pytorch_test.py

from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import multiprocessing
from py3nvml.py3nvml import *
import time 

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, args.conv1, kernel_size=5)
        self.conv2 = nn.Conv2d(args.conv1, args.conv2, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(16*args.conv2, args.fc1)
        self.fc2 = nn.Linear(args.fc1, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 16*args.conv2)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x)

def train(epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data), Variable(target)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.data[0]))

def test():
    model.eval()
    test_loss = 0
    correct = 0
    for data, target in test_loader:
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data, volatile=True), Variable(target)
        output = model(data)
        test_loss += F.nll_loss(output, target, size_average=False).data[0] # sum up batch loss
        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
        correct += pred.eq(target.data.view_as(pred)).cpu().sum()

    test_loss /= len(test_loader.dataset)
    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

def memory_monitor(interval,args):
    nvmlInit()
    handle=nvmlDeviceGetHandleByIndex(0)
    while True:
        info = nvmlDeviceGetMemoryInfo(handle)
        f = open("result-{}-{}-{}.csv".format(args.conv1,args.conv2,args.fc1),"a")
        print("{}, {}".format(time.time(),info.used),file=f)
        time.sleep(interval)

if __name__=="__main__":

    # Training settings
    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
    parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                        help='input batch size for training (default: 64)')
    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                        help='input batch size for testing (default: 1000)')
    parser.add_argument('--epochs', type=int, default=10, metavar='N',
                        help='number of epochs to train (default: 10)')
    parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
                        help='learning rate (default: 0.01)')
    parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
                        help='SGD momentum (default: 0.5)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    parser.add_argument('--seed', type=int, default=1, metavar='S',
                        help='random seed (default: 1)')
    parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                        help='how many batches to wait before logging training status')
    parser.add_argument('--fashion', action='store_true', default=False,
                        help='use MNIST fashion instead')
    parser.add_argument('--memory-log-interval',type=float, default=0.1,help="interval between memory log")
    parser.add_argument('--conv1',type=int, default=10,help="conv1 size")
    parser.add_argument('--conv2',type=int,default=10,help="conv2 size")
    parser.add_argument('--fc1',type=int,default=50,help="fc size")
    args = parser.parse_args()
    args.cuda = not args.no_cuda and torch.cuda.is_available()
    p=multiprocessing.Process(target=memory_monitor,args=(args.memory_log_interval,args))
    p.daemon=True
    p.start()
    torch.manual_seed(args.seed)
    if args.cuda:
        torch.cuda.manual_seed(args.seed)


    kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
    if not args.fashion:
        print ("using normal MNIST")
        train_loader = torch.utils.data.DataLoader(
            datasets.MNIST('../data', train=True, download=True,
                        transform=transforms.Compose([
                            transforms.ToTensor(),
                            transforms.Normalize((0.1307,), (0.3081,))
                        ])),
            batch_size=args.batch_size, shuffle=True, **kwargs)
        test_loader = torch.utils.data.DataLoader(
            datasets.MNIST('../data', train=False, transform=transforms.Compose([
                            transforms.ToTensor(),
                            transforms.Normalize((0.1307,), (0.3081,))
                        ])),
            batch_size=args.test_batch_size, shuffle=True, **kwargs)
    else:
        print ("using fashion MNIST")
        train_loader = torch.utils.data.DataLoader(
            datasets.FashionMNIST('../fashion_data', train=True, download=True,
                        transform=transforms.Compose([
                            transforms.ToTensor(),
                            transforms.Normalize((0.1307,), (0.3081,))
                        ])),
            batch_size=args.batch_size, shuffle=True, **kwargs)
        test_loader = torch.utils.data.DataLoader(
            datasets.FashionMNIST('../fashion_data', train=False, transform=transforms.Compose([
                            transforms.ToTensor(),
                            transforms.Normalize((0.1307,), (0.3081,))
                        ])),
            batch_size=args.test_batch_size, shuffle=True, **kwargs)

    model = Net()
    if args.cuda:
        model.cuda()

    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)



    for epoch in range(1, args.epochs + 1):
        train(epoch)
        test()