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Replace list comprehension with generator expressions. 

PiperOrigin-RevId: 285822581
Change-Id: I679256cc6f5890fa93ff3a2bfb9136b5d679d3ac
This commit is contained in:
A. Unique TensorFlower 2019-12-16 12:14:09 -08:00 committed by TensorFlower Gardener
parent 708e5729bb
commit a78fa541d8
69 changed files with 132 additions and 134 deletions
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2
tensorflow/python/compiler/tensorrt/test/tf_trt_integration_test_base.py
View File

@ -586,7 +586,7 @@ class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase):
trt_op_names.append(node.name)
# Remove the function name prefix.
def _Canonicalize(names):
return set([self._ToString(name.split("/")[-1]) for name in names])
return set(self._ToString(name.split("/")[-1]) for name in names)
all_op_names = _Canonicalize(all_op_names)
trt_op_names = _Canonicalize(trt_op_names)

12
tensorflow/python/compiler/tensorrt/trt_convert.py
View File

@ -253,8 +253,8 @@ def _check_trt_version_compatibility():
if loaded_version < linked_version:
tf_logging.error(
"Loaded TensorRT %s but linked TensorFlow against TensorRT %s. " %
(".".join([str(x) for x in loaded_version]),
".".join([str(x) for x in linked_version])) +
(".".join(str(x) for x in loaded_version), ".".join(
str(x) for x in linked_version)) +
"TensorRT does not support forward compatibility. " +
"It is also required to use the same major version of TensorRT " +
"during compilation and runtime.")
@ -262,16 +262,16 @@ def _check_trt_version_compatibility():
if loaded_version[0] > linked_version[0]:
tf_logging.error(
"Loaded TensorRT %s but linked TensorFlow against TensorRT %s. " %
(".".join([str(x) for x in loaded_version]),
".".join([str(x) for x in linked_version])) +
(".".join(str(x) for x in loaded_version), ".".join(
str(x) for x in linked_version)) +
"It is required to use the same major version " +
"of TensorRT during compilation and runtime.")
raise RuntimeError("Incompatible TensorRT major version")
if loaded_version != linked_version:
tf_logging.info(
"Loaded TensorRT %s and linked TensorFlow against TensorRT %s. " %
(".".join([str(x) for x in loaded_version]),
".".join([str(x) for x in linked_version])) +
(".".join(str(x) for x in loaded_version), ".".join(
str(x) for x in linked_version)) +
"This is supported because TensorRT " +
" minor/patch upgrades are backward compatible")

4
tensorflow/python/data/experimental/benchmarks/csv_dataset_benchmark.py
View File

@ -54,8 +54,8 @@ class CsvDatasetBenchmark(test.Benchmark):
with open(fn, 'wb') as f:
# Just write 100 rows and use `repeat`... Assumes the cost
# of creating an iterator is not significant
row = ','.join([str_val for _ in range(n)])
f.write('\n'.join([row for _ in range(100)]))
row = ','.join(str_val for _ in range(n))
f.write('\n'.join(row for _ in range(100)))
self._filenames.append(fn)
def _tear_down(self):

2
tensorflow/python/data/experimental/kernel_tests/optimize_dataset_test.py
View File

@ -221,7 +221,7 @@ class OptimizeDatasetTest(test_base.DatasetTestBase, parameterized.TestCase):
" To enable rewrites, use resource variables instead by "
"calling `tf.enable_resource_variables()` at the start of the "
"program." % (", ".join(options._graph_rewrites())))
self.assertTrue(any([expected in str(warning) for warning in w]))
self.assertTrue(any(expected in str(warning) for warning in w))
# Check that outputs are the same in the optimized and unoptimized cases,
# when the variable value is changing.

2
tensorflow/python/data/experimental/kernel_tests/override_threadpool_test.py
View File

@ -120,7 +120,7 @@ class OverrideThreadpoolTest(test_base.DatasetTestBase,
graph = graph_pb2.GraphDef().FromString(
self.evaluate(dataset._as_serialized_graph()))
self.assertTrue(
any([node.op != "MaxIntraOpParallelismDataset" for node in graph.node]))
any(node.op != "MaxIntraOpParallelismDataset" for node in graph.node))
if __name__ == "__main__":

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

@ -52,7 +52,7 @@ class DatasetTest(test_base.DatasetTestBase, parameterized.TestCase):
dataset = dataset_ops.Dataset.range(10)
graph = graph_pb2.GraphDef().FromString(
self.evaluate(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))
def testAsSerializedGraphStateful(self):
dataset = dataset_ops.Dataset.range(10).map(

6
tensorflow/python/data/kernel_tests/text_line_dataset_test.py
View File

@ -103,13 +103,13 @@ class TextLineDatasetTest(test_base.DatasetTestBase, parameterized.TestCase):
# Basic test: read from both files.
expected_output = [self._lineText(0, i) for i in range(5)]
expected_output.extend([self._lineText(1, i) for i in range(5)])
expected_output.extend(self._lineText(1, i) for i in range(5))
self.assertDatasetProduces(
dataset_fn(test_filenames, 1), expected_output=expected_output)
# Test repeated iteration through both files.
expected_output = [self._lineText(0, i) for i in range(5)]
expected_output.extend([self._lineText(1, i) for i in range(5)])
expected_output.extend(self._lineText(1, i) for i in range(5))
self.assertDatasetProduces(
dataset_fn(test_filenames, 10), expected_output=expected_output * 10)
@ -125,7 +125,7 @@ class TextLineDatasetTest(test_base.DatasetTestBase, parameterized.TestCase):
files = dataset_ops.Dataset.from_tensor_slices(test_filenames).repeat(10)
expected_output = []
for j in range(10):
expected_output.extend([self._lineText(j, i) for i in range(10)])
expected_output.extend(self._lineText(j, i) for i in range(10))
dataset = readers.TextLineDataset(files, num_parallel_reads=4)
self.assertDatasetProduces(
dataset, expected_output=expected_output * 10, assert_items_equal=True)

