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150 lines
5.6 KiB
150 lines
5.6 KiB
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from __future__ import print_function
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import argparse
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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from torchvision import datasets, transforms
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from torch.autograd import Variable
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from models.binarized_modules import BinarizeLinear,BinarizeConv2d
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from models.binarized_modules import Binarize,HingeLoss
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# Training settings
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parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
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parser.add_argument('--batch-size', type=int, default=64, metavar='N',
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help='input batch size for training (default: 256)')
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parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
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help='input batch size for testing (default: 1000)')
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parser.add_argument('--epochs', type=int, default=100, metavar='N',
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help='number of epochs to train (default: 10)')
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parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
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help='learning rate (default: 0.001)')
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parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
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help='SGD momentum (default: 0.5)')
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parser.add_argument('--no-cuda', action='store_true', default=False,
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help='disables CUDA training')
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parser.add_argument('--seed', type=int, default=1, metavar='S',
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help='random seed (default: 1)')
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parser.add_argument('--gpus', default=3,
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help='gpus used for training - e.g 0,1,3')
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parser.add_argument('--log-interval', type=int, default=10, metavar='N',
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help='how many batches to wait before logging training status')
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args = parser.parse_args()
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args.cuda = not args.no_cuda and torch.cuda.is_available()
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torch.manual_seed(args.seed)
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if args.cuda:
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torch.cuda.manual_seed(args.seed)
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kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
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train_loader = torch.utils.data.DataLoader(
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datasets.MNIST('../data', train=True, download=True,
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transform=transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.1307,), (0.3081,))
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])),
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batch_size=args.batch_size, shuffle=True, **kwargs)
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test_loader = torch.utils.data.DataLoader(
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datasets.MNIST('../data', train=False, transform=transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.1307,), (0.3081,))
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])),
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batch_size=args.test_batch_size, shuffle=True, **kwargs)
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class Net(nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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self.infl_ratio=3
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self.fc1 = BinarizeLinear(784, 2048*self.infl_ratio)
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self.htanh1 = nn.Hardtanh()
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self.bn1 = nn.BatchNorm1d(2048*self.infl_ratio)
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self.fc2 = BinarizeLinear(2048*self.infl_ratio, 2048*self.infl_ratio)
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self.htanh2 = nn.Hardtanh()
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self.bn2 = nn.BatchNorm1d(2048*self.infl_ratio)
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self.fc3 = BinarizeLinear(2048*self.infl_ratio, 2048*self.infl_ratio)
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self.htanh3 = nn.Hardtanh()
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self.bn3 = nn.BatchNorm1d(2048*self.infl_ratio)
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self.fc4 = nn.Linear(2048*self.infl_ratio, 10)
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self.logsoftmax=nn.LogSoftmax()
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self.drop=nn.Dropout(0.5)
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def forward(self, x):
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x = x.view(-1, 28*28)
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x = self.fc1(x)
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x = self.bn1(x)
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x = self.htanh1(x)
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x = self.fc2(x)
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x = self.bn2(x)
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x = self.htanh2(x)
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x = self.fc3(x)
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x = self.drop(x)
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x = self.bn3(x)
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x = self.htanh3(x)
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x = self.fc4(x)
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return self.logsoftmax(x)
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model = Net()
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if args.cuda:
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torch.cuda.set_device(3)
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model.cuda()
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.Adam(model.parameters(), lr=args.lr)
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def train(epoch):
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model.train()
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for batch_idx, (data, target) in enumerate(train_loader):
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if args.cuda:
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data, target = data.cuda(), target.cuda()
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data, target = Variable(data), Variable(target)
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optimizer.zero_grad()
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output = model(data)
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loss = criterion(output, target)
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if epoch%40==0:
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optimizer.param_groups[0]['lr']=optimizer.param_groups[0]['lr']*0.1
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optimizer.zero_grad()
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loss.backward()
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for p in list(model.parameters()):
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if hasattr(p,'org'):
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p.data.copy_(p.org)
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optimizer.step()
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for p in list(model.parameters()):
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if hasattr(p,'org'):
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p.org.copy_(p.data.clamp_(-1,1))
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if batch_idx % args.log_interval == 0:
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print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
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epoch, batch_idx * len(data), len(train_loader.dataset),
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100. * batch_idx / len(train_loader), loss.item()))
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def test():
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model.eval()
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test_loss = 0
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correct = 0
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with torch.no_grad():
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for data, target in test_loader:
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if args.cuda:
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data, target = data.cuda(), target.cuda()
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data, target = Variable(data), Variable(target)
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output = model(data)
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test_loss += criterion(output, target).item() # sum up batch loss
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pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
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correct += pred.eq(target.data.view_as(pred)).cpu().sum()
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test_loss /= len(test_loader.dataset)
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print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
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test_loss, correct, len(test_loader.dataset),
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100. * correct / len(test_loader.dataset)))
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for epoch in range(1, args.epochs + 1):
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train(epoch)
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test()
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if epoch%40==0:
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optimizer.param_groups[0]['lr']=optimizer.param_groups[0]['lr']*0.1
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