tflite 学习——生成.tflite 模型与验证-优快云博客

用Resnet 模型进行验证

Step 1 导入resnet 50模型

import numpy as np
import tensorflow as tf



physical_devices = tf.config.list_physical_devices('GPU')
for device in physical_devices: # 使用GPU 
    tf.config.experimental.set_memory_growth(device, True)

model = tf.keras.applications.resnet50.ResNet50(weights='imagenet') # 导入resnet 模型 
img = tf.keras.utils.load_img('123.jpg', target_size=[224, 224，3])  # 载入图片并给出size 

x = tf.keras.preprocessing.image.img_to_array(img)  # 处理输入图片尺寸 
x = np.expand_dims(x, axis=0)
x = tf.keras.applications.resnet50.preprocess_input(x)


preds = model.predict(x)  # 预测结果 
# 将结果解码为元组列表 (class, description, probability)
# (一个列表代表批次中的一个样本）
print('Predicted:', tf.keras.applications.resnet50.decode_predictions(preds, top=3)[0])

结果： Predicted: [('n03887697', 'paper_towel', 0.71528304), ('n15075141', 'toilet_tissue', 0.16052581), ('n02948072', 'candle', 0.03291733)]

Step 2 存储Resnet 模型为 Savemodel的格式两种方法

（1）低级API

存储 tf.saved_model.save (model, path)

这种形式保存以后文件夹是这样的

加载 tf.saved_model.load(path_to_dir)

具体代码如下

load_model = tf.saved_model.load('resnet50_low_level_save/') # 加载模型
labeling = load_model(x)   # 
imagenet_labels = np.array(open('ImageNetLabels.txt').read().splitlines())  # ImageNetLabels.txt 文件中包含1000个被识别的物体名字列表 和 一个backgroud 所以下面的物体标号是+1 
decoded = imagenet_labels[np.argsort(labeling)[0, ::-1][:3] + 1]
print(decoded)

（2）高级API

存储 tf.keras.models.save_model(model, path)

这种形式保存以后文件夹是这样的多了一个keras_metadata.pb的文件

加载 tf.keras.models.save_model(path_to_dir)

具体代码如下

tf.keras.models.save_model(model, 'resnet50_high_level_save/')
load_model = tf.keras.models.load_model('resnet50_high_level_save/') # 加载模型
preds = load_model.predict(x)
decoded = imagenet_labels[np.argsort(preds)[0, ::-1][:3] + 1]

这种方法好处在于可以继续使用keras 的方法

Step 3 利用 Savedmodel 生成.tflite模型

(1) 低级API

convert = tf.lite.TFLiteConverter.from_saved_model('resnet50_low_level_save')
tflite_model = convert.convert()

tflite_model_file = pathlib.Path('resnet50_low_level.tflite')
tflite_model_file.write_bytes(tflite_model)

(2) 高级API

convert = tf.lite.TFLiteConverter.from_saved_model('resnet50_high_level_save')
tflite_model = convert.convert()

tflite_model_file = pathlib.Path('resnet50_high_level.tflite')
tflite_model_file.write_bytes(tflite_model)

Step4 .tflite 模型结果验证

（1）低级API 输入图片的处理利用tensorflow 处理

import numpy as np
import tensorflow as tf

interpreter = tf.lite.Interpreter(model_path="resnet50_low_level.tflite")
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
interpreter.allocate_tensors()
imagenet_labels = np.array(open('ImageNetLabels.txt').read().splitlines())


################################### 输入矩阵处理方式 1 ###################################
img_path = '123.jpg'
print(np.shape(img_path))
img = tf.keras.preprocessing.image.load_img(img_path, target_size=(224, 224, 3))   # 输入矩阵处理方式 1
x = tf.keras.preprocessing.image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = tf.keras.applications.resnet50.preprocess_input(x)


print(np.shape(x))

interpreter.set_tensor(input_details[0]['index'], x)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])

decoded = imagenet_labels[np.argsort(output_data)[0, ::-1][:3] + 1]
print(output_data[:,[np.argsort(output_data)[0, ::-1][:3]]])
print("Result before saving:\n", decoded)

结果：

与前面的结果基本一样，所以证明一切正确。

（2）低级API 输入图片的处理利用Opencv 处理

import numpy as np
import tensorflow as tf
import cv2
interpreter = tf.lite.Interpreter(model_path="resnet50_low_level.tflite")
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
interpreter.allocate_tensors()
imagenet_labels = np.array(open('ImageNetLabels.txt').read().splitlines())


################################### 输入矩阵处理方式 2 利用Opencv ###################################
img = cv2.imread("123.jpg")
new_img = cv2.resize(img, (224, 224))
new_img = new_img.astype(dtype=np.float32)
# new_img = tf.cast(new_img , dtype=np.float32)
x = np.expand_dims(new_img, axis=0)

print(np.shape(x))

interpreter.set_tensor(input_details[0]['index'], x)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])

decoded = imagenet_labels[np.argsort(output_data)[0, ::-1][:3] + 1]
print(output_data[:,[np.argsort(output_data)[0, ::-1][:3]]])
print("Result before saving:\n", decoded)