使用 Python 中的量化 tflite model “INT8” 运行推理

Question

**Hello everyone, I converted a tensorflow float model to a tflite quantized INT8 model recently, in the end I got the model without errors. 我想在 python 中用这个 model 做推断，但我不能得到好的结果。 代码如下：**

转换TF model

 def representative_dataset_gen():
    for i in range(20):
        data_x, data_y = validation_generator.next()
        for data_xx in data_x:
            data = tf.reshape(data, shape=[-1, 128, 128, 3])
            yield [data]

converter = tf.lite.TFLiteConverter.from_keras_model(model)

converter.optimizations = [tf.lite.Optimize.DEFAULT]

converter.representative_dataset = representative_dataset_gen

converter.target_spec.supported_ops =[tf.lite.OpsSet.TFLITE_BUILTINS_INT8]

converter.inference_input_type  = tf.int8

converter.inference_output_type = tf.int8

quantized_model = converter.convert()

open("/content/drive/My Drive/model.tflite", "wb").write(quantized_model)

运行推理

tflite_file='./model_google.tflite'
img_name='./img_test/1_2.jpg'

test_image = load_img(img_name, target_size=(128, 128))
test_image = img_to_array(test_image)

test_image = test_image.reshape(1, 128, 128,3)
#test_image = test_image.astype('float32')


interpreter = tf.lite.Interpreter(model_path=(tflite_file))
interpreter.allocate_tensors()

input_details = interpreter.get_input_details()[0]


input_scale, input_zero_point = input_details['quantization']


test_image_int = test_image / input_scale + input_zero_point
test_image_int=test_image_int.astype(input_details['dtype'])




interpreter.set_tensor(input_details['index'], test_image_int)
interpreter.invoke()

output_details = interpreter.get_output_details()[0]

output = interpreter.get_tensor(output_details['index'])

scale, zero_point= output_details['quantization']

tflite_output=output.astype(np.float32)
tflite_output= (tflite_output- zero_point)* scale

print(input_scale)
print(tflite_output)
print(input_details["quantization"])

你能告诉我如何用这个量化的 model 预测 class（输入和 output 转换为 INT8）并具有正确的概率值

Answer 1

您好 Jae，谢谢您的回答，附上代表性数据集代码：

train_datagen =  ImageDataGenerator(
    rescale=1. / 255,
    rotation_range=30,
    width_shift_range=0.1,
    height_shift_range=0.1,
    shear_range=0.1,
    zoom_range=[0.6, 1.1],
    horizontal_flip=True,
    brightness_range=[0.8, 1.3],
    channel_shift_range=2.0,
    fill_mode='nearest')

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    classes=classes,
    class_mode='categorical',
    )
def representative_dataset_gen():
    for i in range(10):
        data_x, data_y = train_generator.next()
        for data in data_x:
            data = tf.reshape(data, shape=[-1, 128, 128, 3])
            yield [data]

我使用来自训练数据集的数据进行量化，你能告诉我在将其发送到输入之前如何进行图像处理以及如何读取 output 的推理吗 谢谢