feat: parameterize batch-size in gst pipelines via environment variable

Use env var BATCH_SIZE to parameterize batch-size element in gst pipelines, specifically gvadetect command CLOSES: intel-retail#29 Signed-off-by: Jim Wang <[email protected]>
jim-wang-intel · Feb 7, 2024 · 074adb0 · 074adb0
1 parent b971e6e
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Showing 6 changed files with 30 additions and 11 deletions.
diff --git a/configs/opencv-ovms/envs/gst.env b/configs/opencv-ovms/envs/gst.env
@@ -1,4 +1,5 @@
 COLOR_WIDTH=1920
 COLOR_HEIGHT=1080
 COLOR_FRAMERATE=15
-OCR_SPECIFIED=5
+OCR_SPECIFIED=5
+BATCH_SIZE=0
diff --git a/configs/opencv-ovms/gst/framework-pipelines/yolov5_pipeline/yolov5s.sh b/configs/opencv-ovms/gst/framework-pipelines/yolov5_pipeline/yolov5s.sh
@@ -1,6 +1,6 @@
  #!/bin/bash
 #
-# Copyright (C) 2023 Intel Corporation.
+# Copyright (C) 2024 Intel Corporation.
 #
 # SPDX-License-Identifier: Apache-2.0
 #
@@ -16,9 +16,9 @@ else
     OUTPUT="${OUTPUT:="! fpsdisplaysink video-sink=fakesink sync=true --verbose"}"
 fi
 
-echo "Run yolov5s pipeline"
+echo "Run yolov5s pipeline: batch size = $BATCH_SIZE"
 
-gstLaunchCmd="GST_DEBUG=\"GST_TRACER:7\" GST_TRACERS=\"latency_tracer(flags=pipeline,interval=100)\" gst-launch-1.0 $inputsrc ! $DECODE ! gvadetect model-instance-id=odmodel name=detection model=models/yolov5s/FP16-INT8/1/yolov5s.xml model-proc=models/yolov5s/FP16-INT8/1/yolov5s.json threshold=.5 device=$DEVICE $PRE_PROCESS ! $AGGREGATE gvametaconvert name=metaconvert add-empty-results=true ! gvametapublish name=destination file-format=2 file-path=/tmp/results/r$cid_count.jsonl $OUTPUT 2>&1 | tee >/tmp/results/gst-launch_$cid_count.log >(stdbuf -oL sed -n -e 's/^.*current: //p' | stdbuf -oL cut -d , -f 1 > /tmp/results/pipeline$cid_count.log)"
+gstLaunchCmd="GST_DEBUG=\"GST_TRACER:7\" GST_TRACERS=\"latency_tracer(flags=pipeline,interval=100)\" gst-launch-1.0 $inputsrc ! $DECODE ! gvadetect batch-size=$BATCH_SIZE model-instance-id=odmodel name=detection model=models/yolov5s/FP16-INT8/1/yolov5s.xml model-proc=models/yolov5s/FP16-INT8/1/yolov5s.json threshold=.5 device=$DEVICE $PRE_PROCESS ! $AGGREGATE gvametaconvert name=metaconvert add-empty-results=true ! gvametapublish name=destination file-format=2 file-path=/tmp/results/r$cid_count.jsonl $OUTPUT 2>&1 | tee >/tmp/results/gst-launch_$cid_count.log >(stdbuf -oL sed -n -e 's/^.*current: //p' | stdbuf -oL cut -d , -f 1 > /tmp/results/pipeline$cid_count.log)"
 
 echo "$gstLaunchCmd"
 

diff --git a/configs/opencv-ovms/gst/framework-pipelines/yolov5_pipeline/yolov5s_effnetb0.sh b/configs/opencv-ovms/gst/framework-pipelines/yolov5_pipeline/yolov5s_effnetb0.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 #
-# Copyright (C) 2023 Intel Corporation.
+# Copyright (C) 2024 Intel Corporation.
 #
 # SPDX-License-Identifier: Apache-2.0
 #
@@ -23,8 +23,8 @@ else
     OUTPUT="${OUTPUT:="! fpsdisplaysink video-sink=fakesink sync=true --verbose"}"
 fi
 
