def _preprocessing_fn_with_chained_ptransforms(inputs):
class FakeChainable(tfx_namedtuple.namedtuple('FakeChainable', ['label']),
nodes.OperationDef):
def __new__(cls):
scope = tf.compat.v1.get_default_graph().get_name_scope()
label = '{}[{}]'.format(cls.__name__, scope)
return super(FakeChainable, cls).__new__(cls, label=label)
with tf.compat.v1.name_scope('x'):
input_values_node = nodes.apply_operation(analyzer_nodes.TensorSource,
tensors=[inputs['x']])
with tf.compat.v1.name_scope('ptransform1'):
intermediate_value_node = nodes.apply_operation(
FakeChainable, input_values_node)
with tf.compat.v1.name_scope('ptransform2'):
output_value_node = nodes.apply_operation(FakeChainable,
intermediate_value_node)
x_chained = analyzer_nodes.bind_future_as_tensor(
output_value_node,
analyzer_nodes.TensorInfo(tf.float32, (17, 27), None))
return {'x_chained': x_chained}
def _preprocessing_fn_for_generalized_chained_ptransforms(inputs):
class FakeChainablePartitionable(
collections.namedtuple('FakeChainablePartitionable', ['label']),
nodes.OperationDef):
def __new__(cls, label=None):
if label is None:
scope = tf.compat.v1.get_default_graph().get_name_scope()
label = '{}[{}]'.format(cls.__name__, scope)
return super(FakeChainablePartitionable, cls).__new__(cls,
label=label)
@property
def num_outputs(self):
return 1
@property
def is_partitionable(self):
return True
class FakeChainableCacheable(
collections.namedtuple('FakeChainableCacheable', ['label']),
nodes.OperationDef):
def __new__(cls, label=None):
if label is None:
scope = tf.compat.v1.get_default_graph().get_name_scope()
label = '{}[{}]'.format(cls.__name__, scope)
return super(FakeChainableCacheable, cls).__new__(cls, label=label)
@property
def num_outputs(self):
return 1
@property
def is_partitionable(self):
return True
@property
def cache_coder(self):
return 'Not-a-coder-but-thats-ok!'
class FakeChainable(collections.namedtuple('FakeChainable', ['label']),
nodes.OperationDef):
def __new__(cls, label=None):
if label is None:
scope = tf.compat.v1.get_default_graph().get_name_scope()
label = '{}[{}]'.format(cls.__name__, scope)
return super(FakeChainable, cls).__new__(cls, label=label)
@property
def num_outputs(self):
return 1
@property
def is_partitionable(self):
return False
with tf.compat.v1.name_scope('x'):
input_values_node = nodes.apply_operation(analyzer_nodes.TensorSource,
tensors=[inputs['x']])
with tf.compat.v1.name_scope('partitionable1'):
partitionable_outputs = nodes.apply_multi_output_operation(
FakeChainablePartitionable, input_values_node)
with tf.compat.v1.name_scope('cacheable1'):
intermediate_cached_value_node = nodes.apply_multi_output_operation(
FakeChainableCacheable, *partitionable_outputs)
with tf.compat.v1.name_scope('partitionable2'):
partitionable_outputs = nodes.apply_multi_output_operation(
FakeChainablePartitionable, *intermediate_cached_value_node)
with tf.compat.v1.name_scope('cacheable2'):
cached_value_node = nodes.apply_multi_output_operation(
FakeChainableCacheable, *partitionable_outputs)
with tf.compat.v1.name_scope('partitionable3'):
output_value_node = nodes.apply_multi_output_operation(
FakeChainablePartitionable, *cached_value_node)
with tf.compat.v1.name_scope('merge'):
output_value_node = nodes.apply_operation(FakeChainable,
*output_value_node)
with tf.compat.v1.name_scope('not-cacheable'):
non_cached_output = nodes.apply_operation(FakeChainable,
input_values_node)
x_chained = analyzer_nodes.bind_future_as_tensor(
output_value_node,
analyzer_nodes.TensorInfo(tf.float32, (17, 27), False))
x_plain = analyzer_nodes.bind_future_as_tensor(
non_cached_output,
analyzer_nodes.TensorInfo(tf.int64, (7, 13), False))
return {'x_chained': x_chained, 'x_plain': x_plain}
