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Validation: Test AUTO plugin timetests (#17508)

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* Validation: Test AUTO plugin

* config

* Add debug logs against AUTO

* Iteration

* iteration

* iteration

* iteration

* iter

* iteration

* iteration

* iteration

* iteration

---------

Co-authored-by: Daria Ilina <daria.ilina@intel.com>
This commit is contained in:
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2023-05-31 16:38:57 +03:00
committed by GitHub
parent b655fa55a1
commit f2017e8c2e
4 changed files with 685 additions and 6 deletions
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673
tests/time_tests/.automation/auto_plugin_test_config.yml Normal file
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- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/onnx/FP16/resnet-50-pytorch.xml
name: resnet-50-pytorch
precision: FP16
framework: onnx
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/onnx/FP16/resnet-50-pytorch.xml
name: resnet-50-pytorch
precision: FP16
framework: onnx
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/onnx/FP16/resnet-50-pytorch.xml
name: resnet-50-pytorch
precision: FP16
framework: onnx
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/onnx/FP16-INT8/resnet-50-pytorch.xml
name: resnet-50-pytorch
precision: FP16-INT8
framework: onnx
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/onnx/FP16-INT8/resnet-50-pytorch.xml
name: resnet-50-pytorch
precision: FP16-INT8
framework: onnx
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/onnx/FP16-INT8/resnet-50-pytorch.xml
name: resnet-50-pytorch
precision: FP16-INT8
framework: onnx
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe/FP16/mobilenet-v2.xml
name: mobilenet-v2
precision: FP16
framework: caffe
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe/FP16/mobilenet-v2.xml
name: mobilenet-v2
precision: FP16
framework: caffe
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe/FP16/mobilenet-v2.xml
name: mobilenet-v2
precision: FP16
framework: caffe
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe/FP16-INT8/mobilenet-v2.xml
name: mobilenet-v2
precision: FP16-INT8
framework: caffe
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe/FP16-INT8/mobilenet-v2.xml
name: mobilenet-v2
precision: FP16-INT8
framework: caffe
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe/FP16-INT8/mobilenet-v2.xml
name: mobilenet-v2
precision: FP16-INT8
framework: caffe
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16/faster-rcnn-resnet101-coco-sparse-60-0001.xml
name: faster-rcnn-resnet101-coco-sparse-60-0001
precision: FP16
framework: tf
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16/faster-rcnn-resnet101-coco-sparse-60-0001.xml
name: faster-rcnn-resnet101-coco-sparse-60-0001
precision: FP16
framework: tf
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16/faster-rcnn-resnet101-coco-sparse-60-0001.xml
name: faster-rcnn-resnet101-coco-sparse-60-0001
precision: FP16
framework: tf
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16-INT8/faster-rcnn-resnet101-coco-sparse-60-0001.xml
name: faster-rcnn-resnet101-coco-sparse-60-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16-INT8/faster-rcnn-resnet101-coco-sparse-60-0001.xml
name: faster-rcnn-resnet101-coco-sparse-60-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16-INT8/faster-rcnn-resnet101-coco-sparse-60-0001.xml
name: faster-rcnn-resnet101-coco-sparse-60-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/googlenet-v1/tf/FP16/googlenet-v1.xml
name: googlenet-v1
precision: FP16
framework: tf
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/googlenet-v1/tf/FP16/googlenet-v1.xml
name: googlenet-v1
precision: FP16
framework: tf
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/googlenet-v1/tf/FP16/googlenet-v1.xml
name: googlenet-v1
precision: FP16
framework: tf
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/googlenet-v1/tf/FP16-INT8/googlenet-v1.xml
name: googlenet-v1
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/googlenet-v1/tf/FP16-INT8/googlenet-v1.xml
name: googlenet-v1
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/googlenet-v1/tf/FP16-INT8/googlenet-v1.xml
name: googlenet-v1
precision: FP16-INT8
framework: tf
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/googlenet-v3/tf/FP16/googlenet-v3.xml
name: googlenet-v3
precision: FP16
framework: tf
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/googlenet-v3/tf/FP16/googlenet-v3.xml
name: googlenet-v3
precision: FP16
framework: tf
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/googlenet-v3/tf/FP16/googlenet-v3.xml
name: googlenet-v3
precision: FP16
framework: tf
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/googlenet-v3/tf/FP16-INT8/googlenet-v3.xml
name: googlenet-v3
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/googlenet-v3/tf/FP16-INT8/googlenet-v3.xml
name: googlenet-v3
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/googlenet-v3/tf/FP16-INT8/googlenet-v3.xml
name: googlenet-v3
precision: FP16-INT8
framework: tf
