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Update MemCheckTests OMZ part: get_testdata.py and configs (#2722)

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Vitaliy Urusovskij 2020-10-30 00:37:02 +03:00 committed by GitHub
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tests/stress_tests/.automation/memcheck_tests/nightly_configs/desktop_references_config.xml
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@ -1,6 +1,323 @@
<?xml version="1.0"?>
<attributes>
<models>
# References were collected from DB with next query: {"target_branch": "releases/2020/4", "commit_date":
"2020-06-15 13:21:41+00:00"}
# and modified on FACTOR = 1.3
<model device="GPU" path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" test="create_exenetwork" vmhwm="329206"
vmpeak="687460" vmrss="329206" vmsize="687460"/>
<model device="GPU" path="public/ctdet_coco_dlav0_512/FP32/ctdet_coco_dlav0_512.xml"
test="infer_request_inference" vmhwm="1071985" vmpeak="1598854" vmrss="1071985" vmsize="1513657"/>
<model device="GPU" path="public/brain-tumor-segmentation-0001/FP32/brain-tumor-segmentation-0001.xml"
test="create_exenetwork" vmhwm="5488875" vmpeak="5846682" vmrss="5417256" vmsize="5774787"/>
<model device="CPU" path="public/brain-tumor-segmentation-0002/FP32/brain-tumor-segmentation-0002.xml"
test="create_exenetwork" vmhwm="153509" vmpeak="2130762" vmrss="131820" vmsize="2109010"/>
<model device="CPU" path="public/googlenet-v1-tf/FP32/googlenet-v1-tf.xml" test="infer_request_inference"
vmhwm="143520" vmpeak="1230450" vmrss="117785" vmsize="1230450"/>
<model device="CPU" path="public/googlenet-v4-tf/FP32/googlenet-v4-tf.xml" test="infer_request_inference"
vmhwm="715135" vmpeak="1690878" vmrss="517982" vmsize="1483570"/>
<model device="GPU" path="public/yolo-v2-tf/FP32/yolo-v2-tf.xml" test="infer_request_inference" vmhwm="1357283"
vmpeak="1707222" vmrss="1055761" vmsize="1487714"/>
<model device="GPU" path="public/alexnet/FP32/alexnet.xml" test="create_exenetwork" vmhwm="1673422"
vmpeak="2031624" vmrss="1013547" vmsize="1371312"/>
<model device="CPU" path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" test="infer_request_inference" vmhwm="32110"
vmpeak="1137089" vmrss="32110" vmsize="1137089"/>
<model device="GPU" path="public/ssd_mobilenet_v2_coco/FP32/ssd_mobilenet_v2_coco.xml" test="create_exenetwork"
vmhwm="1023994" vmpeak="1370938" vmrss="959743" vmsize="1306188"/>
<model device="CPU" path="public/brain-tumor-segmentation-0001/FP32/brain-tumor-segmentation-0001.xml"
test="create_exenetwork" vmhwm="845915" vmpeak="2927428" vmrss="655449" vmsize="2736734"/>
<model device="GPU" path="public/ctdet_coco_dlav0_384/FP32/ctdet_coco_dlav0_384.xml"
test="infer_request_inference" vmhwm="942968" vmpeak="1470898" vmrss="942968" vmsize="1385701"/>
<model device="CPU" path="public/mobilenet-v2/FP32/mobilenet-v2.xml" test="create_exenetwork" vmhwm="90064"
vmpeak="1030208" vmrss="74037" vmsize="1014031"/>
<model device="CPU" path="public/mobilenet-ssd/FP32/mobilenet-ssd.xml" test="infer_request_inference"
vmhwm="126630" vmpeak="1222676" vmrss="110188" vmsize="1137479"/>
<model device="CPU" path="public/mtcnn/mtcnn-o/FP32/mtcnn-o.xml" test="infer_request_inference" vmhwm="33727"
vmpeak="1141301" vmrss="33727" vmsize="1141301"/>
<model device="GPU" path="public/densenet-169/FP32/densenet-169.xml" test="infer_request_inference"
vmhwm="1462349" vmpeak="1990289" vmrss="1462349" vmsize="1905092"/>
<model device="CPU" path="public/efficientnet-b0/FP32/efficientnet-b0.xml" test="infer_request_inference"
vmhwm="128403" vmpeak="1194440" vmrss="116745" vmsize="1194440"/>
<model device="GPU" path="public/brain-tumor-segmentation-0002/FP32/brain-tumor-segmentation-0002.xml"
test="infer_request_inference" vmhwm="2300573" vmpeak="2732880" vmrss="2217467" vmsize="2647684"/>
<model device="CPU" path="public/ctdet_coco_dlav0_512/FP32/ctdet_coco_dlav0_512.xml" test="create_exenetwork"
vmhwm="323060" vmpeak="1302262" vmrss="233859" vmsize="1212931"/>
<model device="CPU" path="public/brain-tumor-segmentation-0002/FP32/brain-tumor-segmentation-0002.xml"
test="infer_request_inference" vmhwm="1157234" vmpeak="2449824" vmrss="1157234" vmsize="2364627"/>
<model device="GPU" path="public/brain-tumor-segmentation-0002/FP32/brain-tumor-segmentation-0002.xml"
test="create_exenetwork" vmhwm="2281832" vmpeak="2638927" vmrss="2207790" vmsize="2564796"/>
<model device="CPU" path="public/vgg19/FP32/vgg19.xml" test="infer_request_inference" vmhwm="2219224"
vmpeak="3182665" vmrss="1523537" vmsize="2453885"/>
<model device="GPU" path="public/yolo-v2-tiny-tf/FP32/yolo-v2-tiny-tf.xml" test="create_exenetwork"
vmhwm="538304" vmpeak="895824" vmrss="490417" vmsize="847880"/>
<model device="GPU" path="public/ctdet_coco_dlav0_384/FP32/ctdet_coco_dlav0_384.xml" test="create_exenetwork"
vmhwm="957179" vmpeak="1314721" vmrss="957179" vmsize="1314721"/>
<model device="GPU" path="public/mobilenet-v2/FP32/mobilenet-v2.xml" test="create_exenetwork" vmhwm="594932"
vmpeak="952640" vmrss="594932" vmsize="952640"/>
<model device="CPU" path="public/googlenet-v3/FP32/googlenet-v3.xml" test="create_exenetwork" vmhwm="415412"
vmpeak="1326598" vmrss="296753" vmsize="1207601"/>
<model device="CPU" path="public/googlenet-v1-tf/FP32/googlenet-v1-tf.xml" test="create_exenetwork"
vmhwm="142896" vmpeak="1079457" vmrss="112377" vmsize="1048611"/>
<model device="GPU" path="public/alexnet/FP32/alexnet.xml" test="infer_request_inference" vmhwm="1677910"
vmpeak="2036039" vmrss="1018867" vmsize="1460924"/>
<model device="GPU" path="public/googlenet-v4-tf/FP32/googlenet-v4-tf.xml" test="create_exenetwork"
vmhwm="1662434" vmpeak="2019430" vmrss="1662434" vmsize="2019430"/>
