +++ b/samples/lva_ai_extension/client/__main__.py @@ -86,18 +86,6 @@ def _log_options(args): logging.info(banner) -def _remove_empty_lists(dictionary): - if not isinstance(dictionary, (dict, list)): - return dictionary - if isinstance(dictionary, list): - return [v for v in (_remove_empty_lists(v) for v in dictionary) if v or v == 0] - return { - k: v - for k, v in ((k, _remove_empty_lists(v)) for k, v in dictionary.items()) - if v or v == 0 - } - - def _log_result(response, output, log_result=True): if not log_result: return @@ -125,15 +113,10 @@ def _log_result(response, output, log_result=True): ) # default value field is used to avoid not including values set to 0, # but it also causes empty lists to be included - # empty and none values are filtered out in response_dict returned_dict = MessageToDict( response.media_sample, including_default_value_fields=True ) - response_dict = _remove_empty_lists(returned_dict) - if response_dict.get("inferences"): - for inference in response_dict["inferences"]: - inference["type"] = inference["type"].lower() - output.write("{}\n".format(json.dumps(response_dict))) + output.write("{}\n".format(json.dumps(returned_dict))) def _log_fps(start_time, frames_received, prev_fps_delta, fps_interval): diff --git a/samples/lva_ai_extension/tests/README.md b/samples/lva_ai_extension/tests/README.md new file mode 100644 index 0000000..323db03 --- /dev/null +++ b/samples/lva_ai_extension/tests/README.md @@ -0,0 +1,11 @@ + +Use --entrypoint-args to specify pytest arguments. e.g. + +./run.sh --entrypoint-args "-k test_lva_parameter_string_argument" + +To run a specific test from the test case + +./run.sh --entrypoint-args "-k test_pipeline_execution_positive[object_tracking_person_vehicle_bike_tracking_cpu]" + +By default only CPU tests will run. --gpu adds GPU tests. --myriad adds NCS2 tests. --no-cpu excludes cpu tests. + diff --git a/samples/lva_ai_extension/tests/conftest.py b/samples/lva_ai_extension/tests/conftest.py new file mode 100644 index 0000000..8eae65b --- /dev/null +++ b/samples/lva_ai_extension/tests/conftest.py @@ -0,0 +1,179 @@ + +import subprocess +import time +import os +import json +import pytest + +from jsonschema import validate +from collections.abc import Mapping + +class Helpers: + def __init__(self): + self.server_process = None + self.client_process = None + + def run_server(self, params): + server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", "-p", str(params["port"])] + if params.get("pipeline_parameters"): + if isinstance(params["pipeline_parameters"], Mapping): + params["pipeline_parameters"] = json.dumps(params["pipeline_parameters"]) + server_args.extend(["--pipeline-parameters", params["pipeline_parameters"]]) + if params.get("pipeline_name"): + server_args.extend(["--pipeline-name", params["pipeline_name"]]) + if params.get("pipeline_version"): + server_args.extend(["--pipeline-version", params["pipeline_version"]]) + print(' '.join(server_args)) + self.server_process = subprocess.Popen(server_args) + time.sleep(params.get("sleep_period",0.25)) + return self.server_process + + def run_client(self, params): + client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", + "-s", "127.0.0.1:" + str(params["port"]), + "-l", str(params.get("loop_count", 1)), + "-f", params["source"]] + if params.get("shared_memory", False): + client_args.append("-m") + if params.get("output_location"): + client_args.extend(["-o", params["output_location"]]) + print(' '.join(client_args)) + self.client_process = subprocess.run(client_args) + assert self.client_process.returncode is not None + assert self.client_process.returncode == params.get("expected_return_code", 0) + return self.client_process.returncode + + def remove_empty_lists(self, dictionary): + if not isinstance(dictionary, (dict, list)): + return dictionary + if isinstance(dictionary, list): + return [v for v in (self.remove_empty_lists(v) for v in dictionary) if v or v == 0] + return {k: v for k, v in ((k, self.remove_empty_lists(v)) for k, v in dictionary.items()) if v or v == 0} + + def validate_output_against_schema(self, output_location): + json_schema = None + json_schema_file = os.path.join(os.path.dirname(__file__), 'common/Extension_Data_Schema.json') + with open(json_schema_file, "r") as read_file: + json_schema = json.load(read_file) + + #Read each inference result and compare against the schema + with open(output_location, "r") as file_: + for line in file_: + if line and line != '': + dictionary = json.loads(line) + dictionary = self.remove_empty_lists(dictionary) + if dictionary.get("inferences"): + for inference in dictionary["inferences"]: + inference["type"] = inference["type"].lower() + validate(instance=dictionary,schema=json_schema) + + def get_results_from_file(self, output_location, results): + with open(output_location) as results_file: + for x in results_file: + if (len(x.strip()) != 0): + results.append(json.loads(x)) + + def cmp_results(self, measured, expected, tolerance): + if measured == expected: + return True + + assert type(measured) == type(expected), "Type Comparison Mismatch" + + if isinstance(measured, int) or isinstance(measured, float): + if expected != 0: + msg = "Measured Value {} not within tolerance ({}) of Expected Value {}" + assert (abs(measured-expected) / abs(expected)) < tolerance, \ + msg.format(measured, tolerance, expected) + else: + msg = "Measured Value {} not within tolerance ({}) of Expected Value {}" + assert tolerance > 1, \ + msg.format(measured, tolerance, expected) + + return True + + if isinstance(measured, list): + assert len(measured) == len(expected), "List length not equal" + + for measured1, expected1 in zip(measured, expected): + assert self.cmp_results(measured1, expected1, tolerance), "List items not equal" + return True + + if isinstance(measured, dict): + assert len(measured) == len(expected), "Dictionary length not equal" + for key in measured: + assert key in expected, "Dictionary keys not equal" + if key.endswith("id"): + assert measured[key] == expected[key], "{} doesn't match".format(key) + return True + assert self.cmp_results(measured[key], expected[key], tolerance), "Dictionaries not equal" + return True + + assert measured == expected, "Values not equal" + return True + + def cleanup_processes(self): + if self.server_process is not None: + self.server_process.kill() + +@pytest.fixture +def helpers(): + helper = Helpers() + yield helper + helper.cleanup_processes() + +def pytest_addoption(parser): + parser.addoption("--generate", action="store_true", help="generate expected results", + default=False) + parser.addoption("--cpu", action="store_true", help="Run CPU tests", + default=True) + parser.addoption("--no-cpu", action="store_false", dest='cpu', help="Disable CPU tests") + parser.addoption("--gpu", action="store_true", help="Run GPU tests", + default=False) + parser.addoption("--myriad", action="store_true", help="Run MYRIAD tests", + default=False) + + +#Parse the test_cases folder to load config files for parameterizing tests and checking results +def load_test_cases(metafunc, directory): + dir_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_cases", directory) + list_of_dir_paths = [dir_path] + + filenames = [] + if metafunc.config.getoption("cpu"): + cpu_path = os.path.join(dir_path, "cpu") + if os.path.isdir(cpu_path): + list_of_dir_paths.append(cpu_path) + if metafunc.config.getoption("gpu"): + gpu_path = os.path.join(dir_path, "gpu") + if os.path.isdir(gpu_path): + list_of_dir_paths.append(gpu_path) + + if metafunc.config.getoption("myriad"): + gpu_path = os.path.join(dir_path, "myriad") + if os.path.isdir(gpu_path): + list_of_dir_paths.append(gpu_path) + + for path in list_of_dir_paths: + dir_filenames = [(os.path.abspath(os.path.join(path, fn)), + os.path.splitext(fn)[0]) for fn in os.listdir(path) + if os.path.isfile(os.path.join(path, fn)) and + os.path.splitext(fn)[1] == '.json'] + filenames.extend(dir_filenames) + test_cases = [] + test_names = [] + generate = metafunc.config.getoption("generate") + for filepath, testname in filenames: + try: + with open(filepath) as json_file: + test_cases.append((json.load(json_file), filepath, generate)) + test_names.append(testname) + except Exception as error: + print(error) + assert False, "Error Reading Test Case" + return (test_cases, test_names) + +def pytest_generate_tests(metafunc): + if "pipeline_execution_positive" in metafunc.function.__name__: + test_cases, test_names = load_test_cases(metafunc, "pipeline_execution_positive") + metafunc.parametrize("test_case,test_filename,generate", test_cases, ids=test_names) + diff --git a/samples/lva_ai_extension/tests/run.sh b/samples/lva_ai_extension/tests/run.sh index 77e206a..6d54f78 100755 --- a/samples/lva_ai_extension/tests/run.sh +++ b/samples/lva_ai_extension/tests/run.sh @@ -7,4 +7,4 @@ LOCAL_RESULTS_DIR="$CURRENT_DIR/results" DOCKER_RESULTS_DIR="/home/video-analytics-serving/samples/lva_ai_extension/tests/results" mkdir -p "$LOCAL_RESULTS_DIR" RESULTS_VOLUME_MOUNT="-v $LOCAL_RESULTS_DIR:$DOCKER_RESULTS_DIR " -"$ROOT_DIR/docker/run.sh" --image $IMAGE -v /dev/shm:/dev/shm $RESULTS_VOLUME_MOUNT --pipelines "$LVA_DIR/pipelines" -p 5001:5001 --user $UID "$@" +"$ROOT_DIR/docker/run.sh" --image $IMAGE -v /dev/shm:/dev/shm -v "$LVA_DIR/tests/test_cases:/home/video-analytics-serving/samples/lva_ai_extension/tests/test_cases" $RESULTS_VOLUME_MOUNT --pipelines "$LVA_DIR/pipelines" -p 5001:5001 --user $UID "$@" diff --git a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_classification_vehicle_attributes_recognition_cpu.json b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_classification_vehicle_attributes_recognition_cpu.json new file mode 100644 index 0000000..2db198b --- /dev/null +++ b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_classification_vehicle_attributes_recognition_cpu.json @@ -0,0 +1,24 @@ +{ + "server_params": { + "pipeline_name":"object_classification", + "pipeline_version":"vehicle_attributes_recognition", + "pipeline_parameters": { + "device":"CPU", + "model-instance-id":"" + }, + "sleep_period":0.25, + "port":5001 + }, + "client_params": { + "source":"/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/sample01.png", + "output_location":"", + "shared_memory":true, + "loop_count":1, + "sleep_period":0.25, + "port":5001, + "timeout":300, + "expected_return_code":0 + }, + "num_of_concurrent_clients":1, + "golden_results":false +} diff --git a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_classification_vehicle_attributes_recognition_cpu_video_golden_truth.json 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"shared_memory":true, + "loop_count":1, + "sleep_period":0.25, + "port":5001, + "timeout":300, + "expected_return_code":0 + }, + "num_of_concurrent_clients":1, + "golden_results":false +} diff --git a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/myriad/object_detection_person_vehicle_bike_detection_myriad_video.json b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/myriad/object_detection_person_vehicle_bike_detection_myriad_video.json new file mode 100644 index 0000000..b00678b --- /dev/null +++ b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/myriad/object_detection_person_vehicle_bike_detection_myriad_video.json @@ -0,0 +1,24 @@ +{ + "server_params": { + "pipeline_name":"object_detection", + "pipeline_version":"person_vehicle_bike_detection", + "pipeline_parameters": { + "device":"MYRIAD", + "model-instance-id":"" + }, + "sleep_period":0.25, + "port":5001 + }, + "client_params": { + "source":"/home/video-analytics-serving/person-bicycle-car-detection.mp4", + "output_location":"", + "shared_memory":true, + "loop_count":1, + "sleep_period":0.25, + "port":5001, + "timeout":300, + "expected_return_code":0 + }, + "num_of_concurrent_clients":1, + "golden_results":false +} diff --git a/samples/lva_ai_extension/tests/test_client_server.py b/samples/lva_ai_extension/tests/test_client_server.py deleted file mode 100644 index 95931df..0000000 --- a/samples/lva_ai_extension/tests/test_client_server.py +++ /dev/null @@ -1,51 +0,0 @@ -''' -* Copyright (C) 2019-2020 Intel Corporation. -* -* SPDX-License-Identifier: MIT License -''' - -import subprocess -import time -import os - -class TestLvaClientServer: - def teardown_method(self, test_method): - if self.server_process is not None: - self.server_process.kill() - - def run_server(self, sleep_period = 0.25, port = 5001): - server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", "-p", str(port)] - print(' '.join(server_args)) - self.server_process = subprocess.Popen(server_args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, - bufsize=1, universal_newlines=True) - time.sleep(sleep_period) - - def run_client(self, source, sleep_period = 0.25, port = 5001, output_location = None, shared_memory = True, timeout = 300): - client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", - "-s", "127.0.0.1:" + str(port), - "-f", source] - if shared_memory: - client_args.append("-m") - if output_location is not None: - client_args.append("-o") - client_args.append(output_location) - print(' '.join(client_args)) - client_process = subprocess.Popen(client_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) - client_process.poll() - elapsed_time = 0 - while client_process.returncode is None and elapsed_time < timeout: - time.sleep(sleep_period) - elapsed_time += sleep_period - client_process.poll() - return client_process.returncode - - def test_lva_client_server(self, sleep_period=0.25, port=5001): - self.run_server(sleep_period=sleep_period, port=port) - - returnCode = self.run_client(source="/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/sample01.png", - sleep_period=sleep_period, - port=port, - shared_memory=False) - - assert returnCode is not None - assert returnCode == 0 diff --git a/samples/lva_ai_extension/tests/test_missing_file.py b/samples/lva_ai_extension/tests/test_missing_file.py deleted file mode 100644 index 3602f52..0000000 --- a/samples/lva_ai_extension/tests/test_missing_file.py +++ /dev/null @@ -1,39 +0,0 @@ -''' -* Copyright (C) 2019-2020 Intel Corporation. -* -* SPDX-License-Identifier: MIT License -''' - -import subprocess -import time -import os - -class TestLvaValidate: - - def run_client(self, source, sleep_period = 0.25, port = 5001, output_location = None, shared_memory = True, timeout = 300): - client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", - "-s", "127.0.0.1:" + str(port), - "-f", source] - if shared_memory: - client_args.append("-m") - if output_location is not None: - client_args.append("-o") - client_args.append(output_location) - print(' '.join(client_args)) - client_process = subprocess.Popen(client_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) - client_process.poll() - elapsed_time = 0 - while client_process.returncode is None and elapsed_time < timeout: - time.sleep(sleep_period) - elapsed_time += sleep_period - client_process.poll() - return client_process.returncode - - def test_lva_missing_file(self, sleep_period=0.25, port=5001): - returnCode = self.run_client(source="/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/nonexistantimage.png", - sleep_period=sleep_period, - port=port, - shared_memory=False) - - assert returnCode is not None - assert returnCode == 1 diff --git a/samples/lva_ai_extension/tests/test_multiple_clients.py b/samples/lva_ai_extension/tests/test_multiple_clients.py deleted file mode 100644 index 53bdd8a..0000000 --- a/samples/lva_ai_extension/tests/test_multiple_clients.py +++ /dev/null @@ -1,42 +0,0 @@ -''' -* Copyright (C) 2019-2020 Intel Corporation. -* -* SPDX-License-Identifier: MIT License -''' - -import subprocess -import time -import os - -class TestLvaMultipleClients: - def teardown_method(self, test_method): - if self.server_process is not None: - self.server_process.kill() - - def test_multiple_clients(self, sleep_period=0.25, port=5001, max_running_pipelines=10): - server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", "-p", str(port)] - print(' '.join(server_args)) - self.server_process = subprocess.Popen(server_args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, - bufsize=1, universal_newlines=True) - time.sleep(sleep_period) - - client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", - "-s", "127.0.0.1:" + str(port), - "-f", "/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/sample01.png"] - print(' '.join(client_args)) - client_process_list = [] - for _ in range(max_running_pipelines): - client_process = subprocess.Popen(client_args, stdout=subprocess.PIPE, - stderr=subprocess.PIPE, bufsize=1, - universal_newlines=True) - client_process_list.append(client_process) - print(client_process_list) - client_process_list[-1].poll() - elapsed_time = 0 - while client_process_list[-1].returncode is None and elapsed_time < 60: - time.sleep(sleep_period) - elapsed_time += sleep_period - client_process_list[-1].poll() - assert client_process_list[-1].returncode is not None - assert client_process_list[-1].returncode == 0 - print("Elapsed time = {}s".format(elapsed_time)) diff --git a/samples/lva_ai_extension/tests/test_nonexistant_source.py b/samples/lva_ai_extension/tests/test_nonexistant_source.py new file mode 100644 index 0000000..271f41e --- /dev/null +++ b/samples/lva_ai_extension/tests/test_nonexistant_source.py @@ -0,0 +1,16 @@ +''' +* Copyright (C) 2019-2020 Intel Corporation. +* +* SPDX-License-Identifier: MIT License +''' + +def test_lva_nonexistant_source(helpers, sleep_period=0.25, port=5001): + client_params = { + "source": "/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/nonexistantimage.png", + "sleep_period": sleep_period, + "port": port, + "shared_memory": False, + "expected_return_code": 1 + } + + helpers.run_client(client_params) diff --git a/samples/lva_ai_extension/tests/test_object_pipelines.py b/samples/lva_ai_extension/tests/test_object_pipelines.py deleted file mode 100644 index 0ab6e4b..0000000 --- a/samples/lva_ai_extension/tests/test_object_pipelines.py +++ /dev/null @@ -1,65 +0,0 @@ -import subprocess -import time -import os -import json -import tempfile -import pytest - -from jsonschema import validate - -class TestLvaObjectPipelines: - def teardown_method(self, test_method): - if self.server_process is not None: - self.server_process.kill() - - def run_client(self, source, sleep_period = 0.25, port = 5001, output_location = None, shared_memory = True, timeout = 300): - client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", - "-s", "127.0.0.1:" + str(port), - "-f", source] - if shared_memory: - client_args.append("-m") - if output_location is not None: - client_args.append("-o") - client_args.append(output_location) - print(' '.join(client_args)) - client_process = subprocess.Popen(client_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) - client_process.poll() - elapsed_time = 0 - while client_process.returncode is None and elapsed_time < timeout: - time.sleep(sleep_period) - elapsed_time += sleep_period - client_process.poll() - assert client_process.returncode is not None - assert client_process.returncode == 0 - - def validate_output(self, output_location): - json_schema = None - json_schema_file = os.path.join(os.path.dirname(__file__), 'common/Extension_Data_Schema.json') - with open(json_schema_file, "r") as read_file: - json_schema = json.load(read_file) - - #Read each inference result and compare against the schema - with open(output_location, "r") as file: - for line in file: - if line and line != '': - validate(instance=json.loads(line),schema=json_schema) - - @pytest.mark.parametrize("pipeline_name,pipeline_version", [("object_detection", "person_vehicle_bike_detection"), ("object_classification", "vehicle_attributes_recognition"), ("object_tracking", "person_vehicle_bike_tracking")]) - def test_lva_object_pipeline(self, pipeline_name, pipeline_version, sleep_period=0.25, port=5001): - server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", "-p", str(port), - "--pipeline-name", "object_detection", "--pipeline-version", "person_vehicle_bike_detection"] - print(' '.join(server_args)) - self.server_process = subprocess.Popen(server_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) - time.sleep(sleep_period) - - #Create temporary directory for saving output - workdir_path = tempfile.TemporaryDirectory() - output_file = "output.jsonl" - output_location = os.path.join(workdir_path.name, output_file) - - self.run_client(source="/home/video-analytics-serving/person-bicycle-car-detection.mp4", - sleep_period=sleep_period, - port=port, - output_location=output_location) - self.validate_output(output_location) - diff --git a/samples/lva_ai_extension/tests/test_parameters.py b/samples/lva_ai_extension/tests/test_parameters.py index 260a9d2..b8eac64 100644 --- a/samples/lva_ai_extension/tests/test_parameters.py +++ b/samples/lva_ai_extension/tests/test_parameters.py @@ -4,47 +4,35 @@ * SPDX-License-Identifier: MIT License ''' -import subprocess -import time import os import tempfile -class TestLvaParameters: - def teardown_method(self, test_method): - if self.server_process is not None: - self.server_process.kill() - - def parameter_run_client_server(self, sleep_period, port, parameters): - server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", "-p", str(port), - "--pipeline-parameters", parameters] - print(' '.join(server_args)) - self.server_process = subprocess.Popen(server_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) - time.sleep(sleep_period) - - client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", - "-s", "127.0.0.1:" + str(port), - "-f", "/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/sample01.png"] - print(' '.join(client_args)) - client_process = subprocess.Popen(client_args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, - bufsize=1, universal_newlines=True) - client_process.poll() - elapsed_time = 0 - while client_process.returncode is None and elapsed_time < 5: - time.sleep(sleep_period) - elapsed_time += sleep_period - client_process.poll() - assert client_process.returncode is not None - assert client_process.returncode == 0 - - def test_lva_parameter_string_argument(self, sleep_period=0.25, port=5001, +def parameter_run_client_server(helpers, sleep_period, port, parameters): + server_params = { + "pipeline_parameters": parameters, + "sleep_period": sleep_period, + "port": port + } + + client_params = { + "source": "/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/sample01.png", + "sleep_period": sleep_period, + "port": port + } + + + helpers.run_server(server_params) + helpers.run_client(client_params) + +def test_lva_parameter_string_argument(helpers, sleep_period=0.25, port=5001, parameter_string="{\"device\":\"CPU\"}"): - self.parameter_run_client_server(sleep_period, port, parameter_string) + parameter_run_client_server(helpers, sleep_period, port, parameter_string) - def test_lva_parameter_file_argument(self, sleep_period=0.25, port=5001): - #Create temporary parameter file - workdir_path = tempfile.TemporaryDirectory() - parameter_path = os.path.join(workdir_path.name, "parameters.json") - with open(parameter_path, "w") as parameter_file: - parameter_file.write("{\"device\":\"CPU\"}") +def test_lva_parameter_file_argument(helpers, sleep_period=0.25, port=5001): + #Create temporary parameter file + workdir_path = tempfile.TemporaryDirectory() + parameter_path = os.path.join(workdir_path.name, "parameters.json") + with open(parameter_path, "w") as parameter_file: + parameter_file.write("{\"device\":\"CPU\"}") - self.parameter_run_client_server(sleep_period, port, parameter_path) + parameter_run_client_server(helpers, sleep_period, port, parameter_path) diff --git a/samples/lva_ai_extension/tests/test_pipeline_execution_positive.py b/samples/lva_ai_extension/tests/test_pipeline_execution_positive.py new file mode 100644 index 0000000..ce5c4e2 --- /dev/null +++ b/samples/lva_ai_extension/tests/test_pipeline_execution_positive.py @@ -0,0 +1,46 @@ +import os +import tempfile +import pytest +import copy +import json + +from multiprocessing import Process + +def test_pipeline_execution_positive(helpers, test_case, test_filename, generate): + #Create copy of test case to create the generated file + _test_case = copy.deepcopy(test_case) + + helpers.run_server(test_case["server_params"]) + + #Create temporary directory for saving output + workdir_path = tempfile.TemporaryDirectory() + + output_locations = [] + proc = [] + counter = 0 + for _ in range(test_case["num_of_concurrent_clients"]): + output_file = "output" + str(counter) + ".jsonl" + counter += 1 + output_location = os.path.join(workdir_path.name, output_file) + output_locations.append(output_location) + test_case["client_params"]["output_location"] = output_location + p = Process(target=helpers.run_client(test_case["client_params"])) + p.start() + proc.append(p) + for p in proc: + p.join() + + for output_file in output_locations: + helpers.validate_output_against_schema(output_file) + + if test_case["golden_results"]: + results = [] + for output_file in output_locations: + helpers.get_results_from_file(output_file, results) + if generate: + _test_case["results"] = results + with open(test_filename+'.generated', "w") as test_output: + json.dump(_test_case, test_output, indent=4) + else: + assert helpers.cmp_results(results, test_case["results"], + test_case["numerical_tolerance"]), "Inference result mismatch" diff --git a/samples/lva_ai_extension/tests/test_validate_schema.py b/samples/lva_ai_extension/tests/test_validate_schema.py deleted file mode 100644 index b288ca9..0000000 --- a/samples/lva_ai_extension/tests/test_validate_schema.py +++ /dev/null @@ -1,63 +0,0 @@ -import subprocess -import time -import os -import json -import tempfile - -from jsonschema import validate - -class TestLvaValidate: - def teardown_method(self, test_method): - if self.server_process is not None: - self.server_process.kill() - - def run_client(self, source, sleep_period = 0.25, port = 5001, output_location = None, shared_memory = True, timeout = 300): - client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", - "-s", "127.0.0.1:" + str(port), - "-f", source] - if shared_memory: - client_args.append("-m") - if output_location is not None: - client_args.append("-o") - client_args.append(output_location) - print(' '.join(client_args)) - client_process = subprocess.Popen(client_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) - client_process.poll() - elapsed_time = 0 - while client_process.returncode is None and elapsed_time < timeout: - time.sleep(sleep_period) - elapsed_time += sleep_period - client_process.poll() - assert client_process.returncode is not None - assert client_process.returncode == 0 - - def validate_output(self, output_location): - json_schema = None - json_schema_file = os.path.join(os.path.dirname(__file__), 'common/Extension_Data_Schema.json') - with open(json_schema_file, "r") as read_file: - json_schema = json.load(read_file) - - #Read each inference result and compare against the schema - with open(output_location, "r") as file: - for line in file: - if line and line != '': - validate(instance=json.loads(line),schema=json_schema) - - def test_lva_validate_schema(self, sleep_period=0.25, port=5001): - server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", "-p", str(port)] - print(' '.join(server_args)) - self.server_process = subprocess.Popen(server_args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, - bufsize=1, universal_newlines=True) - time.sleep(sleep_period) - - #Create temporary parameter file - workdir_path = tempfile.TemporaryDirectory() - output_file = "output.jsonl" - output_location = os.path.join(workdir_path.name, output_file) - - self.run_client(source="/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/sample01.png", - sleep_period=sleep_period, - port=port, - output_location=output_location, - timeout=5) - self.validate_output(output_location) diff --git a/samples/lva_ai_extension/tests/test_video.py b/samples/lva_ai_extension/tests/test_video.py deleted file mode 100644 index e5e13c8..0000000 --- a/samples/lva_ai_extension/tests/test_video.py +++ /dev/null @@ -1,132 +0,0 @@ - -''' -* Copyright (C) 2019-2020 Intel Corporation. -* -* SPDX-License-Identifier: MIT License -''' - -import subprocess -import time -import os -import json -import tempfile -import pytest - -from jsonschema import validate - -class TestLvaVideo: - def teardown_method(self, test_method): - server_process = getattr(self, "server_process", None) - if server_process is not None: - server_process.kill() - - def run_client(self, source, sleep_period = 0.25, port = 5001, output_location = None, shared_memory = True, timeout = 300): - client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", - "-s", "127.0.0.1:" + str(port), - "-f", source] - if shared_memory: - client_args.append("-m") - if output_location is not None: - client_args.append("-o") - client_args.append(output_location) - print(' '.join(client_args)) - client_process = subprocess.Popen(client_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) - client_process.poll() - elapsed_time = 0 - while client_process.returncode is None and elapsed_time < timeout: - time.sleep(sleep_period) - elapsed_time += sleep_period - client_process.poll() - assert client_process.returncode is not None - assert client_process.returncode == 0 - - def validate_output(self, output_location): - json_schema = None - json_schema_file = os.path.join(os.path.dirname(__file__), 'common/Extension_Data_Schema.json') - with open(json_schema_file, "r") as read_file: - json_schema = json.load(read_file) - - #Read each inference result and compare against the schema - with open(output_location, "r") as file: - for line in file: - if line and line != '': - validate(instance=json.loads(line),schema=json_schema) - - - def _download_video(self, uri, target, no_proxy=""): - if not os.path.isfile(target): - arguments = ["wget",no_proxy,"-O",target,uri] - subprocess.run(arguments) - return os.path.isfile(target) - - @pytest.mark.parametrize("filename", ["classroom.mp4","person-bicycle-car-detection.mp4","person-bicycle-car-detection_1920_1080_2min.mp4"]) - @pytest.mark.parametrize("backend",["CAP_GSTREAMER"]) - def test_opencv(self, filename, backend): - os.environ["OPENCV_VIDEOIO_DEBUG"] = "1" - os.environ["OPENCV_VIDEOCAPTURE_DEBUG"] = "1" - import cv2 - - print("OpenCV Version: {}".format(cv2.__version__), flush=True) - - if ("2min" in filename): - assert(self._download_video("https://gitlab.devtools.intel.com/media-analytics-pipeline-zoo/media/-/raw/main/video/person-bicycle-car-detection/{}?inline=false".format(filename), - "/home/video-analytics-serving/{}".format(filename), - "--no-proxy")) - else: - assert(self._download_video("https://github.com/intel-iot-devkit/sample-videos/blob/master/{}?raw=true".format(filename), - "/home/video-analytics-serving/{}".format(filename))) - - print("Opening: {}\n".format(filename),flush=True) - capture = cv2.VideoCapture("/home/video-analytics-serving/{}".format(filename), getattr(cv2,backend)) - assert(capture) - ret, frame = capture.read() - assert(ret and frame is not None) - - - def test_lva_video_consistent_results(self, sleep_period=0.25, port=5001): - server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", "-p", str(port)] - print(' '.join(server_args)) - self.server_process = subprocess.Popen(server_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) - time.sleep(sleep_period) - - #Create temporary parameter file - workdir_path = tempfile.TemporaryDirectory() - output_file = "output.jsonl" - output_location1 = os.path.join(workdir_path.name, output_file) - output_file2 = "output2.jsonl" - output_location2 = os.path.join(workdir_path.name, output_file2) - - self.run_client(source="/home/video-analytics-serving/person-bicycle-car-detection.mp4", - sleep_period=sleep_period, - port=port, - output_location=output_location1) - self.run_client(source="/home/video-analytics-serving/person-bicycle-car-detection.mp4", - sleep_period=sleep_period, - port=port, - output_location=output_location2) - self.validate_output(output_location1) - self.validate_output(output_location2) - - #Compare the two outputs to see if they match - json_data1 = None - json_data2 = None - count = 0 - with open(output_location1, "r") as file1: - with open(output_location2, "r") as file2: - file1_s = file1.readline() - file2_s = file2.readline() - while file1_s != '' and file2_s != '': - count += 1 - json_data1 = json.loads(file1_s) - json_data2 = json.loads(file2_s) - - assert json_data1 == json_data2 - - file1_s = file1.readline() - file2_s = file2.readline() - if file1_s != '' and file2_s == '': - assert False - if file1_s == '' and file2_s != '': - assert False - print("Got {} results".format(count), flush=True) - assert (count == 647) From 3c772073611e7b2e507297fa05192fccec4baef9 Mon Sep 17 00:00:00 2001 From: "Shah, Neelay N" Date: Fri, 15 Jan 2021 15:07:35 -0800 Subject: [PATCH 32/50] Instantiate a new logger for each pipeline --- vaserving/gstreamer_pipeline.py | 61 +++++++++++++++++---------------- 1 file changed, 31 insertions(+), 30 deletions(-) diff --git a/vaserving/gstreamer_pipeline.py b/vaserving/gstreamer_pipeline.py index db6ff80..ea58e1d 100755 --- a/vaserving/gstreamer_pipeline.py +++ b/vaserving/gstreamer_pipeline.py @@ -23,8 +23,6 @@ from vaserving.pipeline import Pipeline # pylint: enable=wrong-import-position -logger = logging.get_logger('GSTPipeline', is_static=True) - class GStreamerPipeline(Pipeline): Gst.init(None) GVA_INFERENCE_ELEMENT_TYPES = ["GstGvaDetect", @@ -35,7 +33,7 @@ class GStreamerPipeline(Pipeline): _inference_element_cache = {} _mainloop = None _mainloop_thread = None - CachedElement = namedtuple("CachedElement",["element","pipelines"]) + CachedElement = namedtuple("CachedElement", ["element", "pipelines"]) @staticmethod def gobject_mainloop(): @@ -81,6 +79,8 @@ def __init__(self, identifier, config, model_manager, request, finished_callback self.appsink_element = None self._app_destination = None self._cached_element_keys = [] + self._logger = logging.get_logger('GSTPipeline', is_static=True) + if (not GStreamerPipeline._mainloop): GStreamerPipeline._mainloop_thread = Thread( target=GStreamerPipeline.gobject_mainloop) @@ -98,9 +98,9 @@ def mainloop_quit(): def _delete_pipeline(self, new_state): self.state = new_state self.stop_time = time.time() - logger.debug("Setting Pipeline {id}" - " State to {next_state}".format(id=self.identifier, - next_state=new_state.name)) + self._logger.debug("Setting Pipeline {id}" + " State to {next_state}".format(id=self.identifier, + next_state=new_state.name)) if self.pipeline: bus = self.pipeline.get_bus() if self._bus_connection_id: @@ -160,7 +160,7 @@ def params(self): return params_obj def status(self): - logger.debug("Called Status") + self._logger.debug("Called Status") if self.start_time is not None: if self.stop_time is not None: elapsed_time = max(0, self.stop_time - self.start_time) @@ -227,16 +227,16 @@ def _set_section_properties(self, request_section, config_section): else: element.set_property( property_name, request[key]) - logger.debug("Setting element: {}, property: {}, value: {}".format( + self._logger.debug("Setting element: {}, property: {}, value: {}".format( element_name, property_name, element.get_property(property_name))) else: - logger.debug("Parameter {} given for element {}" - " but no property found".format( - property_name, element_name)) + self._logger.debug("Parameter {} given for element {}" + " but no property found".format( + property_name, element_name)) else: - logger.debug( + self._logger.debug( "Parameter {} given for element {}" " but no element found".format(property_name, element_name)) @@ -262,7 +262,7 @@ def _set_default_models(self): for element in gva_elements: network = self.model_manager.get_default_network_for_device( element.get_property("device"), element.get_property("model")) - logger.debug("Setting model to {} for element {}".format( + self._logger.debug("Setting model to {} for element {}".format( network, element.get_name())) element.set_property("model", network) @@ -280,7 +280,7 @@ def _set_model_proc(self): if element.get_property("model") in self.model_manager.model_procs: proc = self.model_manager.model_procs[element.get_property("model")] if proc is not None: - logger.debug("Setting model proc to {} for element {}".format( + self._logger.debug("Setting model proc to {} for element {}".format( proc, element.get_name())) element.set_property("model-proc", proc) @@ -292,6 +292,7 @@ def validate_config(config): metaconvert = pipeline.get_by_name("metaconvert") metapublish = pipeline.get_by_name("destination") appsrc_elements = GStreamerPipeline._get_elements_by_type(pipeline, ["GstAppSrc"]) + logger = logging.get_logger('GSTPipeline', is_static=True) if (len(appsrc_elements) > 1): logger.warning("Multiple appsrc elements found") if len(appsink_elements) != 1: @@ -347,7 +348,7 @@ def format_location_callback(self, try: os.makedirs(self._dir_name) except FileExistsError: - logger.debug("Directory already exists") + self._logger.debug("Directory already exists") template = "{dirname}/{adjustedtime}_{time}.mp4" return template.format(dirname=self._dir_name, @@ -389,8 +390,8 @@ def start(self): if (self.start_time is not None): return - logger.debug("Starting Pipeline {id}".format(id=self.identifier)) - logger.debug(self._gst_launch_string) + self._logger.debug("Starting Pipeline {id}".format(id=self.identifier)) + self._logger.debug(self._gst_launch_string) try: self.pipeline = Gst.parse_launch(self._gst_launch_string) @@ -433,7 +434,7 @@ def start(self): self.pipeline.set_state(Gst.State.PLAYING) self.start_time = time.time() except Exception as error: - logger.error("Error on Pipeline {id}: {err}".format( + self._logger.error("Error on Pipeline {id}: {err}".format( id=self.identifier, err=error)) # Context is already within _create_delete_lock self._delete_pipeline(Pipeline.State.ERROR) @@ -470,7 +471,7 @@ def on_need_data_app_source(self, src, _): try: self._app_source.start_frames() except Exception as error: - logger.error("Error on Pipeline {id}: Error in App Source: {err}".format( + self._logger.error("Error on Pipeline {id}: Error in App Source: {err}".format( id=self.identifier, err=error)) src.post_message(Gst.Message.new_error(src, GLib.GError(), "AppSource: {}".format(str(error)))) @@ -479,7 +480,7 @@ def