Default MaxPoolingOp only support NHWC on device type CPU [[node maxpool4/MaxPool]] 是啥意思？
Posted
技术标签:
【中文标题】Default MaxPoolingOp only support NHWC on device type CPU [[node maxpool4/MaxPool]] 是啥意思？【英文标题】：What does Default MaxPoolingOp only supports NHWC on device type CPU [[node maxpool4/MaxPool]] mean?Default MaxPoolingOp only support NHWC on device type CPU [[node maxpool4/MaxPool]] 是什么意思？ 【发布时间】：2020-12-10 00:04:10 【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]]
这里是代码
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])
【问题讨论】：
你可以试试--device=cpu --data_format=NHWC
我最终将 tensorflow 升级到 2.3
【参考方案1】：
为了社区的利益在此处发布答案。
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
【讨论】：
以上是关于Default MaxPoolingOp only support NHWC on device type CPU [[node maxpool4/MaxPool]] 是啥意思？的主要内容，如果未能解决你的问题，请参考以下文章
Incorrect table definition； there can be only one TIMESTAMP column with CURRENT_TIMESTAMP in DEFAULT
Error: Default interface methods are only supported starting with Android N (--min-api 24)类似问题解决(示例代
Error: Default interface methods are only supported starting with Android N (--min-api 24): java.io.
8D8: Default interface methods are only supported starting with Android N (--min-api 24): void(示例代码(
测试错误ERROR StatusLogger No log4j2 configuration file found. Using default configuration: logging only
Can’t import the named export ‘bypassFilter’ from non EcmaScript module (only default export is avai