What is the method for concatenating two models in PyTorch?

In PyTorch, we can concatenate two models using the torch.cat() function. This function allows us to concatenate multiple tensors along a specified dimension. The dimension for concatenation can be any dimension, for example, 0 for concatenating along the 0th dimension, 1 for concatenating along the 1st dimension, and so on.

Here is an example code that demonstrates how to concatenate two models on a specified dimension.

import torch
import torch.nn as nn

# 假设有两个模型model1和model2
model1 = nn.Linear(10, 5)
model2 = nn.Linear(5, 3)

# 获取模型的参数
params1 = model1.parameters()
params2 = model2.parameters()

# 将参数拼接在一起
concat_params = list(params1) + list(params2)

# 创建一个新的模型，其中的参数是拼接后的参数
concat_model = nn.ModuleList(concat_params)

In the above code, we created two models, model1 and model2, using the nn.Linear() function and obtained their parameters, params1 and params2. Then, we converted the two parameter lists into regular Python lists using list() and concatenated them with the + operator. Finally, we passed the concatenated parameter list to the nn.ModuleList() function to create a new model, concat_model, with the concatenated parameters.

It is important to note that the concatenated parameter list should be of type nn.Parameter, not a normal Tensor type. Therefore, before using torch.cat() to concatenate, you need to convert the Tensor type parameters to nn.Parameter type parameters using nn.Parameter() or nn.ParameterList().