def __init__(self, inside_ops=(), passthrough_ts=()):
"""Create a subgraph containing the given ops and the "passthrough" tensors.
Args:
inside_ops: an object convertible to a list of tf.Operation. This list
defines all the operations in the subgraph.
passthrough_ts: an object convertible to a list of tf.Tensor. This list
define all the "passthrough" tensors. A passthrough tensor is a tensor
which goes directly from the input of the subgraph to it output, without
any intermediate operations. All the non passthrough tensors are
silently ignored.
Raises:
TypeError: if inside_ops cannot be converted to a list of tf.Operation or
if passthrough_ts cannot be converted to a list of tf.Tensor.
"""
inside_ops = util.make_list_of_op(inside_ops)
passthrough_ts = util.make_list_of_t(passthrough_ts)
ops_and_ts = inside_ops + passthrough_ts
if ops_and_ts:
self._graph = util.get_unique_graph(ops_and_ts)
else:
self._graph = None
self._ops = inside_ops
# Compute inside and outside tensor
inputs, outputs, insides = select.compute_boundary_ts(inside_ops)
# Compute passthrough tensors, silently ignoring the non-passthrough ones.
all_tensors = frozenset(inputs + outputs + list(insides))
self._passthrough_ts = [t for t in passthrough_ts if t not in all_tensors]
# Set inputs and outputs.
self._input_ts = inputs + self._passthrough_ts
self._output_ts = outputs + self._passthrough_ts
def _remap_default(self, remove_input_map=True, remove_output_map=True):
"""Remap in the place the inputs and/or outputs to the default mapping.
Args:
remove_input_map: if True the input map is reset to the default one.
remove_output_map: if True the output map is reset to the default one.
"""
if not remove_input_map and not remove_output_map:
return
# Compute inside and outside tensor
inputs, outputs, _ = select.compute_boundary_ts(self._ops)
if remove_input_map:
self._input_ts = list(inputs) + self._passthrough_ts
if remove_output_map:
self._output_ts = list(outputs) + self._passthrough_ts
def _remap_default(self, remove_input_map=True, remove_output_map=True):
"""Remap in the place the inputs and/or outputs to the default mapping.
Args:
remove_input_map: if True the input map is reset to the default one.
remove_output_map: if True the output map is reset to the default one.
"""
if not remove_input_map and not remove_output_map:
return
# Compute inside and outside tensor
inputs, outputs, _ = select.compute_boundary_ts(self._ops)
if remove_input_map:
self._input_ts = list(inputs) + self._passthrough_ts
if remove_output_map:
self._output_ts = list(outputs) + self._passthrough_ts
def __init__(self, inside_ops=(), passthrough_ts=()):
"""Create a subgraph containing the given ops and the "passthrough" tensors.
Args:
inside_ops: an object convertible to a list of `tf.Operation`. This list
defines all the operations in the subgraph.
passthrough_ts: an object convertible to a list of `tf.Tensor`. This list
define all the "passthrough" tensors. A passthrough tensor is a tensor
which goes directly from the input of the subgraph to it output, without
any intermediate operations. All the non passthrough tensors are
silently ignored.
Raises:
TypeError: if inside_ops cannot be converted to a list of `tf.Operation`
or if `passthrough_ts` cannot be converted to a list of `tf.Tensor`.
"""
inside_ops = util.make_list_of_op(inside_ops)
passthrough_ts = util.make_list_of_t(passthrough_ts)
ops_and_ts = inside_ops + passthrough_ts
if ops_and_ts:
self._graph = util.get_unique_graph(ops_and_ts)
self._ops = inside_ops
# Compute inside and outside tensor
inputs, outputs, insides = select.compute_boundary_ts(inside_ops)
# Compute passthrough tensors, silently ignoring the non-passthrough ones.
all_tensors = frozenset(inputs + outputs + list(insides))
self._passthrough_ts = [
t for t in passthrough_ts if t not in all_tensors
]
# Set inputs and outputs.
self._input_ts = inputs + self._passthrough_ts
self._output_ts = outputs + self._passthrough_ts
else:
self._graph = None
self._passthrough_ts = []
self._input_ts = []
self._output_ts = []
self._ops = []
def unmap(self, remove_input_map=True, remove_output_map=True):
"""Unmap existing input and/or output mapping.
Args:
remove_input_map: if True the input map is reset to identity.
remove_output_map: if True the output map is reset to identity.
Returns:
A new modified instance of the original subgraph view with its
input and/or output mapping reset to identity.
"""
res = self.copy()
if not remove_input_map and not remove_output_map:
return res
# Compute inside and outside tensor
inputs, outputs, _ = select.compute_boundary_ts(self._ops, keep_order=True)
if remove_input_map:
self._input_ts = list(inputs) + self._passthrough_ts
if remove_output_map:
self._output_ts = list(outputs) + self._passthrough_ts
return res
def unmap(self, remove_input_map=True, remove_output_map=True):
"""Unmap existing input and/or output mapping.
Args:
remove_input_map: if True the input map is reset to identity.
remove_output_map: if True the output map is reset to identity.
Returns:
A new modified instance of the original subgraph view with its
input and/or output mapping reset to identity.
"""
res = self.copy()
if not remove_input_map and not remove_output_map:
return res
# Compute inside and outside tensor
inputs, outputs, _ = select.compute_boundary_ts(self._ops,
keep_order=True)
if remove_input_map:
self._input_ts = list(inputs) + self._passthrough_ts
if remove_output_map:
self._output_ts = list(outputs) + self._passthrough_ts
return res