model.load_state_dict()

model.load_state_dict()是一个PyTorch中的函数，用于加载模型的参数。它可以将预训练模型的参数加载到新的模型中，或者将之前训练好的模型的参数加载到当前模型中，以便继续训练或进行推理。

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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:

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()

model.load_state_dict(model_state, strict=True) optimizer.load_state_dict(optimizer_state)

这是PyTorch中用于加载模型和优化器状态字典的函数。其中，model.load_state_dict()用于加载模型的状态字典，optimizer.load_state_dict()用于加载优化器的状态字典。在加载模型状态字典时，可以通过设置strict参数来控制是否严格匹配状态字典的键值和模型的键值。如果strict=True，则要求状态字典的键值和模型的键值完全匹配；如果strict=False，则允许状态字典的键值是模型键值的子集。