def test(net):
global glob_gau
global glob_blur
global best_acc
glob_blur = 1
net.eval()
test_loss = 0
correct = 0
total = 0
mask_channel = torch.load('mask_null.dat')
mask_channel = utils.setMask(utils.setMask(mask_channel, 3, 1), 4, 0)
if args.mode > 0:
net = utils.netMaskMul(net, mask_channel)
net = utils.addNetwork(net, net2)
if args.fixed == 1:
net = utils.quantize(net, args.pprec)
for batch_idx, (inputs, targets) in enumerate(test_loader):
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = Variable(inputs), Variable(targets)
outputs = net(inputs)
loss = criterion(outputs, targets)
test_loss += loss.data.item()
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
correct += float(predicted.eq(targets.data).cpu().sum())
progress_bar(
batch_idx, len(test_loader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)' %
(test_loss /
(batch_idx + 1), 100. * correct / total, correct, total))
# Save checkpoint.
acc = 100. * correct / total
if acc > best_acc:
state = {
'net': net.module if use_cuda else net,
'acc': acc,
}
if not os.path.isdir('checkpoint'):
os.mkdir('checkpoint')
if args.mode == 0:
pass
else:
print('Saving..')
torch.save(state, './checkpoint/ckpt_20190802_half_clean_B1.t0')
best_acc = acc
return acc
def train(net, epoch):
print('\nEpoch: %d' % epoch)
net.train()
train_loss = 0
correct = 0
total = 0
mask_channel = torch.load('mask_null.dat')
mask_channel = utils.setMask(mask_channel, 4, 1)
for batch_idx, (inputs, targets) in enumerate(train_loader):
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
optimizer.zero_grad()
inputs, targets = Variable(inputs), Variable(targets)
outputs = net(inputs)
loss = criterion(outputs, targets)
loss.backward()
net = utils.netMaskMul(net, mask_channel)
if args.fixed == 1:
net = utils.quantize(net, args.pprec)
optimizer.step()
train_loss += loss.data.item()
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
#correct += predicted.eq(targets.data).cpu().sum().item()
correct += float(predicted.eq(targets.data).cpu().sum())
progress_bar(
batch_idx, len(train_loader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)' %
(train_loss /
(batch_idx + 1), 100. * correct / total, correct, total))
elif args.netsel == 1:
print("Generating ResNet18 network")
net = ResNet18()
elif args.netsel == 2:
print("Generating SqueezeNext network")
net = SqNxt_23_2x_v5(100)
elif args.netsel == 3:
print("Generating MobileNetV2 network")
net = MobileNetV2()
else:
print("netsel == ", args.netsel)
best_acc = 0
if args.channelwidth == 1:
pass
else:
mask_channel = utils.setMask(net, args.channelwidth, 1)
#For pruning and quantization
elif args.mode == 2:
checkpoint = torch.load('./checkpoint/' + args.network)
net = checkpoint['net']
params = utils.paramsGet(net)
if args.channelwidth == 1:
thres = utils.findThreshold(params, args.pr)
else:
thres = utils.findThreshold(
params, 100 - ((100 - args.pr) / 100) * args.channelwidth * 100)
mask_prune = utils.getPruningMask(net, thres)
if args.resume:
print('==> Resuming from checkpoint..')
