model.load_state_dict(weights_dict)
时间: 2023-10-24 09:07:18 浏览: 40
`model.load_state_dict(weights_dict)` 是一个 PyTorch 中用来加载预训练模型权重的函数。它将一个保存了模型参数的字典加载到模型中,使得模型的参数与预训练模型的参数一致。这个函数通常用于迁移学习,将预训练模型的参数加载到新的模型中,以便利用预训练模型在新的任务上的表现。需要注意的是,预训练模型和新的模型的结构必须相同,否则加载参数会出错。
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model.load_state_dict
(model_state_dict)
This function loads the saved state dictionary of a PyTorch model into the model. The argument `model_state_dict` is a dictionary containing the saved state of the model, which typically includes the weights, biases, and other parameters. The function updates the model with the saved state dictionary, allowing you to resume training or use the saved model for inference.
Example usage:
```python
import torch
# define a simple neural network
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = torch.nn.Linear(10, 5)
self.fc2 = torch.nn.Linear(5, 1)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
# create an instance of the network
net = Net()
# save the state dictionary of the model
torch.save(net.state_dict(), 'model.pth')
# create a new instance of the network
net2 = Net()
# load the saved state dictionary into the new network
net2.load_state_dict(torch.load('model.pth'))
# the two networks should have the same state now
assert net.state_dict() == net2.state_dict()
```
.load_state_dict只导入部分权重
.load_state_dict()函数可以用来加载模型的权重。默认情况下,它要求完全匹配,即加载的权重字典的键必须与模型的参数键完全匹配,否则会报错。但是,你可以通过将strict参数设置为False来实现部分匹配,这样只会加载权重字典中存在的参数,不匹配的参数会被跳过。例如,你可以使用以下代码来加载部分权重:
```python
pretrained_dict = torch.load(weight_path)
model.load_state_dict(pretrained_dict, strict=False)
```
这样,只有权重字典中存在的参数才会被加载到模型中,不匹配的参数会被跳过。你还可以使用if语句来进行进一步的筛选,只加载满足特定条件的参数。例如,你可以使用以下代码来加载满足条件的权重:
```python
import pickle
with open(weights_path, 'rb') as f:
obj = f.read()
weights = {key: weight_dict for key, weight_dict in pickle.loads(obj, encoding='latin1').items() if (key in model_dict and 'Prediction' not in key)}
model.load_state_dict(weights)
```
这样,只有满足条件的权重参数才会被加载到模型中。
#### 引用[.reference_title]
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