cifar10-多GPU-pytorch0.3

#version1

import torch
import torchvision
import torchvision.transforms as transforms

transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])
trainset = torchvision.datasets.CIFAR10(root='../../data',train=True,download=False,transform=transform)
testset = torchvision.datasets.CIFAR10(root='../../data',train=False,download=False,transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,shuffle=True,num_workers=2)
testloader = torch.utils.data.DataLoader(testset, batch_size=128,shuffle=False,num_workers=2)
classes = ('plane','car','bird','deer','dog','frog','horse','ship','truck')
print type(classes), trainloader, testloader

import sys 
dataiter = iter(trainloader)
images, labels = dataiter.next()
print images.shape

# 2. Define a Convolution Nerual Network
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__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()
device_ids = [2,3]
net = nn.DataParallel(net, device_ids=device_ids)
net = net.cuda(device_ids[0])

import torch.optim as optim
from torch.autograd import Variable
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9,weight_decay=1e-3)

for epoch in range(100):
    running_loss = 0.0
    for i,data in enumerate(trainloader,0):
        inputs,labels=data
        inputs = Variable(inputs,requires_grad=True).cuda(device_ids[0])
        labels = Variable(labels).cuda(device_ids[0])
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()


        running_loss += loss.data[0]
        if i%100==0:
            print('[%d,%5d] loss: %.3f' % (epoch+1, i+1,running_loss/2000))
            running_loss = 0.0


    correct = 0
    total = 0
    for data in testloader:
        images, labels = data
        outputs = net(Variable(images).cuda(device_ids[0]))
        _, predicted = torch.max(outputs.data,1)
        total += labels.size(0)
        correct += (predicted == labels.cuda(device_ids[0])).sum()

    print('Accuracy of the network on the 10000 test images: %d %%' % (
                                100 * correct / total))

#version2


#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''''''''''''''''''''''''''''''''
     # @Time    : 2018/4/15 16:51
     # @Author  : Awiny
     # @Site    : 
     # @File    : cifar10.py
     # @Software: PyCharm
     # @Github  : https://github.com/FingerRec
     # @Blog    : http://fingerrec.github.io
'''''''''''''''''''''''''''''''''
import scipy.io
import os
import torch
import torchvision
import torchvision.transforms as transforms
import numpy as np
import matplotlib.pyplot as plt

#os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  #close the warning
#---------------------------------------------------download and load dataset---------------------------------
#正则化
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) #均值，标准差

trainset = torchvision.datasets.CIFAR10(root='../../data', train=True,
                                        download=True, transform=transform)
#The output of torchvision datasets are PILImage images of range [0, 1].
#We transform them to Tensors of normalized range [-1, 1].
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')
#---------------------------------------------------functions to show an image----------------------------


# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()



#----------------------------------------------------define an convolutional neural network---------------------
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__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):
        y = 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()
#net.cuda()

#--------------------------------------------------Define a Loss function and optimizer------------------------------
import torch.optim as optim

criterion = nn.CrossEntropyLoss() #交叉熵
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

#-------------------------------------------------Training on GPU-------------------------------------

#you transfer the neural net onto the GPU. This will recursively go over all modules and convert their parameters and buffers to CUDA tensors:
#net.cuda()
#have to send the inputs and targets at every step to the GPU too:
#inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())

#----------------------------------------------------Training on Multiple GPU-------------------

if torch.cuda.device_count() > 1:
  print("Let's use", torch.cuda.device_count(), "GPUs!")
  # dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
  net = nn.DataParallel(net)

if torch.cuda.is_available():
   net.cuda()

#pytorch中CrossEntropyLoss是通过两个步骤计算出来的，第一步是计算log softmax，第二步是计算cross entropy（或者说是negative log likehood）
#---------------------------------------------------Training the network------------------------------------------------
for epoch in range(2):  # loop over the dataset multiple times
# 0， 1
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs
        inputs, labels = data

        # wrap them in Variable
        inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(inputs) # forward
        loss = criterion(outputs, labels)
        loss.backward() # backward
        optimizer.step()
        # print statistics
        #print("Outside: input size", images.size(), "output_size", outputs.size())
        running_loss += loss.data[0]
        if i % 10 == 0:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0


print('Finished Training')