megengine.module.batchnorm 源代码

# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
from typing import Optional

import numpy as np

from ..distributed.group import WORLD, Group
from ..functional.nn import batch_norm, sync_batch_norm
from ..tensor import Parameter, Tensor
from . import init
from .module import Module


class _BatchNorm(Module):
    def __init__(
        self,
        num_features,
        eps=1e-5,
        momentum=0.9,
        affine=True,
        track_running_stats=True,
        freeze=False,
    ):
        super(_BatchNorm, self).__init__()
        self.num_features = num_features
        self.eps = eps
        self.momentum = momentum
        self.affine = affine
        self.track_running_stats = track_running_stats
        self._track_running_stats_saved = track_running_stats
        self.freeze = freeze
        tshape = (1, self.num_features, 1, 1)
        if self.affine:
            self.weight = Parameter(np.ones(tshape, dtype=np.float32))
            self.bias = Parameter(np.zeros(tshape, dtype=np.float32))
        else:
            self.weight = None
            self.bias = None

        if self.track_running_stats:
            self.running_mean = Tensor(np.zeros(tshape, dtype=np.float32))
            self.running_var = Tensor(np.ones(tshape, dtype=np.float32))
        else:
            self.running_mean = None
            self.running_var = None

    def reset_running_stats(self) -> None:
        if self.track_running_stats:
            init.zeros_(self.running_mean)
            init.ones_(self.running_var)

    def reset_parameters(self) -> None:
        self.reset_running_stats()
        if self.affine:
            init.ones_(self.weight)
            init.zeros_(self.bias)

    def _check_input_ndim(self, inp):
        raise NotImplementedError

    def forward(self, inp):
        self._check_input_ndim(inp)
        if self._track_running_stats_saved == False:
            assert (
                self.track_running_stats == False
            ), "track_running_stats can not be initilized to False and changed to True later"

        inp_shape = inp.shape
        _ndims = len(inp_shape)
        if _ndims != 4:
            origin_shape = inp_shape
            if _ndims == 2:
                n, c = inp_shape[0], inp_shape[1]
                new_shape = (n, c, 1, 1)
            elif _ndims == 3:
                n, c, h = inp_shape[0], inp_shape[1], inp_shape[2]
                new_shape = (n, c, h, 1)

            inp = inp.reshape(new_shape)

        if self.freeze and self.training and self._track_running_stats_saved:
            scale = self.weight * (self.running_var + self.eps) ** (-0.5)
            bias = self.bias - self.running_mean * scale
            return inp * scale.detach() + bias.detach()

        if self.training and self.track_running_stats:
            exponential_average_factor = self.momentum
        else:
            exponential_average_factor = 0.0  # useless

        output = batch_norm(
            inp,
            self.running_mean if self.track_running_stats else None,
            self.running_var if self.track_running_stats else None,
            self.weight,
            self.bias,
            training=self.training
            or ((self.running_mean is None) and (self.running_var is None)),
            momentum=exponential_average_factor,
            eps=self.eps,
        )

        if _ndims != 4:
            output = output.reshape(origin_shape)

        return output

    def _module_info_string(self) -> str:
        s = (
            "{num_features}, eps={eps}, momentum={momentum}, affine={affine}, "
            "track_running_stats={track_running_stats}"
        )
        return s.format(**self.__dict__)


