The MLModel extension encapsulates a Core ML model’s prediction methods, configuration, and model description. Inputs=Ĭlassifier_config = ct.ClassifierConfig(class_labels) # Convert to Core ML using the Unified Conversion API The conversion to Core ML format is made possible thanks to the Unified Conversion API. Mobile_net_traced = (mobile_net, input)ĭownload class labels from a separate file: import urllibĬlass_labels = (label_url).read().decode( "utf-8").splitlines()Ĭlass_labels = class_labels # remove the first class which is background assert len(class_labels) = 1000 Convert the TorchScript object to Core ML format using coremltools # Random input with tensor dimensions that match the input model performing I/O or accessing global variables). no conditionals on the data in tensors) and that have no untracked external dependencies (e.g. Tracing correctly records only those functions and modules that are not dependent on the data (e.g. Torch jit trace module takes an input example with the exact same tensor dimensions that the model usually takes as input. Set the model to evaluation mode: mobile_net.eval() Generate the Torchscript object using Use torchvision library to import a MobileNetV2 version trained on ImageNet. Pip install -u coremltools Load a pre-trained version of MobileNetV2 Activate the virtual env and install coremltools:.
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