4
tensorflow/python/data/ops/dataset_ops.py
View File

@ -311,10 +311,10 @@ class DatasetV2(tracking_base.Trackable, composite_tensor.CompositeTensor):
# If the captured tensor is an eager tensor, we cannot trace its inputs.
if isinstance(tensor, ops._EagerTensorBase): # pylint: disable=protected-access
return False
return any([is_tensor_or_parent_ref(x) for x in tensor.op.inputs])
return any(is_tensor_or_parent_ref(x) for x in tensor.op.inputs)
for fn in self._functions():
if any([is_tensor_or_parent_ref(t) for t in fn.function.captured_inputs]):
if any(is_tensor_or_parent_ref(t) for t in fn.function.captured_inputs):
return True
return any(

10
tensorflow/python/data/util/traverse_test.py
View File

@ -53,7 +53,7 @@ class TraverseTest(test.TestCase):
variant_tensor_ops = traverse.obtain_all_variant_tensor_ops(ds)
self.assertSetEqual(
set(["MapDataset", "RangeDataset"]),
set([x.name for x in variant_tensor_ops]))
set(x.name for x in variant_tensor_ops))
@test_util.run_deprecated_v1
def testConcat(self):
@ -63,7 +63,7 @@ class TraverseTest(test.TestCase):
variant_tensor_ops = traverse.obtain_all_variant_tensor_ops(ds)
self.assertSetEqual(
set(["ConcatenateDataset", "RangeDataset", "RangeDataset_1"]),
set([x.name for x in variant_tensor_ops]))
set(x.name for x in variant_tensor_ops))
@test_util.run_deprecated_v1
def testZip(self):
@ -73,7 +73,7 @@ class TraverseTest(test.TestCase):
variant_tensor_ops = traverse.obtain_all_variant_tensor_ops(ds)
self.assertSetEqual(
set(["ZipDataset", "RangeDataset", "RangeDataset_1"]),
set([x.name for x in variant_tensor_ops]))
set(x.name for x in variant_tensor_ops))
@test_util.run_deprecated_v1
def testMultipleVariantTensors(self):
@ -82,7 +82,7 @@ class TraverseTest(test.TestCase):
variant_tensor_ops = traverse.obtain_all_variant_tensor_ops(ds)
self.assertSetEqual(
set(["RangeDataset", "ModelDataset", "PrefetchDataset"]),
set([x.name for x in variant_tensor_ops]))
set(x.name for x in variant_tensor_ops))
@test_util.run_deprecated_v1
def testFlatMap(self):
@ -102,7 +102,7 @@ class TraverseTest(test.TestCase):
set([
"FlatMapDataset", "PrefetchDataset", "RepeatDataset",
"RangeDataset", "RangeDataset_1"
]), set([x.name for x in variant_tensor_ops]))
]), set(x.name for x in variant_tensor_ops))
if __name__ == "__main__":

6
tensorflow/python/debug/cli/analyzer_cli.py
View File

@ -1200,12 +1200,12 @@ class DebugAnalyzer(object):
return debugger_cli_common.rich_text_lines_from_rich_line_list(lines)
path_column_width = max(
max([len(item[0]) for item in source_list]), len(path_head)) + 1
max(len(item[0]) for item in source_list), len(path_head)) + 1
num_nodes_column_width = max(
max([len(str(item[2])) for item in source_list]),
max(len(str(item[2])) for item in source_list),
len(num_nodes_head)) + 1
num_tensors_column_width = max(
max([len(str(item[3])) for item in source_list]),
max(len(str(item[3])) for item in source_list),
len(num_tensors_head)) + 1
head = RL(path_head + " " * (path_column_width - len(path_head)), color)

2
tensorflow/python/debug/cli/curses_ui_test.py
View File

@ -42,7 +42,7 @@ def string_to_codes(cmd):
def codes_to_string(cmd_code):
# Omit non-ASCII key codes.
return "".join([chr(code) for code in cmd_code if code < 256])
return "".join(chr(code) for code in cmd_code if code < 256)
class MockCursesUI(curses_ui.CursesUI):

2
tensorflow/python/debug/cli/profile_analyzer_cli.py
View File

@ -624,7 +624,7 @@ class ProfileAnalyzer(object):
device_stats = self._run_metadata.step_stats.dev_stats[index]
if device_name_regex and not device_name_regex.match(device_stats.device):
continue
profile_data.extend([datum for datum in data_generator(device_stats)])
profile_data.extend(data_generator(device_stats))
source_annotation = source_utils.annotate_source_against_profile(
profile_data,

2
tensorflow/python/debug/lib/debug_grappler_test.py
View File

@ -88,7 +88,7 @@ class SessionDebugGrapplerInteractionTest(test_util.TensorFlowTestCase):
self._dump_root, partition_graphs=run_metadata.partition_graphs,
validate=True)
original_node_names = set([op.name for op in sess.graph.get_operations()])
original_node_names = set(op.name for op in sess.graph.get_operations())
dumped_node_names = set(dump_data.nodes())
grappler_created_node_names = dumped_node_names - original_node_names
grappler_removed_node_names = original_node_names - dumped_node_names

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

@ -1231,7 +1231,7 @@ def choose_the_best(devices, session_config=None):
Returns:
A subclass of `CrossDeviceOps`.
"""
requested_devices = set([device_util.canonicalize(d) for d in devices])
requested_devices = set(device_util.canonicalize(d) for d in devices)
if ops.executing_eagerly_outside_functions():
logical_gpus = context.context().list_logical_devices(device_type="GPU")
physical_gpus = context.context().list_physical_devices(device_type="GPU")

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

@ -722,7 +722,7 @@ def is_indexed_slices(value):
if isinstance(value, ops.IndexedSlices):
return True
assert isinstance(value, value_lib.DistributedValues)
return all([isinstance(v, ops.IndexedSlices) for v in value.values])
return all(isinstance(v, ops.IndexedSlices) for v in value.values)
def split_by_sparsity(values):

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

@ -215,7 +215,7 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase):
for replica in range(1, num_parameter_devices)
]
variables = list(variables) + extended_variables
return set([v + ":0" for v in variables])
return set(v + ":0" for v in variables)
self.assertEqual(
get_expected_variables(len(distribution.extended.parameter_devices)),

