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Replaced list comprehensions inside all/any/sum with generator expressions

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PiperOrigin-RevId: 222429778
This commit is contained in:
Sergei Lebedev 2018-11-21 10:40:10 -08:00 committed by TensorFlower Gardener
parent 4085979982
commit 95d7bbb2fc
56 changed files with 113 additions and 117 deletions
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2
configure.py
View File

@ -859,7 +859,7 @@ def set_tf_cuda_version(environ_cp):
cuda_toolkit_paths_full = [
os.path.join(cuda_toolkit_path, x) for x in cuda_rt_lib_paths
]
if any([os.path.exists(x) for x in cuda_toolkit_paths_full]):
if any(os.path.exists(x) for x in cuda_toolkit_paths_full):
break
# Reset and retry

2
tensorflow/compiler/tests/dense_layer_test.py
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@ -42,7 +42,7 @@ def GetRunMetadataLabels(run_metadata):
def InLabels(labels, substr):
"""Returns true iff one of the labels contains substr."""
return any([substr in x for x in labels])
return any(substr in x for x in labels)
class DenseLayerTest(test.TestCase):

2
tensorflow/compiler/tests/jit_test.py
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@ -75,7 +75,7 @@ def RunMetadataLabels(run_metadata):
def InLabels(labels, substr):
"""Returns true iff one of the labels contains substr."""
return any([substr in x for x in labels])
return any(substr in x for x in labels)
def MetadataHasXlaRunOp(run_metadata):

4
tensorflow/contrib/constrained_optimization/python/constrained_minimization_problem.py
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@ -74,8 +74,8 @@ class ConstrainedMinimizationProblem(object):
if (constraints_shape.ndims is None or
proxy_constraints_shape.ndims is None or
any([ii is None for ii in constraints_shape.as_list()]) or
any([ii is None for ii in proxy_constraints_shape.as_list()])):
any(ii is None for ii in constraints_shape.as_list()) or
any(ii is None for ii in proxy_constraints_shape.as_list())):
raise ValueError(
"constraints and proxy_constraints must have fully-known shapes")
if constraints_shape != proxy_constraints_shape:

6
tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_test.py
View File

@ -778,8 +778,7 @@ class CudnnParamsFormatConverterTest(TensorFlowTestCase,
# Test opaque_params size lower bound
opaque_params_size_v = sess.run(opaque_params_size)
min_params_size = (
np.sum([x.size for x in ws]) + np.sum([x.size for x in bs]))
min_params_size = sum(x.size for x in ws) + np.sum(x.size for x in bs)
logging.info("min_parm_size: %d vs actual_opaque_param_size: %d",
min_params_size, opaque_params_size_v)
self.assertLessEqual(min_params_size, opaque_params_size_v)
@ -853,8 +852,7 @@ class CudnnParamsFormatConverterTest(TensorFlowTestCase,
# Test opaque_params size lower bound
opaque_params_size_v = sess.run(opaque_params_size)
min_params_size = (
np.sum([x.size for x in ws]) + np.sum([x.size for x in bs]))
min_params_size = sum(x.size for x in ws) + sum(x.size for x in bs)
logging.info("min_parm_size: %d vs actual_opaque_param_size: %d",
min_params_size, opaque_params_size_v)
self.assertLessEqual(min_params_size, opaque_params_size_v)

4
tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py
View File

@ -1045,8 +1045,8 @@ class CudnnRNNTestParamsSize(test_util.TensorFlowTestCase):
# Min param size estimate = sum(weights.size) + sum(biases.size)
min_params_size = (
np.sum(list(map(np.prod, rnn.canonical_weight_shapes))) +
np.sum([sp[0] for sp in rnn.canonical_bias_shapes]))
sum(map(np.prod, rnn.canonical_weight_shapes)) +
sum(sp[0] for sp in rnn.canonical_bias_shapes))
opaque_params = rnn.trainable_variables[0]
with self.test_session(use_gpu=True, graph=ops.get_default_graph()):

2
tensorflow/contrib/distribute/python/minimize_loss_test.py
View File

@ -344,7 +344,7 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase):
run_step()
v = all_vars[0]
self.assertTrue(all([v is vi for vi in all_vars[1:]]))
self.assertTrue(all(v is vi for vi in all_vars[1:]))
weight = numpy.squeeze(self.evaluate(v))
# Our model is:
# predict = x * w

2
tensorflow/contrib/distribute/python/tpu_strategy.py
View File

@ -254,7 +254,7 @@ class TPUExtended(distribute_lib.DistributionStrategyExtended):
self, fn, multi_worker_iterator, iterations, initial_loop_values=None):
output_shapes = multi_worker_iterator.output_shapes
shapes = nest.flatten(output_shapes)
if any([not s.is_fully_defined() for s in shapes]):
if any(not s.is_fully_defined() for s in shapes):
raise ValueError(
"TPU currently requires fully defined shapes. Either use "
"set_shape() on the input tensors or use "

4
tensorflow/contrib/gan/python/losses/python/losses_impl.py
View File

@ -36,8 +36,6 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.framework.python.ops import variables as contrib_variables_lib
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_util
@ -817,7 +815,7 @@ def _numerically_stable_global_norm(tensor_list):
Returns:
A scalar tensor with the global norm.
"""
if np.all([x is None for x in tensor_list]):
if all(x is None for x in tensor_list):
return 0.0
list_max = math_ops.reduce_max([math_ops.reduce_max(math_ops.abs(x)) for x in