on_enough_data_app_source(self, src): try: self._app_source.pause_frames() except Exception as error: - logger.error("Error on Pipeline {id}: Error in App Source: {err}".format( + self._logger.error("Error on Pipeline {id}: Error in App Source: {err}".format( id=self.identifier, err=error)) src.post_message(Gst.Message.new_error(src, GLib.GError(), "AppSource: {}".format(str(error)))) @@ -533,14 +534,14 @@ def appsink_probe_callback(unused_pad, info, self): return Gst.PadProbeReturn.OK def on_sample_app_destination(self, sink): - logger.debug("Received Sample from Pipeline {id}".format( + self._logger.debug("Received Sample from Pipeline {id}".format( id=self.identifier)) sample = sink.emit("pull-sample") result = Gst.FlowReturn.OK try: self._app_destination.process_frame(sample) except Exception as error: - logger.error("Error on Pipeline {id}: Error in App Destination: {err}".format( + self._logger.error("Error on Pipeline {id}: Error in App Destination: {err}".format( id=self.identifier, err=error)) result = Gst.FlowReturn.ERROR self.frame_count += 1 @@ -549,7 +550,7 @@ def on_sample_app_destination(self, sink): def on_sample(self, sink): - logger.debug("Received Sample from Pipeline {id}".format( + self._logger.debug("Received Sample from Pipeline {id}".format( id=self.identifier)) sample = sink.emit("pull-sample") try: @@ -558,10 +559,10 @@ def on_sample(self, sink): for meta in GVAJSONMeta.iterate(buf): json_object = json.loads(meta.get_message()) - logger.debug(json.dumps(json_object)) + self._logger.debug(json.dumps(json_object)) except Exception as error: - logger.error("Error on Pipeline {id}: {err}".format( + self._logger.error("Error on Pipeline {id}: {err}".format( id=self.identifier, err=error)) self.frame_count += 1 @@ -571,14 +572,14 @@ def on_sample(self, sink): def bus_call(self, unused_bus, message, unused_data=None): message_type = message.type if message_type == Gst.MessageType.APPLICATION: - logger.info("Pipeline {id} Aborted".format(id=self.identifier)) + self._logger.info("Pipeline {id} Aborted".format(id=self.identifier)) self._delete_pipeline_with_lock(Pipeline.State.ABORTED) if message_type == Gst.MessageType.EOS: - logger.info("Pipeline {id} Ended".format(id=self.identifier)) + self._logger.info("Pipeline {id} Ended".format(id=self.identifier)) self._delete_pipeline_with_lock(Pipeline.State.COMPLETED) elif message_type == Gst.MessageType.ERROR: err, debug = message.parse_error() - logger.error( + self._logger.error( "Error on Pipeline {id}: {err}: {debug}".format(id=self.identifier, err=err, debug=debug)) @@ -590,14 +591,14 @@ def bus_call(self, unused_bus, message, unused_data=None): if self.state is Pipeline.State.ABORTED: self._delete_pipeline_with_lock(Pipeline.State.ABORTED) if self.state is Pipeline.State.QUEUED: - logger.info( + self._logger.info( "Setting Pipeline {id} State to RUNNING".format(id=self.identifier)) self.state = Pipeline.State.RUNNING else: if self._bus_messages: structure = Gst.Message.get_structure(message) if structure: - logger.info("Message header: {name} , Message: {message}".format( + self._logger.info("Message header: {name} , Message: {message}".format( name=Gst.Structure.get_name(structure), message=Gst.Structure.to_string(structure))) return True From 496b08b9dde9f032e1e4a2261eb1b0888956dbeb Mon Sep 17 00:00:00 2001 From: "Thakkalapelli, Thanaji Rao" Date: Fri, 15 Jan 2021 16:21:27 -0800 Subject: [PATCH 33/50] Updated device and model-instance-id pipeline params --- .../emotion_recognition/1/pipeline.json | 32 ++++---- .../object_detection/1/pipeline.json | 2 +- .../object_detection/2/pipeline.json | 10 +-- .../app_src_dst/pipeline.json | 2 +- .../object_detection/edgex/pipeline.json | 10 +-- samples/lva_ai_extension/README.md | 8 +- .../deployment.gpu.grpc.template.json | 2 +- .../deployment.hddl.grpc.template.json | 2 +- .../deployment.movidius.grpc.template.json | 2 +- samples/lva_ai_extension/docker/run_server.sh | 6 +- .../pipeline.json | 51 +++++++------ .../pipeline.json | 53 ++++++++------ .../pipeline.json | 20 +++-- .../pipeline.json | 24 +++--- .../pipeline.json | 73 ++++++++++--------- .../pipeline.json | 73 ++++++++++--------- .../server/media_graph_extension.py | 3 +- samples/lva_ai_extension/server/readme.md | 16 ++-- ...on_vehicle_attributes_recognition_cpu.json | 4 +- ...es_recognition_cpu_video_golden_truth.json | 4 +- ...e_bike_detection_cpu_multiple_clients.json | 3 +- ...rson_vehicle_bike_detection_cpu_video.json | 3 +- ...bike_detection_cpu_video_golden_truth.json | 3 +- ...king_person_vehicle_bike_tracking_cpu.json | 4 +- ..._bike_tracking_cpu_video_golden_truth.json | 4 +- ...rson_vehicle_bike_detection_gpu_video.json | 3 +- ...n_vehicle_bike_detection_myriad_video.json | 3 +- .../lva_ai_extension/tests/test_parameters.py | 4 +- .../object_detection_myriad_gstreamer.json | 2 +- .../rest_api/get_pipelines_gstreamer.json | 36 ++++----- ...est_object_detection_myriad_gstreamer.json | 2 +- 31 files changed, 246 insertions(+), 218 deletions(-) diff --git a/pipelines/gstreamer/emotion_recognition/1/pipeline.json b/pipelines/gstreamer/emotion_recognition/1/pipeline.json index 6be4a27..6be86ff 100644 --- a/pipelines/gstreamer/emotion_recognition/1/pipeline.json +++ b/pipelines/gstreamer/emotion_recognition/1/pipeline.json @@ -22,22 +22,22 @@ "MYRIAD" ] }, - "model-instance-id-detection": { - "element": { - "name": "detection", - "property": "model-instance-id" - }, - "type": "string", - "default": "face_detection_retail0" - }, - "model-instance-id-classification": { - "element": { - "name": "classification", - "property": "model-instance-id" - }, - "type": "string", - "default": "" - }, + "detection-model-instance-id": { + "element": { + "name": "detection", + "property": "model-instance-id" + }, + "type": "string", + "default": "face_detection_retail0" + }, + "classification-model-instance-id": { + "element": { + "name": "classification", + "property": "model-instance-id" + }, + "type": "string", + "default": "" + }, "inference-interval": { "element": "detection", "type": "integer", diff --git a/pipelines/gstreamer/object_detection/1/pipeline.json b/pipelines/gstreamer/object_detection/1/pipeline.json index 63bd9bd..bd02f91 100644 --- a/pipelines/gstreamer/object_detection/1/pipeline.json +++ b/pipelines/gstreamer/object_detection/1/pipeline.json @@ -13,7 +13,7 @@ "element": "detection", "type": "string" }, - "model-instance-id": { + "detection-model-instance-id": { "element": "detection", "type": "string", "default": "inf0" diff --git a/pipelines/gstreamer/object_detection/2/pipeline.json b/pipelines/gstreamer/object_detection/2/pipeline.json index 48c6aaf..0ba3a2f 100644 --- a/pipelines/gstreamer/object_detection/2/pipeline.json +++ b/pipelines/gstreamer/object_detection/2/pipeline.json @@ -15,11 +15,11 @@ "element": "detection", "type": "string" }, - "model-instance-id": { - "element": "detection", - "type": "string", - "default": "inf0" - }, + "detection-model-instance-id": { + "element": "detection", + "type": "string", + "default": "inf0" + }, "inference-interval": { "element": "detection", "type": "integer", diff --git a/pipelines/gstreamer/object_detection/app_src_dst/pipeline.json b/pipelines/gstreamer/object_detection/app_src_dst/pipeline.json index f761fab..840c1dc 100644 --- a/pipelines/gstreamer/object_detection/app_src_dst/pipeline.json +++ b/pipelines/gstreamer/object_detection/app_src_dst/pipeline.json @@ -7,7 +7,7 @@ "parameters": { "type": "object", "properties": { - "model-instance-id": { + "detection-model-instance-id": { "element": "detection", "type": "string", "default": "" diff --git a/pipelines/gstreamer/object_detection/edgex/pipeline.json b/pipelines/gstreamer/object_detection/edgex/pipeline.json index d9bcde4..09d88d9 100644 --- a/pipelines/gstreamer/object_detection/edgex/pipeline.json +++ b/pipelines/gstreamer/object_detection/edgex/pipeline.json @@ -33,11 +33,11 @@ "element": "detection", "type": "string" }, - "model-instance-id": { - "element": "detection", - "type": "string", - "default": "inf0" - }, + "detection-model-instance-id": { + "element": "detection", + "type": "string", + "default": "inf0" + }, "inference-interval": { "element": "detection", "type": "integer", diff --git a/samples/lva_ai_extension/README.md b/samples/lva_ai_extension/README.md index 538b5e3..3c15221 100644 --- a/samples/lva_ai_extension/README.md +++ b/samples/lva_ai_extension/README.md @@ -128,16 +128,18 @@ Run with classification pipeline with iGPU inference specified via environment v ``` $ export PIPELINE_NAME=object_classification $ export PIPELINE_VERSION=vehicle_attributes_recognition -$ export PIPELINE_PARAMETERS={\"device\":\"GPU\"} +$ export PIPELINE_PARAMETERS='{"detection-device":"GPU","classification-device":"GPU"}' $ ./docker/run_server.sh ``` Notes: +* Parameter `device` has changed to `detection-device` for detection model and `classification-device` for classification model, refer to example above on how to set. * Only one pipeline can be enabled per container instance. * If selecting a pipeline both name and version must be specified * The `--debug` option selects debug pipelines that watermark inference results and saves images in `/tmp/vaserving/{--pipeline-version}/{timestamp}/` and can also be set using the environment variable DEBUG_PIPELINE * The `--parameters` option specifies pipeline parameters for the selected pipeline. It can be either a JSON string or the name of a file containing the JSON. See the parameters section of the [pipeline definition](/docs/defining_pipelines.md#pipeline-parameters) document for more details. The individual definition files for [object_detection](/samples/lva_ai_extension/pipelines/object_detection/person_vehicle_bike_detection/pipeline.json), [object_classification](/samples/lva_ai_extension/pipelines/object_classification/vehicle_attributes_recognition/pipeline.json), and [object_tracking](/samples/lva_ai_extension/pipelines/object_tracking/person_vehicle_bike_tracking/pipeline.json) contain the supported parameters for the pre-loaded pipelines. -* To enable GPU inference use `--parameters "{\"device\":\"GPU\"}" +* To enable GPU inference for detection `--parameters '{"detection-device":"GPU"}'` +* To enable MYRIAD(NCS2) inference for detection `--parameters '{"detection-device":"MYRIAD"}'` ### Debug Mode @@ -162,7 +164,7 @@ This will allow you to customize the deployment manifest for a given accelerator The run server script will automatically detect installed accelerators and provide access to their resources. Here we run the default pipeline with inference running on Intel® Integrated Graphics (be careful with escaping the JSON string) ```bash -$ ./docker/run_server.sh --pipeline-parameters '{\"device\":\"GPU\"}' +$ ./docker/run_server.sh --pipeline-parameters '{"detection-device":"GPU"}' ``` ### Logging diff --git a/samples/lva_ai_extension/deployment/deployment.gpu.grpc.template.json b/samples/lva_ai_extension/deployment/deployment.gpu.grpc.template.json index b5a049a..bd9c0b2 100755 --- a/samples/lva_ai_extension/deployment/deployment.gpu.grpc.template.json +++ b/samples/lva_ai_extension/deployment/deployment.gpu.grpc.template.json @@ -110,7 +110,7 @@ "Env":[ "PIPELINE_NAME=object_detection", "PIPELINE_VERSION=person_vehicle_bike_detection", - "PIPELINE_PARAMETERS=\"{\"device\":\"GPU\"}\"" + "PIPELINE_PARAMETERS=\"{\"detection-device\":\"GPU\"}\"" ], "HostConfig": { "Binds": [ diff --git a/samples/lva_ai_extension/deployment/deployment.hddl.grpc.template.json b/samples/lva_ai_extension/deployment/deployment.hddl.grpc.template.json index 9dc8651..6e6bc07 100755 --- a/samples/lva_ai_extension/deployment/deployment.hddl.grpc.template.json +++ b/samples/lva_ai_extension/deployment/deployment.hddl.grpc.template.json @@ -111,7 +111,7 @@ "Env":[ "PIPELINE_NAME=object_detection", "PIPELINE_VERSION=person_vehicle_bike_detection", - "PIPELINE_PARAMETERS= {\"device\":\"HDDL\"}" + "PIPELINE_PARAMETERS= {\"detection-device\":\"HDDL\"}" ], "HostConfig": { "Binds": [ diff --git a/samples/lva_ai_extension/deployment/deployment.movidius.grpc.template.json b/samples/lva_ai_extension/deployment/deployment.movidius.grpc.template.json index 5691cec..963c683 100755 --- a/samples/lva_ai_extension/deployment/deployment.movidius.grpc.template.json +++ b/samples/lva_ai_extension/deployment/deployment.movidius.grpc.template.json @@ -107,7 +107,7 @@ "Env":[ "PIPELINE_NAME=object_detection", "PIPELINE_VERSION=person_vehicle_bike_detection", - "PIPELINE_PARAMETERS= {\"device\":\"MYRIAD\"}" + "PIPELINE_PARAMETERS= {\"detection-device\":\"MYRIAD\"}" ], "HostConfig": { "Binds": [ diff --git a/samples/lva_ai_extension/docker/run_server.sh b/samples/lva_ai_extension/docker/run_server.sh index ecdfb39..dc640a0 100755 --- a/samples/lva_ai_extension/docker/run_server.sh +++ b/samples/lva_ai_extension/docker/run_server.sh @@ -56,7 +56,7 @@ while [[ "$#" -gt 0 ]]; do MODE="DEV" ;; *) - ENTRYPOINT_ARGS+="--entrypoint-args $1 " + ENTRYPOINT_ARGS+="--entrypoint-args '$1' " ;; esac @@ -78,11 +78,11 @@ if [ ! -z "$DEBUG_PIPELINE" ]; then fi if [ ! -z "$PARAMETERS" ]; then - ENV+="-e PARAMETERS=$PARAMETERS " + ENV+="-e PARAMETERS='$PARAMETERS' " fi if [ ! -z "$PIPELINE_PARAMETERS" ]; then - ENV+="-e PIPELINE_PARAMETERS=$PIPELINE_PARAMETERS " + ENV+="-e PIPELINE_PARAMETERS='$PIPELINE_PARAMETERS' " fi if [ ! -z "$GST_DEBUG" ]; then diff --git a/samples/lva_ai_extension/pipelines/object_classification/debug_vehicle_attributes_recognition/pipeline.json b/samples/lva_ai_extension/pipelines/object_classification/debug_vehicle_attributes_recognition/pipeline.json index 6e87e83..971263f 100755 --- a/samples/lva_ai_extension/pipelines/object_classification/debug_vehicle_attributes_recognition/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_classification/debug_vehicle_attributes_recognition/pipeline.json @@ -10,13 +10,18 @@ "parameters": { "type": "object", "properties": { - "device": { - "element": - [ {"name":"detection", - "property":"device"}, - {"name":"classification", - "property":"device"} - ], + "detection-device": { + "element": { + "name": "detection", + "property": "device" + }, + "type": "string" + }, + "classification-device": { + "element": { + "name": "classification", + "property": "device" + }, "type": "string" }, "batch-size": { @@ -79,22 +84,22 @@ ], "type": "string" }, - "model-instance-id-detection": { - "element": { - "name": "detection", - "property": "model-instance-id" - }, - "type": "string", - "default": "person_vehicle_bike_detection" - }, - "model-instance-id-classification": { - "element": { - "name": "classification", - "property": "model-instance-id" - }, - "type": "string", - "default": "vehicle_attributes_recognition" - }, + "detection-model-instance-id": { + "element": { + "name": "detection", + "property": "model-instance-id" + }, + "type": "string", + "default": "person_vehicle_bike_detection" + }, + "classification-model-instance-id": { + "element": { + "name": "classification", + "property": "model-instance-id" + }, + "type": "string", + "default": "vehicle_attributes_recognition" + }, "pre-process-backend": { "element": [ {"name":"detection", diff --git a/samples/lva_ai_extension/pipelines/object_classification/vehicle_attributes_recognition/pipeline.json b/samples/lva_ai_extension/pipelines/object_classification/vehicle_attributes_recognition/pipeline.json index d9fd118..e7619de 100755 --- a/samples/lva_ai_extension/pipelines/object_classification/vehicle_attributes_recognition/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_classification/vehicle_attributes_recognition/pipeline.json @@ -8,15 +8,20 @@ "parameters": { "type": "object", "properties": { - "device": { - "element": - [ {"name":"detection", - "property":"device"}, - {"name":"classification", - "property":"device"} - ], - "type": "string" + "detection-device": { + "element": { + "name": "detection", + "property": "device" }, + "type": "string" + }, + "classification-device": { + "element": { + "name": "classification", + "property": "device" + }, + "type": "string" + }, "batch-size": { "element": [ {"name":"detection", @@ -77,22 +82,22 @@ ], "type": "string" }, - "model-instance-id-detection": { - "element": { - "name": "detection", - "property": "model-instance-id" - }, - "type": "string", - "default": "person_vehicle_bike_detection" - }, - "model-instance-id-classification": { - "element": { - "name": "classification", - "property": "model-instance-id" - }, - "type": "string", - "default": "vehicle_attributes_recognition" - }, + "detection-model-instance-id": { + "element": { + "name": "detection", + "property": "model-instance-id" + }, + "type": "string", + "default": "person_vehicle_bike_detection" + }, + "classification-model-instance-id": { + "element": { + "name": "classification", + "property": "model-instance-id" + }, + "type": "string", + "default": "vehicle_attributes_recognition" + }, "pre-process-backend": { "element": [ {"name":"detection", diff --git a/samples/lva_ai_extension/pipelines/object_detection/debug_person_vehicle_bike_detection/pipeline.json b/samples/lva_ai_extension/pipelines/object_detection/debug_person_vehicle_bike_detection/pipeline.json index 7972bab..6cffb93 100755 --- a/samples/lva_ai_extension/pipelines/object_detection/debug_person_vehicle_bike_detection/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_detection/debug_person_vehicle_bike_detection/pipeline.json @@ -9,15 +9,21 @@ "parameters": { "type": "object", "properties": { - "device": { - "element": "detection", + "detection-device": { + "element": { + "name": "detection", + "property": "device" + }, "type": "string" }, - "model-instance-id": { - "element": "detection", - "type": "string", - "default": "person_vehicle_bike_detection" - }, + "detection-model-instance-id": { + "element": { + "name": "detection", + "property": "model-instance-id" + }, + "type": "string", + "default": "person_vehicle_bike_detection" + }, "batch-size": { "element": "detection", "type": "integer" diff --git a/samples/lva_ai_extension/pipelines/object_detection/person_vehicle_bike_detection/pipeline.json b/samples/lva_ai_extension/pipelines/object_detection/person_vehicle_bike_detection/pipeline.json index 59ae3c0..8001128 100755 --- a/samples/lva_ai_extension/pipelines/object_detection/person_vehicle_bike_detection/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_detection/person_vehicle_bike_detection/pipeline.json @@ -7,15 +7,21 @@ "parameters": { "type": "object", "properties": { - "device": { - "element": "detection", + "detection-device": { + "element": { + "name": "detection", + "property": "device" + }, "type": "string" }, - "model-instance-id": { - "element": "detection", - "type": "string", - "default": "person_vehicle_bike_detection" - }, + "detection-model-instance-id": { + "element": { + "name": "detection", + "property": "model-instance-id" + }, + "type": "string", + "default": "person_vehicle_bike_detection" + }, "batch-size": { "element": "detection", "type": "integer" @@ -46,10 +52,6 @@ "element": "detection", "type": "string" }, - "model-instance-id": { - "element": "detection", - "type": "string" - }, "pre-process-backend": { "element": "detection", "type": "string" diff --git a/samples/lva_ai_extension/pipelines/object_tracking/debug_person_vehicle_bike_tracking/pipeline.json b/samples/lva_ai_extension/pipelines/object_tracking/debug_person_vehicle_bike_tracking/pipeline.json index 6aee515..44dd621 100755 --- a/samples/lva_ai_extension/pipelines/object_tracking/debug_person_vehicle_bike_tracking/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_tracking/debug_person_vehicle_bike_tracking/pipeline.json @@ -11,15 +11,31 @@ "parameters": { "type": "object", "properties": { - "device": { + "detection-device": { + "element": { + "name": "detection", + "property": "device" + }, + "type": "string" + }, + "classification-device": { + "element": { + "name": "classification", + "property": "device" + }, + "type": "string" + }, + "tracking-device": { "element": - [ {"name":"detection", - "property":"device"}, - {"name":"classification", - "property":"device"} + [ {"name":"tracking", + "property":"device"} ], - "type": "string" - }, + "type": "string", + "enum": [ + "CPU", + "VPU" + ] + }, "batch-size": { "element": [ {"name":"detection", @@ -80,22 +96,22 @@ ], "type": "string" }, - "model-instance-id-detection": { - "element": { - "name": "detection", - "property": "model-instance-id" - }, - "type": "string", - "default": "person_vehicle_bike_detection" - }, - "model-instance-id-classification": { - "element": { - "name": "classification", - "property": "model-instance-id" - }, - "type": "string", - "default": "vehicle_attributes_recognition" - }, + "detection-model-instance-id": { + "element": { + "name": "detection", + "property": "model-instance-id" + }, + "type": "string", + "default": "person_vehicle_bike_detection" + }, + "classification-model-instance-id": { + "element": { + "name": "classification", + "property": "model-instance-id" + }, + "type": "string", + "default": "vehicle_attributes_recognition" + }, "pre-process-backend": { "element": [ {"name":"detection", @@ -152,17 +168,6 @@ "element": "classification", "type": "integer" }, - "gvatrack_device": { - "element": - [ {"name":"tracking", - "property":"device"} - ], - "type": "string", - "enum": [ - "CPU", - "VPU" - ] - }, "tracking-type": { "element": "tracking", "type": "string", diff --git a/samples/lva_ai_extension/pipelines/object_tracking/person_vehicle_bike_tracking/pipeline.json b/samples/lva_ai_extension/pipelines/object_tracking/person_vehicle_bike_tracking/pipeline.json index fe24ce0..5ce1258 100755 --- a/samples/lva_ai_extension/pipelines/object_tracking/person_vehicle_bike_tracking/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_tracking/person_vehicle_bike_tracking/pipeline.json @@ -9,15 +9,31 @@ "parameters": { "type": "object", "properties": { - "device": { + "detection-device": { + "element": { + "name": "detection", + "property": "device" + }, + "type": "string" + }, + "classification-device": { + "element": { + "name": "classification", + "property": "device" + }, + "type": "string" + }, + "tracking-device": { "element": - [ {"name":"detection", - "property":"device"}, - {"name":"classification", - "property":"device"} + [ {"name":"tracking", + "property":"device"} ], - "type": "string" - }, + "type": "string", + "enum": [ + "CPU", + "VPU" + ] + }, "batch-size": { "element": [ {"name":"detection", @@ -78,22 +94,22 @@ ], "type": "string" }, - "model-instance-id-detection": { - "element": { - "name": "detection", - "property": "model-instance-id" - }, - "type": "string", - "default": "person_vehicle_bike_detection" - }, - "model-instance-id-classification": { - "element": { - "name": "classification", - "property": "model-instance-id" - }, - "type": "string", - "default": "vehicle_attributes_recognition" - }, + "detection-model-instance-id": { + "element": { + "name": "detection", + "property": "model-instance-id" + }, + "type": "string", + "default": "person_vehicle_bike_detection" + }, + "classification-model-instance-id": { + "element": { + "name": "classification", + "property": "model-instance-id" + }, + "type": "string", + "default": "vehicle_attributes_recognition" + }, "pre-process-backend": { "element": [ {"name":"detection", @@ -150,17 +166,6 @@ "element": "classification", "type": "integer" }, - "gvatrack_device": { - "element": - [ {"name":"tracking", - "property":"device"} - ], - "type": "string", - "enum": [ - "CPU", - "VPU" - ] - }, "tracking-type": { "element": "tracking", "type": "string", diff --git a/samples/lva_ai_extension/server/media_graph_extension.py b/samples/lva_ai_extension/server/media_graph_extension.py index 5be1fa1..3025dbd 100644 --- a/samples/lva_ai_extension/server/media_graph_extension.py +++ b/samples/lva_ai_extension/server/media_graph_extension.py @@ -304,7 +304,8 @@ def ProcessMediaStream(self, requestIterator, context): pipeline_parameters = json.loads(self._pipeline_parameter_arg) final_pipeline_parameters.update(pipeline_parameters) except ValueError: - self._logger.error("Issue loading json parameters") + self._logger.error("Issue loading json parameters: {}".format(self._pipeline_parameter_arg)) + raise self._logger.info("Pipeline Name : {}".format(self._pipeline)) self._logger.info("Pipeline Version : {}".format(self._version)) diff --git a/samples/lva_ai_extension/server/readme.md b/samples/lva_ai_extension/server/readme.md index b68e041..ca95a40 100644 --- a/samples/lva_ai_extension/server/readme.md +++ b/samples/lva_ai_extension/server/readme.md @@ -1,10 +1,12 @@ To build and run the server +``` +../docker/build.sh +../docker/run_server.sh +``` -./build.sh +To communicate and test, use the lva client that is available from new terminal +``` +../docker/run_client.sh +``` -NOTE: At this time, build the main VA Serving Docker image with openvino base image before building this Dockerfile -./docker/build.sh --base openvino/ubuntu18_data_dev:2020.4 - -./run.sh - -To communicate and test, use the lva client that is available +Refer to [Documentation here](../README.md) for more details and settings. \ No newline at end of file diff --git a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_classification_vehicle_attributes_recognition_cpu.json b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_classification_vehicle_attributes_recognition_cpu.json index 2db198b..f8fe629 100644 --- a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_classification_vehicle_attributes_recognition_cpu.json +++ b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_classification_vehicle_attributes_recognition_cpu.json @@ -3,8 +3,8 @@ "pipeline_name":"object_classification", "pipeline_version":"vehicle_attributes_recognition", "pipeline_parameters": { - "device":"CPU", - "model-instance-id":"" + "detection-device": "CPU", + "classification-device":"CPU" }, "sleep_period":0.25, "port":5001 diff --git a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_classification_vehicle_attributes_recognition_cpu_video_golden_truth.json b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_classification_vehicle_attributes_recognition_cpu_video_golden_truth.json index c8cc91a..6c670ce 100644 --- a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_classification_vehicle_attributes_recognition_cpu_video_golden_truth.json +++ b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_classification_vehicle_attributes_recognition_cpu_video_golden_truth.json @@ -3,8 +3,8 @@ "pipeline_name": "object_classification", "pipeline_version": "vehicle_attributes_recognition", "pipeline_parameters": { - "device": "CPU", - "model-instance-id": "" + "detection-device": "CPU", + "classification-device":"CPU" }, "sleep_period": 0.25, "port": 5001 diff --git a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_detection_person_vehicle_bike_detection_cpu_multiple_clients.json b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_detection_person_vehicle_bike_detection_cpu_multiple_clients.json index d625268..ca7573a 100644 --- a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_detection_person_vehicle_bike_detection_cpu_multiple_clients.json +++ b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_detection_person_vehicle_bike_detection_cpu_multiple_clients.json @@ -3,8 +3,7 @@ "pipeline_name":"object_detection", "pipeline_version":"person_vehicle_bike_detection", "pipeline_parameters": { - "device":"CPU", - "model-instance-id":"" + "detection-device":"CPU" }, "sleep_period":0.25, "port":5001 diff --git a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_detection_person_vehicle_bike_detection_cpu_video.json b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_detection_person_vehicle_bike_detection_cpu_video.json index 154fc6f..311f285 100644 --- a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_detection_person_vehicle_bike_detection_cpu_video.json +++ b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_detection_person_vehicle_bike_detection_cpu_video.json @@ -3,8 +3,7 @@ "pipeline_name":"object_detection", "pipeline_version":"person_vehicle_bike_detection", "pipeline_parameters": { - "device":"CPU", - "model-instance-id":"" + "detection-device":"CPU" }, "sleep_period":0.25, "port":5001 diff --git a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_detection_person_vehicle_bike_detection_cpu_video_golden_truth.json b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_detection_person_vehicle_bike_detection_cpu_video_golden_truth.json index e20c9b2..f060039 100644 --- a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_detection_person_vehicle_bike_detection_cpu_video_golden_truth.json +++ b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_detection_person_vehicle_bike_detection_cpu_video_golden_truth.json @@ -3,8 +3,7 @@ "pipeline_name": "object_detection", "pipeline_version": "person_vehicle_bike_detection", "pipeline_parameters": { - "device": "CPU", - "model-instance-id": "" + "detection-device": "CPU" }, "sleep_period": 0.25, "port": 5001 diff --git a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_tracking_person_vehicle_bike_tracking_cpu.json b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_tracking_person_vehicle_bike_tracking_cpu.json index 6cfd313..b9a2c24 100644 --- a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_tracking_person_vehicle_bike_tracking_cpu.json +++ b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_tracking_person_vehicle_bike_tracking_cpu.json @@ -3,8 +3,8 @@ "pipeline_name":"object_tracking", "pipeline_version":"person_vehicle_bike_tracking", "pipeline_parameters": { - "device":"CPU", - "model-instance-id":"" + "detection-device": "CPU", + "classification-device":"CPU" }, "sleep_period":0.25, "port":5001 diff --git a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_tracking_person_vehicle_bike_tracking_cpu_video_golden_truth.json b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_tracking_person_vehicle_bike_tracking_cpu_video_golden_truth.json index 82db33d..73222b0 100644 --- a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_tracking_person_vehicle_bike_tracking_cpu_video_golden_truth.json +++ b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_tracking_person_vehicle_bike_tracking_cpu_video_golden_truth.json @@ -3,8 +3,8 @@ "pipeline_name": "object_tracking", "pipeline_version": "person_vehicle_bike_tracking", "pipeline_parameters": { - "device": "CPU", - "model-instance-id": "" + "detection-device": "CPU", + "classification-device":"CPU" }, "sleep_period": 0.25, "port": 5001 diff --git a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/gpu/object_detection_person_vehicle_bike_detection_gpu_video.json b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/gpu/object_detection_person_vehicle_bike_detection_gpu_video.json index 5cb9d57..2e9796c 100644 --- a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/gpu/object_detection_person_vehicle_bike_detection_gpu_video.json +++ b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/gpu/object_detection_person_vehicle_bike_detection_gpu_video.json @@ -3,8 +3,7 @@ "pipeline_name":"object_detection", "pipeline_version":"person_vehicle_bike_detection", "pipeline_parameters": { - "device":"GPU", - "model-instance-id":"" + "detection-device":"GPU" }, "sleep_period":0.25, "port":5001 diff --git a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/myriad/object_detection_person_vehicle_bike_detection_myriad_video.json b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/myriad/object_detection_person_vehicle_bike_detection_myriad_video.json index b00678b..38911fb 100644 --- a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/myriad/object_detection_person_vehicle_bike_detection_myriad_video.json +++ b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/myriad/object_detection_person_vehicle_bike_detection_myriad_video.json @@ -3,8 +3,7 @@ "pipeline_name":"object_detection", "pipeline_version":"person_vehicle_bike_detection", "pipeline_parameters": { - "device":"MYRIAD", - "model-instance-id":"" + "detection-device":"MYRIAD" }, "sleep_period":0.25, "port":5001 diff --git a/samples/lva_ai_extension/tests/test_parameters.py b/samples/lva_ai_extension/tests/test_parameters.py index b8eac64..8d90d49 100644 --- a/samples/lva_ai_extension/tests/test_parameters.py +++ b/samples/lva_ai_extension/tests/test_parameters.py @@ -25,7 +25,7 @@ def parameter_run_client_server(helpers, sleep_period, port, parameters): helpers.run_client(client_params) def test_lva_parameter_string_argument(helpers, sleep_period=0.25, port=5001, - parameter_string="{\"device\":\"CPU\"}"): + parameter_string="{\"detection-device\":\"CPU\"}"): parameter_run_client_server(helpers, sleep_period, port, parameter_string) def test_lva_parameter_file_argument(helpers, sleep_period=0.25, port=5001): @@ -33,6 +33,6 @@ def test_lva_parameter_file_argument(helpers, sleep_period=0.25, port=5001): workdir_path = tempfile.TemporaryDirectory() parameter_path = os.path.join(workdir_path.name, "parameters.json") with open(parameter_path, "w") as parameter_file: - parameter_file.write("{\"device\":\"CPU\"}") + parameter_file.write("{\"detection-device\":\"CPU\"}") parameter_run_client_server(helpers, sleep_period, port, parameter_path) diff --git a/tests/test_cases/pipeline_execution/myriad/object_detection_myriad_gstreamer.json b/tests/test_cases/pipeline_execution/myriad/object_detection_myriad_gstreamer.json index 4a202c6..e6f885c 100644 --- a/tests/test_cases/pipeline_execution/myriad/object_detection_myriad_gstreamer.json +++ b/tests/test_cases/pipeline_execution/myriad/object_detection_myriad_gstreamer.json @@ -16,7 +16,7 @@ }, "parameters": { "device": "MYRIAD", - "model-instance-id":"" + "detection-model-instance-id":"" } }, "numerical_tolerance": 0.3, diff --git a/tests/test_cases/rest_api/get_pipelines_gstreamer.json b/tests/test_cases/rest_api/get_pipelines_gstreamer.json index 29e1c66..ce122c4 100644 --- a/tests/test_cases/rest_api/get_pipelines_gstreamer.json +++ b/tests/test_cases/rest_api/get_pipelines_gstreamer.json @@ -38,10 +38,10 @@ "minimum": 1, "type": "integer" }, - "model-instance-id": { - "default": "inf0", + "detection-model-instance-id": { "element": "detection", - "type": "string" + "type": "string", + "default": "inf0" }, "n-threads": { "default": 1, @@ -78,7 +78,7 @@ "name": "object_detection", "parameters": { "properties": { - "model-instance-id": { + "detection-model-instance-id": { "default": "", "element": "detection", "type": "string" @@ -118,10 +118,10 @@ "minimum": 1, "type": "integer" }, - "model-instance-id": { - "default": "inf0", + "detection-model-instance-id": { "element": "detection", - "type": "string" + "type": "string", + "default": "inf0" }, "n-threads": { "default": 1, @@ -171,10 +171,10 @@ "minimum": 1, "type": "integer" }, - "model-instance-id": { - "default": "inf0", + "detection-model-instance-id": { "element": "detection", - "type": "string" + "type": "string", + "default": "inf0" }, "n-threads": { "default": 1, @@ -259,21 +259,21 @@ "minimum": 1, "type": "integer" }, - "model-instance-id-classification": { - "default": "", + "detection-model-instance-id": { "element": { - "name": "classification", + "name": "detection", "property": "model-instance-id" }, - "type": "string" + "type": "string", + "default": "face_detection_retail0" }, - "model-instance-id-detection": { - "default": "face_detection_retail0", + "classification-model-instance-id": { "element": { - "name": "detection", + "name": "classification", "property": "model-instance-id" }, - "type": "string" + "type": "string", + "default": "" }, "n-threads": { "default": 1, diff --git a/tests/test_cases/rest_execution/myriad/rest_object_detection_myriad_gstreamer.json b/tests/test_cases/rest_execution/myriad/rest_object_detection_myriad_gstreamer.json index f07a905..85c25a5 100644 --- a/tests/test_cases/rest_execution/myriad/rest_object_detection_myriad_gstreamer.json +++ b/tests/test_cases/rest_execution/myriad/rest_object_detection_myriad_gstreamer.json @@ -13,7 +13,7 @@ }, "parameters": { "device": "MYRIAD", - "model-instance-id":"" + "detection-model-instance-id":"" } } }, From 1c63cee1b0f9270508e5fff5f928156a6566e0aa Mon Sep 17 00:00:00 2001 From: "Khoshnevissan, Farid" Date: Fri, 15 Jan 2021 17:09:36 -0800 Subject: [PATCH 34/50] Add documentation on model-instance-id --- docs/defining_pipelines.md | 34 +++++++++++++++++++++++++++++++++- 1 file changed, 33 insertions(+), 1 deletion(-) diff --git a/docs/defining_pipelines.md b/docs/defining_pipelines.md index 7634b14..dc3e88c 100644 --- a/docs/defining_pipelines.md +++ b/docs/defining_pipelines.md @@ -117,7 +117,7 @@ The `object_detection` template demonstrates how to set the used to detect objects in a video frame. ``` -gvadetect model-instance-id=inf0 model={models[object_detection][1][network]} model-proc={models[object_detection][1][proc]} name=detection +gvadetect model-instance-id=detect0 model={models[object_detection][1][network]} model-proc={models[object_detection][1][proc]} name=detection ``` The `model` and `model-proc` properties reference file paths to the @@ -127,6 +127,38 @@ python dictionary associating model names and versions to their absolute paths enabling pipeline templates to reference them by name. More details are provided in the [Deep Learning Models](#deep-learning-models) section. +#### Special Handling of Model-Instance-ID in OpenVINO Gstreamer elements + +In the section above, the example code for gvadetect has the element property + +``` +model-instance-id= +``` + +This is a special optional property that will hold the model in memory instead +of releasing it when the pipeline completes. This will save time and memory +usage when launching the same pipeline multiple times. The model is associated +with the given ID to allow subsequent runs to use the same model instance. + +Its important to be careful when using this property when dealing with +multiple hardware targets as models are loaded for a specific device. For +example, if a model is loaded on the CPU and is given an instance ID of 'inf0', +then that ID must not be used to run the model on the GPU. + +When using the same pipeline with different accelerators, the model-instance-id +must be parameterized so that a unique id can be provided by the request for each +accelerator + +In the same vein as the hardware targets mentioned above, when working with +various models ensure