7
tensorflow/python/eager/function.py
View File

@ -2251,10 +2251,9 @@ def _convert_inputs_to_signature(inputs, input_signature, flat_input_signature):
"""Convert inputs to pass into a function with an explicit signature."""
def format_error_message(inputs, input_signature):
return (" inputs: (\n" + " " +
",\n ".join([str(i) for i in inputs]) + ")\n" +
" input_signature: (\n" + " " +
",\n ".join([str(i) for i in input_signature]) + ")")
return (" inputs: (\n" + " " + ",\n ".join(str(i) for i in inputs) +
")\n" + " input_signature: (\n" + " " +
",\n ".join(str(i) for i in input_signature) + ")")
try:
# TODO(b/124370185): Use all elements as inputs to throw an error if there

2
tensorflow/python/eager/lift_to_graph.py
View File

@ -246,7 +246,7 @@ def lift_to_graph(tensors,
# Check that the initializer does not depend on any placeholders.
sources = object_identity.ObjectIdentitySet(sources or [])
visited_ops = set([x.op for x in sources])
visited_ops = set(x.op for x in sources)
op_outputs = collections.defaultdict(set)
# First we extract the subgraph between init_tensors and sources.

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

@ -94,13 +94,13 @@ def convert_structure_to_signature(structure, arg_names=None):
# of the function argument.
name = user_specified_name
else:
name = "/".join([str(p) for p in path])
name = "/".join(str(p) for p in path)
return tensor_spec.TensorSpec(arg.shape, arg.dtype, name)
if isinstance(arg, composite_tensor.CompositeTensor):
# TODO(b/133606651) Do we need to inject arg_name?
return arg._type_spec # pylint: disable=protected-access
if isinstance(arg, resource_variable_ops.BaseResourceVariable):
name = "/".join([str(p) for p in path])
name = "/".join(str(p) for p in path)
return resource_variable_ops.VariableSpec(arg.shape, arg.dtype, name)
if isinstance(arg, (
int,

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

@ -1296,7 +1296,7 @@ def get_extra_args():
def _type_list_to_str(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])
return "".join(_DTYPE_TO_STR[_] for _ in types)
# NOTE: The list needs to be extended when more data types are added.

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

@ -170,7 +170,7 @@ def graph_to_function_def(graph, operations, inputs, outputs, out_names=None):
else:
func.signature.output_arg.extend(
[_tensor_to_argdef(o, name=n) for o, n in zip(outputs, out_names)])
func_arg_placeholders = set([i.name for i in inputs])
func_arg_placeholders = set(i.name for i in inputs)
input_dict = _create_input_dict(graph, func_arg_placeholders,
initial_value=initial_dict)

24
tensorflow/python/framework/meta_graph.py
View File

@ -297,9 +297,10 @@ def _find_extraneous_saver_nodes(graph_def, saver_def):
# but it seems unnecessarily complex given the name scope solution.
# load the graph DAG in minimal form, without initializing a full Graph object
nodes = {node_def.name:
(set([_op_name(x) for x in node_def.input]), node_def.op)
for node_def in graph_def.node}
nodes = {
node_def.name: (set(_op_name(x) for x in node_def.input), node_def.op)
for node_def in graph_def.node
}
retain_scope_save = None
retain_scope_restore = None
@ -313,12 +314,12 @@ def _find_extraneous_saver_nodes(graph_def, saver_def):
retain_scope_restore = _get_scope(restore_op_name) + "/"
retain_scope_save = _get_scope(save_op_name) + "/"
all_saver_node_names = set([name for name, (_, op) in nodes.items()
if op in SAVE_AND_RESTORE_OPS])
all_saver_node_names = set(
name for name, (_, op) in nodes.items() if op in SAVE_AND_RESTORE_OPS)
all_saver_scopes = (set([_get_scope(x) for x in all_saver_node_names])
- all_saver_node_names)
all_saver_scopes = set([x + "/" for x in all_saver_scopes])
all_saver_scopes = (
set(_get_scope(x) for x in all_saver_node_names) - all_saver_node_names)
all_saver_scopes = set(x + "/" for x in all_saver_scopes)
extraneous_scopes = all_saver_scopes - set([retain_scope_save,
retain_scope_restore])
@ -766,9 +767,10 @@ def import_scoped_meta_graph_with_return_elements(
sorted([compat.as_str(v) for v in field.value]) !=
sorted(input_map)):
raise ValueError("Graph contains unbound inputs: %s. Must "
"provide these inputs through input_map." %
",".join([compat.as_str(v) for v in field.value
if not input_map or v not in input_map]))
"provide these inputs through input_map." % ",".join(
compat.as_str(v)
for v in field.value
if not input_map or v not in input_map))
break
# Sets graph to default graph if it's not passed in.

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

@ -3283,7 +3283,7 @@ class Graph(object):
node_def = _NodeDef(op_type, name, attrs)
input_ops = set([t.op for t in inputs])
input_ops = set(t.op for t in inputs)
control_inputs = self._control_dependencies_for_inputs(input_ops)
# _create_op_helper mutates the new Operation. `_mutation_lock` ensures a
# Session.run call cannot occur between creating and mutating the op.
@ -4442,7 +4442,7 @@ class Graph(object):
# Don't add a control input if we already have a data dependency on i.
# NOTE(mrry): We do not currently track transitive data dependencies,
# so we may add redundant control inputs.
ret.extend([c for c in controller.control_inputs if c not in input_ops])
ret.extend(c for c in controller.control_inputs if c not in input_ops)
return ret
def _record_op_seen_by_control_dependencies(self, op):

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

@ -2409,7 +2409,7 @@ class TensorFlowTestCase(googletest.TestCase):
path=None,
msg=None):
path = path or []
path_str = (("[" + "][".join([str(p) for p in path]) + "]") if path else "")
path_str = (("[" + "][".join(str(p) for p in path) + "]") if path else "")
msg = msg if msg else ""
# Check if a and/or b are namedtuples.

