wehub-resource-sync
142 lines
5.1 KiB
Python
142 lines
5.1 KiB
Python
# Copyright (c) 2023 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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import paddle
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from paddle import _legacy_C_ops
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from paddle.framework import in_dynamic_mode
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class FusedDropout(paddle.nn.Layer):
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r"""
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Dropout is a regularization technique for reducing overfitting by preventing
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neuron co-adaption during training as described in the paper:
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`Improving neural networks by preventing co-adaptation of feature detectors <https://arxiv.org/abs/1207.0580>`_
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The dropout operator randomly sets the outputs of some units to zero, while upscale others
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according to the given dropout probability.
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It is an optimized implementation for ``paddle.nn.Dropout``.
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In dygraph mode, please use ``eval()`` to switch to evaluation mode, where dropout is disabled.
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Parameters:
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p (float|int, optional): Probability of setting units to zero. Default: 0.5
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axis (int|list|tuple, optional): The axis along which the dropout is performed. Default: None.
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mode(str, optional): ['upscale_in_train'(default) | 'downscale_in_infer']
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1. upscale_in_train (default), upscale the output at training time
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- train: :math:`out = input \times \frac{mask}{(1.0 - p)}`
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- inference: :math:`out = input`
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2. downscale_in_infer, downscale the output at inference
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- train: :math:`out = input \times mask`
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- inference: :math:`out = input \times (1.0 - p)`
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name (str, optional): Name for the operation, Default: None. For more information, please refer to :ref:`api_guide_Name`.
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Shape:
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- input: N-D tensor.
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- output: N-D tensor, the same shape as input.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> paddle.seed(2023)
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>>> x = paddle.to_tensor([[1, 2, 3], [4, 5, 6]], dtype="float32")
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>>> m = paddle.incubate.nn.FusedDropout(p=0.5)
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>>> y_train = m(x)
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>>> print(y_train)
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Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
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[[0., 0., 6.],
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[0., 0., 0.]])
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>>> m.eval() # switch the model to test phase
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>>> y_test = m(x)
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>>> print(y_test)
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Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
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[[1., 2., 3.],
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[4., 5., 6.]])
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"""
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def __init__(self, p=0.5, axis=None, mode="upscale_in_train", name=None):
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super().__init__()
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if not isinstance(p, (float, int)):
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raise TypeError("p argument should be a number")
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if p < 0 or p > 1:
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raise ValueError("p argument should between 0 and 1")
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mode = (
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'downgrade_in_infer' if mode == 'downscale_in_infer' else mode
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) # semantic transfer
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if mode not in ('downscale_in_infer', 'upscale_in_train'):
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raise ValueError(
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"mode argument should be 'downscale_in_infer' or 'upscale_in_train'"
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)
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if axis and not isinstance(axis, (int, list, tuple)):
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raise TypeError("datatype of axis argument should be int or list")
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self.p = p
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self.mode = mode
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self.name = name
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self.axis = None
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if axis is not None:
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self.axis = [axis] if isinstance(axis, int) else list(axis)
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def forward(self, input):
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# fast return for p == 0
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if self.p == 0:
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return input
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if self.axis is not None and in_dynamic_mode():
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seed = None
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if paddle.static.default_main_program().random_seed != 0:
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seed = paddle.static.default_main_program().random_seed
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out, mask = _legacy_C_ops.dropout_nd(
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input,
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'dropout_prob',
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self.p,
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'is_test',
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not self.training,
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'fix_seed',
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seed is not None,
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'seed',
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seed if seed is not None else 0,
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'dropout_implementation',
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self.mode,
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'axis',
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self.axis,
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)
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else:
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out = paddle.nn.functional.dropout(
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input,
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p=self.p,
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axis=self.axis,
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training=self.training,
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mode=self.mode,
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name=self.name,
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)
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return out
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def extra_repr(self):
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name_str = f', name={self.name}' if self.name else ''
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return f'p={self.p}, axis={self.axis}, mode={self.mode}{name_str}'
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