torch 保存提取

image.png

import torch
from torch.autograd import Variable
import matplotlib.pyplot as plt

torch.manual_seed(1) # reproducible

# fake data
x = torch.unsqueeze(torch.linspace(-1,1,100),dim=1)
y = x.pow(2) + 0.2*torch.rand(x.size())
x,y = Variable(x,requires_grad=False) ,Variable(y,requires_grad=False)

def save():
    # save net1
    net1 = torch.nn.Sequential(
        torch.nn.Linear(1,10),
        torch.nn.ReLU(),
        torch.nn.Linear(10,1),
    )
    optimizer = torch.optim.SGD(net1.parameters(),lr=0.5)
    loss_func = torch.nn.MSELoss()

    for t in range(100):
        prediction = net1(x)
        loss = loss_func(prediction,y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    # plot result
    plt.figure(1,figsize=(10,3))
    plt.subplot(131)
    plt.title('Net1')
    plt.scatter(x.data.numpy(),y.data.numpy())
    plt.plot(x.data.numpy(),prediction.data.numpy(),'r-',lw=5)
    # 2 ways to save the net
    torch.save(net1,'net.pkl') # save entire net
    torch.save(net1.state_dict(),'net_params.pkl') # save only the parameters

def restore_net():
    # restore entire net1 to net2
    net2 = torch.load('net.pkl')
    prediction = net2(x)
    #plot result
    plt.subplot(132)
    plt.title('Net2')
    plt.scatter(x.data.numpy(),y.data.numpy())
    plt.plot(x.data.numpy(),prediction.data.numpy(),'r-',lw=5)

def restore_params():
    # restore only the parameters in net1 to net3
    net3 = torch.nn.Sequential(
        torch.nn.Linear(1,10),
        torch.nn.ReLU(),
        torch.nn.Linear(10,1),
    )
    # copy net1's parameters into net3
    net3.load_state_dict(torch.load('net_params.pkl'))
    prediction = net3(x)
    # plot result
    plt.subplot(133)
    plt.title('Net3')
    plt.scatter(x.data.numpy(),y.data.numpy())
    plt.plot(x.data.numpy(),prediction.data.numpy(),'r-',lw=5)
    plt.show()

# save net1
save()
# restore entire net
restore_net()
# restore only the net parameters
restore_params()