2
tensorflow/python/grappler/memory_optimizer_test.py
View File

@ -90,7 +90,7 @@ class MemoryOptimizerSwapTest(test.TestCase):
self.assertEqual(len(graph.node), graph_size + 2)
self.assertTrue(
set([node.name for node in graph.node]) > set(
set(node.name for node in graph.node) > set(
['a', 'b', 'c', 'd', 'swap_in_d_0', 'swap_out_d_0']))
for node in graph.node:
if node.name == 'swap_in_d_0':

2
tensorflow/python/keras/applications/resnet.py
View File

@ -410,7 +410,7 @@ def block3(x,
output_shape = x_shape + (groups,
c) if backend.backend() == 'theano' else None
x = layers.Lambda(
lambda x: sum([x[:, :, :, :, i] for i in range(c)]),
lambda x: sum(x[:, :, :, :, i] for i in range(c)),
output_shape=output_shape,
name=name + '_2_reduce')(
x)

12
tensorflow/python/keras/backend.py
View File

@ -1035,7 +1035,7 @@ def placeholder(shape=None,
dtype = floatx()
if not shape:
if ndim:
shape = tuple([None for _ in range(ndim)])
shape = (None,) * ndim
with get_graph().as_default():
if sparse:
x = array_ops.sparse_placeholder(dtype, shape=shape, name=name)
@ -1768,7 +1768,7 @@ def batch_dot(x, y, axes=None):
else:
axes = [x_ndim - 1, y_ndim - 2]
if py_any([isinstance(a, (list, tuple)) for a in axes]):
if py_any(isinstance(a, (list, tuple)) for a in axes):
raise ValueError('Multiple target dimensions are not supported. ' +
'Expected: None, int, (int, int), ' +
'Provided: ' + str(axes))
@ -4580,7 +4580,7 @@ def sparse_categorical_crossentropy(target, output, from_logits=False, axis=-1):
target = flatten(target)
output = array_ops.reshape(output, [-1, output_shape[-1]])
if py_any([_is_symbolic_tensor(v) for v in [target, output]]):
if py_any(_is_symbolic_tensor(v) for v in [target, output]):
with get_graph().as_default():
res = nn.sparse_softmax_cross_entropy_with_logits_v2(
labels=target, logits=output)
@ -5424,9 +5424,9 @@ def local_conv(inputs,
if data_format == 'channels_first':
slices.append(slice(None))
slices.extend([slice(position[d] * strides[d],
position[d] * strides[d] + kernel_size[d])
for d in spatial_dimensions])
slices.extend(
slice(position[d] * strides[d], position[d] * strides[d] +
kernel_size[d]) for d in spatial_dimensions)
if data_format == 'channels_last':
slices.append(slice(None))

2
tensorflow/python/keras/datasets/imdb.py
View File

@ -113,7 +113,7 @@ def load_data(path='imdb.npz',
str(maxlen) + ', no sequence was kept. '
'Increase maxlen.')
if not num_words:
num_words = max([max(x) for x in xs])
num_words = max(max(x) for x in xs)
# by convention, use 2 as OOV word
# reserve 'index_from' (=3 by default) characters:

2
tensorflow/python/keras/datasets/reuters.py
View File

@ -99,7 +99,7 @@ def load_data(path='reuters.npz',
xs, labels = _remove_long_seq(maxlen, xs, labels)
if not num_words:
num_words = max([max(x) for x in xs])
num_words = max(max(x) for x in xs)
# by convention, use 2 as OOV word
# reserve 'index_from' (=3 by default) characters:

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

@ -266,7 +266,7 @@ class TensorLikeDataAdapter(DataAdapter):
msg = "Data cardinality is ambiguous:\n"
for label, data in zip(["x", "y", "sample_weight"], inputs):
msg += " {} sizes: {}\n".format(
label, ", ".join([str(i.shape[0]) for i in nest.flatten(data)]))
label, ", ".join(str(i.shape[0]) for i in nest.flatten(data)))
msg += "Please provide data which shares the same first dimension."
raise ValueError(msg)
num_samples = num_samples.pop()

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

@ -1319,7 +1319,7 @@ class Model(network.Network, version_utils.VersionSelector):
"""
if not self._is_compiled:
return
if sample_weights and any([s is not None for s in sample_weights]):
if sample_weights and any(s is not None for s in sample_weights):
for endpoint in self._training_endpoints:
endpoint.sample_weight_mode = (
endpoint.sample_weight_mode or 'samplewise')
@ -1330,8 +1330,8 @@ class Model(network.Network, version_utils.VersionSelector):
def _recompile_weights_loss_and_weighted_metrics(self):
if not self._is_compiled:
return False
recompile = any([e.sample_weights_mismatch()
for e in self._training_endpoints])
recompile = any(
e.sample_weights_mismatch() for e in self._training_endpoints)
if recompile:
self._compile_weights_loss_and_weighted_metrics()

2
tensorflow/python/keras/engine/training_v1.py
View File

@ -1492,7 +1492,7 @@ class Model(training_lib.Model):
"""
if not self._is_compiled:
return
if sample_weights and any([s is not None for s in sample_weights]):
if sample_weights and any(s is not None for s in sample_weights):
for endpoint in self._training_endpoints:
endpoint.sample_weight_mode = (
endpoint.sample_weight_mode or 'samplewise')

7
tensorflow/python/keras/layers/core.py
View File

@ -592,8 +592,7 @@ class Flatten(Layer):
if (self.data_format == 'channels_first'
and K.ndim(inputs) is not None and K.ndim(inputs) > 1):
permutation = [0]
permutation.extend([i for i in
range(2, K.ndim(inputs))])
permutation.extend(range(2, K.ndim(inputs)))
permutation.append(1)
inputs = array_ops.transpose(inputs, perm=permutation)
@ -858,7 +857,7 @@ class Lambda(Layer):
untracked_new_vars = [v for v in created_variables
if v.experimental_ref() not in tracked_weights]
if untracked_new_vars:
variable_str = '\n'.join([' {}'.format(i) for i in untracked_new_vars])
variable_str = '\n'.join(' {}'.format(i) for i in untracked_new_vars)
error_str = textwrap.dedent(
'''
The following Variables were created within a Lambda layer ({name})
@ -875,7 +874,7 @@ class Lambda(Layer):
untracked_used_vars = [v for v in accessed_variables
if v.experimental_ref() not in tracked_weights]
if untracked_used_vars and not self._already_warned:
variable_str = '\n'.join([' {}'.format(i) for i in untracked_used_vars])
variable_str = '\n'.join(' {}'.format(i) for i in untracked_used_vars)
self._warn(textwrap.dedent(
'''
The following Variables were used a Lambda layer's call ({name}), but

