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CNN-pytorch.py
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CNN-pytorch.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
print(torch.__version__)
import torchvision
import torchvision.transforms as TF
import matplotlib.pyplot as plt
import numpy as np
from tqdm import trange
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# 1. load the CIFAR data set and normalize
# compose several transforms together
# ToTensor() convert a PIL image or np.ndarray to tensor
# Normalize(mean,std)
# ps: we can use trasnform.resize, Grayscale
# transforms.lambda(lambda) functional transform
transform=TF.Compose([TF.ToTensor(),TF.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])
trainset=torchvision.datasets.CIFAR10(root='./data',train=True,download=True,transform=transform)
trainloader=torch.utils.data.DataLoader(trainset,batch_size=4,shuffle=True,num_workers=2)
testset=torchvision.datasets.CIFAR10(root='./data',train=False,download=True,transform=transform)
testloader=torch.utils.data.DataLoader(testset,batch_size=4,shuffle=False,num_workers=2)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
# show samples of the images
def imshow(img):
img=img/2+0.5 # unnormalize
npimg=img.numpy()
plt.imshow(np.transpose(npimg,(1,2,0)))
plt.show()
# get some random training images
# dataiter=iter(trainloader)
# images,labels=dataiter.next()
# show images and labels
# imshow(torchvision.utils.make_grid(images))
# print(' '.join("%s" % classes[labels[j]] for j in range(4)))
# define the neural network
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1=nn.Conv2d(3,6,5)
self.pool=nn.MaxPool2d(2,2)
self.conv2=nn.Conv2d(6,16,5)
self.fc1=nn.Linear(16*5*5,120)
self.fc2=nn.Linear(120,84)
self.fc3=nn.Linear(84,10)
def forward(self,x):
x=self.pool(F.relu(self.conv1(x)))
x=self.pool(F.relu(self.conv2(x)))
x=x.view(-1,16*5*5)
x=F.relu(self.fc1(x))
x=F.relu(self.fc2(x))
x=self.fc3(x)
return x
net=Net().to(device)
Train=False
path='./cifar_net.pth'
if Train:
# define the loss
criterion=nn.CrossEntropyLoss()
optimizer=optim.SGD(net.parameters(),lr=0.001,momentum=0.9)
# train the network
for epoch in range(20):
running_loss=0.0
for i,data in enumerate(trainloader,0):
inputs,labels=data[0].to(device),data[1].to(device)
optimizer.zero_grad()
output=net(inputs)
loss=criterion(output,labels)
loss.backward()
optimizer.step()
running_loss+=loss.item()
if i%2000==1999:
print("[%5d,%5d] loss : %.3f" % (epoch+1,i+1,running_loss/2000))
running_loss=0.0
print("The end")
# save the model
torch.save(net.state_dict(),path)
else:
dataiter=iter(testloader)
images,labels=dataiter.next()[0].to(device),dataiter.next()[1].to(device)
imshow(torchvision.utils.make_grid(images.cpu()))
print('Ground truth:',' '.join("%5s" % classes[labels[j]]for j in range(4)))
net.load_state_dict(torch.load(path))
output=net(images)
_,predicted=torch.max(output,1)
print("predicted",' '.join("%5s" % classes[labels[j]]for j in range(4)))
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data[0].to(device),data[1].to(device)
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
for data in testloader:
images, labels = data[0].to(device),data[1].to(device)
outputs = net(images)
_, predicted = torch.max(outputs, 1)
c = (predicted == labels).squeeze()
for i in range(4):
label = labels[i]
class_correct[label] += c[i].item()
class_total[label] += 1
for i in range(10):
print('Accuracy of %5s : %2d %%' % (
classes[i], 100 * class_correct[i] / class_total[i]))