[Enhancement]: Added static config and CI tests for OpenVINO. (#218)
* Add openvino_static. * Add openvino-dev. * Fix skipping ORT tests in test_mmocr_models. * Updated docs. * Fix print. * Fix * Fix * Fix other backends * Fix is_available * fix ncnn * Add constrict for get rewrite output * add not * Fix * fix * Fix * Fix * Improve tests * Remove rebundant `cuda` * Prevent None object and rename variable * Fix multi-line string * rename get_backend_checker * Add Troubleshooting to doc. * Fix postprocessing_masks with empty masks. * Fix tests * lint * Update docs. Co-authored-by: SingleZombie <singlezombie@163.com>pull/12/head
Semyon Bevzyuk
GitHub
parent
f4b3db188e
commit
bb9b0a98de
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@ -0,0 +1,3 @@
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_base_ = ['./base_static.py', '../../_base_/backends/openvino.py']
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onnx_config = dict(input_shape=(1344, 800))
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@ -0,0 +1 @@
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_base_ = ['../_base_/base_openvino_static-800x1344.py']
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@ -3,9 +3,30 @@
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This tutorial is based on Linux systems like Ubuntu-18.04.
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### Installation
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It is recommended to create a virtual environment for the project.
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1. Install [OpenVINO](https://docs.openvino.ai/2021.4/get_started.html). For example, you can install OpenVINO with [pip](https://pypi.org/project/openvino-dev/).
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```bash
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pip install openvino-dev
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```
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2. Install [PyTorch](https://pytorch.org/get-started/locally/).
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```bash
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pip install torch torchvision
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```
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3. Install [MMCV](https://mmcv.readthedocs.io/en/latest/get_started/installation.html). It is advisable to install the latest version `mmcv-full`.
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```bash
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pip install mmcv-full
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```
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4. Install MMDeploy following the [instructions](../build.md).
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1. Install [OpenVINO](https://docs.openvinotoolkit.org/latest/installation_guides.html).
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2. Install MMDeploy following the [instructions](../build.md).
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To work with models from [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/get_started.md), you may need to install it additionally.
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### Troubleshooting
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#### ImportError: libpython3.7m.so.1.0: cannot open shared object file: No such file or directory
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To resolve missing external dependency on Ubuntu*, execute the following command:
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```bash
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sudo apt-get install libpython3.7
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```
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### Usage
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@ -41,7 +62,13 @@ The table below lists the models that are guaranteed to be exportable to OpenVIN
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| Faster R-CNN + DCN | `configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py` | Y |
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| VFNet | `configs/vfnet/vfnet_r50_fpn_1x_coco.py` | Y |
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Notes:
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- Custom operations from OpenVINO use the domain `org.openvinotoolkit`.
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- For faster work in OpenVINO in the Faster-RCNN, Mask-RCNN, Cascade-RCNN, Cascade-Mask-RCNN models
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the RoiAlign operation is replaced with the [ExperimentalDetectronROIFeatureExtractor](https://docs.openvinotoolkit.org/latest/openvino_docs_ops_detection_ExperimentalDetectronROIFeatureExtractor_6.html) operation in the ONNX graph.
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- Models "VFNet" and "Faster R-CNN + DCN" use the custom "DeformableConv2D" operation.
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### FAQs
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- None
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@ -50,3 +50,6 @@ Build the inference engine extension libraries you need.
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cd ${MMDEPLOY_DIR} # To mmdeploy root directory
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pip install -e .
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```
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Some dependencies are optional. Simply running `pip install -e .` will only install the minimum runtime requirements.
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To use optional dependencies install them manually with `pip install -r requirements/optional.txt` or specify desired extras when calling `pip` (e.g. `pip install -e .[optional]`).
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Valid keys for the extras field are: `all`, `tests`, `build`, `optional`.