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

@ -387,7 +387,7 @@ class Concatenate(_Merge):
'except for the concat axis. Got inputs shapes: %s' %
input_shape)
# Make sure all the shapes have same ranks.
ranks = set([len(shape) for shape in shape_set])
ranks = set(len(shape) for shape in shape_set)
if len(ranks) != 1:
raise ValueError(err_msg)
# Get the only rank for the set.
@ -395,8 +395,8 @@ class Concatenate(_Merge):
for axis in range(rank):
# Skip the Nones in the shape since they are dynamic, also the axis for
# concat has been removed above.
unique_dims = set([shape[axis] for shape in shape_set
if shape[axis] is not None])
unique_dims = set(
shape[axis] for shape in shape_set if shape[axis] is not None)
if len(unique_dims) > 1:
raise ValueError(err_msg)

2
tensorflow/python/keras/layers/rnn_cell_wrapper_v2_test.py
View File

@ -175,7 +175,7 @@ class RNNCellWrapperTest(test.TestCase, parameterized.TestCase):
_ = rnn_cell(inputs, [state, state])
weights = base_cell._cells[0].weights
self.assertLen(weights, expected_len=2)
self.assertTrue(all(["_wrapper" in v.name for v in weights]))
self.assertTrue(all("_wrapper" in v.name for v in weights))
@parameterized.parameters(
[rnn_cell_wrapper_v2.DropoutWrapper, rnn_cell_wrapper_v2.ResidualWrapper])

4
tensorflow/python/keras/optimizer_v2/adam_test.py
View File

@ -539,8 +539,8 @@ class AdamOptimizerTest(test.TestCase):
opt = adam.Adam(1.)
opt.minimize(lambda: v1 + v2, var_list=[v1, v2])
# There should be iteration, and two unique slot variables for v1 and v2.
self.assertEqual(
5, len(set([v.experimental_ref() for v in opt.variables()])))
self.assertEqual(5,
len(set(v.experimental_ref() for v in opt.variables())))
self.assertEqual(
self.evaluate(opt.variables()[0]), self.evaluate(opt.iterations))

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

@ -89,7 +89,7 @@ class Optimizer(object):
function not implemented).
"""
grads = K.gradients(loss, params)
if any([g is None for g in grads]):
if any(g is None for g in grads):
raise ValueError('An operation has `None` for gradient. '
'Please make sure that all of your ops have a '
'gradient defined (i.e. are differentiable). '

3
tensorflow/python/keras/saving/hdf5_format.py
View File

@ -804,8 +804,7 @@ def save_attributes_to_hdf5_group(group, name, data):
if bad_attributes:
raise RuntimeError('The following attributes cannot be saved to HDF5 '
'file because they are larger than %d bytes: %s' %
(HDF5_OBJECT_HEADER_LIMIT,
', '.join([x for x in bad_attributes])))
(HDF5_OBJECT_HEADER_LIMIT, ', '.join(bad_attributes)))
data_npy = np.asarray(data)

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

@ -681,7 +681,7 @@ def to_list(x):
def object_list_uid(object_list):
"""Creates a single string from object ids."""
object_list = nest.flatten(object_list)
return ', '.join([str(abs(id(x))) for x in object_list])
return ', '.join(str(abs(id(x))) for x in object_list)
def to_snake_case(name):

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

@ -402,7 +402,7 @@ def assert_no_legacy_layers(layers):
# isinstance check for tf.layers.Layer introduces a circular dependency.
legacy_layers = [l for l in layers if getattr(l, '_is_legacy_layer', None)]
if legacy_layers:
layer_str = '\n'.join([' ' + str(l) for l in legacy_layers])
layer_str = '\n'.join(' ' + str(l) for l in legacy_layers)
raise TypeError(
'The following are legacy tf.layers.Layers:\n{}\nTo use keras as a '
'framework (for instance using the Network, Model, or Sequential '

2
tensorflow/python/kernel_tests/basic_gpu_test.py
View File

@ -273,7 +273,7 @@ class GpuMultiSessionMemoryTest(test_util.TensorFlowTestCase):
for thread in threads:
thread.join()
flat_results = set([x for x in itertools.chain(*results)])
flat_results = set(itertools.chain(*results))
self.assertEqual(1,
len(flat_results),
'Expected single value, got %r' % flat_results)

2
tensorflow/python/kernel_tests/proto/decode_proto_op_test_base.py
View File

@ -330,7 +330,7 @@ class DecodeProtoOpTestBase(test_base.ProtoOpTestBase, parameterized.TestCase):
# Test against all 3! permutations of fragments, and for each permutation
# test parsing all possible combination of 2 fields.
for indices in itertools.permutations(range(len(fragments))):
proto = b''.join([fragments[i] for i in indices])
proto = b''.join(fragments[i] for i in indices)
for i in indices:
if i == 1:
expected_message_values = [

2
tensorflow/python/kernel_tests/variable_scope_test.py
View File

@ -118,7 +118,7 @@ class VariableScopeTest(test.TestCase):
vs.get_variable("v2", [2])
expected_names = ["%s:0" % name for name in ["v1", "v2"]]
self.assertEqual(
set(expected_names), set([v.name for v in vs._vars.values()]))
set(expected_names), set(v.name for v in vs._vars.values()))
# TODO(mihaimaruseac): Not converted to use wrap_function because of
# TypeError: Expected tf.group() expected Tensor arguments not 'None' with

2
tensorflow/python/layers/utils.py
View File

@ -231,7 +231,7 @@ def constant_value(pred):
def object_list_uid(object_list):
"""Creates a single string from object ids."""
object_list = nest.flatten(object_list)
return ', '.join([str(abs(id(x))) for x in object_list])
return ', '.join(str(abs(id(x))) for x in object_list)
def static_shape(x):

