I just follow the video and add the codes, but when I switch to spatial video capturing, the videoPreviewLayer shows black.
<<<< FigCaptureSessionRemote >>>> Fig assert: "! storage->connectionDied" at bail (FigCaptureSessionRemote.m:405) - (err=0)
<<<< FigCaptureSessionRemote >>>> captureSessionRemote_getObjectID signalled err=-16405 (kFigCaptureSessionError_ServerConnectionDied) (Server connection was lost) at FigCaptureSessionRemote.m:405
<<<< FigCaptureSessionRemote >>>> Fig assert: "err == 0 " at bail (FigCaptureSessionRemote.m:421) - (err=-16405)
<<<< FigCaptureSessionRemote >>>> Fig assert: "msg" at bail (FigCaptureSessionRemote.m:744) - (err=0)
Did I miss something?
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Deploy machine learning and AI models on-device with Core ML say the performance report can see the ops run on which unit and why it cannot run on Neural Engine.
I tested my model and the report shows a gray checkmark at the Neural Engine, indicating it can run on the Neural Engine. However, it's not executing on the Neural Engine but on the CPU. Why is this happening?
Hi all,
I tried the "isSpatialVideoCaptureEnabled" with AVCaptureMovieFileOutput mentioned in WWDC24: Build compelling spatial photo and video experiences, and it works.
But there are some issues and questions:
Below codes, the change.newValue always nil so the code seems not work.
let observation = videoDevice.observe(\.spatialCaptureDiscomfortReasons) { (device, change) in
guard let newValue = change.newValue else { return }
if newValue.contains(.subjectTooClose) {
// Guide user to move back
}
if newValue.contains(.notEnoughLight) {
// Guide user to find a brighter environment
}
}
AVCaptureMovieFileOutput is support spatial video capturing.
May I ask if AVCaptureVideoDataOutput will also support spatial video capturing?
I want to try an any resolution image input Core ML model.
So I wrote the model following the Core ML Tools "Set the Range for Each Dimensionas" sample code, modified as below:
# Trace the model with random input.
example_input = torch.rand(1, 3, 50, 50)
traced_model = torch.jit.trace(model.eval(), example_input)
# Set the input_shape to use RangeDim for each dimension.
input_shape = ct.Shape(shape=(1,
3,
ct.RangeDim(lower_bound=25, upper_bound=1920, default=45),
ct.RangeDim(lower_bound=25, upper_bound=1920, default=45)))
scale = 1/(0.226*255.0)
bias = [- 0.485/(0.229) , - 0.456/(0.224), - 0.406/(0.225)]
# Convert the model with input_shape.
mlmodel = ct.convert(traced_model,
inputs=[ct.ImageType(shape=input_shape, name="input", scale=scale, bias=bias)],
outputs=[ct.TensorType(name="output")],
convert_to="mlprogram",
)
# Save the Core ML model
mlmodel.save("image_resize_model.mlpackage")
It converts OK but when I predict the result with an image It will get the error as below:
You will not be able to run predict() on this Core ML model. Underlying exception message was: {
NSLocalizedDescription = "Failed to build the model execution plan using a model architecture file '/private/var/folders/8z/vtz02xrj781dxvz1v750skz40000gp/T/model-small.mlmodelc/model.mil' with error code: -7.";
}
Where did I do wrong?