CoreML 模型在应用内更新后分类没有不同
CoreML model is not classifying differently after updating it in-app
我正在为我的论文创建一个应用程序，到目前为止，我已经成功地创建了一个可更新的图像分类模型，我使用 Keras 对其进行了训练并使用 coremltools 对其进行了转换。 （我将提供用于训练和转换模型的代码，因为我认为它与我的问题有关，以及我用来在应用程序内更新模型的代码。）我可以成功地对图像进行分类并且模型有效正如预期的那样，但更新部分没有。在我对图像执行更新并尝试使用 "updated" 模型对同一图像进行分类后，我得到了相同的分类。连一点信心上的差别都没有。
模型生成和转换在 Python 中完成。应用程序是用 Swift.
编写的
这是创建模型的代码：
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense, BatchNormalization
from keras import backend as K
img_width, img_height = 224, 224
train_data_dir = 'data/train/'
validation_data_dir = 'data/validate/'
nb_train_samples = 500
nb_validation_samples = 40
epochs = 20
batch_size = 25
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
model = Sequential()
model.add(Conv2D(32, (2, 2), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(BatchNormalization())
model.add(Conv2D(64, (2, 2)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(2))
model.add(Activation('softmax'))
model.summary()
model.compile(loss = 'categorical_crossentropy',
optimizer = 'sgd',
metrics = ['accuracy'])
train_datagen = ImageDataGenerator()
test_datagen = ImageDataGenerator()
train_generator = train_datagen.flow_from_directory(train_data_dir,
target_size =(img_width, img_height),
batch_size = batch_size, class_mode ='categorical')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size =(img_width, img_height),
batch_size = batch_size, class_mode ='categorical')
model.fit_generator(train_generator,
steps_per_epoch = nb_train_samples // batch_size,
epochs = epochs, validation_data = validation_generator,
validation_steps = nb_validation_samples // batch_size)
model.save('ModelsH5/catndog_small.h5')
以下是将其转换为 CoreML 并使其可更新的代码：
import coremltools
from coremltools.models.neural_network import SgdParams
from coremltools.models import MLModel
model = "catndog_small"
coreml_model_path = 'ModelsML/' + model + '.mlmodel'
neuralnetwork_spec = coremltools.utils.load_spec(coreml_model_path)
builder = coremltools.models.neural_network.NeuralNetworkBuilder(spec=neuralnetwork_spec)
builder.inspect_layers(last=3)
builder.inspect_input_features()
neuralnetwork_spec.description.metadata.author = 'Ervins Balodis'
neuralnetwork_spec.description.metadata.license = 'No license'
neuralnetwork_spec.description.metadata.shortDescription = ('Cat and Dog Classifier converted from a Keras model')
model_spec = builder.spec
builder.make_updatable(['dense_2'])
builder.set_categorical_cross_entropy_loss(name='lossLayer', input='classLabelProbs')
from coremltools.models.neural_network import SgdParams
builder.set_sgd_optimizer(SgdParams(lr=0.1, batch=1))
builder.set_epochs(10)
model_spec.isUpdatable = True
model_spec.specificationVersion = coremltools._MINIMUM_UPDATABLE_SPEC_VERSION
model_spec.description.trainingInput[0].shortDescription = 'Image for training and updating the model'
model_spec.description.trainingInput[1].shortDescription = 'Set the value as Cat or Dog and update the model'
updateableModel = 'ModelsML/' + model + '_updateable' + '.mlmodel'
coremltools.utils.save_spec(model_spec, updateableModel)
这是从应用内更新模型的代码：
func createTrainingData(imageArray: [UIImage], outputLabel: String) -> MLArrayBatchProvider{
var featureProviders = [MLFeatureProvider]()
let inputName = "image"
let outputName = outputLabel
var trainingImages = [UIImage]()
trainingImages.append(theImage!)
for image in trainingImages {
let inputValue = try? MLFeatureValue(cgImage: image.cgImage!,
pixelsWide: 224,
pixelsHigh: 224,
pixelFormatType: kCVPixelFormatType_OneComponent8,
options: nil)
let outputValue = MLFeatureValue(string: String(outputName))
let dataPointFeatures: [String: MLFeatureValue] = [inputName: inputValue!,
outputName: outputValue]
if let provider = try? MLDictionaryFeatureProvider(dictionary: dataPointFeatures) {
featureProviders.append(provider)
}
else{
print("Failed to append feature provider.")
}
}
return MLArrayBatchProvider(array: featureProviders)
}
func updateModel(at url: URL,
with trainingData: MLArrayBatchProvider,
completionHandler: @escaping (MLUpdateContext) -> Void) {
// Create an Update Task.
guard let updateTask = try? MLUpdateTask(forModelAt: url,
trainingData: trainingData,
configuration: nil,
completionHandler: completionHandler)
else {
print("Couldn't create an MLUpdateTask.")
return
}
updateTask.resume()
}
func performUpdate(){
//Might need to change from a batch to a single image
let updateImages: [UIImage] = [theImage!]
let imageBatch = createTrainingData(imageArray: updateImages, outputLabel: "dog") // temp outputLabel
updateModel(at: globalCompiledModel!, with: imageBatch, completionHandler:{ param in
print("Model updated!")
