def set_input(scope, op, inputs, place):
def np_value_to_fluid_value(input):
if input.dtype == np.float16:
input = input.view(np.uint16)
return input
def __set_input__(var_name, var):
if isinstance(var, tuple) or isinstance(var, np.ndarray):
tensor = scope.find_var(var_name).get_tensor()
if isinstance(var, tuple):
tensor.set_recursive_sequence_lengths(var[1])
var = var[0]
tensor._set_dims(var.shape)
tensor.set(np_value_to_fluid_value(var), place)
elif isinstance(var, float):
scope.find_var(var_name).set_float(var)
elif isinstance(var, int):
scope.find_var(var_name).set_int(var)
for in_name, in_dup in Operator.get_op_inputs(op.type()):
if in_name in inputs:
if in_dup:
sub_in = inputs[in_name]
for item in sub_in:
sub_in_name, sub_in_val = item[0], item[1]
__set_input__(sub_in_name, sub_in_val)
else:
__set_input__(in_name, inputs[in_name])
def create_op(scope, op_type, inputs, outputs, attrs):
kwargs = dict()
def __create_var__(name, var_name):
scope.var(var_name).get_tensor()
kwargs[name].append(var_name)
for in_name, in_dup in Operator.get_op_inputs(op_type):
if in_name in inputs:
kwargs[in_name] = []
if in_dup:
sub_in = inputs[in_name]
for item in sub_in:
sub_in_name, _ = item[0], item[1]
__create_var__(in_name, sub_in_name)
else:
__create_var__(in_name, in_name)
for out_name, out_dup in Operator.get_op_outputs(op_type):
if out_name in outputs:
kwargs[out_name] = []
if out_dup:
sub_out = outputs[out_name]
for item in sub_out:
sub_out_name, _ = item[0], item[1]
__create_var__(out_name, sub_out_name)
else:
__create_var__(out_name, out_name)
for attr_name in Operator.get_op_attr_names(op_type):
if attr_name in attrs:
kwargs[attr_name] = attrs[attr_name]
return Operator(op_type, **kwargs)
def create_op(scope, op_type, inputs, outputs, attrs, cache_list=None):
kwargs = dict()
op_maker = core.op_proto_and_checker_maker
op_role_attr_name = op_maker.kOpRoleAttrName()
if op_role_attr_name not in attrs:
attrs[op_role_attr_name] = int(op_maker.OpRole.Forward)
def __create_var__(name, var_name):
scope.var(var_name).get_tensor()
kwargs[name].append(var_name)
for in_name, in_dup in Operator.get_op_inputs(op_type):
if in_name in inputs:
kwargs[in_name] = []
if in_dup:
sub_in = inputs[in_name]
for item in sub_in:
sub_in_name, _ = item[0], item[1]
__create_var__(in_name, sub_in_name)
else:
__create_var__(in_name, in_name)
if cache_list != None and isinstance(cache_list, list):
for name in cache_list:
kwargs[name] = []
scope.var(name)
kwargs[name].append(name)
for out_name, out_dup in Operator.get_op_outputs(op_type):
if out_name in outputs:
kwargs[out_name] = []
if out_dup:
sub_out = outputs[out_name]
for item in sub_out:
sub_out_name, _ = item[0], item[1]
__create_var__(out_name, sub_out_name)
else:
__create_var__(out_name, out_name)
for attr_name in Operator.get_op_attr_names(op_type):
if attr_name in attrs:
kwargs[attr_name] = attrs[attr_name]
return Operator(op_type, **kwargs)
def create_op(scope, op_type, inputs, outputs, attrs):
kwargs = dict()
op_maker = core.op_proto_and_checker_maker
op_role_attr_name = op_maker.kOpRoleAttrName()
if op_role_attr_name not in attrs:
attrs[op_role_attr_name] = int(op_maker.OpRole.Forward)
def __create_var__(name, var_name):
scope.var(var_name).get_tensor()
kwargs[name].append(var_name)
for in_name, in_dup in Operator.get_op_inputs(op_type):
if in_name in inputs:
kwargs[in_name] = []
if in_dup:
sub_in = inputs[in_name]
for item in sub_in:
sub_in_name, _ = item[0], item[1]
__create_var__(in_name, sub_in_name)
else:
__create_var__(in_name, in_name)
for out_name, out_dup in Operator.get_op_outputs(op_type):
if out_name in outputs:
kwargs[out_name] = []
if out_dup:
sub_out = outputs[out_name]
for item in sub_out:
sub_out_name, _ = item[0], item[1]
__create_var__(out_name, sub_out_name)
else:
__create_var__(out_name, out_name)
for attr_name in Operator.get_op_attr_names(op_type):
if attr_name in attrs:
kwargs[attr_name] = attrs[attr_name]
return Operator(op_type, **kwargs)
def set_input(scope, op, inputs, place):
def __set_input__(var_name, var):
if isinstance(var, tuple) or isinstance(var, np.ndarray):
tensor = scope.find_var(var_name).get_tensor()
if isinstance(var, tuple):
tensor.set_lod(var[1])
var = var[0]
tensor.set_dims(var.shape)
tensor.set(var, place)
elif isinstance(var, float):
scope.find_var(var_name).set_float(var)
elif isinstance(var, int):
scope.find_var(var_name).set_int(var)
for in_name, in_dup in Operator.get_op_inputs(op.type()):
if in_name in inputs:
if in_dup:
sub_in = inputs[in_name]
for item in sub_in:
sub_in_name, sub_in_val = item[0], item[1]
__set_input__(sub_in_name, sub_in_val)
else:
__set_input__(in_name, inputs[in_name])
def set_input(scope, op, inputs, place):
def __set_input__(var_name, var):
if isinstance(var, tuple) or isinstance(var, np.ndarray):
tensor = scope.find_var(var_name).get_tensor()
if isinstance(var, tuple):
tensor.set_lod(var[1])
var = var[0]
tensor.set_dims(var.shape)
tensor.set(var, place)
elif isinstance(var, float):
scope.find_var(var_name).set_float(var)
elif isinstance(var, int):
scope.find_var(var_name).set_int(var)
for in_name, in_dup in Operator.get_op_inputs(op.type()):
if in_name in inputs:
if in_dup:
sub_in = inputs[in_name]
for item in sub_in:
sub_in_name, sub_in_val = item[0], item[1]
__set_input__(sub_in_name, sub_in_val)
else:
__set_input__(in_name, inputs[in_name])