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/ssd512/caffe/FP16/ssd512.xml
name: ssd512
precision: FP16
framework: caffe
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/ssd512/caffe/FP16/ssd512.xml
name: ssd512
precision: FP16
framework: caffe
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/ssd512/caffe/FP16/ssd512.xml
name: ssd512
precision: FP16
framework: caffe
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/ssd512/caffe/FP16-INT8/ssd512.xml
name: ssd512
precision: FP16-INT8
framework: caffe
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/ssd512/caffe/FP16-INT8/ssd512.xml
name: ssd512
precision: FP16-INT8
framework: caffe
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/ssd512/caffe/FP16-INT8/ssd512.xml
name: ssd512
precision: FP16-INT8
framework: caffe
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-ava-0001/tf/FP16/yolo-v2-ava-0001.xml
name: yolo-v2-ava-0001
precision: FP16
framework: tf
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-ava-0001/tf/FP16/yolo-v2-ava-0001.xml
name: yolo-v2-ava-0001
precision: FP16
framework: tf
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-ava-0001/tf/FP16/yolo-v2-ava-0001.xml
name: yolo-v2-ava-0001
precision: FP16
framework: tf
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-ava-0001/tf/FP16-INT8/yolo-v2-ava-0001.xml
name: yolo-v2-ava-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-ava-0001/tf/FP16-INT8/yolo-v2-ava-0001.xml
name: yolo-v2-ava-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-ava-0001/tf/FP16-INT8/yolo-v2-ava-0001.xml
name: yolo-v2-ava-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-35-0001/tf/FP16/yolo-v2-ava-sparse-35-0001.xml
name: yolo-v2-ava-sparse-35-0001
precision: FP16
framework: tf
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-35-0001/tf/FP16/yolo-v2-ava-sparse-35-0001.xml
name: yolo-v2-ava-sparse-35-0001
precision: FP16
framework: tf
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-35-0001/tf/FP16/yolo-v2-ava-sparse-35-0001.xml
name: yolo-v2-ava-sparse-35-0001
precision: FP16
framework: tf
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-35-0001/tf/FP16-INT8/yolo-v2-ava-sparse-35-0001.xml
name: yolo-v2-ava-sparse-35-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-35-0001/tf/FP16-INT8/yolo-v2-ava-sparse-35-0001.xml
name: yolo-v2-ava-sparse-35-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-35-0001/tf/FP16-INT8/yolo-v2-ava-sparse-35-0001.xml
name: yolo-v2-ava-sparse-35-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-70-0001/tf/FP16/yolo-v2-ava-sparse-70-0001.xml
name: yolo-v2-ava-sparse-70-0001
precision: FP16
framework: tf
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-70-0001/tf/FP16/yolo-v2-ava-sparse-70-0001.xml
name: yolo-v2-ava-sparse-70-0001
precision: FP16
framework: tf
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-70-0001/tf/FP16/yolo-v2-ava-sparse-70-0001.xml
name: yolo-v2-ava-sparse-70-0001
precision: FP16
framework: tf
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-70-0001/tf/FP16-INT8/yolo-v2-ava-sparse-70-0001.xml
name: yolo-v2-ava-sparse-70-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-70-0001/tf/FP16-INT8/yolo-v2-ava-sparse-70-0001.xml
name: yolo-v2-ava-sparse-70-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-70-0001/tf/FP16-INT8/yolo-v2-ava-sparse-70-0001.xml
name: yolo-v2-ava-sparse-70-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-0001/tf/FP16/yolo-v2-tiny-ava-0001.xml
name: yolo-v2-tiny-ava-0001
precision: FP16
framework: tf
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-0001/tf/FP16/yolo-v2-tiny-ava-0001.xml
name: yolo-v2-tiny-ava-0001
precision: FP16
framework: tf
- device:
name: AUTO
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-0001/tf/FP16/yolo-v2-tiny-ava-0001.xml
name: yolo-v2-tiny-ava-0001
precision: FP16
framework: tf
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-0001/tf/FP16-INT8/yolo-v2-tiny-ava-0001.xml
name: yolo-v2-tiny-ava-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-0001/tf/FP16-INT8/yolo-v2-tiny-ava-0001.xml
name: yolo-v2-tiny-ava-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-0001/tf/FP16-INT8/yolo-v2-tiny-ava-0001.xml
name: yolo-v2-tiny-ava-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16/yolo-v2-tiny-ava-sparse-30-0001.xml
name: yolo-v2-tiny-ava-sparse-30-0001
precision: FP16
framework: tf
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16/yolo-v2-tiny-ava-sparse-30-0001.xml
name: yolo-v2-tiny-ava-sparse-30-0001
precision: FP16
framework: tf
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16/yolo-v2-tiny-ava-sparse-30-0001.xml
name: yolo-v2-tiny-ava-sparse-30-0001
precision: FP16
framework: tf
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-30-0001.xml
name: yolo-v2-tiny-ava-sparse-30-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-30-0001.xml
name: yolo-v2-tiny-ava-sparse-30-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-30-0001.xml
name: yolo-v2-tiny-ava-sparse-30-0001
precision: FP16-INT8
framework: tf
