def train(epoch,args):
net.train()
train_loss = 0
correct = 0
total = 0
batch_idx = 0
ds = ImageDataset(args.dataset,dataset_load,'data/casia_landmark.txt',name=args.net+':train',
bs=args.bs,shuffle=True,nthread=6,imagesize=128)
while True:
img,label = ds.get()
if img is None: break
inputs = torch.from_numpy(img).float()
targets = torch.from_numpy(label[:,0]).long()
if use_cuda: inputs, targets = inputs.cuda(), targets.cuda()
optimizer.zero_grad()
inputs, targets = Variable(inputs), Variable(targets)
outputs_1, outputs_2 = net(inputs)
outputs =
loss = criterion(outputs, targets)
lossd = loss.data[0]
loss.backward()
optimizer.step()
train_loss += loss.data[0]
outputs = outputs[0] # 0=cos_theta 1=phi_theta
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
correct += predicted.eq(targets.data).cpu().sum()
printoneline(dt(),'Te=%d Loss=%.4f | AccT=%.4f%% (%d/%d) %.4f %.2f %d'
% (epoch,train_loss/(batch_idx+1), 100.0*correct/total, correct, total,
lossd, criterion.lamb, criterion.it))
batch_idx += 1
print('')
def train(epoch,args):
featureNet.train()
maskNet.train()
fcNet.train()
train_loss = 0
classification_loss = 0
correct = 0
total = 0
batch_idx = 0
ds = ImageDataset(args.dataset,dataset_load,'data/casia_landmark.txt',name=args.net+':train',
bs=args.bs,shuffle=True,nthread=6,imagesize=128)
while True:
img,label = ds.get()
if img is None: break
inputs = torch.from_numpy(img).float()
targets = torch.from_numpy(label[:,0]).long()
if use_cuda: inputs, targets = inputs.cuda(), targets.cuda()
optimizerMask.zero_grad()
inputs, targets = Variable(inputs), Variable(targets)
features = featureNet(inputs)
mask = maskNet(features)
maskedFeatures = torch.mul(mask, features)
outputs = fcNet(maskedFeatures)
outputs1 = outputs[0] # 0=cos_theta 1=phi_theta
_, predicted = torch.max(outputs1.data, 1)
total += targets.size(0)
correct += predicted.eq(targets.data).cpu().sum()
# training the advNet:
lossAdv = criterion(outputs, targets)
lossCompact = torch.sum(conv2d(mask, laplacianKernel, stride=1, groups=512))
# lossSize #L1 norm of the mask to make the mask sparse.
lossSize = F.l1_loss(mask, target=torch.ones(mask.size()).cuda(), size_average = False)
print("advnet:", - criterion2(outputs1, targets).data/10, lossCompact.data/1000000, lossSize.data/10000)
loss = - criterion2(outputs1, targets)/10 + lossCompact/1000000 + lossSize/10000
lossd = loss.data
loss.backward(retain_graph=True)
optimizerMask.step()
optimizerFC.zero_grad()
lossC = criterion(outputs, targets)
lossClassification = lossC.data
lossC.backward()
optimizerFC.step()
classification_loss += lossClassification
train_loss += loss.data
print("classification loss:", classification_loss / (batch_idx + 1))
printoneline(dt(),'Te=%d Loss=%.4f | AccT=%.4f%% (%d/%d) %.4f %.2f %d\n'
% (epoch,train_loss/(batch_idx+1), 100.0*correct/total, correct, total,
lossd, criterion.lamb, criterion.it))
batch_idx += 1
# break
print('')
def train(epoch, args):
net.train()
train_loss = 0
correct = 0
total = 0
batch_idx = 0
ds = ImageDataset(imageroot=args.dataset,
callback=dataset_load,
imagelistfile=args.data_list,
name=args.net + ':train',
batchsize=args.batchsize,
shuffle=True,
nthread=args.nthread,
imagesize=128)
batch_num = ds.imagenum // args.batchsize
while True:
img, label = ds.get()
if img is None:
break
inputs = torch.from_numpy(img).float()
targets = torch.from_numpy(label[:, 0]).long()
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()
optimizer.step()
train_loss += loss.item()
outputs = outputs[0] # 0=cos_theta 1=phi_theta
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
correct += predicted.eq(targets.data).sum().item()
if batch_idx % 10 == 0:
print(
dt(), 'Epoch=%d batch: %d/%d Loss=%.4f | Acc=%.4f%%' %
(epoch, batch_idx, batch_num, train_loss /
(batch_idx + 1), correct * 100.0 / total))
batch_idx += 1
