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Align time_tests models with master (#3270)

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* Add new model to `tgl_test_config.yml` (#3236)

* Fix wrong path for `yolo-v2-tiny-ava-0001` for time_tests

* Add several new models to `tgl_test_config.yml` in time_tests
This commit is contained in:
Vitaliy Urusovskij
2020-11-24 11:06:37 +03:00
committed by GitHub
parent f4ed2572a5
commit 71cde81827
1 changed files with 421 additions and 1 deletions
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422
tests/time_tests/test_runner/.automation/tgl_test_config.yml
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@@ -53,4 +53,424 @@
path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe2/FP16-INT8/mobilenet-v2.xml
name: mobilenet-v2
precision: FP16-INT8
framework: caffe2
framework: caffe2
- device:
name: CPU
model:
path: ${VPUX_MODELS_PKG}/faster_rcnn_resnet101_coco/tf/FP16/faster_rcnn_resnet101_coco.xml
name: faster_rcnn_resnet101_coco
precision: FP16
framework: tf
- device:
name: GPU
model:
path: ${VPUX_MODELS_PKG}/faster_rcnn_resnet101_coco/tf/FP16/faster_rcnn_resnet101_coco.xml
name: faster_rcnn_resnet101_coco
precision: FP16
framework: tf
- device:
name: CPU
model:
path: ${VPUX_MODELS_PKG}/faster_rcnn_resnet101_coco/tf/FP16-INT8/faster_rcnn_resnet101_coco.xml
name: faster_rcnn_resnet101_coco
precision: FP16-INT8
framework: tf
- device:
name: GPU
model:
path: ${VPUX_MODELS_PKG}/faster_rcnn_resnet101_coco/tf/FP16-INT8/faster_rcnn_resnet101_coco.xml
name: faster_rcnn_resnet101_coco
precision: FP16-INT8
framework: tf
- device:
name: 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: 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: 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: 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: CPU
model:
path: ${VPUX_MODELS_PKG}/googlenet-v1/tf/FP16/googlenet-v1.xml
name: googlenet-v1
precision: FP16
framework: tf
- device:
name: GPU
model:
path: ${VPUX_MODELS_PKG}/googlenet-v1/tf/FP16/googlenet-v1.xml
name: googlenet-v1
precision: FP16
framework: tf
- device:
name: CPU
model:
path: ${VPUX_MODELS_PKG}/googlenet-v1/tf/FP16-INT8/googlenet-v1.xml
name: googlenet-v1
precision: FP16-INT8
framework: tf
- device:
name: GPU
model:
path: ${VPUX_MODELS_PKG}/googlenet-v1/tf/FP16-INT8/googlenet-v1.xml
name: googlenet-v1
precision: FP16-INT8
framework: tf
- device:
name: CPU
model:
path: ${VPUX_MODELS_PKG}/googlenet-v3/tf/FP16/googlenet-v3.xml
name: googlenet-v3
precision: FP16
framework: tf
- device:
name: GPU
model:
path: ${VPUX_MODELS_PKG}/googlenet-v3/tf/FP16/googlenet-v3.xml
name: googlenet-v3
precision: FP16
framework: tf
- device:
name: CPU
model:
path: ${VPUX_MODELS_PKG}/googlenet-v3/tf/FP16-INT8/googlenet-v3.xml
name: googlenet-v3
precision: FP16-INT8
framework: tf
- device:
name: GPU
model:
path: ${VPUX_MODELS_PKG}/googlenet-v3/tf/FP16-INT8/googlenet-v3.xml
name: googlenet-v3
precision: FP16-INT8
framework: tf
- device:
name: CPU
model:
path: ${VPUX_MODELS_PKG}/ssd512/caffe/FP16/ssd512.xml
name: ssd512
precision: FP16
framework: caffe
- device:
name: GPU
model:
path: ${VPUX_MODELS_PKG}/ssd512/caffe/FP16/ssd512.xml
name: ssd512
precision: FP16
framework: caffe
- device:
name: CPU
model:
path: ${VPUX_MODELS_PKG}/ssd512/caffe/FP16-INT8/ssd512.xml
name: ssd512
precision: FP16-INT8
framework: caffe
- device:
name: GPU
model:
path: ${VPUX_MODELS_PKG}/ssd512/caffe/FP16-INT8/ssd512.xml
name: ssd512
precision: FP16-INT8
framework: caffe
- device:
name: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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:
name: GPU
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:
name: CPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-60-0001.xml
name: yolo-v2-tiny-ava-sparse-60-0001
precision: FP16-INT8
framework: tf
- device:
name: GPU
model:
path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-60-0001.xml
name: yolo-v2-tiny-ava-sparse-60-0001
precision: FP16-INT8
framework: tf
- device:
name: CPU
model:
path: ${VPUX_MODELS_PKG}/squeezenet1.1/tf/FP16/squeezenet1.1.xml
name: squeezenet1.1
precision: FP16
framework: caffe2
- device:
name: GPU
model:
path: ${VPUX_MODELS_PKG}/squeezenet1.1/tf/FP16/squeezenet1.1.xml
name: squeezenet1.1
precision: FP16
framework: caffe2
- device:
name: CPU
model:
path: ${VPUX_MODELS_PKG}/squeezenet1.1/tf/FP16-INT8/squeezenet1.1.xml
name: squeezenet1.1
precision: FP16-INT8
framework: caffe2
- device:
name: GPU
model:
path: ${VPUX_MODELS_PKG}/squeezenet1.1/tf/FP16-INT8/squeezenet1.1.xml
name: squeezenet1.1
precision: FP16-INT8
framework: caffe2
- device:
name: 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:
name: GPU
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: CPU
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: GPU
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: CPU
model:
path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16/icnet-camvid-ava-sparse-30-0001.xml
name: icnet-camvid-ava-sparse-30-0001
precision: FP16
framework: tf
- device:
name: GPU
model:
path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16/icnet-camvid-ava-sparse-30-0001.xml
name: icnet-camvid-ava-sparse-30-0001
precision: FP16
framework: tf
- device:
name: 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: 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: 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: 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: 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: 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