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upgrade to latest openvino version (#11563)

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Blake Blackshear 2024-05-27 15:49:35 -05:00 committed by GitHub
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6 changed files with 83 additions and 41 deletions
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3
.gitignore vendored
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@ -16,4 +16,5 @@ web/node_modules
web/coverage
core
!/web/**/*.ts
.idea/*
.idea/*
.ipynb_checkpoints

11
docker/main/Dockerfile
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@ -57,9 +57,11 @@ RUN apt-get -qq update \
&& pip install -r /requirements-ov.txt
# Get OpenVino Model
RUN mkdir /models \
&& cd /models && omz_downloader --name ssdlite_mobilenet_v2 \
&& cd /models && omz_converter --name ssdlite_mobilenet_v2 --precision FP16
RUN --mount=type=bind,source=docker/main/build_ov_model.py,target=/build_ov_model.py \
mkdir /models && cd /models \
&& wget http://download.tensorflow.org/models/object_detection/ssdlite_mobilenet_v2_coco_2018_05_09.tar.gz \
&& tar -xvf ssdlite_mobilenet_v2_coco_2018_05_09.tar.gz \
&& python3 /build_ov_model.py
# libUSB - No Udev
@ -97,7 +99,8 @@ RUN wget -qO edgetpu_model.tflite https://github.com/google-coral/test_data/raw/
RUN wget -qO cpu_model.tflite https://github.com/google-coral/test_data/raw/release-frogfish/ssdlite_mobiledet_coco_qat_postprocess.tflite
COPY labelmap.txt .
# Copy OpenVino model
COPY --from=ov-converter /models/public/ssdlite_mobilenet_v2/FP16 openvino-model
COPY --from=ov-converter /models/ssdlite_mobilenet_v2.xml openvino-model/
COPY --from=ov-converter /models/ssdlite_mobilenet_v2.bin openvino-model/
RUN wget -q https://github.com/openvinotoolkit/open_model_zoo/raw/master/data/dataset_classes/coco_91cl_bkgr.txt -O openvino-model/coco_91cl_bkgr.txt && \
sed -i 's/truck/car/g' openvino-model/coco_91cl_bkgr.txt
# Get Audio Model and labels

11
docker/main/build_ov_model.py Normal file
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@ -0,0 +1,11 @@
import openvino as ov
from openvino.tools import mo
ov_model = mo.convert_model(
"/models/ssdlite_mobilenet_v2_coco_2018_05_09/frozen_inference_graph.pb",
compress_to_fp16=True,
transformations_config="/usr/local/lib/python3.9/dist-packages/openvino/tools/mo/front/tf/ssd_v2_support.json",
tensorflow_object_detection_api_pipeline_config="/models/ssdlite_mobilenet_v2_coco_2018_05_09/pipeline.config",
reverse_input_channels=True,
)
ov.save_model(ov_model, "/models/ssdlite_mobilenet_v2.xml")

6
docker/main/requirements-ov.txt
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@ -1,5 +1,3 @@
numpy
# Openvino Library - Custom built with MYRIAD support
openvino @ https://github.com/NateMeyer/openvino-wheels/releases/download/multi-arch_2022.3.1/openvino-2022.3.1-1-cp39-cp39-manylinux_2_31_x86_64.whl; platform_machine == 'x86_64'
openvino @ https://github.com/NateMeyer/openvino-wheels/releases/download/multi-arch_2022.3.1/openvino-2022.3.1-1-cp39-cp39-linux_aarch64.whl; platform_machine == 'aarch64'
openvino-dev[tensorflow2] @ https://github.com/NateMeyer/openvino-wheels/releases/download/multi-arch_2022.3.1/openvino_dev-2022.3.1-1-py3-none-any.whl
tensorflow
openvino-dev>=2024.0.0