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

@ -351,7 +351,7 @@ def _binary_assert(sym, opname, op_func, static_func, x, y, data, summarize,
raise errors.InvalidArgumentError(
node_def=None,
op=None,
message=('\n'.join([_pretty_print(d, summarize) for d in data])))
message=('\n'.join(_pretty_print(d, summarize) for d in data)))
else: # not context.executing_eagerly()
if data is None:

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

@ -82,7 +82,7 @@ def _as_shape_list(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]):
if any(shape.dims is None for shape in shapes):
raise ValueError("All shapes must have a defined rank: %s" % shapes)
return shapes

4
tensorflow/python/ops/gradients_util.py
View File

@ -834,9 +834,9 @@ def _LogOpGradients(op, out_grads, in_grads):
return True
logging.vlog(1, " in --> %s",
", ".join([x.name for x in out_grads if _FilterGrad(x)]))
", ".join(x.name for x in out_grads if _FilterGrad(x)))
logging.vlog(1, " out --> %s",
", ".join([x.name for x in in_grads if _FilterGrad(x)]))
", ".join(x.name for x in in_grads if _FilterGrad(x)))
def _MultiDeviceAddN(tensor_list, gradient_uid):

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

@ -275,7 +275,7 @@ def _EinsumGrad(op, grad):
set(output_subs + other_subs + "."))
# Obtain the input subscripts with the reduced axis labels removed. E.g.
# "ac" in the above example.
left_subs = "".join([s for s in input_subs if s not in reduced_label_set])
left_subs = "".join(s for s in input_subs if s not in reduced_label_set)
# Compute the gradient wrt the input, without accounting for the operation
# "abc->ac". So, now we have the VJP of the operation "ac,cd->ad".

4
tensorflow/python/ops/op_selector.py
View File

@ -339,7 +339,7 @@ def _path_from(from_op, tensor, sources):
if isinstance(from_op, ops.Tensor):
from_op = from_op.op
visited_ops = set([x.op for x in sources])
visited_ops = set(x.op for x in sources)
ops_to_visit = [_as_operation(tensor)]
some_op_output = {}
while ops_to_visit:
@ -354,7 +354,7 @@ def _path_from(from_op, tensor, sources):
while path_op != final_op:
path_op = some_op_output[path_op]
path.append(path_op)
return " <- ".join(["%s (%s)" % (x.name, x.type) for x in reversed(path)])
return " <- ".join("%s (%s)" % (x.name, x.type) for x in reversed(path))
else:
for inp in graph_inputs(op):
if inp not in visited_ops and inp not in sources:

2
tensorflow/python/ops/parallel_for/control_flow_ops.py
View File

@ -74,7 +74,7 @@ def for_loop(loop_fn, loop_fn_dtypes, iters, parallel_iterations=None):
len(fn_output)))
outputs = []
del is_none_list[:]
is_none_list.extend([x is None for x in fn_output])
is_none_list.extend(x is None for x in fn_output)
for out, ta in zip(fn_output, ta_list):
# TODO(agarwal): support returning Operation objects from loop_fn.
if out is not None:

2
tensorflow/python/ops/parallel_for/control_flow_ops_test.py
View File

@ -315,7 +315,7 @@ class BitwiseTest(PForTestCase):
y1 = array_ops.gather(y, i)
outputs = [op(x, y), op(x1, y), op(x, y1), op(x1, y1), op(x1, x1)]
del output_dtypes[:]
output_dtypes.extend([t.dtype for t in outputs])
output_dtypes.extend(t.dtype for t in outputs)
return outputs
# pylint: enable=cell-var-from-loop
self._test_loop_fn(loop_fn, 3)

4
tensorflow/python/ops/parallel_for/math_test.py
View File

@ -65,7 +65,7 @@ class MathTest(PForTestCase, parameterized.TestCase):
if grad is not None:
outputs.append(grad)
del output_dtypes[:]
output_dtypes.extend([t.dtype for t in outputs])
output_dtypes.extend(t.dtype for t in outputs)
return outputs
# pylint: enable=cell-var-from-loop
@ -215,7 +215,7 @@ class MathTest(PForTestCase, parameterized.TestCase):
y1 = array_ops.gather(y, i)
outputs = [op(x, y), op(x1, y), op(x, y1), op(x1, y1), op(x1, x1)]
del output_dtypes[:]
output_dtypes.extend([t.dtype for t in outputs])
output_dtypes.extend(t.dtype for t in outputs)
return outputs
# pylint: enable=cell-var-from-loop

4
tensorflow/python/ops/parallel_for/pfor.py
View File

@ -121,7 +121,7 @@ class WhileOp(object):
"""
self._pfor_config = pfor_config
self._pfor_ops = set(pfor_ops)
self._pfor_op_ids = set([x._id for x in pfor_ops])
self._pfor_op_ids = set(x._id for x in pfor_ops)
assert isinstance(exit_node, ops.Tensor)
self._while_context = exit_node.op._get_control_flow_context()
assert isinstance(self._while_context, control_flow_ops.WhileContext)
@ -1176,7 +1176,7 @@ class PFor(object):
self._conversion_map = object_identity.ObjectIdentityDictionary()
self._conversion_map[loop_var] = wrap(self.all_indices, True)
self._pfor_ops = set(pfor_ops)
self._pfor_op_ids = set([x._id for x in pfor_ops])
self._pfor_op_ids = set(x._id for x in pfor_ops)
self._pfor_config = pfor_config
def op_is_inside_loop(self, op):

4
tensorflow/python/ops/ragged/ragged_tensor_shape.py
View File

@ -126,8 +126,8 @@ class RaggedTensorDynamicShape(object):
# Convert dimension size tensors to a single dtype.
if dim_size_dtype is None:
dim_size_dtypes = set([p.dtype for p in partitioned_dim_sizes
if p.shape.ndims == 1])
dim_size_dtypes = set(
p.dtype for p in partitioned_dim_sizes if p.shape.ndims == 1)
if not dim_size_dtypes:
dim_size_dtype = dtypes.int64
elif len(dim_size_dtypes) == 1:

