训练后动态范围量化

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在 TensorFlow.org 上查看 在 Google Colab 中运行 在 Github 上查看源代码 下载笔记本 查看 TF Hub 模型

概述

TensorFlow Lite 现在支持将权重转换为 8 位精度,作为从 TensorFlow GraphDef 到 TensorFlow Lite FlatBuffer 格式的模型转换的一部分。动态范围量化能使模型大小缩减至原来的四分之一。此外,TFLite 支持对激活进行实时量化和反量化以实现以下效果:

  1. 在可用时使用量化内核加快实现速度。
  2. 将计算图不同部分的浮点内核与量化内核混合。

激活始终以浮点进行存储。对于支持量化内核的算子,激活会在处理前动态量化为 8 位精度,并在处理后反量化为浮点精度。根据被转换的模型,这可以提供比纯浮点计算更快的速度。

量化感知训练相比,在此方法中,权重会在训练后量化,激活会在推断时动态量化。因此,不会重新训练模型权重以补偿量化引起的误差。请务必检查量化模型的准确率,以确保下降程度可以接受。

本教程将从头开始训练一个 MNIST 模型,在 TensorFlow 中检查其准确率,然后使用动态范围量化将此模型转换为 Tensorflow Lite FlatBuffer 格式。最后,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。

构建 MNIST 模型

设置

import logging
logging.getLogger("tensorflow").setLevel(logging.DEBUG)

import tensorflow as tf
from tensorflow import keras
import numpy as np
import pathlib
2022-08-11 18:56:26.133891: 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 18:56:26.955636: 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 18:56:26.955943: 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 18:56:26.955956: 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.

训练 TensorFlow 模型

# Load MNIST dataset
mnist = 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 / 255.0
test_images = test_images / 255.0

# Define the model architecture
model = keras.Sequential([
  keras.layers.InputLayer(input_shape=(28, 28)),
  keras.layers.Reshape(target_shape=(28, 28, 1)),
  keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation=tf.nn.relu),
  keras.layers.MaxPooling2D(pool_size=(2, 2)),
  keras.layers.Flatten(),
  keras.layers.Dense(10)
])

# Train the digit classification model
model.compile(optimizer='adam',
              loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
model.fit(
  train_images,
  train_labels,
  epochs=1,
  validation_data=(test_images, test_labels)
)
1875/1875 [==============================] - 7s 3ms/step - loss: 0.2869 - accuracy: 0.9191 - val_loss: 0.1435 - val_accuracy: 0.9580
<keras.callbacks.History at 0x7fd2e188a5e0>

在此示例中,由于您只对模型进行了一个周期的训练,因此只训练到约 96% 的准确率。

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 模型。

现在使用 TFLiteConverter 加载模型:

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/tmpal6qprbe/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpal6qprbe/assets
2022-08-11 18:56:40.651101: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.
2022-08-11 18:56:40.651139: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.

将其写入 TFLite 文件:

tflite_models_dir = pathlib.Path("/tmp/mnist_tflite_models/")
tflite_models_dir.mkdir(exist_ok=True, parents=True)
tflite_model_file = tflite_models_dir/"mnist_model.tflite"
tflite_model_file.write_bytes(tflite_model)
84824

要在导出时量化模型,请设置 optimizations 标记以优化大小:

converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_quant_model = converter.convert()
tflite_model_quant_file = tflite_models_dir/"mnist_model_quant.tflite"
tflite_model_quant_file.write_bytes(tflite_quant_model)
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/tmp7yko6o6w/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmp7yko6o6w/assets
2022-08-11 18:56:41.815256: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.
2022-08-11 18:56:41.815297: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.
24072

请注意,生成文件的大小约为 1/4

ls -lh {tflite_models_dir}
total 152K
-rw-rw-r-- 1 kbuilder kbuilder 83K Aug 11 18:56 mnist_model.tflite
-rw-rw-r-- 1 kbuilder kbuilder 24K Aug 11 18:56 mnist_model_quant.tflite
-rw-rw-r-- 1 kbuilder kbuilder 44K Aug 11 18:54 mnist_model_quant_f16.tflite

运行 TFLite 模型

使用 Python TensorFlow Lite 解释器运行 TensorFlow Lite 模型。

将模型加载到解释器中

interpreter = tf.lite.Interpreter(model_path=str(tflite_model_file))
interpreter.allocate_tensors()
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
interpreter_quant = tf.lite.Interpreter(model_path=str(tflite_model_quant_file))
interpreter_quant.allocate_tensors()