2
tensorflow/contrib/gan/python/train_test.py
View File

@ -759,7 +759,7 @@ class TensorPoolAdjusteModelTest(test.TestCase):
# For [pool_size, ?), the pool is full, tensor2 must be equal to some
# historical values of tensor1 (which is previously stored in the
# pool).
self.assertTrue(any([(v == t2).all() for v in history_values]))
self.assertTrue(any((v == t2).all() for v in history_values))
def _make_new_model_and_check(self, model, pool_size):
pool_fn = lambda x: random_tensor_pool.tensor_pool(x, pool_size=pool_size)

2
tensorflow/contrib/kernel_methods/python/kernel_estimators.py
View File

@ -90,7 +90,7 @@ def _update_features_and_columns(features, feature_columns,
mapped_column_name = column_name + "_MAPPED"
# Construct new feature columns based on provided kernel_mappers.
column_kernel_mappers = kernel_mappers_dict[feature_column]
new_dim = sum([mapper.output_dim for mapper in column_kernel_mappers])
new_dim = sum(mapper.output_dim for mapper in column_kernel_mappers)
mapped_columns.add(
layers.feature_column.real_valued_column(mapped_column_name, new_dim))

2
tensorflow/contrib/layers/python/layers/regularizers_test.py
View File

@ -141,7 +141,7 @@ class RegularizerTest(test.TestCase):
dummy_regularizer = lambda x: math_ops.reduce_sum(2 * x)
array_weights_list = [[1.5], [2, 3, 4.2], [10, 42, 666.6]]
tensor_weights_list = [constant_op.constant(x) for x in array_weights_list]
expected = sum([2 * x for l in array_weights_list for x in l])
expected = sum(2 * x for l in array_weights_list for x in l)
with self.cached_session():
result = regularizers.apply_regularization(dummy_regularizer,
tensor_weights_list)

4
tensorflow/contrib/learn/python/learn/estimators/dnn.py
View File

@ -150,10 +150,10 @@ def _dnn_model_fn(features, labels, mode, params, config=None):
"input_from_feature_columns",
values=tuple(six.itervalues(features)),
partitioner=input_layer_partitioner) as input_layer_scope:
if all([
if all(
isinstance(fc, feature_column._FeatureColumn) # pylint: disable=protected-access
for fc in feature_columns
]):
):
net = layers.input_from_feature_columns(
columns_to_tensors=features,
feature_columns=feature_columns,

8
tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py
View File

@ -236,10 +236,10 @@ def _dnn_linear_combined_model_fn(features, labels, mode, params, config=None):
"input_from_feature_columns",
values=tuple(six.itervalues(features)),
partitioner=input_layer_partitioner) as dnn_input_scope:
if all([
if all(
isinstance(fc, feature_column_lib._FeatureColumn) # pylint: disable=protected-access
for fc in dnn_feature_columns
]):
):
net = layers.input_from_feature_columns(
columns_to_tensors=features,
feature_columns=dnn_feature_columns,
@ -292,8 +292,8 @@ def _dnn_linear_combined_model_fn(features, labels, mode, params, config=None):
linear_parent_scope,
values=tuple(six.itervalues(features)),
partitioner=linear_partitioner) as scope:
if all([isinstance(fc, feature_column_lib._FeatureColumn) # pylint: disable=protected-access
for fc in linear_feature_columns]):
if all(isinstance(fc, feature_column_lib._FeatureColumn) # pylint: disable=protected-access
for fc in linear_feature_columns):
if joint_linear_weights:
linear_logits, _, _ = layers.joint_weighted_sum_from_feature_columns(
columns_to_tensors=features,

6
tensorflow/contrib/learn/python/learn/estimators/estimator.py
View File

@ -1066,11 +1066,11 @@ class BaseEstimator(sklearn.BaseEstimator, evaluable.Evaluable,
chief_hooks = []
if (self._config.save_checkpoints_secs or
self._config.save_checkpoints_steps):
saver_hook_exists = any([
saver_hook_exists = any(
isinstance(h, basic_session_run_hooks.CheckpointSaverHook)
for h in (all_hooks + model_fn_ops.training_hooks + chief_hooks +
model_fn_ops.training_chief_hooks)
])
)
if not saver_hook_exists:
chief_hooks = [
basic_session_run_hooks.CheckpointSaverHook(
@ -1493,7 +1493,7 @@ class Estimator(BaseEstimator):
# pylint: disable=protected-access
class SKCompat(sklearn.BaseEstimator):
"""Scikit learn wrapper for TensorFlow Learn Estimator.
THIS CLASS IS DEPRECATED. See
[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
for general migration instructions.

