wehub-resource-sync
119 lines
4.1 KiB
Python
119 lines
4.1 KiB
Python
# Copyright (c) 2022 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 __future__ import annotations
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from typing import TYPE_CHECKING
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import paddle
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from .data_feeder import check_type
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if TYPE_CHECKING:
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from paddle.nn.initializer import Initializer
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__all__ = []
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_global_weight_initializer_ = None
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_global_bias_initializer_ = None
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def _global_weight_initializer() -> Initializer | None:
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"""
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Return the global weight initializer, The user doesn't need to use it.
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"""
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return _global_weight_initializer_
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def _global_bias_initializer() -> Initializer | None:
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"""
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Return the global weight initializer, The user doesn't need to use it.
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"""
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return _global_bias_initializer_
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def set_global_initializer(
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weight_init: Initializer | None, bias_init: Initializer | None = None
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) -> None:
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"""
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This API is used to set up global model parameter initializer in framework.
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After this API is invoked, the global initializer will takes effect in subsequent code.
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The model parameters include ``weight`` and ``bias`` . In the framework, they correspond
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to ``paddle.ParamAttr`` , which is inherited from ``paddle.Tensor`` , and is a persistable Variable.
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This API only takes effect for model parameters, not for variables created through apis such as
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:ref:`api_paddle_static_create_global_var` , :ref:`api_paddle_Tensor_create_tensor`.
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If the initializer is also set up by ``param_attr`` or ``bias_attr`` when creating a network layer,
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the global initializer setting here will not take effect because it has a lower priority.
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If you want to cancel the global initializer in framework, please set global initializer to ``None`` .
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Args:
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weight_init (Initializer|None): set the global initializer for ``weight`` of model parameters.
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bias_init (Initializer|None, optional): set the global initializer for ``bias`` of model parameters.
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Default: None.
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Returns:
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None
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> import paddle.nn as nn
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>>> nn.initializer.set_global_initializer(nn.initializer.Uniform(), nn.initializer.Constant())
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>>> x_var = paddle.uniform((2, 4, 8, 8), dtype='float32', min=-1.0, max=1.0)
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>>> # The weight of conv1 is initialized by Uniform
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>>> # The bias of conv1 is initialized by Constant
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>>> conv1 = nn.Conv2D(4, 6, (3, 3))
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>>> y_var1 = conv1(x_var)
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>>> # If set param_attr/bias_attr too, global initializer will not take effect
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>>> # The weight of conv2 is initialized by Xavier
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>>> # The bias of conv2 is initialized by Normal
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>>> conv2 = nn.Conv2D(
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... 4,
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... 6,
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... (3, 3),
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... weight_attr=nn.initializer.XavierUniform(),
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... bias_attr=nn.initializer.Normal(),
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... )
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>>> y_var2 = conv2(x_var)
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>>> # Cancel the global initializer in framework, it will takes effect in subsequent code
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>>> nn.initializer.set_global_initializer(None)
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"""
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check_type(
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weight_init,
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'weight_init',
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(paddle.nn.initializer.Initializer, type(None)),
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'set_global_initializer',
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)
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global _global_weight_initializer_
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_global_weight_initializer_ = weight_init
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check_type(
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bias_init,
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'bias_init',
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(paddle.nn.initializer.Initializer, type(None)),
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'set_global_initializer',
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)
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global _global_bias_initializer_
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_global_bias_initializer_ = bias_init
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