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在 tensorflow 2.3 中执行量化感知训练 (QAT) 时无法访问正则化损失值。

AttributeError：“张量”对象没有属性“numpy”

我创建了一个测试 mnist 模型，并使用 TFMOT QAT 库在模型中添加了量化节点。在尝试访问在model.lossestensorflow 示例中提到的收集到的正则化损失的值时，我收到了上述错误。当我在没有量化（QAT）的情况下运行相同的值时，可以很好地访问这些值。这可能是什么原因造成的？我正在使用功能模型并执行自定义训练操作。

重现问题的代码

import numpy as np
import tensorflow as tf
from tensorflow_model_optimization.python.core.quantization.keras import quantize
from tensorflow.python import keras
from tensorflow.python.keras.utils import losses_utils
l = keras.layers

tf.config.run_functions_eagerly(True)
physical_devices = tf.config.list_physical_devices('GPU')
for device in physical_devices:
    tf.config.experimental.set_memory_growth(device, True)

def functional_model():
  """Builds an MNIST functional model."""
  inp = keras.Input(image_input_shape())
  x = l.Conv2D(32, 5, padding='same', activation='relu', activity_regularizer=tf.keras.regularizers.l2(l=0.0001), kernel_regularizer=tf.keras.regularizers.l2(l=0.0001))(inp)
  x = l.MaxPooling2D((2, 2), (2, 2), padding='same')(x)
  # TODO(pulkitb): Add BatchNorm when transformations are ready.
  # x = l.BatchNormalization()(x)
  x = l.Conv2D(64, 5, padding='same', activation='relu', activity_regularizer=tf.keras.regularizers.l2(l=0.0001), kernel_regularizer=tf.keras.regularizers.l2(l=0.0001))(x)
  x = l.MaxPooling2D((2, 2), (2, 2), padding='same')(x)
  x = l.Flatten()(x)
  x = l.Dense(1024, activation='relu')(x)
  x = l.Dropout(0.4)(x)
  out = l.Dense(10, activation='softmax')(x)

  return keras.models.Model([inp], [out])


def image_input_shape(img_rows=28, img_cols=28):
  if tf.keras.backend.image_data_format() == 'channels_first':
    return 1, img_rows, img_cols
  else:
    return img_rows, img_cols, 1

def preprocessed_data(img_rows=28,
                      img_cols=28,
                      num_classes=10):
  """Get data for mnist training and evaluation."""
  (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

  if tf.keras.backend.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
  else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)

  x_train = x_train.astype('float32')
  x_test = x_test.astype('float32')
  x_train /= 255
  x_test /= 255

  # convert class vectors to binary class matrices
  y_train = tf.keras.utils.to_categorical(y_train, num_classes)
  y_test = tf.keras.utils.to_categorical(y_test, num_classes)

  return x_train, y_train, x_test, y_test

@tf.function
def trainStep(model, optimizer, images, labels):
    with tf.GradientTape() as tape:
        nnRunOutput = model(images, training=True)
        regTotalLoss = 0.0
        if model.losses:
            regLoss = losses_utils.cast_losses_to_common_dtype(model.losses)
            regTotalLoss = tf.math.add_n(regLoss)
        loss = tf.cast(tf.keras.losses.CategoricalCrossentropy(from_logits=True)(labels, nnRunOutput), dtype=tf.float32)
        totalLossTensor = tf.math.add_n([loss, regTotalLoss])
    gradients = tape.gradient(totalLossTensor, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    print(loss.numpy())
    print(totalLossTensor.numpy())
    print(tf.math.add_n(regTotalLoss).numpy())
    trainLossItems = [loss.numpy(), tf.math.add_n(regTotalLoss).numpy()]
    print(trainLossItems)


with tf.device('/device:GPU:' + "0"):
    model = functional_model() #sequential_model()
    model.summary()
    x_train, y_train, x_test, y_test = preprocessed_data()
    print("Quantizing model")

    quantized_model = quantize.quantize_model(model)
    print(quantized_model.losses)


    optimizer = tf.keras.optimizers.SGD()
    trainImgBatches = np.array_split(x_train, len(x_train)/500)
    trainLabelBatches = np.array_split(y_train, len(y_train)/500)
    for idx in range(0,len(trainImgBatches)):
        trainStep(quantized_model, optimizer, trainImgBatches[idx], trainLabelBatches[idx])

错误输出

Quantizing model
[<tf.Tensor 'conv2d/ActivityRegularizer_2/truediv:0' shape=() dtype=float32>, <tf.Tensor: shape=(), dtype=float32, numpy=0.00019313334>, <tf.Tensor 'conv2d_1/ActivityRegularizer_2/truediv:0' shape=() dtype=float32>, <tf.Tensor: shape=(), dtype=float32, numpy=0.0042751725>]
2021-09-28 12:39:32.366940: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2021-09-28 12:39:32.947998: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2.3025882
Traceback (most recent call last):
  File "/home/user/git/archive-code/tempTrain.py", line 219, in <module>
    trainStep(quantized_model, optimizer, trainImgBatches[idx], trainLabelBatches[idx])
  File "/home/user/TF2MainEnv/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py", line 761, in __call__
    return self._python_function(*args, **kwds)
  File "/home/user/git/archive-code/tempTrain.py", line 186, in trainStep
    print(totalLossTensor.numpy())
AttributeError: 'Tensor' object has no attribute 'numpy'

Process finished with exit code 1
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