tensorflow federated：使用自定义数据集和模型时出现类型错误
tensorflow federated : TypeError when using customized dataset and model
我正在按照教程 'Federated Learning for Image Classification' 进行操作，但使用的是我自己的数据集和 resnet50。当 运行 iterative_process.next.
时出现此错误
我认为这是由 tf.data.Dataset.from_generator 引起的
这是我的代码：
par1_train_data_dir = './par1/train'
par2_train_data_dir = './par2/train'
input_shape = (img_height, img_width, 3)
img_gen = ImageDataGenerator(preprocessing_function=preprocess_input)
ds_par1 = tf.data.Dataset.from_generator(
img_gen.flow_from_directory, args=[par1_train_data_dir,(img_height,img_width)],
output_types=(tf.float32, tf.float32),
output_shapes=([batch_size,img_height,img_width,3], [batch_size,num_classes])
)
ds_par2 = tf.data.Dataset.from_generator(
img_gen.flow_from_directory, args=[par2_train_data_dir,(img_height,img_width)],
output_types=(tf.float32, tf.float32),
output_shapes=([batch_size,img_height,img_width,3], [batch_size,num_classes])
)
dataset_dict={}
dataset_dict['1'] = ds_par1
dataset_dict['2'] = ds_par2
def create_tf_dataset_for_client_fn(client_id):
return dataset_dict[client_id]
train_data = tff.simulation.ClientData.from_clients_and_fn(['1','2'],create_tf_dataset_for_client_fn)
def make_federated_data(client_data, client_ids):
return [client_data.create_tf_dataset_for_client(x)
for x in client_ids]
federated_train_data = make_federated_data(train_data, train_data.client_ids)
images, labels = next(img_gen.flow_from_directory(par1_train_data_dir,batch_size=batch_size,target_size=(img_height,img_width)))
sample_batch = (images,labels)
def create_compiled_keras_model():
pretrain_model = tf.keras.applications.resnet.ResNet50(include_top=False, weights='imagenet',
input_tensor=tf.keras.layers.Input(shape=(img_height,
img_width, 3)), pooling=None)
Inp = Input((img_height, img_width, 3))
x = pretrain_model(Inp)
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(10, activation='softmax')(x)
model = Model(inputs=Inp, outputs=predictions,name='resnet50_transfer')
model.compile(
loss=tf.keras.losses.categorical_crossentropy,
optimizer=tf.keras.optimizers.SGD(learning_rate=0.02))
return model
def model_fn():
keras_model = create_compiled_keras_model()
return tff.learning.from_compiled_keras_model(keras_model, sample_batch)
iterative_process = tff.learning.build_federated_averaging_process(model_fn)
state = iterative_process.initialize()
NUM_ROUNDS = 11
for round_num in range(2, NUM_ROUNDS):
state, metrics = iterative_process.next(state, federated_train_data)
print('round {:2d}, metrics={}'.format(round_num, metrics))
我收到错误
InvalidArgumentError: TypeError: endswith first arg must be bytes or a tuple of bytes, not str
这里有更多信息
InvalidArgumentError Traceback (most recent call last)
<ipython-input-48-b01b66dc0dcd> in <module>
1 NUM_ROUNDS = 11
2 for round_num in range(2, NUM_ROUNDS):
----> 3 state, metrics = iterative_process.next(state, federated_train_data)
4 print('round {:2d}, metrics={}'.format(round_num, metrics))
~/miniconda3/lib/python3.6/site-packages/tensorflow_federated/python/core/impl/utils/function_utils.py in __call__(self, *args, **kwargs)
~/miniconda3/lib/python3.6/site-packages/tensorflow_federated/python/core/impl/utils/function_utils.py in pack_args(parameter_type, args, kwargs, context)
~/miniconda3/lib/python3.6/site-packages/tensorflow_federated/python/core/impl/utils/function_utils.py in pack_args_into_anonymous_tuple(args, kwargs, type_spec, context)
~/miniconda3/lib/python3.6/site-packages/tensorflow_federated/python/core/impl/reference_executor.py in ingest(self, arg, type_spec)
627 intrinsic_defs.FEDERATED_MEAN.uri:
628 self._federated_mean,
--> 629 intrinsic_defs.FEDERATED_BROADCAST.uri:
630 self._federated_broadcast,
631 intrinsic_defs.FEDERATED_COLLECT.uri:
~/miniconda3/lib/python3.6/site-packages/tensorflow_federated/python/core/impl/reference_executor.py in to_representation_for_type(value, type_spec, callable_handler)
239 else:
240 return [
--> 241 to_representation_for_type(v, type_spec.member, callable_handler)
242 for v in value
243 ]
~/miniconda3/lib/python3.6/site-packages/tensorflow_federated/python/core/impl/reference_executor.py in <listcomp>(.0)