that pipelines have unique IDs for each model to prevent +overloading models to the same instance-id. For example, if you have a +detection model and a classification model, they can not have the same +model-instance-id value, in any VA-Serving pipeline. + +Different pipelines can have the same model-instance-id value, as long as +the model is the same one for all instances of that id, and for the same +hardware target + #### More Information For more information and examples of media analytics pipelines created From 6b8f9696510bcfb9085c2350feb18c781a6125dd Mon Sep 17 00:00:00 2001 From: darrin Date: Wed, 13 Jan 2021 22:22:41 -0800 Subject: [PATCH 35/50] Removed model-proc from sample and reference pipelines. Updated documentation. --- docs/changing_object_detection_models.md | 2 +- docs/defining_pipelines.md | 9 ++++++--- pipelines/gstreamer/audio_detection/1/pipeline.json | 2 +- pipelines/gstreamer/emotion_recognition/1/pipeline.json | 4 ++-- pipelines/gstreamer/object_detection/1/pipeline.json | 2 +- pipelines/gstreamer/object_detection/2/pipeline.json | 2 +- .../gstreamer/object_detection/app_src_dst/pipeline.json | 2 +- pipelines/gstreamer/object_detection/edgex/pipeline.json | 2 +- .../debug_vehicle_attributes_recognition/pipeline.json | 4 ++-- .../vehicle_attributes_recognition/pipeline.json | 4 ++-- .../debug_person_vehicle_bike_detection/pipeline.json | 2 +- .../person_vehicle_bike_detection/pipeline.json | 2 +- .../debug_person_vehicle_bike_tracking/pipeline.json | 4 ++-- .../person_vehicle_bike_tracking/pipeline.json | 4 ++-- 14 files changed, 24 insertions(+), 21 deletions(-) diff --git a/docs/changing_object_detection_models.md b/docs/changing_object_detection_models.md index f052300..d59d3ec 100644 --- a/docs/changing_object_detection_models.md +++ b/docs/changing_object_detection_models.md @@ -106,7 +106,7 @@ $ cp -r pipelines/gstreamer/object_detection/1 pipelines/gstreamer/object_detect The pipeline template is shown below: ``` "template": ["urisourcebin name=source ! concat name=c ! decodebin ! video/x-raw ! videoconvert name=videoconvert", - " ! gvadetect model-instance-id=inf0 model={models[object_detection][1][network]} model-proc={models[object_detection][1][proc]} name=detection", + " ! gvadetect model={models[object_detection][1][network]} name=detection", " ! gvametaconvert name=metaconvert ! queue ! gvametapublish name=destination", " ! appsink name=appsink" ] diff --git a/docs/defining_pipelines.md b/docs/defining_pipelines.md index dc3e88c..cb02cb2 100644 --- a/docs/defining_pipelines.md +++ b/docs/defining_pipelines.md @@ -86,7 +86,7 @@ the calling application. **Example:** ``` "template": ["urisourcebin name=source ! concat name=c ! decodebin ! video/x-raw ! videoconvert name=videoconvert", - " ! gvadetect model-instance-id=inf0 model={models[object_detection][1][network]} model-proc={models[object_detection][1][proc]} name=detection", + " ! gvadetect model={models[object_detection][1][network]} name=detection", " ! gvametaconvert name=metaconvert ! queue ! gvametapublish name=destination", " ! appsink name=appsink" ] @@ -117,7 +117,7 @@ The `object_detection` template demonstrates how to set the used to detect objects in a video frame. ``` -gvadetect model-instance-id=detect0 model={models[object_detection][1][network]} model-proc={models[object_detection][1][proc]} name=detection +gvadetect model={models[object_detection][1][network]} model-proc={models[object_detection][1][proc]} name=detection ``` The `model` and `model-proc` properties reference file paths to the @@ -125,7 +125,7 @@ deep learning model as discovered and populated by the Video Analytics Serving `model_manager` module. The `model_manager` module provides a python dictionary associating model names and versions to their absolute paths enabling pipeline templates to reference them by -name. More details are provided in the [Deep Learning Models](#deep-learning-models) section. +name. You can use the `model-proc` property to point to custom model-proc by specifying absolute path. More details are provided in the [Deep Learning Models](#deep-learning-models) section. #### Special Handling of Model-Instance-ID in OpenVINO Gstreamer elements @@ -615,6 +615,9 @@ filters make use of an additional JSON file specifying the input and output processing instructions for a model. Processing instructions include details such as the expected color format and resolution of the input as well labels to associate with a models outputs. +Video Analytics Serving automatically looks for this file in the path +`models/model-alias/model-version/*.json`. Note that the model manager will +fail to load if there are multiple ".json" model-proc files in this directory. ### DL Streamer For more information on DL Streamer `model-proc` files and samples for diff --git a/pipelines/gstreamer/audio_detection/1/pipeline.json b/pipelines/gstreamer/audio_detection/1/pipeline.json index 424ae01..1beecc4 100755 --- a/pipelines/gstreamer/audio_detection/1/pipeline.json +++ b/pipelines/gstreamer/audio_detection/1/pipeline.json @@ -4,7 +4,7 @@ "template": ["urisourcebin name=\"source\" ! decodebin ! audioresample ! audioconvert", " ! audio/x-raw, channels=1,format=S16LE,rate=16000 ! audiomixer name=\"audiomixer\"", " ! level name=\"level\"", - " ! gvaaudiodetect model=\"{models[audio_detection][1][network]}\" model-proc=\"{models[audio_detection][1][proc]}\" name=\"detection\"", + " ! gvaaudiodetect model=\"{models[audio_detection][1][network]}\" name=\"detection\"", " ! gvametaconvert name=metaconvert ! gvametapublish name=destination", " ! appsink name=appsink" ], diff --git a/pipelines/gstreamer/emotion_recognition/1/pipeline.json b/pipelines/gstreamer/emotion_recognition/1/pipeline.json index 6be86ff..b86973d 100644 --- a/pipelines/gstreamer/emotion_recognition/1/pipeline.json +++ b/pipelines/gstreamer/emotion_recognition/1/pipeline.json @@ -2,8 +2,8 @@ "type": "GStreamer", "template": ["urisourcebin name=source ! concat name=c ! decodebin ! video/x-raw ", " ! videoconvert name=videoconvert", - " ! gvadetect model={models[face_detection_retail][1][network]} model-proc={models[face_detection_retail][1][proc]} name=detection", - " ! queue ! gvaclassify model={models[emotion_recognition][1][network]} model-proc={models[emotion_recognition][1][proc]} name=classification", + " ! gvadetect model={models[face_detection_retail][1][network]} name=detection", + " ! queue ! gvaclassify model={models[emotion_recognition][1][network]} name=classification", " ! queue ! gvametaconvert name=metaconvert ! queue ! gvametapublish name=destination", " ! appsink name=appsink" ], diff --git a/pipelines/gstreamer/object_detection/1/pipeline.json b/pipelines/gstreamer/object_detection/1/pipeline.json index bd02f91..a258a4f 100644 --- a/pipelines/gstreamer/object_detection/1/pipeline.json +++ b/pipelines/gstreamer/object_detection/1/pipeline.json @@ -1,7 +1,7 @@ { "type": "GStreamer", "template": ["urisourcebin name=source ! concat name=c ! decodebin ! video/x-raw ! videoconvert name=videoconvert", - " ! gvadetect model={models[object_detection][1][network]} model-proc={models[object_detection][1][proc]} name=detection", + " ! gvadetect model={models[object_detection][1][network]} name=detection", " ! gvametaconvert name=metaconvert ! queue ! gvametapublish name=destination", " ! appsink name=appsink" ], diff --git a/pipelines/gstreamer/object_detection/2/pipeline.json b/pipelines/gstreamer/object_detection/2/pipeline.json index 0ba3a2f..8c411bc 100644 --- a/pipelines/gstreamer/object_detection/2/pipeline.json +++ b/pipelines/gstreamer/object_detection/2/pipeline.json @@ -2,7 +2,7 @@ "type": "GStreamer", "template": ["urisourcebin name=source ! tee name=t ! queue ! decodebin ! video/x-raw ! videoconvert name=videoconvert", " ! queue", - " ! gvadetect model={models[object_detection][1][network]} model-proc={models[object_detection][1][proc]} name=detection", + " ! gvadetect model={models[object_detection][1][network]} name=detection", " ! gvametaconvert name=metaconvert ! queue ! gvametapublish name=destination", " ! appsink name=appsink", " t. ! queue ! qtdemux ! splitmuxsink name=splitmuxsink" diff --git a/pipelines/gstreamer/object_detection/app_src_dst/pipeline.json b/pipelines/gstreamer/object_detection/app_src_dst/pipeline.json index 840c1dc..b15c4bd 100644 --- a/pipelines/gstreamer/object_detection/app_src_dst/pipeline.json +++ b/pipelines/gstreamer/object_detection/app_src_dst/pipeline.json @@ -1,7 +1,7 @@ { "type": "GStreamer", "template": ["appsrc name=source", - " ! gvadetect model={models[object_detection][1][network]} model-proc={models[object_detection][1][proc]} name=detection", + " ! gvadetect model={models[object_detection][1][network]} name=detection", " ! appsink name=destination"], "description": "Object Detection Pipeline", "parameters": { diff --git a/pipelines/gstreamer/object_detection/edgex/pipeline.json b/pipelines/gstreamer/object_detection/edgex/pipeline.json index 09d88d9..151cee5 100644 --- a/pipelines/gstreamer/object_detection/edgex/pipeline.json +++ b/pipelines/gstreamer/object_detection/edgex/pipeline.json @@ -1,7 +1,7 @@ { "type": "GStreamer", "template": ["urisourcebin name=source ! concat name=c ! decodebin ! video/x-raw ! videoconvert name=videoconvert", - " ! gvadetect model={models[object_detection][1][network]} model-proc={models[object_detection][1][proc]} name=detection", + " ! gvadetect model={models[object_detection][1][network]} name=detection", " ! gvametaconvert name=metaconvert ", " ! gvapython name=edgexbridge module=/home/video-analytics-serving/extensions/edgex/edgex_transform.py class=Bridge", " ! queue ! gvametapublish name=destination", diff --git a/samples/lva_ai_extension/pipelines/object_classification/debug_vehicle_attributes_recognition/pipeline.json b/samples/lva_ai_extension/pipelines/object_classification/debug_vehicle_attributes_recognition/pipeline.json index 971263f..bdc5cd7 100755 --- a/samples/lva_ai_extension/pipelines/object_classification/debug_vehicle_attributes_recognition/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_classification/debug_vehicle_attributes_recognition/pipeline.json @@ -1,8 +1,8 @@ { "type": "GStreamer", "template": ["appsrc name=source", - " ! gvadetect model={models[person_vehicle_bike_detection][1][network]} model-proc={models[person_vehicle_bike_detection][1][proc]} name=detection", - " ! gvaclassify model={models[vehicle_attributes_recognition][1][network]} model-proc={models[vehicle_attributes_recognition][1][proc]} name=classification", + " ! gvadetect model={models[person_vehicle_bike_detection][1][network]} name=detection", + " ! gvaclassify model={models[vehicle_attributes_recognition][1][network]} name=classification", " ! tee name = t ! queue ! gvawatermark ! videoconvert ! jpegenc", " ! multifilesink name=filesink t. ! queue", " ! appsink name=destination"], diff --git a/samples/lva_ai_extension/pipelines/object_classification/vehicle_attributes_recognition/pipeline.json b/samples/lva_ai_extension/pipelines/object_classification/vehicle_attributes_recognition/pipeline.json index e7619de..ca92f31 100755 --- a/samples/lva_ai_extension/pipelines/object_classification/vehicle_attributes_recognition/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_classification/vehicle_attributes_recognition/pipeline.json @@ -1,8 +1,8 @@ { "type": "GStreamer", "template": ["appsrc name=source", - " ! gvadetect model={models[person_vehicle_bike_detection][1][network]} model-proc={models[person_vehicle_bike_detection][1][proc]} name=detection", - " ! gvaclassify model={models[vehicle_attributes_recognition][1][network]} model-proc={models[vehicle_attributes_recognition][1][proc]} name=classification", + " ! gvadetect model={models[person_vehicle_bike_detection][1][network]} name=detection", + " ! gvaclassify model={models[vehicle_attributes_recognition][1][network]} name=classification", " ! appsink name=destination"], "description": "Object Classification using gstreamer gvadetect & gvaclassify elements, check more about gvadetect at https://github.com/openvinotoolkit/dlstreamer_gst/wiki/gvadetect and gvaclassify at https://github.com/openvinotoolkit/dlstreamer_gst/wiki/gvaclassify", "parameters": { diff --git a/samples/lva_ai_extension/pipelines/object_detection/debug_person_vehicle_bike_detection/pipeline.json b/samples/lva_ai_extension/pipelines/object_detection/debug_person_vehicle_bike_detection/pipeline.json index 6cffb93..8928f59 100755 --- a/samples/lva_ai_extension/pipelines/object_detection/debug_person_vehicle_bike_detection/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_detection/debug_person_vehicle_bike_detection/pipeline.json @@ -1,7 +1,7 @@ { "type": "GStreamer", "template": ["appsrc name=source", - " ! gvadetect model={models[person_vehicle_bike_detection][1][network]} model-proc={models[person_vehicle_bike_detection][1][proc]} name=detection", + " ! gvadetect model={models[person_vehicle_bike_detection][1][network]} name=detection", " ! tee name = t ! queue ! gvawatermark ! videoconvert ! jpegenc", " ! multifilesink name=filesink t. ! queue", " ! appsink name=destination"], diff --git a/samples/lva_ai_extension/pipelines/object_detection/person_vehicle_bike_detection/pipeline.json b/samples/lva_ai_extension/pipelines/object_detection/person_vehicle_bike_detection/pipeline.json index 8001128..9ebb680 100755 --- a/samples/lva_ai_extension/pipelines/object_detection/person_vehicle_bike_detection/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_detection/person_vehicle_bike_detection/pipeline.json @@ -1,7 +1,7 @@ { "type": "GStreamer", "template": ["appsrc name=source", - " ! gvadetect model={models[person_vehicle_bike_detection][1][network]} model-proc={models[person_vehicle_bike_detection][1][proc]} name=detection", + " ! gvadetect model={models[person_vehicle_bike_detection][1][network]} name=detection", " ! appsink name=destination"], "description": "Object Detection using gstreamer gvadetect element, check more about gvadetect at https://github.com/openvinotoolkit/dlstreamer_gst/wiki/gvadetect", "parameters": { diff --git a/samples/lva_ai_extension/pipelines/object_tracking/debug_person_vehicle_bike_tracking/pipeline.json b/samples/lva_ai_extension/pipelines/object_tracking/debug_person_vehicle_bike_tracking/pipeline.json index 44dd621..b2da0dd 100755 --- a/samples/lva_ai_extension/pipelines/object_tracking/debug_person_vehicle_bike_tracking/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_tracking/debug_person_vehicle_bike_tracking/pipeline.json @@ -1,9 +1,9 @@ { "type": "GStreamer", "template": ["appsrc name=source", - " ! gvadetect model={models[person_vehicle_bike_detection][1][network]} model-proc={models[person_vehicle_bike_detection][1][proc]} name=detection", + " ! gvadetect model={models[person_vehicle_bike_detection][1][network]} name=detection", " ! queue ! gvatrack name=tracking", - " ! gvaclassify model={models[vehicle_attributes_recognition][1][network]} model-proc={models[vehicle_attributes_recognition][1][proc]} name=classification", + " ! gvaclassify model={models[vehicle_attributes_recognition][1][network]} name=classification", " ! tee name = t ! queue ! gvawatermark ! videoconvert ! jpegenc", " ! multifilesink name=filesink t. ! queue", " ! appsink name=destination"], diff --git a/samples/lva_ai_extension/pipelines/object_tracking/person_vehicle_bike_tracking/pipeline.json b/samples/lva_ai_extension/pipelines/object_tracking/person_vehicle_bike_tracking/pipeline.json index 5ce1258..42b45ca 100755 --- a/samples/lva_ai_extension/pipelines/object_tracking/person_vehicle_bike_tracking/pipeline.json +++ b/samples/lva_ai_extension/pipelines/object_tracking/person_vehicle_bike_tracking/pipeline.json @@ -1,9 +1,9 @@ { "type": "GStreamer", "template": ["appsrc name=source", - " ! gvadetect model={models[person_vehicle_bike_detection][1][network]} model-proc={models[person_vehicle_bike_detection][1][proc]} name=detection", + " ! gvadetect model={models[person_vehicle_bike_detection][1][network]} name=detection", " ! queue ! gvatrack name=tracking", - " ! gvaclassify model={models[vehicle_attributes_recognition][1][network]} model-proc={models[vehicle_attributes_recognition][1][proc]} name=classification", + " ! gvaclassify model={models[vehicle_attributes_recognition][1][network]} name=classification", " ! appsink name=destination"], "description": "Object Tracking using gstreamer gvadetect, gvaclassify & gvatrack elements, check more about gvadetect at https://github.com/openvinotoolkit/dlstreamer_gst/wiki/gvadetect, gvaclassify at https://github.com/openvinotoolkit/dlstreamer_gst/wiki/gvaclassify and gvatrack at https://github.com/openvinotoolkit/dlstreamer_gst/wiki/gvatrack", "parameters": { From 909a74060fe1f837b639a07e268a914ff50bc6c6 Mon Sep 17 00:00:00 2001 From: Thanaji Rao Thakkalapelli Date: Tue, 19 Jan 2021 15:23:13 -0800 Subject: [PATCH 36/50] change topologyfile to github topology url --- samples/lva_ai_extension/topologies/operations.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/samples/lva_ai_extension/topologies/operations.json b/samples/lva_ai_extension/topologies/operations.json index 121d4cb..7957c8d 100644 --- a/samples/lva_ai_extension/topologies/operations.json +++ b/samples/lva_ai_extension/topologies/operations.json @@ -4,7 +4,7 @@ { "opName": "GraphTopologySet", "opParams": { - "topologyFile": "" + "topologyUrl": "https://raw.githubusercontent.com/intel/video-analytics-serving/master/samples/lva_ai_extension/topologies/topology.json" } }, { From 310b97f9381fd6cdbf328badceaa616a69b570c3 Mon Sep 17 00:00:00 2001 From: "Bruce, Henry" Date: Wed, 20 Jan 2021 14:45:38 -0800 Subject: [PATCH 37/50] Clarify that pipelines build directory must be within docker build context --- docs/build_script_reference.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/build_script_reference.md b/docs/build_script_reference.md index 4d17815..c0c704e 100644 --- a/docs/build_script_reference.md +++ b/docs/build_script_reference.md @@ -25,7 +25,7 @@ usage: build.sh [--environment-file read and set environment variables from a file. Can be supplied multiple times.] [--dry-run print docker commands without running] ``` -All command line options are optional. Details of key options and their default values are shown below: +All command line options are optional. Details of key options and their default values are shown below: ## Framework (--framework) VA Serving can use either `gstreamer` or `ffmpeg` for pipeline construction. Select framework with `--framework` option. Default is `gstreamer`. @@ -33,13 +33,13 @@ VA Serving can use either `gstreamer` or `ffmpeg` for pipeline construction. Sel This is the image that docker builds on. It must contain the full set of framework dependencies needed for either [DL Streamer](https://github.com/opencv/gst-video-analytics) or [FFmpeg Video Analytics](https://github.com/VCDP/FFmpeg-patch) and must match the framework selected with the `--framework` option. If a base image is not defined you must provide the location of the Dockerfile to build the base image (see `--base-build-context` and `--base-build-dockerfile`). ## Base Build Context (--base-build-context) -This option is used in conjunction with `--base-build-dockerfile` to specify the docker build file and its context. It must be a git repo URL, path to tarball or path to locally cloned folder. +This option is used in conjunction with `--base-build-dockerfile` to specify the docker build file and its context. It must be a git repo URL, path to tarball or path to locally cloned folder. ## Base Build Dockerfile (--base-build-dockerfile) This option is used in conjunction with `--base-build-context` to specify the docker build file and its context. Default values are framework dependent. If framework is `gstreamer` the dockerfile for `DL Streamer` is selected, otherwise the dockerfile is set for the `FFmpeg Video Analytics` image. ## Pipeline Directory (--pipelines) -Relative path to VA Serving pipelines directory from root of video-analytics-serving project. If not specified, [sample pipelines](../pipelines/gstreamer) are included in the image. If set to `NONE` no pipelines are included and the user must ensure pipelines are made available at runtime by volume mounting. +Path to VA Serving pipelines. Path must be within docker build context which defaults to the root of the video-analytics-serving project. If not specified, [sample pipelines](../pipelines/gstreamer) are included in the image. If set to `NONE` no pipelines are included and the user must ensure pipelines are made available at runtime by [volume mounting](running_video_analytics_serving.md#selecting-pipelines-and-models-at-runtime). ## Model Directory/File List (--models) This option can be used to specify path to models directory or a model list file. When its a directory, models used by pipelines are expected to be in this directory. When its a file, the models listed in the file are downloaded and converted to IR format if needed by the [model download tool](../tools/model_downloader/README.md) during build time. If nothing is specified, default models listed in the file `models_list/models.list.yml` are downloaded, converted to IR format if needed and included in the image. If set to `NONE` no models are included and the user must ensure models are made available at runtime by volume mounting. From 9baf93e2d5d47ea915abc1d89944d6dd05fe9ded Mon Sep 17 00:00:00 2001 From: "Bruce, Henry" Date: Wed, 20 Jan 2021 18:58:18 -0800 Subject: [PATCH 38/50] Document NCS2 "single model" limitation --- docs/running_video_analytics_serving.md | 8 ++++++-- samples/lva_ai_extension/README.md | 3 ++- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/docs/running_video_analytics_serving.md b/docs/running_video_analytics_serving.md index b87e590..580e147 100644 --- a/docs/running_video_analytics_serving.md +++ b/docs/running_video_analytics_serving.md @@ -167,8 +167,8 @@ $ ./docker/run.sh --framework gstreamer --pipelines /path/to/my-pipelines --mode # Enabling Hardware Accelerators The run script automatically gives docker access (i.e. device, volume mount and device cgroup rule) to the following accelerators * iGPU -* Myriad NCS2 -* HDDL-R +* Intel® Neural Compute Stick 2 (NCS2) +* HDDL-R cards You also need to specify the inference device in the parameters section of the VA Serving request. Example for GPU below @@ -189,6 +189,10 @@ The following the table shows docker configuration and inference device name for | NCS2 | | /dev/bus/usb |c 189:* rmw| MYRIAD | | HDDL-R | /dev/ion | /var/tmp | | HDDL | +## NCS2 Limitation +DL Streamer pipelines can only target a single neural network model to each NCS2 accelerator in a system. For pipelines that contain multiple models +(for example, [emotion_recognition](/pipelines/gstreamer/emotion_recognition/1/pipeline.json)), only a single element can have its device property set to MYRIAD. Other elements in the pipeline must target other accelerators (for example, CPU, GPU). In the case the system has `N` NCS2 accelerators available then up to `N` elements can have their device property set to MYRIAD. + # Developer Mode The run script includes a `--dev` flag which starts the diff --git a/samples/lva_ai_extension/README.md b/samples/lva_ai_extension/README.md index 3c15221..71848f1 100644 --- a/samples/lva_ai_extension/README.md +++ b/samples/lva_ai_extension/README.md @@ -139,7 +139,8 @@ Notes: * The `--debug` option selects debug pipelines that watermark inference results and saves images in `/tmp/vaserving/{--pipeline-version}/{timestamp}/` and can also be set using the environment variable DEBUG_PIPELINE * The `--parameters` option specifies pipeline parameters for the selected pipeline. It can be either a JSON string or the name of a file containing the JSON. See the parameters section of the [pipeline definition](/docs/defining_pipelines.md#pipeline-parameters) document for more details. The individual definition files for [object_detection](/samples/lva_ai_extension/pipelines/object_detection/person_vehicle_bike_detection/pipeline.json), [object_classification](/samples/lva_ai_extension/pipelines/object_classification/vehicle_attributes_recognition/pipeline.json), and [object_tracking](/samples/lva_ai_extension/pipelines/object_tracking/person_vehicle_bike_tracking/pipeline.json) contain the supported parameters for the pre-loaded pipelines. * To enable GPU inference for detection `--parameters '{"detection-device":"GPU"}'` -* To enable MYRIAD(NCS2) inference for detection `--parameters '{"detection-device":"MYRIAD"}'` +* To enable MYRIAD(NCS2) inference for detection `--parameters '{"detection-device":"MYRIAD"}'`. +Note that each NCS2 accelerator can only support [one model per pipeline](/docs/running_video_analytics_serving.md#ncs2-limitation) ### Debug Mode From 8ba4d89e1db392e867babd66a7d31bdb309e3e79 Mon Sep 17 00:00:00 2001 From: "Shah, Neelay N" Date: Thu, 21 Jan 2021 12:23:21 -0800 Subject: [PATCH 39/50] Set LVA version to 0.4.1 and remove section on docker pull --- samples/lva_ai_extension/README.md | 18 +++++------------- samples/lva_ai_extension/docker/build.sh | 2 +- samples/lva_ai_extension/tests/Dockerfile | 2 +- 3 files changed, 7 insertions(+), 15 deletions(-) diff --git a/samples/lva_ai_extension/README.md b/samples/lva_ai_extension/README.md index 71848f1..f9e4af0 100644 --- a/samples/lva_ai_extension/README.md +++ b/samples/lva_ai_extension/README.md @@ -18,14 +18,6 @@ The OpenVINOâ„¢ DL Streamer - Edge AI Extension module is a microservice based o The OpenVINOâ„¢ DL Streamer - Edge AI Extension module is available as a pre-built docker image. The image can run as a standalone microservice or as a module within an Live Video Analytics graph. For more information on deploying the module as part of a Live Video Analytics graph please see [Configuring the AI Extension Module for Live Video Analytics](#configuring-the-ai-extension-module-for-live-video-analytics) and refer to the [Live Video Analytics documentation](https://azure.microsoft.com/en-us/services/media-services/live-video-analytics/). The following instructions demonstrate running the microservice and test client outside of Live Video Analytics. -## Pulling the Image from Docker Hub - -Pull the pre-built image using the following command. For instructions on building your own image please see [Building an Edge AI Extension Module Image](#building-an-edge-ai-extension-module-image). - -``` -docker pull intel/video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension -``` - ## Running the Edge AI Extension Module To run the module as a standalone microservice with an `object_detection` pipeline use the `run_server.sh` script with default options. For examples of additional options see [Additional Standalone Edge AI Extension Examples](#additional-standalone-edge-ai-extension-examples). @@ -171,7 +163,7 @@ $ ./docker/run_server.sh --pipeline-parameters '{"detection-device":"GPU"}' ### Logging Run the following command to monitor the logs from the docker container ```bash -$ docker logs video-analytics-serving_0.4.0-dlstreamer-edge-ai-extension -f +$ docker logs video-analytics-serving_0.4.1-dlstreamer-edge-ai-extension -f ``` ### Developer Mode @@ -226,7 +218,7 @@ Run the docker image build script. ``` $ ./docker/build.sh ``` -Resulting image name is `video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension` +Resulting image name is `video-analytics-serving:0.4.1-dlstreamer-edge-ai-extension` # Updating or Changing Detection and Classification Models Before updating the models used by a pipeline please see the format of @@ -293,7 +285,7 @@ models ### Check that images contains the new model and pipeline As the LVA API does not support model or pipeline queries, start the container with an interactive shell and check that expected model and pipeline are present. ```bash -$ docker run -it --entrypoint /bin/bash video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension +$ docker run -it --entrypoint /bin/bash video-analytics-serving:0.4.1-dlstreamer-edge-ai-extension vaserving@82dd59743ca3:~$ ls models person_vehicle_bike_detection vehicle_attributes_recognition yolo vaserving@82dd59743ca3:~$ ls pipelines/object_detection/ @@ -303,7 +295,7 @@ debug_person_vehicle_bike_detection person_vehicle_bike_detection yolo ### Re-start service Restart the service to ensure we are using the image with the yolo-v2-tiny-tf model ``` -$ docker stop video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension +$ docker stop video-analytics-serving:0.4.1-dlstreamer-edge-ai-extension $ docker/run_server.sh --pipeline-name object_detection --pipeline-version yolo ``` ### Run the client @@ -322,4 +314,4 @@ $ docker/run_client.sh [AIXC] [2021-01-07 06:51:13,082] [MainThread ] [INFO]: - person (0.57) [0.76, 0.46, 0.13, 0.23] [] [AIXC] [2021-01-07 06:51:13,082] [MainThread ] [INFO]: - person (0.55) [0.41, 0.44, 0.03, 0.10] [] -``` \ No newline at end of file +``` diff --git a/samples/lva_ai_extension/docker/build.sh b/samples/lva_ai_extension/docker/build.sh index dd2f2ad..9c470c5 100755 --- a/samples/lva_ai_extension/docker/build.sh +++ b/samples/lva_ai_extension/docker/build.sh @@ -6,7 +6,7 @@ SAMPLE_BUILD_ARGS=$(env | cut -f1 -d= | grep -E '_(proxy|REPO|VER)$' | sed 's/^/ MODELS="models/models.list.yml" BASE_IMAGE= OMZ_VERSION= -TAG="video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension" +TAG="video-analytics-serving:0.4.1-dlstreamer-edge-ai-extension" #Get options passed into script function get_options { diff --git a/samples/lva_ai_extension/tests/Dockerfile b/samples/lva_ai_extension/tests/Dockerfile index 462bfb0..55d5b00 100644 --- a/samples/lva_ai_extension/tests/Dockerfile +++ b/samples/lva_ai_extension/tests/Dockerfile @@ -1,4 +1,4 @@ -ARG BASE="video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension" +ARG BASE="video-analytics-serving:0.4.1-dlstreamer-edge-ai-extension" FROM ${BASE} as video-analytics-serving-lva-tests USER root From 4f25bda42eb0906fd91f86aece5bc8949cab37b8 Mon Sep 17 00:00:00 2001 From: "Shah, Neelay N" Date: Thu, 21 Jan 2021 16:01:46 -0800 Subject: [PATCH 40/50] update downloader to use dev mode to avoid port mapping --- docker/build.sh | 4 ++++ tools/model_downloader/model_downloader.sh | 6 +++++- 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/docker/build.sh b/docker/build.sh index 10943e3..3c5f528 100755 --- a/docker/build.sh +++ b/docker/build.sh @@ -247,6 +247,10 @@ get_options() { OPEN_MODEL_ZOO_VERSION=2021.1 fi + if [ ! -d "$SOURCE_DIR/models" ]; then + $RUN_PREFIX mkdir $SOURCE_DIR/models + fi + $SOURCE_DIR/tools/model_downloader/model_downloader.sh --model-list $MODELS --output $SOURCE_DIR $FORCE_MODEL_DOWNLOAD --open-model-zoo-version $OPEN_MODEL_ZOO_VERSION $DRY_RUN elif [ -d "$MODELS" ]; then diff --git a/tools/model_downloader/model_downloader.sh b/tools/model_downloader/model_downloader.sh index 506714d..9a6f9c0 100755 --- a/tools/model_downloader/model_downloader.sh +++ b/tools/model_downloader/model_downloader.sh @@ -108,4 +108,8 @@ if [ ! -z "$TEAMCITY_VERSION" ]; then NON_INTERACTIVE=--non-interactive fi -$SOURCE_DIR/docker/run.sh --user "$UID" $NON_INTERACTIVE --image openvino/ubuntu18_data_dev:$OPEN_MODEL_ZOO_VERSION $VOLUME_MOUNT $DRY_RUN --entrypoint /bin/bash --entrypoint-args "\"-i\" \"-c\" \"pip3 install -r /home/video-analytics-serving/tools/model_downloader/requirements.txt ; python3 -u /home/video-analytics-serving/tools/model_downloader --model-list /models_yml/$YML_FILE_NAME --output /output $FORCE\"" +if [ ! -d "$OUTPUT_DIR/models" ]; then + mkdir $OUTPUT_DIR/models +fi + +$SOURCE_DIR/docker/run.sh --user "$UID" $NON_INTERACTIVE --dev --image openvino/ubuntu18_data_dev:$OPEN_MODEL_ZOO_VERSION $VOLUME_MOUNT $DRY_RUN --entrypoint /bin/bash --entrypoint-args "\"-i\" \"-c\" \"pip3 install -r /home/video-analytics-serving/tools/model_downloader/requirements.txt ; python3 -u /home/video-analytics-serving/tools/model_downloader --model-list /models_yml/$YML_FILE_NAME --output /output $FORCE\"" From 965b395a2758604270e6684544a9ecd517906c98 Mon Sep 17 00:00:00 2001 From: "Shah, Neelay N" Date: Thu, 21 Jan 2021 16:38:41 -0800 Subject: [PATCH 41/50] update run script to set user correctly on any volume mount --- docker/run.sh | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/docker/run.sh b/docker/run.sh index 6dfeb00..f0cb0ca 100755 --- a/docker/run.sh +++ b/docker/run.sh @@ -286,12 +286,13 @@ fi if [ ! -z "$MODELS" ]; then VOLUME_MOUNT+="-v $MODELS:/home/video-analytics-serving/models " - if [ -z "$USER" ]; then - USER="--user $UID" - fi fi + if [ ! -z "$PIPELINES" ]; then VOLUME_MOUNT+="-v $PIPELINES:/home/video-analytics-serving/pipelines " +fi + +if [ ! -z "$VOLUME_MOUNT" ]; then if [ -z "$USER" ]; then USER="--user $UID" fi From fecc366056b22bbb228bc3d7bfd928f7ac5690ac Mon Sep 17 00:00:00 2001 From: "Dunham, Darrin" Date: Thu, 21 Jan 2021 17:14:16 -0800 Subject: [PATCH 42/50] Fix JSON escaping in LVA deployment templates --- .../deployment/deployment.gpu.grpc.template.json | 3 +-- .../deployment/deployment.hddl.grpc.template.json | 12 +++++------- ...ate.json => deployment.myriad.grpc.template.json} | 12 +++++------- 3 files changed, 11 insertions(+), 16 deletions(-) rename samples/lva_ai_extension/deployment/{deployment.movidius.grpc.template.json => deployment.myriad.grpc.template.json} (93%) diff --git a/samples/lva_ai_extension/deployment/deployment.gpu.grpc.template.json b/samples/lva_ai_extension/deployment/deployment.gpu.grpc.template.json index bd9c0b2..3d92b0b 100755 --- a/samples/lva_ai_extension/deployment/deployment.gpu.grpc.template.json +++ b/samples/lva_ai_extension/deployment/deployment.gpu.grpc.template.json @@ -90,7 +90,6 @@ "Binds": [ "$INPUT_VIDEO_FOLDER_ON_DEVICE:/live/mediaServer/media" ] - } } } @@ -110,7 +109,7 @@ "Env":[ "PIPELINE_NAME=object_detection", "PIPELINE_VERSION=person_vehicle_bike_detection", - "PIPELINE_PARAMETERS=\"{\"detection-device\":\"GPU\"}\"" + "PIPELINE_PARAMETERS={\"detection-device\":\"GPU\"}" ], "HostConfig": { "Binds": [ diff --git a/samples/lva_ai_extension/deployment/deployment.hddl.grpc.template.json b/samples/lva_ai_extension/deployment/deployment.hddl.grpc.template.json index 6e6bc07..aec561e 100755 --- a/samples/lva_ai_extension/deployment/deployment.hddl.grpc.template.json +++ b/samples/lva_ai_extension/deployment/deployment.hddl.grpc.template.json @@ -87,16 +87,14 @@ "image": "mcr.microsoft.com/lva-utilities/rtspsim-live555:1.2", "createOptions": { "HostConfig": { - "$CONTAINER_REGISTRY_USERNAME_myacr":{ - "username":"$CONTAINER_REGISTRY_USERNAME_myacr", - "password":"$CONTAINER_REGISTRY_PASSWORD_myacr", - "address": "$CONTAINER_REGISTRY_USERNAME_myacr.azurecr.io" - } + "Binds": [ + "$INPUT_VIDEO_FOLDER_ON_DEVICE:/live/mediaServer/media" + ] } } } }, - "lvaextension" : { + "lvaExtension" : { "version": "1.0", "type": "docker", "status": "running", @@ -111,7 +109,7 @@ "Env":[ "PIPELINE_NAME=object_detection", "PIPELINE_VERSION=person_vehicle_bike_detection", - "PIPELINE_PARAMETERS= {\"detection-device\":\"HDDL\"}" + "PIPELINE_PARAMETERS={\"detection-device\":\"HDDL\"}" ], "HostConfig": { "Binds": [ diff --git a/samples/lva_ai_extension/deployment/deployment.movidius.grpc.template.json b/samples/lva_ai_extension/deployment/deployment.myriad.grpc.template.json similarity index 93% rename from samples/lva_ai_extension/deployment/deployment.movidius.grpc.template.json rename to samples/lva_ai_extension/deployment/deployment.myriad.grpc.template.json index 963c683..a517e3e 100755 --- a/samples/lva_ai_extension/deployment/deployment.movidius.grpc.template.json +++ b/samples/lva_ai_extension/deployment/deployment.myriad.grpc.template.json @@ -10,11 +10,9 @@ "minDockerVersion": "v1.25", "loggingOptions": "", "registryCredentials": { - "$CONTAINER_REGISTRY_USERNAME_myacr":{ - "username":"$CONTAINER_REGISTRY_USERNAME_myacr", - "password":"$CONTAINER_REGISTRY_PASSWORD_myacr", - "address": "$CONTAINER_REGISTRY_USERNAME_myacr.azurecr.io" - } + "Binds": [ + "$INPUT_VIDEO_FOLDER_ON_DEVICE:/live/mediaServer/media" + ] } } }, @@ -92,7 +90,7 @@ } } }, - "lvaextension" : { + "lvaExtension" : { "version": "1.0", "type": "docker", "status": "running", @@ -107,7 +105,7 @@ "Env":[ "PIPELINE_NAME=object_detection", "PIPELINE_VERSION=person_vehicle_bike_detection", - "PIPELINE_PARAMETERS= {\"detection-device\":\"MYRIAD\"}" + "PIPELINE_PARAMETERS={\"detection-device\":\"MYRIAD\"}" ], "HostConfig": { "Binds": [ From 158af7d6714aac7db892b3c6ae5c7230210aea2e Mon Sep 17 00:00:00 2001 From: nnshah1 Date: Thu, 21 Jan 2021 08:08:38 -0800 Subject: [PATCH 43/50] remove note on build time - no longer relevant for default build --- README.md | 5 ----- 1 file changed, 5 deletions(-) diff --git a/README.md b/README.md index 7a0d79c..2a7ef6f 100644 --- a/README.md +++ b/README.md @@ -54,11 +54,6 @@ Build the sample microservice with the following command: The script will automatically include the sample models, pipelines and required dependencies. -> **Note:** When running this command for the first time, the default -> base image for Video Analytics Serving will take a long time to -> build (likely over an hour). For instructions on how to re-use -> pre-built base images to speed up the build time please see the -> following [documentation](docs/building_video_analytics_serving.md#using-pre-built-media-analytics-base-images). To verify the build succeeded execute the following command: From d6f8880055d48929822384e13d13289e1f665171 Mon Sep 17 00:00:00 2001 From: "Shah, Neelay N" Date: Thu, 21 Jan 2021 18:43:40 -0800 Subject: [PATCH 44/50] Update object detection tutorial --- docs/changing_object_detection_models.md | 337 ++++++++++++++++++----- samples/lva_ai_extension/README.md | 42 ++- 2 files changed, 296 insertions(+), 83 deletions(-) diff --git a/docs/changing_object_detection_models.md b/docs/changing_object_detection_models.md index d59d3ec..7c78433 100644 --- a/docs/changing_object_detection_models.md +++ b/docs/changing_object_detection_models.md @@ -1,47 +1,75 @@ -# Changing Object Detection Models -| [Tutorial Overview](#tutorial-overview) | [Understand Model Formats](#1-understand-model-formats) | [Run Existing Object Detection Pipeline](#2-run-existing-object-detection-pipeline) | [Add New Model](#3-add-new-model) | [Re-build the Image](#4-re-build-the-image) | [Run Pipeline with New Model](#5-run-pipeline-with-new-model) | +# Changing Object Detection