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@ -8,14 +8,22 @@ Please refer to [get_started.md](https://github.com/open-mmlab/mmdetection/blob/
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### List of MMDetection models supported by MMDeploy
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| model | task | OnnxRuntime | TensorRT | NCNN | PPL | OpenVINO | model config file(example) |
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|:-------------|:-------------|:-----------:|:--------:|:----:|:---:|:--------:|:------------------------------------------------------------------|
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| RetinaNet | single-stage | Y | Y | Y | Y | Y | $MMDET_DIR/configs/retinanet/retinanet_r50_fpn_1x_coco.py |
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| YOLOv3 | single-stage | Y | Y | Y | Y | Y | $MMDET_DIR/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py |
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| FCOS | single-stage | Y | Y | Y | N | Y | $MMDET_DIR/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py |
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| FSAF | single-stage | Y | Y | Y | Y | Y | $MMDET_DIR/configs/fsaf/fsaf_r50_fpn_1x_coco.py |
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| Faster R-CNN | two-stage | Y | Y | Y | Y | Y | $MMDET_DIR/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py |
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| Mask R-CNN | two-stage | Y | Y | N | Y | Y | $MMDET_DIR/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py |
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| model | task | OnnxRuntime | TensorRT | NCNN | PPL | OpenVINO | model config file(example) |
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|:-------------------|:-------------|:-----------:|:--------:|:----:|:---:|:--------:|:---------------------------------------------------------------------|
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| ATSS | single-stage | Y | ? | ? | ? | Y | $MMDET_DIR/configs/atss/atss_r50_fpn_1x_coco.py |
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| FCOS | single-stage | Y | Y | Y | N | Y | $MMDET_DIR/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py |
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| FoveaBox | single-stage | Y | ? | ? | ? | Y | $MMDET_DIR/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py |
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| FSAF | single-stage | Y | Y | Y | Y | Y | $MMDET_DIR/configs/fsaf/fsaf_r50_fpn_1x_coco.py |
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| RetinaNet | single-stage | Y | Y | Y | Y | Y | $MMDET_DIR/configs/retinanet/retinanet_r50_fpn_1x_coco.py |
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| SSD | single-stage | Y | Y | Y | Y | Y | $MMDET_DIR/configs/ssd/ssd300_coco.py |
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| VFNet | single-stage | Y | ? | ? | ? | Y | $MMDET_DIR/configs/vfnet/vfnet_r50_fpn_1x_coco.py |
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| YOLOv3 | single-stage | Y | Y | Y | Y | Y | $MMDET_DIR/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py |
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| YOLOX | single-stage | Y | ? | ? | ? | Y | $MMDET_DIR/configs/yolox/yolox_tiny_8x8_300e_coco.py |
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| Cascade R-CNN | two-stage | Y | ? | ? | Y | Y | $MMDET_DIR/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py |
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| Faster R-CNN | two-stage | Y | Y | Y | Y | Y | $MMDET_DIR/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py |
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| Faster R-CNN + DCN | two-stage | Y | Y | Y | Y | Y | $MMDET_DIR/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py` |
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| Mask Cascade R-CNN | two-stage | Y | ? | ? | Y | Y | $MMDET_DIR/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py |
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| Mask R-CNN | two-stage | Y | Y | N | Y | Y | $MMDET_DIR/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py |
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### Reminder
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| model | OnnxRuntime | TensorRT | NCNN | PPL | OpenVino | model config file(example) |
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|:------------------------------|:-----------:|:--------:|:----:|:---:|:--------:|:-----------------------------------------------------------------------------------|
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| FCN | Y | Y | Y | Y | ? | ${MMSEG_DIR}/configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py |
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| PSPNet[*](#pspnet) | Y | Y | N | Y | ? | ${MMSEG_DIR}/configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py |
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| PSPNet[*](#pspnet) | Y | Y | N | Y | ? | ${MMSEG_DIR}/configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py |
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| DeepLabV3 | Y | Y | Y | Y | ? | ${MMSEG_DIR}/configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py |
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| DeepLabV3+ | Y | Y | Y | Y | ? | ${MMSEG_DIR}/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py |
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import mmcv
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from mmdeploy.utils import get_input_shape
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def get_input_shape_from_cfg(config: mmcv.Config) -> List[int]:
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"""Get the input shape from the model config for OpenVINO Model Optimizer.