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

@ -1237,7 +1237,7 @@ class MultiRNNCell(RNNCell):
if not nest.is_sequence(cells):
raise TypeError("cells must be a list or tuple, but saw: %s." % cells)
if len(set([id(cell) for cell in cells])) < len(cells):
if len(set(id(cell) for cell in cells)) < len(cells):
logging.log_first_n(
logging.WARN, "At least two cells provided to MultiRNNCell "
"are the same object and will share weights.", 1)

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

@ -360,8 +360,8 @@ def _einsum_v1_parse_and_resolve_equation(equation, input_shapes):
# tensors of different length and unlabeled output.
ellipsis_axes = ''
if '...' in equation:
unused = ''.join([c for c in string.ascii_letters
if c not in ''.join(input_axis_labels)])
unused = ''.join(
c for c in string.ascii_letters if c not in ''.join(input_axis_labels))
for i, ax in enumerate(input_axis_labels):
if '...' in ax:
parts = ax.split('...')
@ -381,7 +381,7 @@ def _einsum_v1_parse_and_resolve_equation(equation, input_shapes):
if len(replace_axes) > len(ellipsis_axes):
ellipsis_axes = replace_axes
if any(['.' in ax for ax in input_axis_labels]):
if any('.' in ax for ax in input_axis_labels):
raise ValueError('period "." found outside of ellipsis')
if output_axis_labels is not None:

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

@ -804,7 +804,7 @@ class _EagerTensorArray(object):
None, None,
"Tried to write to index %d but array is not resizeable and size "
"is: %d" % (index, size))
self._tensor_array.extend([None for _ in range(index - size + 1)])
self._tensor_array.extend(None for _ in range(index - size + 1))
if not isinstance(value, ops.EagerTensor):
# TODO(b/129870929): Fix after all callers provide proper init dtype.

4
tensorflow/python/ops/variables.py
View File

@ -1324,9 +1324,9 @@ class Variable(six.with_metaclass(VariableMetaclass, trackable.Trackable)):
@property
def spec(self):
"""Computes the spec string used for saving."""
full_shape_str = " ".join(["%d" % d for d in self.full_shape]) + " "
full_shape_str = " ".join("%d" % d for d in self.full_shape) + " "
sl_spec = ":".join(
["%d,%d" % (o, s) for o, s in zip(self.var_offset, self.var_shape)])
"%d,%d" % (o, s) for o, s in zip(self.var_offset, self.var_shape))
return full_shape_str + sl_spec
def to_proto(self, export_scope=None):

2
tensorflow/python/profiler/model_analyzer_test.py
View File

@ -229,7 +229,7 @@ class PrintModelAnalysisTest(test.TestCase):
with gfile.Open(outfile, 'r') as f:
lines = f.read().split('\n')
self.assertGreater(len(lines), 5)
result = '\n'.join([l[:min(len(l), 80)] for l in lines])
result = '\n'.join(l[:min(len(l), 80)] for l in lines)
self.assertTrue(
compat.as_text(lib.CheckAndRemoveDoc(result))
.startswith('node name | # parameters | # float_ops'))

3
tensorflow/python/saved_model/function_deserialization.py
View File

@ -244,8 +244,7 @@ def recreate_function(saved_function, concrete_functions):
def _pretty_format_positional(positional):
return "Positional arguments ({} total):\n * {}".format(
len(positional),
"\n * ".join([str(a) for a in positional]))
len(positional), "\n * ".join(str(a) for a in positional))
for index, function_name in enumerate(saved_function.concrete_functions):
concrete_function = concrete_functions[function_name]

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

@ -96,7 +96,7 @@ def get_header_from_ops_and_kernels(ops_and_kernels,
Returns:
the string of the header that should be written as ops_to_register.h.
"""
ops = set([op for op, _ in ops_and_kernels])
ops = set(op for op, _ in ops_and_kernels)
result_list = []
def append(s):

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

@ -111,7 +111,7 @@ def topological_sort(g):
if op_in_degree[consumer] < 0:
raise ValueError('consumer:%s degree mismatch'%consumer.name)
left_ops = set([op for (op, degree) in op_in_degree.items() if degree > 0])
left_ops = set(op for (op, degree) in op_in_degree.items() if degree > 0)
if left_ops:
return (True, left_ops)
else:

6
tensorflow/python/tpu/tpu.py
View File

@ -321,8 +321,8 @@ class TPUReplicateContext(control_flow_ops.XLAControlFlowContext):
def report_unsupported_operations(self):
if self._unsupported_ops:
op_str = "\n".join([" %s (%s)" % (op.type, op.name)
for op in self._unsupported_ops[:_MAX_WARNING_LINES]])
op_str = "\n".join(" %s (%s)" % (op.type, op.name)
for op in self._unsupported_ops[:_MAX_WARNING_LINES])
logging.warning("%d unsupported operations found: \n%s",
len(self._unsupported_ops), op_str)
if len(self._unsupported_ops) > _MAX_WARNING_LINES:
@ -1200,7 +1200,7 @@ def split_compile_and_replicate(computation,
if host_compute_core:
attr_value = attr_value_pb2.AttrValue()
attr_value.list.s.extend([compat.as_bytes(x) for x in host_compute_core])
attr_value.list.s.extend(compat.as_bytes(x) for x in host_compute_core)
metadata._set_attr("host_compute_core", attr_value) # pylint: disable=protected-access
with ops.control_dependencies([metadata]):

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

@ -199,7 +199,7 @@ def master_job(master, cluster_def):
if (not cluster_def or not cluster_def.job):
return _DEFAULT_JOB_NAME
job_names = set([job.name for job in cluster_def.job])
job_names = set(job.name for job in cluster_def.job)
if _DEFAULT_JOB_NAME in job_names:
# b/37868888 tracks allowing ClusterSpec propagation to reuse job names.
raise ValueError('Currently, tpu_worker is not an allowed job name.')