4
tensorflow/contrib/learn/python/learn/estimators/linear.py
View File

@ -155,8 +155,8 @@ def _linear_model_fn(features, labels, mode, params, config=None):
parent_scope,
values=tuple(six.itervalues(features)),
partitioner=partitioner) as scope:
if all([isinstance(fc, feature_column._FeatureColumn) # pylint: disable=protected-access
for fc in feature_columns]):
if all(isinstance(fc, feature_column._FeatureColumn) # pylint: disable=protected-access
for fc in feature_columns):
if joint_weights:
layer_fn = layers.joint_weighted_sum_from_feature_columns
else:

4
tensorflow/contrib/quantize/python/quantize.py
View File

@ -160,7 +160,7 @@ def Quantize(graph,
# shouldn't quantize it, since the activation will be Fused into the
# Add at inference time.
consumers = input_to_ops_map.ConsumerOperations(layer_match.bypass_op)
if any([consumer.type in _ACTIVATION_TYPES for consumer in consumers]):
if any(consumer.type in _ACTIVATION_TYPES for consumer in consumers):
logging.info('Skipping %s, because its followed by an activation.',
layer_match.bypass_op.name)
else:
@ -195,7 +195,7 @@ def Quantize(graph,
# Add at inference time.
consumers = input_to_ops_map.ConsumerOperations(
layer_match.post_activation_bypass_op)
if any([consumer.type in _RELU_TYPES for consumer in consumers]):
if any(consumer.type in _RELU_TYPES for consumer in consumers):
logging.info('Skipping %s, because its followed by an activation.',
layer_match.post_activation_bypass_op.name)
else:

4
tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py
View File

@ -154,8 +154,8 @@ class AttentionWrapperTest(test.TestCase):
if attention_layer_sizes is not None:
# Compute sum of attention_layer_sizes. Use encoder_output_depth if None.
attention_depth = sum([attention_layer_size or encoder_output_depth
for attention_layer_size in attention_layer_sizes])
attention_depth = sum(attention_layer_size or encoder_output_depth
for attention_layer_size in attention_layer_sizes)
elif attention_layers is not None:
# Compute sum of attention_layers output depth.
attention_depth = sum(

2
tensorflow/contrib/tpu/python/tpu/tpu.py
View File

@ -1111,7 +1111,7 @@ def validate_inference_rewrite_for_variables(graph):
Raises:
RuntimeError: if validation failed.
"""
if not any([x.type == "GuaranteeConst" for x in graph.get_operations()]):
if not any(x.type == "GuaranteeConst" for x in graph.get_operations()):
raise RuntimeError(
"No GuaranteeConst ops found in the graph after running "
"tpu.rewrite_for_inference(...). Please check that you are using "

2
tensorflow/python/autograph/utils/type_check.py
View File

@ -30,4 +30,4 @@ def is_tensor(*args):
Returns:
True if any *args are TensorFlow types, False if none are.
"""
return any([tensor_util.is_tensor(a) for a in args])
return any(tensor_util.is_tensor(a) for a in args)

2
tensorflow/python/data/experimental/kernel_tests/optimization/map_vectorization_test.py
View File

@ -459,7 +459,7 @@ class MapVectorizationBenchmark(test.Benchmark):
return median_time
def _compare(self, input_dataset, map_fn, batch_size, input_size, str_id):
num_elems = int(np.sum([np.prod(x) for x in input_size]))
num_elems = sum(np.prod(x) for x in input_size)
name_template = "{}__batch_size_{}_input_element_size_{}_{}"
unoptimized = input_dataset.map(map_fn).batch(batch_size)
unoptimized_op = unoptimized.make_one_shot_iterator().get_next()

6
tensorflow/python/data/experimental/kernel_tests/serialization/filter_dataset_serialization_test.py
View File

@ -17,8 +17,6 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.data.experimental.kernel_tests.serialization import dataset_serialization_test_base
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.framework import sparse_tensor
@ -35,7 +33,7 @@ class FilterDatasetSerializationTest(
def testFilterCore(self):
div = 3
num_outputs = np.sum([x % 3 != 2 for x in range(100)])
num_outputs = sum(x % 3 != 2 for x in range(100))
self.run_core_tests(lambda: self._build_filter_range_graph(div),
lambda: self._build_filter_range_graph(div * 2),
num_outputs)
@ -47,7 +45,7 @@ class FilterDatasetSerializationTest(
lambda d: d["foo"] + d["bar"])
def testFilterDictCore(self):
num_outputs = np.sum([(x**2) % 2 == 0 for x in range(10)])
num_outputs = sum((x**2) % 2 == 0 for x in range(10))
self.run_core_tests(self._build_filter_dict_graph, None, num_outputs)
def _build_sparse_filter(self):

4
tensorflow/python/data/experimental/ops/parsing_ops.py
View File

@ -138,10 +138,10 @@ def parse_example_dataset(features, num_parallel_calls=1):
def _apply_fn(dataset):
"""Function from `Dataset` to `Dataset` that applies the transformation."""
out_dataset = _ParseExampleDataset(dataset, features, num_parallel_calls)
if any([
if any(
isinstance(feature, parsing_ops.SparseFeature)
for _, feature in features.items()
]):
):
# pylint: disable=protected-access
# pylint: disable=g-long-lambda
out_dataset = out_dataset.map(

2
tensorflow/python/data/kernel_tests/dataset_ops_test.py
View File

@ -42,7 +42,7 @@ class DatasetOpsTest(test_base.DatasetTestBase, parameterized.TestCase):
with self.cached_session() as sess:
graph = graph_pb2.GraphDef().FromString(
sess.run(dataset._as_serialized_graph()))
self.assertTrue(any([node.op != "RangeDataset" for node in graph.node]))
self.assertTrue(any(node.op != "RangeDataset" for node in graph.node))
@staticmethod
def make_apply_fn(dataset):

2
tensorflow/python/data/util/sparse.py
View File

@ -34,7 +34,7 @@ def any_sparse(classes):
Returns:
`True` if `classes` contains a sparse tensor type and `False` otherwise.
"""
return any([c is sparse_tensor.SparseTensor for c in nest.flatten(classes)])
return any(c is sparse_tensor.SparseTensor for c in nest.flatten(classes))
def as_dense_shapes(shapes, classes):

