[BUG] `[system] [critical] Fatal error. Please report to developers. Log: 'Flic' '89'`
See original GitHub issueDescribe the bug I was try load 5 models to OAK-D but program raise this error:
[14442C10E11DCAD200] [17.050] [NeuralNetwork(11)] [warning] Network compiled for 4 shaves, maximum available 10, compiling for 5 shaves likely will yield in better performance
[14442C10E11DCAD200] [17.051] [NeuralNetwork(14)] [warning] Network compiled for 4 shaves, maximum available 10, compiling for 5 shaves likely will yield in better performance
[14442C10E11DCAD200] [17.106] [system] [critical] Fatal error. Please report to developers. Log: 'Flic' '89'
[14442C10E11DCAD200] [17.052] [NeuralNetwork(17)] [warning] Network compiled for 4 shaves, maximum available 10, compiling for 5 shaves likely will yield in better performance
[14442C10E11DCAD200] [17.053] [NeuralNetwork(2)] [warning] Network compiled for 4 shaves, maximum available 10, compiling for 5 shaves likely will yield in better performance
[14442C10E11DCAD200] [17.096] [NeuralNetwork(5)] [warning] Network compiled for 4 shaves, maximum available 10, compiling for 5 shaves likely will yield in better performance
[14442C10E11DCAD200] [17.097] [NeuralNetwork(8)] [warning] Network compiled for 1 shaves, maximum available 10, compiling for 5 shaves likely will yield in better performance
RuntimeError: Couldn't read data from stream: '__rpc_main' (X_LINK_ERROR)
I guessed shaves had non-enough then i’m was try convert 2 models to 1 shave but this error was raised.
This error only raise when i was tried add palm_detection.blob
model. (4 models code is still running)
I’m was tried to replace palm_detection.blob
with 1 shave running model but program raised the same error
To Reproduce This is my code:
def create_nn(self, model_path: str, model_name: str, first: bool = False):
"""
:param model_path: model path
:param model_name: model abbreviation
:param first: Is it the first model
:return:
"""
# NeuralNetwork
print(f"Creating {model_path} Neural Network...")
model_nn = self.pipeline.createNeuralNetwork()
model_nn.setBlobPath(str(Path(f"{model_path}").resolve().absolute()))
model_nn.input.setBlocking(False)
if first and self.camera:
print("linked cam.preview to model_nn.input")
# Create face detection resize manip
detection_manip = self.pipeline.createImageManip()
detection_manip.setResize(320, 320)
# Link camera output to detection_manip input
self.cam.preview.link(detection_manip.inputImage)
# Link to face detection neural network input
detection_manip.out.link(model_nn.input)
# self.cam.preview.link(model_nn.input)
else:
model_in = self.pipeline.createXLinkIn()
model_in.setStreamName(f"{model_name}_in")
model_in.out.link(model_nn.input)
model_nn_xout = self.pipeline.createXLinkOut()
model_nn_xout.setStreamName(f"{model_name}_nn")
model_nn.out.link(model_nn_xout.input)
def create_nns(self):
if self.camera:
self.create_nn(
"models/scrfd_detection_openvino_2021.2_4shave.blob",
"mfd",
first=self.camera,
)
self.create_nn(
"models/scrfd_detection_openvino_2021.2_4shave.blob",
"mfd_db",
first=False,
)
self.create_nn(
"models/mobilenetv2_50_48_83-0001.blob",
"gm"
)
self.create_nn(
"models/face-recognition_2021.2_4shave.blob",
"arcface",
)
# Create facial landmarks nn
self.create_nn(
"models/facial-landmarks-35-adas-0002.blob",
"landmarks"
)
self.create_nn(
"models/palm_detection.blob",
"palm"
)
Issue Analytics
- State:
- Created 2 years ago
- Comments:5
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Yeah, i’ll keep my system uptodate ❤️
Great, thanks. I suggest updating to latest regularly since we are improving performance and stability constantly.