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

@ -330,7 +330,7 @@ def _init_from_checkpoint(ckpt_dir_or_file, assignment_map):
))
var_name = var.name
else:
var_name = ",".join([v.name for v in var])
var_name = ",".join(v.name for v in var)
_set_variable_or_list_initializer(var, ckpt_file, tensor_name_in_ckpt)
logging.debug("Initialize variable %s from checkpoint %s with %s",
var_name, ckpt_dir_or_file, tensor_name_in_ckpt)

20
tensorflow/python/training/input_test.py
View File

@ -401,7 +401,7 @@ class SliceInputProducerTest(test_lib.TestCase):
frequency[e] = 0
for _ in range(num_epochs):
output = [self.evaluate(slices) for _ in range(len(source_strings))]
key = b",".join([s + compat.as_bytes(str(i)) for s, i in output])
key = b",".join(s + compat.as_bytes(str(i)) for s, i in output)
self.assertIn(key, expected)
frequency[key] += 1
@ -1083,7 +1083,7 @@ class BatchJoinTest(test_lib.TestCase):
self.assertEqual(len(which_a) + len(which_b), batch_size)
if which_a and which_b:
saw_both += 1
all_a.extend([results[0][i] for i in which_a])
all_a.extend(results[0][i] for i in which_a)
seen_b += len(which_b)
self.assertAllEqual([99] * len(which_b),
[results[0][i] for i in which_b])
@ -1185,7 +1185,7 @@ class BatchJoinTest(test_lib.TestCase):
self.assertEqual(len(which_a) + len(which_b), batch_size)
if which_a and which_b:
saw_both += 1
all_a.extend([results[0][i] for i in which_a])
all_a.extend(results[0][i] for i in which_a)
seen_b += len(which_b)
self.assertAllEqual([99] * len(which_b),
[results[0][i] for i in which_b])
@ -1271,7 +1271,7 @@ class BatchJoinTest(test_lib.TestCase):
self.assertEqual(len(which_a) + len(which_b), batch_size)
if which_a and which_b:
saw_both += 1
all_a.extend([results[0][i] for i in which_a])
all_a.extend(results[0][i] for i in which_a)
seen_b += len(which_b)
self.assertAllEqual([99] * len(which_b),
[results[0][i] for i in which_b])
@ -1291,7 +1291,7 @@ class BatchJoinTest(test_lib.TestCase):
self.assertEqual(len(which_a) + len(which_b), 2 * extra_elements)
if which_a and which_b:
saw_both += 1
all_a.extend([results[0][i] for i in which_a])
all_a.extend(results[0][i] for i in which_a)
seen_b += len(which_b)
# We'd like to see some minimum level of mixing of the results of both
@ -1369,7 +1369,7 @@ class BatchJoinTest(test_lib.TestCase):
self.assertEqual(len(which_a) + len(which_b), batch_size)
if which_a and which_b:
saw_both += 1
all_a.extend([results[0][i] for i in which_a])
all_a.extend(results[0][i] for i in which_a)
seen_b += len(which_b)
self.assertAllEqual([99] * len(which_b),
[results[0][i] for i in which_b])
@ -1389,7 +1389,7 @@ class BatchJoinTest(test_lib.TestCase):
self.assertEqual(len(which_a) + len(which_b), 2 * extra_elements)
if which_a and which_b:
saw_both += 1
all_a.extend([results[0][i] for i in which_a])
all_a.extend(results[0][i] for i in which_a)
seen_b += len(which_b)
# We'd like to see some minimum level of mixing of the results of both
@ -2099,7 +2099,7 @@ class ShuffleBatchJoinTest(test_lib.TestCase):
self.assertEqual(len(which_a) + len(which_b), batch_size)
if which_a and which_b:
saw_both += 1
all_a.extend([results[0][i] for i in which_a])
all_a.extend(results[0][i] for i in which_a)
seen_b += len(which_b)
self.assertAllEqual([99] * len(which_b),
[results[0][i] for i in which_b])
@ -2194,7 +2194,7 @@ class ShuffleBatchJoinTest(test_lib.TestCase):
self.assertEqual(len(which_a) + len(which_b), batch_size)
if which_a and which_b:
saw_both += 1
all_a.extend([results[0][i] for i in which_a])
all_a.extend(results[0][i] for i in which_a)
seen_b += len(which_b)
self.assertAllEqual([99] * len(which_b),
[results[0][i] for i in which_b])
@ -2213,7 +2213,7 @@ class ShuffleBatchJoinTest(test_lib.TestCase):
self.assertEqual(len(which_a) + len(which_b), 2 * extra_elements)
if which_a and which_b:
saw_both += 1
all_a.extend([results[0][i] for i in which_a])
all_a.extend(results[0][i] for i in which_a)
seen_b += len(which_b)
# Some minimum level of mixing of the results of both threads.

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

@ -554,7 +554,7 @@ class ExponentialMovingAverage(object):
for v in moving_avg_variables:
name_map[self.average_name(v)] = v
# Make sure we restore variables without moving averages as well.
moving_avg_variable_names = set([v.name for v in moving_avg_variables])
moving_avg_variable_names = set(v.name for v in moving_avg_variables)
for v in list(set(variables.global_variables())):
if v.name not in moving_avg_variable_names and v.op.name not in name_map:
name_map[v.op.name] = v

4
tensorflow/python/util/all_util.py
View File

@ -46,8 +46,8 @@ def make_all(module_name, doc_string_modules=None):
"""
if doc_string_modules is None:
doc_string_modules = [_sys.modules[module_name]]
cur_members = set([name for name, _
in _tf_inspect.getmembers(_sys.modules[module_name])])
cur_members = set(
name for name, _ in _tf_inspect.getmembers(_sys.modules[module_name]))
results = set()
for doc_module in doc_string_modules:

2
tensorflow/python/util/object_identity.py
View File

@ -172,7 +172,7 @@ class ObjectIdentitySet(collections_abc.MutableSet):
"""Like the built-in set, but compares objects with "is"."""
def __init__(self, *args):
self._storage = set([self._wrap_key(obj) for obj in list(*args)])
self._storage = set(self._wrap_key(obj) for obj in list(*args))
@staticmethod
def _from_storage(storage):