16
tensorflow/python/distribute/cross_device_ops.py
View File

@ -103,10 +103,10 @@ def _validate_value_destination_pairs(value_destination_pairs):
# pylint: disable=g-missing-docstring
if not value_destination_pairs: return False
if not isinstance(value_destination_pairs, (list, tuple)): return False
if not all([isinstance(pair, tuple) for pair in value_destination_pairs]):
if not all(isinstance(pair, tuple) for pair in value_destination_pairs):
return False
if not all([isinstance(v[0], value_lib.PerReplica)
for v in value_destination_pairs]):
if not all(isinstance(v[0], value_lib.PerReplica)
for v in value_destination_pairs):
return False
return True
@ -132,10 +132,10 @@ def _devices_match(left, right):
def _all_devices_match(value_destination_pairs):
if not all([_devices_match(v, d) for v, d in value_destination_pairs]):
if not all(_devices_match(v, d) for v, d in value_destination_pairs):
return False
if not all([_devices_match(v, value_destination_pairs[0][0])
for v, _ in value_destination_pairs[1:]]):
if not all(_devices_match(v, value_destination_pairs[0][0])
for v, _ in value_destination_pairs[1:]):
return False
return True
@ -401,7 +401,7 @@ class ConcatAndSplitPacker(object):
# all gradient shapes are defined, we use another method to get the
# total size.
# TODO(yuefengz): move this logic to array_ops.size.
if all([g.shape.is_fully_defined() for g, _ in device_grads_and_vars]):
if all(g.shape.is_fully_defined() for g, _ in device_grads_and_vars):
total_grad_size = sum(
[g.shape.num_elements() for g, _ in device_grads_and_vars])
else:
@ -941,7 +941,7 @@ def choose_the_best(devices, session_config=None):
"TensorFlow sessions.")
return ReductionToOneDeviceCrossDeviceOps()
if any([d.device_type.lower() != "gpu" for d in using_devices]):
if any(d.device_type.lower() != "gpu" for d in using_devices):
logging.warning("Not all devices in DistributionStrategy are visible to "
"TensorFlow session.")
return ReductionToOneDeviceCrossDeviceOps()

2
tensorflow/python/distribute/cross_device_utils.py
View File

@ -420,7 +420,7 @@ def sum_gradients_all_reduce(dev_prefixes, replica_grads, num_workers, alg,
Returns:
list of reduced tensors
"""
alg_contains_shuffle = any([n in alg for n in ['pscpu', 'psgpu']])
alg_contains_shuffle = any(n in alg for n in ['pscpu', 'psgpu'])
is_hierarchical = '/' in alg
if 'pscpu' in alg:
aux_devices = [prefix + '/cpu:0' for prefix in dev_prefixes]

6
tensorflow/python/eager/backprop.py
View File

@ -547,11 +547,11 @@ def _aggregate_grads(gradients):
if len(gradients) == 1:
return gradients[0]
if all([isinstance(g, ops.Tensor) for g in gradients]):
if all(isinstance(g, ops.Tensor) for g in gradients):
return gen_math_ops.add_n(gradients)
else:
assert all([isinstance(g, (ops.Tensor, ops.IndexedSlices))
for g in gradients])
assert all(isinstance(g, (ops.Tensor, ops.IndexedSlices))
for g in gradients)
indexed_slices_list = []
for grad in gradients:
# TODO(xpan): Support nested IndexedSlices and core IndexedSlices

4
tensorflow/python/eager/function.py
View File

@ -89,8 +89,8 @@ def _parse_func_attrs(attributes):
"""
attrs = {}
for key, value in attributes.items():
if not any([re.match(reg, key)
for reg in WHITELIST_FUNCTION_ATTRIBUTE_REGEX]):
if not any(re.match(reg, key)
for reg in WHITELIST_FUNCTION_ATTRIBUTE_REGEX):
raise ValueError("Attribute name is not whitelisted. "
"Whitelisted: prefix %s, got: %s" %
(WHITELIST_FUNCTION_ATTRIBUTE_REGEX, key))

4
tensorflow/python/framework/function.py
View File

@ -874,7 +874,7 @@ def func_graph_from_py_func(func, arg_names, arg_types, name=None,
# If func only returned one value, make it a tuple.
if not isinstance(outputs, (list, tuple)):
outputs = (outputs,)
if any([_ is None for _ in outputs]):
if any(_ is None for _ in outputs):
raise ValueError("Function %s can not return None." % name)
# Ensures each output is a Tensor in the function graph.
outputs = [ops.convert_to_tensor(t) for t in outputs]
@ -1190,7 +1190,7 @@ def get_extra_args():
def _type_list_to_str(types):
if any([_ not in _DTYPE_TO_STR for _ in types]):
if any(_ not in _DTYPE_TO_STR for _ in types):
raise ValueError("Unsupported dtypes: %s" % types)
return "".join([_DTYPE_TO_STR[_] for _ in types])

4
tensorflow/python/framework/meta_graph_test.py
View File

@ -600,11 +600,11 @@ class ScopedMetaGraphTest(test.TestCase):
with graph.as_default():
variables.Variable(initial_value=1.0, trainable=True)
self.assertTrue(
all([
all(
graph.get_collection(key)
for key in
[ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.TRAINABLE_VARIABLES]
]))
))
meta_graph.export_scoped_meta_graph(
filename=meta_graph_filename, graph=graph)

