pytorch_online_plotter/mnist.py at master · kuixu/pytorch_online_plotter · GitHub

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from __future__ import print_function
import os
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
from torchnet.logger import MeterLogger
# 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=50, 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=100, metavar='N',
                    help='how many batches to wait before logging training status')
parser.add_argument('--server', type=str, default="localhost", metavar='N',
                    help='server name that visdom service on (default: localhost)')
parser.add_argument('--port', type=int, default=8097, metavar='N',
                    help='port that visdom service on (default: 8097)')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()

torch.manual_seed(args.seed)
if args.cuda:
    torch.cuda.manual_seed(args.seed)

# online ploter
#os.system("python -m visdom.server -port "+str(args.port)+" &")
mlog = MeterLogger(server=args.server, port=args.port, nclass=10,title="mnist_ConvNet")

kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
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)

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 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, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x)

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

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

def train(epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
	# online ploter
	mlog.timer.reset()
        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()
	# online ploter
	mlog.update_loss(loss, meter='loss')
	#mlog.updateLoss(l1_loss, meter='l1_loss')
	mlog.update_meter(output, target, meters={'accuracy', 'map'})
        if batch_idx % args.log_interval == 0:
	    # online ploter
	    mlog.print_meter("Train", epoch, batch_idx, len(train_loader))

    # online ploter
    mlog.reset_meter(epoch, mode='Train')

def test():
    model.eval()
    test_loss = 0
    correct = 0
    for batch_idx, (data, target) in enumerate(test_loader):
	# online ploter
	mlog.timer.reset()
        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)
	# online ploter
	mlog.update_loss(test_loss, meter='loss')
	mlog.update_meter(output, target, meters={'accuracy', 'map'})
        if batch_idx % args.log_interval == 0:
	    # online ploter
	    mlog.print_meter("Test", epoch, batch_idx, len(test_loader))

    # online ploter
    mlog.reset_meter(epoch, mode='Test')

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