-echo "Run run yolov5s with efficientnet classification pipeline"
-gstLaunchCmd="gst-launch-1.0 $inputsrc ! $DECODE ! gvadetect model-instance-id=odmodel name=detection model=/home/pipeline-server/models/yolov5s/FP16-INT8/1/yolov5s.xml model-proc=/home/pipeline-server/models/yolov5s/FP16-INT8/1/yolov5s.json threshold=.5 device=$DEVICE $PRE_PROCESS $DETECTION_OPTIONS ! gvatrack name=tracking tracking-type=zero-term-imageless ! queue max-size-bytes=0 max-size-buffers=0 max-size-time=0 ! gvaclassify model-instance-id=clasifier labels=/home/pipeline-server/models/efficientnet-b0/1/imagenet_2012.txt model=/home/pipeline-server/models/efficientnet-b0/FP32-INT8/1/efficientnet-b0.xml model-proc=/home/pipeline-server/models/efficientnet-b0/efficientnet-b0.json device=$DEVICE inference-region=roi-list name=classification $PRE_PROCESS $CLASSIFICATION_OPTIONS ! gvametaconvert name=metaconvert add-empty-results=true ! gvametapublish name=destination file-format=2 file-path=/tmp/results/r$cid_count.jsonl $OUTPUT 2>&1 | tee >/tmp/results/gst_launch$cid_count.log >(stdbuf -oL sed -n -e 's/^.*current: //p' | stdbuf -oL cut -d , -f 1 > /tmp/results/pipeline$cid_count.log)"
+echo "Run run yolov5s with efficientnet classification pipeline: BATCH_SIZE=$BATCH_SIZE"
+gstLaunchCmd="gst-launch-1.0 $inputsrc ! $DECODE ! gvadetect batch-size=$BATCH_SIZE model-instance-id=odmodel name=detection model=/home/pipeline-server/models/yolov5s/FP16-INT8/1/yolov5s.xml model-proc=/home/pipeline-server/models/yolov5s/FP16-INT8/1/yolov5s.json threshold=.5 device=$DEVICE $PRE_PROCESS $DETECTION_OPTIONS ! gvatrack name=tracking tracking-type=zero-term-imageless ! queue max-size-bytes=0 max-size-buffers=0 max-size-time=0 ! gvaclassify model-instance-id=clasifier labels=/home/pipeline-server/models/efficientnet-b0/1/imagenet_2012.txt model=/home/pipeline-server/models/efficientnet-b0/FP32-INT8/1/efficientnet-b0.xml model-proc=/home/pipeline-server/models/efficientnet-b0/efficientnet-b0.json device=$DEVICE inference-region=roi-list name=classification $PRE_PROCESS $CLASSIFICATION_OPTIONS ! gvametaconvert name=metaconvert add-empty-results=true ! gvametapublish name=destination file-format=2 file-path=/tmp/results/r$cid_count.jsonl $OUTPUT 2>&1 | tee >/tmp/results/gst_launch$cid_count.log >(stdbuf -oL sed -n -e 's/^.*current: //p' | stdbuf -oL cut -d , -f 1 > /tmp/results/pipeline$cid_count.log)"
 
 echo "$gstLaunchCmd"
 

diff --git a/configs/opencv-ovms/gst/framework-pipelines/yolov5_pipeline/yolov5s_full.sh b/configs/opencv-ovms/gst/framework-pipelines/yolov5_pipeline/yolov5s_full.sh
@@ -1,6 +1,6 @@
  #!/bin/bash
 #
-# Copyright (C) 2023 Intel Corporation.
+# Copyright (C) 2024 Intel Corporation.
 #
 # SPDX-License-Identifier: Apache-2.0
 #
@@ -23,8 +23,8 @@ else
     OUTPUT="${OUTPUT:="! fpsdisplaysink video-sink=fakesink sync=true --verbose"}"
 fi
 
