Mirrors/blakeblackshear.frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2024-11-30 19:09:13 +01:00
Code Issues Projects Releases Wiki Activity

Adds support for YOLO-NAS in OpenVino (#11645)

Browse Source 
* update onnxruntime

* support for yolo-nas in openvino

* cleanup notebook

* update docs

* improve docs

* handle AUTO issue and update docs
This commit is contained in:
Blake Blackshear 2024-06-07 06:52:08 -05:00 committed by GitHub
parent 4e569ad644
commit 4313fd97aa
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
6 changed files with 279 additions and 46 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
docker/main/requirements-wheels.txt
View File

@ -29,5 +29,5 @@ norfair == 2.2.*
setproctitle == 1.3.*
ws4py == 0.5.*
unidecode == 1.3.*
onnxruntime == 1.16.*
onnxruntime == 1.18.*
openvino == 2024.1.*

91
docker/main/rootfs/labelmap/coco.txt Normal file
View File

@ -0,0 +1,91 @@
0 person
1 bicycle
2 car
3 motorcycle
4 airplane
5 bus
6 train
7 car
8 boat
9 traffic light
10 fire hydrant
11 street sign
12 stop sign
13 parking meter
14 bench
15 bird
16 cat
17 dog
18 horse
19 sheep
20 cow
21 elephant
22 bear
23 zebra
24 giraffe
25 hat
26 backpack
27 umbrella
28 shoe
29 eye glasses
30 handbag
31 tie
32 suitcase
33 frisbee
34 skis
35 snowboard
36 sports ball
37 kite
38 baseball bat
39 baseball glove
40 skateboard
41 surfboard
42 tennis racket
43 bottle
44 plate
45 wine glass
46 cup
47 fork
48 knife
49 spoon
50 bowl
51 banana
52 apple
53 sandwich
54 orange
55 broccoli
56 carrot
57 hot dog
58 pizza
59 donut
60 cake
61 chair
62 couch
63 potted plant
64 bed
65 mirror
66 dining table
67 window
68 desk
69 toilet
70 door
71 tv
72 laptop
73 mouse
74 remote
75 keyboard
76 cell phone
77 microwave
78 oven
79 toaster
80 sink
81 refrigerator
82 blender
83 book
84 clock
85 vase
86 scissors
87 teddy bear
88 hair drier
89 toothbrush
90 hair brush