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def get_input_shape_from_cfg(deploy_cfg: mmcv.Config,
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model_cfg: mmcv.Config) -> List[int]:
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"""Get the input shape from the configs for OpenVINO Model Optimizer. The
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value from config 'deploy_cfg' has the highest priority, then 'model_cfg'.
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If there is no input shape in configs, then the default value will be used.
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Args:
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config (mmcv.Config): Model config.
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deploy_cfg (mmcv.Config): Deployment config.
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model_cfg (mmcv.Config): Model config.
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Returns:
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List[int]: The input shape in [1, 3, H, W] format from config
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or [1, 3, 800, 1344].
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"""
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shape = []
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test_pipeline = config.get('test_pipeline', None)
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if test_pipeline is not None:
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img_scale = test_pipeline[1]['img_scale']
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shape = [1, 3, img_scale[1], img_scale[0]]
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shape = [1, 3]
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is_use_deploy_cfg = False
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try:
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input_shape = get_input_shape(deploy_cfg)
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if input_shape is not None:
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is_use_deploy_cfg = True
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except KeyError:
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is_use_deploy_cfg = False
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if is_use_deploy_cfg:
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shape += [input_shape[1], input_shape[0]]
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else:
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shape = [1, 3, 800, 1344]
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test_pipeline = model_cfg.get('test_pipeline', None)
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if test_pipeline is not None:
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img_scale = test_pipeline[1]['img_scale']
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shape += [img_scale[1], img_scale[0]]
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else:
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shape += [800, 1344]
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return shape
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@ -1,4 +1,5 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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import logging
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import os.path as osp
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import subprocess
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from subprocess import CalledProcessError, run
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@ -16,7 +17,7 @@ def is_mo_available() -> bool:
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"""
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is_available = True
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try:
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run('mo.py -h',
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run('mo -h',
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stdout=subprocess.DEVNULL,
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stderr=subprocess.DEVNULL,
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shell=True,
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@ -68,11 +69,11 @@ def onnx2openvino(input_info: Dict[str, Union[List[int], torch.Size]],
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f'--input="{input_names}" ' \
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f'--input_shape="{input_shapes}" ' \
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f'--disable_fusing '
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command = f'mo.py {mo_args}'
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print(f'Args for mo.py: {command}')
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command = f'mo {mo_args}'
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logging.info(f'Args for mo: {command}')
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mo_output = run(command, capture_output=True, shell=True, check=True)
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print(mo_output.stdout.decode())
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print(mo_output.stderr.decode())
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logging.info(mo_output.stdout.decode())
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logging.debug(mo_output.stderr.decode())
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model_xml = get_output_model_file(onnx_path, work_dir)
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print(f'Successfully exported OpenVINO model: {model_xml}')
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logging.info(f'Successfully exported OpenVINO model: {model_xml}')
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device_name=self.device.upper(),
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num_requests=1)
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def __process_outputs(
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self, outputs: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
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"""Converts tensors from 'torch' to 'numpy' and fixes the names of the
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outputs.
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Args:
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outputs Dict[str, torch.Tensor]: The output name and tensor pairs.
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Returns:
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Dict[str, torch.Tensor]: The output name and tensor pairs
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after processing.
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"""
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outputs = {
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name: torch.from_numpy(tensor)
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for name, tensor in outputs.items()
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}
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for output_name in outputs.keys():
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if '.' in output_name:
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new_output_name = output_name.split('.')[0]
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outputs[new_output_name] = outputs.pop(output_name)
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return outputs
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def forward(self, inputs: Dict[str,
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torch.Tensor]) -> Dict[str, torch.Tensor]:
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"""Run forward inference.