<model device="GPU" path="public/densenet-121/FP32/densenet-121.xml" test="create_exenetwork" vmhwm="1094308"
vmpeak="1452172" vmrss="1094308" vmsize="1452172"/>
<model device="CPU" path="public/yolo-v1-tiny-tf/FP32/yolo-v1-tiny-tf.xml" test="create_exenetwork"
vmhwm="272740" vmpeak="1216732" vmrss="192602" vmsize="1136304"/>
<model device="GPU" path="public/mask_rcnn_resnet101_atrous_coco/FP32/mask_rcnn_resnet101_atrous_coco.xml"
test="create_exenetwork" vmhwm="4859030" vmpeak="5206666" vmrss="4102753" vmsize="4450399"/>
<model device="CPU" path="public/googlenet-v1/FP32/googlenet-v1.xml" test="create_exenetwork" vmhwm="147898"
vmpeak="1114651" vmrss="115112" vmsize="1053561"/>
<model device="GPU" path="public/yolo-v1-tiny-tf/FP32/yolo-v1-tiny-tf.xml" test="infer_request_inference"
vmhwm="680680" vmpeak="1054289" vmrss="527285" vmsize="969092"/>
<model device="CPU" path="public/octave-resnext-50-0.25/FP32/octave-resnext-50-0.25.xml"
test="infer_request_inference" vmhwm="464297" vmpeak="1802980" vmrss="400145" vmsize="1717783"/>
<model device="GPU" path="public/ctdet_coco_dlav0_512/FP32/ctdet_coco_dlav0_512.xml" test="create_exenetwork"
vmhwm="1071673" vmpeak="1429012" vmrss="1071673" vmsize="1429012"/>
<model device="GPU" path="public/ssd_mobilenet_v1_coco/FP32/ssd_mobilenet_v1_coco.xml"
test="infer_request_inference" vmhwm="672250" vmpeak="1199031" vmrss="672250" vmsize="1113834"/>
<model device="CPU" path="public/efficientnet-b0/FP32/efficientnet-b0.xml" test="create_exenetwork"
vmhwm="128388" vmpeak="1033484" vmrss="107822" vmsize="1012533"/>
<model device="CPU" path="public/ssd_mobilenet_v1_coco/FP32/ssd_mobilenet_v1_coco.xml"
test="infer_request_inference" vmhwm="143041" vmpeak="1332099" vmrss="121971" vmsize="1246902"/>
<model device="GPU" path="public/yolo-v2-tf/FP32/yolo-v2-tf.xml" test="create_exenetwork" vmhwm="1357709"
vmpeak="1707929" vmrss="1054398" vmsize="1401415"/>
<model device="GPU" path="public/se-resnet-50/FP32/se-resnet-50.xml" test="infer_request_inference"
vmhwm="1207304" vmpeak="1725323" vmrss="1207304" vmsize="1640126"/>
<model device="GPU" path="public/efficientnet-b0/FP32/efficientnet-b0.xml" test="create_exenetwork"
vmhwm="810825" vmpeak="1168039" vmrss="810825" vmsize="1168039"/>
<model device="GPU" path="public/se-inception/FP32/se-inception.xml" test="create_exenetwork" vmhwm="944112"
vmpeak="1291321" vmrss="944112" vmsize="1291321"/>
<model device="CPU" path="public/googlenet-v3/FP32/googlenet-v3.xml" test="infer_request_inference"
vmhwm="415594" vmpeak="1390048" vmrss="305515" vmsize="1390048"/>
<model device="CPU" path="public/mask_rcnn_resnet101_atrous_coco/FP32/mask_rcnn_resnet101_atrous_coco.xml"
test="infer_request_inference" vmhwm="2099572" vmpeak="3280186" vmrss="2099572" vmsize="2998964"/>
<model device="CPU" path="public/se-resnet-50/FP32/se-resnet-50.xml" test="infer_request_inference"
vmhwm="476954" vmpeak="1581210" vmrss="351046" vmsize="1496014"/>
<model device="CPU"
path="public/faster_rcnn_inception_resnet_v2_atrous_coco/FP32/faster_rcnn_inception_resnet_v2_atrous_coco.xml"
test="create_exenetwork" vmhwm="1011462" vmpeak="2969579" vmrss="760396" vmsize="2718393"/>
<model device="CPU"
path="public/faster_rcnn_inception_resnet_v2_atrous_coco/FP32/faster_rcnn_inception_resnet_v2_atrous_coco.xml"
test="infer_request_inference" vmhwm="1731979" vmpeak="2994170" vmrss="1731979" vmsize="2908973"/>
<model device="CPU" path="public/ctpn/FP32/ctpn.xml" test="infer_request_inference" vmhwm="497364"
vmpeak="1607845" vmrss="497364" vmsize="1529418"/>
<model device="CPU" path="public/mobilenet-v2/FP32/mobilenet-v2.xml" test="infer_request_inference"
vmhwm="90126" vmpeak="1281170" vmrss="82409" vmsize="1195974"/>
<model device="CPU" path="public/yolo-v2-tf/FP32/yolo-v2-tf.xml" test="infer_request_inference" vmhwm="812780"
vmpeak="1815288" vmrss="625066" vmsize="1744184"/>
<model device="CPU" path="public/alexnet/FP32/alexnet.xml" test="create_exenetwork" vmhwm="964272"
vmpeak="1895368" vmrss="654877" vmsize="1585792"/>
<model device="CPU" path="public/mobilenet-v1-1.0-224-tf/FP32/mobilenet-v1-1.0-224-tf.xml"
test="infer_request_inference" vmhwm="97141" vmpeak="1279350" vmrss="81712" vmsize="1194154"/>
<model device="CPU" path="public/mask_rcnn_resnet101_atrous_coco/FP32/mask_rcnn_resnet101_atrous_coco.xml"
test="create_exenetwork" vmhwm="1128233" vmpeak="3280191" vmrss="780743" vmsize="2932467"/>
<model device="CPU" path="public/mobilenet-v2-1.4-224/FP32/mobilenet-v2-1.4-224.xml"
test="infer_request_inference" vmhwm="130343" vmpeak="1311367" vmrss="111295" vmsize="1226170"/>
<model device="CPU" path="public/se-inception/FP32/se-inception.xml" test="create_exenetwork" vmhwm="232299"
vmpeak="1191814" vmrss="176898" vmsize="1136148"/>
<model device="GPU" path="public/se-resnet-152/FP32/se-resnet-152.xml" test="infer_request_inference"
vmhwm="2734373" vmpeak="3261367" vmrss="2734373" vmsize="3176170"/>
<model device="CPU" path="public/se-resnext-50/FP32/se-resnext-50.xml" test="create_exenetwork" vmhwm="476860"
vmpeak="1483336" vmrss="339549" vmsize="1345760"/>
<model device="CPU" path="public/ssd_mobilenet_v2_coco/FP32/ssd_mobilenet_v2_coco.xml"
test="infer_request_inference" vmhwm="300705" vmpeak="1445402" vmrss="235357" vmsize="1360205"/>
<model device="CPU" path="public/brain-tumor-segmentation-0001/FP32/brain-tumor-segmentation-0001.xml"
test="infer_request_inference" vmhwm="1768228" vmpeak="2992033" vmrss="1768228" vmsize="2821946"/>
<model device="CPU" path="public/caffenet/FP32/caffenet.xml" test="infer_request_inference" vmhwm="965572"
vmpeak="1894677" vmrss="658169" vmsize="1585953"/>
<model device="GPU" path="public/mobilenet-ssd/FP32/mobilenet-ssd.xml" test="infer_request_inference"
vmhwm="664773" vmpeak="1181507" vmrss="664773" vmsize="1096310"/>
