为什么KerasLambda层会导致Mask_RCNN出现问题？

我使用的是从这个回购协议Mask_RCNN包：https://github.com/matterport/Mask_RCNN。

我尝试使用这个包训练我自己的数据集，但它在开始时给了我一个错误。

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.

我查找了负责该问题的代码部分（位于file: /mrcnn/model.py line: 1935repo 中）：
IN[0]: anchors = KL.Lambda(lambda x: tf.Variable(anchors), name="anchors")(input_image)

如果有人知道如何解决它或已经解决了它，请提及解决方案。

回答

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

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

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