TensorFlow系列：第五讲：移动端部署模型

项目地址：https://github.com/LionJackson/imageClassification
Flutter项目地址：https://github.com/LionJackson/flutter_image

一. 模型转换

编写tflite模型工具类：

import osimport PIL
import tensorflow as tf
import keras
import numpy as np
from PIL.Image import Image
from matplotlib import pyplot as pltfrom utils.dataset_loader import DatasetLoader
from utils.utils import Utils"""
tflite模型工具类
"""class TFLiteUtil:def __init__(self, saved_model_dir, path_url):self.save_model_dir = saved_model_dirself.path_url = path_url# 训练的模型生成标签列表def get_folder_names(self):folder_names = []for root, dirs, files in os.walk(self.path_url + '/train'):for dir_name in dirs:folder_names.append(dir_name)with open(self.save_model_dir + '.label', 'w') as file:for name in folder_names:file.write(name + '\n')return folder_names# 模型转成tflite格式def convert_tflite(self):self.get_folder_names()converter = tf.lite.TFLiteConverter.from_saved_model(self.save_model_dir)tflite_model = converter.convert()# 将转换后的 TFLite 模型保存为文件with open(self.save_model_dir + '.tflite', 'wb') as f:f.write(tflite_model)print("转换成功，已保存为 tflite")# 加载keras并转成tflitedef convert_model_tflite(self):self.get_folder_names()model = keras.models.load_model(self.save_model_dir + ".keras")converter = tf.lite.TFLiteConverter.from_keras_model(model)converter.optimizations = [tf.lite.Optimize.DEFAULT]converter.target_spec.supported_types = [tf.float16]tflite_model = converter.convert()# 将转换后的 TFLite 模型保存为文件with open(self.save_model_dir + '.tflite', 'wb') as f:f.write(tflite_model)print("转换成功(model)，已保存为 tflite")# 批量识别 进行可视化显示def batch_evaluation(self, class_mode='categorical', image_size=(224, 224), num_images=25):dataset_loader = DatasetLoader(self.path_url, image_size=image_size, class_mode=class_mode)train_ds, val_ds, test_ds, class_names = dataset_loader.load_data()interpreter = tf.lite.Interpreter(self.save_model_dir + '.tflite')interpreter.allocate_tensors()# 获取输入和输出张量的信息input_details = interpreter.get_input_details()output_details = interpreter.get_output_details()plt.figure(figsize=(10, 10))for images, labels in test_ds.take(1):outputs = []for img in images:img_expanded = np.expand_dims(img, axis=0)interpreter.set_tensor(input_details[0]['index'], img_expanded)interpreter.invoke()output = interpreter.get_tensor(output_details[0]['index'])outputs.append(output)for i in range(num_images):plt.subplot(5, 5, i + 1)image = np.array(images[i]).astype("uint8")plt.imshow(image)index = int(np.argmax(outputs[i]))prediction = outputs[i][0][index]percentage_str = "{:.2f}%".format(prediction * 100)plt.title(f"{class_names[index]}: {percentage_str}")plt.axis("off")plt.subplots_adjust(hspace=0.5, wspace=0.5)plt.show()# 查看tflite模型信息def tflite_analyzer(self):# 加载 TFLite 模型interpreter = tf.lite.Interpreter(model_path=self.save_model_dir + '.tflite')interpreter.allocate_tensors()# 获取输入和输出的详细信息input_details = interpreter.get_input_details()output_details = interpreter.get_output_details()# 打印输入和输出的详细信息print("Input Details:")for detail in input_details:print(detail)print("\nOutput Details:")for detail in output_details:print(detail)# 列出所有使用的算子tensor_details = interpreter.get_tensor_details()print("\nTensor Details:")for tensor_detail in tensor_details:print("Index:", tensor_detail['index'])print("Name:", tensor_detail['name'])print("Shape:", tensor_detail['shape'])print("Shape Signature:", tensor_detail['shape_signature'])print("dtype:", tensor_detail['dtype'])print("Quantization:", tensor_detail['quantization'])print("Quantization Parameters:", tensor_detail['quantization_parameters'])print("Sparsity Parameters:", tensor_detail['sparsity_parameters'])print()

引用工具类：

if __name__ == '__main__':# train()# model_util = ModelUtil(SAVED_MODEL_DIR, PATH_URL)# model_util.batch_evaluation()tflite_util = TFLiteUtil(SAVED_MODEL_DIR, PATH_URL)tflite_util.convert_tflite()tflite_util.tflite_analyzer()tflite_util.batch_evaluation()

此时会生成tflite模型文件：

在这里插入图片描述

二. 使用模型

创建flutter项目，引入以下库：

  image: ^4.0.17path: ^1.8.3path_provider: ^2.0.15image_picker: ^0.8.8tflite_flutter: ^0.10.4camera: ^0.10.5+2

把模型文件拷贝到项目中：

在这里插入图片描述
核心代码：

import 'dart:developer';
import 'dart:io';
import 'dart:isolate';import 'package:camera/camera.dart';
import 'package:flutter/services.dart';
import 'package:image/image.dart';
import 'package:tflite_flutter/tflite_flutter.dart';import 'isolate_inference.dart';class ImageClassificationHelper {static const modelPath = 'assets/models/fruits.tflite';static const labelsPath = 'assets/models/fruits.label';late final Interpreter interpreter;late final List<String> labels;late final IsolateInference isolateInference;late Tensor inputTensor;late Tensor outputTensor;// Load modelFuture<void> _loadModel() async {final options = InterpreterOptions();// Use XNNPACK Delegateif (Platform.isAndroid) {options.addDelegate(XNNPackDelegate());}// Use GPU Delegate// doesn't work on emulator// if (Platform.isAndroid) {//   options.addDelegate(GpuDelegateV2());// }// Use Metal Delegateif (Platform.isIOS) {options.addDelegate(GpuDelegate());}// Load model from assetsinterpreter = await Interpreter.fromAsset(modelPath, options: options);// Get tensor input shape [1, 224, 224, 3]inputTensor = interpreter.getInputTensors().first;// Get tensor output shape [1, 1001]outputTensor = interpreter.getOutputTensors().first;log('Interpreter loaded successfully');}// Load labels from assetsFuture<void> _loadLabels() async {final labelTxt = await rootBundle.loadString(labelsPath);labels = labelTxt.split('\n');}Future<void> initHelper() async {_loadLabels();_loadModel();isolateInference = IsolateInference();await isolateInference.start();}Future<Map<String, double>> _inference(InferenceModel inferenceModel) async {ReceivePort responsePort = ReceivePort();isolateInference.sendPort.send(inferenceModel..responsePort = responsePort.sendPort);// get inference result.var results = await responsePort.first;return results;}// inference camera frameFuture<Map<String, double>> inferenceCameraFrame(CameraImage cameraImage) async {var isolateModel = InferenceModel(cameraImage, null, interpreter.address,labels, inputTensor.shape, outputTensor.shape);return _inference(isolateModel);}// inference still imageFuture<Map<String, double>> inferenceImage(Image image) async {var isolateModel = InferenceModel(null, image, interpreter.address, labels,inputTensor.shape, outputTensor.shape);return _inference(isolateModel);}Future<void> close() async {isolateInference.close();}
}