4
docker/main/requirements-wheels.txt
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@ -30,6 +30,4 @@ setproctitle == 1.3.*
ws4py == 0.5.*
unidecode == 1.3.*
onnxruntime == 1.16.*
# Openvino Library - Custom built with MYRIAD support
openvino @ https://github.com/NateMeyer/openvino-wheels/releases/download/multi-arch_2022.3.1/openvino-2022.3.1-1-cp39-cp39-manylinux_2_31_x86_64.whl; platform_machine == 'x86_64'
openvino @ https://github.com/NateMeyer/openvino-wheels/releases/download/multi-arch_2022.3.1/openvino-2022.3.1-1-cp39-cp39-linux_aarch64.whl; platform_machine == 'aarch64'
openvino == 2024.1.*

89
frigate/detectors/plugins/openvino.py
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@ -1,7 +1,7 @@
import logging
import numpy as np
import openvino.runtime as ov
import openvino as ov
from pydantic import Field
from typing_extensions import Literal
@ -23,28 +23,56 @@ class OvDetector(DetectionApi):
def __init__(self, detector_config: OvDetectorConfig):
self.ov_core = ov.Core()
self.ov_model = self.ov_core.read_model(detector_config.model.path)
self.ov_model_type = detector_config.model.model_type
self.h = detector_config.model.height
self.w = detector_config.model.width
self.interpreter = self.ov_core.compile_model(
model=self.ov_model, device_name=detector_config.device
model=detector_config.model.path, device_name=detector_config.device
)
logger.info(f"Model Input Shape: {self.interpreter.input(0).shape}")
self.output_indexes = 0
self.model_invalid = False
# Ensure the SSD model has the right input and output shapes
if self.ov_model_type == ModelTypeEnum.ssd:
model_inputs = self.interpreter.inputs
model_outputs = self.interpreter.outputs
if len(model_inputs) != 1:
logger.error(
f"SSD models must only have 1 input. Found {len(model_inputs)}."
)
self.model_invalid = True
if len(model_outputs) != 1:
logger.error(
f"SSD models must only have 1 output. Found {len(model_outputs)}."
)
self.model_invalid = True
if model_inputs[0].get_shape() != ov.Shape([1, self.w, self.h, 3]):
logger.error(
f"SSD model input doesn't match. Found {model_inputs[0].get_shape()}."
)
self.model_invalid = True
output_shape = model_outputs[0].get_shape()
if output_shape[0] != 1 or output_shape[1] != 1 or output_shape[3] != 7:
logger.error(f"SSD model output doesn't match. Found {output_shape}.")
self.model_invalid = True
while True:
try:
tensor_shape = self.interpreter.output(self.output_indexes).shape
logger.info(f"Model Output-{self.output_indexes} Shape: {tensor_shape}")
self.output_indexes += 1
except Exception:
logger.info(f"Model has {self.output_indexes} Output Tensors")
break
if self.ov_model_type == ModelTypeEnum.yolox:
self.output_indexes = 0
while True:
try:
tensor_shape = self.interpreter.output(self.output_indexes).shape
logger.info(
f"Model Output-{self.output_indexes} Shape: {tensor_shape}"
)
self.output_indexes += 1
except Exception:
logger.info(f"Model has {self.output_indexes} Output Tensors")
break
self.num_classes = tensor_shape[2] - 5
logger.info(f"YOLOX model has {self.num_classes} classes")
self.set_strides_grids()
@ -81,29 +109,32 @@ class OvDetector(DetectionApi):
def detect_raw(self, tensor_input):
infer_request = self.interpreter.create_infer_request()
infer_request.infer([tensor_input])
# TODO: see if we can use shared_memory=True
input_tensor = ov.Tensor(array=tensor_input)
infer_request.infer(input_tensor)
if self.ov_model_type == ModelTypeEnum.ssd:
results = infer_request.get_output_tensor()
detections = np.zeros((20, 6), np.float32)
i = 0
for object_detected in results.data[0, 0, :]:
if object_detected[0] != -1:
logger.debug(object_detected)
if object_detected[2] < 0.1 or i == 20:
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):
if i == 20:
break
detections[i] = [
object_detected[1], # Label ID
float(object_detected[2]), # Confidence
object_detected[4], # y_min
object_detected[3], # x_min
object_detected[6], # y_max
object_detected[5], # x_max
class_id,
float(score),
ymin,
xmin,
ymax,
xmax,
]
i += 1
return detections
elif self.ov_model_type == ModelTypeEnum.yolox:
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],
results = out_tensor.data