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