I have a file of a few thousand sentences I'd like to convert into something that NLEmbedding can use. (In searching around it looks like none of the sample code for "Make Apps Smarter with Natural Language" from WWDC 2020 was ever released.) Can anyone put me on the right track on how to do this?

This video example can not find all the code, some details have doubts, I would like to ask you to help me, thank you very much! email: wu.shaopeng@aol. Please forgive me, this mailbox also has (com)

I am trying to deploy our first Core ML model through the dashboard at https://ml.developer.apple.com/. Immediately after I go into the site, I get a message that reads "Your request is invalid.". I click "+" to add a model collection, I enter a collection ID, a description and a single model ID and then tap on "Create". I get a message that reads "One of the fields was invalid.". I have tried changing the IDs and the description, but there is no way to make it work. I have been trying this for hours. Could you please guide me on how to make it work?

Is it possible to use Core ML on video files? My goal is to have a recorded video and process it for text in the video. When that text or video is found mark it and then extract clips form the video based on where the text was found. Making highlights basically. I am kind of lost as to where to start.

I have set up a project using DeepLabV3 for image semantic segmentation using the pre-trained DeepLabV3 model from Apple. This works consistently well for delineating (creating a black/white mask) the main object in a given image, but only as long as that main object is a person. When I try other random objects (coffee cups, tractor, stapler), with very clear outline/contrast, I get very poor results, if not a completely black mask. Is the pre-trained DeepLabV3 model just for people? If I need to do something (re-training, config, etc), what is it? Thanks, Neal

Hi there, I am trying to create a CoreML Custom layer that runs on the GPU, using Objective-C for CoreML setup and Metal for GPU programming. I have created the CoreML model with the custom layer and can successfully execute on the GPU, I wish to create an MTLBuffer from an input MTLTexture in my setup actual GPU execution, although I can't seem to do so, or get access to the memory address to the MTLTexture memory. When defining a custom layer in CoreML to run on the GPU, the following function needs to be defined, with the given prototype; (BOOL) encodeToCommandBuffer:(id<MTLCommandBuffer>)commandBuffer inputs:(NSArray<id<MTLTexture>> *)inputs outputs:(NSArray<id<MTLTexture>> *)outputs error:(NSError *__autoreleasing _Nullable *)error{ // GPU Setup, moving data, encoding, execution and so on here } Here, the inputs are passed as an NSArray of MTLTexture's, we then pass these texture's on to the Metal Shader for computation. My problem is that I want to pass an MTLBuffer to the Metal Shader, which points to the input data, say inputs[0], but I am having troubling copying the input MTLTexture to an MTLBuffer. I have tried using the MTLBlitCommandEncoder to copy the data from the MTLTexture to an MTLBuffer like so; id<MTLBuffer> test_buffer = [command_PSO.device newBufferWithLength:(8) options:MTLResourceStorageModeShared]; id <MTLBlitCommandEncoder> blitCommandEncoder = [commandBuffer blitCommandEncoder]; [blitCommandEncoder copyFromTexture:inputs[0] sourceSlice:0 sourceLevel:0 sourceOrigin:MTLOriginMake(0, 0, 0) sourceSize:MTLSizeMake(1, 1, 1) toBuffer:test_buffer destinationOffset:0 destinationBytesPerRow:8 destinationBytesPerImage:8]; [blitCommandEncoder endEncoding]; The above example should copy a single pixel from the MTLTexture, inputs[0], to the MTLBuffer, test_buffer, but this is not the case. MTLTextures, getBytes also doesn't work as the inputs have MTLResourceStorageModePrivate set. When I inspect the input MTLTexture I note that the attribute buffer = <null> and I'm wondering if this could be an issue since the texture was not created from a buffer, and perhaps doesn't store the address to memory easily, but surely we should be able to get the memory address somewhere? For further reference, here is the input MTLTexture definition; <CaptureMTLTexture: 0x282469500> -> <AGXA14FamilyTexture: 0x133d9bb00> label = <none> textureType = MTLTextureType2DArray pixelFormat = MTLPixelFormatRGBA16Float width = 8 height = 1 depth = 1 arrayLength = 1 mipmapLevelCount = 1 sampleCount = 1 cpuCacheMode = MTLCPUCacheModeDefaultCache storageMode = MTLStorageModePrivate hazardTrackingMode = MTLHazardTrackingModeTracked resourceOptions = MTLResourceCPUCacheModeDefaultCache MTLResourceStorageModePrivate MTLResourceHazardTrackingModeTracked usage = MTLTextureUsageShaderRead MTLTextureUsageShaderWrite shareable = 0 framebufferOnly = 0 purgeableState = MTLPurgeableStateNonVolatile swizzle = [MTLTextureSwizzleRed, MTLTextureSwizzleGreen, MTLTextureSwizzleBlue, MTLTextureSwizzleAlpha] isCompressed = 0 parentTexture = <null> parentRelativeLevel = 0 parentRelativeSlice = 0 buffer = <null> bufferOffset = 0 bufferBytesPerRow = 0 iosurface = 0x0 iosurfacePlane = 0 allowGPUOptimizedContents = YES label = <none>

