# ClearML - Example of pytorch with tensorboardX # from __future__ import print_function import argparse import os from tempfile import gettempdir import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from tensorboardX import SummaryWriter from torch.autograd import Variable from torchvision import datasets, transforms from clearml import Task 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, dim=1) def train(model, epoch, train_loader, args, optimizer, writer): 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.item())) niter = epoch*len(train_loader)+batch_idx writer.add_scalar('Train/Loss', loss.data.item(), niter) def test(model, test_loader, args, optimizer, writer): model.eval() test_loss = 0 correct = 0 for niter, (data, target) in enumerate(test_loader): if args.cuda: data, target = data.cuda(), target.cuda() data, target = Variable(data), Variable(target) output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').data.item() # sum up batch loss pred = output.data.max(1)[1] # get the index of the max log-probability pred = pred.eq(target.data).cpu().sum() writer.add_scalar('Test/Loss', pred, niter) correct += pred if niter % 100 == 0: writer.add_image('test', data[0, :, :, :], niter) 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 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=2, 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') args = parser.parse_args() args.cuda = not args.no_cuda and torch.cuda.is_available() # Connecting ClearML with the current process, # from here on everything is logged automatically task = Task.init(project_name='examples', task_name='PyTorch with tensorboardX') writer = SummaryWriter('runs') writer.add_text('TEXT', 'This is some text', 0) torch.manual_seed(args.seed) if args.cuda: torch.cuda.manual_seed(args.seed) kwargs = {'num_workers': 4, '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) 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(model, epoch, train_loader, args, optimizer, writer) torch.save(model, os.path.join(gettempdir(), 'model{}'.format(epoch))) test(model, test_loader, args, optimizer, writer) if __name__ == "__main__": # Hack for supporting Windows OS - https://pytorch.org/docs/stable/notes/windows.html#usage-multiprocessing main()