239 else:
240 return [
--> 241 to_representation_for_type(v, type_spec.member, callable_handler)
242 for v in value
243 ]
~/miniconda3/lib/python3.6/site-packages/tensorflow_federated/python/core/impl/reference_executor.py in to_representation_for_type(value, type_spec, callable_handler)
198 if tf.executing_eagerly():
199 return [
--> 200 to_representation_for_type(v, type_spec.element, callable_handler)
201 for v in value
202 ]
~/miniconda3/lib/python3.6/site-packages/tensorflow_federated/python/core/impl/reference_executor.py in <listcomp>(.0)
197 if isinstance(value, tf.data.Dataset):
198 if tf.executing_eagerly():
--> 199 return [
200 to_representation_for_type(v, type_spec.element, callable_handler)
201 for v in value
~/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/data/ops/iterator_ops.py in __next__(self)
620
621 def __next__(self): # For Python 3 compatibility
--> 622 return self.next()
623
624 def _next_internal(self):
~/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/data/ops/iterator_ops.py in next(self)
664 """Returns a nested structure of `Tensor`s containing the next element."""
665 try:
--> 666 return self._next_internal()
667 except errors.OutOfRangeError:
668 raise StopIteration
~/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/data/ops/iterator_ops.py in _next_internal(self)
649 self._iterator_resource,
650 output_types=self._flat_output_types,
--> 651 output_shapes=self._flat_output_shapes)
652
653 try:
~/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/ops/gen_dataset_ops.py in iterator_get_next_sync(iterator, output_types, output_shapes, name)
2671 _ctx = _context._context or _context.context()
2672 if _ctx is not None and _ctx._thread_local_data.is_eager:
-> 2673 try:
2674 _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
2675 _ctx._context_handle, _ctx._thread_local_data.device_name,
~/miniconda3/lib/python3.6/site-packages/six.py in raise_from(value, from_value)
InvalidArgumentError: TypeError: endswith first arg must be bytes or a tuple of bytes, not str
Traceback (most recent call last):
File "/root/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/data/ops/dataset_ops.py", line 464, in get_iterator
self._next_id += 1
KeyError: 2
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/root/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/ops/script_ops.py", line 221, in __call__
"""
File "/root/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/data/ops/dataset_ops.py", line 585, in generator_py_func
File "/root/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/data/ops/dataset_ops.py", line 466, in get_iterator
# NOTE(mrry): Explicitly create an array of `np.int64` because implicit
File "/root/miniconda3/lib/python3.6/site-packages/keras_preprocessing/image/image_data_generator.py", line 540, in flow_from_directory
interpolation=interpolation
File "/root/miniconda3/lib/python3.6/site-packages/keras_preprocessing/image/directory_iterator.py", line 126, in __init__
classes, filenames = res.get()
File "/root/miniconda3/lib/python3.6/multiprocessing/pool.py", line 644, in get
raise self._value
File "/root/miniconda3/lib/python3.6/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/root/miniconda3/lib/python3.6/site-packages/keras_preprocessing/image/utils.py", line 216, in _list_valid_filenames_in_directory
for root, fname in valid_files:
File "/root/miniconda3/lib/python3.6/site-packages/keras_preprocessing/image/utils.py", line 172, in _iter_valid_files
if fname.lower().endswith('.tiff'):
TypeError: endswith first arg must be bytes or a tuple of bytes, not str
[[{{node PyFunc}}]] [Op:IteratorGetNextSync]
我的环境
tensorboard==1.15.0
tensorcache==0.4.2
tensorflow==1.15.2
tensorflow-addons==0.6.0
tensorflow-estimator==1.15.1
tensorflow-federated==0.4.0
更新
我已经升级了tf==2.1.0和tff==0.12.0，错误消失了，但我又遇到了另一个错误。
似乎生成器到达最后一批，与输入形状不匹配。
但是ImageDataGenerator不需要设置drop_remainder。我的代码有什么问题吗？
InvalidArgumentError: ValueError: `generator` yielded an element of shape (50, 224, 224, 3) where an element of shape (64, 224, 224, 3) was expected.
