 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 |
概述
现在，将模型从 TensorFlow 转换为 TensorFlow Lite 的 FlatBuffer 格式时，TensorFlow Lite 支持将激活转换为 16 位整数值，同时将权重转换为 8 位整数值。我们将此模式称为“16x8 量化模式”。当激活对量化敏感时，此模式可以大幅提高量化模型的准确率，同时还可以将模型大小缩减四分之一至四分之三。此外，这种完全量化的模型可供仅支持整数的硬件加速器使用。
一些可以从这种训练后量化模式受益的示例模型包括：
- 超分辨率，
- 音频信号处理，如噪声消除和波束成形，
- 图像降噪，
- 基于单张图像的 HDR 重建
在本教程中，您将从头开始训练一个 MNIST 模型，并在 TensorFlow 中检查其准确率，然后使用此模式将该模型转换为 Tensorflow Lite FlatBuffer。最后，您将检查转换的模型的准确率，并将其与原始 float32 模型进行对比。请注意，本示例旨在演示此模式的用法，并不会展现与 TensorFlow Lite 中提供的其他量化技术相比的优势。
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
检查 16x8 量化模式是否可用
tf.lite.OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8
<OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8: 'EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8'>
训练并导出模型
# 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)
)
2021-08-13 21:10:34.217149: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:34.226026: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:34.226944: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:34.228589: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2021-08-13 21:10:34.229187: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:34.230116: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:34.231004: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:34.820411: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:34.821367: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:34.822256: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:34.823079: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14648 MB memory: -> device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0 2021-08-13 21:10:35.698373: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2) 2021-08-13 21:10:36.427390: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8100 2021-08-13 21:10:36.948287: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory 1875/1875 [==============================] - 6s 2ms/step - loss: 0.2773 - accuracy: 0.9217 - val_loss: 0.1299 - val_accuracy: 0.9622 <keras.callbacks.History at 0x7fbf585e3b10>
在此示例中，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 Python TFLiteConverter 将训练的模型转换为 TensorFlow Lite 模型。
现在，使用 TFliteConverter 将模型转换为默认的 float32 格式：
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
2021-08-13 21:10:41.486118: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them. INFO:tensorflow:Assets written to: /tmp/tmpyetg5jcr/assets 2021-08-13 21:10:41.855311: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:41.855664: I tensorflow/core/grappler/devices.cc:66] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1 2021-08-13 21:10:41.855778: I tensorflow/core/grappler/clusters/single_machine.cc:357] Starting new session 2021-08-13 21:10:41.856128: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:41.856572: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:41.856861: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:41.857220: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:41.857524: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:41.857790: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14648 MB memory: -> device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0 2021-08-13 21:10:41.859122: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:1137] Optimization results for grappler item: graph_to_optimize function_optimizer: function_optimizer did nothing. time = 0.007ms. function_optimizer: function_optimizer did nothing. time = 0.001ms. 2021-08-13 21:10:41.889267: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:351] Ignored output_format. 2021-08-13 21:10:41.889304: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:354] Ignored drop_control_dependency. 2021-08-13 21:10:41.892454: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:210] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
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)
84500
要改为将模型量化为 16x8 量化模式，首先将 optimizations 标记设置为使用默认优化。然后将 16x8 量化模式指定为目标规范中要求的受支持运算：
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8]
对于 int8 训练后量化，通过将转换器选项 inference_input(output)_type 设置为 tf.int16，可以产生全整数量化模型。
设置校准数据：
mnist_train, _ = tf.keras.datasets.mnist.load_data()
images = tf.cast(mnist_train[0], tf.float32) / 255.0
mnist_ds = tf.data.Dataset.from_tensor_slices((images)).batch(1)
def representative_data_gen():
for input_value in mnist_ds.take(100):
# Model has only one input so each data point has one element.
yield [input_value]
converter.representative_dataset = representative_data_gen
最后，像往常一样转换模型。请注意，为了方便调用，转换后的模型默认仍将使用浮点输入和输出。
tflite_16x8_model = converter.convert()
tflite_model_16x8_file = tflite_models_dir/"mnist_model_quant_16x8.tflite"
tflite_model_16x8_file.write_bytes(tflite_16x8_model)
INFO:tensorflow:Assets written to: /tmp/tmpfhuog02w/assets INFO:tensorflow:Assets written to: /tmp/tmpfhuog02w/assets 2021-08-13 21:10:42.892609: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:42.892955: I tensorflow/core/grappler/devices.cc:66] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1 2021-08-13 21:10:42.893072: I tensorflow/core/grappler/clusters/single_machine.cc:357] Starting new session 2021-08-13 21:10:42.893454: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:42.893801: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:42.894061: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:42.894381: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:42.894644: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-08-13 21:10:42.894888: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14648 MB memory: -> device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0 2021-08-13 21:10:42.896278: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:1137] Optimization results for grappler item: graph_to_optimize function_optimizer: function_optimizer did nothing. time = 0.008ms. function_optimizer: function_optimizer did nothing. time = 0.001ms. 2021-08-13 21:10:42.927173: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:351] Ignored output_format. 2021-08-13 21:10:42.927204: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:354] Ignored drop_control_dependency. 24768
请注意，生成文件的大小约为原来的 1/3。
ls -lh {tflite_models_dir}
total 180K -rw-rw-r-- 1 kbuilder kbuilder 83K Aug 13 21:10 mnist_model.tflite -rw-rw-r-- 1 kbuilder kbuilder 24K Aug 13 21:09 mnist_model_quant.tflite -rw-rw-r-- 1 kbuilder kbuilder 25K Aug 13 21:10 mnist_model_quant_16x8.tflite -rw-rw-r-- 1 kbuilder kbuilder 44K Aug 13 21:08 mnist_model_quant_f16.tflite
运行 TensorFlow Lite 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()
interpreter_16x8 = tf.lite.Interpreter(model_path=str(tflite_model_16x8_file))
interpreter_16x8.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)
test_image = np.expand_dims(test_images[0], axis=0).astype(np.float32)
input_index = interpreter_16x8.get_input_details()[0]["index"]
output_index = interpreter_16x8.get_output_details()[0]["index"]
interpreter_16x8.set_tensor(input_index, test_image)
interpreter_16x8.invoke()
predictions = interpreter_16x8.get_tensor(output_index)
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)
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))
0.9622
在 16x8 量化模型上重复评估：
# NOTE: This quantization mode is an experimental post-training mode,
# it does not have any optimized kernels implementations or
# specialized machine learning hardware accelerators. Therefore,
# it could be slower than the float interpreter.
print(evaluate_model(interpreter_16x8))
0.9625
在此示例中，您已将模型量化为 16x8 模型，准确率没有任何差异，但文件大小只有原来的 1/3。