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name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16/yolo-v2-tiny-ava-sparse-60-0001.xml
name: yolo-v2-tiny-ava-sparse-60-0001
precision: FP16
framework: tf
- device:
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model:
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name: yolo-v2-tiny-ava-sparse-60-0001
precision: FP16
framework: tf
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name: AUTO:GPU,CPU
model:
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precision: FP16
framework: tf
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model:
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precision: FP16-INT8
framework: tf
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model:
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precision: FP16-INT8
framework: tf
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model:
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name: yolo-v2-tiny-ava-sparse-60-0001
precision: FP16-INT8
framework: tf
- device:
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model:
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name: squeezenet1.1
precision: FP16
framework: caffe
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model:
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name: squeezenet1.1
precision: FP16
framework: caffe
- device:
name: AUTO:GPU,CPU
model:
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name: squeezenet1.1
precision: FP16
framework: caffe
- device:
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model:
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name: squeezenet1.1
precision: FP16-INT8
framework: caffe
- device:
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model:
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name: squeezenet1.1
precision: FP16-INT8
framework: caffe
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/squeezenet1.1/caffe/FP16-INT8/squeezenet1.1.xml
name: squeezenet1.1
precision: FP16-INT8
framework: caffe
- device:
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model:
path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16/icnet-camvid-ava-0001.xml
name: icnet-camvid-ava-0001
precision: FP16
framework: tf
- device:
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model:
path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16/icnet-camvid-ava-0001.xml
name: icnet-camvid-ava-0001
precision: FP16
framework: tf
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16/icnet-camvid-ava-0001.xml
name: icnet-camvid-ava-0001
precision: FP16
framework: tf
- device:
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model:
path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16-INT8/icnet-camvid-ava-0001.xml
name: icnet-camvid-ava-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16-INT8/icnet-camvid-ava-0001.xml
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precision: FP16-INT8
framework: tf
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model:
path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16-INT8/icnet-camvid-ava-0001.xml
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precision: FP16-INT8
framework: tf
- device:
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model:
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name: icnet-camvid-ava-sparse-30-0001
precision: FP16
framework: tf
- device:
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model:
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name: icnet-camvid-ava-sparse-30-0001
precision: FP16
framework: tf
- device:
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model:
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name: icnet-camvid-ava-sparse-30-0001
precision: FP16
framework: tf
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model:
path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-30-0001.xml
name: icnet-camvid-ava-sparse-30-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-30-0001.xml
name: icnet-camvid-ava-sparse-30-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-30-0001.xml
name: icnet-camvid-ava-sparse-30-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16/icnet-camvid-ava-sparse-60-0001.xml
name: icnet-camvid-ava-sparse-60-0001
precision: FP16
framework: tf
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16/icnet-camvid-ava-sparse-60-0001.xml
name: icnet-camvid-ava-sparse-60-0001
precision: FP16
framework: tf
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16/icnet-camvid-ava-sparse-60-0001.xml
name: icnet-camvid-ava-sparse-60-0001
precision: FP16
framework: tf
- device:
name: AUTO:CPU
model:
path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-60-0001.xml
name: icnet-camvid-ava-sparse-60-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU
model:
path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-60-0001.xml
name: icnet-camvid-ava-sparse-60-0001
precision: FP16-INT8
framework: tf
- device:
name: AUTO:GPU,CPU
model:
path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-60-0001.xml
name: icnet-camvid-ava-sparse-60-0001
precision: FP16-INT8
framework: tf