<model device="CPU" path="public/ctdet_coco_dlav0_384/FP32/ctdet_coco_dlav0_384.xml" test="create_exenetwork"
vmhwm="321599" vmpeak="1278414" vmrss="232367" vmsize="1189084"/>
<model device="CPU" path="public/ssd_mobilenet_v2_coco/FP32/ssd_mobilenet_v2_coco.xml" test="create_exenetwork"
vmhwm="301511" vmpeak="1258327" vmrss="221260" vmsize="1177727"/>
<model device="GPU" path="public/yolo-v3-tf/FP32/yolo-v3-tf.xml" test="create_exenetwork" vmhwm="1711231"
vmpeak="2075855" vmrss="1532403" vmsize="1889888"/>
<model device="CPU" path="public/vgg19/FP32/vgg19.xml" test="create_exenetwork" vmhwm="2219448" vmpeak="3182665"
vmrss="1490070" vmsize="2453094"/>
<model device="CPU" path="public/googlenet-v1/FP32/googlenet-v1.xml" test="infer_request_inference"
vmhwm="149328" vmpeak="1320654" vmrss="123120" vmsize="1235457"/>
<model device="GPU" path="public/ctpn/FP32/ctpn.xml" test="create_exenetwork" vmhwm="2404532" vmpeak="2762448"
vmrss="2386540" vmsize="2743967"/>
<model device="CPU" path="public/i3d-rgb-tf/FP32/i3d-rgb-tf.xml" test="create_exenetwork" vmhwm="292292"
vmpeak="1390573" vmrss="230708" vmsize="1328849"/>
<model device="GPU" path="public/mtcnn/mtcnn-o/FP32/mtcnn-o.xml" test="create_exenetwork" vmhwm="339934"
vmpeak="698349" vmrss="339934" vmsize="698349"/>
<model device="CPU" path="public/yolo-v3-tf/FP32/yolo-v3-tf.xml" test="infer_request_inference" vmhwm="988608"
vmpeak="1965574" vmrss="720434" vmsize="1841538"/>
<model device="CPU" path="public/yolo-v1-tiny-tf/FP32/yolo-v1-tiny-tf.xml" test="infer_request_inference"
vmhwm="271902" vmpeak="1405289" vmrss="206668" vmsize="1320092"/>
<model device="CPU" path="public/se-resnext-50/FP32/se-resnext-50.xml" test="infer_request_inference"
vmhwm="477490" vmpeak="1483336" vmrss="371966" vmsize="1346592"/>
<model device="CPU" path="public/yolo-v3-tf/FP32/yolo-v3-tf.xml" test="create_exenetwork" vmhwm="991343"
vmpeak="1970904" vmrss="679114" vmsize="1658664"/>
<model device="GPU" path="public/caffenet/FP32/caffenet.xml" test="create_exenetwork" vmhwm="1678461"
vmpeak="2036767" vmrss="1018586" vmsize="1376455"/>
<model device="CPU" path="public/se-resnet-152/FP32/se-resnet-152.xml" test="infer_request_inference"
vmhwm="1114266" vmpeak="2174572" vmrss="828776" vmsize="2056496"/>
<model device="GPU" path="public/mtcnn/mtcnn-o/FP32/mtcnn-o.xml" test="infer_request_inference" vmhwm="340433"
vmpeak="868753" vmrss="340433" vmsize="783556"/>
<model device="CPU" path="public/Sphereface/FP32/Sphereface.xml" test="infer_request_inference" vmhwm="374836"
vmpeak="1459000" vmrss="263281" vmsize="1373803"/>
<model device="GPU" path="public/i3d-rgb-tf/FP32/i3d-rgb-tf.xml" test="infer_request_inference" vmhwm="1485442"
vmpeak="1881984" vmrss="1365530" vmsize="1796787"/>
<model device="GPU" path="public/se-resnet-152/FP32/se-resnet-152.xml" test="create_exenetwork" vmhwm="2746140"
vmpeak="3103349" vmrss="2746140" vmsize="3103349"/>
<model device="CPU" path="public/alexnet/FP32/alexnet.xml" test="infer_request_inference" vmhwm="963482"
vmpeak="1895368" vmrss="658283" vmsize="1767693"/>
<model device="GPU" path="public/mobilenet-ssd/FP32/mobilenet-ssd.xml" test="create_exenetwork" vmhwm="665277"
vmpeak="1012148" vmrss="665277" vmsize="1012148"/>
<model device="GPU" path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" test="infer_request_inference" vmhwm="329435"
vmpeak="857864" vmrss="329435" vmsize="772668"/>
<model device="GPU" path="public/ssd_mobilenet_v1_coco/FP32/ssd_mobilenet_v1_coco.xml" test="create_exenetwork"
vmhwm="671990" vmpeak="1028981" vmrss="671990" vmsize="1028981"/>
<model device="CPU" path="public/yolo-v2-tf/FP32/yolo-v2-tf.xml" test="create_exenetwork" vmhwm="813150"
vmpeak="2102848" vmrss="554668" vmsize="1737689"/>
<model device="GPU"
path="public/faster_rcnn_inception_resnet_v2_atrous_coco/FP32/faster_rcnn_inception_resnet_v2_atrous_coco.xml"
test="create_exenetwork" vmhwm="5051202" vmpeak="5408088" vmrss="5051202" vmsize="5408088"/>
<model device="CPU" path="public/octave-resnext-50-0.25/FP32/octave-resnext-50-0.25.xml"
test="create_exenetwork" vmhwm="463372" vmpeak="1638936" vmrss="360770" vmsize="1535965"/>
<model device="CPU" path="public/i3d-rgb-tf/FP32/i3d-rgb-tf.xml" test="infer_request_inference" vmhwm="392787"
vmpeak="1390573" vmrss="392787" vmsize="1389242"/>
<model device="CPU" path="public/se-resnet-50/FP32/se-resnet-50.xml" test="create_exenetwork" vmhwm="477682"
vmpeak="1454668" vmrss="337610" vmsize="1314164"/>
<model device="CPU" path="public/ssd_mobilenet_v1_coco/FP32/ssd_mobilenet_v1_coco.xml" test="create_exenetwork"
vmhwm="142844" vmpeak="1097834" vmrss="109626" vmsize="1064325"/>
<model device="CPU" path="public/se-resnet-152/FP32/se-resnet-152.xml" test="create_exenetwork" vmhwm="1113491"
vmpeak="2174577" vmrss="813904" vmsize="1874657"/>
<model device="CPU" path="public/densenet-121/FP32/densenet-121.xml" test="create_exenetwork" vmhwm="182317"
vmpeak="1197773" vmrss="164658" vmsize="1111999"/>
<model device="CPU" path="public/ctdet_coco_dlav0_512/FP32/ctdet_coco_dlav0_512.xml"
test="infer_request_inference" vmhwm="322951" vmpeak="1484464" vmrss="279208" vmsize="1399268"/>
<model device="GPU" path="public/se-resnext-50/FP32/se-resnext-50.xml" test="create_exenetwork" vmhwm="1193774"
vmpeak="1540479" vmrss="1193774" vmsize="1540479"/>
<model device="CPU" path="public/mtcnn/mtcnn-o/FP32/mtcnn-o.xml" test="create_exenetwork" vmhwm="34309"
vmpeak="962114" vmrss="32546" vmsize="960247"/>
<model device="GPU" path="public/mobilenet-v1-1.0-224-tf/FP32/mobilenet-v1-1.0-224-tf.xml"
test="create_exenetwork" vmhwm="494447" vmpeak="852124" vmrss="494447" vmsize="852124"/>
<model device="GPU" path="public/mobilenet-v2-1.4-224/FP32/mobilenet-v2-1.4-224.xml"