Traceback (most recent call last):
File "/root/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/ops/script_ops.py", line 236, in __call__
ret = func(*args)
File "/root/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/data/ops/dataset_ops.py", line 825, in generator_py_func
"of shape %s was expected." % (ret_array.shape, expected_shape))
ValueError: `generator` yielded an element of shape (50, 224, 224, 3) where an element of shape (64, 224, 224, 3) was expected.
[[{{node PyFunc}}]]
[[import/StatefulPartitionedCall_1/ReduceDataset]] [Op:__inference_wrapped_function_277930]
Function call stack:
wrapped_function
TensorFlow Federated 版本 0.4.0 仅经过测试可与 TensorFlow 版本 1.13.1 一起使用（请参阅 TensorFlow Federated Compatibility table）。是否可以升级到最新的 0.12.0 版本的 TensorFlow Federated？
更新：
我相信您的分析是正确的，该代码正在设置来自生成器的数据集，该生成器预计会准确地生成 batch_size 批次，但从 img_gen.flow_from_directory 生成器接收到不同大小的批次.
在设置数据集期间，为批大小传递 None 以指示批大小可能是可变的可能有效。
具体来说，更改这些行：
... = tf.data.Dataset.from_generator(
img_gen.flow_from_directory, args=[par1_train_data_dir,(img_height,img_width)],
output_types=(tf.float32, tf.float32),
output_shapes=([batch_size,img_height,img_width,3], [batch_size,num_classes])
)
收件人：
... = tf.data.Dataset.from_generator(
img_gen.flow_from_directory, args=[par1_train_data_dir,(img_height,img_width)],
output_types=(tf.float32, tf.float32),
output_shapes=([None,img_height,img_width,3], [None,num_classes])
)
我正在按照教程 'Federated Learning for Image Classification' 进行操作，但使用的是我自己的数据集和 resnet50。当 运行 iterative_process.next.
我认为这是由 tf.data.Dataset.from_generator 引起的
这是我的代码：
par1_train_data_dir = './par1/train'
par2_train_data_dir = './par2/train'
input_shape = (img_height, img_width, 3)
img_gen = ImageDataGenerator(preprocessing_function=preprocess_input)
ds_par1 = tf.data.Dataset.from_generator(
img_gen.flow_from_directory, args=[par1_train_data_dir,(img_height,img_width)],
output_types=(tf.float32, tf.float32),
output_shapes=([batch_size,img_height,img_width,3], [batch_size,num_classes])
)
ds_par2 = tf.data.Dataset.from_generator(
img_gen.flow_from_directory, args=[par2_train_data_dir,(img_height,img_width)],
output_types=(tf.float32, tf.float32),
output_shapes=([batch_size,img_height,img_width,3], [batch_size,num_classes])
)
dataset_dict={}
dataset_dict['1'] = ds_par1
dataset_dict['2'] = ds_par2
def create_tf_dataset_for_client_fn(client_id):
return dataset_dict[client_id]
train_data = tff.simulation.ClientData.from_clients_and_fn(['1','2'],create_tf_dataset_for_client_fn)
def make_federated_data(client_data, client_ids):
return [client_data.create_tf_dataset_for_client(x)
for x in client_ids]
federated_train_data = make_federated_data(train_data, train_data.client_ids)
images, labels = next(img_gen.flow_from_directory(par1_train_data_dir,batch_size=batch_size,target_size=(img_height,img_width)))
sample_batch = (images,labels)
def create_compiled_keras_model():
pretrain_model = tf.keras.applications.resnet.ResNet50(include_top=False, weights='imagenet',
input_tensor=tf.keras.layers.Input(shape=(img_height,
img_width, 3)), pooling=None)
Inp = Input((img_height, img_width, 3))
x = pretrain_model(Inp)
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(10, activation='softmax')(x)
model = Model(inputs=Inp, outputs=predictions,name='resnet50_transfer')
