 在 TensorFlow.org 查看 |
 在 Google Colab 中运行 |
 在 GitHub 上查看源代码 |
 下载笔记本 |
概述
整数量化是一种优化策略，可将 32 位浮点数（如权重和激活输出）转换为 8 位定点数。这样可以缩减模型大小并加快推理速度，这对低功耗设备（如微控制器）很有价值。仅支持整数的加速器（如 Edge TPU）也需要使用此数据格式。
在本教程中，您将从头开始训练一个 MNIST 模型、将其转换为 TensorFlow Lite 文件，并使用训练后量化对其进行量化。最后，您将检查转换后模型的准确率并将其与原始浮点模型进行比较。
实际上，对模型进行量化的程度有几种选项。在本教程中，您将执行“全整数量化”，它会将所有权重和激活输出转换为 8 位整数数据，而其他策略可能会将部分数据保留为浮点。
要详细了解各种量化策略，请阅读 TensorFlow Lite 模型优化。
设置
为了量化输入和输出张量，我们需要使用 TensorFlow 2.3 中新添加的 API：
import logging
logging.getLogger("tensorflow").setLevel(logging.DEBUG)
import tensorflow as tf
import numpy as np
print("TensorFlow version: ", tf.__version__)
2022-08-11 19:07:46.234983: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered 2022-08-11 19:07:47.061607: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvrtc.so.11.1: cannot open shared object file: No such file or directory 2022-08-11 19:07:47.061943: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvrtc.so.11.1: cannot open shared object file: No such file or directory 2022-08-11 19:07:47.061957: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly. TensorFlow version: 2.10.0-rc0
生成 TensorFlow 模型
我们将构建一个简单的模型来对 MNIST 数据集中的数字进行分类。
此训练不会花很长时间，因为只对模型进行 5 个周期的训练，训练到约 98% 的准确率。
# Load MNIST dataset
mnist = tf.keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
# Normalize the input image so that each pixel value is between 0 to 1.
train_images = train_images.astype(np.float32) / 255.0
test_images = test_images.astype(np.float32) / 255.0
# Define the model architecture
model = tf.keras.Sequential([
tf.keras.layers.InputLayer(input_shape=(28, 28)),
tf.keras.layers.Reshape(target_shape=(28, 28, 1)),
tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(10)
])
# Train the digit classification model
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True),
metrics=['accuracy'])
model.fit(
train_images,
train_labels,
epochs=5,
validation_data=(test_images, test_labels)
)
Epoch 1/5 1875/1875 [==============================] - 7s 3ms/step - loss: 0.2688 - accuracy: 0.9261 - val_loss: 0.1208 - val_accuracy: 0.9632 Epoch 2/5 1875/1875 [==============================] - 5s 3ms/step - loss: 0.1062 - accuracy: 0.9701 - val_loss: 0.0870 - val_accuracy: 0.9746 Epoch 3/5 1875/1875 [==============================] - 5s 3ms/step - loss: 0.0805 - accuracy: 0.9766 - val_loss: 0.0746 - val_accuracy: 0.9770 Epoch 4/5 1875/1875 [==============================] - 5s 3ms/step - loss: 0.0668 - accuracy: 0.9801 - val_loss: 0.0683 - val_accuracy: 0.9783 Epoch 5/5 1875/1875 [==============================] - 5s 3ms/step - loss: 0.0581 - accuracy: 0.9832 - val_loss: 0.0679 - val_accuracy: 0.9775 <keras.callbacks.History at 0x7fc9011f51c0>
转换为 TensorFlow Lite 模型
现在，您可以使用TensorFlow Lite Converter 将训练后的模型转换为 TensorFlow Lite 格式，并应用不同程度的量化。
请注意，某些版本的量化会将部分数据保留为浮点格式。因此，以下各个部分将以量化程度不断增加的顺序展示每个选项，直到获得完全由 int8 或 uint8 数据组成的模型。（请注意，我们在每个部分中重复了一些代码，使您能够看到每个选项的全部量化步骤。）
首先，下面是一个没有量化的转换后模型：
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op while saving (showing 1 of 1). These functions will not be directly callable after loading. INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpkjury1v_/assets INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpkjury1v_/assets 2022-08-11 19:08:22.226815: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format. 2022-08-11 19:08:22.226853: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.
