为什么Keras Lambda层会导致问题Mask_RCNN？

oknwwptz 于 2022-11-30 发布在其他

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我正在使用此存储库中的Mask_RCNN包：https://github.com/matterport/Mask_RCNN .
我试着用这个包训练我自己的数据集，但是它一开始就给了我一个错误。

2020-11-30 12:13:16.577252: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
2020-11-30 12:13:16.587017: E tensorflow/stream_executor/cuda/cuda_driver.cc:314] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
2020-11-30 12:13:16.587075: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (7612ade969e5): /proc/driver/nvidia/version does not exist
2020-11-30 12:13:16.587479: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2020-11-30 12:13:16.593569: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 2300000000 Hz
2020-11-30 12:13:16.593811: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1b2aa00 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-11-30 12:13:16.593846: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
Traceback (most recent call last):
  File "machines.py", line 345, in <module>
    model_dir=args.logs)
  File "/content/Mask_RCNN/mrcnn/model.py", line 1837, in __init__
    self.keras_model = self.build(mode=mode, config=config)
  File "/content/Mask_RCNN/mrcnn/model.py", line 1934, in build
    anchors = KL.Lambda(lambda x: tf.Variable(anchors), name="anchors")(input_image)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py", line 926, in __call__
    input_list)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py", line 1117, in _functional_construction_call
    outputs = call_fn(cast_inputs, *args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/core.py", line 904, in call
    self._check_variables(created_variables, tape.watched_variables())
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/core.py", line 931, in _check_variables
    raise ValueError(error_str)
ValueError: 
The following Variables were created within a Lambda layer (anchors)
but are not tracked by said layer:
  <tf.Variable 'anchors/Variable:0' shape=(1, 261888, 4) dtype=float32>
The layer cannot safely ensure proper Variable reuse across multiple
calls, and consquently this behavior is disallowed for safety. Lambda
layers are not well suited to stateful computation; instead, writing a
subclassed Layer is the recommend way to define layers with
Variables.

我查找了导致该问题的代码部分（位于repo中的file: /mrcnn/model.pyline: 1935）：IN[0]: anchors = KL.Lambda(lambda x: tf.Variable(anchors), name="anchors")(input_image)
如果有人知道如何解决这个问题，或者已经解决了这个问题，请提出解决方案。

keras

来源：https://stackoverflow.com/questions/65073434/why-keras-lambda-layer-cause-problem-mask-rcnn

2条答案

按热度按时间

rta7y2nd1#

请访问mrcnn/model.py并添加：

class AnchorsLayer(KL.Layer):
    def __init__(self, anchors, name="anchors", **kwargs):
        super(AnchorsLayer, self).__init__(name=name, **kwargs)
        self.anchors = tf.Variable(anchors)

    def call(self, dummy):
        return self.anchors

    def get_config(self):
        config = super(AnchorsLayer, self).get_config()
        return config

然后找到行：

anchors = KL.Lambda(lambda x: tf.Variable(anchors), name="anchors")(input_image)

并将其替换为：

anchors = AnchorsLayer(anchors, name="anchors")(input_image)

在TF 2.4中工作起来就像一个魅力！

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55ooxyrt2#

根本原因：在Tensorflow 1.X中Keras的Lambda层的行为与Tensorflow 1.X中Keras的Lambda层的行为有所不同，所有tf.Variable和tf.get_variable都通过变量创建者上下文自动跟踪到layer.weights中，因此它们自动接收梯度和可训练。这种方法在自动图编译中存在问题，自动图编译将Python代码转换为Tensorflow 2中的执行图。X，所以它被删除了，现在Lambda层有代码来检查变量创建并引发错误，如你所见。简而言之，Tensorflow 2.X中的Lambda层必须是无状态的。如果你想创建变量，Tensorflow 2.X中的正确方法是将层类子类化，并将可训练权重添加为类成员。
解决方案：有两种选择-
1.更改为使用Tensorflow 1.X..不会引发此错误。
1.用Keras层的子类替换Lambda层：

class AnchorsLayer(tensorflow.keras.layers.Layer):

   def __init__(self, anchors):
     super(AnchorLayer, self).__init__()
     self.anchors_v = tf.Variable(anchors)
   
   def call(self):
     return self.anchors_v

# Then replace the Lambda call with this:
   
   anchors_layer = AnchorLayers(anchors)
   anchors = anchors_layer()

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为什么Keras Lambda层会导致问题Mask_RCNN？

2条答案

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