model.compile(
loss=tf.keras.losses.categorical_crossentropy,
optimizer=tf.keras.optimizers.SGD(learning_rate=0.02))
return model
def model_fn():
keras_model = create_compiled_keras_model()
return tff.learning.from_compiled_keras_model(keras_model, sample_batch)
iterative_process = tff.learning.build_federated_averaging_process(model_fn)
state = iterative_process.initialize()
NUM_ROUNDS = 11
for round_num in range(2, NUM_ROUNDS):
state, metrics = iterative_process.next(state, federated_train_data)
print('round {:2d}, metrics={}'.format(round_num, metrics))
我收到错误
InvalidArgumentError: TypeError: endswith first arg must be bytes or a tuple of bytes, not str
这里有更多信息
InvalidArgumentError Traceback (most recent call last)
<ipython-input-48-b01b66dc0dcd> in <module>
1 NUM_ROUNDS = 11
2 for round_num in range(2, NUM_ROUNDS):
----> 3 state, metrics = iterative_process.next(state, federated_train_data)
4 print('round {:2d}, metrics={}'.format(round_num, metrics))
~/miniconda3/lib/python3.6/site-packages/tensorflow_federated/python/core/impl/utils/function_utils.py in __call__(self, *args, **kwargs)
~/miniconda3/lib/python3.6/site-packages/tensorflow_federated/python/core/impl/utils/function_utils.py in pack_args(parameter_type, args, kwargs, context)
~/miniconda3/lib/python3.6/site-packages/tensorflow_federated/python/core/impl/utils/function_utils.py in pack_args_into_anonymous_tuple(args, kwargs, type_spec, context)
~/miniconda3/lib/python3.6/site-packages/tensorflow_federated/python/core/impl/reference_executor.py in ingest(self, arg, type_spec)
627 intrinsic_defs.FEDERATED_MEAN.uri:
628 self._federated_mean,
--> 629 intrinsic_defs.FEDERATED_BROADCAST.uri:
630 self._federated_broadcast,
631 intrinsic_defs.FEDERATED_COLLECT.uri:
~/miniconda3/lib/python3.6/site-packages/tensorflow_federated/python/core/impl/reference_executor.py in to_representation_for_type(value, type_spec, callable_handler)
239 else:
240 return [
--> 241 to_representation_for_type(v, type_spec.member, callable_handler)
242 for v in value
243 ]
~/miniconda3/lib/python3.6/site-packages/tensorflow_federated/python/core/impl/reference_executor.py in <listcomp>(.0)
239 else:
240 return [
--> 241 to_representation_for_type(v, type_spec.member, callable_handler)
242 for v in value
243 ]
~/miniconda3/lib/python3.6/site-packages/tensorflow_federated/python/core/impl/reference_executor.py in to_representation_for_type(value, type_spec, callable_handler)
198 if tf.executing_eagerly():
199 return [
--> 200 to_representation_for_type(v, type_spec.element, callable_handler)
201 for v in value
202 ]
~/miniconda3/lib/python3.6/site-packages/tensorflow_federated/python/core/impl/reference_executor.py in <listcomp>(.0)
197 if isinstance(value, tf.data.Dataset):
198 if tf.executing_eagerly():
--> 199 return [
200 to_representation_for_type(v, type_spec.element, callable_handler)
201 for v in value
~/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/data/ops/iterator_ops.py in __next__(self)
620
621 def __next__(self): # For Python 3 compatibility
--> 622 return self.next()
623
624 def _next_internal(self):
~/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/data/ops/iterator_ops.py in next(self)
664 """Returns a nested structure of `Tensor`s containing the next element."""
