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Paddle/python/paddle/fluid/dygraph/base.py

109 lines
3.1 KiB

# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..wrapped_decorator import signature_safe_contextmanager, wrap_decorator
import contextlib
import numpy as np
from paddle.fluid import core
from paddle.fluid import framework
from .tracer import Tracer
__all__ = [
'enabled',
'no_grad',
'not_support',
'guard',
'to_variable',
]
def enabled():
return framework.in_dygraph_mode()
@contextlib.contextmanager
def _switch_tracer_mode_guard_(is_train=True):
tracer = framework._dygraph_tracer()
if tracer:
mode = tracer._train_mode
tracer._train_mode = is_train
yield
tracer._train_mode = mode
else:
yield
def _dygraph_not_support_(func):
def __impl__(*args, **kwargs):
assert not framework.in_dygraph_mode(
), "We don't support %s in Dygraph mode" % func.__name__
return func(*args, **kwargs)
return __impl__
def _no_grad_(func):
def __impl__(*args, **kwargs):
with _switch_tracer_mode_guard_(is_train=False):
return func(*args, **kwargs)
return __impl__
no_grad = wrap_decorator(_no_grad_)
not_support = wrap_decorator(_dygraph_not_support_)
@signature_safe_contextmanager
def guard(place=None):
train = framework.Program()
startup = framework.Program()
tracer = Tracer(train.current_block().desc)
if place is None:
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
with framework.program_guard(train, startup):
with framework.unique_name.guard():
with framework._dygraph_guard(tracer):
with framework._dygraph_place_guard(place):
yield
def to_variable(value, block=None, name=None):
if isinstance(value, np.ndarray):
assert enabled(), "to_variable could only be called in dygraph mode"
if not block:
block = framework.default_main_program().current_block()
py_var = framework.Variable(
block,
type=core.VarDesc.VarType.LOD_TENSOR,
name=name,
shape=value.shape,
dtype=value.dtype,
stop_gradient=True)
var = py_var._ivar.value()
tensor = var.get_tensor()
tensor.set(value, framework._current_expected_place())
return py_var
elif isinstance(value, framework.Variable):
return value
else:
raise TypeError(
"to_variable only accepts 'ndarray' and 'Variable' as value's input")