def test_multihead(self, task_id, testloader):
self.net.eval()
test_loss = 0.0
correct = 0
total = 0
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(self.device), targets.to(
self.device)
inputs = Variable(inputs)
targets = Variable(targets)
outputs, _ = self.net(inputs)
loss = self.criterion(outputs, targets)
test_loss += loss.item()
_, predicted = outputs[:, args.classes_per_task *
task_id:args.classes_per_task *
(task_id + 1)].max(1)
total += targets.size(0)
correct += (
predicted +
args.classes_per_task * task_id).eq(targets).sum().item()
progress_bar(
batch_idx, len(testloader),
'Loss:%.3f|Acc:%.3f%% (%d/%d)--Test' %
(test_loss /
(batch_idx + 1), 100. * correct / total, correct, total))
return correct / total
def test(self, testloader):
self.net.eval()
test_loss = 0.0
correct = 0
total = 0
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(self.device), targets.to(self.device)
inputs = Variable(inputs)
targets = Variable(targets)
outputs = self.net(inputs)
loss = self.criterion(outputs, targets)
test_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
progress_bar(batch_idx, len(testloader), 'Loss:%.3f|Acc:%.3f%% (%d/%d)--Test'
% (test_loss/(batch_idx+1), 100.*correct/total, correct, total))
#
# print('target', targets)
# print('predicted', predicted)
return correct/total
def train(self, epoch, trainloader):
if args.resume:
# Load checkpoint.
print('==> Resuming from checkpoint..')
assert os.path.isdir(
'checkpoint'), 'Error: no checkpoint directory found!'
checkpoint = torch.load('./checkpoint/ckpt.t7')
self.net.load_state_dict(checkpoint['net'])
best_acc = checkpoint['acc']
start_epoch = checkpoint['epoch']
print('\nEpoch: %d lr: %s' % (epoch, self.scheduler.get_lr()))
self.scheduler.step()
self.net.train()
train_loss = 0.0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
inputs, targets = inputs.to(self.device), targets.to(self.device)
inputs_var = Variable(inputs)
targets_var = Variable(targets)
self.optimizer.zero_grad()
outputs, _ = self.net(inputs_var)
loss = self.criterion(outputs, targets_var)
loss.backward()
self.optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1) # outputs.shape: (batch, classes)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
self.loss = train_loss
progress_bar(
batch_idx, len(trainloader),
'Loss:%.3f|Acc:%.3f%% (%d/%d)--Train' %
(train_loss /
(batch_idx + 1), 100. * correct / total, correct, total))
return correct / total
def train_fc(self, epoch, trainloader):
for name, param in self.net.named_parameters():
if re.search('conv', name) or re.search('bn', name):
param.requires_grad = False
elif re.search('linear', name):
param.requires_grad = True
self.optimizer = optim.SGD(filter(lambda p: p.requires_grad,
self.net.parameters()),
lr=args.lr,
momentum=0.9,
weight_decay=args.weight_decay)
logging.info('\nEpoch: %d lr: %s' % (epoch, self.scheduler.get_lr()))
self.optimizer.step()
self.scheduler.step()
self.net.train()
train_loss = 0.0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
inputs, targets = inputs.to(self.device), targets.to(self.device)
inputs_var = Variable(inputs)
targets_var = Variable(targets)
self.optimizer.zero_grad()
outputs = self.net(inputs_var)
loss = self.criterion(outputs, targets_var)
loss.backward()
self.optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1) # outputs.shape: (batch, classes)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
self.loss = train_loss
progress_bar(
batch_idx, len(trainloader),
'Loss:%.3f|Acc:%.3f%% (%d/%d)--Train' %
(train_loss /
(batch_idx + 1), 100. * correct / total, correct, total))
return correct / total
def train_with_mask_with_KD(self, epoch, trainloader, KD_target_list, len_onehot):
mask_dict = pickle.load(open(save_mask_file, "rb"))
mask_reverse_dict = pickle.load(open(save_mask_fileR, "rb"))
lr_list = self.scheduler.get_lr()
logging.info('\nEpoch: %d lr: %s' % (epoch, self.scheduler.get_lr()))
self.scheduler.step()
self.net.train()
