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Add OpenVINO inference (#6179)

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Glenn Jocher 2022-01-03 15:41:26 -08:00 committed by GitHub
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commit 63a4d862aa
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4 changed files with 35 additions and 20 deletions
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2
detect.py
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@ -16,7 +16,7 @@ Usage - formats:
yolov5s.torchscript # TorchScript yolov5s.torchscript # TorchScript
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
yolov5s.mlmodel # CoreML (under development) yolov5s.mlmodel # CoreML (under development)
yolov5s_openvino_model # OpenVINO (under development) yolov5s.xml # OpenVINO
yolov5s_saved_model # TensorFlow SavedModel yolov5s_saved_model # TensorFlow SavedModel
yolov5s.pb # TensorFlow protobuf yolov5s.pb # TensorFlow protobuf
yolov5s.tflite # TensorFlow Lite yolov5s.tflite # TensorFlow Lite

20
export.py
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@ -20,16 +20,16 @@ Usage:
$ python path/to/export.py --weights yolov5s.pt --include torchscript onnx coreml openvino saved_model tflite tfjs $ python path/to/export.py --weights yolov5s.pt --include torchscript onnx coreml openvino saved_model tflite tfjs
Inference: Inference:
$ python path/to/detect.py --weights yolov5s.pt $ python path/to/detect.py --weights yolov5s.pt # PyTorch
yolov5s.torchscript yolov5s.torchscript # TorchScript
yolov5s.onnx yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
yolov5s.mlmodel (under development) yolov5s.mlmodel # CoreML (under development)
yolov5s_openvino_model (under development) yolov5s.xml # OpenVINO
yolov5s_saved_model yolov5s_saved_model # TensorFlow SavedModel
yolov5s.pb yolov5s.pb # TensorFlow protobuf
yolov5s.tflite yolov5s.tflite # TensorFlow Lite
yolov5s_edgetpu.tflite yolov5s_edgetpu.tflite # TensorFlow Edge TPU
yolov5s.engine yolov5s.engine # TensorRT
TensorFlow.js: TensorFlow.js:
$ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example

31
models/common.py
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@ -282,6 +282,7 @@ class DetectMultiBackend(nn.Module):
# PyTorch: weights = *.pt # PyTorch: weights = *.pt
# TorchScript: *.torchscript # TorchScript: *.torchscript
# CoreML: *.mlmodel # CoreML: *.mlmodel
# OpenVINO: *.xml
# TensorFlow: *_saved_model # TensorFlow: *_saved_model
# TensorFlow: *.pb # TensorFlow: *.pb
# TensorFlow Lite: *.tflite # TensorFlow Lite: *.tflite
@ -294,31 +295,38 @@ class DetectMultiBackend(nn.Module):
super().__init__() super().__init__()
w = str(weights[0] if isinstance(weights, list) else weights) w = str(weights[0] if isinstance(weights, list) else weights)
suffix = Path(w).suffix.lower() suffix = Path(w).suffix.lower()
suffixes = ['.pt', '.torchscript', '.onnx', '.engine', '.tflite', '.pb', '', '.mlmodel'] suffixes = ['.pt', '.torchscript', '.onnx', '.engine', '.tflite', '.pb', '', '.mlmodel', '.xml']
check_suffix(w, suffixes) # check weights have acceptable suffix check_suffix(w, suffixes) # check weights have acceptable suffix
pt, jit, onnx, engine, tflite, pb, saved_model, coreml = (suffix == x for x in suffixes) # backend booleans pt, jit, onnx, engine, tflite, pb, saved_model, coreml, xml = (suffix == x for x in suffixes) # backends
stride, names = 64, [f'class{i}' for i in range(1000)] # assign defaults stride, names = 64, [f'class{i}' for i in range(1000)] # assign defaults
w = attempt_download(w) # download if not local w = attempt_download(w) # download if not local
if data: # data.yaml path (optional) if data: # data.yaml path (optional)
with open(data, errors='ignore') as f: with open(data, errors='ignore') as f:
names = yaml.safe_load(f)['names'] # class names names = yaml.safe_load(f)['names'] # class names
if jit: # TorchScript if pt: # PyTorch
model = attempt_load(weights if isinstance(weights, list) else w, map_location=device)
stride = int(model.stride.max()) # model stride
names = model.module.names if hasattr(model, 'module') else model.names # get class names
self.model = model # explicitly assign for to(), cpu(), cuda(), half()
elif jit: # TorchScript
LOGGER.info(f'Loading {w} for TorchScript inference...') LOGGER.info(f'Loading {w} for TorchScript inference...')
extra_files = {'config.txt': ''} # model metadata extra_files = {'config.txt': ''} # model metadata
model = torch.jit.load(w, _extra_files=extra_files) model = torch.jit.load(w, _extra_files=extra_files)
if extra_files['config.txt']: if extra_files['config.txt']:
d = json.loads(extra_files['config.txt']) # extra_files dict d = json.loads(extra_files['config.txt']) # extra_files dict
stride, names = int(d['stride']), d['names'] stride, names = int(d['stride']), d['names']
elif pt: # PyTorch
model = attempt_load(weights if isinstance(weights, list) else w, map_location=device)
stride = int(model.stride.max()) # model stride
names = model.module.names if hasattr(model, 'module') else model.names # get class names
self.model = model # explicitly assign for to(), cpu(), cuda(), half()
elif coreml: # CoreML elif coreml: # CoreML
LOGGER.info(f'Loading {w} for CoreML inference...') LOGGER.info(f'Loading {w} for CoreML inference...')
import coremltools as ct import coremltools as ct
model = ct.models.MLModel(w) model = ct.models.MLModel(w)
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()
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)
elif dnn: # ONNX OpenCV DNN elif dnn: # ONNX OpenCV DNN
LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...') LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')
check_requirements(('opencv-python>=4.5.4',)) check_requirements(('opencv-python>=4.5.4',))
@ -403,6 +411,13 @@ class DetectMultiBackend(nn.Module):
y = self.net.forward() y = self.net.forward()
else: # ONNX Runtime else: # ONNX Runtime
y = self.session.run([self.session.get_outputs()[0].name], {self.session.get_inputs()[0].name: im})[0] 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))
elif self.engine: # TensorRT elif self.engine: # TensorRT
assert im.shape == self.bindings['images'].shape, (im.shape, self.bindings['images'].shape) assert im.shape == self.bindings['images'].shape, (im.shape, self.bindings['images'].shape)
self.binding_addrs['images'] = int(im.data_ptr()) self.binding_addrs['images'] = int(im.data_ptr())

2
val.py
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@ -10,7 +10,7 @@ Usage - formats:
yolov5s.torchscript # TorchScript yolov5s.torchscript # TorchScript
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
yolov5s.mlmodel # CoreML (under development) yolov5s.mlmodel # CoreML (under development)
yolov5s_openvino_model # OpenVINO (under development) yolov5s.xml # OpenVINO
yolov5s_saved_model # TensorFlow SavedModel yolov5s_saved_model # TensorFlow SavedModel
yolov5s.pb # TensorFlow protobuf yolov5s.pb # TensorFlow protobuf
yolov5s.tflite # TensorFlow Lite yolov5s.tflite # TensorFlow Lite