def binary_output(dataloader):
net = AlexNetPlusLatent(args.bits)
net.load_state_dict(torch.load('./model/%d' % args.pretrained))
use_cuda = torch.cuda.is_available()
if use_cuda:
net.cuda()
full_batch_output = torch.cuda.FloatTensor()
full_batch_label = torch.cuda.LongTensor()
net.eval()
for batch_idx, (inputs, targets) in enumerate(dataloader):
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = Variable(inputs, volatile=True), Variable(targets)
outputs, _ = net(inputs)
full_batch_output = torch.cat((full_batch_output, outputs.data), 0)
full_batch_label = torch.cat((full_batch_label, targets.data), 0)
return torch.round(full_batch_output), full_batch_label
def binary_output(dataloader):
net = AlexNetPlusLatent(args.bits)
net.load_state_dict(torch.load(args.modelpath))
use_cuda = torch.cuda.is_available()
if use_cuda:
net.cuda()
full_batch_output = torch.cuda.FloatTensor()
full_batch_label = torch.cuda.LongTensor()
net.eval()
for batch_idx, inputs in enumerate(dataloader):
if use_cuda:
inputs = inputs.cuda()
inputs = Variable(inputs)
inputs = inputs.unsqueeze(0)
outputs, _ = net(inputs)
full_batch_output = torch.cat((full_batch_output, outputs.data), 0)
return torch.round(full_batch_output)
def binary_output(dataloader):
net = AlexNetPlusLatent(48)
net.load_state_dict(torch.load('./{}/{}'.format('model', 92)))
use_cuda = torch.cuda.is_available()
if use_cuda:
net.cuda()
full_batch_output = torch.cuda.FloatTensor()
full_batch_label = torch.cuda.LongTensor()
net.eval()
for batch_idx, (inputs, targets) in enumerate(dataloader):
print batch_idx
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = Variable(inputs), Variable(targets)
outputs, _ = net(inputs)
full_batch_output = torch.cat((full_batch_output, outputs.data), 0)
full_batch_label = torch.cat((full_batch_label, targets.data), 0)
return torch.round(full_batch_output), full_batch_label
trainset = datasets.CIFAR10(root='./data', train=True, download=True,
transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,
shuffle=True, num_workers=2)
testset = datasets.CIFAR10(root='./data', train=False, download=True,
transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=100,
shuffle=True, num_workers=2)
net = AlexNetPlusLatent(args.bits)
use_cuda = torch.cuda.is_available()
if use_cuda:
net.cuda()
softmaxloss = nn.CrossEntropyLoss().cuda()
optimizer4nn = torch.optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=0.0005)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer4nn, milestones=[64], gamma=0.1)
def train(epoch):
print('\nEpoch: %d' % epoch)
net.train()
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
if use_cuda:
