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Replace `openvino-dev` with OpenVINO Runtime inference (#7843) 

* Uses OpenVINO runtime instead of openvino-dev

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* export with openvino package

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Revert export.py

* Update common.py

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
pull/7344/head^2
Nanohana 2022-05-16 16:03:02 -03:00 committed by GitHub
parent 2b64b45cd8
commit 05d4fc9db4
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1 changed files with 7 additions and 10 deletions
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models/common.py
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@ -354,13 +354,14 @@ class DetectMultiBackend(nn.Module):
stride, names = int(meta['stride']), eval(meta['names'])
elif xml: # OpenVINO
LOGGER.info(f'Loading {w} for OpenVINO inference...')
check_requirements(('openvino-dev',)) # requires openvino-dev: https://pypi.org/project/openvino-dev/
import openvino.inference_engine as ie
core = ie.IECore()
check_requirements(('openvino',)) # requires openvino-dev: https://pypi.org/project/openvino-dev/
from openvino.runtime import Core
ie = Core()
if not Path(w).is_file(): # if not *.xml
w = next(Path(w).glob('*.xml')) # get *.xml file from *_openvino_model dir
network = core.read_network(model=w, weights=Path(w).with_suffix('.bin')) # *.xml, *.bin paths
executable_network = core.load_network(network, device_name='CPU', num_requests=1)
network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))
executable_network = ie.compile_model(model=network, device_name="CPU")
self.output_layer = next(iter(executable_network.outputs))
elif engine: # TensorRT
LOGGER.info(f'Loading {w} for TensorRT inference...')
import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download
@ -444,11 +445,7 @@ class DetectMultiBackend(nn.Module):
y = self.session.run([self.session.get_outputs()[0].name], {self.session.get_inputs()[0].name: im})[0]
elif self.xml: # OpenVINO
im = im.cpu().numpy() # FP32
desc = self.ie.TensorDesc(precision='FP32', dims=im.shape, layout='NCHW') # Tensor Description
request = self.executable_network.requests[0] # inference request
request.set_blob(blob_name='images', blob=self.ie.Blob(desc, im)) # name=next(iter(request.input_blobs))
request.infer()
y = request.output_blobs['output'].buffer # name=next(iter(request.output_blobs))
y = self.executable_network([im])[self.output_layer]
elif self.engine: # TensorRT
assert im.shape == self.bindings['images'].shape, (im.shape, self.bindings['images'].shape)
self.binding_addrs['images'] = int(im.data_ptr())