test="infer_request_inference" vmhwm="645127" vmpeak="1162184" vmrss="645127" vmsize="1076987"/>
<model device="GPU" path="public/vgg19/FP32/vgg19.xml" test="create_exenetwork" vmhwm="3663301" vmpeak="4022153"
vmrss="2013772" vmsize="2371428"/>
<model device="GPU" path="public/ssd_mobilenet_v2_coco/FP32/ssd_mobilenet_v2_coco.xml"
test="infer_request_inference" vmhwm="1022548" vmpeak="1475224" vmrss="959036" vmsize="1390027"/>
<model device="GPU"
path="public/faster_rcnn_inception_resnet_v2_atrous_coco/FP32/faster_rcnn_inception_resnet_v2_atrous_coco.xml"
test="infer_request_inference" vmhwm="5038597" vmpeak="5561036" vmrss="5038597" vmsize="5475839"/>
<model device="GPU" path="public/i3d-rgb-tf/FP32/i3d-rgb-tf.xml" test="create_exenetwork" vmhwm="1361396"
vmpeak="1708865" vmrss="1361396" vmsize="1708865"/>
<model device="GPU" path="public/mobilenet-v1-1.0-224-tf/FP32/mobilenet-v1-1.0-224-tf.xml"
test="infer_request_inference" vmhwm="491171" vmpeak="1018591" vmrss="491171" vmsize="933394"/>
<model device="GPU" path="public/octave-resnext-50-0.25/FP32/octave-resnext-50-0.25.xml"
test="infer_request_inference" vmhwm="1976426" vmpeak="2503280" vmrss="1976426" vmsize="2418083"/>
<model device="GPU" path="public/googlenet-v1/FP32/googlenet-v1.xml" test="infer_request_inference"
vmhwm="691709" vmpeak="1207856" vmrss="691709" vmsize="1122659"/>
<model device="GPU" path="public/efficientnet-b0/FP32/efficientnet-b0.xml" test="infer_request_inference"
vmhwm="820570" vmpeak="1338064" vmrss="820570" vmsize="1252867"/>
<model device="GPU" path="public/Sphereface/FP32/Sphereface.xml" test="infer_request_inference" vmhwm="863813"
vmpeak="1215136" vmrss="698453" vmsize="1129939"/>
<model device="CPU" path="public/mobilenet-ssd/FP32/mobilenet-ssd.xml" test="create_exenetwork" vmhwm="125866"
vmpeak="1110345" vmrss="98670" vmsize="1045501"/>
<model device="GPU" path="public/se-resnet-50/FP32/se-resnet-50.xml" test="create_exenetwork" vmhwm="1210544"
vmpeak="1557566" vmrss="1210544" vmsize="1557566"/>
<model device="GPU" path="public/mobilenet-v2/FP32/mobilenet-v2.xml" test="infer_request_inference"
vmhwm="596081" vmpeak="1120735" vmrss="596081" vmsize="1035538"/>
<model device="GPU" path="public/yolo-v1-tiny-tf/FP32/yolo-v1-tiny-tf.xml" test="create_exenetwork"
vmhwm="680950" vmpeak="1038434" vmrss="520566" vmsize="877874"/>
<model device="CPU" path="public/yolo-v2-tiny-tf/FP32/yolo-v2-tiny-tf.xml" test="infer_request_inference"
vmhwm="200688" vmpeak="1273443" vmrss="159364" vmsize="1273443"/>
<model device="GPU" path="public/Sphereface/FP32/Sphereface.xml" test="create_exenetwork" vmhwm="849794"
vmpeak="1208750" vmrss="683867" vmsize="1041315"/>
<model device="CPU" path="public/densenet-169/FP32/densenet-169.xml" test="create_exenetwork" vmhwm="290888"
vmpeak="1281810" vmrss="257852" vmsize="1248598"/>
<model device="CPU" path="public/ctdet_coco_dlav0_384/FP32/ctdet_coco_dlav0_384.xml"
test="infer_request_inference" vmhwm="321152" vmpeak="1278378" vmrss="258684" vmsize="1194871"/>
<model device="GPU" path="public/googlenet-v3/FP32/googlenet-v3.xml" test="create_exenetwork" vmhwm="1110356"
vmpeak="1467570" vmrss="1110356" vmsize="1467570"/>
<model device="CPU" path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" test="create_exenetwork" vmhwm="30227"
vmpeak="1037795" vmrss="30227" vmsize="956035"/>
<model device="GPU" path="public/googlenet-v1/FP32/googlenet-v1.xml" test="create_exenetwork" vmhwm="682973"
vmpeak="1039568" vmrss="682973" vmsize="1039568"/>
<model device="CPU" path="public/googlenet-v4-tf/FP32/googlenet-v4-tf.xml" test="create_exenetwork"
vmhwm="716154" vmpeak="1690884" vmrss="507525" vmsize="1482078"/>
<model device="CPU" path="public/densenet-121/FP32/densenet-121.xml" test="infer_request_inference"
vmhwm="182390" vmpeak="1197778" vmrss="173716" vmsize="1112763"/>
<model device="GPU" path="public/densenet-121/FP32/densenet-121.xml" test="infer_request_inference"
vmhwm="1093898" vmpeak="1621344" vmrss="1093898" vmsize="1536147"/>
<model device="GPU" path="public/yolo-v2-tiny-tf/FP32/yolo-v2-tiny-tf.xml" test="infer_request_inference"
vmhwm="541606" vmpeak="1021555" vmrss="494780" vmsize="936358"/>
<model device="GPU" path="public/octave-resnext-50-0.25/FP32/octave-resnext-50-0.25.xml"
test="create_exenetwork" vmhwm="1975246" vmpeak="2332423" vmrss="1975246" vmsize="2332423"/>
<model device="GPU" path="public/se-resnext-50/FP32/se-resnext-50.xml" test="infer_request_inference"
vmhwm="1214886" vmpeak="1731776" vmrss="1214886" vmsize="1646580"/>
<model device="CPU" path="public/se-inception/FP32/se-inception.xml" test="infer_request_inference"
vmhwm="231592" vmpeak="1403303" vmrss="181334" vmsize="1318106"/>
<model device="GPU" path="public/googlenet-v1-tf/FP32/googlenet-v1-tf.xml" test="create_exenetwork"
vmhwm="684533" vmpeak="1041762" vmrss="684533" vmsize="1041762"/>
<model device="CPU" path="public/ctpn/FP32/ctpn.xml" test="create_exenetwork" vmhwm="312821" vmpeak="1613175"
vmrss="229080" vmsize="1529262"/>
<model device="GPU" path="public/caffenet/FP32/caffenet.xml" test="infer_request_inference" vmhwm="1678918"
vmpeak="2036923" vmrss="1019933" vmsize="1461808"/>
<model device="GPU" path="public/mask_rcnn_resnet101_atrous_coco/FP32/mask_rcnn_resnet101_atrous_coco.xml"
test="infer_request_inference" vmhwm="4857247" vmpeak="5206427" vmrss="4128420" vmsize="4553484"/>
<model device="GPU" path="public/googlenet-v4-tf/FP32/googlenet-v4-tf.xml" test="infer_request_inference"
vmhwm="1670255" vmpeak="2196989" vmrss="1670255" vmsize="2111792"/>
<model device="CPU" path="public/mobilenet-v1-1.0-224-tf/FP32/mobilenet-v1-1.0-224-tf.xml"
test="create_exenetwork" vmhwm="97193" vmpeak="1087216" vmrss="76294" vmsize="1012232"/>