})
}
我对 globalCompiledModel 持怀疑态度。更新模型后，您需要重新加载它，即从保存更新模型的 URL 创建一个新实例。我也看不到您在代码中的哪个位置保存更新后的模型。
（好的，从技术上讲，您 没有 来保存更新后的模型，但是您 需要使用来自之后 MLUpdateTaskContext。）
我正在为我的论文创建一个应用程序，到目前为止，我已经成功地创建了一个可更新的图像分类模型，我使用 Keras 对其进行了训练并使用 coremltools 对其进行了转换。 （我将提供用于训练和转换模型的代码，因为我认为它与我的问题有关，以及我用来在应用程序内更新模型的代码。）我可以成功地对图像进行分类并且模型有效正如预期的那样，但更新部分没有。在我对图像执行更新并尝试使用 "updated" 模型对同一图像进行分类后，我得到了相同的分类。连一点信心上的差别都没有。
模型生成和转换在 Python 中完成。应用程序是用 Swift.
编写的这是创建模型的代码：
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense, BatchNormalization
from keras import backend as K
img_width, img_height = 224, 224
train_data_dir = 'data/train/'
validation_data_dir = 'data/validate/'
nb_train_samples = 500
nb_validation_samples = 40
epochs = 20
batch_size = 25
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
model = Sequential()
model.add(Conv2D(32, (2, 2), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(BatchNormalization())
model.add(Conv2D(64, (2, 2)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(2))
model.add(Activation('softmax'))
model.summary()
model.compile(loss = 'categorical_crossentropy',
optimizer = 'sgd',
metrics = ['accuracy'])
train_datagen = ImageDataGenerator()
test_datagen = ImageDataGenerator()
train_generator = train_datagen.flow_from_directory(train_data_dir,
target_size =(img_width, img_height),
batch_size = batch_size, class_mode ='categorical')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size =(img_width, img_height),
batch_size = batch_size, class_mode ='categorical')
model.fit_generator(train_generator,
steps_per_epoch = nb_train_samples // batch_size,
epochs = epochs, validation_data = validation_generator,
validation_steps = nb_validation_samples // batch_size)
model.save('ModelsH5/catndog_small.h5')
以下是将其转换为 CoreML 并使其可更新的代码：
import coremltools
from coremltools.models.neural_network import SgdParams
from coremltools.models import MLModel
model = "catndog_small"
coreml_model_path = 'ModelsML/' + model + '.mlmodel'
neuralnetwork_spec = coremltools.utils.load_spec(coreml_model_path)
builder = coremltools.models.neural_network.NeuralNetworkBuilder(spec=neuralnetwork_spec)
builder.inspect_layers(last=3)
builder.inspect_input_features()
neuralnetwork_spec.description.metadata.author = 'Ervins Balodis'
neuralnetwork_spec.description.metadata.license = 'No license'
neuralnetwork_spec.description.metadata.shortDescription = ('Cat and Dog Classifier converted from a Keras model')
model_spec = builder.spec
builder.make_updatable(['dense_2'])
builder.set_categorical_cross_entropy_loss(name='lossLayer', input='classLabelProbs')
from coremltools.models.neural_network import SgdParams
builder.set_sgd_optimizer(SgdParams(lr=0.1, batch=1))
builder.set_epochs(10)
model_spec.isUpdatable = True
model_spec.specificationVersion = coremltools._MINIMUM_UPDATABLE_SPEC_VERSION
model_spec.description.trainingInput[0].shortDescription = 'Image for training and updating the model'
model_spec.description.trainingInput[1].shortDescription = 'Set the value as Cat or Dog and update the model'
updateableModel = 'ModelsML/' + model + '_updateable' + '.mlmodel'
coremltools.utils.save_spec(model_spec, updateableModel)
这是从应用内更新模型的代码：
func createTrainingData(imageArray: [UIImage], outputLabel: String) -> MLArrayBatchProvider{
var featureProviders = [MLFeatureProvider]()
let inputName = "image"
let outputName = outputLabel
var trainingImages = [UIImage]()
trainingImages.append(theImage!)
for image in trainingImages {
let inputValue = try? MLFeatureValue(cgImage: image.cgImage!,
pixelsWide: 224,
pixelsHigh: 224,
pixelFormatType: kCVPixelFormatType_OneComponent8,
options: nil)
let outputValue = MLFeatureValue(string: String(outputName))
let dataPointFeatures: [String: MLFeatureValue] = [inputName: inputValue!,
outputName: outputValue]
if let provider = try? MLDictionaryFeatureProvider(dictionary: dataPointFeatures) {
featureProviders.append(provider)
}
else{
print("Failed to append feature provider.")
}
}
return MLArrayBatchProvider(array: featureProviders)
}
func updateModel(at url: URL,
with trainingData: MLArrayBatchProvider,
completionHandler: @escaping (MLUpdateContext) -> Void) {
// Create an Update Task.
guard let updateTask = try? MLUpdateTask(forModelAt: url,
trainingData: trainingData,
configuration: nil,
completionHandler: completionHandler)
else {
print("Couldn't create an MLUpdateTask.")
return
}
updateTask.resume()
}
func performUpdate(){
//Might need to change from a batch to a single image
let updateImages: [UIImage] = [theImage!]
let imageBatch = createTrainingData(imageArray: updateImages, outputLabel: "dog") // temp outputLabel
updateModel(at: globalCompiledModel!, with: imageBatch, completionHandler:{ param in
print("Model updated!")
})
}
我对 globalCompiledModel 持怀疑态度。更新模型后，您需要重新加载它，即从保存更新模型的 URL 创建一个新实例。我也看不到您在代码中的哪个位置保存更新后的模型。
（好的，从技术上讲，您 没有 来保存更新后的模型，但是您 需要使用来自之后 MLUpdateTaskContext。）