hello, When I used xcode to generate the model encryption key, an error was reported, the error was 'Failed to Generate Encryption Key and Sign in with you Apple ID in the Apple ID pane in System Preferences and retry '.But I have logged in my apple id in the system preferences, and this error still occurs.I reinstalled xcode and re-logged in to my apple id. This error still exists. Xcode Version 12.4 macOS Catalina 10.15.7 thanks

Following the guide found here, I've been able to preview image classification in Create ML and Xcode. However, when I swap out the MobileNet model for my own and try running it as an app, images are not classified accurately. When I check the same images using my model in its Xcode preview tab, the guesses are accurate. I've tried changing this line to the different available options, but it doesn't seem to help: imageClassificationRequest.imageCropAndScaleOption = .centerCrop Does anyone know why a model would work well in preview but not while running in the app? Thanks in advance.

I converted PyTorch model to CoreML model and want to use it in iOS device. I wrote the swift code, but the code works normally in the simulator but not in the real device. My CoreML model information and experimental environment are as follows. Model Type: Neural Network / Size: 60.8 MB / Document Type: Core ML Model / Availability: iOS 14.0+ / torch == 1.8.1 / coremltools == 5.2.0 / Experimental iOS version: 15.0 / Device: iPhone 12 Pro / And the error message is: 2022-04-19 10:36:24.404508+0900 test[2488:1369794] [espresso] [Espresso::handle_ex_plan] exception=Espresso exception: "Invalid blob shape": generic_elementwise_kernel: cannot broadcast: (1, 256, 128, 130) (1, 256, 128, 132)  status=-7 2022-04-19 10:36:24.404716+0900 test[2488:1369794] [coreml] Error in adding network -7. 2022-04-19 10:36:24.405000+0900 test[2488:1369794] [coreml] MLModelAsset: load failed with error Error Domain=com.apple.CoreML Code=0 "Error in declaring network." UserInfo={NSLocalizedDescription=Error in declaring network.} 2022-04-19 10:36:24.405029+0900 test[2488:1369794] [coreml] MLModelAsset: modelWithError: load failed with error Error Domain=com.apple.CoreML Code=0 "Error in declaring network." UserInfo={NSLocalizedDescription=Error in declaring network.} test/ContentView.swift:16: Fatal error: 'try!' expression unexpectedly raised an error: Error Domain=com.apple.CoreML Code=0 "Error in declaring network." UserInfo={NSLocalizedDescription=Error in declaring network.} 2022-04-19 10:36:24.405408+0900 test[2488:1369794] test/ContentView.swift:16: Fatal error: 'try!' expression unexpectedly raised an error: Error Domain=com.apple.CoreML Code=0 "Error in declaring network." UserInfo={NSLocalizedDescription=Error in declaring network.} (lldb)  I would appreciate it if you could let me know if you know the solution or if you know the related references. Thank you for reading it.

I try to transfer this Xcode sample project to the Playground app project. However, when I move all swift files and the CoreML file called 'ExerciseClassifier.mlmodel' on the folder to the Playground app project, it shows the error, "Cannot find type 'ExerciseClassifier' in scope". What could I do to remove the error and make a proper working project on Playground?

Dear Teams, I try to transfer this Xcode sample project to the Playground app project. However, when I move all swift files and the CoreML file called 'ExerciseClassifier.mlmodel' on the folder to the Playground app project, it shows the error, "Cannot find type 'ExerciseClassifier' in scope". What could I do to remove the error and make a proper working project on Playground? Thanks.

How can I gather data from a watch for an activity classifier model? Are there any tools to help facilitate this?

