Mask_rcnn: AttributeError: 'ParallelModel' object has no attribute '_is_graph_network'

[linquanxu]

hi, when I run 'python3 parallel_model.py ', an error has happened.any ideas?

[zymale]

I had the same problem.Have you solved it?

[zymale]

hi, when I run 'python3 parallel_model.py ', an error has happened.any ideas?
The code is updated. Please look at Pull requests.My problem has been solved.

[keineahnung2345]

The problems happens to me when I was using keras 2.2.2, when I downgraded to keras 2.1.3, the problem runs away.

[linquanxu]

The problems happens to me when I was using keras 2.2.2, when I downgraded to keras 2.1.3, the problem runs away.

thanks,you are right!

[dcyoung]

I believe this was related to the parallel model's init function.
I was able to get Keras v2.2.2 working by changing the parallel model like so:

def make_parallel(keras_model, gpu_count):
    """Creates a new wrapper model that consists of multiple replicas of
    the original model placed on different GPUs.
    Args:
        keras_model: the input model to replicate on multiple gpus
        gpu_count: the number of replicas to build
    Returns:
        Multi-gpu model
    """
    # Slice inputs. Slice inputs on the CPU to avoid sending a copy
    # of the full inputs to all GPUs. Saves on bandwidth and memory.
    input_slices = {name: tf.split(x, gpu_count)
                    for name, x in zip(keras_model.input_names,
                                       keras_model.inputs)}

    output_names = keras_model.output_names
    outputs_all = []
    for i in range(len(keras_model.outputs)):
        outputs_all.append([])

    # Run the model call() on each GPU to place the ops there
    for i in range(gpu_count):
        with tf.device('/gpu:%d' % i):
            with tf.name_scope('tower_%d' % i):
                # Run a slice of inputs through this replica
                zipped_inputs = zip(keras_model.input_names,
                                    keras_model.inputs)
                inputs = [
                    KL.Lambda(lambda s: input_slices[name][i],
                              output_shape=lambda s: (None,) + s[1:])(tensor)
                    for name, tensor in zipped_inputs]
                # Create the model replica and get the outputs
                outputs = keras_model(inputs)
                if not isinstance(outputs, list):
                    outputs = [outputs]
                # Save the outputs for merging back together later
                for l, o in enumerate(outputs):
                    outputs_all[l].append(o)

    # Merge outputs on CPU
    with tf.device('/cpu:0'):
        merged = []
        for outputs, name in zip(outputs_all, output_names):
            # Concatenate or average outputs?
            # Outputs usually have a batch dimension and we concatenate
            # across it. If they don't, then the output is likely a loss
            # or a metric value that gets averaged across the batch.
            # Keras expects losses and metrics to be scalars.
            if K.int_shape(outputs[0]) == ():
                # Average
                m = KL.Lambda(lambda o: tf.add_n(
                    o) / len(outputs), name=name)(outputs)
            else:
                # Concatenate
                m = KL.Concatenate(axis=0, name=name)(outputs)
            merged.append(m)
    return merged


class ParallelModel(KM.Model):
    """Subclasses the standard Keras Model and adds multi-GPU support.
    It works by creating a copy of the model on each GPU. Then it slices
    the inputs and sends a slice to each copy of the model, and then
    merges the outputs together and applies the loss on the combined
    outputs.
    """

    def __init__(self, keras_model, gpu_count):
        """Class constructor.
        keras_model: The Keras model to parallelize
        gpu_count: Number of GPUs. Must be > 1
        """
        merged_outputs = make_parallel(
            keras_model=keras_model, gpu_count=gpu_count)
        super(ParallelModel, self).__init__(inputs=keras_model.inputs,
                                            outputs=merged_outputs)
        self.inner_model = keras_model

    def __getattribute__(self, attrname):
        """Redirect loading and saving methods to the inner model. That's where
        the weights are stored."""
        if 'load' in attrname or 'save' in attrname:
            return getattr(self.inner_model, attrname)
        return super(ParallelModel, self).__getattribute__(attrname)

    def summary(self, *args, **kwargs):
        """Override summary() to display summaries of both, the wrapper
        and inner models."""
        super(ParallelModel, self).summary(*args, **kwargs)
        self.inner_model.summary(*args, **kwargs)

[lunasdejavu]

I am using keras 2.2.4 tensorflow1.14.0
@dcyoung there is a new error that

    def __getattribute__(self, attrname):
        """Redirect loading and saving methods to the inner model. That's where
        the weights are stored."""
        if 'load' in attrname or 'save' in attrname:
            return getattr(self.inner_model, attrname)
        return super(ParallelModel, self).__getattribute__(attrname)

it showed

Traceback (most recent call last):
  File "lpr_train.py", line 283, in <module>
    layers='all',augmentation=augmentation)
  File "/data/workspace/willy_sung/Mask_RCNN/mrcnn/model.py", line 2369, in train
    use_multiprocessing=True,
  File "/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py", line 1418, in fit_generator
    initial_epoch=initial_epoch)
  File "/usr/local/lib/python3.6/dist-packages/keras/engine/training_generator.py", line 251, in fit_generator
    callbacks.on_epoch_end(epoch, epoch_logs)
  File "/usr/local/lib/python3.6/dist-packages/keras/callbacks.py", line 79, in on_epoch_end
    callback.on_epoch_end(epoch, logs)
  File "/usr/local/lib/python3.6/dist-packages/keras/callbacks.py", line 444, in on_epoch_end
    self.model.save_weights(filepath, overwrite=True)
  File "/data/workspace/willy_sung/Mask_RCNN/mrcnn/parallel_model.py", line 98, in __getattribute__
    return getattr(self.inner_model, attrname)
  File "/data/workspace/willy_sung/Mask_RCNN/mrcnn/parallel_model.py", line 99, in __getattribute__
    return super(ParallelModel, self).__getattribute__(attrname)
AttributeError: 'ParallelModel' object has no attribute 'inner_model'

it seems ttaht there is no innwe_model anymore
how to modify it