pytorch框架網路引數儲存和過載torch.save,torch.load,Unet
阿新 • • 發佈:2018-12-05
首先,定義NET,然後訓練,然後儲存:
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')
重新載入訓練好的網路引數
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'))
點評:網路結構和網路引數的理解