The release version of my app will Not collect user data. I wanted to know if I can ask for users permission to collect trained ml models based on beta users data on test flight after presenting them the necessary documentations like privacy policy. I also don’t want the user to be able to use the beta app if they don’t agree to it. are these two things possible? Thanks

I created a style transfer model using CreateML and can not save the generated styled image to tempDirectory, unsure if it is to do with the way I create the pixelBuffer? (below): import Vision import CoreML import CoreVideo let model = style1() // set input size of the model var modelInputSize = CGSize(width: 512, height: 512) // create a cvpixel buffer var pixelBuffer: CVPixelBuffer? let attrs = [kCVPixelBufferCGImageCompatibilityKey: kCFBooleanTrue, kCVPixelBufferCGBitmapContextCompatibilityKey: kCFBooleanTrue] as CFDictionary CVPixelBufferCreate(kCFAllocatorDefault, Int(modelInputSize.width), Int(modelInputSize.height), kCVPixelFormatType_32BGRA, attrs, &pixelBuffer) // put bytes into pixelBuffer let context = CIContext() let argPathUrl = "file:///pathhere" let modelImageUrl: URL = URL(string: argPathUrl)!; guard let CiImageData = CIImage(contentsOf: modelImageUrl) else { return } context.render(CiImageData, to: pixelBuffer!) // predict image let output = try? model.prediction(image: pixelBuffer!) let predImage = CIImage(cvPixelBuffer: (output?.stylizedImage)!) let context2 = CIContext() let format = kCIFormatRGBA16 try! context2.writePNGRepresentation(of: predImage, to: FileManager.default.temporaryDirectory.appendingPathComponent("testcgi.png"), format: format, colorSpace: CGColorSpace(name: CGColorSpace.sRGB)!, options: [:]) let saveUrl = "testcgi.png" return;

Hi, while I was able to successfully retrieve MLModelCollection with a list of model identifiers from Apple's CoreML Deployment Dashboard, loading encrypted models from a collection results in the following error: NSUnderlyingError=0x281ffb810 {Error Domain=com.apple.CoreML Code=3 "failed to invoke mremap_encrypted with result = -1, error = 12" UserInfo={NSLocalizedDescription=failed to invoke mremap_encrypted with result = -1, error = 12}}} I use the same MLModel.load(contentsOf:configuration:completionHandler:) method with model URLs (from MLModelCollection) which works just fine for non–encrypted models. Is there any workaround for this issue?

Is there a Machine Learning API that can take handwriting (either as a bitmap or as a list of points) and convert it to text? I know Scribble can be used to allow handwriting input into text fields, but in this API it is Scribble which controls the rendering of the handwriting. Is there an API where my app can render the handwriting and get information about the text content? In the Keynote demo Craig was able to get text content from a photo of a whiteboard. Are there APIs which would allow an app developer to create something similar?

Hi everyone I'm trying to use createML hand action classifier to detect some simple actions, I'm having some trouble because the model only detects one hand at a time in the scene(even in the preview of the model, without any coding) and I would need both hands, is it a bug or am I doing something wrong? Thank you in advance

Hi,I have made several ML models previously with more than one feature column, always worked great.But now in a new project I have a set of data (with multiple csv files) with a single feature column. Create ML loads the data well, but when I try to train the model I keep getting this error message:"Feature column Y is empty on row 0 of input data table"Y is the name of the column, that's correct. But I have no row 0. I tried to convert CSV files to JSON, didn't work, I keep getting the same error.I would appreciate any help 🙂Best Regards,Joel Balagué

I am using a CoreML model from https://github.com/PeterL1n/RobustVideoMatting. I have an M1Macbook13 16G and an M1Max Macbook 16 64G. When "computeUnits" using .all or default, M1Max 16 is much slower than M1 13, finish one prediction time is 0.202 and 0.155. Using .cpuOnly, M1Max 16 is fast a little, time is 0.129 and 0.146. Using .cpuAndGPU, M1Max 16 is much fast than M1 13, time is 0.057 and 0.086. And when I use .all or default, M1Max will appear error messages like this: H11ANEDevice::H11ANEDeviceOpen IOServiceOpen failed result= 0xe00002e2 H11ANEDevice::H11ANEDeviceOpen kH11ANEUserClientCommand_DeviceOpen call failed result=0xe00002bc Error opening LB - status=0xe00002bc.. Skipping LB and retrying But M1 13 doesn't have any errors. So I want to know is this a bug of CoreML or M1Max? My Codes is like this: let config = MLModelConfiguration() config.computeUnits = .all let model = try rvm_mobilenetv3_1920x1080_s0_25_int8_ANE(configuration: config) let image1 = NSImage(named: "test1")?.cgImage(forProposedRect: nil, context: nil, hints: nil) let input = try? rvm_mobilenetv3_1920x1080_s0_25_int8_ANEInput(srcWith:image1!, r1i: MLMultiArray(), r2i: MLMultiArray(), r3i: MLMultiArray(), r4i: MLMultiArray())  _ = try? model.prediction(input: input!)