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首页 > 解决方案 > Default MaxPoolingOp only support NHWC on device type CPU [[{{node maxpool4/MaxPool}}]] 是什么意思？

python - Default MaxPoolingOp only support NHWC on device type CPU [[{{node maxpool4/MaxPool}}]] 是什么意思？

问题描述

我正在尝试编写在 python 中的 matlab 中找到的squeezenet CNN。在我尝试这样做时，我遇到了一个错误。我在 github 上找到了此代码的灵感，并将链接此人以确保他们获得应得的荣誉。 https://github.com/chasingbob/squeezenet-keras

```
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-21-faad1ec2e17b> in <module>
 23             validation_data=val_data_gen,
 24             #nb_val_samples=21,
 ---> 25             callbacks=[checkpoint])
 
 in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, 
 validation_data, validation_steps, validation_freq, class_weight, 
 max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
 1294         shuffle=shuffle,
 1295         initial_epoch=initial_epoch,
 ->1296         steps_name='steps_per_epoch')
 1297 
 1298   def evaluate_generator(self,

 in model_iteration(model, data, steps_per_epoch, epochs, verbose, callbacks, 
 validation_data, validation_steps, validation_freq, class_weight, 
 max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, 
 batch_size, steps_name, **kwargs)
 263 
 264       is_deferred = not model._is_compiled
 -->  265       batch_outs = batch_function(*batch_data)
 266       if not isinstance(batch_outs, list):
 267         batch_outs = [batch_outs]

 in train_on_batch(self, x, y, sample_weight, class_weight, reset_metrics)
 1015       self._update_sample_weight_modes(sample_weights=sample_weights)
 1016       self._make_train_function()
 -> 1017       outputs = self.train_function(ins)  # pylint: disable=not- 
 callable
 1018 
 1019     if reset_metrics:

 in __call__(self, inputs)
 3471         feed_symbols != self._feed_symbols or self.fetches != 
 self._fetches or
 3472         session != self._session):
 -> 3473       self._make_callable(feed_arrays, feed_symbols, symbol_vals, session)
 3474 
 3475     fetched = self._callable_fn(*array_vals,

 in _make_callable(self, feed_arrays, feed_symbols, symbol_vals, session)
 3408       callable_opts.run_options.CopyFrom(self.run_options)
 3409     # Create callable.
 -> 3410     callable_fn = session._make_callable_from_options(callable_opts)
 3411     # Cache parameters corresponding to the generated callable, so that
 3412     # we can detect future mismatches and refresh the callable.

 in _make_callable_from_options(self, callable_options)
 1503     """
 1504     self._extend_graph()
 -> 1505     return BaseSession._Callable(self, callable_options)
 1506 
 1507 

 in __init__(self, session, callable_options)
 1458       try:
 1459         self._handle = tf_session.TF_SessionMakeCallable(
 -> 1460             session._session, options_ptr)
 1461       finally:
 1462         tf_session.TF_DeleteBuffer(options_ptr)

 InvalidArgumentError: Default MaxPoolingOp only supports NHWC on device type 
 CPU
 [[{{node maxpool4/MaxPool}}]]

这是代码

batch_size = 10
epochs = 18
IMG_HEIGHT = 227
IMG_WIDTH = 227

train_image_generator = ImageDataGenerator(rescale=1./255) # Generator for our 
training data
validation_image_generator = ImageDataGenerator(rescale=1./255) # Generator for 
our validation data

train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,
                                                           directory=train_dir,
                                                           shuffle=False,
                                                           target_size=(IMG_HEIGHT, IMG_WIDTH),
                                                           class_mode='categorical')



val_data_gen = validation_image_generator.flow_from_directory(batch_size=batch_size,
                                                          directory=validation_dir,
                                                          target_size=(IMG_HEIGHT, IMG_WIDTH),
                                                          class_mode='categorical')


def SqueezeNet(nb_classes, inputs=(227,227,3)):
    # Keras Implementation of SqueezeNet(arXiv 1602.07360)
    #Arguments:
        #nb_classes: total number of final categories
        
        #inputs -- shape of the input images (channel, cols, rows)
    
    input_img = Input(shape=(227,227,3))
    conv1 = Convolution2D(
        96, 7, 7, activation='relu', kernel_initializer='glorot_uniform',#strides=(2,2),
         padding='same', name='conv1')(input_img)
    
    maxpool1 = MaxPooling2D(
        pool_size=(1, 1), strides=(2, 2), name='maxpool1')(conv1)

    fire2_squeeze = Convolution2D(
        16, 1, 1, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire2_squeeze')(maxpool1)
    fire2_expand1 = Convolution2D(
        64, 3, 3, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire2_expand1')(fire2_squeeze)
    fire2_expand2 = Convolution2D(
        64, 3, 3, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire2_expand2')(fire2_squeeze)
    merge2 = concatenate(
        [fire2_expand1, fire2_expand2], axis=1)

    fire3_squeeze = Convolution2D(
        16, 1, 1, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire3_squeeze')(merge2)
    fire3_expand1 = Convolution2D(
        64, 3, 3, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire3_expand1')(fire3_squeeze)
    fire3_expand2 = Convolution2D(