在单个图像上测试模型

test_image = np.expand_dims(test_images[0], axis=0).astype(np.float32)

input_index = interpreter.get_input_details()[0]["index"]
output_index = interpreter.get_output_details()[0]["index"]

interpreter.set_tensor(input_index, test_image)
interpreter.invoke()
predictions = interpreter.get_tensor(output_index)
import matplotlib.pylab as plt

plt.imshow(test_images[0])
template = "True:{true}, predicted:{predict}"
_ = plt.title(template.format(true= str(test_labels[0]),
                              predict=str(np.argmax(predictions[0]))))
plt.grid(False)

png

评估模型

# A helper function to evaluate the TF Lite model using "test" dataset.
def evaluate_model(interpreter):
  input_index = interpreter.get_input_details()[0]["index"]
  output_index = interpreter.get_output_details()[0]["index"]

  # Run predictions on every image in the "test" dataset.
  prediction_digits = []
  for test_image in test_images:
    # Pre-processing: add batch dimension and convert to float32 to match with
    # the model's input data format.
    test_image = np.expand_dims(test_image, axis=0).astype(np.float32)
    interpreter.set_tensor(input_index, test_image)

    # Run inference.
    interpreter.invoke()

    # Post-processing: remove batch dimension and find the digit with highest
    # probability.
    output = interpreter.tensor(output_index)
    digit = np.argmax(output()[0])
    prediction_digits.append(digit)

  # Compare prediction results with ground truth labels to calculate accuracy.
  accurate_count = 0
  for index in range(len(prediction_digits)):
    if prediction_digits[index] == test_labels[index]:
      accurate_count += 1
  accuracy = accurate_count * 1.0 / len(prediction_digits)

  return accuracy
print(evaluate_model(interpreter))
0.958

在动态范围量化模型上重复评估,以获得如下结果:

print(evaluate_model(interpreter_quant))
0.9579

在此示例中,压缩后的模型在准确率方面没有差别。

优化现有模型

带有预激活层的 ResNet (ResNet-v2) 被广泛用于视觉应用。用于 ResNet-v2-101 的预训练冻结计算图可在 Tensorflow Hub 上获得。

您可以通过执行以下代码,使用量化将冻结计算图转换为 TensorFLow Lite FlatBuffer 格式:

import tensorflow_hub as hub

resnet_v2_101 = tf.keras.Sequential([
  keras.layers.InputLayer(input_shape=(224, 224, 3)),
  hub.KerasLayer("https://tfhub.dev/google/imagenet/resnet_v2_101/classification/4")
])

converter = tf.lite.TFLiteConverter.from_keras_model(resnet_v2_101)
WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.data_structures has been moved to tensorflow.python.trackable.data_structures. The old module will be deleted in version 2.11.
WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.data_structures has been moved to tensorflow.python.trackable.data_structures. The old module will be deleted in version 2.11.
# Convert to TF Lite without quantization
resnet_tflite_file = tflite_models_dir/"resnet_v2_101.tflite"
resnet_tflite_file.write_bytes(converter.convert())
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpgfyg0h86/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpgfyg0h86/assets
2022-08-11 18:57:15.894863: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.
2022-08-11 18:57:15.894917: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.
178422332
# Convert to TF Lite with quantization
converter.optimizations = [tf.lite.Optimize.DEFAULT]
resnet_quantized_tflite_file = tflite_models_dir/"resnet_v2_101_quantized.tflite"
resnet_quantized_tflite_file.write_bytes(converter.convert())
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmprt6zi9pz/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmprt6zi9pz/assets
2022-08-11 18:57:46.225193: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.
2022-08-11 18:57:46.225249: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.
45733976
ls -lh {tflite_models_dir}/*.tflite
-rw-rw-r-- 1 kbuilder kbuilder  83K Aug 11 18:56 /tmp/mnist_tflite_models/mnist_model.tflite
-rw-rw-r-- 1 kbuilder kbuilder  24K Aug 11 18:56 /tmp/mnist_tflite_models/mnist_model_quant.tflite
-rw-rw-r-- 1 kbuilder kbuilder  44K Aug 11 18:54 /tmp/mnist_tflite_models/mnist_model_quant_f16.tflite
-rw-rw-r-- 1 kbuilder kbuilder 171M Aug 11 18:57 /tmp/mnist_tflite_models/resnet_v2_101.tflite
-rw-rw-r-- 1 kbuilder kbuilder  44M Aug 11 18:58 /tmp/mnist_tflite_models/resnet_v2_101_quantized.tflite

模型大小从 171 MB 减小到 43 MB。可以使用为 TFLite 准确率测量提供的脚本来评估此模型在 ImageNet 上的准确率。

优化后模型的 Top-1 准确率为 76.8,与浮点模型相同。