Models in a Pipeline +| [Background](#background) | [Run Existing Pipeline](#step-1-run-existing-object-detection-pipeline) | [Download New Model](#step-2-download-new-model) | [Create New Pipeline](#step-3-create-new-object-detection-pipeline) +| [Run New Pipeline](#step-4-run-microservice-with-added-model-and-pipeline) | [Rebuild Microservice](#step-6-rebuild-microservice-with-new-model-and-pipeline) | [Further Reading](#further-reading) -VA Serving Object Detection pipelines are customizable. A simple pipeline customization is to use a different model. +Video Analytics Serving pipeline definitions are designed to make +pipeline customization and model selection easy. This tutorial +provides step by step instructions for changing the object detection +reference pipeline to use a different object detection model. +## Models Used: +Original: [mobilenet-ssd](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/mobilenet-ssd/mobilenet-ssd.md) -## Tutorial Overview +After Change: [yolo-v2-tiny-tf](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/yolo-v2-tiny-tf/yolo-v2-tiny-tf.md) -It is assumed that you are familiar with VA Serving [docker operation](building_video_analytics_serving.md) and [pipeline definition files](defining_pipelines.md). It is also assumed that you have python3 installed on your Linux host. +## Sample Videos Used: -This tutorial consists of the following sections: -1. Understand model formats -2. Run existing object detection pipeline -3. Add a new model and update the pipeline definition to use it -4. Re-build image to include new model and pipeline -5. Run the pipeline with the new model +The tutorial uses [bottle-detection.mp4](https://github.com/intel-iot-devkit/sample-videos/raw/master/bottle-detection.mp4) -Once you complete this tutorial you should be able to quickly obtain and use a new model in a VA Serving pipeline. -> **Note:** The tutorial assumes use of a GStreamer base image. +![bottle-detection.gif](https://github.com/intel-iot-devkit/sample-videos/raw/master/preview/bottle-detection.gif) -## 1. Understand Model Formats -OpenVINO* provides a set of ready to use models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) (OMZ). -OMZ hosts optimized deep learning models to expedite development of inference applications by using free pre-trained models instead of training your own models. +# Background -This tutorial assumes you are using a model from OMZ that has a `model-proc`, which is a file that describes the input and output formats supported by the model along with inference output labels. For more information see [DL Streamer Model Preparation](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Model-preparation). A list of supported models can be found in the [DL Streamer repo](https://github.com/openvinotoolkit/dlstreamer_gst/tree/v1.2.1/samples/model_proc). +Object detection is a combination of object localization and object +classification. Object detection models take images as input and +produce bounding boxes identifying objects in the image along with +their class or label. Different model architectures and training +datasets have different performance and accuracy characteristics and +it is important to choose the best model for your application. -> **Note:** Model-proc files are optional but they ensure better inference by checking the input video format -and advertising labels that can be used by downstream elements. +## Step 1. Run Existing Object Detection Pipeline -VA Serving has a built in tool `model_downloader` that obtains a model from OMZ and converts it to the [Intermediate Representation](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_IR_and_opsets.html) (IR) format required by the OpenVINO* inference engine. The tool also gets the associated `model-proc` file. +Before modifying the pipeline, we'll start by detecting objects in a +sample video using the existing `mobilenet-ssd` model. -The following `object_detection` models and pipelines are included in the Video Analytics Serving library. +#### Build the Microservice -| Pipeline Endpoint | Model Name | Implementation | OMZ Model Name | -| ------------- | --------------------------- | -----------------------------------| -------------- | -| `pipelines/object_detection/1` | SSD with MobileNet | Caffe | [mobilenet-ssd](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mobilenet-ssd) | +Build the sample microservice with the following command: +```bash +./docker/build.sh +``` + +#### Run the Microservice + +Start the sample microservice with the following command: + +```bash +./docker/run.sh -v /tmp:/tmp --name vaserving +``` + +#### Run Interactive Session + +In a second terminal window launch an interactive session: -## 2. Run Existing Object Detection Pipeline -With the service running, you can start the `object_detection` version `1` pipeline as follows: ```bash -$ samples/sample.py --pipeline object_detection --version 1 +./docker/run.sh --dev --name vaclient +``` + +#### Detect Objects on Sample Video + +In the interactive session run the sample client to detect objects on +the sample video using version `1` of the reference pipeline +`object-detection`. + +```bash +./samples/sample.py --pipeline object_detection --version 1 +``` + +Expected output (abbreviated): +``` Pipeline Status: @@ -80,16 +108,42 @@ Pipeline Status: "timestamp": 39553072625 } ``` +#### Stop the Microservice + +In the original terminal window, stop the service using `CTRL-C`, or in a new terminal window issue a docker kill command: + +```bash +docker kill vaserving +``` +```bash +docker kill vaclient +``` + +## Step 2. Download New Model + +On start-up Video Analytics Serving discovers models that have been +downloaded and makes them available for reference within pipelines. + +Models can be downloaded either as part of the normal Video Analytics +Serving build process or using a stand-alone tool. + +#### Add `yolo-v2-tiny-tf` to the Model Downloader List file -## 3. Add New Model +The model downloader tool uses a yml file to specify models to +download along with details on how they will be referenced in +pipelines. The following edits to the file will download +`yolo-v2-tiny-tf` and alias it for reference in pipelines as +`object_detection` version `yolo-v2-tiny-tf`. -This example will show how to update the object detection pipeline to use the [yolo-v2-tiny-tf](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/yolo-v2-tiny-tf/yolo-v2-tiny-tf.md) object detection model. +Create a copy of the existing model list file `models_list/models.list.yml` and name it `models_list/yolo-models.list.yml`. -### Select the model -For this example we are using `yolo-v2-tiny-tf`. We'll use the VA Serving model `alias` feature to make this `object_detection` version `yolo-v2-tiny-tf`. +```bash +cp ./models_list/models.list.yml ./models_list/yolo-models.list.yml +``` + +Add an entry for the new object detection model `yolo-v2-tiny-tf` at +the end of the file. -### Add model to model list -Create a copy of the existing model list file `models_list/models.list.yml`, say `models_list/yolo.yml`, then add an entry for the new object detection model `yolo-v2-tiny-tf`. ```yml - model: yolo-v2-tiny-tf alias: object_detection @@ -97,13 +151,61 @@ Create a copy of the existing model list file `models_list/models.list.yml`, say precision: [FP16,FP32] ``` -### Update Pipeline Definition File to Use New Model + +#### Run Model Downloader + +``` +./tools/model_downloader/model_downloader.sh --model-list models_list/yolo-models.list.yml +``` +Expected output (abbreviated): + +``` +[ SUCCESS ] Generated IR version 10 model. +[ SUCCESS ] XML file: /tmp/tmp8mq6f1ti/public/yolo-v2-tiny-tf/FP32/yolo-v2-tiny-tf.xml +[ SUCCESS ] BIN file: /tmp/tmp8mq6f1ti/public/yolo-v2-tiny-tf/FP32/yolo-v2-tiny-tf.bin +[ SUCCESS ] Total execution time: 5.75 seconds. +[ SUCCESS ] Memory consumed: 533 MB. +It's been a while, check for a new version of Intel(R) Distribution of OpenVINO(TM) toolkit here https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit/choose-download.html?cid=other&source=Prod&campid=ww_2020_bu_IOTG_OpenVINO-2021-1&content=upg_pro&medium=organic_uid_agjj or on the GitHub* + +Downloaded yolo-v2-tiny-tf model-proc file from gst-video-analytics repo +``` + +The model will now be in `models` folder in the root of the project: +``` +models +└── object_detection + └── yolo-v2-tiny-tf + ├── FP16 + │   ├── yolo-v2-tiny-tf.bin + │   ├── yolo-v2-tiny-tf.mapping + │   └── yolo-v2-tiny-tf.xml + ├── FP32 + │   ├── yolo-v2-tiny-tf.bin + │   ├── yolo-v2-tiny-tf.mapping + │   └── yolo-v2-tiny-tf.xml + └── yolo-v2-tiny-tf.json +``` + +## Step 3. Create New Object Detection Pipeline + +#### Copy and Rename Existing Object Detection Pipeline + Make a copy of the `object_detection` version `1` pipeline definition file and change the version to `yolo-v2-tiny-tf`. -> **Note:** You can also update the existing version `1` to point to the new model instead of creating a new version. + ``` $ cp -r pipelines/gstreamer/object_detection/1 pipelines/gstreamer/object_detection/yolo-v2-tiny-tf ``` -The pipeline template is shown below: + +> **Note:** You can also update the existing version `1` to point to the new model instead of creating a new version. + +#### Edit the Pipeline Template + +Video Analytics Serving pipeline definition files contain a template +that specifies which model to use. The template needs to be updated to +select a different model. + +Original pipeline template: + ``` "template": ["urisourcebin name=source ! concat name=c ! decodebin ! video/x-raw ! videoconvert name=videoconvert", " ! gvadetect model={models[object_detection][1][network]} name=detection", @@ -111,63 +213,94 @@ The pipeline template is shown below: " ! appsink name=appsink" ] ``` + We need to change the model references used by the `gvadetect` element to use version `yolo-v2-tiny-tf`. You can use your favorite editor to do this. The below command makes this change using the `sed` utility program. + +```bash +sed -i -e s/\\[1\\]/\\[yolo-v2-tiny-tf\\]/g pipelines/gstreamer/object_detection/yolo-v2-tiny-tf/pipeline.json ``` -$ sed -i -e s/\\[1\\]/\\[yolo-v2-tiny-tf\\]/g pipelines/gstreamer/object_detection/yolo-v2-tiny-tf/pipeline.json -``` -Now the template will look like this: + +Edited pipeline template: + ``` "template": ["urisourcebin name=source ! concat name=c ! decodebin ! video/x-raw ! videoconvert name=videoconvert", - " ! gvadetect model-instance-id=inf0 model={models[object_detection][yolo-v2-tiny-tf][network]} model-proc={models[object_detection][yolo-v2-tiny-tf][proc]} name=detection", + " ! gvadetect model-instance-id=inf0 model={models[object_detection][yolo-v2-tiny-tf][network]} name=detection", " ! gvametaconvert name=metaconvert ! queue ! gvametapublish name=destination", " ! appsink name=appsink" ] ``` -## 4. Re-build the Image -Build the image to include the new model and pipeline that references it. -``` -$ docker/build.sh --models models_list/yolo.yml +## Step 4. Run Microservice with Added Model and Pipeline + +The sample microservice supports volume mounting of models and +pipelines to make testing local changes easier. + +```bash +./docker/run.sh -v /tmp:/tmp --name vaserving --models models --pipelines pipelines/gstreamer ``` -The model will now be in `models` folder in the root of the project: + +Expected output (abbreviated): + ``` -models -└── object_detection - └── yolo-v2-tiny-tf - ├── FP16 - │   ├── yolo-v2-tiny-tf.bin - │   ├── yolo-v2-tiny-tf.mapping - │   └── yolo-v2-tiny-tf.xml - ├── FP32 - │   ├── yolo-v2-tiny-tf.bin - │   ├── yolo-v2-tiny-tf.mapping - │   └── yolo-v2-tiny-tf.xml - └── yolo-v2-tiny-tf.json + +{"levelname": "INFO", "asctime": "2021-01-21 06:58:11,099", "message": "==============", "module": "model_manager"} +{"levelname": "INFO", "asctime": "2021-01-21 06:58:11,099", "message": "Loading Models", "module": "model_manager"} +{"levelname": "INFO", "asctime": "2021-01-21 06:58:11,099", "message": "==============", "module": "model_manager"} +{"levelname": "INFO", "asctime": "2021-01-21 06:58:11,099", "message": "Loading Models from Path /home/video-analytics-serving/models", "module": "model_manager"} +{"levelname": "WARNING", "asctime": "2021-01-21 06:58:11,099", "message": "Models directory is mount point", "module": "model_manager"} +{"levelname": "INFO", "asctime": "2021-01-21 06:58:11,100", "message": "Loading Model: audio_detection version: 1 type: IntelDLDT from {'FP32': '/home/video-analytics-serving/models/audio_detection/1/FP32/aclnet.xml', 'FP16': '/home/video-analytics-serving/models/audio_detection/1/FP16/aclnet.xml', 'model-proc': '/home/video-analytics-serving/models/audio_detection/1/aclnet.json'}", "module": "model_manager"} +{"levelname": "INFO", "asctime": "2021-01-21 06:58:11,100", "message": "Loading Model: person_vehicle_bike_detection version: 1 type: IntelDLDT from {'FP32': '/home/video-analytics-serving/models/person_vehicle_bike_detection/1/FP32/person-vehicle-bike-detection-crossroad-0078.xml', 'FP16': '/home/video-analytics-serving/models/person_vehicle_bike_detection/1/FP16/person-vehicle-bike-detection-crossroad-0078.xml', 'model-proc': '/home/video-analytics-serving/models/person_vehicle_bike_detection/1/person-vehicle-bike-detection-crossroad-0078.json'}", "module": "model_manager"} +{"levelname": "INFO", "asctime": "2021-01-21 06:58:11,100", "message": "Loading Model: face_detection_retail version: 1 type: IntelDLDT from {'FP32': '/home/video-analytics-serving/models/face_detection_retail/1/FP32/face-detection-retail-0004.xml', 'FP16': '/home/video-analytics-serving/models/face_detection_retail/1/FP16/face-detection-retail-0004.xml', 'model-proc': '/home/video-analytics-serving/models/face_detection_retail/1/face-detection-retail-0004.json'}", "module": "model_manager"} +{"levelname": "INFO", "asctime": "2021-01-21 06:58:11,100", "message": "Loading Model: object_detection version: 1 type: IntelDLDT from {'FP32': '/home/video-analytics-serving/models/object_detection/1/FP32/mobilenet-ssd.xml', 'FP16': '/home/video-analytics-serving/models/object_detection/1/FP16/mobilenet-ssd.xml', 'model-proc': '/home/video-analytics-serving/models/object_detection/1/mobilenet-ssd.json'}", "module": "model_manager"} +{"levelname": "INFO", "asctime": "2021-01-21 06:58:11,100", "message": "Loading Model: object_detection version: yolo-v2-tiny-tf type: IntelDLDT from {'FP32': '/home/video-analytics-serving/models/object_detection/yolo-v2-tiny-tf/FP32/yolo-v2-tiny-tf.xml', 'FP16': '/home/video-analytics-serving/models/object_detection/yolo-v2-tiny-tf/FP16/yolo-v2-tiny-tf.xml', 'model-proc': '/home/video-analytics-serving/models/object_detection/yolo-v2-tiny-tf/yolo-v2-tiny-tf.json'}", "module": "model_manager"} + +{"levelname": "INFO", "asctime": "2021-01-21 06:58:11,549", "message": "Loading Pipeline: object_detection version: yolo-v2-tiny-tf type: GStreamer from /home/video-analytics-serving/pipelines/object_detection/yolo-v2-tiny-tf/pipeline.json", "module": "pipeline_manager"} ``` -## 5. Run Pipeline with New Model -1. Restart the service to ensure we are using the image with the yolo-v2-tiny-tf model -``` -$ docker stop video-analytics-serving-gstreamer -$ docker/run.sh -v /tmp:/tmp +Once started you can verify that the new model and pipeline have been loaded via `curl`: + +```bash +curl localhost:8080/models | grep yolo ``` -2. Check whether the new model and pipeline are loaded +Expected Output: ``` -$ curl localhost:8080/models | grep yolo "model-proc": "/home/video-analytics-serving/models/object_detection/yolo-v2-tiny-tf/yolo-v2-tiny-tf.json", "FP16": "/home/video-analytics-serving/models/object_detection/yolo-v2-tiny-tf/FP16/yolo-v2-tiny-tf.xml", "FP32": "/home/video-analytics-serving/models/object_detection/yolo-v2-tiny-tf/FP32/yolo-v2-tiny-tf.xml" "version": "yolo-v2-tiny-tf" -$ curl localhost:8080/pipelines | grep yolo +``` +```bash +curl localhost:8080/pipelines | grep yolo +``` +Expected Output: +``` "version": "yolo-v2-tiny-tf" ``` -3. Start pipeline -Start the `object_detection` version `yolo-v2-tiny-tf` pipeline. -> **Note:** The lower fps is due to a higher complexity model. + +## Step 5. Detect Objects on Sample Video with New Pipeline + +#### Run interactive session + +In a second terminal window launch an interactive session: + +```bash +./docker/run.sh --dev --name vaclient +``` + +#### Detect Objects on Sample Video + +In the interactive session run the sample client to detect objects on +the sample video using version `1` of the reference pipeline +`object-detection`. ```bash -$ samples/sample.py --pipeline object_detection --version yolo-v2-tiny-tf +./samples/sample.py --pipeline object_detection --version yolo-v2-tiny-tf +``` + +Expected output (abbreviated): + +``` Pipeline Status: @@ -229,6 +362,66 @@ e-detection.mp4?raw=true", } ``` -## Related Links -1. For more information on the [Model Download Tool](../tools/model_downloader/README.md) -2. [Converting models to IR format](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_Converting_Model.html) +## Step 6. Rebuild Microservice with New Model and Pipeline + +Once the new pipeline is ready for deployment you can rebuild the +microservice with the new model and pipeline so it no longer needs to +be volume mounted locally. This step is optional. + +```bash +./docker/build.sh --models ./models_list/yolo-models.list.yml --pipelines pipelines/gstreamer +``` + +Verify the models and pipelines are included: + +```bash +./docker/run.sh -v /tmp:/tmp --name vaserving +``` + +Expected output (abbreviated): + +``` + +{"levelname": "INFO", "asctime": "2021-01-21 06:58:11,099", "message": "==============", "module": "model_manager"} +{"levelname": "INFO", "asctime": "2021-01-21 06:58:11,099", "message": "Loading Models", "module": "model_manager"} +{"levelname": "INFO", "asctime": "2021-01-21 06:58:11,099", "message": "==============", "module": "model_manager"} +{"levelname": "INFO", "asctime": "2021-01-21 06:58:11,099", "message": "Loading Models from Path /home/video-analytics-serving/models", "module": "model_manager"} +{"levelname": "WARNING", "asctime": "2021-01-21 06:58:11,099", "message": "Models directory is mount point", "module": "model_manager"} +{"levelname": "INFO", "asctime": "2021-01-21 06:58:11,100", "message": "Loading Model: audio_detection version: 1 type: IntelDLDT from {'FP32': '/home/video-analytics-serving/models/audio_detection/1/FP32/aclnet.xml', 'FP16': '/home/video-analytics-serving/models/audio_detection/1/FP16/aclnet.xml', 'model-proc': '/home/video-analytics-serving/models/audio_detection/1/aclnet.json'}", "module": "model_manager"} +{"levelname": "INFO", "asctime": "2021-01-21 06:58:11,100", "message": "Loading Model: person_vehicle_bike_detection version: 1 type: IntelDLDT from {'FP32': '/home/video-analytics-serving/models/person_vehicle_bike_detection/1/FP32/person-vehicle-bike-detection-crossroad-0078.xml', 'FP16': '/home/video-analytics-serving/models/person_vehicle_bike_detection/1/FP16/person-vehicle-bike-detection-crossroad-0078.xml', 'model-proc': '/home/video-analytics-serving/models/person_vehicle_bike_detection/1/person-vehicle-bike-detection-crossroad-0078.json'}", "module": "model_manager"} +{"levelname": "INFO", "asctime": "2021-01-21 06:58:11,100", "message": "Loading Model: face_detection_retail version: 1 type: IntelDLDT from {'FP32': '/home/video-analytics-serving/models/face_detection_retail/1/FP32/face-detection-retail-0004.xml', 'FP16': '/home/video-analytics-serving/models/face_detection_retail/1/FP16/face-detection-retail-0004.xml', 'model-proc': '/home/video-analytics-serving/models/face_detection_retail/1/face-detection-retail-0004.json'}", "module": "model_manager"} +{"levelname": "INFO", "asctime": "2021-01-21 06:58:11,100", "message": "Loading Model: object_detection version: 1 type: IntelDLDT from {'FP32': '/home/video-analytics-serving/models/object_detection/1/FP32/mobilenet-ssd.xml', 'FP16': '/home/video-analytics-serving/models/object_detection/1/FP16/mobilenet-ssd.xml', 'model-proc': '/home/video-analytics-serving/models/object_detection/1/mobilenet-ssd.json'}", "module": "model_manager"} +{"levelname": "INFO", "asctime": "2021-01-21 06:58:11,100", "message": "Loading Model: object_detection version: yolo-v2-tiny-tf type: IntelDLDT from {'FP32': '/home/video-analytics-serving/models/object_detection/yolo-v2-tiny-tf/FP32/yolo-v2-tiny-tf.xml', 'FP16': '/home/video-analytics-serving/models/object_detection/yolo-v2-tiny-tf/FP16/yolo-v2-tiny-tf.xml', 'model-proc': '/home/video-analytics-serving/models/object_detection/yolo-v2-tiny-tf/yolo-v2-tiny-tf.json'}", "module": "model_manager"} + +{"levelname": "INFO", "asctime": "2021-01-21 06:58:11,549", "message": "Loading Pipeline: object_detection version: yolo-v2-tiny-tf type: GStreamer from /home/video-analytics-serving/pipelines/object_detection/yolo-v2-tiny-tf/pipeline.json", "module": "pipeline_manager"} +``` + +Once started you can verify that the new model and pipeline have been loaded via `curl`: + +```bash +curl localhost:8080/models | grep yolo +``` +Expected Output: +``` +"model-proc": "/home/video-analytics-serving/models/object_detection/yolo-v2-tiny-tf/yolo-v2-tiny-tf.json", + "FP16": "/home/video-analytics-serving/models/object_detection/yolo-v2-tiny-tf/FP16/yolo-v2-tiny-tf.xml", + "FP32": "/home/video-analytics-serving/models/object_detection/yolo-v2-tiny-tf/FP32/yolo-v2-tiny-tf.xml" + "version": "yolo-v2-tiny-tf" +``` +```bash +curl localhost:8080/pipelines | grep yolo +``` +Expected Output: +``` + "version": "yolo-v2-tiny-tf" +``` + +# Further Reading + +For more information on the build, run, pipeline definition and model download please see: + +* [Getting Started](/README.md#getting-started) +* [Building Video Analytics Serving](/docs/building_video_analytics_serving.md) +* [Running Video Analytics Serving](/docs/running_video_analytics_serving.md) +* [Defining Pipelines](/docs/defining_pipelines.md) +* [Model Downloader Tool](/tools/model_downloader/README.md) diff --git a/samples/lva_ai_extension/README.md b/samples/lva_ai_extension/README.md index f9e4af0..fd4a423 100644 --- a/samples/lva_ai_extension/README.md +++ b/samples/lva_ai_extension/README.md @@ -230,6 +230,19 @@ Extension module. The LVA specific steps are called out in the following section ## Run Existing Object Detection Pipeline Get baseline results for existing object_detection model `person-vehicle-bike-detection-crossroad-0078` + +``` +$./docker/run_server.sh + +/object_classification/vehicle_attributes_recognition/pipeline.json", "module": "pipeline_manager"} +{"levelname": "INFO", "asctime": "2021-01-21 12:10:10,288", "message": "===========================", "module": "pipeline_manager"} +{"levelname": "INFO", "asctime": "2021-01-21 12:10:10,288", "message": "Completed Loading Pipelines", "module": "pipeline_manager"} +{"levelname": "INFO", "asctime": "2021-01-21 12:10:10,289", "message": "===========================", "module": "pipeline_manager"} +{"levelname": "INFO", "asctime": "2021-01-21 12:10:10,292", "message": "Starting DL Streamer Edge AI Extension on port: 5001", "module": "__main__"} +``` + +In a seperate terminal: + ``` $ ./docker/run_client.sh @@ -243,10 +256,10 @@ $ ./docker/run_client.sh [AIXC] [2020-11-20 23:29:11,418] [MainThread ] [INFO]: - person (0.60) [0.84, 0.44, 0.05, 0.29] ``` -## Add New Model -### Add Model to Models List -As per tutorial, we will add the model `yolo-v2-tiny-tf`. Copy existing model list models/models.list.yml to models/yolo.yml -then add the following entry +## Add New Model to Models List + +Copy the existing model list `models/models.list.yml` to `models/yolo-models.list.yml` then add the following entry: + ```yml - model: yolo-v2-tiny-tf alias: yolo @@ -254,18 +267,23 @@ then add the following entry precision: [FP16,FP32] ``` -### Update Pipeline Definition File to Use New Model -Follow [instructions in tutorial](/docs/changing_object_detection_models.md#3-add-new-model), but base new pipeline on `pipelines/object_detection/person_vehicle_bike_detection`. The tutorial has all the details, but the changes can be made with the following commands +## Update Pipeline Definition File to Use New Model + +Copy, rename and update the exising object detection pipeline to reference `yolo-v2-tiny-tf` model: + ```bash $ cp -r pipelines/object_detection/person_vehicle_bike_detection pipelines/object_detection/yolo $ sed -i -e s/person_vehicle_bike_detection/yolo/g pipelines/object_detection/yolo/pipeline.json ``` -Then re-build the image with `yolo.yml` as the input model list file. +## Rebuild Edge AI Extension with new Model and Pipeline + ``` -$ ./docker/build.sh --models models/yolo.yml +$ ./docker/build.sh --models models/yolo-models.list.yml ``` + The model will now be in `models` folder in the root of the project: + ``` models └── yolo @@ -281,9 +299,8 @@ models └── yolo-v2-tiny-tf.json ``` -## 5. Run Pipeline with New Model -### Check that images contains the new model and pipeline -As the LVA API does not support model or pipeline queries, start the container with an interactive shell and check that expected model and pipeline are present. +Check that expected model and pipeline are present in the built image: + ```bash $ docker run -it --entrypoint /bin/bash video-analytics-serving:0.4.1-dlstreamer-edge-ai-extension vaserving@82dd59743ca3:~$ ls models @@ -292,6 +309,9 @@ vaserving@82dd59743ca3:~$ ls pipelines/object_detection/ debug_person_vehicle_bike_detection person_vehicle_bike_detection yolo ``` +## Run Edge AI Extension with new Model and Pipeline + + ### Re-start service Restart the service to ensure we are using the image with the yolo-v2-tiny-tf model ``` From b46d47faefea4230acbc3e660f6676d00d34ed4e Mon Sep 17 00:00:00 2001 From: "Thakkalapelli, Thanaji Rao" Date: Thu, 21 Jan 2021 19:38:16 -0800 Subject: [PATCH 45/50] Update image name of client server --- samples/lva_ai_extension/docker/run_client.sh | 2 +- samples/lva_ai_extension/docker/run_server.sh | 2 +- samples/lva_ai_extension/tests/build.sh | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/samples/lva_ai_extension/docker/run_client.sh b/samples/lva_ai_extension/docker/run_client.sh index 982cf3f..2bb27c0 100755 --- a/samples/lva_ai_extension/docker/run_client.sh +++ b/samples/lva_ai_extension/docker/run_client.sh @@ -7,7 +7,7 @@ SAMPLE_FILE_PATH=$LVA_ROOT/sampleframes/sample01.png OUTPUT_FILE_PATH=/tmp/result SHARED_MEMORY= INTERACTIVE= -IMAGE=video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension +IMAGE=video-analytics-serving:0.4.1-dlstreamer-edge-ai-extension NUMBER_OF_STREAMS=1 SCRIPT_DIR=$(dirname $(readlink -f "$0")) SAMPLE_DIR=$(dirname $SCRIPT_DIR) diff --git a/samples/lva_ai_extension/docker/run_server.sh b/samples/lva_ai_extension/docker/run_server.sh index dc640a0..f43200d 100755 --- a/samples/lva_ai_extension/docker/run_server.sh +++ b/samples/lva_ai_extension/docker/run_server.sh @@ -4,7 +4,7 @@ CURRENT_DIR=$(dirname $(readlink -f "$0")) ROOT_DIR=$(readlink -f "$CURRENT_DIR/../../..") LVA_DIR=$(dirname $CURRENT_DIR) LVA_ROOT=/home/video-analytics-serving/samples/lva_ai_extension -IMAGE=video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension +IMAGE=video-analytics-serving:0.4.1-dlstreamer-edge-ai-extension VASERVING_ROOT=/home/video-analytics-serving NAME=${IMAGE//[\:]/_} PORT=5001 diff --git a/samples/lva_ai_extension/tests/build.sh b/samples/lva_ai_extension/tests/build.sh index a3082cc..8673ffa 100755 --- a/samples/lva_ai_extension/tests/build.sh +++ b/samples/lva_ai_extension/tests/build.sh @@ -2,7 +2,7 @@ WORK_DIR=$(dirname $(readlink -f "$0")) ROOT_DIR=$(readlink -f "$WORK_DIR/../../..") LVA_DIR=$(dirname $WORK_DIR) -EXTENSION_IMAGE_TAG="video-analytics-serving:0.4.0-dlstreamer-edge-ai-extension" +EXTENSION_IMAGE_TAG="video-analytics-serving:0.4.1-dlstreamer-edge-ai-extension" TEST_IMAGE_TAG="video-analytics-serving-lva-tests" SAMPLE_BUILD_ARGS=$(env | cut -f1 -d= | grep -E '_(proxy|REPO|VER)$' | sed 's/^/--build-arg / ' | tr '\n' ' ') From 2db86eaa64f1ac9d7a748eb02c8670d2cc395428 Mon Sep 17 00:00:00 2001 From: "Bruce, Henry" Date: Fri, 22 Jan 2021 09:27:43 -0800 Subject: [PATCH 46/50] Add LVA pipeline diagrams --- samples/lva_ai_extension/README.md | 11 +++--- .../object-classification.png | Bin 0 -> 45338 bytes .../pipeline_diagrams/object-detection.png | Bin 0 -> 36647 bytes .../pipeline_diagrams/object-tracking.png | Bin 0 -> 40354 bytes .../pipeline_diagrams.mermaid | 36 ++++++++++++++++++ 5 files changed, 42 insertions(+), 5 deletions(-) create mode 100644 samples/lva_ai_extension/pipeline_diagrams/object-classification.png create mode 100644 samples/lva_ai_extension/pipeline_diagrams/object-detection.png create mode 100644 samples/lva_ai_extension/pipeline_diagrams/object-tracking.png create mode 100644 samples/lva_ai_extension/pipeline_diagrams/pipeline_diagrams.mermaid diff --git a/samples/lva_ai_extension/README.md b/samples/lva_ai_extension/README.md index fd4a423..2e094b3 100644 --- a/samples/lva_ai_extension/README.md +++ b/samples/lva_ai_extension/README.md @@ -80,11 +80,12 @@ The module can be configured using command line options or environment variables The following pipelines are included in the AI Extension: -| Pipeline Name | Pipeline Version | Pipeline Definition -| ------------- | ------------- | --------------- | -| object_detection | person_vehicle_bike_detection | [definition](/samples/lva_ai_extension/pipelines/object_detection/person_vehicle_bike_detection/pipeline.json) -| object_classification | vehicle_attributes_recognition | [definition](/samples/lva_ai_extension/pipelines/object_classification/vehicle_attributes_recognition/pipeline.json) -| object_tracking | person_vehicle_bike_tracking | [definition](/samples/lva_ai_extension/pipelines/object_tracking/person_vehicle_bike_tracking/pipeline.json) +| Name | Version | Definition | Diagram | +| ------------- | ------------- | --------------- | ------- | +| object_detection | person_vehicle_bike_detection | [definition](/samples/lva_ai_extension/pipelines/object_detection/person_vehicle_bike_detection/pipeline.json)|![diagram](pipeline_diagrams/object-detection.png)| +| object_classification | vehicle_attributes_recognition | [definition](/samples/lva_ai_extension/pipelines/object_classification/vehicle_attributes_recognition/pipeline.json)|![diagram](pipeline_diagrams/object-classification.png)| +| object_tracking | person_vehicle_bike_tracking | 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zHC44R^uCXyM^HsNPeoP_+%?TwNwVdfi5l@Izm44ZZLfgQ*UPN?j4i6_CVEHxduNRa zXTKtFP-DMQ428i)tSp#X>IAf!lOCYo$~vu>YWw-3+{PyQucqx&wl$u3JF z%4XY{U3w{{ZuP~(3RgdQ z@mIeig^5WGrRYx-cdGs%yl3-AlIp7j%P8%=IwIa1^Vj!wUNXZT}owr8gjn`Cr|pAX{OqOiHnuD`ss}hhx+;PI@#1*P_MXy?;Nk{I-pv zskT~_;D=7tP6hlMh@a2csAMkBDSGS#{d=zqdR}bppJa={v29J3KgLmo#X+91P5WID zz%`)2Y@$Art9}TsmIFudrlf7xh>NMSr6`$arMJodg9Ttb&e(9f9jXke*UoP>0r|H$ zF_CwV+znfMMEE+d{rHV637DLY2|bt6%#3&!iglCb%goEHFzo}65MgC=bco%5+Txw3 zY@Nt>5E$lf-~|&mbP03(%C6RH?2Q)zcs-hdh`uZ_dO^<3!72*LNU(g8jOEJj?UxH% z!0vZuGBIBN6GKSX9lb8uAs2q*Cp=}L*mW~sl@RRK1FfXs&?#woans+CZI|L3y+Eue zZy}`IahWv~EKmCTr_XTrLY*?0>V)4WkSp|8e7N~f&7U@hzk>h2TmLFsZvxQyNBJK! zqmICe@h^kF%ib*jF8WXHKNX$7mXiNc3%Cv}M*pRTrv+B6|57`>_y6jUw%qb1<+1appsrc + subgraph "Object Detection" + appsrc-->gavdetect + gavdetect-->appsink + end + appsink-->meta_data +``` + +### Object Classification +```mermaid +graph LR + frame-->appsrc + subgraph "Object Classification" + appsrc-->gavdetect + gavdetect-->gvaclassify + gvaclassify-->appsink + end + appsink-->meta_data +``` + +### Object Tracking +```mermaid +graph LR + frame-->appsrc + subgraph "Object Tracking" + appsrc-->gavdetect + gavdetect-->gvatrack + gvatrack-->gvaclassify + gvaclassify-->appsink + end + appsink-->meta_data +``` From 658980db491201964153a5db967566477f11fcf1 Mon Sep 17 00:00:00 2001 From: "Thakkalapelli, Thanaji Rao" Date: Fri, 22 Jan 2021 14:07:44 -0800 Subject: [PATCH 47/50] Update deployment.myriad.grpc.template.json --- .../deployment.myriad.grpc.template.json | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/samples/lva_ai_extension/deployment/deployment.myriad.grpc.template.json b/samples/lva_ai_extension/deployment/deployment.myriad.grpc.template.json index a517e3e..d9b483b 100755 --- a/samples/lva_ai_extension/deployment/deployment.myriad.grpc.template.json +++ b/samples/lva_ai_extension/deployment/deployment.myriad.grpc.template.json @@ -10,9 +10,11 @@ "minDockerVersion": "v1.25", "loggingOptions": "", "registryCredentials": { - "Binds": [ - "$INPUT_VIDEO_FOLDER_ON_DEVICE:/live/mediaServer/media" - ] + "$CONTAINER_REGISTRY_USERNAME_myacr":{ + "username":"$CONTAINER_REGISTRY_USERNAME_myacr", + "password":"$CONTAINER_REGISTRY_PASSWORD_myacr", + "address": "$CONTAINER_REGISTRY_USERNAME_myacr.azurecr.io" + } } } }, @@ -85,7 +87,9 @@ "image": "mcr.microsoft.com/lva-utilities/rtspsim-live555:1.2", "createOptions": { "HostConfig": { - + "Binds": [ + "$INPUT_VIDEO_FOLDER_ON_DEVICE:/live/mediaServer/media" + ] } } } @@ -109,9 +113,7 @@ ], "HostConfig": { "Binds": [ - "/var/tmp:/var/tmp", "/tmp:/tmp", - "/dev/shm:/dev/shm", "/dev/bus/usb:/dev/bus/usb" ], "DeviceCgroupRules":[ @@ -174,4 +176,4 @@ } } } -} +} \ No newline at end of file From c39d34ee8727ea2a647a90366b604af0bc4e57d5 Mon Sep 17 00:00:00 2001 From: "Khoshnevissan, Farid" Date: Fri, 22 Jan 2021 17:20:14 -0800 Subject: [PATCH 48/50] Documentation for configuring NCS2 hardware access --- docs/running_video_analytics_serving.md | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) diff --git a/docs/running_video_analytics_serving.md b/docs/running_video_analytics_serving.md index 580e147..291c406 100644 --- a/docs/running_video_analytics_serving.md +++ b/docs/running_video_analytics_serving.md @@ -1,5 +1,5 @@ # Running Video Analytics Serving -| [Video Analytics Serving Microservice](#video-analytics-serving-microservice) | [Interacting with the Microservice](#interacting-with-the-microservice) | [Selecting Pipelines and Models at Runtime](#selecting-pipelines-and-models-at-runtime) | [Developer Mode](#developer-mode) | +| [Video Analytics Serving Microservice](#video-analytics-serving-microservice) | [Interacting with the Microservice](#interacting-with-the-microservice) | [Selecting Pipelines and Models at Runtime](#selecting-pipelines-and-models-at-runtime) | [Developer Mode](#developer-mode) | [Enabling Hardware Accelerators](#enabling-hardware-accelerators) | Video Analytics Serving docker images can be started using standard `docker run` and `docker compose` commands. For convenience a simplified run script is provided to pass common options to `docker run` such as proxies, device mounts, and to expose the default microservice port (8080). @@ -177,7 +177,7 @@ of the VA Serving request. Example for GPU below "device": "GPU" } ``` -See [Customizing Pipeline Requests](customizing_pipeline_requests.md) for more information of forming requests. +See [Customizing Pipeline Requests](customizing_pipeline_requests.md) for more information. The following the table shows docker configuration and inference device name for all accelerators. > **Note:** Open Visual Cloud base images only support the GPU accelerator. @@ -189,7 +189,20 @@ The following the table shows docker configuration and inference device name for | NCS2 | | /dev/bus/usb |c 189:* rmw| MYRIAD | | HDDL-R | /dev/ion | /var/tmp | | HDDL | -## NCS2 Limitation +## Specific Instructions for NCS2 + +### User Permissions +NCS2 accelerators require users to have special permissions for hardware access. To configure your system please follow the steps outlined in the OpenVINO [documentation](https://docs.openvinotoolkit.org/latest/openvino_docs_install_guides_installing_openvino_linux.html#additional-NCS-steps) + +> **Note:** These steps require the file `97-myriad-usbboot.rules` which can be extracted from the Video Analytics Serving docker container using the following command: +> +> ```bash +> ./docker/run.sh -v ${PWD}:/tmp --entrypoint cp --entrypoint-args "/opt/intel/openvino_2021/inference_engine/external/97-myriad-usbboot.rules /tmp" +> ``` +> +> Once extracted the file will be in the current directory. Follow the instructions given in the OpenVINO documentation to copy it to the correct location. + +### Limitations DL Streamer pipelines can only target a single neural network model to each NCS2 accelerator in a system. For pipelines that contain multiple models (for example, [emotion_recognition](/pipelines/gstreamer/emotion_recognition/1/pipeline.json)), only a single element can have its device property set to MYRIAD. Other elements in the pipeline must target other accelerators (for example, CPU, GPU). In the case the system has `N` NCS2 accelerators available then up to `N` elements can have their device property set to MYRIAD. From 35af612719ca02f819df6f9aa1c77073854b6f71 Mon Sep 17 00:00:00 2001 From: Henry Bruce Date: Fri, 22 Jan 2021 16:24:12 -0800 Subject: [PATCH 49/50] Corrections for README files --- README.md | 35 ++----- samples/lva_ai_extension/README.md | 144 +++++++++++++++-------------- 2 files changed, 83 insertions(+), 96 deletions(-) diff --git a/README.md b/README.md index 2a7ef6f..15cf2b6 100644 --- a/README.md +++ b/README.md @@ -31,7 +31,7 @@ The sample microservice includes three media analytics pipelines. | | | |---------------------------------------------|---------| -| **object_detection** | Detect and label objects such as bottles and bicycles. +| **object_detection** | Detect and label objects such as bottles and bicycles. | **emotion_recognition** | Detect the emotions of a person within a video stream. | **audio_detection** | Analyze audio streams for events such as breaking glass or barking dogs. @@ -65,7 +65,7 @@ Expected output: ```bash REPOSITORY TAG IMAGE ID CREATED SIZE -video-analytics-serving-gstreamer latest f51f2695639f 2 minutes ago 1.39GB +video-analytics-serving-gstreamer latest f51f2695639f 2 minutes ago 3.03GB ``` ## Running the Microservice @@ -99,7 +99,7 @@ Expected output: {"levelname": "INFO", "asctime": "2020-08-06 12:37:13,333", "message": "Starting Tornado Server on port: 8080", "module": "__main__"} ``` -## Detecting Objects in a Video

+## Detecting Objects in a Video