2
tensorflow/python/framework/op_def_library.py
View File

@ -570,7 +570,7 @@ class OpDefLibrary(object):
"than minimum length %d." %
(input_name, op_type_name, len(values), num_attr.minimum))
# All tensors must have the same base type.
if any([bt != base_types[0] for bt in base_types]):
if any(bt != base_types[0] for bt in base_types):
raise TypeError(
"All tensors passed to '%s' of '%s' Op "
"must have the same type." %

2
tensorflow/python/framework/test_util.py
View File

@ -1044,7 +1044,7 @@ def is_gpu_available(cuda_only=False, min_cuda_compute_capability=None):
return True
return False
except errors_impl.NotFoundError as e:
if not all([x in str(e) for x in ["CUDA", "not find"]]):
if not all(x in str(e) for x in ["CUDA", "not find"]):
raise e
else:
logging.error(str(e))

2
tensorflow/python/keras/backend.py
View File

@ -2325,7 +2325,7 @@ def concatenate(tensors, axis=-1):
else:
axis = 0
if py_all([is_sparse(x) for x in tensors]):
if py_all(is_sparse(x) for x in tensors):
return sparse_ops.sparse_concat(axis, tensors)
else:
return array_ops.concat([to_dense(x) for x in tensors], axis)

7
tensorflow/python/keras/engine/base_layer.py
View File

@ -768,7 +768,7 @@ class Layer(checkpointable.CheckpointableBase):
if context.executing_eagerly():
# Accept NumPy inputs by converting to Tensors when executing eagerly.
if all([isinstance(x, (np.ndarray, float, int)) for x in input_list]):
if all(isinstance(x, (np.ndarray, float, int)) for x in input_list):
inputs = nest.map_structure(ops.convert_to_tensor, inputs)
input_list = nest.flatten(inputs)
@ -1442,8 +1442,7 @@ class Layer(checkpointable.CheckpointableBase):
', but the layer isn\'t built. '
'You can build it manually via: `' + self.name +
'.build(batch_input_shape)`.')
weight_shapes = [w.shape.as_list() for w in self.weights]
return int(sum([np.prod(w) for w in weight_shapes]))
return int(sum(np.prod(w.shape.as_list()) for w in self.weights))
@property
def output_shape(self):
@ -1758,7 +1757,7 @@ def have_all_keras_metadata(iterable_or_element):
iterable = [iterable_or_element]
else:
iterable = nest.flatten(iterable_or_element)
return all([hasattr(x, '_keras_history') for x in iterable])
return all(hasattr(x, '_keras_history') for x in iterable)
def collect_previous_mask(input_tensors):

6
tensorflow/python/keras/engine/distributed_training_utils.py
View File

@ -383,7 +383,11 @@ def validate_inputs(x, y, distribution_strategy):
for i in [x, y]:
if isinstance(i, dataset_ops.Dataset):
shapes = nest.flatten(i.output_shapes)
if any([not s.is_fully_defined() for s in shapes]):
try:
s = next(s for s in shapes if not s.is_fully_defined())
except StopIteration:
continue
else:
raise ValueError(
'Using TPUs currently requires fully defined shapes. Either use '
'set_shape() on the input tensors or use '

4
tensorflow/python/keras/engine/network.py
View File

@ -428,8 +428,8 @@ class Network(base_layer.Layer):
@property
def stateful(self):
return any([(hasattr(layer, 'stateful') and layer.stateful)
for layer in self.layers])
return any((hasattr(layer, 'stateful') and layer.stateful)
for layer in self.layers)
def reset_states(self):
for layer in self.layers:

2
tensorflow/python/keras/engine/saving.py
View File

@ -917,7 +917,7 @@ def save_attributes_to_hdf5_group(group, name, data):
chunked_data = np.array_split(data_npy, num_chunks)
# This will never loop forever thanks to the test above.
while any([x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data]):
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data):
num_chunks += 1
chunked_data = np.array_split(data_npy, num_chunks)

6
tensorflow/python/keras/engine/training_utils.py
View File

@ -58,10 +58,10 @@ def _map_nested(data, func):
def _nested_all(data, cond_func):
"""Checks if all elements in a nested structure satisfy cond_func."""
if isinstance(data, (tuple, list)):
return all([_nested_all(nested_data, cond_func) for nested_data in data])
return all(_nested_all(nested_data, cond_func) for nested_data in data)
elif isinstance(data, dict):
return all(
[_nested_all(nested_data, cond_func) for nested_data in data.values()])
_nested_all(nested_data, cond_func) for nested_data in data.values())
else:
return cond_func(data)
@ -69,7 +69,7 @@ def _nested_all(data, cond_func):
def _nested_any(data, cond_func):
"""Checks if any nested_elements in a nested structure satisfy cond_func."""
if isinstance(data, (tuple, list)):
return any([_nested_any(nested_data, cond_func) for nested_data in data])
return any(_nested_any(nested_data, cond_func) for nested_data in data)
elif isinstance(data, dict):
return any(
[_nested_any(nested_data, cond_func) for nested_data in data.values()])

