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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。

  1. 张量流版本：'2.4.0-dev20200903'
  2. 蟒蛇版本：3.6.9

编码：

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，请帮忙？

4

1 回答 1

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背后的原因是因为您的层目前还不支持 QAT。如果您想量化它，您必须通过 quantize_annotate_layer 自行编写量化并将其传递给 quantize_scope 并通过 quantize_apply 应用于您的模型，如下所述：https ://www.tensorflow.org/model_optimization/guide/quantization/training_comprehensive_guide ?hl=en#quantize_custom_keras_layer

我在这里创建了一个 batch_norm_layer作为示例

TensorFlow 2.x 对于 QAT 层不完整，请考虑使用 tf1.x，在操作符后添加 FakeQuant。

于 2020-09-10T06:51:05.017 回答