android - 如何保存图像分类模型并将其用于 android

Question

我如何使用 Keras 和 Tensorflow 将图像分类模型保存为 .pb 文件及其 label.txt，以便在 android 上使用这两个文件。我有一个开始代码，代码只保存 .pb 文件但不是 label.txt

我已经完成了打洞的事情，但还没有完成 label.txt这是代码

import pandas as pd 
import numpy as np
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import keras
from keras.models import Sequential
from keras.layers import Conv2D,MaxPooling2D,Dense,Flatten,Dropout,Activation
from keras.optimizers import Adam
from keras.callbacks import TensorBoard
from keras.layers.core import Lambda
from keras.optimizers import Adam
import keras 
import keras.backend as k
import tensorflow as tf
from tensorflow.python.framework import graph_util
print(keras.__version__)
print(tf.__version__)
import os
train_df = pd.read_csv('fashionmnist/fashion-mnist_train.csv',sep=',')
test_df = pd.read_csv('fashionmnist/fashion-mnist_test.csv',sep=',')


train_data =np.array(train_df,dtype = 'float32')
test_data = np.array(test_df,dtype = 'float32')
x_train = train_data[:,1:]/255
y_train = train_data[:,0]
x_test = train_data[:,1:]/255
y_test = train_data[:,0]
x_train,x_validate,y_train,y_validate=train_test_split(x_train,y_train,test_size = 0.2,random_state = 12345)
image = x_train[50,:].reshape((28,28))
plt.imshow(image)
plt.show()

image_rows =28
image_cols= 28
batch_size =100
image_shape =(image_rows,image_cols,1)



x_train = x_train.reshape(x_train.shape[0],*image_shape)
x_test = x_test.reshape(x_test.shape[0],*image_shape)
x_validate = x_validate.reshape(x_validate.shape[0],*image_shape)


def build_network(is_training=True):
    model = Sequential()
    model.add(Conv2D(32, (3, 3), input_shape=image_shape,  padding='same',name="1_conv"))
    model.add(Activation('relu'))
    model.add(Conv2D(32, (3, 3), padding='same',name="2_conv"))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2),name="1_pool"))

    model.add(Conv2D(64, (3, 3), padding='same',name="3_conv"))
    model.add(Activation('relu'))
    model.add(Conv2D(64,(3, 3), padding='same',name="4_conv"))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2),name="2_pool"))

    model.add(Conv2D(128,(3, 3),padding='same',name="5_conv"))
    model.add(Activation('relu'))
    model.add(Conv2D(128, (3, 3),padding='same',name="6_conv"))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2),name="3_pool"))

    model.add(Conv2D(256,(3, 3), padding='same',name="7_conv"))
    model.add(Activation('relu'))
    model.add(Conv2D(256, (3, 3), padding='same',name="8_conv"))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2),name="4_pool"))

    model.add(Flatten())
    model.add(Dense(512,name="fc_1"))
    model.add(Activation('relu'))


    if (is_training):
        #model.add(Dense(512, activation='relu'))
        #model.add(Dropout(0.5, name="drop_1"))
        model.add(Lambda(lambda x:k.dropout(x,level=0.5),name="drop_1"))



    model.add(Dense(10,name="fc_2"))
    model.add(Activation('softmax',name="class_result"))
    #model.summary()
    return model


    tf.reset_default_graph()
sess = tf.Session()
k.set_session(sess)
model=build_network()

history_dict = {}
model.compile(loss='sparse_categorical_crossentropy',optimizer = Adam(),metrics=['accuracy'])




class TFCheckpointCallback(keras.callbacks.Callback):
    def __init__(self,saver,sess):
        self.saver=saver
        self.sess=sess

    def on_epoch_end(self,epoch,log=None):
        self.saver.save(self.sess,'fMnist/ckpt',global_step=epoch)


tf_saver= tf.train.Saver(max_to_keep=2)
checkpoint_callback= TFCheckpointCallback(tf_saver,sess)
%time
tf_graph=sess.graph
tf.train.write_graph(tf_graph.as_graph_def(),'freeze','fm_graph.pdtxt',as_text=True)
%time
history = model.fit(x_train,
                    y_train,
                    batch_size=batch_size,
                    epochs=50,
                    callbacks=[checkpoint_callback],
                    shuffle=True,
                    verbose=1,
                    validation_data=(x_validate,y_validate)
                   )

sess.close()


