Tensorflow无法量化重塑功能

如何解决Tensorflow无法量化重塑功能

我将训练我的模型量化意识。但是，当我使用它时，tensorflow_model_optimization无法量化tf.reshape函数，并引发错误。

tensorflow版本：'2.4.0-dev20200903'
python版本：3.6.9

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '3'
from tensorflow.keras.applications import VGG16
import tensorflow_model_optimization as tfmot
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
quantize_model = tfmot.quantization.keras.quantize_model
inputs = keras.Input(shape=(784,))
# img_inputs = keras.Input(shape=(32,32,3))

dense = layers.Dense(64,activation="relu")
x = dense(inputs)
x = layers.Dense(64,activation="relu")(x)
outputs = layers.Dense(10)(x)
outputs = tf.reshape(outputs,[-1,2,5])
model = keras.Model(inputs=inputs,outputs=outputs,name="mnist_model")

# keras.utils.plot_model(model,"my_first_model.png")


q_aware_model = quantize_model(model)

和输出：

Traceback (most recent call last):

  File "<ipython-input-39-af601b78c010>",line 14,in <module>
    q_aware_model = quantize_model(model)

  File "/home/essys/.local/lib/python3.6/site-packages/tensorflow_model_optimization/python/core/quantization/keras/quantize.py",line 137,in quantize_model
    annotated_model = quantize_annotate_model(to_quantize)

  File "/home/essys/.local/lib/python3.6/site-packages/tensorflow_model_optimization/python/core/quantization/keras/quantize.py",line 210,in quantize_annotate_model
    to_annotate,input_tensors=None,clone_function=_add_quant_wrapper)
...

  File "/home/essys/anaconda3/envs/tf_gpu/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py",line 667,in wrapper
    raise e.ag_error_Metadata.to_exception(e)

TypeError: in user code:


    TypeError: tf__call() got an unexpected keyword argument 'shape'

如果有人知道，请帮助？

解决方法

背后的原因是因为您的图层目前尚不支持QAT。如果要量化，则必须通过quantize_annotate_layer自行编写量化并通过quantize_scope传递，然后通过quantize_apply将其应用于模型，如此处所述：https://www.tensorflow.org/model_optimization/guide/quantization/training_comprehensive_guide?hl=en#quantize_custom_keras_layer

我已在here中创建了一个batch_norm_layer为例

针对QAT层的Tensorflow 2.x尚不完善，请考虑通过在运算符后添加FakeQuant来使用tf1.x。