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@ -104,8 +126,7 @@ class OpenVINOWrapper(BaseWrapper):
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inputs = self.__update_device(inputs)
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self.__reshape(inputs)
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outputs = self.__openvino_execute(inputs)
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for output_name, numpy_tensor in outputs.items():
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outputs[output_name] = torch.from_numpy(numpy_tensor)
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outputs = self.__process_outputs(outputs)
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return outputs
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@TimeCounter.count_time()
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@ -96,28 +96,33 @@ class End2EndModel(BaseBackendModel):
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return outputs
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@staticmethod
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def __postprocessing_masks(det_bboxes: np.ndarray,
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det_masks: np.ndarray,
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img_w: int,
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img_h: int,
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mask_thr_binary: float = 0.5) -> np.ndarray:
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def postprocessing_masks(det_bboxes: np.ndarray,
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det_masks: np.ndarray,
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img_w: int,
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img_h: int,
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mask_thr_binary: float = 0.5) -> np.ndarray:
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"""Additional processing of masks. Resizes masks from [num_det, 28, 28]
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to [num_det, img_w, img_h]. Analog of the 'mmdeploy.codebase.mmdet.
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models.roi_heads.fcn_mask_head._do_paste_mask' function.
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Args:
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det_bboxes (np.ndarray): Bbox of shape [num_det, 5]
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det_bboxes (np.ndarray): Bbox of shape [num_det, 4]
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det_masks (np.ndarray): Masks of shape [num_det, 28, 28].
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img_w (int): Width of the original image.
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img_h (int): Height of the original image.
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mask_thr_binary (float): The threshold for the mask.
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Returns:
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np.ndarray: masks of shape [N, num_det, img_w, img_h].
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np.ndarray: masks of shape [N, num_det, img_h, img_w].
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"""
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masks = det_masks
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bboxes = det_bboxes
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num_det = bboxes.shape[0]
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# Skip postprocessing if no detections are found.
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if num_det == 0:
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return np.zeros((0, img_h, img_w))
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if isinstance(masks, np.ndarray):
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masks = torch.tensor(masks)
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bboxes = torch.tensor(bboxes)
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@ -214,7 +219,7 @@ class End2EndModel(BaseBackendModel):
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export_postprocess_mask = mmdet_deploy_cfg.get(
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'export_postprocess_mask', True)
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if not export_postprocess_mask:
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masks = End2EndModel.__postprocessing_masks(
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masks = End2EndModel.postprocessing_masks(
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dets[:, :4], masks, ori_w, ori_h)
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else:
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masks = masks[:, :img_h, :img_w]
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@ -225,7 +230,7 @@ class End2EndModel(BaseBackendModel):
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masks = torch.nn.functional.interpolate(
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masks.unsqueeze(0), size=(ori_h, ori_w))
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masks = masks.squeeze(0).detach().numpy()
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if masks.dtype != np.bool:
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if masks.dtype != bool:
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masks = masks >= 0.5
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segms_results = [[] for _ in range(len(self.CLASSES))]
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for j in range(len(dets)):
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@ -5,4 +5,5 @@ mmocr>=0.3.0
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mmsegmentation
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ncnn
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onnxruntime>=1.8.0
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openvino-dev[onnx,pytorch]
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tensorrt
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@ -3,6 +3,7 @@ import os
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import os.path as osp
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import tempfile
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import mmcv
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import numpy as np
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import pytest
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import torch
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@ -104,14 +105,25 @@ def test_get_input_shape_from_cfg():
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from mmdeploy.apis.openvino import get_input_shape_from_cfg
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# Test with default value
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model_cfg = {}
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input_shape = get_input_shape_from_cfg(model_cfg)
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deploy_cfg = mmcv.Config()
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model_cfg = mmcv.Config()
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input_shape = get_input_shape_from_cfg(deploy_cfg, model_cfg)
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assert input_shape == [1, 3, 800, 1344], \
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'The function returned a different default shape.'
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# Test with config that contains the required data.
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# Test with model_cfg that contains the required data.
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height, width = 800, 1200
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model_cfg = {'test_pipeline': [{}, {'img_scale': (width, height)}]}
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input_shape = get_input_shape_from_cfg(model_cfg)
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model_cfg = mmcv.Config(
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{'test_pipeline': [{}, {
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'img_scale': (width, height)
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}]})
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input_shape = get_input_shape_from_cfg(deploy_cfg, model_cfg)
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assert input_shape == [1, 3, height, width], \
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'The shape in the config does not match the output shape.'
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'The shape in the model_cfg does not match the output shape.'
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# Test with deploy_cfg that contains the required data.