它现在是一个 TensorFlow Lite 模型，但所有参数数据仍使用 32 位浮点值。
使用动态范围量化进行转换
现在，我们启用默认的 optimizations 标记来量化所有固定参数（例如权重）：
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model_quant = converter.convert()
WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op while saving (showing 1 of 1). These functions will not be directly callable after loading. INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpjt4keerb/assets INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpjt4keerb/assets 2022-08-11 19:08:23.373341: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format. 2022-08-11 19:08:23.373380: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.
现在，进行了权重量化的模型要略小一些，但其他变量数据仍为浮点格式。
使用浮点回退量化进行转换
要量化可变数据（例如模型输入/输出和层之间的中间体），您需要提供 RepresentativeDataset。这是一个生成器函数，它提供一组足够大的输入数据来代表典型值。转换器可以通过该函数估算所有可变数据的动态范围。（相比训练或评估数据集，此数据集不必唯一。）为了支持多个输入，每个代表性数据点都是一个列表，并且列表中的元素会根据其索引被馈送到模型。
def representative_data_gen():
for input_value in tf.data.Dataset.from_tensor_slices(train_images).batch(1).take(100):
# Model has only one input so each data point has one element.
yield [input_value]
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_data_gen
tflite_model_quant = converter.convert()
WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op while saving (showing 1 of 1). These functions will not be directly callable after loading.
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpqs29u3y8/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpqs29u3y8/assets
/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/lite/python/convert.py:766: UserWarning: Statistics for quantized inputs were expected, but not specified; continuing anyway.
warnings.warn("Statistics for quantized inputs were expected, but not "
2022-08-11 19:08:24.526139: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.
2022-08-11 19:08:24.526176: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.
fully_quantize: 0, inference_type: 6, input_inference_type: FLOAT32, output_inference_type: FLOAT32
现在，所有权重和可变数据都已量化，并且与原始 TensorFlow Lite 模型相比，该模型要小得多。
但是，为了与传统上使用浮点模型输入和输出张量的应用保持兼容，TensorFlow Lite 转换器将模型的输入和输出张量保留为浮点：
interpreter = tf.lite.Interpreter(model_content=tflite_model_quant)
input_type = interpreter.get_input_details()[0]['dtype']
print('input: ', input_type)
output_type = interpreter.get_output_details()[0]['dtype']
print('output: ', output_type)
input: <class 'numpy.float32'> output: <class 'numpy.float32'>
这通常对兼容性有利，但它无法兼容执行全整数运算的设备（如 Edge TPU）。
此外，如果 TensorFlow Lite 不包括某个运算的量化实现，则上述过程可能会将该运算保留为浮点格式。您仍能通过此策略完成转换，并得到一个更小、更高效的模型，但它还是不兼容仅支持整数的硬件。（此 MNIST 模型中的所有算子都有量化的实现。）
因此，为了确保端到端全整数模型，您还需要几个参数…
使用仅整数量化进行转换
为了量化输入和输出张量，并让转换器在遇到无法量化的运算时引发错误，使用一些附加参数再次转换模型：
def representative_data_gen():
for input_value in tf.data.Dataset.from_tensor_slices(train_images).batch(1).take(100):
yield [input_value]
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_data_gen
# Ensure that if any ops can't be quantized, the converter throws an error
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
# Set the input and output tensors to uint8 (APIs added in r2.3)
converter.inference_input_type = tf.uint8
converter.inference_output_type = tf.uint8
tflite_model_quant = converter.convert()
WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op while saving (showing 1 of 1). These functions will not be directly callable after loading.
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmphnqo4e52/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmphnqo4e52/assets
/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/lite/python/convert.py:766: UserWarning: Statistics for quantized inputs were expected, but not specified; continuing anyway.
warnings.warn("Statistics for quantized inputs were expected, but not "
2022-08-11 19:08:26.493820: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.