-echo "Run yolov5s with efficientnet classification, horizontal text detection, and barcode detection pipeline"
-gstLaunchCmd="gst-launch-1.0 $inputsrc ! $DECODE $VA_SURFACE ! gvadetect model-instance-id=odmodel name=detection model=/home/pipeline-server/models/yolov5s/FP16-INT8/1/yolov5s.xml model-proc=/home/pipeline-server/models/yolov5s/FP16-INT8/1/yolov5s.json threshold=.5 device=$DEVICE $PRE_PROCESS $DETECTION_OPTIONS  gpu-throughput-streams=4 nireq=4 batch-size=1 ! gvatrack name=tracking tracking-type=zero-term-imageless $PARALLEL_PIPELINE ! gvaclassify model-instance-id=clasifier labels=/home/pipeline-server/models/efficientnet-b0/1/imagenet_2012.txt model=/home/pipeline-server/models/efficientnet-b0/FP32-INT8/1/efficientnet-b0.xml model-proc=/home/pipeline-server/models/efficientnet-b0/efficientnet-b0.json reclassify-interval=1  device=$DEVICE inference-region=roi-list name=classification $PRE_PROCESS reclassify_interval=$OCR_RECLASSIFY_INTERVAL batch-size=8 nireq=4 gpu-throughput-streams=1 $PARALLEL_AGGRAGATE ! gvapython class=ObjectFilter module=/home/pipeline-server/extensions/tracked_object_filter.py kwarg=\"{\\\"reclassify_interval\\\": $OCR_RECLASSIFY_INTERVAL}\" name=tracked_object_filter ! gvadetect model-instance-id=ocr threshold=.2 model=/home/pipeline-server/models/horizontal-text-detection-0002/FP16-INT8/1/horizontal-text-detection-0002.xml model-proc=/home/pipeline-server/models/horizontal-text-detection-0002/FP16-INT8/1/horizontal-text-detection-0002.json name=text_detection device=$DEVICE inference-region=roi-list $PRE_PROCESS ! gvainference model-instance-id=ocr2 nireq=4 gpu-throughput-streams=2 batch-size=32 device=$DEVICE model=/home/pipeline-server/models/text-recognition-0012-mod/FP16-INT8/1/text-recognition-0012-mod.xml model-proc=/home/pipeline-server/models/text-recognition-0012-mod/FP16-INT8/1/text-recognition-0012-mod.json inference-region=roi-list name=text_recognition object-class=text ! gvapython class=OCR module=/home/pipeline-server/extensions/OCR_post_processing_0012.py name=ocr_postprocess $AGGREGATE ! gvapython name=barcode class=BarcodeDetection module=/home/pipeline-server/extensions/barcode_nv12_to_gray.py kwarg=\"{\\\"reclassify_interval\\\": $BARCODE_RECLASSIFY_INTERVAL}\" !  gvametaconvert name=metaconvert add-empty-results=true ! gvametapublish name=destination file-format=2 file-path=/tmp/results/r$cid_count.jsonl $OUTPUT 2>&1 | tee >/tmp/results/gst-launch_$cid_count.log >(stdbuf -oL sed -n -e 's/^.*current: //p' | stdbuf -oL cut -d , -f 1 > /tmp/results/pipeline$cid_count.log)"
+echo "Run yolov5s with efficientnet classification, horizontal text detection, and barcode detection pipeline: BATCH_SIZE=$BATCH_SIZE"
+gstLaunchCmd="gst-launch-1.0 $inputsrc ! $DECODE $VA_SURFACE ! gvadetect batch-size=$BATCH_SIZE model-instance-id=odmodel name=detection model=/home/pipeline-server/models/yolov5s/FP16-INT8/1/yolov5s.xml model-proc=/home/pipeline-server/models/yolov5s/FP16-INT8/1/yolov5s.json threshold=.5 device=$DEVICE $PRE_PROCESS $DETECTION_OPTIONS  gpu-throughput-streams=4 nireq=4 batch-size=1 ! gvatrack name=tracking tracking-type=zero-term-imageless $PARALLEL_PIPELINE ! gvaclassify model-instance-id=clasifier labels=/home/pipeline-server/models/efficientnet-b0/1/imagenet_2012.txt model=/home/pipeline-server/models/efficientnet-b0/FP32-INT8/1/efficientnet-b0.xml model-proc=/home/pipeline-server/models/efficientnet-b0/efficientnet-b0.json reclassify-interval=1  device=$DEVICE inference-region=roi-list name=classification $PRE_PROCESS reclassify_interval=$OCR_RECLASSIFY_INTERVAL batch-size=8 nireq=4 gpu-throughput-streams=1 $PARALLEL_AGGRAGATE ! gvapython class=ObjectFilter module=/home/pipeline-server/extensions/tracked_object_filter.py kwarg=\"{\\\"reclassify_interval\\\": $OCR_RECLASSIFY_INTERVAL}\" name=tracked_object_filter ! gvadetect model-instance-id=ocr threshold=.2 model=/home/pipeline-server/models/horizontal-text-detection-0002/FP16-INT8/1/horizontal-text-detection-0002.xml model-proc=/home/pipeline-server/models/horizontal-text-detection-0002/FP16-INT8/1/horizontal-text-detection-0002.json name=text_detection device=$DEVICE inference-region=roi-list $PRE_PROCESS ! gvainference model-instance-id=ocr2 nireq=4 gpu-throughput-streams=2 batch-size=32 device=$DEVICE model=/home/pipeline-server/models/text-recognition-0012-mod/FP16-INT8/1/text-recognition-0012-mod.xml model-proc=/home/pipeline-server/models/text-recognition-0012-mod/FP16-INT8/1/text-recognition-0012-mod.json inference-region=roi-list name=text_recognition object-class=text ! gvapython class=OCR module=/home/pipeline-server/extensions/OCR_post_processing_0012.py name=ocr_postprocess $AGGREGATE ! gvapython name=barcode class=BarcodeDetection module=/home/pipeline-server/extensions/barcode_nv12_to_gray.py kwarg=\"{\\\"reclassify_interval\\\": $BARCODE_RECLASSIFY_INTERVAL}\" !  gvametaconvert name=metaconvert add-empty-results=true ! gvametapublish name=destination file-format=2 file-path=/tmp/results/r$cid_count.jsonl $OUTPUT 2>&1 | tee >/tmp/results/gst-launch_$cid_count.log >(stdbuf -oL sed -n -e 's/^.*current: //p' | stdbuf -oL cut -d , -f 1 > /tmp/results/pipeline$cid_count.log)"
 