6
tensorflow/python/keras/layers/merge.py
View File

@ -212,7 +212,7 @@ class _Merge(Layer):
if len(mask) != len(inputs):
raise ValueError('The lists `inputs` and `mask` '
'should have the same length.')
if all([m is None for m in mask]):
if all(m is None for m in mask):
return None
masks = [array_ops.expand_dims(m, axis=0) for m in mask if m is not None]
return K.all(K.concatenate(masks, axis=0), axis=0, keepdims=False)
@ -378,7 +378,7 @@ class Concatenate(_Merge):
if not isinstance(input_shape, list) or len(input_shape) < 2:
raise ValueError('A `Concatenate` layer should be called '
'on a list of at least 2 inputs')
if all([shape is None for shape in input_shape]):
if all(shape is None for shape in input_shape):
return
reduced_inputs_shapes = [list(shape) for shape in input_shape]
shape_set = set()
@ -418,7 +418,7 @@ class Concatenate(_Merge):
if len(mask) != len(inputs):
raise ValueError('The lists `inputs` and `mask` '
'should have the same length.')
if all([m is None for m in mask]):
if all(m is None for m in mask):
return None
# Make a list of masks while making sure
# the dimensionality of each mask

2
tensorflow/python/keras/utils/layer_utils.py
View File

@ -77,7 +77,7 @@ def count_params(weights):
Returns:
The total number of scalars composing the weights
"""
return int(np.sum([np.prod(p.get_shape().as_list()) for p in set(weights)]))
return int(sum(np.prod(p.get_shape().as_list()) for p in set(weights)))
def print_summary(model, line_length=None, positions=None, print_fn=None):

6
tensorflow/python/kernel_tests/control_flow_ops_py_test.py
View File

@ -863,13 +863,13 @@ class ControlFlowTest(test.TestCase):
# Should just be [1, 1], but possibly a sparse representation
gv, gi = sess.run([grad.values, grad.indices], feed_dict={c: 1})
dense_gv = [
sum([y for (x, y) in zip(gi, gv) if x == i]) for i in range(2)
sum(y for (x, y) in zip(gi, gv) if x == i) for i in range(2)
]
self.assertAllEqual(dense_gv, [1.0, 1.0])
# Should be [0, 2], as the else forwards v1[1] twice
gv, gi = sess.run([grad.values, grad.indices], feed_dict={c: 3})
dense_gv = [
sum([y for (x, y) in zip(gi, gv) if x == i]) for i in range(2)
sum(y for (x, y) in zip(gi, gv) if x == i) for i in range(2)
]
self.assertAllEqual(dense_gv, [0.0, 2.0])
@ -2809,7 +2809,7 @@ class ControlFlowTest(test.TestCase):
self.assertAllClose([156.0, 400.0], sess.run(r, feed_dict=feed_dict))
name = "gradients/while/stopped_grad"
all_ops = x.graph.get_operations()
self.assertFalse(any([name in op.name for op in all_ops]))
self.assertFalse(any(name in op.name for op in all_ops))
@test_util.disable_control_flow_v2("b/117954949")
def testWhileGradGradFail(self):

2
tensorflow/python/ops/bitwise_ops_test.py
View File

@ -59,7 +59,7 @@ class BitwiseOpTest(test_util.TensorFlowTestCase):
2**31 - 1, 2**31, 2**32 - 1, 2**32, -2**32 + 1, -2**32,
-2**63 + 1, 2**63 - 1]
def count_bits(x):
return sum([bin(z).count("1") for z in six.iterbytes(x.tobytes())])
return sum(bin(z).count("1") for z in six.iterbytes(x.tobytes()))
for dtype in dtype_list:
with self.cached_session(use_gpu=True) as sess:
print("PopulationCount test: ", dtype)

12
tensorflow/python/ops/control_flow_ops.py
View File

@ -158,7 +158,7 @@ def Assert(condition, data, summarize=None, name=None):
with ops.name_scope(name, "Assert", [condition, data]) as name:
xs = ops.convert_n_to_tensor(data)
if all([x.dtype in {dtypes.string, dtypes.int32} for x in xs]):
if all(x.dtype in {dtypes.string, dtypes.int32} for x in xs):
# As a simple heuristic, we assume that string and int32 are
# on host to avoid the need to use cond. If it is not case,
# we will pay the price copying the tensor to host memory.
@ -457,19 +457,19 @@ def merge(inputs, name=None):
ValueError: If any of the inputs is None, or inputs are IndexedSlices and
some but not all have a dense_shape property.
"""
if any([inp is None for inp in inputs]):
if any(inp is None for inp in inputs):
raise ValueError("At least one of the merge inputs is None: %s" % inputs)
with ops.name_scope(name, "Merge", inputs) as name:
inputs = [
ops.internal_convert_to_tensor_or_indexed_slices(inp, as_ref=True)
for inp in inputs
]
if all([isinstance(v, ops.Tensor) for v in inputs]):
if all([v.dtype._is_ref_dtype for v in inputs]): # pylint: disable=protected-access
if all(isinstance(v, ops.Tensor) for v in inputs):
if all(v.dtype._is_ref_dtype for v in inputs): # pylint: disable=protected-access
return gen_control_flow_ops.ref_merge(inputs, name)
else:
return gen_control_flow_ops.merge(inputs, name)
elif all([isinstance(v, sparse_tensor.SparseTensor) for v in inputs]):
elif all(isinstance(v, sparse_tensor.SparseTensor) for v in inputs):
# Only handle the case when all inputs are SparseTensor.
values, _ = merge([inp.values for inp in inputs], name=name)
indices, chosen_index = gen_control_flow_ops.merge(
@ -557,7 +557,7 @@ def _SetShapeInvariants(input_vars, enter_vars, shapes):
if shapes is None:
return
flat_shapes = nest.flatten(shapes)
if not all([isinstance(s, tensor_shape.TensorShape) for s in flat_shapes]):
if not all(isinstance(s, tensor_shape.TensorShape) for s in flat_shapes):
raise ValueError("`shapes` must be a (possibly nested) list of shapes.")
# Check that the shapes of the inputs are less than the shape invariants,
# and set the shapes of `enter_vars` to the shape invariants.