82
docs/docs/configuration/object_detectors.md
View File

@ -109,9 +109,13 @@ detectors:
The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel VPU hardware. To configure an OpenVINO detector, set the `"type"` attribute to `"openvino"`.
The OpenVINO device to be used is specified using the `"device"` attribute according to the naming conventions in the [Device Documentation](https://docs.openvino.ai/latest/openvino_docs_OV_UG_Working_with_devices.html). Other supported devices could be `AUTO`, `CPU`, `GPU`, `MYRIAD`, etc. If not specified, the default OpenVINO device will be selected by the `AUTO` plugin.
The OpenVINO device to be used is specified using the `"device"` attribute according to the naming conventions in the [Device Documentation](https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes.html). The most common devices are `CPU` and `GPU`. Currently, there is a known issue with using `AUTO`. For backwards compatibility, Frigate will attempt to use `GPU` if `AUTO` is set in your configuration.
OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will also run on AMD CPUs despite having no official support for it. A supported Intel platform is required to use the `GPU` device with OpenVINO. The `MYRIAD` device may be run on any platform, including Arm devices. For detailed system requirements, see [OpenVINO System Requirements](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/system-requirements.html)
OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will also run on AMD CPUs despite having no official support for it. A supported Intel platform is required to use the `GPU` device with OpenVINO. For detailed system requirements, see [OpenVINO System Requirements](https://docs.openvino.ai/2024/about-openvino/release-notes-openvino/system-requirements.html)
### Supported Models
#### SSDLite MobileNet v2
An OpenVINO model is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an FP16 precision IR model. Use the model configuration shown below when using the OpenVINO detector with the default model.
@ -119,27 +123,26 @@ An OpenVINO model is provided in the container at `/openvino-model/ssdlite_mobil
detectors:
ov:
type: openvino
device: AUTO
model:
path: /openvino-model/ssdlite_mobilenet_v2.xml
device: GPU
model:
width: 300
height: 300
input_tensor: nhwc
input_pixel_format: bgr
path: /openvino-model/ssdlite_mobilenet_v2.xml
labelmap_path: /openvino-model/coco_91cl_bkgr.txt
```
This detector also supports YOLOX. Other YOLO variants are not officially supported/tested. Frigate does not come with any yolo models preloaded, so you will need to supply your own models. This detector has been verified to work with the [yolox_tiny](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolox-tiny) model from Intel's Open Model Zoo. You can follow [these instructions](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolox-tiny#download-a-model-and-convert-it-into-openvino-ir-format) to retrieve the OpenVINO-compatible `yolox_tiny` model. Make sure that the model input dimensions match the `width` and `height` parameters, and `model_type` is set accordingly. See [Full Configuration Reference](/configuration/reference.md) for a list of possible `model_type` options. Below is an example of how `yolox_tiny` can be used in Frigate:
#### YOLOX
This detector also supports YOLOX. Frigate does not come with any YOLOX models preloaded, so you will need to supply your own models. This detector has been verified to work with the [yolox_tiny](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolox-tiny) model from Intel's Open Model Zoo. You can follow [these instructions](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolox-tiny#download-a-model-and-convert-it-into-openvino-ir-format) to retrieve the OpenVINO-compatible `yolox_tiny` model. Make sure that the model input dimensions match the `width` and `height` parameters, and `model_type` is set accordingly. See [Full Configuration Reference](/configuration/reference.md) for a list of possible `model_type` options. Below is an example of how `yolox_tiny` can be used in Frigate:
```yaml
detectors:
ov:
type: openvino
device: AUTO
model:
path: /path/to/yolox_tiny.xml
device: GPU
model:
width: 416
@ -147,38 +150,41 @@ model:
input_tensor: nchw
input_pixel_format: bgr
model_type: yolox
path: /path/to/yolox_tiny.xml
labelmap_path: /path/to/coco_80cl.txt
```
### Intel NCS2 VPU and Myriad X Setup
#### YOLO-NAS
Intel produces a neural net inference acceleration chip called Myriad X. This chip was sold in their Neural Compute Stick 2 (NCS2) which has been discontinued. If intending to use the MYRIAD device for acceleration, additional setup is required to pass through the USB device. The host needs a udev rule installed to handle the NCS2 device.
[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
```bash
sudo usermod -a -G users "$(whoami)"
cat <<EOF > 97-myriad-usbboot.rules
SUBSYSTEM=="usb", ATTRS{idProduct}=="2485", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
SUBSYSTEM=="usb", ATTRS{idProduct}=="f63b", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
EOF
sudo cp 97-myriad-usbboot.rules /etc/udev/rules.d/
sudo udevadm control --reload-rules
sudo udevadm trigger
:::warning
The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
:::
The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.
After placing the downloaded onnx model in your config folder, you can use the following configuration:
```yaml
detectors:
ov:
type: openvino
device: GPU
model:
model_type: yolonas
width: 320 # <--- should match whatever was set in notebook
height: 320 # <--- should match whatever was set in notebook
input_tensor: nchw
input_pixel_format: bgr
path: /config/yolo_nas_s.onnx
labelmap_path: /labelmap/coco-80.txt
```
Additionally, the Frigate docker container needs to run with the following configuration:
```bash
--device-cgroup-rule='c 189:\* rmw' -v /dev/bus/usb:/dev/bus/usb
```
or in your compose file:
```yml
device_cgroup_rules:
- "c 189:* rmw"
volumes:
- /dev/bus/usb:/dev/bus/usb
```
Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
## NVidia TensorRT Detector
@ -354,15 +360,15 @@ model: # required
The correct labelmap must be loaded for each model. If you use a custom model (see notes below), you must make sure to provide the correct labelmap. The table below lists the correct paths for the bundled models:
| `path` | `labelmap_path` |
| ------------------- | --------------------- |
| deci-fp16-yolonas_* | /labelmap/coco-80.txt |
| `path` | `labelmap_path` |
| --------------------- | --------------------- |
| deci-fp16-yolonas\_\* | /labelmap/coco-80.txt |
### Choosing a model
:::warning
yolo-nas models use weights from DeciAI. These weights are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
:::

2
frigate/detectors/detection_api.py
View File

@ -1,5 +1,6 @@
import logging
from abc import ABC, abstractmethod
from typing import List
import numpy as np
@ -10,6 +11,7 @@ logger = logging.getLogger(__name__)
class DetectionApi(ABC):
type_key: str
supported_models: List[ModelTypeEnum]
@abstractmethod
def __init__(self, detector_config):