<model device="CPU" path="public/caffenet/FP32/caffenet.xml" test="create_exenetwork" vmhwm="964386"
vmpeak="1900002" vmrss="654992" vmsize="1590425"/>
<model device="GPU" path="public/mobilenet-v2-1.4-224/FP32/mobilenet-v2-1.4-224.xml" test="create_exenetwork"
vmhwm="640260" vmpeak="988109" vmrss="640260" vmsize="988109"/>
<model device="CPU" path="public/mobilenet-v2-1.4-224/FP32/mobilenet-v2-1.4-224.xml" test="create_exenetwork"
vmhwm="130213" vmpeak="1074086" vmrss="100630" vmsize="1044352"/>
<model device="CPU" path="public/densenet-169/FP32/densenet-169.xml" test="infer_request_inference"
vmhwm="290009" vmpeak="1430551" vmrss="266780" vmsize="1430551"/>
<model device="GPU" path="public/densenet-169/FP32/densenet-169.xml" test="create_exenetwork" vmhwm="1465521"
vmpeak="1823016" vmrss="1465521" vmsize="1823016"/>
<model device="GPU" path="public/ctpn/FP32/ctpn.xml" test="infer_request_inference" vmhwm="2408104"
vmpeak="2888906" vmrss="2363524" vmsize="2803710"/>
<model device="GPU" path="public/googlenet-v3/FP32/googlenet-v3.xml" test="infer_request_inference"
vmhwm="1109373" vmpeak="1637074" vmrss="1109373" vmsize="1551877"/>
<model device="CPU" path="public/Sphereface/FP32/Sphereface.xml" test="create_exenetwork" vmhwm="375663"
vmpeak="1307919" vmrss="260686" vmsize="1192750"/>
<model device="GPU" path="public/brain-tumor-segmentation-0001/FP32/brain-tumor-segmentation-0001.xml"
test="infer_request_inference" vmhwm="5498755" vmpeak="5913622" vmrss="5393434" vmsize="5828425"/>
<model device="GPU" path="public/vgg19/FP32/vgg19.xml" test="infer_request_inference" vmhwm="3676467"
vmpeak="4025216" vmrss="2028057" vmsize="2459688"/>
<model device="GPU" path="public/googlenet-v1-tf/FP32/googlenet-v1-tf.xml" test="infer_request_inference"
vmhwm="681803" vmpeak="1209452" vmrss="681803" vmsize="1124255"/>
<model device="GPU" path="public/se-inception/FP32/se-inception.xml" test="infer_request_inference"
vmhwm="947564" vmpeak="1465734" vmrss="947564" vmsize="1380537"/>
<model device="CPU" path="public/yolo-v2-tiny-tf/FP32/yolo-v2-tiny-tf.xml" test="create_exenetwork"
vmhwm="201552" vmpeak="1170317" vmrss="144762" vmsize="1089290"/>
<model device="GPU" path="public/yolo-v3-tf/FP32/yolo-v3-tf.xml" test="infer_request_inference" vmhwm="1711876"
vmpeak="2076287" vmrss="1521967" vmsize="1963785"/>
<model path="public/ssd300/FP32/ssd300.xml" test="create_exenetwork" device="CPU" vmsize="1046988"
vmpeak="1179042" vmrss="307990" vmhwm="439457"/> # values from {"target_branch": "releases/2020/2", "commit_date": "2020-05-14 11:19:36+00:00"} and *= 1.3
<model path="public/ssd300/FP32/ssd300.xml" test="create_exenetwork" device="GPU" vmsize="1267775"
vmpeak="1279647" vmrss="932672" vmhwm="944626"/> # values from {"target_branch": "releases/2020/2", "commit_date": "2020-05-14 11:19:36+00:00"} and *= 1.3
<model path="public/ssd300/FP32/ssd300.xml" test="infer_request_inference" device="CPU" vmsize="1321819"
vmpeak="1321819" vmrss="374207" vmhwm="439748"/> # values from {"target_branch": "releases/2020/2", "commit_date": "2020-05-14 11:19:36+00:00"} and *= 1.3
<model path="public/ssd300/FP32/ssd300.xml" test="infer_request_inference" device="GPU" vmsize="1356565"
vmpeak="1441762" vmrss="941418" vmhwm="947060"/> # values from {"target_branch": "releases/2020/2", "commit_date": "2020-05-14 11:19:36+00:00"} and *= 1.3
<model device="CPU" path="public/googlenet-v2/FP32/googlenet-v2.xml" test="create_exenetwork" vmhwm="214182"
vmpeak="1067034" vmrss="162011" vmsize="1014452"/> # values from {"target_branch": "releases/2020/2", "commit_date": "2020-05-14 11:19:36+00:00"} and *= 1.3
<model device="CPU" path="public/ssd512/FP32/ssd512.xml" test="create_exenetwork" vmhwm="453237"
vmpeak="1498151" vmrss="319966" vmsize="1361952"/> # values from {"target_branch": "releases/2020/2", "commit_date": "2020-05-14 11:19:36+00:00"} and *= 1.3
<model device="GPU" path="public/googlenet-v2/FP32/googlenet-v2.xml" test="create_exenetwork" vmhwm="642766"
vmpeak="975322" vmrss="606226" vmsize="938501"/> # values from {"target_branch": "releases/2020/2", "commit_date": "2020-05-14 11:19:36+00:00"} and *= 1.3
<model device="GPU" path="public/ssd512/FP32/ssd512.xml" test="create_exenetwork" vmhwm="1110293"
vmpeak="1441819" vmrss="1110293" vmsize="1441819"/> # values from {"target_branch": "releases/2020/2", "commit_date": "2020-05-14 11:19:36+00:00"} and *= 1.3
<model device="CPU" path="public/googlenet-v2/FP32/googlenet-v2.xml" test="infer_request_inference"
vmhwm="213876" vmpeak="1180764" vmrss="167460" vmsize="1095567"/> # values from {"target_branch": "releases/2020/2", "commit_date": "2020-05-14 11:19:36+00:00"} and *= 1.3
<model device="CPU" path="public/ssd512/FP32/ssd512.xml" test="infer_request_inference" vmhwm="501009"
vmpeak="1543474" vmrss="501009" vmsize="1458277"/> # values from {"target_branch": "releases/2020/2", "commit_date": "2020-05-14 11:19:36+00:00"} and *= 1.3
<model device="GPU" path="public/googlenet-v2/FP32/googlenet-v2.xml" test="infer_request_inference"
vmhwm="644373" vmpeak="1111749" vmrss="610048" vmsize="1026552"/> # values from {"target_branch": "releases/2020/2", "commit_date": "2020-05-14 11:19:36+00:00"} and *= 1.3
<model device="GPU" path="public/ssd512/FP32/ssd512.xml" test="infer_request_inference" vmhwm="1127287"
vmpeak="1619753" vmrss="1127287" vmsize="1534556"/> # values from {"target_branch": "releases/2020/2", "commit_date": "2020-05-14 11:19:36+00:00"} and *= 1.3
</models>
</attributes>

136
tests/stress_tests/.automation/memcheck_tests/nightly_configs/desktop_test_config.xml
View File

@ -5,14 +5,9 @@
</devices>
<models>
<model name="mobilenet-v2-1.4-224" source="omz" />
<model name="resnet-101" source="omz" />