6
tensorflow/python/ops/data_flow_ops.py
View File

@ -79,7 +79,7 @@ def _as_shape_list(shapes,
shapes = [shapes]
shapes = [tensor_shape.as_shape(shape) for shape in shapes]
if not unknown_dim_allowed:
if any([not shape.is_fully_defined() for shape in shapes]):
if any(not shape.is_fully_defined() for shape in shapes):
raise ValueError("All shapes must be fully defined: %s" % shapes)
if not unknown_rank_allowed:
if any([shape.dims is None for shape in shapes]):
@ -198,11 +198,11 @@ class QueueBase(object):
raise TypeError("A list of queues expected")
dtypes = queues[0].dtypes
if not all([dtypes == q.dtypes for q in queues[1:]]):
if not all(dtypes == q.dtypes for q in queues[1:]):
raise TypeError("Queues do not have matching component dtypes.")
names = queues[0].names
if not all([names == q.names for q in queues[1:]]):
if not all(names == q.names for q in queues[1:]):
raise TypeError("Queues do not have matching component names.")
queue_shapes = [q.shapes for q in queues]

8
tensorflow/python/ops/gradients_impl.py
View File

@ -895,7 +895,7 @@ def _HasAnyNotNoneGrads(grads, op):
if isinstance(out_grad, (ops.Tensor, ops.IndexedSlices)):
return True
if out_grad and isinstance(out_grad, collections.Sequence):
if any([g is not None for g in out_grad]):
if any(g is not None for g in out_grad):
return True
return False
@ -1110,11 +1110,11 @@ def _AggregatedGrads(grads,
assert control_flow_util.IsLoopSwitch(op)
continue
# Grads have to be Tensors or IndexedSlices
if (isinstance(out_grad, collections.Sequence) and not all([
if (isinstance(out_grad, collections.Sequence) and not all(
isinstance(g, (ops.Tensor, ops.IndexedSlices))
for g in out_grad
if g is not None
])):
)):
raise TypeError("gradients have to be either all Tensors "
"or all IndexedSlices")
# Aggregate multiple gradients, and convert [] to None.
@ -1122,7 +1122,7 @@ def _AggregatedGrads(grads,
if len(out_grad) < 2:
used = "nop"
out_grads[i] = out_grad[0]
elif all([isinstance(g, ops.Tensor) for g in out_grad if g is not None]):
elif all(isinstance(g, ops.Tensor) for g in out_grad if g is not None):
tensor_shape = _AccumulatorShape(out_grad)
if (aggregation_method == AggregationMethod.EXPERIMENTAL_ACCUMULATE_N
and len(out_grad) > 2 and tensor_shape.is_fully_defined()):

7
tensorflow/python/ops/parallel_for/pfor.py
View File

@ -1152,9 +1152,8 @@ class PFor(object):
continue
converted_inputs = [self._conversion_map[inp] for inp in y_op.inputs]
some_input_converted = any(
[self._was_converted(x) for x in y_op.inputs])
some_input_stacked = any([x.is_stacked for x in converted_inputs])
some_input_converted = any(self._was_converted(x) for x in y_op.inputs)
some_input_stacked = any(x.is_stacked for x in converted_inputs)
converted_control_ops = set()
some_control_input_converted = False
@ -1198,7 +1197,7 @@ class PFor(object):
# All inputs are unstacked or uncoverted but some control inputs are
# converted.
# TODO(rachelim): Handle the case where some inputs are sparsely
# stacked (i.e. any([x.is_sparse_stacked for x in converted_inputs]))
# stacked (i.e. any(x.is_sparse_stacked for x in converted_inputs))
new_op = _create_op(y_op.type, [x.t for x in converted_inputs],
[x.dtype for x in y_op.outputs],
y_op.node_def.attr)

2
tensorflow/python/ops/rnn.py
View File

@ -117,7 +117,7 @@ def _infer_state_dtype(explicit_dtype, state):
inferred_dtypes = [element.dtype for element in nest.flatten(state)]
if not inferred_dtypes:
raise ValueError("Unable to infer dtype from empty state.")
all_same = all([x == inferred_dtypes[0] for x in inferred_dtypes])
all_same = all(x == inferred_dtypes[0] for x in inferred_dtypes)
if not all_same:
raise ValueError(
"State has tensors of different inferred_dtypes. Unable to infer a "

2
tensorflow/python/ops/rnn_cell_impl.py
View File

@ -1456,7 +1456,7 @@ class MultiRNNCell(RNNCell):
if self._state_is_tuple:
return tuple(cell.state_size for cell in self._cells)
else:
return sum([cell.state_size for cell in self._cells])
return sum(cell.state_size for cell in self._cells)
@property
def output_size(self):