### Example Request: @@ -156,7 +156,7 @@ Start a new shell and execute the following command to issue an HTTP POST reques ```bash curl localhost:8080/pipelines/object_detection/1 -X POST -H \ 'Content-Type: application/json' -d \ -'{ +'{ "source": { "uri": "https://github.com/intel-iot-devkit/sample-videos/blob/master/bottle-detection.mp4?raw=true", "type": "uri" @@ -280,7 +280,7 @@ Start a new shell and execute the following command to issue an HTTP POST reques ```bash curl localhost:8080/pipelines/emotion_recognition/1 -X POST -H \ 'Content-Type: application/json' -d \ -'{ +'{ "source": { "uri": "https://github.com/intel-iot-devkit/sample-videos/blob/master/head-pose-face-detection-male.mp4?raw=true", "type": "uri" @@ -350,7 +350,7 @@ After pretty-printing:

Audio Event Detection -## Detecting Audio Events in an Audio Recording +## Detecting Audio Events in an Audio Recording ### Example Request: @@ -406,7 +406,7 @@ Start a new shell and execute the following command to issue an HTTP POST reques ```bash curl localhost:8080/pipelines/audio_detection/1 -X POST -H \ 'Content-Type: application/json' -d \ -'{ +'{ "source": { "uri": "https://github.com/opencv/gst-video-analytics/blob/preview/audio-detect/samples/gst_launch/audio_detect/how_are_you_doing.wav?raw=true", "type": "uri" @@ -472,24 +472,9 @@ After pretty-printing: | ------------ | ------------------ | -----------------| | **-** [GStreamer](https://gstreamer.freedesktop.org/documentation/?gi-language=c)*
**-** [GStreamer* Overview](docs/gstreamer_overview.md)
**-** [FFmpeg](https://ffmpeg.org/)* | **-** [OpenVINO Toolkit](https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html)
**-** [OpenVINO Toolkit DL Streamer](https://github.com/opencv/gst-video-analytics)
**-** [FFmpeg* Video Analytics](https://github.com/VCDP/FFmpeg-patch) | **-** [Open Visual Cloud Smart City Sample](https://github.com/OpenVisualCloud/Smart-City-Sample)
**-** [Open Visual Cloud Ad-Insertion Sample](https://github.com/OpenVisualCloud/Ad-Insertion-Sample)
**-** [Edge Insights for Retail](https://software.intel.com/content/www/us/en/develop/articles/real-time-sensor-fusion-for-loss-detection.html) ---- -\* Other names and brands may be claimed as the property of others. # Known Issues -## Default GStreamer Build Fails -If the build fails with the error `ModuleNotFoundError: No module named 'skbuild'` -follow instructions in this [github issue](https://github.com/intel/video-analytics-serving/issues/25) for a workaround. - -## Service Will Not Start Due to Missing Audio Plugin -`GStreamer` framework base images are expected to include the -[audio detection inference](https://github.com/opencv/gst-video-analytics/wiki/gvaaudiodetect) plugin `libgstaudioanalytics.so`. If this plugin is missing the audio detection pipeline will not load, see error message below, and the Video Analytics Serving service will not start. -``` -{"levelname": "ERROR", "asctime": "2020-08-26 01:49:40,114", "message": "Failed to Load Pipeline from: /home/video-analytics-serving/pipelines/audio_detection/1/pipeline.json", "module": "pipeline_manager"} -``` -Currently this plugin is only present in the DL Streamer audio preview so will be not be in any base images obtained from dockerhub. -Thus GStreamer images based on Open Visual Cloud or OpenVINO will exhibit this problem. +Known issues are tracked on [GitHub](https://github.com/intel/video-analytics-serving/issues)* -As a workaround you can configure the service to ignore initialization errors when you start it. -```bash -docker/run.sh -v /tmp:/tmp -e IGNORE_INIT_ERRORS=True -``` +--- +\* Other names and brands may be claimed as the property of others. diff --git a/samples/lva_ai_extension/README.md b/samples/lva_ai_extension/README.md index 2e094b3..601d712 100644 --- a/samples/lva_ai_extension/README.md +++ b/samples/lva_ai_extension/README.md @@ -16,7 +16,25 @@ The OpenVINOâ„¢ DL Streamer - Edge AI Extension module is a microservice based o # Getting Started -The OpenVINOâ„¢ DL Streamer - Edge AI Extension module is available as a pre-built docker image. The image can run as a standalone microservice or as a module within an Live Video Analytics graph. For more information on deploying the module as part of a Live Video Analytics graph please see [Configuring the AI Extension Module for Live Video Analytics](#configuring-the-ai-extension-module-for-live-video-analytics) and refer to the [Live Video Analytics documentation](https://azure.microsoft.com/en-us/services/media-services/live-video-analytics/). The following instructions demonstrate running the microservice and test client outside of Live Video Analytics. +The OpenVINOâ„¢ DL Streamer - Edge AI Extension module can run as a standalone microservice or as a module within an Live Video Analytics graph. For more information on deploying the module as part of a Live Video Analytics graph please see [Configuring the AI Extension Module for Live Video Analytics](#configuring-the-ai-extension-module-for-live-video-analytics) and refer to the [Live Video Analytics documentation](https://azure.microsoft.com/en-us/services/media-services/live-video-analytics/). The following instructions demonstrate building and running the microservice and test client outside of Live Video Analytics. + +## Building the Edge AI Extension Module Image + +### Prerequisites +Building the image requires a modern Linux distro with the following packages installed: + +| | | +|---------------------------------------------|------------------| +| **Docker** | Video Analytics Serving requires Docker for it's build, development, and runtime environments. Please install the latest for your platform. [Docker](https://docs.docker.com/install). | +| **bash** | Video Analytics Serving's build and run scripts require bash and have been tested on systems using versions greater than or equal to: `GNU bash, version 4.3.48(1)-release (x86_64-pc-linux-gnu)`. Most users shouldn't need to update their version but if you run into issues please install the latest for your platform. Instructions for macOS®* users [here](/docs/installing_bash_macos.md). | + +### Building the Image: + +Run the docker image build script. +``` +$ ./docker/build.sh +``` +Resulting image name is `video-analytics-serving:0.4.1-dlstreamer-edge-ai-extension` ## Running the Edge AI Extension Module @@ -25,7 +43,7 @@ To run the module as a standalone microservice with an `object_detection` pipeli ```bash $ ./docker/run_server.sh -Starting Protocol Server Application on port 5001 +{"levelname": "INFO", "asctime": "2021-01-22 15:27:00,009", "message": "Starting DL Streamer Edge AI Extension on port: 5001", "module": "__main__"} ``` ## Sending a Test Frame for Object Detection @@ -34,34 +52,45 @@ To send a test frame to the microservice and receive `object_detection` results ```bash $ ./docker/run_client.sh -[AIXC] [2020-11-20 23:29:10,817] [MainThread ] [INFO]: ======================= -[AIXC] [2020-11-20 23:29:10,817] [MainThread ] [INFO]: Options for __main__.py -[AIXC] [2020-11-20 23:29:10,817] [MainThread ] [INFO]: ======================= -[AIXC] [2020-11-20 23:29:10,817] [MainThread ] [INFO]: grpc_server_address == 127.0.0.1:5001 -[AIXC] [2020-11-20 23:29:10,817] [MainThread ] [INFO]: ======================= -[AIXC] [2020-11-20 23:29:10,817] [MainThread ] [INFO]: sample_file == /home/video-analytics-serving/samples/lva_ai_extension/sampleframes/sample01.png -[AIXC] [2020-11-20 23:29:10,817] [MainThread ] [INFO]: ======================= -[AIXC] [2020-11-20 23:29:10,817] [MainThread ] [INFO]: loop_count == 1 -[AIXC] [2020-11-20 23:29:10,818] [MainThread ] [INFO]: ======================= -[AIXC] [2020-11-20 23:29:10,818] [MainThread ] [INFO]: use_shared_memory == False -[AIXC] [2020-11-20 23:29:10,818] [MainThread ] [INFO]: ======================= -[AIXC] [2020-11-20 23:29:10,818] [MainThread ] [INFO]: output_file == /tmp/results.jsonl -[AIXC] [2020-11-20 23:29:10,818] [MainThread ] [INFO]: ======================= -[AIXC] [2020-11-20 23:29:10,842] [Thread-2 ] [INFO]: MediaStreamDescriptor request #1 -[AIXC] [2020-11-20 23:29:10,842] [Thread-2 ] [INFO]: MediaSample request #2 -[AIXC] [2020-11-20 23:29:10,843] [MainThread ] [INFO]: [Received] AckNum: 1 -[AIXC] [2020-11-20 23:29:10,849] [Thread-2 ] [INFO]: MediaSample request #3 -[AIXC] [2020-11-20 23:29:11,417] [Thread-3 ] [INFO]: [Received] AckNum: 2 -[AIXC] [2020-11-20 23:29:11,417] [MainThread ] [INFO]: Inference result 2 -[AIXC] [2020-11-20 23:29:11,417] [MainThread ] [INFO]: - person (1.00) [0.30, 0.47, 0.09, 0.39] -[AIXC] [2020-11-20 23:29:11,417] [MainThread ] [INFO]: - person (0.97) [0.36, 0.40, 0.05, 0.24] -[AIXC] [2020-11-20 23:29:11,417] [MainThread ] [INFO]: - person (0.94) [0.44, 0.42, 0.08, 0.43] -[AIXC] [2020-11-20 23:29:11,418] [MainThread ] [INFO]: - person (0.92) [0.57, 0.38, 0.05, 0.25] -[AIXC] [2020-11-20 23:29:11,418] [MainThread ] [INFO]: - person (0.91) [0.69, 0.56, 0.12, 0.43] -[AIXC] [2020-11-20 23:29:11,418] [MainThread ] [INFO]: - person (0.90) [0.68, 0.42, 0.04, 0.24] -[AIXC] [2020-11-20 23:29:11,418] [MainThread ] [INFO]: - person (0.82) [0.64, 0.36, 0.05, 0.27] -[AIXC] [2020-11-20 23:29:11,418] [MainThread ] [INFO]: - person (0.60) [0.84, 0.44, 0.05, 0.29] -[AIXC] [2020-11-20 23:29:11,422] [MainThread ] [INFO]: Client finished execution +[AIXC] [2021-01-22 15:28:06,956] [MainThread ] [INFO]: ======================= +[AIXC] [2021-01-22 15:28:06,957] [MainThread ] [INFO]: Options for __main__.py +[AIXC] [2021-01-22 15:28:06,957] [MainThread ] [INFO]: ======================= +[AIXC] [2021-01-22 15:28:06,957] [MainThread ] [INFO]: grpc_server_address == localhost:5001 +[AIXC] [2021-01-22 15:28:06,957] [MainThread ] [INFO]: ======================= +[AIXC] [2021-01-22 15:28:06,957] [MainThread ] [INFO]: grpc_server_ip == localhost +[AIXC] [2021-01-22 15:28:06,957] [MainThread ] [INFO]: ======================= +[AIXC] [2021-01-22 15:28:06,957] [MainThread ] [INFO]: grpc_server_port == 5001 +[AIXC] [2021-01-22 15:28:06,957] [MainThread ] [INFO]: ======================= +[AIXC] [2021-01-22 15:28:06,957] [MainThread ] [INFO]: sample_file == /home/video-analytics-serving/samples/lva_ai_extension/sampleframes/sample01.png +[AIXC] [2021-01-22 15:28:06,957] [MainThread ] [INFO]: ======================= +[AIXC] [2021-01-22 15:28:06,957] [MainThread ] [INFO]: loop_count == 0 +[AIXC] [2021-01-22 15:28:06,957] [MainThread ] [INFO]: ======================= +[AIXC] [2021-01-22 15:28:06,957] [MainThread ] [INFO]: fps_interval == 2 +[AIXC] [2021-01-22 15:28:06,957] [MainThread ] [INFO]: ======================= +[AIXC] [2021-01-22 15:28:06,957] [MainThread ] [INFO]: frame_rate == -1 +[AIXC] [2021-01-22 15:28:06,957] [MainThread ] [INFO]: ======================= +[AIXC] [2021-01-22 15:28:06,957] [MainThread ] [INFO]: frame_queue_size == 200 +[AIXC] [2021-01-22 15:28:06,957] [MainThread ] [INFO]: ======================= +[AIXC] [2021-01-22 15:28:06,958] [MainThread ] [INFO]: use_shared_memory == False +[AIXC] [2021-01-22 15:28:06,958] [MainThread ] [INFO]: ======================= +[AIXC] [2021-01-22 15:28:06,958] [MainThread ] [INFO]: output_file == /tmp/result.jsonl +[AIXC] [2021-01-22 15:28:06,958] [MainThread ] [INFO]: ======================= +[AIXC] [2021-01-22 15:28:07,341] [Thread-2 ] [INFO]: MediaStreamDescriptor request #1 +[AIXC] [2021-01-22 15:28:07,364] [Thread-2 ] [INFO]: MediaSample request #2 +[AIXC] [2021-01-22 15:28:07,365] [MainThread ] [INFO]: [Received] AckNum: 1 +[AIXC] [2021-01-22 15:28:07,371] [Thread-2 ] [INFO]: MediaSample request #3 +[AIXC] [2021-01-22 15:28:07,940] [Thread-3 ] [INFO]: [Received] AckNum: 2 +[AIXC] [2021-01-22 15:28:07,940] [MainThread ] [INFO]: Inference result 2 +[AIXC] [2021-01-22 15:28:07,941] [MainThread ] [INFO]: - person (1.00) [0.30, 0.47, 0.09, 0.39] [] +[AIXC] [2021-01-22 15:28:07,941] [MainThread ] [INFO]: - person (0.97) [0.36, 0.40, 0.05, 0.24] [] +[AIXC] [2021-01-22 15:28:07,941] [MainThread ] [INFO]: - person (0.94) [0.44, 0.42, 0.08, 0.43] [] +[AIXC] [2021-01-22 15:28:07,941] [MainThread ] [INFO]: - person (0.92) [0.57, 0.38, 0.05, 0.25] [] +[AIXC] [2021-01-22 15:28:07,941] [MainThread ] [INFO]: - person (0.91) [0.69, 0.56, 0.12, 0.43] [] +[AIXC] [2021-01-22 15:28:07,941] [MainThread ] [INFO]: - person (0.90) [0.68, 0.42, 0.04, 0.24] [] +[AIXC] [2021-01-22 15:28:07,941] [MainThread ] [INFO]: - person (0.82) [0.64, 0.36, 0.05, 0.27] [] +[AIXC] [2021-01-22 15:28:07,941] [MainThread ] [INFO]: - person (0.60) [0.84, 0.44, 0.05, 0.29] [] +[AIXC] [2021-01-22 15:28:07,943] [MainThread ] [INFO]: Start Time: 1611347287.3661082 End Time: 1611347287.9434469 Frames Recieved: 1 FPS: 1.7320855292554225 +[AIXC] [2021-01-22 15:28:07,943] [MainThread ] [INFO]: Client finished execution ``` # Edge AI Extension Module Options @@ -131,9 +160,6 @@ Notes: * If selecting a pipeline both name and version must be specified * The `--debug` option selects debug pipelines that watermark inference results and saves images in `/tmp/vaserving/{--pipeline-version}/{timestamp}/` and can also be set using the environment variable DEBUG_PIPELINE * The `--parameters` option specifies pipeline parameters for the selected pipeline. It can be either a JSON string or the name of a file containing the JSON. See the parameters section of the [pipeline definition](/docs/defining_pipelines.md#pipeline-parameters) document for more details. The individual definition files for [object_detection](/samples/lva_ai_extension/pipelines/object_detection/person_vehicle_bike_detection/pipeline.json), [object_classification](/samples/lva_ai_extension/pipelines/object_classification/vehicle_attributes_recognition/pipeline.json), and [object_tracking](/samples/lva_ai_extension/pipelines/object_tracking/person_vehicle_bike_tracking/pipeline.json) contain the supported parameters for the pre-loaded pipelines. -* To enable GPU inference for detection `--parameters '{"detection-device":"GPU"}'` -* To enable MYRIAD(NCS2) inference for detection `--parameters '{"detection-device":"MYRIAD"}'`. -Note that each NCS2 accelerator can only support [one model per pipeline](/docs/running_video_analytics_serving.md#ncs2-limitation) ### Debug Mode @@ -168,19 +194,11 @@ $ docker logs video-analytics-serving_0.4.1-dlstreamer-edge-ai-extension -f ``` ### Developer Mode -The run script includes a `--dev` flag which starts the -container in "developer" mode. "Developer" mode sets `docker run` -options to make development and modification of media analytics -pipelines easier by allowing editing of source files on your host. - -Here is an example of starting the AI Extension module in developer mode -``` +The server run script includes a `--dev` flag which starts the container in "developer" mode. +This mode runs with files from the host, not the container, which is useful for quick iteration and development. +```bash $ ./docker/run_server.sh --dev -vaserving@host:~$ python3 samples/lva_ai_extension/server - -Starting Protocol Server Application on port 5001 ``` -The python application supports the same [options](#edge-ai-extension-module-options) as the `run_server.sh` script. # Test Client A test client is provided to demonstrate the capabilities of the Edge AI Extension module. @@ -188,39 +206,24 @@ The test client script `run_client.sh` sends frames(s) to the extension module a Use the --help option to see how to use the script. All arguments are optional. ``` -$ ./docker/run_client.sh --help -usage: ./run_client.sh +$ ./docker/run_client.sh +All arguments are optional, usage is as follows [ --server-ip : Specify the server ip to connect to ] (defaults to 127.0.0.1) [ --server-port : Specify the server port to connect to ] (defaults to 5001) [ --shared-memory : Enables and uses shared memory between client and server ] (defaults to off) [ --sample-file-path : Specify the sample file path to run] (defaults to samples/lva_ai_extension/sampleframes/sample01.png) - [ --output-file-path : Specify the output file path to save inference results in jsonl format (defaults to /tmp/results.jsonl) ] -``` -Notes + [ --output-file-path : Specify the output file path to save inference results in jsonl format] (defaults to /tmp/results.jsonl) + [ --number-of-streams : Specify number of streams (one client process per stream)] + [--fps-interval FPS_INTERVAL] (interval between frames in seconds, defaults to 0) + [--frame-rate FRAME_RATE] (send frames at given fps, default is no limit) + [ --dev : Mount local source code] (use for development) + ``` +Notes: * Media or log file must be inside container or in volume mounted path * Either png or mp4 media files are supported * If not using shared memory, decoded image frames must be less than 4MB (the maximum gPRC message size) * If you are behind a firewall ensure `no_proxy` contains `127.0.0.1` in docker config and system settings. -# Building an Edge AI Extension Module Image -To build your own image follow the instructions below. - -### Prerequisites -Building the image requires a modern Linux distro with the following packages installed: - -| | | -|---------------------------------------------|------------------| -| **Docker** | Video Analytics Serving requires Docker for it's build, development, and runtime environments. Please install the latest for your platform. [Docker](https://docs.docker.com/install). | -| **bash** | Video Analytics Serving's build and run scripts require bash and have been tested on systems using versions greater than or equal to: `GNU bash, version 4.3.48(1)-release (x86_64-pc-linux-gnu)`. Most users shouldn't need to update their version but if you run into issues please install the latest for your platform. Instructions for macOS®* users [here](/docs/installing_bash_macos.md). | - -## Building the Image: - -Run the docker image build script. -``` -$ ./docker/build.sh -``` -Resulting image name is `video-analytics-serving:0.4.1-dlstreamer-edge-ai-extension` - # Updating or Changing Detection and Classification Models Before updating the models used by a pipeline please see the format of [pipeline definition files](/docs/defining_pipelines.md) and read the @@ -242,7 +245,7 @@ $./docker/run_server.sh {"levelname": "INFO", "asctime": "2021-01-21 12:10:10,292", "message": "Starting DL Streamer Edge AI Extension on port: 5001", "module": "__main__"} ``` -In a seperate terminal: +In a separate terminal: ``` $ ./docker/run_client.sh @@ -270,7 +273,7 @@ Copy the existing model list `models/models.list.yml` to `models/yolo-models.lis ## Update Pipeline Definition File to Use New Model -Copy, rename and update the exising object detection pipeline to reference `yolo-v2-tiny-tf` model: +Copy, rename and update the existing object detection pipeline to reference `yolo-v2-tiny-tf` model: ```bash $ cp -r pipelines/object_detection/person_vehicle_bike_detection pipelines/object_detection/yolo @@ -312,7 +315,6 @@ debug_person_vehicle_bike_detection person_vehicle_bike_detection yolo ## Run Edge AI Extension with new Model and Pipeline - ### Re-start service Restart the service to ensure we are using the image with the yolo-v2-tiny-tf model ``` From 33f57a387e2e139ff084c337b2bfe600dca9f661 Mon Sep 17 00:00:00 2001 From: Henry Bruce Date: Fri, 22 Jan 2021 18:19:27 -0800 Subject: [PATCH 50/50] Remove tests --- samples/lva_ai_extension/tests/Dockerfile | 35 - samples/lva_ai_extension/tests/README.md | 11 - samples/lva_ai_extension/tests/build.sh | 73 - .../tests/common/Extension_Data_Schema.json | 366 - samples/lva_ai_extension/tests/conftest.py | 179 - .../multiple_client_stress_test/readme.md | 9 - .../run_client_multiple_times.sh | 10 - .../tests/requirements.tests.txt | 7 - 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tests/vas_pylint_ignore.py diff --git a/samples/lva_ai_extension/tests/Dockerfile b/samples/lva_ai_extension/tests/Dockerfile deleted file mode 100644 index 55d5b00..0000000 --- a/samples/lva_ai_extension/tests/Dockerfile +++ /dev/null @@ -1,35 +0,0 @@ -ARG BASE="video-analytics-serving:0.4.1-dlstreamer-edge-ai-extension" -FROM ${BASE} as video-analytics-serving-lva-tests - -USER root - -# Dependencies from apt-get -RUN DEBIAN_FRONTEND=noninteractive apt-get update && \ - apt-get install -y -q --no-install-recommends \ - python3-dev \ - gcc \ - wget && \ - rm -rf /var/lib/apt/lists/* - - -# Dependencies installed via pip -COPY ./requirements.tests.txt /home/video-analytics-serving -RUN pip3 install --no-cache-dir -r /home/video-analytics-serving/requirements.tests.txt -RUN rm -f /home/video-analytics-serving/requirements.tests.txt - -COPY . /home/video-analytics-serving/samples/lva_ai_extension/tests - -ENV PIPELINE_NAME 'object_detection' -ENV PIPELINE_VERSION 'person_vehicle_bike_detection' - -RUN wget -O classroom.mp4 https://github.com/intel-iot-devkit/sample-videos/blob/master/classroom.mp4?raw=true -RUN wget -O person-bicycle-car-detection.mp4 https://github.com/intel-iot-devkit/sample-videos/blob/master/person-bicycle-car-detection.mp4?raw=true -RUN wget --no-proxy -O person-bicycle-car-detection_1920_1080_2min.mp4 https://gitlab.devtools.intel.com/media-analytics-pipeline-zoo/media/-/raw/main/video/person-bicycle-car-detection/person-bicycle-car-detection_1920_1080_2min.mp4?inline=false - -ARG USER=vaserving - -USER ${USER} - -WORKDIR /home/video-analytics-serving/samples/lva_ai_extension - -ENTRYPOINT [ "pytest", "-s", "--html=tests/results/report.html", "--self-contained-html"] diff --git a/samples/lva_ai_extension/tests/README.md b/samples/lva_ai_extension/tests/README.md deleted file mode 100644 index 323db03..0000000 --- a/samples/lva_ai_extension/tests/README.md +++ /dev/null @@ -1,11 +0,0 @@ - -Use --entrypoint-args to specify pytest arguments. e.g. - -./run.sh --entrypoint-args "-k test_lva_parameter_string_argument" - -To run a specific test from the test case - -./run.sh --entrypoint-args "-k test_pipeline_execution_positive[object_tracking_person_vehicle_bike_tracking_cpu]" - -By default only CPU tests will run. --gpu adds GPU tests. --myriad adds NCS2 tests. --no-cpu excludes cpu tests. - diff --git a/samples/lva_ai_extension/tests/build.sh b/samples/lva_ai_extension/tests/build.sh deleted file mode 100755 index 8673ffa..0000000 --- a/samples/lva_ai_extension/tests/build.sh +++ /dev/null @@ -1,73 +0,0 @@ -#!/bin/bash -e -WORK_DIR=$(dirname $(readlink -f "$0")) -ROOT_DIR=$(readlink -f "$WORK_DIR/../../..") -LVA_DIR=$(dirname $WORK_DIR) -EXTENSION_IMAGE_TAG="video-analytics-serving:0.4.1-dlstreamer-edge-ai-extension" -TEST_IMAGE_TAG="video-analytics-serving-lva-tests" -SAMPLE_BUILD_ARGS=$(env | cut -f1 -d= | grep -E '_(proxy|REPO|VER)$' | sed 's/^/--build-arg / ' | tr '\n' ' ') - -function launch { $@ - local exit_code=$? - if [ $exit_code -ne 0 ]; then - echo "ERROR: error with $1" >&2 - exit $exit_code - fi - return $exit_code -} - -#Get options passed into script -function get_options { - while :; do - case $1 in - -h | -\? | --help) - show_help - exit - ;; - --lva-image) - if [ "$2" ]; then - LVA_IMAGE=$2 - SAMPLE_BUILD_ARGS+=" --build-arg BASE=$LVA_IMAGE" - shift - else - error 'ERROR: "--lva-image" requires an argument.' - fi - ;; - --docker-cache) - if [ "$2" ]; then - CACHE_PREFIX=$2 - shift - else - error 'ERROR: "--docker-cache" requires an argument.' - fi - ;; - # For backwards compatbility with scripts that took cache prefix as $1 - *cache*) - CACHE_PREFIX=$1 - ;; - *) - break - ;; - esac - shift - done -} - -function show_help { - echo "usage: ./run_server.sh" - echo " [ --lva-image : LVA extension image to base test image on ] " - echo " [ --docker-cache : Docker cache prefix ] " -} - -get_options "$@" - -# Build LVA image if not specified -if [ -z "$LVA_IMAGE" ]; then - echo Building $EXTENSION_IMAGE_TAG - # Export to allow nested scripts to digest CACHE_PREFIX - export CACHE_PREFIX=${CACHE_PREFIX} - launch "$LVA_DIR/docker/build.sh" -fi - -# Add tests layer -echo "docker build -f $WORK_DIR/Dockerfile $SAMPLE_BUILD_ARGS -t $TEST_IMAGE_TAG $WORK_DIR" -launch "docker build -f $WORK_DIR/Dockerfile $SAMPLE_BUILD_ARGS -t $TEST_IMAGE_TAG $WORK_DIR" diff --git a/samples/lva_ai_extension/tests/common/Extension_Data_Schema.json b/samples/lva_ai_extension/tests/common/Extension_Data_Schema.json deleted file mode 100644 index 7192056..0000000 --- a/samples/lva_ai_extension/tests/common/Extension_Data_Schema.json +++ /dev/null @@ -1,366 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-07/schema", - "title": "Live Video Analytics Inference Data Schema", - "type": "object", - "description": "Top level inference results.", - "properties": { - "inferences": { - "type": "array", - "description": "Array of inferences for a single media sample. It must contain at least one result, otherwise inferences must not be sent.", - "minItems": 1, - "items": { - "type": "object", - "anyOf": [ - { - "$ref": "#/definitions/inference" - } - ] - } - } - }, - "definitions": { - "tag": { - "type": "object", - "title": "Tag", - "description": "Tag or label associated with the result.", - "properties": { - "value": { - "type": "string", - "title": "Value", - "description": "The tag value." - }, - "confidence": { - "type": "number", - "title": "Confidence", - "description": "Confidence value associated with the tag.", - "minimum": 0.0, - "maximum": 1.0 - } - }, - "required": [ - "value" - ], - "additionalProperties": false - }, - "attribute": { - "type": "object", - "title": "Attribute", - "description": "Additional attribute associated with the result.", - "properties": { - "name": { - "type": "string", - "title": "Name", - "description": "Attribute name or identifier." - }, - "value": { - "type": "string", - "title": "Value", - "description": "Attribute value." - }, - "confidence": { - "type": "number", - "title": "Confidence", - "description": "Confidence value associated with the attribute value.", - "minimum": 0.0, - "maximum": 1.0 - } - }, - "required": [ - "name", - "value" - ], - "additionalProperties": false - }, - "attributeList": { - "type": "array", - "description": "Optional list of attributes.", - "items": { - "anyOf": [ - { - "$ref": "#/definitions/attribute" - } - ] - }, - "additionalItems": false - }, - "rectangle": { - "type": "object", - "title": "Rectangle", - "description": "Rectangular region relative to the image top left corner.", - "required": [ - "l", - "t", - "w", - "h" - ], - "properties": { - "l": { - "type": "number", - "title": "Left", - "description": "Left edge distance from the origin, normalized to image width.", - "default": 0.0, - "minimum": 0.0, - "maximum": 1.0 - }, - "t": { - "type": "number", - "title": "Top", - "description": "Top edge distance from the origin, normalized to image height.", - "default": 0.0, - "minimum": 0.0, - "maximum": 1.0 - }, - "w": { - "type": "number", - "title": "Width", - "description": "Rectangle width, normalized to image width.", - "default": 0.0, - "minimum": 0.0, - "maximum": 1.0 - }, - "h": { - "type": "number", - "title": "Height", - "description": "Rectangle height, normalized to image height.", - "default": 0.0, - "minimum": 0.0, - "maximum": 1.0 - } - }, - "additionalProperties": false - }, - "classification": { - "type": "object", - "title": "Classification", - "description": "Label of classifier often applicable to the whole media sample.", - "properties": { - "tag": { - "description": "Classification label", - "$ref": "#/definitions/tag" - }, - "attributes": { - "$ref": "#/definitions/attributeList" - } - }, - "required": [ - "tag" - ], - "additionalProperties": false - }, - "entity": { - "type": "object", - "title": "Entity", - "description": "Entity detected or identified on the sample.", - "properties": { - "id": { - "type": "string", - "title": "Id", - "description": "Optional entity identification (persistent or transient)." - }, - "tag": { - "description": "Entity identifier", - "$ref": "#/definitions/tag" - }, - "attributes": { - "$ref": "#/definitions/attributeList" - }, - "box": { - "description": "Region in the image where entity has been detected or identified.", - "$ref": "#/definitions/rectangle" - } - }, - "required": [ - "tag", - "box" - ], - "additionalProperties": false - }, - "event": { - "type": "object", - "title": "Event", - "description": "Event detected on the sample.", - "properties": { - "name": { - "description": "Event name or identifier.", - "type": "string" - }, - "properties": { - "description": "Event specific properties", - "type": "object", - "additionalProperties": {"type": "string"} - }, - "box": { - "$ref": "#/definitions/rectangle" - } - }, - "required": [ - "name", - "properties" - ], - "additionalProperties": false - }, - "motion": { - "type": "object", - "title": "Motion", - "description": "Motion detected on the sample.", - "properties": { - "box": { - "$ref": "#/definitions/rectangle" - } - }, - "additionalProperties": false - }, - "text": { - "type": "object", - "title": "Text", - "description": "Text associated with the sample.", - "properties": { - "value": { - "type": "string", - "title": "Text value", - "description": "Text associated with the sample." - }, - "language": { - "type": "string", - "title": "Language", - "description": "Text language." - }, - "startTimestamp": { - "type": "integer", - "title": "Start Timestamp", - "description": "Timestamp for the text start." - }, - "endTimestamp": { - "type": "integer", - "title": "End timestamp", - "description": "Timestamp for the text end." - } - }, - "required": [ - "value" - ], - "additionalProperties": false - }, - "other": { - "type": "object", - "title": "Other generic payload", - "description": "Allows for opaque payloads to be serialized.", - "properties": { - "contentType": { - "type": "string", - "title": "Content Type", - "description": "IANA defined content type." - }, - "contentBytes": { - "type": "string", - "title": "Content", - "description": "Base64 encoded content bytes." - } - }, - "additionalProperties": false - }, - "inference": { - "type": "object", - "description": "Single inference data associated with a sample.", - "properties": { - "type": { - "type": "string", - "title": "Inference Type", - "description": "Inference type identifier", - "enum": [ - "classification", - "motion", - "entity", - "text", - "event", - "other" - ] - }, - "subtype": { - "type": "string", - "title": "Inference Subtype", - "description": "A extension defined inference subtype.", - "pattern": "^\\S(\\s*\\S)*$", - "examples": [ - "objectDetection", - "peopleDetection", - "vehicleIdentification" - ] - }, - "inferenceId": { - "type": "string", - "title": "Optional inference identifier", - "description": "Optional inference id which can be used for referencing." - }, - "relatedInferences": { - "type": "array", - "title": "Set of related inferences", - "description": "A set of of inference identifiers which are related with this inference result.", - "default": [], - "items": { - "type": "string" - } - }, - "classification": { - "$ref": "#/definitions/classification" - }, - "motion": { - "$ref": "#/definitions/motion" - }, - "entity": { - "$ref": "#/definitions/entity" - }, - "event": { - "$ref": "#/definitions/event" - }, - "text": { - "$ref": "#/definitions/text" - }, - "other": { - "$ref": "#/definitions/other" - }, - "extensions": { - "$id": "#/properties/extensions", - "type": "object", - "title": "The extensions schema", - "description": "An explanation about the purpose of this instance.", - "default": {}, - "examples": [ - { - "name1": "value1", - "name2": "value2" - } - ], - "additionalProperties": {"type": "string"} - } - }, - "oneOf": [ - { - "required": ["type", "classification"], - "not": { "required":["motion", "entity", "event", "text", "other"] } - }, - { - "required": ["type", "motion"], - "not": { "required":["classification", "entity", "event", "text", "other"] } - }, - { - "required": ["type", "entity"], - "not": { "required":["classification", "motion", "event", "text", "other"] } - }, - { - "required": ["type", "event"], - "not": { "required":["classification", "motion", "entity", "text", "other"] } - }, - { - "required": ["type", "text"], - "not": { "required":["classification", "motion", "entity", "event", "other"] } - }, - { - "required": ["type", "other"], - "not": { "required":["classification", "motion", "entity", "event", "text"] } - } - ], - "additionalProperties": false - } - } -} \ No newline at end of file diff --git a/samples/lva_ai_extension/tests/conftest.py b/samples/lva_ai_extension/tests/conftest.py deleted file mode 100644 index 8eae65b..0000000 --- a/samples/lva_ai_extension/tests/conftest.py +++ /dev/null @@ -1,179 +0,0 @@ - -import subprocess -import time -import os -import json -import pytest - -from jsonschema import validate -from collections.abc import Mapping - -class Helpers: - def __init__(self): - self.server_process = None - self.client_process = None - - def run_server(self, params): - server_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/server", "-p", str(params["port"])] - if params.get("pipeline_parameters"): - if isinstance(params["pipeline_parameters"], Mapping): - params["pipeline_parameters"] = json.dumps(params["pipeline_parameters"]) - server_args.extend(["--pipeline-parameters", params["pipeline_parameters"]]) - if params.get("pipeline_name"): - server_args.extend(["--pipeline-name", params["pipeline_name"]]) - if params.get("pipeline_version"): - server_args.extend(["--pipeline-version", params["pipeline_version"]]) - print(' '.join(server_args)) - self.server_process = subprocess.Popen(server_args) - time.sleep(params.get("sleep_period",0.25)) - return self.server_process - - def run_client(self, params): - client_args = ["python3", "/home/video-analytics-serving/samples/lva_ai_extension/client", - "-s", "127.0.0.1:" + str(params["port"]), - "-l", str(params.get("loop_count", 1)), - "-f", params["source"]] - if params.get("shared_memory", False): - client_args.append("-m") - if params.get("output_location"): - client_args.extend(["-o", params["output_location"]]) - print(' '.join(client_args)) - self.client_process = subprocess.run(client_args) - assert self.client_process.returncode is not None - assert self.client_process.returncode == params.get("expected_return_code", 0) - return self.client_process.returncode - - def remove_empty_lists(self, dictionary): - if not isinstance(dictionary, (dict, list)): - return dictionary - if isinstance(dictionary, list): - return [v for v in (self.remove_empty_lists(v) for v in dictionary) if v or v == 0] - return {k: v for k, v in ((k, self.remove_empty_lists(v)) for k, v in dictionary.items()) if v or v == 0} - - def validate_output_against_schema(self, output_location): - json_schema = None - json_schema_file = os.path.join(os.path.dirname(__file__), 'common/Extension_Data_Schema.json') - with open(json_schema_file, "r") as read_file: - json_schema = json.load(read_file) - - #Read each inference result and compare against the schema - with open(output_location, "r") as file_: - for line in file_: - if line and line != '': - dictionary = json.loads(line) - dictionary = self.remove_empty_lists(dictionary) - if dictionary.get("inferences"): - for inference in dictionary["inferences"]: - inference["type"] = inference["type"].lower() - validate(instance=dictionary,schema=json_schema) - - def get_results_from_file(self, output_location, results): - with open(output_location) as results_file: - for x in results_file: - if (len(x.strip()) != 0): - results.append(json.loads(x)) - - def cmp_results(self, measured, expected, tolerance): - if measured == expected: - return True - - assert type(measured) == type(expected), "Type Comparison Mismatch" - - if isinstance(measured, int) or isinstance(measured, float): - if expected != 0: - msg = "Measured Value {} not within tolerance ({}) of Expected Value {}" - assert (abs(measured-expected) / abs(expected)) < tolerance, \ - msg.format(measured, tolerance, expected) - else: - msg = "Measured Value {} not within tolerance ({}) of Expected Value {}" - assert tolerance > 1, \ - msg.format(measured, tolerance, expected) - - return True - - if isinstance(measured, list): - assert len(measured) == len(expected), "List length not equal" - - for measured1, expected1 in zip(measured, expected): - assert self.cmp_results(measured1, expected1, tolerance), "List items not equal" - return True - - if isinstance(measured, dict): - assert len(measured) == len(expected), "Dictionary length not equal" - for key in measured: - assert key in expected, "Dictionary keys not equal" - if key.endswith("id"): - assert measured[key] == expected[key], "{} doesn't match".format(key) - return True - assert self.cmp_results(measured[key], expected[key], tolerance), "Dictionaries not equal" - return True - - assert measured == expected, "Values not equal" - return True - - def cleanup_processes(self): - if self.server_process is not None: - self.server_process.kill() - -@pytest.fixture -def helpers(): - helper = Helpers() - yield helper - helper.cleanup_processes() - -def pytest_addoption(parser): - parser.addoption("--generate", action="store_true", help="generate expected results", - default=False) - parser.addoption("--cpu", action="store_true", help="Run CPU tests", - default=True) - parser.addoption("--no-cpu", action="store_false", dest='cpu', help="Disable CPU tests") - parser.addoption("--gpu", action="store_true", help="Run GPU tests", - default=False) - parser.addoption("--myriad", action="store_true", help="Run MYRIAD tests", - default=False) - - -#Parse the test_cases folder to load config files for parameterizing tests and checking results -def load_test_cases(metafunc, directory): - dir_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_cases", directory) - list_of_dir_paths = [dir_path] - - filenames = [] - if metafunc.config.getoption("cpu"): - cpu_path = os.path.join(dir_path, "cpu") - if os.path.isdir(cpu_path): - list_of_dir_paths.append(cpu_path) - if metafunc.config.getoption("gpu"): - gpu_path = os.path.join(dir_path, "gpu") - if os.path.isdir(gpu_path): - list_of_dir_paths.append(gpu_path) - - if metafunc.config.getoption("myriad"): - gpu_path = os.path.join(dir_path, "myriad") - if os.path.isdir(gpu_path): - list_of_dir_paths.append(gpu_path) - - for path in list_of_dir_paths: - dir_filenames = [(os.path.abspath(os.path.join(path, fn)), - os.path.splitext(fn)[0]) for fn in os.listdir(path) - if os.path.isfile(os.path.join(path, fn)) and - os.path.splitext(fn)[1] == '.json'] - filenames.extend(dir_filenames) - test_cases = [] - test_names = [] - generate = metafunc.config.getoption("generate") - for filepath, testname in filenames: - try: - with open(filepath) as json_file: - test_cases.append((json.load(json_file), filepath, generate)) - test_names.append(testname) - except Exception as error: - print(error) - assert False, "Error Reading Test Case" - return (test_cases, test_names) - -def pytest_generate_tests(metafunc): - if "pipeline_execution_positive" in metafunc.function.