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height, width = 600, 1000
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deploy_cfg = mmcv.Config({'onnx_config': {'input_shape': (width, height)}})
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input_shape = get_input_shape_from_cfg(deploy_cfg, model_cfg)
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assert input_shape == [1, 3, height, width], \
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'The shape in the deploy_cfg does not match the output shape.'
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@ -1,5 +1,4 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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import mmcv
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import numpy as np
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import pytest
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|
|
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@ -561,11 +561,8 @@ def test_cascade_roi_head(backend_type: Backend):
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wrapped_model=wrapped_model,
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model_inputs=model_inputs,
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deploy_cfg=deploy_cfg)
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processed_backend_outputs = []
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if isinstance(backend_outputs, dict):
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processed_backend_outputs = convert_to_list(backend_outputs,
|
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output_names)
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elif isinstance(backend_outputs, (list, tuple)) and \
|
||||
|
||||
if isinstance(backend_outputs, (list, tuple)) and \
|
||||
backend_outputs[0].shape == (1, 0, 5):
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processed_backend_outputs = torch.zeros((1, 80, 5))
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else:
|
||||
|
|
|
|||
|
|
@ -72,6 +72,23 @@ def test_init_backend_model(backend_model):
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|||
assert isinstance(backend_model, End2EndModel)
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||||
|
||||
|
||||
def test_can_postprocess_masks():
|
||||
from mmdeploy.codebase.mmdet.deploy.object_detection_model \
|
||||
import End2EndModel
|
||||
num_dets = [0, 1, 5]
|
||||
for num_det in num_dets:
|
||||
det_bboxes = np.random.randn(num_det, 4)
|
||||
det_masks = np.random.randn(num_det, 28, 28)
|
||||
img_w, img_h = (30, 40)
|
||||
masks = End2EndModel.postprocessing_masks(det_bboxes, det_masks, img_w,
|
||||
img_h)
|
||||
expected_shape = (num_det, img_h, img_w)
|
||||
actual_shape = masks.shape
|
||||
assert actual_shape == expected_shape, \
|
||||
f'The expected shape of masks {expected_shape} ' \
|
||||
f'did not match actual shape {actual_shape}.'
|
||||
|
||||
|
||||
@pytest.mark.parametrize('device', ['cpu', 'cuda:0'])
|
||||
def test_create_input(device):
|
||||
if device == 'cuda:0' and not torch.cuda.is_available():
|
||||
|
|
|
|||
|
|
@ -1,5 +1,4 @@
|
|||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
|
||||
import mmcv
|
||||
import numpy as np
|
||||
import torch
|
||||
|
|
|
|||
|
|
@ -7,11 +7,15 @@ import pytest
|
|||
import torch
|
||||
from mmocr.models.textdet.necks import FPNC
|
||||
|
||||
from mmdeploy.apis.onnxruntime import is_available as ort_available
|
||||
from mmdeploy.core import RewriterContext, patch_model
|
||||
from mmdeploy.utils import Backend
|
||||
from mmdeploy.utils.test import (WrapModel, check_backend, get_model_outputs,
|
||||
get_rewrite_outputs)
|
||||
|
||||
onnxruntime_skip = not ort_available()
|
||||
cuda_skip = not torch.cuda.is_available()
|
||||
|
||||
|
||||
class FPNCNeckModel(FPNC):
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,4 @@
|
|||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
|
||||
import mmcv
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
|
|
|||
|
|
@ -234,7 +234,7 @@ def main():
|
|||
for onnx_path in onnx_files:
|
||||
model_xml_path = get_output_model_file(onnx_path, args.work_dir)
|
||||
input_name = deploy_cfg.onnx_config.input_names
|
||||
input_shape = [get_input_shape_from_cfg(model_cfg)]
|
||||
input_shape = [get_input_shape_from_cfg(deploy_cfg, model_cfg)]
|
||||
input_info = dict(zip(input_name, input_shape))
|
||||
output_names = deploy_cfg.onnx_config.output_names
|
||||
create_process(
|
||||
|
|
|
|||
Loading…
Reference in New Issue