 echo "$gstLaunchCmd"
 

diff --git a/configs/opencv-ovms/scripts/run_gst.sh b/configs/opencv-ovms/scripts/run_gst.sh
@@ -1,10 +1,23 @@
 #!/bin/bash
 #
-# Copyright (C) 2023 Intel Corporation.
+# Copyright (C) 2024 Intel Corporation.
 #
 # SPDX-License-Identifier: Apache-2.0
 #
 
+checkBatchSize() {
+	if [ "$BATCH_SIZE" -lt 0 ]
+	then
+		echo "Invalid: BATCH_SIZE should be >= 0: $BATCH_SIZE"
+		exit 1
+	elif [ "$BATCH_SIZE" -gt 1024 ]
+	then
+		echo "Invalid: BATCH_SIZE should be <= 1024: $BATCH_SIZE"
+		exit 1
+	fi
+	echo "Ok, BATCH_SIZE = $BATCH_SIZE"
+}
+
 cid_count="${cid_count:=0}"
 cameras="${cameras:=}"
 stream_density_mount="${stream_density_mount:=}"
@@ -15,6 +28,7 @@ COLOR_WIDTH="${COLOR_WIDTH:=1920}"
 COLOR_HEIGHT="${COLOR_HEIGHT:=1080}"
 COLOR_FRAMERATE="${COLOR_FRAMERATE:=15}"
 OCR_SPECIFIED="${OCR_SPECIFIED:=5}"
+BATCH_SIZE="${BATCH_SIZE:=0}"
 
 show_help() {
 	echo "usage: "--pipeline_script_choice" requires an argument yolov5s.sh|yolov5s_effnetb0.sh|yolov5s_full.sh"
@@ -51,6 +65,8 @@ while :; do
 
 done
 
+checkBatchSize
+
 if [ "$PIPELINE_SCRIPT" != "yolov5s.sh" ] && [ "$PIPELINE_SCRIPT" != "yolov5s_effnetb0.sh" ] && [ "$PIPELINE_SCRIPT" != "yolov5s_full.sh" ]
 then
 	echo "Error on your input: $PIPELINE_SCRIPT"
@@ -118,6 +134,7 @@ DISPLAY="$DISPLAY" \
 RESULT_DIR="/tmp/result" \
 DECODE="$DECODE" \
 DEVICE="$DEVICE" \
+BATCH_SIZE="$BATCH_SIZE" \
 PRE_PROCESS="$PRE_PROCESS" \
 AGGREGATE="$AGGREGATE" \
 OUTPUTFORMAT="$OUTPUTFORMAT" \

diff --git a/docs_src/dev-tools/environment_variables.md b/docs_src/dev-tools/environment_variables.md
@@ -19,6 +19,7 @@ The table below lists the environment variables (EVs) that can be used as inputs
     |`PARALLEL_PIPELINE` | run pipeline in parallel using the tee branch | "", "! tee name=branch ! queue" |
     |`PARALLEL_AGGRAGATE` | aggregate parallel pipeline results together, paired use with PARALLEL_PIPELINE | "", "! gvametaaggregate name=aggregate ! gvametaconvert name=metaconvert add-empty-results=true ! gvametapublish name=destination file-format=2 file-path=/tmp/results/r$cid_count.jsonl ! fpsdisplaysink video-sink=fakesink sync=true --verbose branch. ! queue !" |
     |`VA_SURFACE` | use video analytics surface from the shared memory if applicable | "", "! "video/x-raw(memory |VASurface)" (GPU only)" |
+    |`BATCH_SIZE` | number of frames batched together for a single inference to be used in [gvadetect batch-size element](https://dlstreamer.github.io/elements/gvadetect.html) | 0, 1 |
 
 === "Common EVs"
     This list of EVs is common for all profiles.