665 try:
--> 666 return self._next_internal()
667 except errors.OutOfRangeError:
668 raise StopIteration
~/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/data/ops/iterator_ops.py in _next_internal(self)
649 self._iterator_resource,
650 output_types=self._flat_output_types,
--> 651 output_shapes=self._flat_output_shapes)
652
653 try:
~/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/ops/gen_dataset_ops.py in iterator_get_next_sync(iterator, output_types, output_shapes, name)
2671 _ctx = _context._context or _context.context()
2672 if _ctx is not None and _ctx._thread_local_data.is_eager:
-> 2673 try:
2674 _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
2675 _ctx._context_handle, _ctx._thread_local_data.device_name,
~/miniconda3/lib/python3.6/site-packages/six.py in raise_from(value, from_value)
InvalidArgumentError: TypeError: endswith first arg must be bytes or a tuple of bytes, not str
Traceback (most recent call last):
File "/root/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/data/ops/dataset_ops.py", line 464, in get_iterator
self._next_id += 1
KeyError: 2
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/root/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/ops/script_ops.py", line 221, in __call__
"""
File "/root/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/data/ops/dataset_ops.py", line 585, in generator_py_func
File "/root/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/data/ops/dataset_ops.py", line 466, in get_iterator
# NOTE(mrry): Explicitly create an array of `np.int64` because implicit
File "/root/miniconda3/lib/python3.6/site-packages/keras_preprocessing/image/image_data_generator.py", line 540, in flow_from_directory
interpolation=interpolation
File "/root/miniconda3/lib/python3.6/site-packages/keras_preprocessing/image/directory_iterator.py", line 126, in __init__
classes, filenames = res.get()
File "/root/miniconda3/lib/python3.6/multiprocessing/pool.py", line 644, in get
raise self._value
File "/root/miniconda3/lib/python3.6/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/root/miniconda3/lib/python3.6/site-packages/keras_preprocessing/image/utils.py", line 216, in _list_valid_filenames_in_directory
for root, fname in valid_files:
File "/root/miniconda3/lib/python3.6/site-packages/keras_preprocessing/image/utils.py", line 172, in _iter_valid_files
if fname.lower().endswith('.tiff'):
TypeError: endswith first arg must be bytes or a tuple of bytes, not str
[[{{node PyFunc}}]] [Op:IteratorGetNextSync]
我的环境
tensorboard==1.15.0
tensorcache==0.4.2
tensorflow==1.15.2
tensorflow-addons==0.6.0
tensorflow-estimator==1.15.1
tensorflow-federated==0.4.0
更新
我已经升级了tf==2.1.0和tff==0.12.0，错误消失了，但我又遇到了另一个错误。
似乎生成器到达最后一批，与输入形状不匹配。
但是ImageDataGenerator不需要设置drop_remainder。我的代码有什么问题吗？
InvalidArgumentError: ValueError: `generator` yielded an element of shape (50, 224, 224, 3) where an element of shape (64, 224, 224, 3) was expected.
Traceback (most recent call last):
File "/root/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/ops/script_ops.py", line 236, in __call__
ret = func(*args)
File "/root/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/data/ops/dataset_ops.py", line 825, in generator_py_func
"of shape %s was expected." % (ret_array.shape, expected_shape))
ValueError: `generator` yielded an element of shape (50, 224, 224, 3) where an element of shape (64, 224, 224, 3) was expected.
[[{{node PyFunc}}]]
[[import/StatefulPartitionedCall_1/ReduceDataset]] [Op:__inference_wrapped_function_277930]
Function call stack:
wrapped_function
TensorFlow Federated 版本 0.4.0 仅经过测试可与 TensorFlow 版本 1.13.1 一起使用（请参阅 TensorFlow Federated Compatibility table）。是否可以升级到最新的 0.12.0 版本的 TensorFlow Federated？
更新：
我相信您的分析是正确的，该代码正在设置来自生成器的数据集，该生成器预计会准确地生成 batch_size 批次，但从 img_gen.flow_from_directory 生成器接收到不同大小的批次.
在设置数据集期间，为批大小传递 None 以指示批大小可能是可变的可能有效。
具体来说，更改这些行：
... = tf.data.Dataset.from_generator(
img_gen.flow_from_directory, args=[par1_train_data_dir,(img_height,img_width)],
output_types=(tf.float32, tf.float32),
output_shapes=([batch_size,img_height,img_width,3], [batch_size,num_classes])
)
收件人：
... = tf.data.Dataset.from_generator(
img_gen.flow_from_directory, args=[par1_train_data_dir,(img_height,img_width)],
output_types=(tf.float32, tf.float32),
output_shapes=([None,img_height,img_width,3], [None,num_classes])
)