<model name="brain-tumor-segmentation-0001" source="omz" />
<model name="octave-resnet-101-0.125" source="omz" />
<model name="faster_rcnn_inception_resnet_v2_atrous_coco" source="omz" />
<model name="efficientnet-b7_auto_aug" source="omz" />
<model name="yolo-v2-tf" source="omz" />
<model name="mobilenet-v2-1.0-224" source="omz" />
<model name="colorization-v2-norebal" source="omz" />
<model name="se-inception" source="omz" />
<model name="efficientnet-b0" source="omz" />
<model name="mobilenet-v1-1.0-224-tf" source="omz" />
@ -23,49 +18,14 @@
<model name="googlenet-v3" source="omz" />
<model name="ssd_mobilenet_v2_coco" source="omz" />
<model name="alexnet" source="omz" />
<model name="license-plate-recognition-barrier-0007" source="omz" />
<model name="mobilenet-v1-0.50-224" source="omz" />
<model name="ssd_mobilenet_v1_fpn_coco" source="omz" />
<model name="vgg16" source="omz" />
<model name="face-recognition-resnet34-arcface" source="omz" />
<model name="gmcnn-places2-tf" source="omz" />
<model name="mobilenet-v1-1.0-224" source="omz" />
<model name="se-resnet-101" source="omz" />
<model name="face-detection-retail-0044" source="omz" />
<model name="face-recognition-mobilefacenet-arcface" source="omz" />
<model name="vehicle-license-plate-detection-barrier-0123" source="omz" />
<model name="densenet-161" source="omz" />
<model name="mask_rcnn_inception_resnet_v2_atrous_coco" source="omz" />
<model name="octave-resnext-101-0.25" source="omz" />
<model name="face-recognition-resnet50-arcface" source="omz" />
<model name="densenet-161-tf" source="omz" />
<model name="octave-resnet-200-0.125" source="omz" />
<model name="mtcnn-p" source="omz" />
<model name="se-resnext-101" source="omz" />
<model name="efficientnet-b5" source="omz" />
<model name="densenet-169-tf" source="omz" />
<model name="densenet-201" source="omz" />
<model name="resnet-50-tf" source="omz" />
<model name="squeezenet1.1" source="omz" />
<model name="squeezenet1.0" source="omz" />
<model name="octave-resnet-26-0.25" source="omz" />
<model name="googlenet-v4-tf" source="omz" />
<model name="ssd300" source="omz" />
<model name="rfcn-resnet101-coco-tf" source="omz" />
<model name="vgg19" source="omz" />
<model name="ctdet_coco_dlav0_384" source="omz" />
<model name="efficientnet-b0_auto_aug" source="omz" />
<model name="googlenet-v1" source="omz" />
<model name="faster_rcnn_inception_v2_coco" source="omz" />
<model name="mask_rcnn_inception_v2_coco" source="omz" />
<model name="inception-resnet-v2-tf" source="omz" />
<model name="deeplabv3" source="omz" />
<model name="yolo-v3-tf" source="omz" />
<model name="resnet-152" source="omz" />
<model name="mtcnn-o" source="omz" />
<model name="octave-se-resnet-50-0.125" source="omz" />
<model name="yolo-v1-tiny-tf" source="omz" />
<model name="resnet-50" source="omz" />
<model name="googlenet-v1-tf" source="omz" />
<model name="yolo-v2-tiny-tf" source="omz" />
<model name="ssd512" source="omz" />
@ -73,111 +33,15 @@
<model name="brain-tumor-segmentation-0002" source="omz" />
<model name="Sphereface" source="omz" />
<model name="googlenet-v2" source="omz" />
<model name="face-recognition-resnet100-arcface" source="omz" />
<model name="mobilenet-v1-0.25-128" source="omz" />
<model name="ctdet_coco_dlav0_512" source="omz" />
<model name="facenet-20180408-102900" source="omz" />
<model name="ctpn" source="omz" />
<model name="ssdlite_mobilenet_v2" source="omz" />
<model name="i3d-rgb-tf" source="omz" />
<model name="mobilenet-v2" source="omz" />
<model name="mobilenet-ssd" source="omz" />
<model name="se-resnext-50" source="omz" />
<model name="caffenet" source="omz" />
<model name="mtcnn-r" source="omz" />
<model name="faster_rcnn_resnet50_coco" source="omz" />
<model name="se-resnet-50" source="omz" />
<model name="mask_rcnn_resnet50_atrous_coco" source="omz" />
<model name="octave-resnet-50-0.125" source="omz" />
<model name="densenet-121-tf" source="omz" />
<model name="mobilenet-v1-0.50-160" source="omz" />
<model name="densenet-121" source="omz" />
<model name="faster_rcnn_resnet101_coco" source="omz" />
<model name="octave-densenet-121-0.125" source="omz" />
<model name="colorization-v2" source="omz" />
<model name="densenet-121-caffe2" source="omz" />
<model name="efficientnet-b0-pytorch" source="omz" />
<model name="efficientnet-b5-pytorch" source="omz" />
<model name="efficientnet-b7-pytorch" source="omz" />
<model name="googlenet-v3-pytorch" source="omz" />
<model name="human-pose-estimation-3d-0001" source="omz" />
<model name="midasnet" source="omz" />
<model name="mobilenet-v2-pytorch" source="omz" />
<model name="resnet-18-pytorch" source="omz" />
<model name="resnet-50-caffe2" source="omz" />
<model name="resnet-50-pytorch" source="omz" />
<model name="single-human-pose-estimation-0001" source="omz" />
<model name="squeezenet1.1-caffe2" source="omz" />
<model name="vgg19-caffe2" source="omz" />
<model name="facial-landmarks-35-adas-0002" source="omz" />
<model name="vehicle-attributes-recognition-barrier-0039" source="omz" />
<model name="person-detection-action-recognition-0006" source="omz" />
<model name="asl-recognition-0004" source="omz" />
<model name="yolo-v2-tiny-ava-sparse-30-0001" source="omz" />
<model name="text-detection-0004" source="omz" />
<model name="person-vehicle-bike-detection-crossroad-1016" source="omz" />
<model name="text-spotting-0002-detector" source="omz" />
<model name="age-gender-recognition-retail-0013" source="omz" />
<model name="vehicle-detection-adas-0002" source="omz" />
<model name="image-retrieval-0001" source="omz" />
<model name="person-detection-retail-0002" source="omz" />
<model name="person-attributes-recognition-crossroad-0230" source="omz" />