73
frigate/detectors/plugins/openvino.py
View File

@ -20,6 +20,7 @@ class OvDetectorConfig(BaseDetectorConfig):
class OvDetector(DetectionApi):
type_key = DETECTOR_KEY
supported_models = [ModelTypeEnum.ssd, ModelTypeEnum.yolonas, ModelTypeEnum.yolox]
def __init__(self, detector_config: OvDetectorConfig):
self.ov_core = ov.Core()
@ -28,12 +29,24 @@ class OvDetector(DetectionApi):
self.h = detector_config.model.height
self.w = detector_config.model.width
if detector_config.device == "AUTO":
logger.warning(
"OpenVINO AUTO device type is not currently supported. Attempting to use GPU instead."
)
detector_config.device = "GPU"
self.interpreter = self.ov_core.compile_model(
model=detector_config.model.path, device_name=detector_config.device
)
self.model_invalid = False
if self.ov_model_type not in self.supported_models:
logger.error(
f"OpenVino detector does not support {self.ov_model_type} models."
)
self.model_invalid = True
# Ensure the SSD model has the right input and output shapes
if self.ov_model_type == ModelTypeEnum.ssd:
model_inputs = self.interpreter.inputs
@ -61,6 +74,34 @@ class OvDetector(DetectionApi):
logger.error(f"SSD model output doesn't match. Found {output_shape}.")
self.model_invalid = True
if self.ov_model_type == ModelTypeEnum.yolonas:
model_inputs = self.interpreter.inputs
model_outputs = self.interpreter.outputs
if len(model_inputs) != 1:
logger.error(
f"YoloNAS models must only have 1 input. Found {len(model_inputs)}."
)
self.model_invalid = True
if len(model_outputs) != 1:
logger.error(
f"YoloNAS models must be exported in flat format and only have 1 output. Found {len(model_outputs)}."
)
self.model_invalid = True
if model_inputs[0].get_shape() != ov.Shape([1, 3, self.w, self.h]):
logger.error(
f"YoloNAS model input doesn't match. Found {model_inputs[0].get_shape()}, but expected {[1, 3, self.w, self.h]}."
)
self.model_invalid = True
output_shape = model_outputs[0].partial_shape
if output_shape[-1] != 7:
logger.error(
f"YoloNAS models must be exported in flat format. Model output doesn't match. Found {output_shape}."
)
self.model_invalid = True
if self.ov_model_type == ModelTypeEnum.yolox:
self.output_indexes = 0
while True:
@ -113,12 +154,12 @@ class OvDetector(DetectionApi):
input_tensor = ov.Tensor(array=tensor_input)
infer_request.infer(input_tensor)
detections = np.zeros((20, 6), np.float32)
if self.model_invalid:
return detections
if self.ov_model_type == ModelTypeEnum.ssd:
detections = np.zeros((20, 6), np.float32)
if self.model_invalid:
return detections
results = infer_request.get_output_tensor(0).data[0][0]
for i, (_, class_id, score, xmin, ymin, xmax, ymax) in enumerate(results):
@ -134,6 +175,26 @@ class OvDetector(DetectionApi):
]
return detections
if self.ov_model_type == ModelTypeEnum.yolonas:
predictions = infer_request.get_output_tensor(0).data
for i, prediction in enumerate(predictions):
if i == 20:
break
(_, x_min, y_min, x_max, y_max, confidence, class_id) = prediction
# when running in GPU mode, empty predictions in the output have class_id of -1
if class_id < 0:
break
detections[i] = [
class_id,
confidence,
y_min / self.h,
x_min / self.w,
y_max / self.h,
x_max / self.w,
]
return detections
if self.ov_model_type == ModelTypeEnum.yolox:
out_tensor = infer_request.get_output_tensor()
# [x, y, h, w, box_score, class_no_1, ..., class_no_80],
@ -155,8 +216,6 @@ class OvDetector(DetectionApi):
ordered = dets[dets[:, 5].argsort()[::-1]][:20]
detections = np.zeros((20, 6), np.float32)
for i, object_detected in enumerate(ordered):
detections[i] = self.process_yolo(
object_detected[6], object_detected[5], object_detected[:4]

75
notebooks/YOLO_NAS_Pretrained_Export.ipynb Normal file
View File

@ -0,0 +1,75 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rmuF9iKWTbdk"
},
"outputs": [],
"source": [
"! pip install -q super_gradients==3.7.1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dTB0jy_NNSFz"
},
"outputs": [],
"source": [
"from super_gradients.common.object_names import Models\n",
"from super_gradients.conversion import DetectionOutputFormatMode\n",
"from super_gradients.training import models\n",
"\n",
"model = models.get(Models.YOLO_NAS_S, pretrained_weights=\"coco\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GymUghyCNXem"
},
"outputs": [],
"source": [
"# export the model for compatibility with Frigate\n",
"\n",
"model.export(\"yolo_nas_s.onnx\",\n",
" output_predictions_format=DetectionOutputFormatMode.FLAT_FORMAT,\n",
" max_predictions_per_image=20,\n",
" confidence_threshold=0.4,\n",
" input_image_shape=(320,320),\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "uBhXV5g4Nh42"
},
"outputs": [],
"source": [
"from google.colab import files\n",
"\n",
"files.download('yolo_nas_s.onnx')"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}