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-
- 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
- from models.binarized_modules import BinarizeLinear,BinarizeConv2d
- from models.binarized_modules import Binarize,HingeLoss
- # 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: 256)')
- 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=100, 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: 0.001)')
- 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('--gpus', default=3,
- help='gpus used for training - e.g 0,1,3')
- 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()
-
- 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 {}
- 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.infl_ratio=3
- self.fc1 = BinarizeLinear(784, 2048*self.infl_ratio)
- self.htanh1 = nn.Hardtanh()
- self.bn1 = nn.BatchNorm1d(2048*self.infl_ratio)
- self.fc2 = BinarizeLinear(2048*self.infl_ratio, 2048*self.infl_ratio)
- self.htanh2 = nn.Hardtanh()
- self.bn2 = nn.BatchNorm1d(2048*self.infl_ratio)
- self.fc3 = BinarizeLinear(2048*self.infl_ratio, 2048*self.infl_ratio)
- self.htanh3 = nn.Hardtanh()
- self.bn3 = nn.BatchNorm1d(2048*self.infl_ratio)
- self.fc4 = nn.Linear(2048*self.infl_ratio, 10)
- self.logsoftmax=nn.LogSoftmax()
- self.drop=nn.Dropout(0.5)
-
- def forward(self, x):
- x = x.view(-1, 28*28)
- x = self.fc1(x)
- x = self.bn1(x)
- x = self.htanh1(x)
- x = self.fc2(x)
- x = self.bn2(x)
- x = self.htanh2(x)
- x = self.fc3(x)
- x = self.drop(x)
- x = self.bn3(x)
- x = self.htanh3(x)
- x = self.fc4(x)
- return self.logsoftmax(x)
-
- model = Net()
- if args.cuda:
- torch.cuda.set_device(3)
- model.cuda()
-
-
- criterion = nn.CrossEntropyLoss()
- optimizer = optim.Adam(model.parameters(), lr=args.lr)
-
-
- 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 = criterion(output, target)
-
- if epoch%40==0:
- optimizer.param_groups[0]['lr']=optimizer.param_groups[0]['lr']*0.1
-
- optimizer.zero_grad()
- loss.backward()
- for p in list(model.parameters()):
- if hasattr(p,'org'):
- p.data.copy_(p.org)
- optimizer.step()
- for p in list(model.parameters()):
- if hasattr(p,'org'):
- p.org.copy_(p.data.clamp_(-1,1))
-
- 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()))
-
- def test():
- model.eval()
- test_loss = 0
- correct = 0
- with torch.no_grad():
- for data, target in test_loader:
- if args.cuda:
- data, target = data.cuda(), target.cuda()
- data, target = Variable(data), Variable(target)
- output = model(data)
- test_loss += criterion(output, target).item() # 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)))
-
-
- for epoch in range(1, args.epochs + 1):
- train(epoch)
- test()
- if epoch%40==0:
- optimizer.param_groups[0]['lr']=optimizer.param_groups[0]['lr']*0.1
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