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 retrain(net, epoch, mask):
print('\nEpoch: %d' % epoch)
global best_acc
net.train()
train_loss = 0
total = 0
correct = 0
mask_channel = torch.load('mask_null.dat')
mask_channel = utils.setMask(utils.setMask(mask_channel, 0, 1), 2, 0)
for batch_idx, (inputs, targets) in enumerate(train_loader):
glob_gau = 0
glob_blur = 0
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)
net = utils.addNetwork(net, net2)
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 += 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))
for batch_idx, (inputs, targets) in enumerate(train_loader):
glob_blur = 1
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)
net = utils.addNetwork(net, net2)
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 += 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))
for batch_idx, (inputs, targets) in enumerate(train_loader):
glob_gau = 1
glob_blur = 1
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)
net = utils.addNetwork(net, net2)
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 += 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))