        64, 3, 3, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire3_expand2')(fire3_squeeze)
    merge3 = concatenate(
        [fire3_expand1, fire3_expand2], axis=1)

    fire4_squeeze = Convolution2D(
        32, 1, 1, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire4_squeeze')(merge3)
    fire4_expand1 = Convolution2D(
        128, 3, 3, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire4_expand1')(fire4_squeeze)
    fire4_expand2 = Convolution2D(
        128, 3, 3, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire4_expand2')(fire4_squeeze)
    merge4 = concatenate(
        [fire4_expand1, fire4_expand2], axis=1)
    maxpool4 = MaxPooling2D(
        pool_size=(1, 1), strides=(2, 2), name='maxpool4',data_format = 'channels_first')(merge4) 

    fire5_squeeze = Convolution2D(
        32, 1, 1, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire5_squeeze')(maxpool4)
    fire5_expand1 = Convolution2D(
        128, 3, 3, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire5_expand1')(fire5_squeeze)
    fire5_expand2 = Convolution2D(
        128, 3, 3, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire5_expand2')(fire5_squeeze)
    merge5 = concatenate(
        [fire5_expand1, fire5_expand2], axis=1)

    fire6_squeeze = Convolution2D(
        48, 1, 1, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire6_squeeze')(merge5)
    fire6_expand1 = Convolution2D(
        192, 3, 3, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire6_expand1')(fire6_squeeze)
    fire6_expand2 = Convolution2D(
        192, 3, 3, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire6_expand2')(fire6_squeeze)
    merge6 = concatenate(
        [fire6_expand1, fire6_expand2], axis=1)

    fire7_squeeze = Convolution2D(
        48, 1, 1, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire7_squeeze')(merge6)
    fire7_expand1 = Convolution2D(
        192, 3, 3, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire7_expand1')(fire7_squeeze)
    fire7_expand2 = Convolution2D(
        192, 3, 3, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire7_expand2')(fire7_squeeze)
    merge7 = concatenate(
        [fire7_expand1, fire7_expand2], axis=1)

    fire8_squeeze = Convolution2D(
        64, 1, 1, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire8_squeeze')(merge7)
    fire8_expand1 = Convolution2D(
        256, 3, 3, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire8_expand1')(fire8_squeeze)
    fire8_expand2 = Convolution2D(
        256, 3, 3, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire8_expand2')(fire8_squeeze)
    merge8 = concatenate(
        [fire8_expand1, fire8_expand2], axis=1)

    maxpool8 = MaxPooling2D(
        pool_size=(1, 1), strides=(2, 2), name='maxpool8',data_format = 'channels_first')(merge8)

    fire9_squeeze = Convolution2D(
        64, 1, 1, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire9_squeeze')(maxpool8)
    fire9_expand1 = Convolution2D(
        256, 3, 3, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire9_expand1')(fire9_squeeze)
    fire9_expand2 = Convolution2D(
        256, 3, 3, activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire9_expand2')(fire9_squeeze)
    merge9 = concatenate(
        [fire9_expand1, fire9_expand2], axis=1)

    fire9_dropout = Dropout(0.6, name='fire9_dropout')(merge9)
    conv10 = Convolution2D(
        nb_classes, 1, 1, kernel_initializer='glorot_uniform',
        padding='valid', name='conv10')(fire9_dropout)
    # The size should match the output of conv10
    avgpool10 = AveragePooling2D((1, 1), name='avgpool10')(conv10)

    flatten = Flatten(name='flatten')(avgpool10)
    softmax = Activation("softmax", name='softmax')(flatten)

    return Model(inputs=input_img, outputs=softmax)


from tensorflow.keras.optimizers import SGD
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
from tensorflow.keras.preprocessing.image import ImageDataGenerator
model = SqueezeNet(2,inputs=(227,227,3))

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
              #loss="mse",
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

batch_size = 32
nb_classes = 10
nb_epoch = 200

early_stopping = EarlyStopping(monitor='val_loss', patience=3, verbose=0)
checkpoint = ModelCheckpoint(                                         
                'weights.{epoch:02d}-{val_loss:.2f}.h5',
                monitor='val_loss',                               
                verbose=0,                                        
                save_best_only=True,                              
                save_weights_only=True,                           
                mode='min',                                       
                period=1)                                         


#model = CustomModel(inputs, outputs)
tf.config.threading.set_intra_op_parallelism_threads(2)
tf.config.threading.set_inter_op_parallelism_threads(2)
model.fit_generator(
            train_data_gen,
            #samples_per_epoch=10,
            #epoch=18,
            validation_data=val_data_gen,
            #nb_val_samples=21, 
            callbacks=[checkpoint])

标签： pythontensorflowscikit-learnconv-neural-network

解决方案

为了社区的利益，在这里发布答案。

将 Tensorflow 版本升级到2.3解决了这个问题。

您可以使用以下行来升级 Tensorflow 版本。

pip install --user --upgrade tensorflow

此外，您还可以在代码开头尝试以下行。

--device=cpu --data_format=NHWC

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