motion-coremltools (coremotiontools) is the wrapper tool for converting neural networks trained with motion sensor data.
The usage is the same as coremltools' Unified Conversation API. Currently, only tensorflow.keras.Model is supported. Also, only 1-demensional CNNs are supported.
import coremltools as ct
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv1D, MaxPooling1D, Flatten, Dense
from tensorflow.keras.models import Model
from coremotiontools import convert
# Build model
inputs = Input(shape=(256*3, 1))
x = Conv1D(16, kernel_size=3, strides=1, padding='same', activation='relu', kernel_initalizer='he_normal')(inputs)
x = MaxPooling1D(pool_size=2, padding='same')(x)
x = Flatten()(x)
x = Dense(1024, activation='relu', kernel_initalizer='he_normal')(x)
outputs = Dense(6, activation='softmax')(x)
model = Model(inputs=inputs, outputs=outputs)
# Convert to Core ML
classifier_config = ct.ClassifierConfig(class_labels=["stay", "walk", "jog", "skip", "stUp", "stDown"])
mlmodel = convert(model, classifier_config=classifier_config)
mlmodel.save("ActivityClassifier.mlmodel")See here for more detailed usages.
When the converted model is used in Core ML, the input type is MLMultiArray.
let input: [Double] = [0.1, 0.2, 0.3, ...] // Order of x-axis, y-axis, z-axis, x, y, z, ...
let mlArray = try! MLMultiArray.fromDouble(input) // MLMultiArray.fromDouble() is extension
// Predict
let model = ActivityClassifier()
let output = try! model.prediction(input: ActivityClassifierInput(input: mlArray))See here for more detailed usages.
Supported pip install
pip install git+https://github.com/Shakshi3104/motion-coremltools.gitcoremltools 4.1tensorflow >=2.1
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