train_loss = 0.0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
targets_KD = self.make_one_hot(targets, len_onehot)
w = KD_target_list[batch_idx].shape[1]
targets_KD[:, 0:w] = KD_target_list[batch_idx][:, 0:w]
inputs, targets_KD, targets = inputs.to(self.device), targets_KD.to(self.device), targets.to(self.device)
inputs_var = Variable(inputs)
targets_var_KD = Variable(targets_KD)
self.optimizer.zero_grad()
# print(targets_var_KD.shape)
outputs = self.net(inputs_var)/args.temperature
loss = self.xentropy_cost(outputs, targets_var_KD)
# break
loss.backward()
self.optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets_var_KD.size(0)
correct += predicted.eq(targets).sum().item()
self.loss = train_loss
progress_bar(batch_idx, len(trainloader), 'Loss:%.3f|Acc:%.3f%% (%d/%d)--Train' % (train_loss/(batch_idx+1), 100.*correct/total, correct, total))
return correct / total
def train(self, epoch, trainloader):
logging.info('\nEpoch: %d lr: %s' % (epoch, self.scheduler.get_lr()))
self.net.train()
train_loss = 0.0
correct = 0
total = 0
self.optimizer.step()
self.scheduler.step()
for batch_idx, (inputs, targets) in enumerate(trainloader):
inputs, targets = inputs.to(self.device), targets.to(self.device)
inputs_var = Variable(inputs)
targets_var = Variable(targets)
self.optimizer.zero_grad()
outputs = self.net(inputs_var)
loss = self.criterion(outputs, targets_var)
loss.backward()
self.optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1) # outputs.shape: (batch, classes)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
self.loss = train_loss
acc = 100. * correct / total
progress_bar(
batch_idx, len(trainloader),
'Loss:%.3f|Acc:%.3f%% (%d/%d)--Train' %
(train_loss / (batch_idx + 1), acc, correct, total))
if epoch == args.epoch - 1:
self.save_checkpoint_t7(epoch, acc, train_loss)
return correct / total
def train_with_mask_with_EWC(self, current_trainloader, previous_trainloader, fisher_estimation_sample_size=256): # retrain percentage
all_loader = []
all_loader.append(previous_trainloader)
all_loader.append(current_trainloader)
for task_id, trainloader in enumerate(all_loader):
# self.initialization(args.lr_mutant, args.lr_mutant_step_size, args.weight_decay_2)
self.optimizer = optim.SGD(self.net.parameters(), lr=args.lr_mutant, momentum=0.9, weight_decay=args.weight_decay_2)
self.scheduler = optim.lr_scheduler.StepLR(self.optimizer, step_size=args.lr_mutant_step_size, gamma=args.lr_gamma)
# self.save_mutant(55, 1)
train_acc = np.zeros([1, args.num_epoch])
test_acc = np.zeros([1, args.num_epoch])
for epoch in range(args.num_epoch):
lr_list = self.scheduler.get_lr()
logging.info('\nEpoch: %d lr: %s' % (epoch, self.scheduler.get_lr()))
self.scheduler.step()
self.net.train()
train_loss = 0.0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
# print(batch_idx)
# targets = self.make_one_hot(targets, len_onehot)
# print(target)
inputs, targets = inputs.to(self.device), targets.to(self.device)
inputs_var = Variable(inputs)
targets_var = Variable(targets)
self.optimizer.zero_grad()
outputs = self.net(inputs_var)
loss = self.criterion(outputs, targets_var)
if args.ewc and task_id > 0 :
ewc_loss = self.net.ewc_loss(cuda = self.device)
else:
ewc_loss = 0.0
loss = loss + ewc_loss
loss.backward()
self.optimizer.step()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
self.loss = loss
progress_bar(batch_idx, len(trainloader), ' | Loss:%.3f| ewc_loss:%.3f | Acc:%.3f%% (%d/%d) -- Train Task(%d/%d)'
% ( loss, ewc_loss, 100.*correct/total, correct, total, task_id, len(all_loader)))
train_acc[0, epoch] = correct / total
# test_acc[0, epoch] = self.test(trainloader)
if args.consolidate and task_id < len(all_loader):
# estimate the fisher information of the parameters and consolidate
# them in the network.
print(
'=> Estimating diagonals of the fisher information matrix...',
end='', flush=True
)
self.net.consolidate(self.net.estimate_fisher(
trainloader, fisher_estimation_sample_size
))
print(' Done!')