model_folder='fMnist/'
def prepare_graph_for_freezing(model_folder):
    model=build_network(is_training=False)
    checkpoint=tf.train.get_checkpoint_state(model_folder)
    input_checkpoint=checkpoint.model_checkpoint_path
    saver=tf.train.Saver()
    with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
        k.set_session(sess)
        saver.restore(sess,input_checkpoint)
        tf.gfile.MakeDirs(model_folder+'freeze')
        saver.save(sess,model_folder + 'freeze/ckpt',global_step=0)


def freeze_graph(model_folder):
    checkpoint =tf.train.get_checkpoint_state(model_folder)
    print(model_folder+'freeze/')
    input_checkpoint = checkpoint.model_checkpoint_path
    absolut_model_folder="/".join(input_checkpoint.split('/')[:-1])
    output_graph=absolut_model_folder + "/fm_freazen_model.pb"
    print(output_graph)
    output_node_name = "class_result/Softmax"
    clear_devices = True
    new_saver=  tf.train.import_meta_graph(input_checkpoint + '.meta',clear_devices=clear_devices)

    graph = tf.get_default_graph()
    input_graph_def = graph.as_graph_def()


    with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess2:
        print(input_checkpoint)
        new_saver.restore(sess2,input_checkpoint)

        output_graph_def=graph_util.convert_variables_to_constants(
        sess2,
        input_graph_def,
        output_node_name.split(","))

        with tf.gfile.GFile(output_graph,"wb") as f:
            f.write(output_graph_def.SerializeToString())
        print("%d ops in the final graph."% len(output_graph_def.node))
tf.reset_default_graph()
prepare_graph_for_freezing("freeze/")
freeze_graph("freeze/")

我有检查点和.pb 文件

但是我没有label.txt文件

Answer 1

就Android上的Image Classification而言，我推荐你使用TensorFlow Lite而不是直接使用 Protocol Buffer 。

首先，您需要将 Keras 模型 ( .h5 ) 转换为 TensorFlow Lite 模型 (.tflite )。

converter = tf.lite.TFLiteConverter.from_keras_model_file( 'model.h5' )
tflite_buffer = converter.convert()
open( 'tflite_model.tflite' , 'wb' ).write( tflite_buffer )

模型已准备好加载到 Android 上。要检查输入和输出 dtype 和 shape，请参阅 this文件。

现在在 Android 上，首先在 build.gradle 中添加 TensorFlow Lite 依赖。

dependencies {
...
   implementation 'org.tensorflow:tensorflow-lite:1.13.1'
...
}

现在我们将模型加载为 MappedByteBuffer 对象。

@Throws(IOException::类)

private fun loadModelFile(): MappedByteBuffer {
    val MODEL_ASSETS_PATH = "model.tflite"
    val assetFileDescriptor = assets.openFd(MODEL_ASSETS_PATH)
    val fileInputStream = FileInputStream(assetFileDescriptor.getFileDescriptor())
    val fileChannel = fileInputStream.getChannel()
    val startoffset = assetFileDescriptor.getStartOffset()
    val declaredLength = assetFileDescriptor.getDeclaredLength()
    return fileChannel.map(FileChannel.MapMode.READ_ONLY, startoffset, declaredLength)
}

使用 interpreter.run() 方法，我们在给定一些输入的情况下产生推理。看这个file .此文件包含使用 Bitmap.createScaledBitmap 方法调整 Bitmap 大小并将其转换为 float[][]

的方法

val interpreter = Interpreter( loadModelFile() )
val inputs : Array<FloatArray> = arrayOf( some_input_image. )
val outputs : Array<FloatArray> = arrayOf( floatArrayOf( 0.0f , 0.0f ) )
interpreter.run( inputs , outputs )
val output = outputs[0]

就是这样。 TFLite 比 TensorFlow Mobile 快得多。

Note: TF Lite supports only a few number of ops. Since, ops related with CNNs are fully supported, we can use TFLite for image classification in Android and iOS too.

提示:

1. 要减小 .tflite 文件的大小，请在使用 Python 转换模型时使用 post_training_quantize 标志。

   converter = tf.lite.TFLiteConverter.from_keras_model_file( 'model.h5' )
   converter.post_training_quantize = True
   tflite_buffer = converter.convert()
   open( 'tflite_model.tflite' , 'wb' ).write( tflite_buffer )
   

2. 此外，尝试使用 Firebase MLKit 在 Firebase 中托管自定义模型。

3. 我已经创建了许多使用 TF 对图像和文本进行分类的应用。

https://github.com/shubham0204/Spam_Classification_Android_Demo

https://github.com/shubham0204/Skinly_for_Melanoma