pytorch框架网络参数保存和重载torch.save,torch.load,Unet

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/Arctic_Beacon/article/details/84789707

首先，定义NET，然后训练，然后保存：

import torch
import torchvision
import torchvision.transforms as transforms

import matplotlib.pyplot as plt
import numpy as np
import torch.nn as nn
import torch.nn.functional as F

import torch.optim as optim
import random


# construct a Unet
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.maxpool = nn.MaxPool2d(2, 2)
        self.bn1 = nn.BatchNorm2d(64)
        self.bn2 = nn.BatchNorm2d(128)
        self.bn3 = nn.BatchNorm2d(256)
        self.conv1_1 = nn.Conv2d(1, 64, 3)
        self.conv1_2 = nn.Conv2d(64, 64, 3)
        self.conv2_1 = nn.Conv2d(64, 128, 3)
        self.conv2_2 = nn.Conv2d(128, 128, 3)
        self.conv3_1 = nn.Conv2d(128, 256, 3)
        self.conv3_2 = nn.Conv2d(256, 256, 3)
        self.conv3_1 = nn.Conv2d(128, 256, 3)
        self.conv3_2 = nn.Conv2d(256, 256, 3)
        self.upconv4 = nn.Conv2d(256, 1, 1)
        self.fc2 = nn.Linear(120, 80)
        self.fc3 = nn.Linear(80, 40)
        self.fc4 = nn.Linear(40, 20)
        self.fc5 = nn.Linear(20, 2)
        self.m = nn.Softmax()
                                       

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1_2(F.relu(self.conv1_1(x)))))
        #print('x1 size: %s'%str(x.size()))
        x = F.relu(self.bn2(self.conv2_2(F.relu(self.conv2_1(self.maxpool(x))))))
        #print('x2 size: %s'%str(x.size()))
        x = F.relu(self.bn3(self.conv3_2(F.relu(self.conv3_1(self.maxpool(x))))))
        #print('x3 size: %s'%str(x.size()))
        x = F.relu(self.upconv4(self.maxpool(x)))
        #print('x4 size: %s'%str(x.size()))
        m=x.size(2)
        n=x.size(3)
        x = x.view(-1, m*n)
        #print('x5 size: %s'%str(x.size()))
        fc1 = nn.Linear(m*n, 120)
        x = F.relu(fc1(x))
        x = F.relu(self.fc2(x))
        x = F.relu(self.fc3(x))
        x = F.relu(self.fc4(x))
        x = self.fc5(x)
        
        #print('x size: %s'%str(x.size()))
        x = F.softmax(x,dim=1)
        return x



net = Net()


#input training data and label
input = torch.Tensor(batch,channel,m,n)
label = torch.Tensor(batch,channel,m,n)




#train the network

criterion = nn.MSELoss()
#criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.99)
for epoch in range(80):  # loop over the dataset multiple times
    
       running_loss = 0.0
    
       

        # zero the parameter gradients
       optimizer.zero_grad()

        # forward + backward + optimize
       outputs = net(x)
       loss = criterion(outputs, label)
       loss.backward()
       optimizer.step()

        # print statistics
       running_loss += loss.item()
       print('[epoch%d] loss: %.3f' %
                  (epoch + 1,running_loss))
       
       running_loss = 0.0
    

print('Finished Training')

#save the net
torch.save(net.state_dict(), 'net_parameters.pkl') 

重新载入训练好的网络参数

import torch
import torchvision
import torchvision.transforms as transforms

import matplotlib.pyplot as plt
import numpy as np
import torch.nn as nn
import torch.nn.functional as F

import torch.optim as optim


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.maxpool = nn.MaxPool2d(2, 2)
        self.bn1 = nn.BatchNorm2d(64)
        self.bn2 = nn.BatchNorm2d(128)
        self.bn3 = nn.BatchNorm2d(256)
        self.conv1_1 = nn.Conv2d(1, 64, 3)
        self.conv1_2 = nn.Conv2d(64, 64, 3)
        self.conv2_1 = nn.Conv2d(64, 128, 3)
        self.conv2_2 = nn.Conv2d(128, 128, 3)
        self.conv3_1 = nn.Conv2d(128, 256, 3)
        self.conv3_2 = nn.Conv2d(256, 256, 3)
        self.conv3_1 = nn.Conv2d(128, 256, 3)
        self.conv3_2 = nn.Conv2d(256, 256, 3)
        self.upconv4 = nn.Conv2d(256, 1, 1)
        self.fc2 = nn.Linear(120, 80)
        self.fc3 = nn.Linear(80, 40)
        self.fc4 = nn.Linear(40, 20)
        self.fc5 = nn.Linear(20, 2)
        self.m = nn.Softmax()
         

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1_2(F.relu(self.conv1_1(x)))))
        #print('x1 size: %s'%str(x.size()))
        x = F.relu(self.bn2(self.conv2_2(F.relu(self.conv2_1(self.maxpool(x))))))
        #print('x2 size: %s'%str(x.size()))
        x = F.relu(self.bn3(self.conv3_2(F.relu(self.conv3_1(self.maxpool(x))))))
        #print('x3 size: %s'%str(x.size()))
        x = F.relu(self.upconv4(self.maxpool(x)))
        #print('x4 size: %s'%str(x.size()))
        m=x.size(2)
        n=x.size(3)
        x = x.view(-1, m*n)
        #print('x5 size: %s'%str(x.size()))
        fc1 = nn.Linear(m*n, 120)
        x = F.relu(fc1(x))
        x = F.relu(self.fc2(x))
        x = F.relu(self.fc3(x))
        x = F.relu(self.fc4(x))
        x = self.fc5(x)
        
        #print('x size: %s'%str(x.size()))
        x = F.softmax(x,dim=1)
        return x





net = Net()

classes = ('Absense of flow: Left Middle Cerebral Artery ', 'Absense of flow: Right Middle Cerebral Artery ', 'Normal')

net.load_state_dict(torch.load('net_parameters.pkl'))

点评：网络结构和网络参数的理解