Comparing the function difference with torch.nn.Module.named_children

torch.nn.Module.named_children

torch.nn.Module.named_children()

For more information, see torch.nn.Module.named_children.

mindspore.nn.Cell.name_cells

mindspore.nn.Cell.name_cells()

For more information, see mindspore.nn.Cell.name_cells.

Differences

PyTorch: The function returns a Generator over immediate children modules, yielding both the names of the modules as well as the modules themselves.

MindSpore：The function returns OrderedDict over immediate cells, containing both the names of the cells as well as the cells themselves.

Code Example

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.name_cells() with MindSpore.
net = MyNet()
print(net.name_cells())

# Out:
OrderedDict([('build_block', SequentialCell<
  (0): ConvBN<
    (conv): Conv2d<input_channels=3, output_channels=64, kernel_size=(3, 3),stride=(1, 1),  pad_mode=same, padding=0, dilation=(1, 1), group=1, has_bias=Falseweight_init=normal, bias_init=zeros, format=NCHW>
    (bn): BatchNorm2d<num_features=64, eps=1e-05, momentum=0.09999999999999998, gamma=Parameter (name=build_block.0.bn.gamma, shape=(64,), dtype=Float32, requires_grad=True), beta=Parameter (name=build_block.0.bn.beta, shape=(64,), dtype=Float32, requires_grad=True), moving_mean=Parameter (name=build_block.0.bn.moving_mean, shape=(64,), dtype=Float32, requires_grad=False), moving_variance=Parameter (name=build_block.0.bn.moving_variance, shape=(64,), dtype=Float32, requires_grad=False)>
    >
  (1): ReLU<>
  >)])

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.named_children() with torch.
net = MyNet()
print(net.named_children(), "\n")
for name, child in net.named_children():
  print("Name: ", name)
  print("Child: ", child)

# Out:
<generator object Module.named_children at 0x7f6a6134abd0>

Name:  build_block
Child:  Sequential(
  (0): ConvBN(
    (conv): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1))
    (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  )
  (1): ReLU()
)