__name__: - test_cases, test_names = load_test_cases(metafunc, "pipeline_execution_positive") - metafunc.parametrize("test_case,test_filename,generate", test_cases, ids=test_names) - diff --git a/samples/lva_ai_extension/tests/manual/multiple_client_stress_test/readme.md b/samples/lva_ai_extension/tests/manual/multiple_client_stress_test/readme.md deleted file mode 100644 index ce93a88..0000000 --- a/samples/lva_ai_extension/tests/manual/multiple_client_stress_test/readme.md +++ /dev/null @@ -1,9 +0,0 @@ -This manual test is used to check if the server can handle concurrent streams. - -To validate, monitor the server logs, most pipelines will be set to 'Running' before any finish. - -In the docker folder -./run_server.sh --max-running-pipelines 10 - -In the tests/manual/multiple_client_stress_test -./run_client_multiple_times.sh diff --git a/samples/lva_ai_extension/tests/manual/multiple_client_stress_test/run_client_multiple_times.sh b/samples/lva_ai_extension/tests/manual/multiple_client_stress_test/run_client_multiple_times.sh deleted file mode 100755 index 5a6f93d..0000000 --- a/samples/lva_ai_extension/tests/manual/multiple_client_stress_test/run_client_multiple_times.sh +++ /dev/null @@ -1,10 +0,0 @@ -#!/bin/bash -END=10 - -parent_path=$( cd "$(dirname "${BASH_SOURCE[0]}")" ; pwd -P ) -cd "$parent_path/../../../docker" - -for i in $(seq 1 $END); -do - ./run_client.sh & -done diff --git a/samples/lva_ai_extension/tests/requirements.tests.txt b/samples/lva_ai_extension/tests/requirements.tests.txt deleted file mode 100644 index ab3cce3..0000000 --- a/samples/lva_ai_extension/tests/requirements.tests.txt +++ /dev/null @@ -1,7 +0,0 @@ -psutil == 5.7.0 -pylint == 2.4.4 -pytest == 5.4.1 -pytest-cov == 2.8.1 -pytest-html == 2.1.1 -requests == 2.23.0 -teamcity-messages == 1.28 diff --git a/samples/lva_ai_extension/tests/run.sh b/samples/lva_ai_extension/tests/run.sh deleted file mode 100755 index 6d54f78..0000000 --- a/samples/lva_ai_extension/tests/run.sh +++ /dev/null @@ -1,10 +0,0 @@ -IMAGE=video-analytics-serving-lva-tests -CURRENT_DIR=$(dirname $(readlink -f "$0")) -ROOT_DIR=$(readlink -f "$CURRENT_DIR/../../..") -LVA_DIR=$(dirname $CURRENT_DIR) -docker stop $IMAGE -LOCAL_RESULTS_DIR="$CURRENT_DIR/results" -DOCKER_RESULTS_DIR="/home/video-analytics-serving/samples/lva_ai_extension/tests/results" -mkdir -p "$LOCAL_RESULTS_DIR" -RESULTS_VOLUME_MOUNT="-v $LOCAL_RESULTS_DIR:$DOCKER_RESULTS_DIR " -"$ROOT_DIR/docker/run.sh" --image $IMAGE -v /dev/shm:/dev/shm -v "$LVA_DIR/tests/test_cases:/home/video-analytics-serving/samples/lva_ai_extension/tests/test_cases" $RESULTS_VOLUME_MOUNT --pipelines "$LVA_DIR/pipelines" -p 5001:5001 --user $UID "$@" diff --git a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_classification_vehicle_attributes_recognition_cpu.json b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_classification_vehicle_attributes_recognition_cpu.json deleted file mode 100644 index f8fe629..0000000 --- a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/cpu/object_classification_vehicle_attributes_recognition_cpu.json +++ /dev/null @@ -1,24 +0,0 @@ -{ - 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"server_params": { - "pipeline_name":"object_detection", - "pipeline_version":"person_vehicle_bike_detection", - "pipeline_parameters": { - "detection-device":"GPU" - }, - "sleep_period":0.25, - "port":5001 - }, - "client_params": { - "source":"/home/video-analytics-serving/person-bicycle-car-detection.mp4", - "output_location":"", - "shared_memory":true, - "loop_count":1, - "sleep_period":0.25, - "port":5001, - "timeout":300, - "expected_return_code":0 - }, - "num_of_concurrent_clients":1, - "golden_results":false -} diff --git a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/myriad/object_detection_person_vehicle_bike_detection_myriad_video.json b/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/myriad/object_detection_person_vehicle_bike_detection_myriad_video.json deleted file mode 100644 index 38911fb..0000000 --- a/samples/lva_ai_extension/tests/test_cases/pipeline_execution_positive/myriad/object_detection_person_vehicle_bike_detection_myriad_video.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "server_params": { - "pipeline_name":"object_detection", - "pipeline_version":"person_vehicle_bike_detection", - "pipeline_parameters": { - "detection-device":"MYRIAD" - }, - "sleep_period":0.25, - "port":5001 - }, - "client_params": { - "source":"/home/video-analytics-serving/person-bicycle-car-detection.mp4", - "output_location":"", - "shared_memory":true, - "loop_count":1, - "sleep_period":0.25, - "port":5001, - "timeout":300, - "expected_return_code":0 - }, - "num_of_concurrent_clients":1, - "golden_results":false -} diff --git a/samples/lva_ai_extension/tests/test_nonexistant_source.py b/samples/lva_ai_extension/tests/test_nonexistant_source.py deleted file mode 100644 index 271f41e..0000000 --- a/samples/lva_ai_extension/tests/test_nonexistant_source.py +++ /dev/null @@ -1,16 +0,0 @@ -''' -* Copyright (C) 2019-2020 Intel Corporation. -* -* SPDX-License-Identifier: MIT License -''' - -def test_lva_nonexistant_source(helpers, sleep_period=0.25, port=5001): - client_params = { - "source": "/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/nonexistantimage.png", - "sleep_period": sleep_period, - "port": port, - "shared_memory": False, - "expected_return_code": 1 - } - - helpers.run_client(client_params) diff --git a/samples/lva_ai_extension/tests/test_parameters.py b/samples/lva_ai_extension/tests/test_parameters.py deleted file mode 100644 index 8d90d49..0000000 --- a/samples/lva_ai_extension/tests/test_parameters.py +++ /dev/null @@ -1,38 +0,0 @@ -''' -* Copyright (C) 2019-2020 Intel Corporation. -* -* SPDX-License-Identifier: MIT License -''' - -import os -import tempfile - -def parameter_run_client_server(helpers, sleep_period, port, parameters): - server_params = { - "pipeline_parameters": parameters, - "sleep_period": sleep_period, - "port": port - } - - client_params = { - "source": "/home/video-analytics-serving/samples/lva_ai_extension/sampleframes/sample01.png", - "sleep_period": sleep_period, - "port": port - } - - - helpers.run_server(server_params) - helpers.run_client(client_params) - -def test_lva_parameter_string_argument(helpers, sleep_period=0.25, port=5001, - parameter_string="{\"detection-device\":\"CPU\"}"): - parameter_run_client_server(helpers, sleep_period, port, parameter_string) - -def test_lva_parameter_file_argument(helpers, sleep_period=0.25, port=5001): - #Create temporary parameter file - workdir_path = tempfile.TemporaryDirectory() - parameter_path = os.path.join(workdir_path.name, "parameters.json") - with open(parameter_path, "w") as parameter_file: - parameter_file.write("{\"detection-device\":\"CPU\"}") - - parameter_run_client_server(helpers, sleep_period, port, parameter_path) diff --git a/samples/lva_ai_extension/tests/test_pipeline_execution_positive.py b/samples/lva_ai_extension/tests/test_pipeline_execution_positive.py deleted file mode 100644 index ce5c4e2..0000000 --- a/samples/lva_ai_extension/tests/test_pipeline_execution_positive.py +++ /dev/null @@ -1,46 +0,0 @@ -import os -import tempfile -import pytest -import copy -import json - -from multiprocessing import Process - -def test_pipeline_execution_positive(helpers, test_case, test_filename, generate): - #Create copy of test case to create the generated file - _test_case = copy.deepcopy(test_case) - - helpers.run_server(test_case["server_params"]) - - #Create temporary directory for saving output - workdir_path = tempfile.TemporaryDirectory() - - output_locations = [] - proc = [] - counter = 0 - for _ in range(test_case["num_of_concurrent_clients"]): - output_file = "output" + str(counter) + ".jsonl" - counter += 1 - output_location = os.path.join(workdir_path.name, output_file) - output_locations.append(output_location) - test_case["client_params"]["output_location"] = output_location - p = Process(target=helpers.run_client(test_case["client_params"])) - p.start() - proc.append(p) - for p in proc: - p.join() - - for output_file in output_locations: - helpers.validate_output_against_schema(output_file) - - if test_case["golden_results"]: - results = [] - for output_file in output_locations: - helpers.get_results_from_file(output_file, results) - if generate: - _test_case["results"] = results - with open(test_filename+'.generated', "w") as test_output: - json.dump(_test_case, test_output, indent=4) - else: - assert helpers.cmp_results(results, test_case["results"], - test_case["numerical_tolerance"]), "Inference result mismatch" diff --git a/tests/.gitignore b/tests/.gitignore deleted file mode 100644 index a661690..0000000 --- a/tests/.gitignore +++ /dev/null @@ -1 +0,0 @@ -*results* \ No newline at end of file diff --git a/tests/Dockerfile b/tests/Dockerfile deleted file mode 100644 index 0d782ab..0000000 --- a/tests/Dockerfile +++ /dev/null @@ -1,39 +0,0 @@ -ARG BASE="video-analytics-serving-gstreamer" -FROM ${BASE} as video-analytics-serving-tests - -USER root - -# Dependencies from apt-get -RUN DEBIAN_FRONTEND=noninteractive apt-get update && \ - apt-get install -y -q --no-install-recommends \ - python3-dev \ - clamav \ - clamav-daemon \ - gcc && \ - rm -rf /var/lib/apt/lists/* - - -# Dependencies installed via pip -COPY ./samples/lva_ai_extension/requirements.txt /home/video-analytics-serving/requirements.lva.txt -RUN if [ "$FRAMEWORK" = "gstreamer" ]; then pip3 install --no-cache-dir -r /home/video-analytics-serving/requirements.lva.txt; fi -RUN rm -f /home/video-analytics-serving/requirements.lva.txt -COPY ./tests/requirements.tests.txt /home/video-analytics-serving -RUN pip3 install --no-cache-dir -r /home/video-analytics-serving/requirements.tests.txt -RUN rm -f /home/video-analytics-serving/requirements.tests.txt - -# Copy tests and sample files -COPY tests/ /home/video-analytics-serving/tests/ -COPY samples/ /home/video-analytics-serving/samples/ -COPY models_list/models.list.yml /home/video-analytics-serving/models/models.list.yml - -# Copy and rename for stringified version - -RUN mkdir -p /home/video-analytics-serving/tests/test_cases/pipeline_execution/cpu/emotion_recognition_stringver_cpu_gstreamer_models/emotion_recognition/stringified_version; cp -rf /home/video-analytics-serving/models/emotion_recognition/1/* /home/video-analytics-serving/tests/test_cases/pipeline_execution/cpu/emotion_recognition_stringver_cpu_gstreamer_models/emotion_recognition/stringified_version - -RUN mkdir -p /home/video-analytics-serving/tests/test_cases/pipeline_execution/cpu/emotion_recognition_stringver_cpu_gstreamer_models/face_detection_retail/1; cp -rf /home/video-analytics-serving/models/face_detection_retail/1/* /home/video-analytics-serving/tests/test_cases/pipeline_execution/cpu/emotion_recognition_stringver_cpu_gstreamer_models/face_detection_retail/1 - -ARG USER=vaserving - -USER ${USER} - -ENTRYPOINT [ "./tests/entrypoint/pytest.sh" ] diff --git a/tests/README.md b/tests/README.md deleted file mode 100644 index 27dacf1..0000000 --- a/tests/README.md +++ /dev/null @@ -1,24 +0,0 @@ -# Video Analytics Serving Tests and Scans - -## Tests Run Script Reference -The `run.sh` can be used to run tests or scans with option to select only one at a time, default selected as --pytest-gstreamer. It uses [docker/run.sh](../docker/run.sh) to pass common options to the underlying docker run command [Reference Doc](../docs/run_script_reference.md). -``` -$ tests/run.sh --help -usage: run.sh - [ --pytest-gstreamer : Run gstreamer tests ] - [ --pytest-ffmpeg: Run ffmpeg tests ] - [ --pylint : Run pylint scan ] - [ --pybandit: Run pybandit scan ] - [ --clamav : Run antivirus scan ] -``` - -### Architecture -* RESULTS_DIR : Results directory is created and volume mounted according to test or scan selected, Directory path is Set as environment varible used by entrypoint scripts to save results in the directory. -* ENTRYPOINT: selected in the script according to test or scan selected. -* Entrypoint Args can be changed through environment variable defined in respective entrypoint scripts, or through --entrypoint-args option. - -#### Entrypoint Scripts -Entrypoint directory used to maintain Docker entrypoint scripts to run tests and scans. Each entrypoint script contains below mentioned details if supported. -* Support to read RESULTS_DIR environment variable to save results in that path. -* Other environment args required and that can be updated from run.sh. -* Command to run respective tests/scans. \ No newline at end of file diff --git a/tests/__init__.py b/tests/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/tests/build.sh b/tests/build.sh deleted file mode 100755 index d9ab97f..0000000 --- a/tests/build.sh +++ /dev/null @@ -1,32 +0,0 @@ -#!/bin/bash -e -# -# Copyright (C) 2019 Intel Corporation. -# -# SPDX-License-Identifier: BSD-3-Clause -# - -DIR=$(dirname $(readlink -f "$0")) - -function launch { $@ - local exit_code=$? - if [ $exit_code -ne 0 ]; then - echo "ERROR: error with $1" >&2 - exit $exit_code - fi - return $exit_code -} - -launch ". $DIR/../docker/build.sh $@ --dockerfile-dir $DIR/../docker" - -#VA_SERVING_TAG is used to explicitly define the TAG that was used for building VA Serving -#TAG variable is set through the build script above -VA_SERVING_TAG=$TAG - -DOCKERFILE_DIR=$(dirname "$(readlink -f "$0")") -SOURCE_DIR=$(dirname $DOCKERFILE_DIR) -BUILD_ARGS=$(env | cut -f1 -d= | grep -E '_(proxy|REPO|VER)$' | sed 's/^/--build-arg / ' | tr '\n' ' ') -BUILD_OPTIONS="--network=host --no-cache" -BUILD_ARGS+=" --build-arg BASE=$VA_SERVING_TAG --build-arg FRAMEWORK=$FRAMEWORK" -TAG="$VA_SERVING_TAG-tests:latest" - -launch "docker build -f $DOCKERFILE_DIR/Dockerfile $BUILD_OPTIONS $BUILD_ARGS -t $TAG $SOURCE_DIR" diff --git a/tests/cis_dockerbench.sh b/tests/cis_dockerbench.sh deleted file mode 100755 index c5d7ddb..0000000 --- a/tests/cis_dockerbench.sh +++ /dev/null @@ -1,29 +0,0 @@ -#!/bin/bash -TARGET_IMAGE=${IMAGE:-video-analytics-serving-gstreamer:latest} -TESTS_DIR=$(dirname "$(readlink -f "$0")") -SOURCE_DIR=$(dirname $TESTS_DIR) -RESULTS_DIR=${RESULTS_DIR:-"$TESTS_DIR/results/dockerbench"} -OUTPUT_DIR="/usr/local/bin/results" -OUTPUT_NAME=${OUTPUT_NAME:-"docker.benchmark.results.txt"} -OUTPUT_FILE="$OUTPUT_DIR/$OUTPUT_NAME" - -echo "Sending output to: $RESULTS_DIR/$OUTPUT_NAME" -mkdir -p "$RESULTS_DIR" - -#INTERACTIVE_MODE="-it --entrypoint /bin/sh" -docker_bench_args="-t $TARGET_IMAGE -l $OUTPUT_FILE" - -docker run $INTERACTIVE --net host --pid host --userns host \ - --cap-add audit_control \ - -e DOCKER_CONTENT_TRUST=$DOCKER_CONTENT_TRUST \ - -e TARGET_IMAGE=$TARGET_IMAGE \ - -v /etc:/etc:ro \ - -v /lib/systemd/system:/lib/systemd/system:ro \ - -v /usr/bin/containerd:/usr/bin/containerd:ro \ - -v /usr/bin/runc:/usr/bin/runc:ro \ - -v /usr/lib/systemd:/usr/lib/systemd:ro \ - -v /var/lib:/var/lib:ro \ - -v /var/run/docker.sock:/var/run/docker.sock:ro \ - -v $RESULTS_DIR:$OUTPUT_DIR:rw \ - --label docker_bench_security \ -docker/docker-bench-security $docker_bench_args diff --git a/tests/common/results_processing.py b/tests/common/results_processing.py deleted file mode 100644 index f13e968..0000000 --- a/tests/common/results_processing.py +++ /dev/null @@ -1,137 +0,0 @@ -''' -* Copyright (C) 2019-2020 Intel Corporation. -* -* SPDX-License-Identifier: BSD-3-Clause -''' - -import os -import math -import json -from threading import Thread -from vaserving.common.utils import logging - -def parse_gstreamer_results(output_file): - results = [] - for x in output_file: - if (len(x.strip()) != 0): - results.append(json.loads(x)) - return results - -def parse_ffmpeg_results(output_file): - results = [] - start_marker = "{\"resolution\":" - data = output_file.read(len(start_marker)) - while True: - x = output_file.read(len(start_marker)) - if (x): - data += x - else: - break - end = data.rfind(start_marker, 1) - if (end != -1): - message = data[: end] - data = data[end:] - results.append(json.loads(message)) - results.append(json.loads(data)) - return results - -def get_results_file(test_case, results): - if test_case["request"]["destination"]["type"] == "file": - filename = test_case["request"]["destination"]["path"] - with open(filename) as results_file: - if test_case["request"]["destination"]["format"] == "json": - results.extend(json.load(results_file)) - else: - if (os.environ["FRAMEWORK"] == "ffmpeg"): - parse_func = parse_ffmpeg_results - else: - parse_func = parse_gstreamer_results - - results.extend(parse_func(results_file)) - -def get_results_fifo(test_case, results): - if test_case["request"]["destination"]["type"] == "file": - if "fifo" in test_case["request"]["destination"]["path"]: - thread = Thread(target=_get_results_fifo, args=[test_case, results], daemon=True) - thread.start() - return thread - return None - -def _get_results_fifo(test_case, results): - fifo_name = test_case["request"]["destination"]["path"] - os.makedirs(os.path.dirname(fifo_name), exist_ok=True) - - try: - os.remove(fifo_name) - except: - pass - - os.mkfifo(fifo_name) - - fifo = open(fifo_name, "r") - - if (os.environ["FRAMEWORK"] == "ffmpeg"): - parse_func = parse_ffmpeg_results - else: - parse_func = parse_gstreamer_results - - results.extend(parse_func(fifo)) - fifo.close() - os.remove(fifo_name) - -def clear_results(test_case): - if test_case["request"]["destination"]["type"] == "file": - try: - os.remove(test_case["request"]["destination"]["path"]) - except: - pass - -def cmp_results(measured, expected, tolerance): - - if measured == expected: - return True - - assert type(measured) == type(expected), "Type Comparison Mismatch" - - if isinstance(measured, int): - if expected != 0: - msg = "Measured Value {} not within tolerance ({}) of Expected Value {}" - assert (abs(measured-expected) / abs(expected) <= math.ceil(tolerance)), \ - msg.format(measured, math.ceil(tolerance), expected) - else: - msg = "Measured Value {} not within tolerance ({}) of Expected Value {}" - assert tolerance > 1, \ - msg.format(measured, tolerance, expected) - return True - - if isinstance(measured, float): - if expected != 0: - msg = "Measured Value {} not within tolerance ({}) of Expected Value {}" - assert (abs(measured-expected) / abs(expected)) < tolerance, \ - msg.format(measured, tolerance, expected) - else: - msg = "Measured Value {} not within tolerance ({}) of Expected Value {}" - assert tolerance > 1, \ - msg.format(measured, tolerance, expected) - - return True - - if isinstance(measured, list): - assert len(measured) == len(expected), "List length not equal" - - for measured1, expected1 in zip(measured, expected): - assert cmp_results(measured1, expected1, tolerance), "List items not equal" - return True - - if isinstance(measured, dict): - assert len(measured) == len(expected), "Dictionary length not equal" - for key in measured: - assert key in expected, "Dictionary keys not equal" - if key.endswith("id"): - assert measured[key] == expected[key], "{} doesn't match".format(key) - return True - assert cmp_results(measured[key], expected[key], tolerance), "Dictionaries not equal" - return True - - assert measured == expected, "Values not equal" - return True diff --git a/tests/config/.pylintrc b/tests/config/.pylintrc deleted file mode 100644 index 0016ac5..0000000 --- a/tests/config/.pylintrc +++ /dev/null @@ -1,606 +0,0 @@ -[MASTER] - -# A comma-separated list of package or module names from where C extensions may -# be loaded. Extensions are loading into the active Python interpreter and may -# run arbitrary code. -extension-pkg-whitelist=cv2 - -# Add files or directories to the blacklist. They should be base names, not -# paths. -ignore=CVS - -# Add files or directories matching the regex patterns to the blacklist. The -# regex matches against base names, not paths. -#ignore-patterns=**autogen** - -# VAS0.4.0 - Until --ignore parameter and other methods are fixed, -# we manually ignore subfolders using init-hook (until PyLint issue #2686 is resolved) - -# Python code to execute, usually for sys.path manipulation such as -# pygtk.require(). -init-hook= - import sys; sys.path.append(os.getcwd()); - from tests.vas_pylint_ignore import VASPylintIgnore; - default_ignore_paths = "/home/video-analytics-serving/samples/lva_ai_extension/common/grpc_autogen/," \ - "/home/video-analytics-serving/tests,"\ - "/home/video-analytics-serving/samples/lva_ai_extension/tests" - ignore_paths = os.getenv('PYLINT_IGNORE_PATHS', default_ignore_paths) - VASPylintIgnore(tuple(ignore_paths.split(','))) - -# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the -# number of processors available to use. -jobs=1 - -# Control the amount of potential inferred values when inferring a single -# object. This can help the performance when dealing with large functions or -# complex, nested conditions. -limit-inference-results=100 - -# List of plugins (as comma separated values of python module names) to load, -# usually to register additional checkers. -load-plugins= - -# Pickle collected data for later comparisons. -persistent=yes - -# Specify a configuration file. -#rcfile= - -# When enabled, pylint would attempt to guess common misconfiguration and emit -# user-friendly hints instead of false-positive error messages. -suggestion-mode=yes - -# Allow loading of arbitrary C extensions. Extensions are imported into the -# active Python interpreter and may run arbitrary code. -unsafe-load-any-extension=no - - -[MESSAGES CONTROL] - -# Only show warnings with the listed confidence levels. Leave empty to show -# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED. -confidence= - -# Disable the message, report, category or checker with the given id(s). You -# can either give multiple identifiers separated by comma (,) or put this -# option multiple times (only on the command line, not in the configuration -# file where it should appear only once). You can also use "--disable=all" to -# disable everything first and then reenable specific checks. For example, if -# you want to run only the similarities checker, you can use "--disable=all -# --enable=similarities". If you want to run only the classes checker, but have -# no Warning level messages displayed, use "--disable=all --enable=classes -# --disable=W". -disable= C0116, # docstring - W0511, # fixme - C0415, # import out of top level, - W0703, # broad Except, - W0621, # outer name, - C0115, # class doc string - R0902, # too many instance attributes - R0201, # refactor for self methods - C0325, # superflous parens - W1202, # use of formatting TBD: remove - R0401, # circular import - TBD: remove - print-statement, - parameter-unpacking, - unpacking-in-except, - old-raise-syntax, - backtick, - long-suffix, - old-ne-operator, - old-octal-literal, - import-star-module-level, - non-ascii-bytes-literal, - raw-checker-failed, - bad-inline-option, - locally-disabled, - file-ignored, - suppressed-message, - useless-suppression, - deprecated-pragma, - use-symbolic-message-instead, - apply-builtin, - basestring-builtin, - buffer-builtin, - cmp-builtin, - coerce-builtin, - execfile-builtin, - file-builtin, - long-builtin, - raw_input-builtin, - reduce-builtin, - standarderror-builtin, - unicode-builtin, - xrange-builtin, - coerce-method, - delslice-method, - getslice-method, - setslice-method, - no-absolute-import, - old-division, - dict-iter-method, - dict-view-method, - next-method-called, - metaclass-assignment, - indexing-exception, - raising-string, - reload-builtin, - oct-method, - hex-method, - nonzero-method, - cmp-method, - input-builtin, - round-builtin, - intern-builtin, - unichr-builtin, - map-builtin-not-iterating, - zip-builtin-not-iterating, - range-builtin-not-iterating, - filter-builtin-not-iterating, - using-cmp-argument, - eq-without-hash, - div-method, - idiv-method, - rdiv-method, - exception-message-attribute, - invalid-str-codec, - sys-max-int, - bad-python3-import, - deprecated-string-function, - deprecated-str-translate-call, - deprecated-itertools-function, - deprecated-types-field, - next-method-defined, - dict-items-not-iterating, - dict-keys-not-iterating, - dict-values-not-iterating, - deprecated-operator-function, - deprecated-urllib-function, - xreadlines-attribute, - deprecated-sys-function, - exception-escape, - comprehension-escape - -# Enable the message, report, category or checker with the given id(s). You can -# either give multiple identifier separated by comma (,) or put this option -# multiple time (only on the command line, not in the configuration file where -# it should appear only once). See also the "--disable" option for examples. -enable=c-extension-no-member - - -[REPORTS] - -# Python expression which should return a score less than or equal to 10. You -# have access to the variables 'error', 'warning', 'refactor', and 'convention' -# which contain the number of messages in each category, as well as 'statement' -# which is the total number of statements analyzed. This score is used by the -# global evaluation report (RP0004). -evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10) - -# Template used to display messages. This is a python new-style format string -# used to format the message information. See doc for all details. -#msg-template= - -# Set the output format. Available formats are text, parseable, colorized, json -# and msvs (visual studio). You can also give a reporter class, e.g. -# mypackage.mymodule.MyReporterClass. -output-format=json - -# Tells whether to display a full report or only the messages. -reports=yes - -# Activate the evaluation score. -score=yes - - -[REFACTORING] - -# Maximum number of nested blocks for function / method body -max-nested-blocks=5 - -# Complete name of functions that never returns. When checking for -# inconsistent-return-statements if a never returning function is called then -# it will be considered as an explicit return statement and no message will be -# printed. -never-returning-functions=sys.exit - - -[FORMAT] - -# Expected format of line ending, e.g. empty (any line ending), LF or CRLF. -expected-line-ending-format= - -# Regexp for a line that is allowed to be longer than the limit. -ignore-long-lines=^\s*(# )??$ - -# Number of spaces of indent required inside a hanging or continued line. -indent-after-paren=4 - -# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1 -# tab). -indent-string=' ' - -# Maximum number of characters on a single line. -max-line-length=120 - -# Maximum number of lines in a module. -max-module-lines=1000 - -# List of optional constructs for which whitespace checking is disabled. `dict- -# separator` is used to allow tabulation in dicts, etc.: {1 : 1,\n222: 2}. -# `trailing-comma` allows a space between comma and closing bracket: (a, ). -# `empty-line` allows space-only lines. -no-space-check=trailing-comma, - dict-separator - -# Allow the body of a class to be on the same line as the declaration if body -# contains single statement. -single-line-class-stmt=no - -# Allow the body of an if to be on the same line as the test if there is no -# else. -single-line-if-stmt=no - - -[TYPECHECK] - -# List of decorators that produce context managers, such as -# contextlib.contextmanager. Add to this list to register other decorators that -# produce valid context managers. -contextmanager-decorators=contextlib.contextmanager - -# List of members which are set dynamically and missed by pylint inference -# system, and so shouldn't trigger E1101 when accessed. Python regular -# expressions are accepted. -generated-members= - -# Tells whether missing members accessed in mixin class should be ignored. A -# mixin class is detected if its name ends with "mixin" (case insensitive). -ignore-mixin-members=yes - -# Tells whether to warn about missing members when the owner of the attribute -# is inferred to be None. -ignore-none=yes - -# This flag controls whether pylint should warn about no-member and similar -# checks whenever an opaque object is returned when inferring. The inference -# can return multiple potential results while evaluating a Python object, but -# some branches might not be evaluated, which results in partial inference. In -# that case, it might be useful to still emit no-member and other checks for -# the rest of the inferred objects. -ignore-on-opaque-inference=yes - -# List of class names for which member attributes should not be checked (useful -# for classes with dynamically set attributes). This supports the use of -# qualified names. -ignored-classes=optparse.Values,thread._local,_thread._local, - extension__pb2.MediaStreamMessage, - MediaStreamMessage - -# List of module names for which member attributes should not be checked -# (useful for modules/projects where namespaces are manipulated during runtime -# and thus existing member attributes cannot be deduced by static analysis). It -# supports qualified module names, as well as Unix pattern matching. -ignored-modules= - -# Show a hint with possible names when a member name was not found. The aspect -# of finding the hint is based on edit distance. -missing-member-hint=yes - -# The minimum edit distance a name should have in order to be considered a -# similar match for a missing member name. -missing-member-hint-distance=1 - -# The total number of similar names that should be taken in consideration when -# showing a hint for a missing member. -missing-member-max-choices=1 - -# List of decorators that change the signature of a decorated function. -signature-mutators= - - -[SIMILARITIES] - -# Ignore comments when computing similarities. -ignore-comments=yes - -# Ignore docstrings when computing similarities. -ignore-docstrings=yes - -# Ignore imports when computing similarities. -ignore-imports=no - -# Minimum lines number of a similarity. -# TODO FIXME -min-similarity-lines=15 - - -[MISCELLANEOUS] - -# List of note tags to take in consideration, separated by a comma. -notes=FIXME, - XXX, - TODO - - -[VARIABLES] - -# List of additional names supposed to be defined in builtins. Remember that -# you should avoid defining new builtins when possible. -additional-builtins= - -# Tells whether unused global variables should be treated as a violation. -allow-global-unused-variables=yes - -# List of strings which can identify a callback function by name. A callback -# name must start or end with one of those strings. -callbacks=cb_, - _cb - -# A regular expression matching the name of dummy variables (i.e. expected to -# not be used). -dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_ - -# Argument names that match this expression will be ignored. Default to name -# with leading underscore. -ignored-argument-names=_.*|^ignored_|^unused_ - -# Tells whether we should check for unused import in __init__ files. -init-import=no - -# List of qualified module names which can have objects that can redefine -# builtins. -redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io - - -[BASIC] - -# Naming style matching correct argument names. -argument-naming-style=snake_case - -# Regular expression matching correct argument names. Overrides argument- -# naming-style. -#argument-rgx= - -# Naming style matching correct attribute names. -attr-naming-style=snake_case - -# Regular expression matching correct attribute names. Overrides attr-naming- -# style. -#attr-rgx= - -# Bad variable names which should always be refused, separated by a comma. -bad-names=foo, - bar, - baz, - toto, - tutu, - tata - -# Naming style matching correct class attribute names. -class-attribute-naming-style=any - -# Regular expression matching correct class attribute names. Overrides class- -# attribute-naming-style. -#class-attribute-rgx= - -# Naming style matching correct class names. -class-naming-style=PascalCase - -# Regular expression matching correct class names. Overrides class-naming- -# style. -#class-rgx= - -# Naming style matching correct constant names. -const-naming-style=any - -# Regular expression matching correct constant names. Overrides const-naming- -# style. -#const-rgx= - -# Minimum line length for functions/classes that require docstrings, shorter -# ones are exempt. -docstring-min-length=-1 - -# Naming style matching correct function names. -function-naming-style=snake_case - -# Regular expression matching correct function names. Overrides function- -# naming-style. -#function-rgx= - -# Good variable names which should always be accepted, separated by a comma. -good-names=x, # added x - i, - j, - k, - ex, - Run, - _ - -# Include a hint for the correct naming format with invalid-name. -include-naming-hint=no - -# Naming style matching correct inline iteration names. -inlinevar-naming-style=any - -# Regular expression matching correct inline iteration names. Overrides -# inlinevar-naming-style. -#inlinevar-rgx= - -# Naming style matching correct method names. -method-naming-style=snake_case - -# Regular expression matching correct method names. Overrides method-naming- -# style. -#method-rgx= - -# Naming style matching correct module names. -module-naming-style=snake_case - -# Regular expression matching correct module names. Overrides module-naming- -# style. -#module-rgx= - -# Colon-delimited sets of names that determine each other's naming style when -# the name regexes allow several styles. -name-group= - -# Regular expression which should only match function or class names that do -# not require a docstring. -no-docstring-rgx=^_ - -# List of decorators that produce properties, such as abc.abstractproperty. Add -# to this list to register other decorators that produce valid properties. -# These decorators are taken in consideration only for invalid-name. -property-classes=abc.abstractproperty - -# Naming style matching correct variable names. -variable-naming-style=snake_case - -# Regular expression matching correct variable names. Overrides variable- -# naming-style. -#variable-rgx= - - -[SPELLING] - -# Limits count of emitted suggestions for spelling mistakes. -max-spelling-suggestions=4 - -# Spelling dictionary name. Available dictionaries: none. To make it work, -# install the python-enchant package. -spelling-dict= - -# List of comma separated words that should not be checked. -spelling-ignore-words= - -# A path to a file that contains the private dictionary; one word per line. -spelling-private-dict-file= - -# Tells whether to store unknown words to the private dictionary (see the -# --spelling-private-dict-file option) instead of raising a message. -spelling-store-unknown-words=no - - -[STRING] - -# This flag controls whether the implicit-str-concat-in-sequence should -# generate a warning on implicit string concatenation in sequences defined over -# several lines. -check-str-concat-over-line-jumps=no - - -[LOGGING] - -# Format style used to check logging format string. `old` means using % -# formatting, `new` is for `{}` formatting,and `fstr` is for f-strings. -logging-format-style=old - -# Logging modules to check that the string format arguments are in logging -# function parameter format. -logging-modules=logging - - -[DESIGN] - -# Maximum number of arguments for function / method. -max-args=10 - -# Maximum number of attributes for a class (see R0902). -max-attributes=7 - -# Maximum number of boolean expressions in an if statement (see R0916). -max-bool-expr=5 - -# Maximum number of branch for function / method body. -max-branches=12 - -# Maximum number of locals for function / method body. -max-locals=15 - -# Maximum number of parents for a class (see R0901). -max-parents=7 - -# Maximum number of public methods for a class (see R0904). -max-public-methods=20 - -# Maximum number of return / yield for function / method body. -max-returns=6 - -# Maximum number of statements in function / method body. -max-statements=50 - -# Minimum number of public methods for a class (see R0903). -min-public-methods=2 - - -[IMPORTS] - -# List of modules that can be imported at any level, not just the top level -# one. -allow-any-import-level= - -# Allow wildcard imports from modules that define __all__. -allow-wildcard-with-all=no - -# Analyse import fallback blocks. This can be used to support both Python 2 and -# 3 compatible code, which means that the block might have code that exists -# only in one or another interpreter, leading to false positives when analysed. -analyse-fallback-blocks=no - -# Deprecated modules which should not be used, separated by a comma. -deprecated-modules=optparse,tkinter.tix - -# Create a graph of external dependencies in the given file (report RP0402 must -# not be disabled). -ext-import-graph= - -# Create a graph of every (i.e. internal and external) dependencies in the -# given file (report RP0402 must not be disabled). -import-graph= - -# Create a graph of internal dependencies in the given file (report RP0402 must -# not be disabled). -int-import-graph= - -# Force import order to recognize a module as part of the standard -# compatibility libraries. -known-standard-library= - -# Force import order to recognize a module as part of a third party library. -known-third-party=enchant - -# Couples of modules and preferred modules, separated by a comma. -preferred-modules= - - -[CLASSES] - -# List of method names used to declare (i.e. assign) instance attributes. -defining-attr-methods=__init__, - __new__, - setUp, - __post_init__ - -# List of member names, which should be excluded from the protected access -# warning. -exclude-protected=_asdict, - _fields, - _replace, - _source, - _make - -# List of valid names for the first argument in a class method. -valid-classmethod-first-arg=cls - -# List of valid names for the first argument in a metaclass class method. -valid-metaclass-classmethod-first-arg=cls - - -[EXCEPTIONS] - -# Exceptions that will emit a warning when being caught. Defaults to -# "BaseException, Exception". -overgeneral-exceptions=BaseException, - Exception diff --git a/tests/config/coveragerc_ffmpeg b/tests/config/coveragerc_ffmpeg deleted file mode 100644 index 1f2a709..0000000 --- a/tests/config/coveragerc_ffmpeg +++ /dev/null @@ -1,5 +0,0 @@ -[run] -omit = *gstreamer*,*app_destination*,*app_source* - -[report] -omit = *gstreamer*,*app_destination*,*app_source* diff --git a/tests/config/coveragerc_gstreamer b/tests/config/coveragerc_gstreamer deleted file mode 100644 index 6e61fcd..0000000 --- a/tests/config/coveragerc_gstreamer +++ /dev/null @@ -1,5 +0,0 @@ -[run] -omit = *ffmpeg* - -[report] -omit = *ffmpeg* diff --git a/tests/conftest.py b/tests/conftest.py deleted file mode 100644 index b5b2406..0000000 --- a/tests/conftest.py +++ /dev/null @@ -1,211 +0,0 @@ -''' -* Copyright (C) 2019 Intel Corporation. -* -* SPDX-License-Identifier: BSD-3-Clause -''' - -import pytest -import subprocess -import json -import psutil -import time -import requests -from collections import namedtuple -import os -import sys -from vaserving.vaserving import VAServing as _VAServing -import signal - -TIMEOUT = 30 -MAX_CONNECTION_ATTEMPTS = 5 -class VAServingService: - - VASERVING_ARGS = ["python3", "-m", "vaserving"] - - def kill(self, timeout=10): - graceful_exit = True - if (self._process): - self._process.send_signal(signal.SIGINT) - start = time.time() - while ((self._process.poll() == None) and ((time.time()-start) < timeout)): - time.sleep(1) - if ((self._process.returncode == None) or (self._process.returncode != 0)): - graceful_exit = False - else: - self._process.kill() - self._process = None - return graceful_exit - - def __del__(self): - self.kill() - - def kill_all(self): - for proc in psutil.process_iter(): - if "vaserving" in proc.cmdline(): - proc.kill() - - def get_log_message(self, line): - try: - log_message = json.loads(line) - if "levelname" in log_message and "message" in log_message: - return log_message - except ValueError: - print("Invalid JSON: %s" % (line)) - return None - - def __init__(self): - self.kill_all() - self.host = "http://localhost:8080" - self._process = subprocess.Popen(VAServingService.VASERVING_ARGS, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - bufsize=1, - universal_newlines=True) - self._process.poll() - while self._process.returncode is None: - next_line = self._process.stderr.readline() - try: - message = self.get_log_message(next_line) - if message: - ignore_init_errors = os.getenv('IGNORE_INIT_ERRORS', False) - if message["levelname"] == "ERROR" and not ignore_init_errors: - raise Exception(next_line) - if message["message"] == "Starting Tornado Server on port: 8080": - attempts = MAX_CONNECTION_ATTEMPTS - while (attempts): - try: - result = requests.get("http://localhost:8080", timeout=TIMEOUT) - except requests.ConnectionError as error: - time.sleep(1) - attempts -= 1 - else: - return - raise Exception("VA Serving Not Launched") - except Exception as error: - self._process.kill() - self._process = None - assert False, "VA Serving Not Launched" - raise - self._process.poll() - if self._process.returncode != 0: - assert False - - def get_models(self): - pass - - -def pytest_addoption(parser): - parser.addoption("--generate", action="store_true", help="generate expected results", - default=False) - parser.addoption("--framework", help="ffmpeg or gstreamer", choices=['ffmpeg', 'gstreamer'], - default=os.environ["FRAMEWORK"]) - parser.addoption("--numerical_tolerance", help="percentage numerical difference to tolerate", - type=float, default=0.0001) - parser.addoption("--stability", action="store_true", help="run stability tests", - default=False) - parser.addoption("--stability_duration", type=int, help="duration to run stability tests", - action="store", default=None) - parser.addoption("--cpu", action="store_true", help="Run CPU tests", - default=True) - parser.addoption("--no-cpu", action="store_false", dest='cpu', help="Disable CPU tests") - parser.addoption("--gpu", action="store_true", help="Run GPU tests", - default=False) - parser.addoption("--myriad", action="store_true", help="Run MYRIAD tests", - default=False) - -def pytest_configure(config): - config.addinivalue_line("markers", "stability: run stability tests") - -def pytest_collection_modifyitems(config, items): - if config.getoption("--stability"): - return - skip_stability = pytest.mark.skip(reason="add --stability option to run stability tests") - for item in items: - if "stability" in item.keywords: - item.add_marker(skip_stability) - -@pytest.fixture -def stability_duration(request): - return request.config.getoption("--stability_duration") - -@pytest.fixture -def numerical_tolerance(request): - return request.config.getoption("--numerical_tolerance") - -def load_test_cases(metafunc, directory): - known_frameworks = ['ffmpeg', 'gstreamer'] - dir_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_cases", directory) - list_of_dir_paths = [dir_path] - - filenames = [] - if metafunc.config.getoption("cpu"): - cpu_path = os.path.join(dir_path, "cpu") - if os.path.isdir(cpu_path): - list_of_dir_paths.append(cpu_path) - if metafunc.config.getoption("gpu"): - gpu_path = os.path.join(dir_path, "gpu") - if os.path.isdir(gpu_path): - list_of_dir_paths.append(gpu_path) - - if metafunc.config.getoption("myriad"): - gpu_path = os.path.join(dir_path, "myriad") - if os.path.isdir(gpu_path): - list_of_dir_paths.append(gpu_path) - - for path in list_of_dir_paths: - dir_filenames = [(os.path.abspath(os.path.join(path, fn)), - os.path.splitext(fn)[0]) for fn in os.listdir(path) - if os.path.isfile(os.path.join(path, fn)) and - os.path.splitext(fn)[1] == '.json'] - filenames.extend(dir_filenames) - framework = metafunc.config.getoption("framework") - filenames = [filename for filename in filenames - if filename[1].split('_')[-1] == framework or - filename[1].split('_')[-1] not in known_frameworks] - test_cases = [] - test_names = [] - generate = metafunc.config.getoption("generate") - for filepath, testname in filenames: - try: - with open(filepath) as json_file: - test_cases.append((json.load(json_file), filepath, generate)) - test_names.append(testname) - except Exception as error: - print(error) - assert False, "Error Reading Test Case" - return (test_cases, test_names) - -def pytest_generate_tests(metafunc): - if "rest_api" in metafunc.function.__name__: - test_cases, test_names = load_test_cases(metafunc, "rest_api") - metafunc.parametrize("test_case,test_filename,generate", test_cases, ids=test_names) - if "rest_execution" in metafunc.function.__name__: - test_cases, test_names = load_test_cases(metafunc, "rest_execution") - metafunc.parametrize("test_case,test_filename,generate", test_cases, ids=test_names) - if "initialization" in metafunc.function.__name__: - test_cases, test_names = load_test_cases(metafunc, "initialization") - metafunc.parametrize("test_case,test_filename,generate", test_cases, ids=test_names) - if "pipeline_execution" in metafunc.function.__name__: - test_cases, test_names = load_test_cases(metafunc, "pipeline_execution") - metafunc.parametrize("test_case,test_filename,generate", test_cases, ids=test_names) - if "pipeline_stability" in metafunc.function.__name__: - test_cases, test_names = load_test_cases(metafunc, "pipeline_stability") - metafunc.parametrize("test_case,test_filename,generate", test_cases, ids=test_names) - if "pipeline_performance" in metafunc.function.__name__: - test_cases, test_names = load_test_cases(metafunc, "pipeline_performance") - metafunc.parametrize("test_case,test_filename,generate", test_cases, ids=test_names) - - print(metafunc.fixturenames) - print(metafunc.function, flush=True) - -@pytest.fixture() -def VAServing(request): - _VAServing.stop() - yield _VAServing - _VAServing.stop() - -@pytest.fixture(scope="session") -def service(request): - proxy = VAServingService() - yield proxy - assert proxy.kill(), "VA Serving failed to exit gracefully on sig int" diff --git a/tests/entrypoint/clamav.sh b/tests/entrypoint/clamav.sh deleted file mode 100755 index 5a8791a..0000000 --- a/tests/entrypoint/clamav.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash -ENTRYPOINT_DIR=$(dirname "$(readlink -f "$0")") -TESTS_DIR=$(dirname $ENTRYPOINT_DIR) -SOURCE_DIR=$(dirname $TESTS_DIR) - -RESULTS_DIR=${RESULTS_DIR:-"$TESTS_DIR/results/clamav"} -OUTPUT_FILE="$RESULTS_DIR/report.txt" -SCAN_DIR=${SCAN_DIR:-"/home"} -CLAMAV_ARGS=${CLAMAV_ARGS:-"$@"} - -mkdir -p "$RESULTS_DIR" - -freshclam - -clamscan -r --bell -i ${SCAN_DIR} $CLAMAV_ARGS > ${OUTPUT_FILE} \ No newline at end of file diff --git a/tests/entrypoint/pybandit.sh b/tests/entrypoint/pybandit.sh deleted file mode 100755 index bd0ab6d..0000000 --- a/tests/entrypoint/pybandit.sh +++ /dev/null @@ -1,16 +0,0 @@ -#!/bin/bash -ENTRYPOINT_DIR=$(dirname "$(readlink -f "$0")") -TESTS_DIR=$(dirname $ENTRYPOINT_DIR) -SOURCE_DIR=$(dirname $TESTS_DIR) -SCAN_DIR=${SCAN_DIR:-$SOURCE_DIR} -RESULTS_DIR=${RESULTS_DIR:-"$TESTS_DIR/results/pybandit"} -OUTPUT_FILE="$RESULTS_DIR/pybandit_report.html" -OUTPUT_FORMAT=${OUTPUT_FORMAT:-html} - -bandit --version - -mkdir -p "$RESULTS_DIR" - -echo "Command: bandit -r $SOURCE_DIR -o $OUTPUT_FILE -f $OUTPUT_FORMAT -v -n 5 -ll" -bandit -r $SOURCE_DIR -o $OUTPUT_FILE -f $OUTPUT_FORMAT -v -n 5 -ll - diff --git a/tests/entrypoint/pylint.sh b/tests/entrypoint/pylint.sh deleted file mode 100755 index 4eb3a38..0000000 --- a/tests/entrypoint/pylint.sh +++ /dev/null @@ -1,22 +0,0 @@ -#!/bin/bash -ENTRYPOINT_DIR=$(dirname "$(readlink -f "$0")") -TESTS_DIR=$(dirname $ENTRYPOINT_DIR) -SOURCE_DIR=$(dirname $TESTS_DIR) - -RESULTS_DIR=${RESULTS_DIR:-"$TESTS_DIR/results/pylint"} -OUTPUT_FILE="$RESULTS_DIR/report.txt" -OUTPUT_FORMAT=${OUTPUT_FORMAT:-text} -PYLINT_RC_FILE_PATH=${PYLINT_RC_FILE_PATH:-"$TESTS_DIR/config/.pylintrc"} -PYLINT_ARGS=${PYLINT_ARGS:-"$@"} - -pylint --version - -mkdir -p "$RESULTS_DIR" - -# Enable Recursion into Subdirectories -shopt -s globstar -echo "Processing source files in $SOURCE_DIR. Results will output to: $OUTPUT_FILE" -cd ${SOURCE_DIR}; -python3 -m pylint ${SOURCE_DIR}/**/*.py --reports=y --rcfile="$PYLINT_RC_FILE_PATH" \ - --output-format=$OUTPUT_FORMAT --score=y --exit-zero $PYLINT_ARGS > $OUTPUT_FILE -shopt -u globstar diff --git a/tests/entrypoint/pytest.sh b/tests/entrypoint/pytest.sh deleted file mode 100755 index 1497fde..0000000 --- a/tests/entrypoint/pytest.sh +++ /dev/null @@ -1,20 +0,0 @@ -#!/bin/bash - -ENTRYPOINT_DIR=$(dirname "$(readlink -f "$0")") -TESTS_DIR=$(dirname $ENTRYPOINT_DIR) -SOURCE_DIR=$(dirname $TESTS_DIR) -FRAMEWORK=${FRAMEWORK:-gstreamer} - -RESULTS_DIR=${RESULTS_DIR:-"$TESTS_DIR/results/pytest/$FRAMEWORK"} -PYTEST_ARGS=${PYTEST_ARGS:-"$@"} - -mkdir -p "$RESULTS_DIR" - -COV_CONFIG="$TESTS_DIR/config/coveragerc_${FRAMEWORK}" -COV_DIR="$RESULTS_DIR/coverage" - -rm -rf "$COV_DIR" - -cd ${SOURCE_DIR}; -python3 -m pytest -s --html="$RESULTS_DIR/report.html" --ignore=$SOURCE_DIR/samples --self-contained-html \ - 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z8t=mPG>(C7tT)m9(~Nl*^7I{^yLYxNs+ZA79l>9#@738d^pI`uS{r?gu6_~u6;@!0 z5YdYoGX@+3=|Po{@By@lHTMpq;X#=y_|mZ{8=OlsfinjiaKTYG^j3SMbkjvj|>orJ1l!$+cr;V*dy9Y(aMb diff --git a/tests/requirements.tests.txt b/tests/requirements.tests.txt deleted file mode 100644 index 8265590..0000000 --- a/tests/requirements.tests.txt +++ /dev/null @@ -1,7 +0,0 @@ -psutil == 5.7.0 -pylint == 2.4.4 -pytest == 5.4.1 -pytest-cov == 2.8.1 -teamcity-messages == 1.28 -pytest-html == 2.1.1 -bandit == 1.6.2 diff --git a/tests/run.sh b/tests/run.sh deleted file mode 100755 index cd8952c..0000000 --- a/tests/run.sh +++ /dev/null @@ -1,89 +0,0 @@ -#!/bin/bash - -ENTRYPOINT_ARGS= -DOCKER_RUN_OPTIONS= -TESTS_DIR=$(dirname "$(readlink -f "$0")") -SOURCE_DIR=$(dirname $TESTS_DIR) -INTERACTIVE=--non-interactive -FRAMEWORK=gstreamer -DOCKER_TESTS_DIR="/home/video-analytics-serving/tests" -RESULTS_DIR="results" -PYTEST_GSTREAMER_RESULTS_DIR="$RESULTS_DIR/pytest/gstreamer" -PYTEST_FFMPEG_RESULTS_DIR="$RESULTS_DIR/pytest/ffmpeg" -PYLINT_RESULTS_DIR="$RESULTS_DIR/pylint" -PYBANDIT_RESULTS_DIR="$RESULTS_DIR/pybandit" -CLAMAV_RESULTS_DIR="$RESULTS_DIR/clamav" - -# Default selected as gstreamer pytests -FRAMEWORK=gstreamer -OUTPUT_DIR="$PYTEST_GSTREAMER_RESULTS_DIR" -SELECTED="--pytest-gstreamer" -ENTRYPOINT="--entrypoint ./tests/entrypoint/pytest.sh" - -# For default gstreamer image, it requires extra permisions to generate coverage report. needs to investigate more. -# TODO: fix user permsion issue for generating reports -USER="--user root" - -function show_help { - echo "usage: run.sh (options are exclusive)" - echo " [ --pytest-gstreamer : Run gstreamer tests ]" - echo " [ --pytest-ffmpeg: Run ffmpeg tests ] " - echo " [ --pylint : Run pylint scan ] " - echo " [ --pybandit: Run pybandit scan ] " - echo " [ --clamav : Run antivirus scan ] " -} - -function error { - printf '%s\n' "$1" >&2 - exit -} - -ARGS=$@ -while [[ "$#" -gt 0 ]]; do - case $1 in - -h | -\? | --help) - show_help - exit - ;; - --pytest-gstreamer) - ;; - --pytest-ffmpeg) - OUTPUT_DIR="$PYTEST_FFMPEG_RESULTS_DIR" - FRAMEWORK=ffmpeg - SELECTED="$1" - ;; - --pylint) - OUTPUT_DIR="$PYLINT_RESULTS_DIR" - ENTRYPOINT="--entrypoint ./tests/entrypoint/pylint.sh" - SELECTED="$1" - ;; - --pybandit) - OUTPUT_DIR="$PYBANDIT_RESULTS_DIR" - ENTRYPOINT="--entrypoint ./tests/entrypoint/pybandit.sh" - SELECTED="$1" - ;; - --clamav) - OUTPUT_DIR="$CLAMAV_RESULTS_DIR" - ENTRYPOINT="--entrypoint ./tests/entrypoint/clamav.sh" - SELECTED="$1" - ;; - *) - break - ;; - esac - shift -done - -IMAGE=video-analytics-serving-${FRAMEWORK}-tests:latest - -DOCKER_RESULTS_DIR="$DOCKER_TESTS_DIR/$OUTPUT_DIR" -LOCAL_RESULTS_DIR="$TESTS_DIR/$OUTPUT_DIR" - -echo "running $SELECTED" -ENVIRONMENT="-e RESULTS_DIR=$DOCKER_RESULTS_DIR" - -mkdir -p "$LOCAL_RESULTS_DIR" - -VOLUME_MOUNT="-v $LOCAL_RESULTS_DIR:$DOCKER_RESULTS_DIR " - -$SOURCE_DIR/docker/run.sh --image $IMAGE --framework $FRAMEWORK $USER $VOLUME_MOUNT $ENVIRONMENT $INTERACTIVE $ENTRYPOINT "$@" diff --git a/tests/run_script_tests.sh b/tests/run_script_tests.sh deleted file mode 100755 index 575fa13..0000000 --- a/tests/run_script_tests.sh +++ /dev/null @@ -1,50 +0,0 @@ -#!/bin/bash -set -e - -TEST_DIR=$(dirname "$(readlink -f "$0")") -SOURCE_DIR=$(dirname $TEST_DIR) -TAG=##teamcity -echo " ${TAG}[testSuiteStarted name='script-tests'] " - - -error() { - - # Return an error message from test script - # OUTPUT and RETURN_CODE are taken from environment - - echo "=======================" - echo "Test Failed" - echo "Return Code: $RETURN_CODE" - echo "Detail: $1" - echo "=======================" - echo "Output Begin" - echo "=======================" - echo "$OUTPUT" - echo "=======================" - echo "Output End" - echo "=======================" - exit 1 -} - -# export function for use in test scripts -typeset -fx error - -run_test() { - TEST_NAME=$(basename $1) - printf " %s[testStarted name='%s' captureStandardOutput='true']\n" "$TAG" "$TEST_NAME" - (TEST_DIR=$TEST_DIR SOURCE_DIR=$SOURCE_DIR $1) || { - printf " %s[testFailed name='%s']\n" "$TAG" "$TEST_NAME" - } - printf " %s[testFinished name='%s']\n" "$TAG" "$TEST_NAME" -} - -if [ ! -z "$1" ]; then - TEST=$TEST_DIR/script_tests/$(basename $1) - run_test $TEST -else - for TEST in $TEST_DIR/script_tests/* ; do - run_test $TEST - done -fi - -echo " ${TAG}[testSuiteFinished name='script-tests'] " diff --git a/tests/script_tests/model_downloader b/tests/script_tests/model_downloader deleted file mode 100755 index fffccd2..0000000 --- a/tests/script_tests/model_downloader +++ /dev/null @@ -1,51 +0,0 @@ -#!/bin/bash -MODEL_NAMES=(mobilenet-ssd) -MODEL_NAMES+=(emotions-recognition-retail-0003) -MODEL_NAMES+=(landmarks-regression-retail-0009) -MODEL_NAMES+=(face-detection-retail-0004) - -ALIAS_NAMES=(object_detection) -ALIAS_NAMES+=(emotion_recognition) -ALIAS_NAMES+=(landmarks_regression) -ALIAS_NAMES+=(face_detection_retail) - -tmp_dir=$(mktemp -d -t model-downloader-XXXXXXXX) - -check_directory() { - model_path=$tmp_dir/models/$2/1 - if [ ! -d "$model_path" ]; then - error "Missing Model Path: $model_path" - fi - model_proc=$model_path/$1.json - if [ ! -f "$model_proc" ]; then - error "Missing Model Proc: $model_proc" - fi - model_precision=$model_path/FP32 - if [ ! -d "$model_precision" ]; then - error "Missing Model Precision: $model_precision" - fi - model_xml=$model_precision/$1.xml - if [ ! -f "$model_xml" ]; then - error "Missing Model XML: $model_xml" - fi - model_bin=$model_precision/$1.bin - if [ ! -f "$model_bin" ]; then - error "Missing Model BIN: $model_bin" - fi -} - -OUTPUT=$($SOURCE_DIR/tools/model_downloader/model_downloader.sh --model-list $SOURCE_DIR/models_list/models.list.yml --output $tmp_dir 2>&1) -RETURN_CODE=$? - -if [[ $RETURN_CODE != 0 ]]; then - rm -rf $tmp_dir - error -fi - -i=0 -for model_name in ${MODEL_NAMES[@]}; do - check_directory $model_name ${ALIAS_NAMES[$i]} - i=$i+1 -done - -rm -rf $tmp_dir diff --git a/tests/script_tests/no_models_no_pipelines_build b/tests/script_tests/no_models_no_pipelines_build deleted file mode 100755 index 9dc69b0..0000000 --- a/tests/script_tests/no_models_no_pipelines_build +++ /dev/null @@ -1,24 +0,0 @@ -#!/bin/bash - -# Test for building container without models and pipelines -# Verifies correct build argument passed to docker build -# Does not verify end to end functionality - -MODELS_COMMAND="--build-arg MODELS_COMMAND=do_not_copy_models " -PIPELINES_COMMAND="--build-arg PIPELINES_COMMAND=do_not_copy_pipelines " - -OUTPUT=$($SOURCE_DIR/docker/build.sh --models NONE --pipelines NONE --dry-run 2>&1) -RETURN_CODE=$? - -if [[ $RETURN_CODE != 0 ]]; then - error -fi - -if [[ ! "$OUTPUT" == *"$MODELS_COMMAND"* ]]; then - error "Invalid MODELS_COMMAND build-arg" -fi - -if [[ ! "$OUTPUT" == *"$PIPELINES_COMMAND"* ]]; then - error "Invalid PIPELINES_COMMAND build-arg" -fi - diff --git a/tests/script_tests/spaces_in_test_run_arguments b/tests/script_tests/spaces_in_test_run_arguments deleted file mode 100755 index 871deb6..0000000 --- a/tests/script_tests/spaces_in_test_run_arguments +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash - -ENTRYPOINT_ARGS="-c echo arg1 arg2" -EXPECTED="EntrypointArgs: '$ENTRYPOINT_ARGS '" - -OUTPUT=$($SOURCE_DIR/docker/run.sh --entrypoint /bin/bash --entrypoint-args "$ENTRYPOINT_ARGS" --dry-run) -RETURN_CODE=$? - -if [[ $RETURN_CODE != 0 ]]; then - error "docker/run.sh has failed" -fi - -if [[ ! "$OUTPUT" == *"$EXPECTED"* ]]; then - error "Invalid Entrypoint Args" -fi diff --git a/tests/stop.sh b/tests/stop.sh deleted file mode 100755 index 9450cd5..0000000 --- a/tests/stop.sh +++ /dev/null @@ -1,48 +0,0 @@ -#!/bin/bash -e -# -# Copyright (C) 2019 Intel Corporation. -# -# SPDX-License-Identifier: BSD-3-Clause -# - -#!/bin/bash - -echo "Stopping all docker containers with name starts with video-analytics-serving" -docker stop $(docker ps -f name=video-analytics-serving) || true - -echo "Removing all docker containers with name starts with video-analytics-serving" -docker rm $(docker ps -f name=video-analytics-serving) || true - -#Get options passed into script -function get_options { - while :; do - case $1 in - -h | -\? | --help) - show_help - exit - ;; - --remove) - echo "Removing all docker images with name starts with video-analytics-serving" - docker rmi $(docker images --format '{{.Repository}}:{{.Tag}}' | grep 'video-analytics-serving') || true - shift - ;; - *) - break - ;; - esac - - shift - done -} - -function show_help { - echo "usage: ./stop.sh" - echo " [ remove : removes all docker images starts with video-analytics-serving ]" -} - -function error { - printf '%s\n' "$1" >&2 - exit -} - -get_options "$@" \ No newline at end of file diff --git a/tests/test_cases/initialization/invalid_pipeline_description_gstreamer.json b/tests/test_cases/initialization/invalid_pipeline_description_gstreamer.json deleted file mode 100644 index 79bb664..0000000 --- a/tests/test_cases/initialization/invalid_pipeline_description_gstreamer.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "options": { - "ignore_init_errors" : "False" - }, - "exception": {"type":"Exception","value":"Error Initializing Pipelines"} -} diff --git a/tests/test_cases/initialization/invalid_pipeline_description_gstreamer_pipelines/invalid/1/invalid.json b/tests/test_cases/initialization/invalid_pipeline_description_gstreamer_pipelines/invalid/1/invalid.json deleted file mode 100644 index a4b1d78..0000000 --- a/tests/test_cases/initialization/invalid_pipeline_description_gstreamer_pipelines/invalid/1/invalid.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "type":"GStreamer", - "template":"identity ! identity" -} diff --git a/tests/test_cases/initialization/invalid_pipeline_type.json b/tests/test_cases/initialization/invalid_pipeline_type.json deleted file mode 100644 index fb61e79..0000000 --- a/tests/test_cases/initialization/invalid_pipeline_type.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "options": { - "ignore_init_errors" : "False" - }, - "exception": {"type":"Exception","value":"Error Initializing Pipelines"} -} diff --git a/tests/test_cases/initialization/invalid_pipeline_type_pipelines/.gitignore b/tests/test_cases/initialization/invalid_pipeline_type_pipelines/.gitignore deleted file mode 100644 index e69de29..0000000 diff --git a/tests/test_cases/initialization/invalid_pipeline_type_pipelines/invalid/1/invalid.json b/tests/test_cases/initialization/invalid_pipeline_type_pipelines/invalid/1/invalid.json deleted file mode 100644 index 9085648..0000000 --- a/tests/test_cases/initialization/invalid_pipeline_type_pipelines/invalid/1/invalid.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "type":"_invalid_", - "description":"Identity", - "template":"identity ! identity" -} diff --git a/tests/test_cases/initialization/valid_pipeline_ffmpeg.json b/tests/test_cases/initialization/valid_pipeline_ffmpeg.json deleted file mode 100644 index 9f3350f..0000000 --- a/tests/test_cases/initialization/valid_pipeline_ffmpeg.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "options": { - } -} diff --git a/tests/test_cases/initialization/valid_pipeline_ffmpeg_pipelines/valid/1/valid.json b/tests/test_cases/initialization/valid_pipeline_ffmpeg_pipelines/valid/1/valid.json deleted file mode 100644 index bd8791d..0000000 --- a/tests/test_cases/initialization/valid_pipeline_ffmpeg_pipelines/valid/1/valid.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "type": "FFmpeg", - "description": "Identity", - "template": [ - ] -} diff --git a/tests/test_cases/initialization/valid_pipeline_gstreamer.json b/tests/test_cases/initialization/valid_pipeline_gstreamer.json deleted file mode 100644 index 9f3350f..0000000 --- a/tests/test_cases/initialization/valid_pipeline_gstreamer.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "options": { - } -} diff --git a/tests/test_cases/initialization/valid_pipeline_gstreamer_pipelines/valid/1/valid.json b/tests/test_cases/initialization/valid_pipeline_gstreamer_pipelines/valid/1/valid.json deleted file mode 100644 index e95239c..0000000 --- a/tests/test_cases/initialization/valid_pipeline_gstreamer_pipelines/valid/1/valid.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "type": "GStreamer", - "template":"identity ! identity", - "description":"Identity" -} \ No newline at end of file diff --git a/tests/test_cases/initialization/valid_pipeline_string_version_ffmpeg.json b/tests/test_cases/initialization/valid_pipeline_string_version_ffmpeg.json deleted file mode 100644 index 9f3350f..0000000 --- a/tests/test_cases/initialization/valid_pipeline_string_version_ffmpeg.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "options": { - } -} diff --git a/tests/test_cases/initialization/valid_pipeline_string_version_ffmpeg_pipelines/valid/stringified_version/valid.json b/tests/test_cases/initialization/valid_pipeline_string_version_ffmpeg_pipelines/valid/stringified_version/valid.json deleted file mode 100644 index bd8791d..0000000 --- a/tests/test_cases/initialization/valid_pipeline_string_version_ffmpeg_pipelines/valid/stringified_version/valid.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "type": "FFmpeg", - "description": "Identity", - "template": [ - ] -} diff --git a/tests/test_cases/initialization/valid_pipeline_string_version_gstreamer.json b/tests/test_cases/initialization/valid_pipeline_string_version_gstreamer.json deleted file mode 100644 index 9f3350f..0000000 --- a/tests/test_cases/initialization/valid_pipeline_string_version_gstreamer.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "options": { - } -} diff --git a/tests/test_cases/initialization/valid_pipeline_string_version_gstreamer_pipelines/valid/stringified_version/valid.json b/tests/test_cases/initialization/valid_pipeline_string_version_gstreamer_pipelines/valid/stringified_version/valid.json deleted file mode 100644 index e95239c..0000000 --- a/tests/test_cases/initialization/valid_pipeline_string_version_gstreamer_pipelines/valid/stringified_version/valid.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "type": "GStreamer", - "template":"identity ! identity", - "description":"Identity" -} \ No newline at end of file diff --git a/tests/test_cases/pipeline_execution/cpu/audio_detection_gstreamer.json b/tests/test_cases/pipeline_execution/cpu/audio_detection_gstreamer.json deleted file mode 100644 index a116604..0000000 --- a/tests/test_cases/pipeline_execution/cpu/audio_detection_gstreamer.json +++ /dev/null @@ -1,307 +0,0 @@ -{ - "options": {}, - "pipeline": { - "name": "audio_detection", - "version": 1 - }, - "request": { - "source": { - "type": "uri", - "uri": "file:///home/video-analytics-serving/tests/media/how_are_you_doing.wav" - }, - "destination": { - "type": "file", - "path": "/tmp/audio_detection_results.json", - "format": "json-lines" - } - }, - "numerical_tolerance": 0.2, - "result": [ - { - "channels": 1, - "events": [ - { - "detection": { - "confidence": 0.9070715308189392, - "label": "Rain", - "label_id": 11, - "segment": { - "end_timestamp": 1000000000, - "start_timestamp": 0 - } - }, - "end_timestamp": 1000000000, - "event_type": "Rain", - "start_timestamp": 0 - } - ], - "rate": 16000, - "source": "file:///home/video-analytics-serving/tests/media/how_are_you_doing.wav", - "tags": {} - }, - { - "channels": 1, - "events": [ - { - "detection": { - "confidence": 0.5903555750846863, - "label": "Insects (flying)", - "label_id": 8, - "segment": { - "end_timestamp": 1200000000, - "start_timestamp": 200000000 - } - }, - "end_timestamp": 1200000000, - "event_type": "Insects (flying)", - "start_timestamp": 200000000 - } - ], - "rate": 16000, - "source": "file:///home/video-analytics-serving/tests/media/how_are_you_doing.wav", - "tags": {} - }, - { - "channels": 1, - "events": [ - { - "detection": { - "confidence": 0.7713412642478943, - "label": "Insects (flying)", - "label_id": 8, - "segment": { - "end_timestamp": 1400000000, - "start_timestamp": 400000000 - } - }, - "end_timestamp": 1400000000, - "event_type": "Insects (flying)", - "start_timestamp": 400000000 - } - ], - "rate": 16000, - "source": "file:///home/video-analytics-serving/tests/media/how_are_you_doing.wav", - "tags": {} - }, - { - "channels": 1, - "events": [ - { - "detection": { - "confidence": 0.9999994039535522, - "label": "Speech", - "label_id": 53, - "segment": { - "end_timestamp": 1600000000, - "start_timestamp": 600000000 - } - }, - "end_timestamp": 1600000000, - "event_type": "Speech", - "start_timestamp": 600000000 - } - ], - "rate": 16000, - "source": "file:///home/video-analytics-serving/tests/media/how_are_you_doing.wav", - "tags": {} - }, - { - "channels": 1, - "events": [ - { - "detection": { - "confidence": 1.0, - "label": "Speech", - "label_id": 53, - "segment": { - "end_timestamp": 1800000000, - "start_timestamp": 800000000 - } - }, - "end_timestamp": 1800000000, - "event_type": "Speech", - "start_timestamp": 800000000 - } - ], - "rate": 16000, - "source": "file:///home/video-analytics-serving/tests/media/how_are_you_doing.wav", - "tags": {} - }, - { - "channels": 1, - "events": [ - { - "detection": { - "confidence": 1.0, - "label": "Speech", - "label_id": 53, - "segment": { - "end_timestamp": 2000000000, - "start_timestamp": 1000000000 - } - }, - "end_timestamp": 2000000000, - "event_type": "Speech", - "start_timestamp": 1000000000 - } - ], - "rate": 16000, - "source": "file:///home/video-analytics-serving/tests/media/how_are_you_doing.wav", - "tags": {} - }, - { - "channels": 1, - "events": [ - { - "detection": { - "confidence": 1.0, - "label": "Speech", - "label_id": 53, - "segment": { - "end_timestamp": 2200000000, - "start_timestamp": 1200000000 - } - }, - "end_timestamp": 2200000000, - "event_type": "Speech", - "start_timestamp": 1200000000 - } - ], - "rate": 16000, - "source": "file:///home/video-analytics-serving/tests/media/how_are_you_doing.wav", - "tags": {} - }, - { - "channels": 1, - "events": [ - { - "detection": { - "confidence": 1.0, - "label": "Speech", - "label_id": 53, - "segment": { - "end_timestamp": 2400000000, - "start_timestamp": 1400000000 - } - }, - "end_timestamp": 2400000000, - "event_type": "Speech", - "start_timestamp": 1400000000 - } - ], - "rate": 16000, - "source": "file:///home/video-analytics-serving/tests/media/how_are_you_doing.wav", - "tags": {} - }, - { - "channels": 1, - "events": [ - { - "detection": { - "confidence": 1.0, - "label": "Speech", - "label_id": 53, - "segment": { - "end_timestamp": 2600000000, - "start_timestamp": 1600000000 - } - }, - "end_timestamp": 2600000000, - "event_type": "Speech", - "start_timestamp": 1600000000 - } - ], - "rate": 16000, - "source": "file:///home/video-analytics-serving/tests/media/how_are_you_doing.wav", - "tags": {} - }, - { - "channels": 1, - "events": [ - { - "detection": { - "confidence": 1.0, - "label": "Speech", - "label_id": 53, - "segment": { - "end_timestamp": 2800000000, - "start_timestamp": 1800000000 - } - }, - "end_timestamp": 2800000000, - "event_type": "Speech", - "start_timestamp": 1800000000 - } - ], - "rate": 16000, - "source": "file:///home/video-analytics-serving/tests/media/how_are_you_doing.wav", - "tags": {} - }, - { - "channels": 1, - "events": [ - { - "detection": { - "confidence": 1.0, - "label": "Speech", - "label_id": 53, - "segment": { - "end_timestamp": 3000000000, - "start_timestamp": 2000000000 - } - }, - "end_timestamp": 3000000000, - "event_type": "Speech", - "start_timestamp": 2000000000 - } - ], - "rate": 16000, - "source": "file:///home/video-analytics-serving/tests/media/how_are_you_doing.wav", - "tags": {} - }, - { - "channels": 1, - "events": [ - { - "detection": { - "confidence": 1.0, - "label": "Speech", - "label_id": 53, - "segment": { - "end_timestamp": 3200000000, - "start_timestamp": 2200000000 - } - }, - "end_timestamp": 3200000000, - "event_type": "Speech", - "start_timestamp": 2200000000 - } - ], - "rate": 16000, - "source": "file:///home/video-analytics-serving/tests/media/how_are_you_doing.wav", - "tags": {} - }, - { - "channels": 1, - "events": [ - { - "detection": { - "confidence": 0.9997380375862122, - "label": "Speech", - "label_id": 53, - "segment": { - "end_timestamp": 3400000000, - "start_timestamp": 2400000000 - } - }, - "end_timestamp": 3400000000, - "event_type": "Speech", - "start_timestamp": 2400000000 - } - ], - "rate": 16000, - "source": "file:///home/video-analytics-serving/tests/media/how_are_you_doing.wav", - "tags": {} - } - ] -} \ No newline at end of file diff --git a/tests/test_cases/pipeline_execution/cpu/emotion_recognition_ffmpeg.json b/tests/test_cases/pipeline_execution/cpu/emotion_recognition_ffmpeg.json deleted file mode 100644 index 670c8c6..0000000 --- a/tests/test_cases/pipeline_execution/cpu/emotion_recognition_ffmpeg.json +++ /dev/null @@ -1,78762 +0,0 @@ -{ - "options": {}, - "pipeline": { - "name": "emotion_recognition", - "version": 1 - }, - "request": { - "source": { - "type": "uri", - "uri": "file:///home/video-analytics-serving/samples/classroom.mp4" - }, - "destination": { - "type": "file", - "path": "/tmp/emotion_recognition_results.json", - "format": "json-lines" - } - }, - "numerical_tolerance": 0.2, - "result": [ - { - "resolution": { - "width": 1920, - "height": 1080 - }, - "source": "file:///home/video-analytics-serving/samples/classroom.mp4", - "tags": {}, - "timestamp": 0, - "objects": [ - { - "detection": { - "bounding_box": { - "x_min": 0.6412968039512634, - "y_min": 0.5694648027420044, - "x_max": 0.6788294911384583, - "y_max": 0.6525497436523438 - }, - "confidence": 0.9999645948410034, - "label_id": 1 - }, - "h": 90, - "w": 72, - "x": 1231, - "y": 615 - }, - { - "detection": { - "bounding_box": { - "x_min": 0.13364143669605255, - "y_min": 0.5634274482727051, - "x_max": 0.1723073273897171, - "y_max": 0.6356501579284668 - }, - 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} - }, - "stability_duration": 600, - "relaunch_on_complete": false, - "numerical_tolerance": 0.3 -} diff --git a/tests/test_cases/pipeline_stability/cpu/stability_object_detection_two_gstreamer_pipelines/1/pipeline.json b/tests/test_cases/pipeline_stability/cpu/stability_object_detection_two_gstreamer_pipelines/1/pipeline.json deleted file mode 100644 index 555fc6b..0000000 --- a/tests/test_cases/pipeline_stability/cpu/stability_object_detection_two_gstreamer_pipelines/1/pipeline.json +++ /dev/null @@ -1,60 +0,0 @@ -{ - "type": "GStreamer", - "template": ["multifilesrc location=/home/video-analytics-serving/samples/bottle_detection.mp4 loop=True ! tee name=t ! queue ! decodebin ! video/x-raw ! videoconvert name=videoconvert", - " ! queue", - " ! gvadetect model={models[object_detection][1][network]} model-proc={models[object_detection][1][proc]} name=detection", - " ! gvametaconvert name=metaconvert ! queue ! gvametapublish name=destination", - " ! appsink name=appsink", - " t. ! queue ! qtdemux ! splitmuxsink name=splitmuxsink" - ], - "description": "Object Detection Pipeline", - "parameters": { - "type": "object", - "properties": { - "device": { - "element": "detection", - "type": "string" - }, - "inference-interval": { - "element": "detection", - "type": "integer", - "minimum": 1, - "maximum": 4294967295, - "default": 1 - }, - "cpu-throughput-streams": { - "element": "detection", - "type": "integer", - "minimum": 0, - "maximum": 4294967295 - }, - "n-threads": { - "element": "videoconvert", - "type": "integer", - "default": 1 - }, - "nireq": { - "element": "detection", - "type": "integer", - "minimum": 1, - "maximum": 64, - "default": 2 - }, - "max-size-time": { - "element": "splitmuxsink", - "type": "integer", - "minimum": 1, - "maximum": 200000000000, - "default": 2000000000 - }, - "recording_prefix": { - "type": "string", - "element": { - "name": "splitmuxsink", - "property": "location" - }, - "default": "/tmp/vaserving" - } - } - } -} diff --git a/tests/test_cases/rest_api/delete_invalid_instance.json b/tests/test_cases/rest_api/delete_invalid_instance.json deleted file mode 100644 index 648a3d7..0000000 --- a/tests/test_cases/rest_api/delete_invalid_instance.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "body": {}, - "path": "pipelines/object_detection/1/10", - "result": "Invalid pipeline, version or instance", - "status_code": 400, - "verb": "delete" -} diff --git a/tests/test_cases/rest_api/get_models.json b/tests/test_cases/rest_api/get_models.json deleted file mode 100644 index 0a36cbe..0000000 --- a/tests/test_cases/rest_api/get_models.json +++ /dev/null @@ -1,74 +0,0 @@ -{ - "body": {}, - "path": "models", - "sort_key": "name", - "result": [ - { - "description": "object_detection", - "name": "object_detection", - "networks": { - "model-proc": "/home/video-analytics-serving/models/object_detection/1/mobilenet-ssd.json", - "networks": { - "FP16": "/home/video-analytics-serving/models/object_detection/1/FP16/mobilenet-ssd.xml", - "FP32": "/home/video-analytics-serving/models/object_detection/1/FP32/mobilenet-ssd.xml" - } - }, - "type": "IntelDLDT", - "version": 1 - }, - { - "description": "landmarks_regression", - "name": "landmarks_regression", - "networks": { - "model-proc": "/home/video-analytics-serving/models/landmarks_regression/1/landmarks-regression-retail-0009.json", - "networks": { - "FP16": "/home/video-analytics-serving/models/landmarks_regression/1/FP16/landmarks-regression-retail-0009.xml", - "FP32": "/home/video-analytics-serving/models/landmarks_regression/1/FP32/landmarks-regression-retail-0009.xml" - } - }, - "type": "IntelDLDT", - "version": 1 - }, - { - "description": "face_detection_retail", - "name": "face_detection_retail", - "networks": { - "model-proc": "/home/video-analytics-serving/models/face_detection_retail/1/face-detection-retail-0004.json", - "networks": { - "FP16": "/home/video-analytics-serving/models/face_detection_retail/1/FP16/face-detection-retail-0004.xml", - "FP32": "/home/video-analytics-serving/models/face_detection_retail/1/FP32/face-detection-retail-0004.xml" - } - }, - "type": "IntelDLDT", - "version": 1 - }, - { - "description": "audio_detection", - "name": "audio_detection", - "networks": { - "model-proc": "/home/video-analytics-serving/models/audio_detection/1/aclnet.json", - "networks": { - "FP16": "/home/video-analytics-serving/models/audio_detection/1/FP16/aclnet.xml", - "FP32": "/home/video-analytics-serving/models/audio_detection/1/FP32/aclnet.xml" - } - }, - "type": "IntelDLDT", - "version": 1 - }, - { - "description": "emotion_recognition", - "name": "emotion_recognition", - "networks": { - "model-proc": "/home/video-analytics-serving/models/emotion_recognition/1/emotions-recognition-retail-0003.json", - "networks": { - "FP16": "/home/video-analytics-serving/models/emotion_recognition/1/FP16/emotions-recognition-retail-0003.xml", - "FP32": "/home/video-analytics-serving/models/emotion_recognition/1/FP32/emotions-recognition-retail-0003.xml" - } - }, - "type": "IntelDLDT", - "version": 1 - } - ], - "status_code": 200, - "verb": "get" -} \ No newline at end of file diff --git a/tests/test_cases/rest_api/get_object_detection_ffmpeg.json b/tests/test_cases/rest_api/get_object_detection_ffmpeg.json deleted file mode 100644 index 96106a2..0000000 --- a/tests/test_cases/rest_api/get_object_detection_ffmpeg.json +++ /dev/null @@ -1,24 +0,0 @@ -{ - "path": "pipelines/object_detection/1", - "verb": "get", - "body": {}, - "status_code": 200, - "result": { - "description": "Object Detection", - "name": "object_detection", - "parameters": { - "properties": { - "inference-interval": { - "default": 1, - "element": "detection", - "maximum": 4294967295, - "minimum": 0, - "type": "integer" - } - }, - "type": "object" - }, - "type": "FFmpeg", - "version": "1" - } -} diff --git a/tests/test_cases/rest_api/get_object_detection_gstreamer.json b/tests/test_cases/rest_api/get_object_detection_gstreamer.json deleted file mode 100644 index 69aa039..0000000 --- a/tests/test_cases/rest_api/get_object_detection_gstreamer.json +++ /dev/null @@ -1,51 +0,0 @@ -{ - "body": {}, - "path": "pipelines/object_detection/1", - "result": { - "description": "Object Detection Pipeline", - "name": "object_detection", - "parameters": { - "properties": { - "cpu-throughput-streams": { - "element": "detection", - "maximum": 4294967295, - "minimum": 0, - "type": "integer" - }, - "device": { - "element": "detection", - "type": "string" - }, - "inference-interval": { - "default": 1, - "element": "detection", - "maximum": 4294967295, - "minimum": 1, - "type": "integer" - }, - "model-instance-id": { - "default": "inf0", - "element": "detection", - "type": "string" - }, - "n-threads": { - "default": 1, - "element": "videoconvert", - "type": "integer" - }, - "nireq": { - "default": 2, - "element": "detection", - "maximum": 64, - "minimum": 1, - "type": "integer" - } - }, - "type": "object" - }, - "type": "GStreamer", - "version": "1" - }, - "status_code": 200, - "verb": "get" -} \ No newline at end of file diff --git a/tests/test_cases/rest_api/get_pipelines_ffmpeg.json b/tests/test_cases/rest_api/get_pipelines_ffmpeg.json deleted file mode 100644 index 2cf969a..0000000 --- a/tests/test_cases/rest_api/get_pipelines_ffmpeg.