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273 lines
8.7 KiB
273 lines
8.7 KiB
3 years ago
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# 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 __future__ import print_function
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from paddle.fluid import framework
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from paddle.fluid.framework import in_dygraph_mode, default_main_program
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import numpy as np
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from paddle.fluid.core import VarDesc
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from paddle.fluid import unique_name
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__all__ = [
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'MSRAInitializer'
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]
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class Initializer(object):
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"""Base class for variable initializers
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Defines the common interface of variable initializers.
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They add operations to the init program that are used
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to initialize variables. Users should not use this class
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directly, but need to use one of its implementations.
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"""
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def __init__(self):
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pass
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def __call__(self, param, block=None):
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"""Add corresponding initialization operations to the network
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"""
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raise NotImplementedError()
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def _check_block(self, block):
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if block is None:
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block = default_main_program().global_block()
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return block
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def _compute_fans(self, var):
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"""Compute the fan_in and the fan_out for layers
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This method computes the fan_in and the fan_out
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for neural network layers, if not specified. It is
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not possible to perfectly estimate fan_in and fan_out.
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This method will estimate it correctly for matrix multiply and
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convolutions.
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Args:
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var: variable for which fan_in and fan_out have to be computed
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Returns:
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tuple of two integers (fan_in, fan_out)
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"""
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shape = var.shape
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if not shape or len(shape) == 0:
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fan_in = fan_out = 1
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elif len(shape) == 1:
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fan_in = fan_out = shape[0]
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elif len(shape) == 2:
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# This is the case for simple matrix multiply
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fan_in = shape[0]
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fan_out = shape[1]
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else:
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# Assume this to be a convolutional kernel
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# In PaddlePaddle, the shape of the kernel is like:
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# [num_filters, num_filter_channels, ...] where the remaining
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# dimensions are the filter_size
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receptive_field_size = np.prod(shape[2:])
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fan_in = shape[1] * receptive_field_size
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fan_out = shape[0] * receptive_field_size
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return (fan_in, fan_out)
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class MSRAInitializer(Initializer):
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r"""Implements the MSRA initializer a.k.a. Kaiming Initializer
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This class implements the weight initialization from the paper
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`Delving Deep into Rectifiers: Surpassing Human-Level Performance on
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ImageNet Classification <https://arxiv.org/abs/1502.01852>`_
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by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. This is a
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robust initialization method that particularly considers the rectifier
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nonlinearities. In case of Uniform distribution, the range is [-x, x], where
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.. math::
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x = \sqrt{\\frac{6.0}{fan\_in}}
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In case of Normal distribution, the mean is 0 and the standard deviation
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is
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.. math::
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\sqrt{\\frac{2.0}{fan\_in}}
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Args:
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uniform (bool): whether to use uniform or normal distribution
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fan_in (float32|None): fan_in for MSRAInitializer. If None, it is\
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inferred from the variable. default is None.
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seed (int32): random seed
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Note:
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It is recommended to set fan_in to None for most cases.
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Examples:
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.. code-block:: python
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import paddle
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import paddle.fluid as fluid
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paddle.enable_static()
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x = fluid.data(name="data", shape=[8, 32, 32], dtype="float32")
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fc = fluid.layers.fc(input=x, size=10,
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param_attr=fluid.initializer.MSRA(uniform=False))
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"""
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def __init__(self, uniform=True, fan_in=None, seed=0):
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"""Constructor for MSRAInitializer
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"""
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assert uniform is not None
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assert seed is not None
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super(MSRAInitializer, self).__init__()
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self._uniform = uniform
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self._fan_in = fan_in
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self._seed = seed
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def __call__(self, var, block=None):
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"""Initialize the input tensor with MSRA initialization.
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Args:
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var(Tensor): Tensor that needs to be initialized.
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block(Block, optional): The block in which initialization ops
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should be added. Used in static graph only, default None.
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Returns:
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The initialization op
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"""
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block = self._check_block(block)
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assert isinstance(var, framework.Variable)
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assert isinstance(block, framework.Block)
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f_in, f_out = self._compute_fans(var)
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# If fan_in is passed, use it
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fan_in = f_in if self._fan_in is None else self._fan_in
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if self._seed == 0:
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self._seed = block.program.random_seed
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# to be compatible of fp16 initalizers
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if var.dtype == VarDesc.VarType.FP16 or (
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var.dtype == VarDesc.VarType.BF16 and not self._uniform):
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out_dtype = VarDesc.VarType.FP32
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out_var = block.create_var(
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name=unique_name.generate(".".join(
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['masra_init', var.name, 'tmp'])),
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shape=var.shape,
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dtype=out_dtype,
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type=VarDesc.VarType.LOD_TENSOR,
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persistable=False)
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else:
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out_dtype = var.dtype
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out_var = var
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if self._uniform:
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limit = np.sqrt(1.0 / float(fan_in))
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op = block.append_op(
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type="uniform_random",
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inputs={},
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outputs={"Out": out_var},
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attrs={
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"shape": out_var.shape,
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"dtype": int(out_dtype),
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"min": -limit,
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"max": limit,
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"seed": self._seed
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},
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stop_gradient=True)
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else:
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std = np.sqrt(2.0 / float(fan_in))
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op = block.append_op(
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type="gaussian_random",
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outputs={"Out": out_var},
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attrs={
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"shape": out_var.shape,
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"dtype": int(out_dtype),
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"mean": 0.0,
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"std": std,
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"seed": self._seed
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},
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stop_gradient=True)
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if var.dtype == VarDesc.VarType.FP16 or (
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var.dtype == VarDesc.VarType.BF16 and not self._uniform):
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block.append_op(
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type="cast",
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inputs={"X": out_var},
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outputs={"Out": var},
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attrs={"in_dtype": out_var.dtype,
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"out_dtype": var.dtype})
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if not framework.in_dygraph_mode():
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var.op = op
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return op
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class KaimingUniform(MSRAInitializer):
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r"""Implements the Kaiming Uniform initializer
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This class implements the weight initialization from the paper
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`Delving Deep into Rectifiers: Surpassing Human-Level Performance on
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ImageNet Classification <https://arxiv.org/abs/1502.01852>`_
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by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. This is a
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robust initialization method that particularly considers the rectifier
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nonlinearities.
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In case of Uniform distribution, the range is [-x, x], where
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.. math::
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x = \sqrt{\frac{6.0}{fan\_in}}
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Args:
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fan_in (float32|None): fan_in for Kaiming uniform Initializer. If None, it is\
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inferred from the variable. default is None.
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Note:
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It is recommended to set fan_in to None for most cases.
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Examples:
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.. code-block:: python
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import paddle
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import paddle.nn as nn
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linear = nn.Linear(2,
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4,
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weight_attr=nn.initializer.KaimingUniform())
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data = paddle.rand([30, 10, 2], dtype='float32')
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res = linear(data)
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"""
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def __init__(self, fan_in=None):
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super(KaimingUniform, self).__init__(
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uniform=True, fan_in=fan_in, seed=0)
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# We short the class name, since users will use the initializer with the package
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# name. The sample code:
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#
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# import paddle.fluid as fluid
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#
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# hidden = fluid.layers.fc(...,
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# param_attr=ParamAttr(fluid.initializer.Xavier()))
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#
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# It is no need to add an `Initializer` as the class suffix
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MSRA = MSRAInitializer
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