<model name="face-detection-0100" source="omz" />
<model name="face-detection-0102" source="omz" />
<model name="person-reidentification-retail-0300" source="omz" />
<model name="instance-segmentation-security-0010" source="omz" />
<model name="instance-segmentation-security-0083" source="omz" />
<model name="face-detection-0105" source="omz" />
<model name="face-detection-0104" source="omz" />
<model name="icnet-camvid-ava-sparse-30-0001" source="omz" />
<model name="action-recognition-0001-decoder" source="omz" />
<model name="face-detection-0106" source="omz" />
<model name="person-detection-action-recognition-teacher-0002" source="omz" />
<model name="person-vehicle-bike-detection-crossroad-0078" source="omz" />
<model name="icnet-camvid-ava-sparse-60-0001" source="omz" />
<model name="face-detection-adas-0001" source="omz" />
<model name="unet-camvid-onnx-0001" source="omz" />
<model name="human-pose-estimation-0001" source="omz" />
<model name="faster-rcnn-resnet101-coco-sparse-60-0001" source="omz" />
<model name="action-recognition-0001-encoder" source="omz" />
<model name="yolo-v2-ava-sparse-35-0001" source="omz" />
<model name="yolo-v2-ava-sparse-70-0001" source="omz" />
<model name="person-reidentification-retail-0248" source="omz" />
<model name="person-detection-raisinghand-recognition-0001" source="omz" />
<model name="person-detection-asl-0001" source="omz" />
<model name="emotions-recognition-retail-0003" source="omz" />
<model name="yolo-v2-tiny-ava-0001" source="omz" />
<model name="license-plate-recognition-barrier-0001" source="omz" />
<model name="person-detection-retail-0013" source="omz" />
<model name="instance-segmentation-security-0050" source="omz" />
<model name="single-image-super-resolution-1032" source="omz" />
<model name="landmarks-regression-retail-0009" source="omz" />
<model name="driver-action-recognition-adas-0002-decoder" source="omz" />
<model name="person-reidentification-retail-0249" source="omz" />
<model name="text-spotting-0002-recognizer-decoder" source="omz" />
<model name="yolo-v2-ava-0001" source="omz" />
<model name="person-detection-action-recognition-0005" source="omz" />
<model name="text-recognition-0012" source="omz" />
<model name="face-detection-retail-0004" source="omz" />
<model name="product-detection-0001" source="omz" />
<model name="yolo-v2-tiny-ava-sparse-60-0001" source="omz" />
<model name="face-reidentification-retail-0095" source="omz" />
<model name="road-segmentation-adas-0001" source="omz" />
<model name="single-image-super-resolution-1033" source="omz" />
<model name="face-detection-retail-0005" source="omz" />
<model name="pedestrian-and-vehicle-detector-adas-0001" source="omz" />
<model name="handwritten-japanese-recognition-0001" source="omz" />
<model name="semantic-segmentation-adas-0001" source="omz" />
<model name="pedestrian-detection-adas-0002" source="omz" />
<model name="driver-action-recognition-adas-0002-encoder" source="omz" />
<model name="text-detection-0003" source="omz" />
<model name="text-spotting-0002-recognizer-encoder" source="omz" />
<model name="handwritten-score-recognition-0003" source="omz" />
<model name="icnet-camvid-ava-0001" source="omz" />
<model name="text-image-super-resolution-0001" source="omz" />
<model name="gaze-estimation-adas-0002" source="omz" />
<model name="head-pose-estimation-adas-0001" source="omz" />
<model name="vehicle-license-plate-detection-barrier-0106" source="omz" />
<model name="instance-segmentation-security-1025" source="omz" />
</models>
</attributes>

150
tests/stress_tests/scripts/get_testdata.py
View File

@ -11,7 +11,6 @@ Usage: ./scrips/get_testdata.py
import argparse
import logging as log
import multiprocessing
import os
import shutil
import subprocess
@ -26,7 +25,6 @@ log.basicConfig(format="{file}: [ %(levelname)s ] %(message)s".format(file=os.pa
# Parameters
OMZ_NUM_ATTEMPTS = 6
DOWNLOADER_JOBS_NUM = 4
def abs_path(relative_path):
@ -120,18 +118,6 @@ def main():
# constants
PRECISION = "FP32"
# parse models from test config
test_conf_obj = ET.parse(str(args.test_conf))
test_conf_root = test_conf_obj.getroot()
models_from_cfg = []
for model_rec in test_conf_root.find("models"):
if "source" in model_rec.attrib and "name" in model_rec.attrib:
if model_rec.attrib["source"] == "omz":
models_from_cfg.append(model_rec.attrib["name"])
else:
log.warning("Record from test config '{}' doesn't contain attributes 'source' or 'name'"
.format(args.test_conf.resolve()))
# prepare Open Model Zoo
if args.omz_repo:
omz_path = Path(args.omz_repo).resolve()
@ -144,69 +130,6 @@ def main():
' https://github.com/opencv/open_model_zoo {omz_path}'.format(omz_path=omz_path)
run_in_subprocess(cmd)
# prepare models list
downloader_path = omz_path / "tools" / "downloader" / "downloader.py"
models_list_path = args.omz_models_out_dir / "models_list.txt"
cmd = '{downloader_path} --print_all'.format(downloader_path=downloader_path)
models_available = subprocess.check_output(cmd, shell=True, universal_newlines=True).split("\n")
models_to_run = set(models_from_cfg).intersection(models_available)
os.makedirs(str(args.omz_models_out_dir), exist_ok=True)
log.info("List of models from {models_list_path} used for downloader.py and converter.py: "
"{models_to_run}".format(models_list_path=models_list_path, models_to_run=",".join(models_to_run)))
with open(str(models_list_path), "w") as file:
file.writelines([name + "\n" for name in models_to_run])
if set(models_from_cfg) - models_to_run:
log.warning("List of models defined in config but not available in OMZ: {}"
.format(",".join(set(models_from_cfg) - models_to_run)))
# update test config with Open Model Zoo info
info_dumper_path = omz_path / "tools" / "downloader" / "info_dumper.py"
