Pytorch 如何为ModuleList中的每个模块命名？

我的模型中有以下组件：

feedfnn = []
for task_name, num_class in self.tasks:
    if self.config.nonlinear_fc:
        ffnn = nn.Sequential(OrderedDict([
            ('dropout1', nn.Dropout(self.config.dropout_fc)),
            ('dense1', nn.Linear(self.config.nhid * self.num_directions * 8, self.config.fc_dim)),
            ('tanh', nn.Tanh()),
            ('dropout2', nn.Dropout(self.config.dropout_fc)),
            ('dense2', nn.Linear(self.config.fc_dim, self.config.fc_dim)),
            ('tanh', nn.Tanh()),
            ('dropout3', nn.Dropout(self.config.dropout_fc)),
            ('dense3', nn.Linear(self.config.fc_dim, num_class))
        ]))
    else:
        ffnn = nn.Sequential(OrderedDict([
            ('dropout1', nn.Dropout(self.config.dropout_fc)),
            ('dense1', nn.Linear(self.config.nhid * self.num_directions * 8, self.config.fc_dim)),
            ('dropout2', nn.Dropout(self.config.dropout_fc)),
            ('dense2', nn.Linear(self.config.fc_dim, self.config.fc_dim)),
            ('dropout3', nn.Dropout(self.config.dropout_fc)),
            ('dense3', nn.Linear(self.config.fc_dim, num_class))
        ]))
    feedfnn.append(ffnn)
self.ffnn = nn.ModuleList(feedfnn)

当我打印我的模型时，我得到上述组件的描述如下：

(ffnn): ModuleList (
(0): Sequential (
  (dropout1): Dropout (p = 0)
  (dense1): Linear (4096 -> 512)
  (dropout2): Dropout (p = 0)
  (dense2): Linear (512 -> 512)
  (dropout3): Dropout (p = 0)
  (dense3): Linear (512 -> 2)
)
(1): Sequential (
  (dropout1): Dropout (p = 0)
  (dense1): Linear (4096 -> 512)
  (dropout2): Dropout (p = 0)
  (dense2): Linear (512 -> 512)
  (dropout3): Dropout (p = 0)
  (dense3): Linear (512 -> 3)
)
(2): Sequential (
  (dropout1): Dropout (p = 0)
  (dense1): Linear (4096 -> 512)
  (dropout2): Dropout (p = 0)
  (dense2): Linear (512 -> 512)
  (dropout3): Dropout (p = 0)
  (dense3): Linear (512 -> 3)
)
)

---

我可以用一个特定的名称，比如

（task1）：Sequential

，

（task2）：Sequential

，而不是

（0）：Sequential

，

（1）：Sequential

？

，这很简单

只需从一个空的

ModuleList

开始，然后对其使用

add\u module

。比如说,

import torch.nn as nn
from collections import OrderedDict

final_module_list = nn.ModuleList()

a_sequential_module_with_names = nn.Sequential(OrderedDict([
        ('dropout1', nn.Dropout(0.1)),
        ('dense1', nn.Linear(10, 10)),
        ('tanh', nn.Tanh()),
        ('dropout2', nn.Dropout(0.1)),
        ('dense2', nn.Linear(10, 10)),
        ('tanh', nn.Tanh()),
        ('dropout3', nn.Dropout(0.1)),
        ('dense3', nn.Linear(10, 10))]))

final_module_list.add_module('Stage 1', a_sequential_module_with_names)
final_module_list.add_module('Stage 2', a_sequential_module_with_names)
etc.

---

这很简单

只需从一个空的

ModuleList

开始，然后对其使用

add\u module

。比如说,

import torch.nn as nn
from collections import OrderedDict

final_module_list = nn.ModuleList()

a_sequential_module_with_names = nn.Sequential(OrderedDict([
        ('dropout1', nn.Dropout(0.1)),
        ('dense1', nn.Linear(10, 10)),
        ('tanh', nn.Tanh()),
        ('dropout2', nn.Dropout(0.1)),
        ('dense2', nn.Linear(10, 10)),
        ('tanh', nn.Tanh()),
        ('dropout3', nn.Dropout(0.1)),
        ('dense3', nn.Linear(10, 10))]))

final_module_list.add_module('Stage 1', a_sequential_module_with_names)
final_module_list.add_module('Stage 2', a_sequential_module_with_names)
etc.

---

似乎有一个悬而未决的问题似乎有一个悬而未决的问题