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Merge pull request #37 from yamichan420/master

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specify list of providers for onnxruntime.InferenceSession()
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Emiliana 2022-05-17 17:31:16 +02:00 • committed by GitHub
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2 changed files with 10 additions and 5 deletions
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3
retinaface.py
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@ -66,7 +66,8 @@ class RetinaFaceDetector():
options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
options.log_severity_level = 3 options.log_severity_level = 3
self.session = onnxruntime.InferenceSession(model_path, sess_options=options) providersList = onnxruntime.capi._pybind_state.get_available_providers()
self.session = onnxruntime.InferenceSession(model_path, sess_options=options, providers=providersList)
self.res_w, self.res_h = res self.res_w, self.res_h = res
with open(json_path, "r") as prior_file: with open(json_path, "r") as prior_file:
self.priorbox = np.array(json.loads(prior_file.read())) self.priorbox = np.array(json.loads(prior_file.read()))

12
tracker.py
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@ -527,8 +527,12 @@ class Tracker():
self.retinaface_scan = RetinaFaceDetector(model_path=os.path.join(model_base_path, "retinaface_640x640_opt.onnx"), json_path=os.path.join(model_base_path, "priorbox_640x640.json"), threads=2, top_k=max_faces, res=(640, 640)) self.retinaface_scan = RetinaFaceDetector(model_path=os.path.join(model_base_path, "retinaface_640x640_opt.onnx"), json_path=os.path.join(model_base_path, "priorbox_640x640.json"), threads=2, top_k=max_faces, res=(640, 640))
self.use_retinaface = use_retinaface self.use_retinaface = use_retinaface
# OTR 1.9 and later requires specifying providers
# explicitly as an argument in InferenceSession()
providersList = onnxruntime.capi._pybind_state.get_available_providers()
# Single face instance with multiple threads # Single face instance with multiple threads
self.session = onnxruntime.InferenceSession(os.path.join(model_base_path, model), sess_options=options) self.session = onnxruntime.InferenceSession(os.path.join(model_base_path, model), sess_options=options, providers=providersList)
# Multiple faces with single threads # Multiple faces with single threads
self.sessions = [] self.sessions = []
@ -544,7 +548,7 @@ class Tracker():
options.intra_op_num_threads += 1 options.intra_op_num_threads += 1
options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
self.sessions.append(onnxruntime.InferenceSession(os.path.join(model_base_path, model), sess_options=options)) self.sessions.append(onnxruntime.InferenceSession(os.path.join(model_base_path, model), sess_options=options, providers=providersList))
self.input_name = self.session.get_inputs()[0].name self.input_name = self.session.get_inputs()[0].name
options = onnxruntime.SessionOptions() options = onnxruntime.SessionOptions()
@ -553,9 +557,9 @@ class Tracker():
options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
options.log_severity_level = 3 options.log_severity_level = 3
self.gaze_model = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_gaze32_split_opt.onnx"), sess_options=options) self.gaze_model = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_gaze32_split_opt.onnx"), sess_options=options, providers=providersList)
self.detection = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_detection_opt.onnx"), sess_options=options) self.detection = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_detection_opt.onnx"), sess_options=options, providers=providersList)
self.faces = [] self.faces = []
# Image normalization constants # Image normalization constants