def testConvertTensors(self):
a = tf.placeholder(tf.int32, [None])
protomap = _make_signature({"a": a}, {}).inputs
# convert constant
in0 = [1, 2, 3]
output = tensor_info.convert_to_input_tensors(protomap, {"a": in0})
self.assertEquals(output["a"].dtype, a.dtype)
# check sparsity
in1 = tf.sparse_placeholder(tf.int32, [])
with self.assertRaisesRegexp(TypeError, "dense"):
tensor_info.convert_to_input_tensors(protomap, {"a": in1})
# check args mismatch
with self.assertRaisesRegexp(TypeError, "missing"):
tensor_info.convert_to_input_tensors(protomap, {"b": in1})
def create_apply_graph(self, signature, inputs, name):
"""See `ModuleImpl.create_apply_graph`."""
signature_def = self._meta_graph.signature_def.get(signature)
input_tensors = tensor_info.convert_to_input_tensors(
signature_def.inputs, inputs)
# Build a input map to feed when importing the apply-graph by augmenting the
# state_map with the input args. This allows an input to override a tensor
# from the state-graph.
feed_map = dict(self._state_map)
feed_map.update(
tensor_info.build_input_map(signature_def.inputs, input_tensors))
# Make state tensors enter the current context. This way the Module can be
# applied inside a control flow structure such as a while_loop.
control_flow = self._graph._get_control_flow_context() # pylint: disable=protected-access
if control_flow:
for key, value in sorted(feed_map.items()):
feed_map[key] = control_flow.AddValue(value)
# Don't mark the name as used at this point - import_scoped_meta_graph will
# start using it.
absolute_scope_name = self._graph.unique_name(name, mark_as_used=False)
relative_scope_name = absolute_scope_name.split("/")[-1]
import_collections = [
# In most cases ASSET_FILEPATHS are only used for the TABLE_INITIALIZERS
# ops, however one could create a graph that uses an asset at any other
# time. As so everytime we bring the tensor with that has the asset
# filename we must annotate it as so, so later re-exports have that
# semantic information and can handle it.
tf.GraphKeys.ASSET_FILEPATHS,
tf.GraphKeys.COND_CONTEXT,
tf.GraphKeys.WHILE_CONTEXT,
]
if self._trainable:
import_collections.extend([tf.GraphKeys.UPDATE_OPS])
tf.train.import_meta_graph(adapted_meta_graph_for_import(
self._meta_graph, absolute_scope_name),
input_map=feed_map,
import_scope=relative_scope_name,
restore_collections_predicate=(
lambda key: key in import_collections))
fix_colocation_after_import(input_map=feed_map,
absolute_import_scope=absolute_scope_name)
def get_tensor(name):
# When trying to output an input tensor there are no nodes created within
# the apply scope. So one must look into the input map.
try:
return feed_map[name]
except KeyError:
return self._graph.get_tensor_by_name(
prepend_name_scope(name, import_scope=absolute_scope_name))
return tensor_info.build_output_map(signature_def.outputs, get_tensor)
def create_apply_graph(self, signature, inputs, name):
"""See `ModuleImpl.create_apply_graph`."""
signature_def = self._meta_graph.signature_def.get(signature)
input_tensors = tensor_info.convert_to_input_tensors(
signature_def.inputs, inputs)
# Build a input map to feed when importing the apply-graph by augmenting the
# state_map with the input args. This allows an input to override a tensor
# from the state-graph.
feed_map = dict(self._state_map)
feed_map.update(
tensor_info.build_input_map(signature_def.inputs, input_tensors))
# Make state tensors enter the current context. This way the Module can be
# applied inside a control flow structure such as a while_loop.
control_flow = self._graph._get_control_flow_context() # pylint: disable=protected-access
if control_flow:
for key, value in sorted(feed_map.items()):
feed_map[key] = control_flow.AddValue(value)
# Don't mark the name as used at this point - import_scoped_meta_graph will
# start using it.
absolute_scope_name = self._graph.unique_name(name, mark_as_used=False)
relative_scope_name = absolute_scope_name.split("/")[-1]
import_collections = [
# In most cases ASSET_FILEPATHS are only used for the TABLE_INITIALIZERS
# ops, however one could create a graph that uses an asset at any other
# time. As so everytime we bring the tensor with that has the asset
# filename we must annotate it as so, so later re-exports have that
# semantic information and can handle it.
tf.GraphKeys.ASSET_FILEPATHS,
tf.GraphKeys.COND_CONTEXT,
tf.GraphKeys.WHILE_CONTEXT,
]
if self._trainable:
import_collections.extend([tf.GraphKeys.UPDATE_OPS])
tf.train.import_meta_graph(
adapted_meta_graph_for_import(self._meta_graph, absolute_scope_name),
input_map=feed_map,
import_scope=relative_scope_name,
restore_collections_predicate=(lambda key: key in import_collections))
fix_colocation_after_import(input_map=feed_map,
absolute_import_scope=absolute_scope_name)
def get_tensor(name):
# When trying to output an input tensor there are no nodes created within
# the apply scope. So one must look into the input map.
try:
return feed_map[name]
except KeyError:
return self._graph.get_tensor_by_name(
prepend_name_scope(name, import_scope=absolute_scope_name))
return tensor_info.build_output_map(signature_def.outputs, get_tensor)