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "path": "pipelines", - "verb": "get", - "body": {}, - "status_code": 200, - "result": [ - { - "description": "Object Detection", - "name": "object_detection", - "parameters": { - "properties": { - "inference-interval": { - "default": 1, - "element": "detection", - "maximum": 4294967295, - "minimum": 0, - "type": "integer" - } - }, - "type": "object" - }, - "type": "FFmpeg", - "version": "1" - }, - { - "description": "Emotion Recognition", - "name": "emotion_recognition", - "parameters": { - "properties": { - "inference-interval": { - "default": 1, - "element": "detection", - "maximum": 4294967295, - "minimum": 0, - "type": "integer" - } - }, - "type": "object" - }, - "type": "FFmpeg", - "version": "1" - } - ] -} \ No newline at end of file diff --git a/tests/test_cases/rest_api/get_pipelines_gstreamer.json b/tests/test_cases/rest_api/get_pipelines_gstreamer.json deleted file mode 100644 index ce122c4..0000000 --- a/tests/test_cases/rest_api/get_pipelines_gstreamer.json +++ /dev/null @@ -1,299 +0,0 @@ -{ - "body": {}, - "path": "pipelines", - "result": [ - { - "description": "Decode Pipeline", - "name": "video_decode", - "type": "GStreamer", - "version": "app_dst" - }, - { - "description": "Object Detection Pipeline", - "name": "object_detection", - "parameters": { - "properties": { - "cpu-throughput-streams": { - "element": "detection", - "maximum": 4294967295, - "minimum": 0, - "type": "integer" - }, - "device": { - "element": "detection", - "type": "string" - }, - "edgexbridge": { - "element": { - "format": "json", - "name": "edgexbridge", - "property": "kwarg" - }, - "type": "object" - }, - "inference-interval": { - "default": 1, - "element": "detection", - "maximum": 4294967295, - "minimum": 1, - "type": "integer" - }, - "detection-model-instance-id": { - "element": "detection", - "type": "string", - "default": "inf0" - }, - "n-threads": { - "default": 1, - "element": "videoconvert", - "type": "integer" - }, - "nireq": { - "default": 2, - "element": "detection", - "maximum": 64, - "minimum": 1, - "type": "integer" - }, - "properties": { - "mqtt_devicename": { - "default": "objectDetect-mqtt", - "element": "edgexbridge", - "type": "string" - }, - "topic": { - "default": "edgex", - "element": "edgexbridge", - "type": "string" - } - } - }, - "type": "object" - }, - "type": "GStreamer", - "version": "edgex" - }, - { - "description": "Object Detection Pipeline", - "name": "object_detection", - "parameters": { - "properties": { - "detection-model-instance-id": { - "default": "", - "element": "detection", - "type": "string" - } - }, - "type": "object" - }, - "type": "GStreamer", - "version": "app_src_dst" - }, - { - "description": "Object Detection Pipeline", - "name": "object_detection", - "parameters": { - "properties": { - "cpu-throughput-streams": { - "element": "detection", - "maximum": 4294967295, - "minimum": 0, - "type": "integer" - }, - "device": { - "element": "detection", - "type": "string" - }, - "inference-interval": { - "default": 1, - "element": "detection", - "maximum": 4294967295, - "minimum": 1, - "type": "integer" - }, - "max-size-time": { - "default": 2000000000, - "element": "splitmuxsink", - "maximum": 200000000000, - "minimum": 1, - "type": "integer" - }, - "detection-model-instance-id": { - "element": "detection", - "type": "string", - "default": "inf0" - }, - "n-threads": { - "default": 1, - "element": "videoconvert", - "type": "integer" - }, - "nireq": { - "default": 2, - "element": "detection", - "maximum": 64, - "minimum": 1, - "type": "integer" - }, - "recording_prefix": { - "default": "/tmp/vaserving", - "element": { - "name": "splitmuxsink", - "property": "location" - }, - "type": "string" - } - }, - "type": "object" - }, - "type": "GStreamer", - "version": "2" - }, - { - "description": "Object Detection Pipeline", - "name": "object_detection", - "parameters": { - "properties": { - "cpu-throughput-streams": { - "element": "detection", - "maximum": 4294967295, - "minimum": 0, - "type": "integer" - }, - "device": { - "element": "detection", - "type": "string" - }, - "inference-interval": { - "default": 1, - "element": "detection", - "maximum": 4294967295, - "minimum": 1, - "type": "integer" - }, - "detection-model-instance-id": { - "element": "detection", - "type": "string", - "default": "inf0" - }, - "n-threads": { - "default": 1, - "element": "videoconvert", - "type": "integer" - }, - "nireq": { - "default": 2, - "element": "detection", - "maximum": 64, - "minimum": 1, - "type": "integer" - } - }, - "type": "object" - }, - "type": "GStreamer", - "version": "1" - }, - { - "description": "Audio Detection Pipeline", - "name": "audio_detection", - "parameters": { - "properties": { - "bus-messages": { - "default": false, - "description": "Prints GstBus messages as logger info", - "type": "boolean" - }, - "device": { - "element": "detection", - "type": "string" - }, - "output-buffer-duration": { - "default": 100000000, - "element": "audiomixer", - "type": "integer" - }, - "post-messages": { - "element": "level", - "type": "boolean" - }, - "sliding-window": { - "default": 0.2, - "element": "detection", - "type": "number" - }, - "threshold": { - "element": "detection", - "type": "number" - } - }, - "type": "object" - }, - "type": "GStreamer", - "version": "1" - }, - { - "description": "Emotion Recognition Pipeline", - "name": "emotion_recognition", - "parameters": { - "properties": { - "cpu-throughput-streams": { - "element": "detection", - "type": "integer" - }, - "device": { - "element": "detection", - "enum": [ - "CPU", - "HDDL", - "GPU", - "VPU", - "MYRIAD" - ], - "type": "string" - }, - "inference-interval": { - "default": 1, - "element": "detection", - "maximum": 4294967295, - "minimum": 1, - "type": "integer" - }, - "detection-model-instance-id": { - "element": { - "name": "detection", - "property": "model-instance-id" - }, - "type": "string", - "default": "face_detection_retail0" - }, - "classification-model-instance-id": { - "element": { - "name": "classification", - "property": "model-instance-id" - }, - "type": "string", - "default": "" - }, - "n-threads": { - "default": 1, - "element": "videoconvert", - "type": "integer" - }, - "nireq": { - "default": 2, - "element": "detection", - "maximum": 1024, - "minimum": 1, - "type": "integer" - } - }, - "type": "object" - }, - "type": "GStreamer", - "version": "1" - } - ], - "status_code": 200, - "verb": "get" -} diff --git a/tests/test_cases/rest_api/post_object_detection_badparam_gstreamer.json b/tests/test_cases/rest_api/post_object_detection_badparam_gstreamer.json deleted file mode 100755 index e263a4b..0000000 --- a/tests/test_cases/rest_api/post_object_detection_badparam_gstreamer.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "body": { "source": { "uri": "https://github.com/intel-iot-devkit/sample-videos/blob/master/head-pose-face-detection-male.mp4?raw=true", "type": "uri" }, "destination": { "type": "file", "path": "/tmp/emotion_results.txt", "format":"json-lines"}, "parameters":{"device":"BPU"}}, - "path": "pipelines/object_detection/1", - "sort_key":"name", - "result": 1, - "status_code": 200, - "verb": "post" -} diff --git a/tests/test_cases/rest_execution/cpu/rest_audio_detection_gstreamer.json b/tests/test_cases/rest_execution/cpu/rest_audio_detection_gstreamer.json deleted file mode 100644 index 732ada7..0000000 --- a/tests/test_cases/rest_execution/cpu/rest_audio_detection_gstreamer.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "path": "pipelines/audio_detection/1", - "start" : { - "body": { - "source": { - "uri": "file:///home/video-analytics-serving/tests/media/how_are_you_doing.wav", - "type": "uri" - }, - "destination": { - "type": "file", - "path": "/tmp/rest_audio_detection_gstreamer", - "format": "json-lines" - } - } - }, - "check_running" : { - "timeout": 1.0 - }, - "abort" : { - "delay" : 0 - }, - "check_stopped" : { - "timeout": 0.5 - } -} diff --git a/tests/test_cases/rest_execution/cpu/rest_emotion_recognition.json b/tests/test_cases/rest_execution/cpu/rest_emotion_recognition.json deleted file mode 100644 index b0d59aa..0000000 --- a/tests/test_cases/rest_execution/cpu/rest_emotion_recognition.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "path": "pipelines/emotion_recognition/1", - "start" : { - "body": { - "source": { - "uri": "file:///home/video-analytics-serving/samples/classroom.mp4", - "type": "uri" - }, - "destination": { - "type": "file", - "path": "/tmp/rest_emotion_recognition", - "format": "json-lines" - } - } - }, - "check_running" : { - "timeout": 2.0 - }, - "abort" : { - "delay" : 0.5 - }, - "check_stopped" : { - "timeout": 0.5 - } -} diff --git a/tests/test_cases/rest_execution/cpu/rest_object_detection.json b/tests/test_cases/rest_execution/cpu/rest_object_detection.json deleted file mode 100644 index dc38e20..0000000 --- a/tests/test_cases/rest_execution/cpu/rest_object_detection.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "path": "pipelines/object_detection/1", - "start" : { - "body": { - "source": { - "uri": "file:///home/video-analytics-serving/samples/pinwheel.ts", - "type": "uri" - }, - "destination": { - "type": "file", - "path": "/tmp/rest_object_detection", - "format": "json-lines" - } - } - }, - "check_running" : { - "timeout": 2.0 - }, - "abort" : { - "delay" : 0.5 - }, - "check_stopped" : { - "timeout": 0.5 - } -} diff --git a/tests/test_cases/rest_execution/cpu/rest_object_detection_bad_param_gstreamer.json b/tests/test_cases/rest_execution/cpu/rest_object_detection_bad_param_gstreamer.json deleted file mode 100644 index ecb0bee..0000000 --- a/tests/test_cases/rest_execution/cpu/rest_object_detection_bad_param_gstreamer.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "path": "pipelines/object_detection/1", - "start" : { - "body": { - "source": { - "uri": "file:///home/video-analytics-serving/samples/pinwheel.ts", - "type": "uri" - }, - "destination": { - "type": "file", - "path": "/tmp/rest_object_detection", - "format": "json-lines" - }, - "parameters": { - "device": "BPU" - } - } - }, - "check_error" : { - "delay" : 0.2, - "timeout" : 0.5 - } -} diff --git a/tests/test_cases/rest_execution/gpu/rest_audio_detection_gpu_gstreamer.json b/tests/test_cases/rest_execution/gpu/rest_audio_detection_gpu_gstreamer.json deleted file mode 100644 index a51514b..0000000 --- a/tests/test_cases/rest_execution/gpu/rest_audio_detection_gpu_gstreamer.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "path": "pipelines/audio_detection/1", - "start" : { - "body": { - "source": { - "uri": "file:///home/video-analytics-serving/tests/media/how_are_you_doing.wav", - "type": "uri" - }, - "destination": { - "type": "file", - "path": "/tmp/rest_audio_detection_gstreamer", - "format": "json-lines" - }, - "parameters": { - "device": "GPU" - } - } - }, - "check_running" : { - "timeout": 30.0 - }, - "abort" : { - "delay" : 0 - }, - "check_stopped" : { - "timeout": 0.5 - } -} diff --git a/tests/test_cases/rest_execution/gpu/rest_emotion_recognition_gpu_gstreamer.json b/tests/test_cases/rest_execution/gpu/rest_emotion_recognition_gpu_gstreamer.json deleted file mode 100644 index 0e26318..0000000 --- a/tests/test_cases/rest_execution/gpu/rest_emotion_recognition_gpu_gstreamer.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "path": "pipelines/emotion_recognition/1", - "start" : { - "body": { - "source": { - "uri": "file:///home/video-analytics-serving/samples/classroom.mp4", - "type": "uri" - }, - "destination": { - "type": "file", - "path": "/tmp/rest_emotion_recognition", - "format": "json-lines" - }, - "parameters": { - "device": "GPU" - } - } - }, - "check_running" : { - "timeout": 30.0 - }, - "abort" : { - "delay" : 0.5 - }, - "check_stopped" : { - "timeout": 0.5 - } -} diff --git a/tests/test_cases/rest_execution/gpu/rest_object_detection_gpu_gstreamer.json b/tests/test_cases/rest_execution/gpu/rest_object_detection_gpu_gstreamer.json deleted file mode 100644 index 861c6a4..0000000 --- a/tests/test_cases/rest_execution/gpu/rest_object_detection_gpu_gstreamer.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "path": "pipelines/object_detection/1", - "start" : { - "body": { - "source": { - "uri": "file:///home/video-analytics-serving/samples/pinwheel.ts", - "type": "uri" - }, - "destination": { - "type": "file", - "path": "/tmp/rest_object_detection", - "format": "json-lines" - }, - "parameters": { - "device": "GPU" - } - } - }, - "check_running" : { - "timeout": 30.0 - }, - "abort" : { - "delay" : 0.5 - }, - "check_stopped" : { - "timeout": 0.5 - } -} diff --git a/tests/test_cases/rest_execution/myriad/rest_object_detection_myriad_gstreamer.json b/tests/test_cases/rest_execution/myriad/rest_object_detection_myriad_gstreamer.json deleted file mode 100644 index 85c25a5..0000000 --- a/tests/test_cases/rest_execution/myriad/rest_object_detection_myriad_gstreamer.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "path": "pipelines/object_detection/1", - "start" : { - "body": { - "source": { - "uri": "file:///home/video-analytics-serving/samples/pinwheel.ts", - "type": "uri" - }, - "destination": { - "type": "file", - "path": "/tmp/rest_object_detection", - "format": "json-lines" - }, - "parameters": { - "device": "MYRIAD", - "detection-model-instance-id":"" - } - } - }, - "check_running" : { - "timeout": 5.0 - }, - "check_stopped" : { - "timeout": 100 - } -} diff --git a/tests/test_initialization.py b/tests/test_initialization.py deleted file mode 100644 index 36d7c49..0000000 --- a/tests/test_initialization.py +++ /dev/null @@ -1,43 +0,0 @@ -''' -* Copyright (C) 2019 Intel Corporation. -* -* SPDX-License-Identifier: BSD-3-Clause -''' - -import os -import json -import requests -from collections import namedtuple -import pytest -import urllib - -def test_initialization(VAServing, test_case, test_filename, generate): - test_prefix = os.path.splitext(os.path.basename(test_filename))[0] - test_model_dir = os.path.join(os.path.dirname(test_filename), - "{0}_models".format(test_prefix)) - test_pipeline_dir = os.path.join(os.path.dirname(test_filename), - "{0}_pipelines".format(test_prefix)) - if "model_dir" not in test_case["options"]: - if os.path.isdir(test_model_dir): - test_case["options"]["model_dir"] = test_model_dir - - if ("pipeline_dir" not in test_case["options"]): - if (os.path.isdir(test_pipeline_dir)): - test_case["options"]["pipeline_dir"] = test_pipeline_dir - - if ("exception" in test_case): - exception_class = None - if (test_case["exception"]["type"] in globals()): - exception_class = globals()[test_case["exception"]["type"]] - elif (test_case["exception"]["type"] in __builtins__): - exception_class = __builtins__[test_case["exception"]["type"]] - - with pytest.raises(exception_class) as exception: - VAServing.start(test_case["options"]) - - if("value" in test_case["exception"]): - assert(str(exception.value) == test_case["exception"]["value"]) - else: - VAServing.start(test_case["options"]) - - VAServing.stop() diff --git a/tests/test_pipeline_execution.py b/tests/test_pipeline_execution.py deleted file mode 100644 index f2374f1..0000000 --- a/tests/test_pipeline_execution.py +++ /dev/null @@ -1,138 +0,0 @@ -''' -* Copyright (C) 2019 Intel Corporation. -* -* SPDX-License-Identifier: BSD-3-Clause -''' - -import os -import json -import time -from vaserving.pipeline import Pipeline -from threading import Thread -import copy -from tests.common import results_processing -from queue import Queue -if os.environ['FRAMEWORK'] == "gstreamer": - import gi - from gstgva.util import libgst, gst_buffer_data, GVAJSONMeta - from gstgva.video_frame import VideoFrame - # pylint: disable=wrong-import-order, wrong-import-position - gi.require_version('Gst', '1.0') - gi.require_version('GstApp', '1.0') - from gi.repository import Gst, GstApp - # pylint: enable=wrong-import-order, wrong-import-position - from vaserving.vaserving import VAServing - from vaserving.app_source import AppSource - from vaserving.app_destination import AppDestination - from vaserving.gstreamer_app_source import GStreamerAppSource - from vaserving.gstreamer_app_source import GvaFrameData - from vaserving.gstreamer_app_destination import GStreamerAppDestination - -PAUSE = 0.1 - -def _get_results_app(test_case, results): - decode_output = Queue() - detect_input = test_case["request"]["source"]["input"] - detect_output = test_case["request"]["destination"]["output"] - decode_cfg = test_case["decode"] - print(decode_cfg) - decode_cfg["destination"]["output"] = decode_output - pipeline = VAServing.pipeline(decode_cfg["pipeline"]["name"], - decode_cfg["pipeline"]["version"]) - pipeline.start(decode_cfg) - sequence_number = 0 - end_of_stream = False - while (not end_of_stream): - if (not decode_output.empty()): - decoded_frame = decode_output.get() - if (decoded_frame): - with gst_buffer_data(decoded_frame.sample.get_buffer(), Gst.MapFlags.READ) as data: - new_sample = GvaFrameData(bytes(data), - decoded_frame.sample.get_caps(), - message={'sequence_number':sequence_number}) - detect_input.put(new_sample) - sequence_number += 1 - else: - detect_input.put(None) - while (not detect_output.empty()): - result = detect_output.get() - if not result: - end_of_stream = True - break - if (result.video_frame): - regions = list(result.video_frame.regions()) - messages = list(result.video_frame.messages()) - if regions and len(regions) > 0: - result_dict = {} - result_dict['message'] = json.loads(messages[0]) - region_results = [] - for region in regions: - region_dict = {} - rect = region.rect() - region_dict['x'] = rect.x - region_dict['y'] = rect.y - region_dict['w'] = rect.w - region_dict['h'] = rect.h - region_dict['label'] = region.label() - region_results.append(region_dict) - result_dict['regions'] = region_results - results.append(result_dict) - #print(result_dict) - -def get_results_app(test_case, results): - thread = Thread(target=_get_results_app, args=[test_case, results], daemon=True) - thread.start() - return thread - -def test_pipeline_execution(VAServing, test_case, test_filename, generate, numerical_tolerance): - _test_case = copy.deepcopy(test_case) - test_prefix = os.path.splitext(os.path.basename(test_filename))[0] - test_model_dir = os.path.join(os.path.dirname(test_filename), - "{0}_models".format(test_prefix)) - test_pipeline_dir = os.path.join(os.path.dirname(test_filename), - "{0}_pipelines".format(test_prefix)) - if "model_dir" not in _test_case["options"]: - if os.path.isdir(test_model_dir): - _test_case["options"]["model_dir"] = test_model_dir - if ("pipeline_dir" not in _test_case["options"]): - if (os.path.isdir(test_pipeline_dir)): - _test_case["options"]["pipeline_dir"] = test_pipeline_dir - if "numerical_tolerance" in _test_case: - numerical_tolerance = _test_case["numerical_tolerance"] - VAServing.start(_test_case["options"]) - pipeline = VAServing.pipeline(_test_case["pipeline"]["name"], - _test_case["pipeline"]["version"]) - results_processing.clear_results(_test_case) - results = [] - src_type = _test_case["request"]["source"]["type"] - print("src_type = {}".format(src_type)) - if src_type == "uri": - thread = results_processing.get_results_fifo(_test_case, results) - elif src_type == "application": - _test_case["request"]["source"]["input"] = Queue() - _test_case["request"]["destination"]["output"] = Queue() - thread = get_results_app(_test_case, results) - pipeline.start(_test_case["request"]) - status = pipeline.status() - transitions = [status] - while (not status.state.stopped()): - if (status.state != transitions[-1].state): - transitions.append(status) - time.sleep(PAUSE) - status = pipeline.status() - transitions.append(status) - assert transitions[0].state == Pipeline.State.QUEUED - assert transitions[-1].state == Pipeline.State.COMPLETED - if (thread): - thread.join() - else: - results_processing.get_results_file(_test_case, results) - VAServing.stop() - if generate: - test_case["result"] = results - with open(test_filename+'.generated', "w") as test_output: - json.dump(test_case, test_output, indent=4) - else: - assert results_processing.cmp_results(results, - _test_case["result"], - numerical_tolerance), "Inference Result Mismatch" diff --git a/tests/test_pipeline_performance.py b/tests/test_pipeline_performance.py deleted file mode 100644 index 70f4d43..0000000 --- a/tests/test_pipeline_performance.py +++ /dev/null @@ -1,119 +0,0 @@ -''' -* Copyright (C) 2019 Intel Corporation. -* -* SPDX-License-Identifier: BSD-3-Clause -''' - -import os -import subprocess -import shlex -import json -import requests -from collections import namedtuple -import pytest -import urllib -import copy -import time -import pytest -from vaserving.pipeline import Pipeline -if os.environ['FRAMEWORK'] == "gstreamer": - import gi - from gstgva.util import libgst, gst_buffer_data, GVAJSONMeta - from gstgva.video_frame import VideoFrame - # pylint: disable=wrong-import-order, wrong-import-position - gi.require_version('Gst', '1.0') - gi.require_version('GstApp', '1.0') - from gi.repository import Gst, GstApp - # pylint: enable=wrong-import-order, wrong-import-position - from vaserving.vaserving import VAServing - from vaserving.app_source import AppSource - from vaserving.app_destination import AppDestination - from vaserving.gstreamer_app_source import GStreamerAppSource - from vaserving.gstreamer_app_source import GvaFrameData - from vaserving.gstreamer_app_destination import GStreamerAppDestination - -PAUSE = 0.001 - -def test_pipeline_performance(VAServing, test_case, test_filename, generate): - iterations = 1 - avg_diff_pct_limit = 10 - start_times_gst_launch = [] - stop_times_gst_launch = [] - start_times_vas_pipeline = [] - stop_times_vas_pipeline = [] - _test_case = copy.deepcopy(test_case) - test_prefix = os.path.splitext(os.path.basename(test_filename))[0] - test_model_dir = os.path.join(os.path.dirname(test_filename), - "{0}_models".format(test_prefix)) - test_pipeline_dir = os.path.join(os.path.dirname(test_filename), - "{0}_pipelines".format(test_prefix)) - if "model_dir" not in _test_case["options"]: - if os.path.isdir(test_model_dir): - _test_case["options"]["model_dir"] = test_model_dir - if ("pipeline_dir" not in _test_case["options"]): - if (os.path.isdir(test_pipeline_dir)): - _test_case["options"]["pipeline_dir"] = test_pipeline_dir - if "gst_launch_string" in _test_case: - gst_launch_string = _test_case["gst_launch_string"] - else: - pytest.fail("Required parameter gst_launch_string missing") - if "iterations" in _test_case: - iterations = _test_case["iterations"] - if "avg_percentage_diff_limit" in _test_case: - avg_diff_pct_limit = _test_case["avg_percentage_diff_limit"] - - run_gst_pipeline(iterations, start_times_gst_launch, gst_launch_string, stop_times_gst_launch) - - run_vas_pipeline(VAServing, _test_case, iterations, - start_times_vas_pipeline, - stop_times_vas_pipeline) - - check_results(iterations, stop_times_gst_launch, start_times_gst_launch, - stop_times_vas_pipeline, start_times_vas_pipeline, - avg_diff_pct_limit) - -def run_gst_pipeline(iterations, start_times_gst_launch, gst_launch_string, stop_times_gst_launch): - for i in range(iterations): - start_times_gst_launch.append(time.time()) - comp_process = subprocess.run(shlex.split(gst_launch_string)) - stop_times_gst_launch.append(time.time()) - assert comp_process.returncode == 0 - print("Completed gst_launch iteration #{iter}".format(iter=i)) - -def run_vas_pipeline(VAServing, _test_case, iterations, start_times_vas_pipeline, - stop_times_vas_pipeline): - VAServing.start(_test_case["options"]) - for i in range(iterations): - pipeline = VAServing.pipeline(_test_case["pipeline"]["name"], - _test_case["pipeline"]["version"]) - start_times_vas_pipeline.append(time.time()) - pipeline.start(_test_case["request"]) - status = pipeline.status() - transitions = [status] - while (not status.state.stopped()): - if (status.state != transitions[-1].state): - transitions.append(status) - time.sleep(PAUSE) - status = pipeline.status() - transitions.append(status) - assert transitions[0].state == Pipeline.State.QUEUED - assert transitions[-1].state == Pipeline.State.COMPLETED - stop_times_vas_pipeline.append(time.time()) - print("Completed vas_launch iteration #{iter}".format(iter=i)) - VAServing.stop() - -def check_results(iterations, stop_times_gst_launch, start_times_gst_launch, - stop_times_vas_pipeline, start_times_vas_pipeline, - avg_diff_pct_limit): - total_gst_launch = 0 - total_vas_pipeline = 0 - for i in range(iterations): - total_gst_launch += stop_times_gst_launch[i] - start_times_gst_launch[i] - total_vas_pipeline += stop_times_vas_pipeline[i] - start_times_vas_pipeline[i] - avg_duration = total_gst_launch / iterations - print("avg_duration time = " + str(avg_duration)) - avg_duration_vas = total_vas_pipeline / iterations - print("avg_duration_vas time = " + str(avg_duration_vas)) - avg_diff_pct = (avg_duration_vas - avg_duration) / avg_duration * 100 - print("avg_diff_pct = " + str(avg_diff_pct)) - assert avg_diff_pct < avg_diff_pct_limit diff --git a/tests/test_pipeline_stability.py b/tests/test_pipeline_stability.py deleted file mode 100644 index 267b6ff..0000000 --- a/tests/test_pipeline_stability.py +++ /dev/null @@ -1,96 +0,0 @@ -''' -* Copyright (C) 2019 Intel Corporation. -* -* SPDX-License-Identifier: BSD-3-Clause -''' - -import os -import json -import requests -from collections import namedtuple -import pytest -import urllib -import time -from vaserving.pipeline import Pipeline -from threading import Thread -import copy -import tempfile -from tests.common import results_processing - -PAUSE = 0.1 - -def start_and_run_pipeline(_test_case, pipeline, stability_duration, start_time): - pipeline.start(_test_case["request"]) - status = pipeline.status() - transitions = [status] - while (not status.state.stopped()): - if (status.state != transitions[-1].state): - transitions.append(status) - time.sleep(PAUSE) - status = pipeline.status() - if (time.time() - start_time) >= stability_duration: - pipeline.stop() - return True - transitions.append(status) - assert transitions[0].state == Pipeline.State.QUEUED - assert transitions[-1].state == Pipeline.State.COMPLETED - return False - -@pytest.mark.stability -def test_pipeline_stability(VAServing, test_case, test_filename, generate, numerical_tolerance, stability_duration): - duration_met = False - _test_case = copy.deepcopy(test_case) - test_prefix = os.path.splitext(os.path.basename(test_filename))[0] - test_model_dir = os.path.join(os.path.dirname(test_filename), - "{0}_models".format(test_prefix)) - test_pipeline_dir = os.path.join(os.path.dirname(test_filename), - "{0}_pipelines".format(test_prefix)) - if "model_dir" not in _test_case["options"]: - if os.path.isdir(test_model_dir): - _test_case["options"]["model_dir"] = test_model_dir - if ("pipeline_dir" not in _test_case["options"]): - if (os.path.isdir(test_pipeline_dir)): - _test_case["options"]["pipeline_dir"] = test_pipeline_dir - if "numerical_tolerance" in _test_case: - numerical_tolerance = _test_case["numerical_tolerance"] - if stability_duration is None and "stability_duration" in _test_case: - stability_duration = _test_case["stability_duration"] - elif stability_duration is None: - stability_duration = 600 - if "relaunch_on_complete" in _test_case: - relaunch_on_complete = _test_case["relaunch_on_complete"] - VAServing.start(_test_case["options"]) - start_time = time.time() - if relaunch_on_complete: - num_loops = 1 - with tempfile.TemporaryDirectory() as temp_dir: - while not duration_met: - _test_case["request"]["destination"]["path"] = temp_dir + "/stability" + \ - str(num_loops) + ".json" - pipeline = VAServing.pipeline(_test_case["pipeline"]["name"], - _test_case["pipeline"]["version"]) - results_processing.clear_results(_test_case) - results = [] - thread = results_processing.get_results_fifo(_test_case, results) - duration_met = start_and_run_pipeline(_test_case, pipeline, - stability_duration, start_time) - if (thread): - thread.join() - elif not duration_met: - results_processing.get_results_file(_test_case, results) - if generate: - test_case["result"] = results - with open(test_filename+'.generated', "w") as test_output: - json.dump(test_case, test_output, indent=4) - elif not duration_met: - assert results_processing.cmp_results(results, - _test_case["result"], - numerical_tolerance), \ - "Inference Result Mismatch" - num_loops = num_loops + 1 - else: - pipeline = VAServing.pipeline(_test_case["pipeline"]["name"], - _test_case["pipeline"]["version"]) - duration_met = start_and_run_pipeline(_test_case, pipeline, stability_duration, start_time) - VAServing.stop() - assert duration_met diff --git a/tests/test_rest_api.py b/tests/test_rest_api.py deleted file mode 100644 index 7b90f70..0000000 --- a/tests/test_rest_api.py +++ /dev/null @@ -1,51 +0,0 @@ -''' -* Copyright (C) 2019 Intel Corporation. -* -* SPDX-License-Identifier: BSD-3-Clause -''' - -import os -import json -import requests -import pytest -import urllib - -TIMEOUT = 30 - -#@pytest.mark.parametrize("test_case",[0],indirect=["test_case"]) -def test_rest_api(service, test_case, test_filename, generate): - url = urllib.parse.urljoin(service.host, test_case['path']) - if test_case["verb"] == "get": - response = requests.get(url, - json=test_case["body"], - timeout=TIMEOUT) - elif test_case["verb"] == "delete": - response = requests.delete(url, - json=test_case["body"], - timeout=TIMEOUT) - elif test_case["verb"] == "post": - response = requests.post(url, - json=test_case["body"], - timeout=TIMEOUT) - else: - assert False, "Unknown Test Case Verb" - if generate: - test_case["status_code"] = response.status_code - test_case["result"] = json.loads(response.text) - with open(test_filename+'.generated', "w") as test_output: - json.dump(test_case, test_output, indent=4) - else: - assert test_case["status_code"] == response.status_code, "Status Code Mismatch" - comparison = json.loads(response.text) - expected = test_case["result"] - assert type(comparison) == type(expected), "Response Type Mismatch" - if isinstance(expected, list): - if 'sort_key' in test_case: - sort_key = test_case['sort_key'] - expected = sorted(expected, key=lambda x: x[sort_key]) - comparison = sorted(comparison, key=lambda x: x[sort_key]) - assert comparison == expected, "Response Value Mismatch" - else: - assert len(comparison) == len(expected), "Response List Length Mismatch" - for entry in expected: - assert entry in comparison, "Response Value Mismatch" diff --git a/tests/test_rest_execution.py b/tests/test_rest_execution.py deleted file mode 100644 index 1775a87..0000000 --- a/tests/test_rest_execution.py +++ /dev/null @@ -1,83 +0,0 @@ -''' -* Copyright (C) 2019 Intel Corporation. -* -* SPDX-License-Identifier: BSD-3-Clause -''' - -import os -import json -import requests -import pytest -import urllib -import time - -TIMEOUT = 30 -HTTP_OK = 200 -states = ["QUEUED", "RUNNING", "ABORTED", "COMPLETED"] -states_negative = ["QUEUED", "ERROR"] - -def get_pipeline_state(status_url, valid_states): - response = requests.get(status_url, timeout=TIMEOUT) - status = json.loads(response.text) - response.close() - state = status.get('state') - if state in valid_states: - print("pipeline_state: %s", state) - return valid_states.index(state) - return -1 - -def wait_for_pipeline_status(instance_url, expected_status, valid_states, timeout): - status_url = instance_url + "/status" - elapsed_time = 0.0 - sleep_time = 0.25 - if not expected_status in valid_states: - return False - expected_state = valid_states.index(expected_status) - print("%s expecting %s" % (instance_url, expected_status)) - state = get_pipeline_state(status_url, valid_states) - while state < expected_state and elapsed_time < timeout: - time.sleep(sleep_time) - state = get_pipeline_state(status_url, valid_states) - elapsed_time = elapsed_time + sleep_time - print("%d %f" % (state, elapsed_time)) - return state >= expected_state - -def test_rest_execution(service, test_case, test_filename, generate): - - url = urllib.parse.urljoin(service.host, test_case['path']) - start_test = test_case["start"] - response = requests.post(url, - json=start_test["body"], - timeout=TIMEOUT) - if generate: - test_case["result"] = json.loads(response.text) - with open(test_filename+'.generated', "w") as test_output: - json.dump(test_case, test_output, indent=4) - return - # Check response to POST - assert response.status_code == HTTP_OK, "Status Code Mismatch" - instance = int(response.text) - assert type(instance) == int, "Response Type Mismatch" - assert instance > 0, "Invalid instance" - # Check pipeline state transitions - instance_url = "{}/{}".format(url, instance) - if "check_error" in test_case: - # Negative tests expect errors before pipeline enters RUNNING - time.sleep(test_case["check_error"]["delay"]) - state_transition_timeout = float(test_case["check_error"]["timeout"]) - assert wait_for_pipeline_status(instance_url, "ERROR", states_negative, - state_transition_timeout), "Pipeline did not error" - else: - state_transition_timeout = float(test_case["check_running"]["timeout"]) - assert wait_for_pipeline_status(instance_url, "RUNNING", states, - state_transition_timeout), "Pipeline did not start" - state_transition_timeout = float(test_case["check_stopped"]["timeout"]) - if "abort" in test_case: - time.sleep(test_case["abort"]["delay"]) - response = requests.delete(instance_url, timeout=TIMEOUT) - assert response.status_code == HTTP_OK, "DELETE Status Code Mismatch" - assert wait_for_pipeline_status(instance_url, "ABORTED", states, - state_transition_timeout), "Pipeline did not abort" - else: - assert wait_for_pipeline_status(instance_url, "COMPLETED", states, - state_transition_timeout), "Pipeline did not complete" diff --git a/tests/util/README.md b/tests/util/README.md deleted file mode 100644 index 1844856..0000000 --- a/tests/util/README.md +++ /dev/null @@ -1,5 +0,0 @@ -# VA Serving Test Listeners - -When VA Serving pipelines are initiated their associated output is directly monitored by methods in PyTest tests. If this output is sent to a message bus, the appropriate client is instantiated and subscribes to the designated topic(s). The messages received by the pipeline execution are then compared against assertion files to confirm expectations were met. - -The scripts found in this folder are used by Team City build agents to configure and launch message brokers and for troubleshooting on the **host**. These are located here in case developers want to configure local brokers that match CI setup. diff --git a/tests/util/docker-compose-kafka-vaserving.yml b/tests/util/docker-compose-kafka-vaserving.yml deleted file mode 100644 index 37112c9..0000000 --- a/tests/util/docker-compose-kafka-vaserving.yml +++ /dev/null @@ -1,28 +0,0 @@ -version: '2' - -networks: - kafka-net: - driver: bridge - -services: - zookeeper: - image: 'bitnami/zookeeper:latest' - networks: - - kafka-net - ports: - - '2181:2181' - environment: - - ALLOW_ANONYMOUS_LOGIN=yes - kafka: - image: 'bitnami/kafka:latest' - networks: - - kafka-net - ports: - - '9092:9092' - environment: - - KAFKA_CFG_AUTO_CREATE_TOPICS_ENABLE=true - - KAFKA_CFG_ZOOKEEPER_CONNECT=zookeeper:2181 - - KAFKA_CFG_ADVERTISED_LISTENERS=PLAINTEXT://localhost:9092 - - ALLOW_PLAINTEXT_LISTENER=yes - depends_on: - - zookeeper diff --git a/tests/util/start_kafka.sh b/tests/util/start_kafka.sh deleted file mode 100755 index f2ca347..0000000 --- a/tests/util/start_kafka.sh +++ /dev/null @@ -1,3 +0,0 @@ -# Start simple Kafka broker -echo "Launching zookeeper and kafka containers with auto-topic creation." -docker-compose -f docker-compose-kafka-vaserving.yml up -d diff --git a/tests/util/start_mqtt.sh b/tests/util/start_mqtt.sh deleted file mode 100755 index 44c1bef..0000000 --- a/tests/util/start_mqtt.sh +++ /dev/null @@ -1 +0,0 @@ -docker run -d --rm --name vaserving_mqtt -p 1883:1883 -p 9001:9001 eclipse-mosquitto diff --git a/tests/util/stop_kafka.sh b/tests/util/stop_kafka.sh deleted file mode 100755 index 23825ad..0000000 --- a/tests/util/stop_kafka.sh +++ /dev/null @@ -1,2 +0,0 @@ -echo "Stopping Kafka broker" -docker-compose -f docker-compose-kafka-vaserving.yml down diff --git a/tests/util/stop_mqtt.sh b/tests/util/stop_mqtt.sh deleted file mode 100755 index 8ff64c8..0000000 --- a/tests/util/stop_mqtt.sh +++ /dev/null @@ -1,2 +0,0 @@ -echo "Stopping MQTT broker" -docker stop vaserving_mqtt diff --git a/tests/vas_pylint_ignore.py b/tests/vas_pylint_ignore.py deleted file mode 100644 index 43088b3..0000000 --- a/tests/vas_pylint_ignore.py +++ /dev/null @@ -1,29 +0,0 @@ -''' -* Copyright (C) 2019-2020 Intel Corporation. -* -* SPDX-License-Identifier: BSD-3-Clause -''' - -#Temp resolution (internal only) -#https://github.com/PyCQA/pylint/issues/2686 - -from pylint.utils import utils - -class VASPylintIgnore: - def __init__(self, *paths): - self.paths = paths - self.original_expand_modules = utils.expand_modules - utils.expand_modules = self.patched_expand - print("IGNORING PATHS: {ignored}".format(ignored=paths)) - - def patched_expand(self, *args, **kwargs): - result, errors = self.original_expand_modules(*args, **kwargs) - - def keep_item(item): - if any(1 for path in self.paths if item['path'].startswith(path)): - print("IGNORING {path}".format(path=item)) - return False - print("KEEPING {path}".format(path=item)) - return True - result = list(filter(keep_item, result)) - return result, errors