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  1. from __future__ import print_function
  2. import argparse
  3. import torch
  4. import torch.nn as nn
  5. import torch.nn.functional as F
  6. import torch.optim as optim
  7. from torchvision import datasets, transforms
  8. from torch.autograd import Variable
  9. from models.binarized_modules import BinarizeLinear,BinarizeConv2d
  10. from models.binarized_modules import Binarize,HingeLoss
  11. # Training settings
  12. parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
  13. parser.add_argument('--batch-size', type=int, default=64, metavar='N',
  14. help='input batch size for training (default: 256)')
  15. parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
  16. help='input batch size for testing (default: 1000)')
  17. parser.add_argument('--epochs', type=int, default=100, metavar='N',
  18. help='number of epochs to train (default: 10)')
  19. parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
  20. help='learning rate (default: 0.001)')
  21. parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
  22. help='SGD momentum (default: 0.5)')
  23. parser.add_argument('--no-cuda', action='store_true', default=False,
  24. help='disables CUDA training')
  25. parser.add_argument('--seed', type=int, default=1, metavar='S',
  26. help='random seed (default: 1)')
  27. parser.add_argument('--gpus', default=3,
  28. help='gpus used for training - e.g 0,1,3')
  29. parser.add_argument('--log-interval', type=int, default=10, metavar='N',
  30. help='how many batches to wait before logging training status')
  31. args = parser.parse_args()
  32. args.cuda = not args.no_cuda and torch.cuda.is_available()
  33. torch.manual_seed(args.seed)
  34. if args.cuda:
  35. torch.cuda.manual_seed(args.seed)
  36. kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
  37. train_loader = torch.utils.data.DataLoader(
  38. datasets.MNIST('../data', train=True, download=True,
  39. transform=transforms.Compose([
  40. transforms.ToTensor(),
  41. transforms.Normalize((0.1307,), (0.3081,))
  42. ])),
  43. batch_size=args.batch_size, shuffle=True, **kwargs)
  44. test_loader = torch.utils.data.DataLoader(
  45. datasets.MNIST('../data', train=False, transform=transforms.Compose([
  46. transforms.ToTensor(),
  47. transforms.Normalize((0.1307,), (0.3081,))
  48. ])),
  49. batch_size=args.test_batch_size, shuffle=True, **kwargs)
  50. class Net(nn.Module):
  51. def __init__(self):
  52. super(Net, self).__init__()
  53. self.infl_ratio=3
  54. self.fc1 = BinarizeLinear(784, 2048*self.infl_ratio)
  55. self.htanh1 = nn.Hardtanh()
  56. self.bn1 = nn.BatchNorm1d(2048*self.infl_ratio)
  57. self.fc2 = BinarizeLinear(2048*self.infl_ratio, 2048*self.infl_ratio)
  58. self.htanh2 = nn.Hardtanh()
  59. self.bn2 = nn.BatchNorm1d(2048*self.infl_ratio)
  60. self.fc3 = BinarizeLinear(2048*self.infl_ratio, 2048*self.infl_ratio)
  61. self.htanh3 = nn.Hardtanh()
  62. self.bn3 = nn.BatchNorm1d(2048*self.infl_ratio)
  63. self.fc4 = nn.Linear(2048*self.infl_ratio, 10)
  64. self.logsoftmax=nn.LogSoftmax()
  65. self.drop=nn.Dropout(0.5)
  66. def forward(self, x):
  67. x = x.view(-1, 28*28)
  68. x = self.fc1(x)
  69. x = self.bn1(x)
  70. x = self.htanh1(x)
  71. x = self.fc2(x)
  72. x = self.bn2(x)
  73. x = self.htanh2(x)
  74. x = self.fc3(x)
  75. x = self.drop(x)
  76. x = self.bn3(x)
  77. x = self.htanh3(x)
  78. x = self.fc4(x)
  79. return self.logsoftmax(x)
  80. model = Net()
  81. if args.cuda:
  82. torch.cuda.set_device(3)
  83. model.cuda()
  84. criterion = nn.CrossEntropyLoss()
  85. optimizer = optim.Adam(model.parameters(), lr=args.lr)
  86. def train(epoch):
  87. model.train()
  88. for batch_idx, (data, target) in enumerate(train_loader):
  89. if args.cuda:
  90. data, target = data.cuda(), target.cuda()
  91. data, target = Variable(data), Variable(target)
  92. optimizer.zero_grad()
  93. output = model(data)
  94. loss = criterion(output, target)
  95. if epoch%40==0:
  96. optimizer.param_groups[0]['lr']=optimizer.param_groups[0]['lr']*0.1
  97. optimizer.zero_grad()
  98. loss.backward()
  99. for p in list(model.parameters()):
  100. if hasattr(p,'org'):
  101. p.data.copy_(p.org)
  102. optimizer.step()
  103. for p in list(model.parameters()):
  104. if hasattr(p,'org'):
  105. p.org.copy_(p.data.clamp_(-1,1))
  106. if batch_idx % args.log_interval == 0:
  107. print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
  108. epoch, batch_idx * len(data), len(train_loader.dataset),
  109. 100. * batch_idx / len(train_loader), loss.item()))
  110. def test():
  111. model.eval()
  112. test_loss = 0
  113. correct = 0
  114. with torch.no_grad():
  115. for data, target in test_loader:
  116. if args.cuda:
  117. data, target = data.cuda(), target.cuda()
  118. data, target = Variable(data), Variable(target)
  119. output = model(data)
  120. test_loss += criterion(output, target).item() # sum up batch loss
  121. pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
  122. correct += pred.eq(target.data.view_as(pred)).cpu().sum()
  123. test_loss /= len(test_loader.dataset)
  124. print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
  125. test_loss, correct, len(test_loader.dataset),
  126. 100. * correct / len(test_loader.dataset)))
  127. for epoch in range(1, args.epochs + 1):
  128. train(epoch)
  129. test()
  130. if epoch%40==0:
  131. optimizer.param_groups[0]['lr']=optimizer.param_groups[0]['lr']*0.1