[文档]class SyncBatchNorm(_BatchNorm): r""" Applies Synchronization Batch Normalization. """ def __init__( self, num_features, eps=1e-5, momentum=0.9, affine=True, track_running_stats=True, freeze=False, group: Optional[Group] = WORLD, ) -> None: super().__init__( num_features, eps, momentum, affine, track_running_stats, freeze ) self.group = group def _check_input_ndim(self, inp): if len(inp.shape) not in {2, 3, 4}: raise ValueError( "expected 2D, 3D or 4D input (got {}D input)".format(len(inp.shape)) )
[文档] def forward(self, inp): self._check_input_ndim(inp) inp_shape = inp.shape _ndims = len(inp_shape) if _ndims != 4: new_shape = Tensor([1, 1, 1, 1], device=inp.device) origin_shape = inp_shape if _ndims == 2: new_shape[:2] = origin_shape[:2] elif _ndims == 3: new_shape[:3] = origin_shape[:3] else: raise ValueError( "expected 2D, 3D or 4D input (got {}D input)".format(len(inp_shape)) ) inp = inp.reshape(new_shape) if self.training and self.track_running_stats: exponential_average_factor = self.momentum else: exponential_average_factor = 0.0 # useless output = sync_batch_norm( inp, self.running_mean, self.running_var, self.weight, self.bias, self.training or not self.track_running_stats, exponential_average_factor, self.eps, group=self.group, ) if _ndims != 4: output = output.reshape(origin_shape) return output
[文档]class BatchNorm1d(_BatchNorm): r""" Applies Batch Normalization over a 2D/3D tensor. Refer to :class:`~.BatchNorm2d` for more information. """ def _check_input_ndim(self, inp): if len(inp.shape) not in {2, 3}: raise ValueError( "expected 2D or 3D input (got {}D input)".format(len(inp.shape)) )
[文档]class BatchNorm2d(_BatchNorm): r""" Applies Batch Normalization over a 4D tensor. .. math:: y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta The mean and standard-deviation are calculated per-dimension over the mini-batches and :math:`\gamma` and :math:`\beta` are learnable parameter vectors. By default, during training this layer keeps running estimates of its computed mean and variance, which are then used for normalization during evaluation. The running estimates are kept with a default :attr:`momentum` of 0.9. If :attr:`track_running_stats` is set to ``False``, this layer will not keep running estimates, batch statistics is used during evaluation time instead. .. note:: This :attr:`momentum` argument is different from one used in optimizer classes and the conventional notion of momentum. Mathematically, the update rule for running statistics here is :math:`\hat{x}_\text{new} = \text{momentum} \times \hat{x} + (1 - \text{momentum}) \times x_t`, where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the new observed value. Because the Batch Normalization is done over the `C` dimension, computing statistics on `(N, H, W)` slices, it's common terminology to call this Spatial Batch Normalization. :type num_features: int :param num_features: usually :math:`C` from an input of shape :math:`(N, C, H, W)` or the highest ranked dimension of an input less than 4D. :type eps: float :param eps: a value added to the denominator for numerical stability. Default: 1e-5 :type momentum: float :param momentum: the value used for the ``running_mean`` and ``running_var`` computation. Default: 0.9 :type affine: bool :param affine: a boolean value that when set to True, this module has learnable affine parameters. Default: True :type track_running_stats: bool :param track_running_stats: when set to True, this module tracks the running mean and variance. When set to False, this module does not track such statistics and always uses batch statistics in both training and eval modes. Default: True :type freeze: bool :param freeze: when set to True, this module does not update the running mean and variance, and uses the running mean and variance instead of the batch mean and batch variance to normalize the input. The parameter takes effect only when the module is initilized with track_running_stats as True and the module is in training mode. Default: False Examples: .. testcode:: import numpy as np import megengine as mge import megengine.module as M # With Learnable Parameters m = M.BatchNorm2d(4) inp = mge.tensor(np.random.rand(1, 4, 3, 3).astype("float32")) oup = m(inp) print(m.weight.numpy().flatten(), m.bias.numpy().flatten()) # Without L`e`arnable Parameters m = M.BatchNorm2d(4, affine=False) oup = m(inp) print(m.weight, m.bias) Outputs: .. testoutput:: [1. 1. 1. 1.] [0. 0. 0. 0.] None None """ def _check_input_ndim(self, inp): if len(inp.shape) != 4: raise ValueError("expected 4D input (got {}D input)".format(len(inp.shape)))