cmd = "{executable} {info_dumper_path} --list {models_list_path}"\
.format(executable=sys.executable, info_dumper_path=info_dumper_path,
models_list_path=models_list_path)
out = subprocess.check_output(cmd, shell=True, universal_newlines=True)
models_info = json.loads(out)
fields_to_add = ["framework", "subdirectory"]
models_root = test_conf_root.find("models")
for model_rec in list(models_root): # convert iterator to list to prevent incorrect removing of records
if model_rec.attrib.get("source") == "omz" and "name" in model_rec.attrib:
if model_rec.attrib["name"] not in models_to_run:
# remove models from test config which aren't available in OMZ
models_root.remove(model_rec)
else:
# Open Model Zoo ensures name uniqueness of every model
models_info_rec = next(iter([rec for rec in models_info if rec["name"] == model_rec.attrib["name"]]))
info_to_add = {key: models_info_rec[key] for key in fields_to_add}
model_rec.attrib.update(info_to_add)
model_rec.attrib["precision"] = PRECISION
model_rec.attrib["path"] = str(
Path(model_rec.attrib["subdirectory"]) / PRECISION / (model_rec.attrib["name"] + ".xml"))
model_rec.attrib["full_path"] = str(
args.omz_irs_out_dir / model_rec.attrib["subdirectory"] / PRECISION / (model_rec.attrib["name"] + ".xml"))
test_conf_obj.write(args.test_conf)
# prepare models
cmd = '{downloader_path} --list {models_list_path}' \
' --num_attempts {num_attempts}' \
' --output_dir {models_dir}' \
' --cache_dir {cache_dir}' \
' --jobs {jobs_num}'.format(downloader_path=downloader_path, models_list_path=models_list_path,
num_attempts=OMZ_NUM_ATTEMPTS,
models_dir=args.omz_models_out_dir,
cache_dir=args.omz_cache_dir,
jobs_num=DOWNLOADER_JOBS_NUM)
run_in_subprocess(cmd, check_call=not args.skip_omz_errors)
# Open Model Zoo doesn't copy downloaded IRs to converter.py output folder where IRs should be stored.
# Do it manually to have only one folder with IRs
for ir_src_path in args.omz_models_out_dir.rglob("*.xml"):
ir_dst_path = args.omz_irs_out_dir / os.path.relpath(ir_src_path, args.omz_models_out_dir)
shutil.copytree(ir_src_path.parent, ir_dst_path.parent)
# prepare virtual environment and install requirements
python_executable = sys.executable
if not args.no_venv:
@ -221,20 +144,65 @@ def main():
Venv.create_n_install_requirements(*requirements)
python_executable = Venv.get_venv_executable()
# convert models to IRs
converter_path = omz_path / "tools" / "downloader" / "converter.py"
# NOTE: remove --precision if both precisions (FP32 & FP16) required
cmd = '{executable} {converter_path} --list {models_list_path}' \
' -p {executable}' \
' --precision={PRECISION}' \
' --output_dir {irs_dir}' \
' --download_dir {models_dir}' \
' --mo {mo_tool} --jobs {workers_num}'.format(executable=python_executable, PRECISION=PRECISION,
converter_path=converter_path,
models_list_path=models_list_path, irs_dir=args.omz_irs_out_dir,
models_dir=args.omz_models_out_dir, mo_tool=args.mo_tool,
workers_num=multiprocessing.cpu_count())
run_in_subprocess(cmd, check_call=not args.skip_omz_errors)
# parse models from test config
test_conf_obj = ET.parse(str(args.test_conf))
test_conf_root = test_conf_obj.getroot()
for model_rec in test_conf_root.find("models"):
if "name" not in model_rec.attrib or model_rec.attrib.get("source") != "omz":
continue
model_name = model_rec.attrib["name"]
info_dumper_path = omz_path / "tools" / "downloader" / "info_dumper.py"
cmd = "{executable} {info_dumper_path} --name {model_name}"\
.format(executable=sys.executable, info_dumper_path=info_dumper_path,
model_name=model_name)
out = subprocess.check_output(cmd, shell=True, universal_newlines=True)
model_info = json.loads(out)[0]
# update model record from test config with Open Model Zoo info
fields_to_add = ["framework", "subdirectory"]
info_to_add = {key: model_info[key] for key in fields_to_add}
model_rec.attrib.update(info_to_add)
model_rec.attrib["precision"] = PRECISION
model_rec.attrib["path"] = str(
Path(model_rec.attrib["subdirectory"]) / PRECISION / (model_rec.attrib["name"] + ".xml"))
model_rec.attrib["full_path"] = str(
args.omz_irs_out_dir / model_rec.attrib["subdirectory"] / PRECISION / (model_rec.attrib["name"] + ".xml"))
# prepare models
downloader_path = omz_path / "tools" / "downloader" / "downloader.py"
cmd = '{downloader_path} --name {model_name}' \
' --precisions={PRECISION}' \
' --num_attempts {num_attempts}' \
' --output_dir {models_dir}' \
' --cache_dir {cache_dir}'.format(downloader_path=downloader_path, model_name=model_name,
PRECISION=PRECISION, num_attempts=OMZ_NUM_ATTEMPTS,
models_dir=args.omz_models_out_dir, cache_dir=args.omz_cache_dir)
run_in_subprocess(cmd, check_call=not args.skip_omz_errors)
# convert models to IRs
converter_path = omz_path / "tools" / "downloader" / "converter.py"
# NOTE: remove --precisions if both precisions (FP32 & FP16) required
cmd = '{executable} {converter_path} --name {model_name}' \
' -p {executable}' \
' --precisions={PRECISION}' \
' --output_dir {irs_dir}' \
' --download_dir {models_dir}' \
' --mo {mo_tool}'.format(executable=python_executable, PRECISION=PRECISION,
converter_path=converter_path,
model_name=model_name, irs_dir=args.omz_irs_out_dir,
models_dir=args.omz_models_out_dir, mo_tool=args.mo_tool)
run_in_subprocess(cmd, check_call=not args.skip_omz_errors)
# rewrite test config with updated records
test_conf_obj.write(args.test_conf)
# Open Model Zoo doesn't copy downloaded IRs to converter.py output folder where IRs should be stored.
# Do it manually to have only one folder with IRs
for ir_src_path in args.omz_models_out_dir.rglob("*.xml"):
ir_dst_path = args.omz_irs_out_dir / os.path.relpath(ir_src_path, args.omz_models_out_dir)
shutil.copytree(ir_src_path.parent, ir_dst_path.parent)
if __name__ == "__main__":