2
tensorflow/python/ops/variable_scope.py
View File

@ -680,7 +680,7 @@ class _VariableStore(object):
"Partitioner returned a partition list that does not match the "
"Variable's rank: %s vs. %s" % (partitions, shape))
if any([p < 1 for p in partitions]):
if any(p < 1 for p in partitions):
raise ValueError(
"Partitioner returned zero partitions for some axes: %s" %
partitions)

8
tensorflow/python/ops/variables.py
View File

@ -2482,21 +2482,21 @@ class PartitionedVariable(object):
"variable_list is not a list or tuple: %s" % variable_list)
if not isinstance(partitions, (list, tuple)):
raise TypeError("partitions is not a list or tuple: %s" % partitions)
if not all([p >= 1 for p in partitions]):
if not all(p >= 1 for p in partitions):
raise ValueError("partition values must be positive: %s" % partitions)
if not variable_list:
raise ValueError("variable_list may not be empty")
# pylint: disable=protected-access
for v in variable_list:
# Sort the variable_list lexicographically according to var offset value.
if not all([v._get_save_slice_info() is not None for v in variable_list]):
if not all(v._get_save_slice_info() is not None for v in variable_list):
raise ValueError(
"All variables must have a save_slice_info available: %s"
% [v.name for v in variable_list])
if len(shape) != len(partitions):
raise ValueError("len(shape) != len(partitions): %s vs. %s"
% (shape, partitions))
if not all([v._get_save_slice_info().full_shape == shape]):
if v._get_save_slice_info().full_shape != shape:
raise ValueError(
"All variables' full shapes must match shape: %s; "
"but full shapes were: %s"
@ -2523,7 +2523,7 @@ class PartitionedVariable(object):
return len(self._variable_list)
def _partition_axes(self):
if all([p == 1 for p in self._partitions]):
if all(p == 1 for p in self._partitions):
return [0]
else:
return [i for i, p in enumerate(self._partitions) if p > 1]

2
tensorflow/python/ops/while_v2.py
View File

@ -509,7 +509,7 @@ def _grad_fn(ys, xs, args, func_graph):
# TODO(b/118712257): Handle the case when grad_outs has None's e.g. when there
# is a tf.StopGradient in the loop body.
assert all([g is not None for g in grad_outs])
assert all(g is not None for g in grad_outs)
counter = args[0]
total_iters = args[1]
return [counter + 1, total_iters] + grad_outs

2
tensorflow/python/tools/inspect_checkpoint.py
View File

@ -63,7 +63,7 @@ def print_tensors_in_checkpoint_file(file_name, tensor_name, all_tensors,
print("It's likely that your checkpoint file has been compressed "
"with SNAPPY.")
if ("Data loss" in str(e) and
(any([e in file_name for e in [".index", ".meta", ".data"]]))):
any(e in file_name for e in [".index", ".meta", ".data"])):
proposed_file = ".".join(file_name.split(".")[0:-1])
v2_file_error_template = """
It's likely that this is a V2 checkpoint and you need to provide the filename

2
tensorflow/python/training/evaluation.py
View File

@ -230,7 +230,7 @@ def _evaluate_once(checkpoint_path,
hooks = list(hooks or [])
if eval_ops is not None:
if any([isinstance(h, _MultiStepStopAfterNEvalsHook) for h in hooks]):
if any(isinstance(h, _MultiStepStopAfterNEvalsHook) for h in hooks):
steps_per_run_variable = \
basic_session_run_hooks.get_or_create_steps_per_run_variable()
update_eval_step = state_ops.assign_add(

6
tensorflow/python/training/warm_starting_util.py
View File

@ -248,7 +248,7 @@ def _warm_start_var_with_vocab(var,
prev_tensor_name = _infer_var_name(var)
# TODO(eddz): Fix functionality for rank-1 Variables (like FC biases).
total_v_first_axis = sum([v.get_shape().as_list()[0] for v in var])
total_v_first_axis = sum(v.get_shape().as_list()[0] for v in var)
for v in var:
v_shape = v.get_shape().as_list()
slice_info = v._get_save_slice_info()
@ -333,12 +333,12 @@ def _get_grouped_variables(vars_to_warm_start):
ops.GraphKeys.TRAINABLE_VARIABLES,
scope=vars_to_warm_start)
elif isinstance(vars_to_warm_start, list):
if all([isinstance(v, str) for v in vars_to_warm_start]):
if all(isinstance(v, str) for v in vars_to_warm_start):
list_of_vars = []
for v in vars_to_warm_start:
list_of_vars += ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES,
scope=v)
elif all([checkpoint_utils._is_variable(v) for v in vars_to_warm_start]): # pylint: disable=protected-access
elif all(checkpoint_utils._is_variable(v) for v in vars_to_warm_start): # pylint: disable=protected-access
list_of_vars = vars_to_warm_start
else:
raise ValueError("If `vars_to_warm_start` is a list, it must be all "

4
tensorflow/tools/api/tests/api_compatibility_test.py
View File

@ -126,9 +126,9 @@ def _FilterNonCoreGoldenFiles(golden_file_list):
filtered_file_list = []
filtered_package_prefixes = ['tensorflow.%s.' % p for p in _NON_CORE_PACKAGES]
for f in golden_file_list:
if any([
if any(
f.rsplit('/')[-1].startswith(pre) for pre in filtered_package_prefixes
]):
):
continue
filtered_file_list.append(f)
return filtered_file_list