def __call__(self, *args):
"""Executes the passed function in eager mode."""
tensor_inputs = [
x for x in nest.flatten(args) if isinstance(x, ops.Tensor)
]
if tape.should_record(tensor_inputs) or tape.should_record(
self._extra_inputs):
if not self._has_backprop:
self._compute_backprop()
return self._backprop_call(tensor_inputs)
if context.in_graph_mode():
g = ops.get_default_graph()
if self._fdef.name not in g._functions: # pylint: disable=protected-access
g._add_function(self._fdef) # pylint: disable=protected-access
signature = self._fdef.definition.signature
args = list(tensor_inputs) + self._extra_inputs
op = g.create_op(
signature.name, [ops.convert_to_tensor(x) for x in args],
[dtypes.DType(x.type) for x in signature.output_arg],
op_def=signature,
name="FunctionCall",
compute_shapes=False)
result = op.outputs
for i, s in enumerate(self._output_shapes):
result[i].set_shape(s)
else:
result = execute.execute(str(self._func_name),
num_outputs=self._num_outputs,
inputs=tensor_inputs + self._extra_inputs)
return self._build_call_outputs(self._returns, result)
def __call__(self, *args):
"""Executes the passed function in eager mode."""
tensor_inputs = [
x for x in nest.flatten(args)
if isinstance(x, ops.Tensor)
]
if tape.should_record(tensor_inputs) or tape.should_record(
self._extra_inputs):
if not self._has_backprop:
self._compute_backprop()
return self._backprop_call(tensor_inputs)
if context.in_graph_mode():
g = ops.get_default_graph()
if self._fdef.name not in g._functions: # pylint: disable=protected-access
g._add_function(self._fdef) # pylint: disable=protected-access
signature = self._fdef.definition.signature
args = list(tensor_inputs) + self._extra_inputs
op = g.create_op(
signature.name, [ops.convert_to_tensor(x) for x in args],
[dtypes.DType(x.type) for x in signature.output_arg],
op_def=signature,
name="FunctionCall",
compute_shapes=False)
result = op.outputs
for i, s in enumerate(self._output_shapes):
result[i].set_shape(s)
else:
result = execute.execute(
str(self._func_name),
num_outputs=self._num_outputs,
inputs=tensor_inputs + self._extra_inputs)
return self._build_call_outputs(self._returns, result)
def __call__(self, *args):
"""Executes the passed function in eager mode."""
for v in self._variables:
if v._trainable: # pylint: disable=protected-access
tape.watch_variable(v)
tensor_inputs = [
x for x in nest.flatten(args) if isinstance(x, ops.Tensor)
]
if tape.should_record(tensor_inputs) or tape.should_record(
self._extra_inputs):
if not self._has_backprop:
self._compute_backprop()
return self._backprop_call(tensor_inputs)
ctx = context.context()
if ctx.in_graph_mode():
g = ops.get_default_graph()
if self._function_def.name not in g._functions: # pylint: disable=protected-access
g._add_function(self._function_def) # pylint: disable=protected-access
for f in self._graph._functions.values(): # pylint: disable=protected-access
if f.name not in g._functions: # pylint: disable=protected-access
g._add_function(f) # pylint: disable=protected-access
signature = self._function_def.definition.signature
args = list(tensor_inputs) + self._extra_inputs
op = g.create_op(
signature.name,
[ops.internal_convert_to_tensor(x, ctx=ctx) for x in args],
tuple(
dtypes_module.DType(x.type) for x in signature.output_arg),
op_def=signature,
name="FunctionCall",
compute_shapes=False)
result = op.outputs
if not result:
return op
for i, s in enumerate(self._output_shapes):
result[i].set_shape(s)
else:
result = execute.execute(str(self._func_name),
num_outputs=self._num_outputs,
inputs=tensor_inputs + self._extra_inputs,
attrs=None,
ctx=ctx)
return self._build_call_outputs(result)
def __call__(self, *args):
"""Executes the passed function in eager mode."""
for v in self._variables:
if v._trainable: # pylint: disable=protected-access
tape.watch_variable(v)
tensor_inputs = [x for x in nest.flatten(args)
if isinstance(x, ops.Tensor)]
if tape.should_record(tensor_inputs) or tape.should_record(
self._extra_inputs):
if not self._has_backprop:
self._compute_backprop()
return self._backprop_call(tensor_inputs)
ctx = context.context()
if ctx.in_graph_mode():
g = ops.get_default_graph()
self.add_to_graph(g)
signature = self._function_def.definition.signature
args = list(tensor_inputs) + self._extra_inputs
op = g.create_op(
signature.name,
[ops.internal_convert_to_tensor(x, ctx=ctx) for x in args],
tuple(dtypes_module.DType(x.type) for x in signature.output_arg),
op_def=signature,
name="FunctionCall",
compute_shapes=False)
result = op.outputs
if not result:
return op
for i, s in enumerate(self._output_shapes):
result[i].set_shape(s)
else:
result = execute.execute(
str(self._func_name),
num_outputs=self._num_outputs,
inputs=tensor_inputs + self._extra_inputs,
attrs=None,
ctx=ctx)
return self._build_call_outputs(result)
def __call__(self, *args):
"""Executes the passed function in eager mode."""
for v in self._variables:
if v.trainable:
tape.watch_variable(v)
tensor_inputs = [x for x in nest.flatten(args) if isinstance(x, ops.Tensor)]
if tape.should_record(tensor_inputs) or tape.should_record(
self._extra_inputs):
if self._backward_function is None:
self._construct_backprop_function()
return self._backprop_call(tensor_inputs)
ctx = context.context()
if ctx.executing_eagerly():
result = execute.execute(
str(self._func_name),
num_outputs=self._num_outputs,
inputs=tensor_inputs + self._extra_inputs,
attrs=None,
ctx=ctx)
else:
g = ops.get_default_graph()
self.add_to_graph(g)
signature = self._function_def.definition.signature
args = list(tensor_inputs) + self._extra_inputs
op = g.create_op(
signature.name,
[ops.internal_convert_to_tensor(x, ctx=ctx) for x in args],
tuple(dtypes_module.DType(x.type) for x in signature.output_arg),
op_def=signature,
name="FunctionCall",
compute_shapes=False)
result = op.outputs
if not result:
return op
for i, s in enumerate(self._output_shapes):
result[i].set_shape(s)
return self._build_call_outputs(result)
