dcslin/train.py

## train.py
"""
This script is modified from https://github.com/pytorch/examples.git
"""
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.optim.lr_scheduler import StepLR
from apex import amp
import time


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout2d(0.25)
        self.dropout2 = nn.Dropout2d(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = F.log_softmax(x, dim=1)
        return output

def train(args, model, device, train_loader, optimizer, epoch):
    start_time = time.time()
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        # loss.backward()

        with amp.scale_loss(loss, optimizer) as scaled_loss:
            scaled_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.item()))
            if args.dry_run:
                break
    end_time=time.time()
    print("training used time %.5f sec" %(end_time-start_time))


def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss
            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()

    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=14, metavar='N',
                        help='number of epochs to train (default: 14)')
    parser.add_argument('--lr', type=float, default=1.0, metavar='LR',
                        help='learning rate (default: 1.0)')
    parser.add_argument('--gamma', type=float, default=0.7, metavar='M',
                        help='Learning rate step gamma (default: 0.7)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    parser.add_argument('--dry-run', action='store_true', default=False,
                        help='quickly check a single pass')
    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('--save-model', action='store_true', default=False,
                        help='For Saving the current Model')
    parser.add_argument('--opt-level', type=str)
    args = parser.parse_args()
    use_cuda = not args.no_cuda and torch.cuda.is_available()

    torch.manual_seed(args.seed)

    device = torch.device("cuda" if use_cuda else "cpu")

    kwargs = {'batch_size': args.batch_size}

    if use_cuda:
        kwargs.update({'num_workers': 1,
                       'pin_memory': True,
                       'shuffle': True},
                     )

    transform=transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
        ])
    dataset1 = datasets.MNIST('../data', train=True, download=True,
                       transform=transform)
    dataset2 = datasets.MNIST('../data', train=False,
                       transform=transform)
    train_loader = torch.utils.data.DataLoader(dataset1,**kwargs)
    test_loader = torch.utils.data.DataLoader(dataset2, **kwargs)

    model = Net().to(device)
    optimizer = optim.Adadelta(model.parameters(), lr=args.lr)

    model, optimizer = amp.initialize(model, optimizer, opt_level=args.opt_level)

    scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
    for epoch in range(1, args.epochs + 1):
        train(args, model, device, train_loader, optimizer, epoch)
        test(model, device, test_loader)
        scheduler.step()

    if args.save_model:
        torch.save(model.state_dict(), "mnist_cnn.pt")


if __name__ == '__main__':
    main()
	"""
	This script is modified from https://github.com/pytorch/examples.git
	"""
	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.optim.lr_scheduler import StepLR
	from apex import amp
	import time


	class Net(nn.Module):
	def __init__(self):
	super(Net, self).__init__()
	self.conv1 = nn.Conv2d(1, 32, 3, 1)
	self.conv2 = nn.Conv2d(32, 64, 3, 1)
	self.dropout1 = nn.Dropout2d(0.25)
	self.dropout2 = nn.Dropout2d(0.5)
	self.fc1 = nn.Linear(9216, 128)
	self.fc2 = nn.Linear(128, 10)

	def forward(self, x):
	x = self.conv1(x)
	x = F.relu(x)
	x = self.conv2(x)
	x = F.relu(x)
	x = F.max_pool2d(x, 2)
	x = self.dropout1(x)
	x = torch.flatten(x, 1)
	x = self.fc1(x)
	x = F.relu(x)
	x = self.dropout2(x)
	x = self.fc2(x)
	output = F.log_softmax(x, dim=1)
	return output

	def train(args, model, device, train_loader, optimizer, epoch):
	start_time = time.time()
	model.train()
	for batch_idx, (data, target) in enumerate(train_loader):
	data, target = data.to(device), target.to(device)
	optimizer.zero_grad()
	output = model(data)
	loss = F.nll_loss(output, target)
	# loss.backward()

	with amp.scale_loss(loss, optimizer) as scaled_loss:
	scaled_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.item()))
	if args.dry_run:
	break
	end_time=time.time()
	print("training used time %.5f sec" %(end_time-start_time))


	def test(model, device, test_loader):
	model.eval()
	test_loss = 0
	correct = 0
	with torch.no_grad():
	for data, target in test_loader:
	data, target = data.to(device), target.to(device)
	output = model(data)
	test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
	pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
	correct += pred.eq(target.view_as(pred)).sum().item()

	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=14, metavar='N',
	help='number of epochs to train (default: 14)')
	parser.add_argument('--lr', type=float, default=1.0, metavar='LR',
	help='learning rate (default: 1.0)')
	parser.add_argument('--gamma', type=float, default=0.7, metavar='M',
	help='Learning rate step gamma (default: 0.7)')
	parser.add_argument('--no-cuda', action='store_true', default=False,
	help='disables CUDA training')
	parser.add_argument('--dry-run', action='store_true', default=False,
	help='quickly check a single pass')
	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('--save-model', action='store_true', default=False,
	help='For Saving the current Model')
	parser.add_argument('--opt-level', type=str)
	args = parser.parse_args()
	use_cuda = not args.no_cuda and torch.cuda.is_available()

	torch.manual_seed(args.seed)

	device = torch.device("cuda" if use_cuda else "cpu")

	kwargs = {'batch_size': args.batch_size}

	if use_cuda:
	kwargs.update({'num_workers': 1,
	'pin_memory': True,
	'shuffle': True},
	)

	transform=transforms.Compose([
	transforms.ToTensor(),
	transforms.Normalize((0.1307,), (0.3081,))
	])
	dataset1 = datasets.MNIST('../data', train=True, download=True,
	transform=transform)
	dataset2 = datasets.MNIST('../data', train=False,
	transform=transform)
	train_loader = torch.utils.data.DataLoader(dataset1,**kwargs)
	test_loader = torch.utils.data.DataLoader(dataset2, **kwargs)

	model = Net().to(device)
	optimizer = optim.Adadelta(model.parameters(), lr=args.lr)

	model, optimizer = amp.initialize(model, optimizer, opt_level=args.opt_level)

	scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
	for epoch in range(1, args.epochs + 1):
	train(args, model, device, train_loader, optimizer, epoch)
	test(model, device, test_loader)
	scheduler.step()

	if args.save_model:
	torch.save(model.state_dict(), "mnist_cnn.pt")


	if __name__ == '__main__':
	main()