5
tests/time_tests/scripts/run_timetest.py
View File

@@ -73,13 +73,14 @@ def run_timetest(args: dict, log=None):
log = logging.getLogger("run_timetest")
cmd_common = prepare_executable_cmd(args)
ov_env = os.environ
ov_env['OPENVINO_LOG_LEVEL'] = '4'
# Run executable and collect statistics
stats = {}
logs = []
for run_iter in range(args["niter"]):
tmp_stats_path = tempfile.NamedTemporaryFile().name
retcode, msg = cmd_exec(cmd_common + ["-s", str(tmp_stats_path)], log=log)
retcode, msg = cmd_exec(cmd_common + ["-s", str(tmp_stats_path)], log=log, env=ov_env)
if os.path.exists(tmp_stats_path):
with open(tmp_stats_path, "r") as file:

6
tests/time_tests/src/timetests/timetest_infer.cpp
View File

@@ -27,6 +27,8 @@ int runPipeline(const std::string &model, const std::string &device, const bool
InferenceEngine::InferRequest inferRequest;
size_t batchSize = 0;
std::string device_prefix = device.substr(0, device.find(':'));
// first_inference_latency = time_to_inference + first_inference
{
SCOPED_TIMER(first_inference_latency);
@@ -34,8 +36,8 @@ int runPipeline(const std::string &model, const std::string &device, const bool
SCOPED_TIMER(time_to_inference);
{
SCOPED_TIMER(load_plugin);
TimeTest::setPerformanceConfig(ie, device);
ie.GetVersions(device);
TimeTest::setPerformanceConfig(ie, device_prefix);
ie.GetVersions(device_prefix);
if (isCacheEnabled)
ie.SetConfig({ {CONFIG_KEY(CACHE_DIR), "models_cache"} });

7
tests/time_tests/src/timetests/timetest_infer_api_2.cpp
View File

@@ -31,6 +31,9 @@ int runPipeline(const std::string &model, const std::string &device, const bool
std::vector<ov::Output<ov::Node>> defaultInputs;
ie.set_property("AUTO", ov::log::level(ov::log::Level::DEBUG));
std::string device_prefix = device.substr(0, device.find(':'));
bool reshape = false;
if (!reshapeShapes.empty()) {
reshape = true;
@@ -51,8 +54,8 @@ int runPipeline(const std::string &model, const std::string &device, const bool
SCOPED_TIMER(time_to_inference);
{
SCOPED_TIMER(load_plugin);
TimeTest::setPerformanceConfig(ie, device);
ie.get_versions(device);
TimeTest::setPerformanceConfig(ie, device_prefix);
ie.get_versions(device_prefix);
if (isCacheEnabled)
ie.set_property({{CONFIG_KEY(CACHE_DIR), "models_cache"}});