How to assign a tensor with another tensor?

import torch
from LeNet5 import LeNet5

model = torch.load('LeNet5.pth')  # 加载模型
layerWeight = model.state_dict()['conv1.0.bias'].clone()
print("----layerWeight is ",layerWeight)
print("----model is ", model.state_dict()['conv1.0.bias'])

model.state_dict()['conv1.0.bias'][0] = 1

print("++++layerWeight is", layerWeight)
print("++++model is", model.state_dict()['conv1.0.bias'])

print("layerWeight type is ",type(layerWeight))
print("model type is",type(model.state_dict()['conv1.0.bias']))

model.state_dict['conv1.0.bias'] = layerWeight

print("****layerWeight is",layerWeight)
print("****model is", model.state_dict()['conv1.0.bias'])

Above the code, I try model.state_dict[‘conv1.0.bias’] = layerWeight,(model.state_dict[‘conv1.0.bias’] and layerWeight are both the type of tensor)but get the error in the picture below.

Please hope you could tell me how to solve it.

The error is raised, since you are not calling the model.state_dict() method.
That being said, a direct assignment will most likely not work and you could use:

sd = model.state_dict()
sd[key] = layerWeight



Why are you so experienced! It’s an amazing thing for me because I could find nothing about that in pytorch document.

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