def _normalize_program(self, program, feed_vars, fetch_vars):
if not isinstance(program, Program):
raise TypeError(
"program type must be `fluid.Program`, but received `%s`" %
type(program))
if not isinstance(feed_vars, list):
feed_vars = [feed_vars]
if not all(isinstance(v, Variable) for v in feed_vars):
raise TypeError(
"feed_vars type must be a Variable or a list of Variable.")
if not isinstance(fetch_vars, list):
fetch_vars = [fetch_vars]
if not all(isinstance(v, Variable) for v in fetch_vars):
raise TypeError(
"fetch_vars type must be a Variable or a list of Variable.")
# remind users to set auc_states to 0 if auc op were found.
for op in program.global_block().ops:
# clear device of Op
device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName(
)
op._set_attr(device_attr_name, "")
if op.type == 'auc':
warnings.warn("Be sure that you have set auc states to 0 "
"before saving inference model.")
break
# serialize program
copy_program = program.clone()
global_block = copy_program.global_block()
remove_op_idx = []
for i, op in enumerate(global_block.ops):
op.desc.set_is_target(False)
if op.type == "feed" or op.type == "fetch":
remove_op_idx.append(i)
for idx in remove_op_idx[::-1]:
global_block._remove_op(idx)
copy_program.desc.flush()
feed_var_names = [var.name for var in feed_vars]
copy_program = copy_program._prune_with_input(
feeded_var_names=feed_var_names, targets=fetch_vars)
copy_program = copy_program._inference_optimize(prune_read_op=True)
fetch_var_names = [var.name for var in fetch_vars]
prepend_feed_ops(copy_program, feed_var_names)
append_fetch_ops(copy_program, fetch_var_names)
copy_program.desc._set_version()
return copy_program
def _normalize_program(program, feed_vars, fetch_vars):
"""
optimize program according feed_vars and fetch_vars.
"""
# remind users to set auc_states to 0 if auc op were found.
for op in program.global_block().ops:
# clear device of Op
device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName()
op._set_attr(device_attr_name, "")
if op.type == 'auc':
warnings.warn("Be sure that you have set auc states to 0 "
"before saving inference model.")
break
# fix the bug that the activation op's output as target will be pruned.
# will affect the inference performance.
# TODO(Superjomn) add an IR pass to remove 1-scale op.
with program_guard(program):
uniq_fetch_vars = []
for i, var in enumerate(fetch_vars):
var = layers.scale(var,
1.,
name="save_infer_model/scale_{}".format(i))
uniq_fetch_vars.append(var)
fetch_vars = uniq_fetch_vars
# serialize program
copy_program = program.clone()
global_block = copy_program.global_block()
remove_op_idx = []
for i, op in enumerate(global_block.ops):
op.desc.set_is_target(False)
if op.type == "feed" or op.type == "fetch":
remove_op_idx.append(i)
for idx in remove_op_idx[::-1]:
global_block._remove_op(idx)
copy_program.desc.flush()
feed_var_names = [var.name for var in feed_vars]
copy_program = copy_program._prune_with_input(
feeded_var_names=feed_var_names, targets=fetch_vars)
copy_program = copy_program._inference_optimize(prune_read_op=True)
fetch_var_names = [var.name for var in fetch_vars]
prepend_feed_ops(copy_program, feed_var_names)
append_fetch_ops(copy_program, fetch_var_names)
copy_program.desc._set_version()
return copy_program
def normalize_program(program, feed_vars, fetch_vars):
"""
:api_attr: Static Graph
Normalize/Optimize a program according to feed_vars and fetch_vars.
Args:
program(Program): Specify a program you want to optimize.
feed_vars(Variable | list[Variable]): Variables needed by inference.
fetch_vars(Variable | list[Variable]): Variables returned by inference.
Returns:
Program: Normalized/Optimized program.
Raises:
TypeError: If `program` is not a Program, an exception is thrown.
TypeError: If `feed_vars` is not a Variable or a list of Variable, an exception is thrown.
TypeError: If `fetch_vars` is not a Variable or a list of Variable, an exception is thrown.
Examples:
.. code-block:: python
import paddle
paddle.enable_static()
path_prefix = "./infer_model"
# User defined network, here a softmax regession example
image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
predict = paddle.static.nn.fc(image, 10, activation='softmax')
loss = paddle.nn.functional.cross_entropy(predict, label)
exe = paddle.static.Executor(paddle.CPUPlace())
exe.run(paddle.static.default_startup_program())
# normalize main program.
program = paddle.static.default_main_program()
normalized_program = paddle.static.normalize_program(program, [image], [predict])
"""
if not isinstance(program, Program):
raise TypeError(
"program type must be `fluid.Program`, but received `%s`" %
type(program))
if not isinstance(feed_vars, list):
feed_vars = [feed_vars]
if not all(isinstance(v, Variable) for v in feed_vars):
raise TypeError(
"feed_vars type must be a Variable or a list of Variable.")
if not isinstance(fetch_vars, list):
fetch_vars = [fetch_vars]
if not all(isinstance(v, Variable) for v in fetch_vars):
raise TypeError(
"fetch_vars type must be a Variable or a list of Variable.")
# remind users to set auc_states to 0 if auc op were found.
for op in program.global_block().ops:
# clear device of Op
device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName()
op._set_attr(device_attr_name, "")
if op.type == 'auc':
warnings.warn("Be sure that you have set auc states to 0 "
"before saving inference model.")
break
# fix the bug that the activation op's output as target will be pruned.
# will affect the inference performance.
# TODO(Superjomn) add an IR pass to remove 1-scale op.
with program_guard(program):
uniq_fetch_vars = []
for i, var in enumerate(fetch_vars):
if var.dtype != paddle.bool:
var = layers.scale(
var, 1., name="save_infer_model/scale_{}".format(i))
uniq_fetch_vars.append(var)
fetch_vars = uniq_fetch_vars
# serialize program
copy_program = program.clone()
global_block = copy_program.global_block()
remove_op_idx = []
for i, op in enumerate(global_block.ops):
op.desc.set_is_target(False)
if op.type == "feed" or op.type == "fetch":
remove_op_idx.append(i)
for idx in remove_op_idx[::-1]:
global_block._remove_op(idx)
copy_program.desc.flush()
feed_var_names = [var.name for var in feed_vars]
copy_program = copy_program._prune_with_input(
feeded_var_names=feed_var_names, targets=fetch_vars)
copy_program = copy_program._inference_optimize(prune_read_op=True)
fetch_var_names = [var.name for var in fetch_vars]
prepend_feed_ops(copy_program, feed_var_names)
append_fetch_ops(copy_program, fetch_var_names)
copy_program.desc._set_version()
return copy_program