比较与torch.nn.Module.buffers的功能差异

torch.nn.Module.buffers

torch.nn.Module.buffers(recurse=True)

更多内容详见torch.nn.Module.buffers。

mindspore.nn.Cell.untrainable_params

mindspore.nn.Cell.untrainable_params(recurse=True)

更多内容详见mindspore.nn.Cell.untrainable_params。

使用方式

PyTorch中，网络有parameter, buffer, state三种概念，其中state为parameter和buffer的合集。parameter可以通过requires_grad属性来区分网络中的参数是否需要优化；buffer多定义为网络中的不变量，例如在定义网络时，BN中的running_mean和running_var会被自动注册为buffer；用户也可以通过相关接口自行注册parameter和buffer。

torch.nn.Module.buffers：获取网络中的buffer，返回类型为生成器。
torch.nn.Module.named_buffers：获取网络中的buffer名称和buffer本身，返回类型为生成器。

MindSpore中目前只有parameter的概念，通过requires_grad属性来区分网络中的参数是否需要优化，例如在定义网络时，BN中的moving_mean和moving_var会被定义为requires_grad=False的parameter。

mindspore.nn.Cell.untrainable_params：获取网络中不需要被优化器优化的参数，返回类型为列表。MindSpore中的Parameter含有属性name，在使用untrainable_params方法获取参数后，可以使用此属性获取名称。

代码示例

import mindspore
import numpy as np
from mindspore import Tensor, nn

class ConvBN(nn.Cell):
  def __init__(self):
    super(ConvBN, self).__init__()
    self.conv = nn.Conv2d(3, 64, 3)
    self.bn = nn.BatchNorm2d(64)
  def construct(self, x):
    x = self.conv(x)
    x = self.bn(x)
    return x

class MyNet(nn.Cell):
  def __init__(self):
    super(MyNet, self).__init__()
    self.build_block = nn.SequentialCell(ConvBN(), nn.ReLU())
  def construct(self, x):
    return self.build_block(x)

# The following implements mindspore.nn.Cell.untrainable_params() with MindSpore.
net = MyNet()
print(type(net.untrainable_params()), "\n")
for params in net.untrainable_params():
  print("Name: ", params.name)
  print("params: ", params)

# Out:
<class 'list'>

Name:  build_block.0.bn.moving_mean
params:  Parameter (name=build_block.0.bn.moving_mean, shape=(64,), dtype=Float32, requires_grad=False)
Name:  build_block.0.bn.moving_variance
params:  Parameter (name=build_block.0.bn.moving_variance, shape=(64,), dtype=Float32, requires_grad=False)

import torch.nn as nn

class ConvBN(nn.Module):
  def __init__(self):
    super(ConvBN, self).__init__()
    self.conv = nn.Conv2d(3, 64, 3)
    self.bn = nn.BatchNorm2d(64)
  def forward(self, x):
    x = self.conv(x)
    x = self.bn(x)
    return x

class MyNet(nn.Module):
  def __init__(self):
    super(MyNet, self).__init__()
    self.build_block = nn.Sequential(ConvBN(), nn.ReLU())
  def construct(self, x):
    return self.build_block(x)

# The following implements torch.nn.Module.buffers() with torch.
net = MyNet()
print(type(net.buffers()), "\n")
for name, params in net.named_buffers():
  print("Name: ", name)
  print("params: ", params.size())

# Out:
<class 'generator'>

Name:  build_block.0.bn.running_mean
params:  torch.Size([64])
Name:  build_block.0.bn.running_var
params:  torch.Size([64])
Name:  build_block.0.bn.num_batches_tracked
params:  torch.Size([])