2022-08-11 19:08:26.493859: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.
fully_quantize: 0, inference_type: 6, input_inference_type: UINT8, output_inference_type: UINT8
内部量化与上文相同，但您可以看到输入和输出张量现在是整数格式：
interpreter = tf.lite.Interpreter(model_content=tflite_model_quant)
input_type = interpreter.get_input_details()[0]['dtype']
print('input: ', input_type)
output_type = interpreter.get_output_details()[0]['dtype']
print('output: ', output_type)
input: <class 'numpy.uint8'> output: <class 'numpy.uint8'>
现在，您有了一个整数量化模型，该模型使用整数数据作为模型的输入和输出张量，因此它兼容仅支持整数的硬件（如 Edge TPU）。
将模型另存为文件
您需要 .tflite 文件才能在其他设备上部署模型。因此，我们将转换的模型保存为文件，然后在下面运行推断时加载它们。
import pathlib
tflite_models_dir = pathlib.Path("/tmp/mnist_tflite_models/")
tflite_models_dir.mkdir(exist_ok=True, parents=True)
# Save the unquantized/float model:
tflite_model_file = tflite_models_dir/"mnist_model.tflite"
tflite_model_file.write_bytes(tflite_model)
# Save the quantized model:
tflite_model_quant_file = tflite_models_dir/"mnist_model_quant.tflite"
tflite_model_quant_file.write_bytes(tflite_model_quant)
24608
运行 TensorFlow Lite 模型
现在，我们使用 TensorFlow Lite Interpreter 运行推断来比较模型的准确率。
首先，我们需要一个函数，该函数使用给定的模型和图像运行推断，然后返回预测值：
# Helper function to run inference on a TFLite model
def run_tflite_model(tflite_file, test_image_indices):
global test_images
# Initialize the interpreter
interpreter = tf.lite.Interpreter(model_path=str(tflite_file))
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()[0]
output_details = interpreter.get_output_details()[0]
predictions = np.zeros((len(test_image_indices),), dtype=int)
for i, test_image_index in enumerate(test_image_indices):
test_image = test_images[test_image_index]
test_label = test_labels[test_image_index]
# Check if the input type is quantized, then rescale input data to uint8
if input_details['dtype'] == np.uint8:
input_scale, input_zero_point = input_details["quantization"]
test_image = test_image / input_scale + input_zero_point
test_image = np.expand_dims(test_image, axis=0).astype(input_details["dtype"])
interpreter.set_tensor(input_details["index"], test_image)
interpreter.invoke()
output = interpreter.get_tensor(output_details["index"])[0]
predictions[i] = output.argmax()
return predictions
在单个图像上测试模型
现在，我们来比较一下浮点模型和量化模型的性能：
tflite_model_file是使用浮点数据的原始 TensorFlow Lite 模型。tflite_model_quant_file是我们使用全整数量化转换的上一个模型（它使用 uint8 数据作为输入和输出）。
我们来创建另一个函数打印预测值：
import matplotlib.pylab as plt
# Change this to test a different image
test_image_index = 1
## Helper function to test the models on one image
def test_model(tflite_file, test_image_index, model_type):
global test_labels
predictions = run_tflite_model(tflite_file, [test_image_index])
plt.imshow(test_images[test_image_index])
template = model_type + " Model \n True:{true}, Predicted:{predict}"
_ = plt.title(template.format(true= str(test_labels[test_image_index]), predict=str(predictions[0])))
plt.grid(False)
现在测试浮点模型：
test_model(tflite_model_file, test_image_index, model_type="Float")
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
然后测试量化模型：
test_model(tflite_model_quant_file, test_image_index, model_type="Quantized")
在所有图像上评估模型
现在，我们使用在本教程开始时加载的所有测试图像来运行两个模型：
# Helper function to evaluate a TFLite model on all images
def evaluate_model(tflite_file, model_type):
global test_images
global test_labels
test_image_indices = range(test_images.shape[0])
predictions = run_tflite_model(tflite_file, test_image_indices)
accuracy = (np.sum(test_labels== predictions) * 100) / len(test_images)
print('%s model accuracy is %.4f%% (Number of test samples=%d)' % (
model_type, accuracy, len(test_images)))
评估浮点模型：
evaluate_model(tflite_model_file, model_type="Float")
Float model accuracy is 97.7500% (Number of test samples=10000)
评估量化模型：
evaluate_model(tflite_model_quant_file, model_type="Quantized")
Quantized model accuracy is 97.7000% (Number of test samples=10000)
现在您有了一个整数量化模型，该模型的准确率与浮点模型相比几乎没有差别。
要详细了解其他量化策略，请阅读 TensorFlow Lite 模型优化。