else:
logging.info('No consolidate/EWC loss available')
return test_acc
def train_with_frozen_filter(self, epoch, trainloader, mask_dict,
mask_dict_R):
param_old_dict = OrderedDict([(k, None)
for k in self.net.state_dict().keys()])
for layer_name, param in self.net.state_dict().items():
param_old_dict[layer_name] = param.clone()
print('\nEpoch: %d lr: %s' % (epoch, self.scheduler.get_lr()))
self.scheduler.step()
self.net.train()
train_loss = 0.0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
inputs, targets = inputs.to(self.device), targets.to(self.device)
inputs_var = Variable(inputs)
targets_var = Variable(targets)
self.optimizer.zero_grad()
outputs, _ = self.net(inputs_var)
loss = self.criterion(outputs, targets)
loss.backward()
self.optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
# apply mask
param_processed = OrderedDict([
(k, None) for k in self.net.state_dict().keys()
])
for layer_name, param_new in self.net.state_dict().items():
param_new = param_new.type(torch.cuda.FloatTensor)
param_old_dict[layer_name] = param_old_dict[layer_name].type(
torch.cuda.FloatTensor)
if re.search('conv', layer_name):
param_processed[layer_name] = Variable(
torch.mul(param_old_dict[layer_name],
mask_dict[layer_name]) +
torch.mul(param_new, mask_dict_R[layer_name]),
requires_grad=True)
elif re.search('shortcut', layer_name):
if len(param_new.shape) == 4: # conv in shortcut
param_processed[layer_name] = Variable(
torch.mul(param_old_dict[layer_name],
mask_dict[layer_name]) +
torch.mul(param_new, mask_dict_R[layer_name]),
requires_grad=True)
else:
param_processed[layer_name] = Variable(
param_new, requires_grad=True)
elif re.search('linear', layer_name):
param_processed[layer_name] = Variable(
torch.mul(param_old_dict[layer_name],
mask_dict[layer_name]) +
torch.mul(param_new, mask_dict_R[layer_name]),
requires_grad=True)
else:
param_processed[layer_name] = Variable(
param_new, requires_grad=True) # num_batches_tracked
# raise ValueError('some parameters are skipped, plz check {}'.format(layer_name)) # num_batches_tracked
self.net.load_state_dict(param_processed)
progress_bar(
batch_idx, len(trainloader),
'Loss:%.3f|Acc:%.3f%% (%d/%d)--Train' %
(train_loss /
(batch_idx + 1), 100. * correct / total, correct, total))
return correct / total
def train_with_frozen_filter(self,
epoch,
trainloader,
mask_dict,
mask_dict_R,
path_postfix=''):
param_old_dict = OrderedDict([(k, None)
for k in self.net.state_dict().keys()])
for layer_name, param in self.net.state_dict().items():
param_old_dict[layer_name] = param.clone()
self.net.train()
logging.info('\nEpoch: %d lr: %s' % (epoch, self.scheduler.get_lr()))
train_loss = 0.0
correct = 0
total = 0
self.optimizer.step()
self.scheduler.step()
for batch_idx, (inputs, targets) in enumerate(trainloader):
inputs, targets = inputs.to(self.device), targets.to(self.device)
inputs_var = Variable(inputs)
targets_var = Variable(targets)
self.optimizer.zero_grad()
outputs = self.net(inputs_var)
loss = self.criterion(outputs, targets)
loss.backward()
self.optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
acc = 100. * correct / total
# apply mask
param_processed = OrderedDict([
(k, None) for k in self.net.state_dict().keys()
])
for layer_name, param_new in self.net.state_dict().items():
param_new = param_new.type(torch.cuda.FloatTensor)
param_old_dict[layer_name] = param_old_dict[layer_name].type(
torch.cuda.FloatTensor)
# print(layer_name)
if re.search('conv', layer_name):
param_processed[layer_name] = Variable(
torch.mul(param_old_dict[layer_name],
mask_dict[layer_name]) +
torch.mul(param_new, mask_dict_R[layer_name]),
requires_grad=True)
# print('new\n', param_new[0:3, 0, :, :])
elif re.search('shortcut', layer_name):
if len(param_new.shape) == 4: # conv in shortcut
param_processed[layer_name] = Variable(
torch.mul(param_old_dict[layer_name],
mask_dict[layer_name]) +
torch.mul(param_new, mask_dict_R[layer_name]),
requires_grad=True)
else:
param_processed[layer_name] = Variable(
param_new, requires_grad=True)
elif re.search('linear', layer_name):
param_processed[layer_name] = Variable(
torch.mul(param_old_dict[layer_name],
mask_dict[layer_name]) +
torch.mul(param_new, mask_dict_R[layer_name]),
requires_grad=True)
else:
param_processed[layer_name] = Variable(
param_new, requires_grad=True) # num_batches_tracked
# print('old\n', param_old_dict['conv1.weight'][0:3, 0, :, :])
# print('mask\n', mask_dict['conv1.weight'][0:3, 0, :, :])
# print('mask_R\n', mask_dict_R['conv1.weight'][0:3, 0, :, :])
# print('param_processed\n', param_processed['conv1.weight'][0:3, 0, :, :])
self.net.load_state_dict(param_processed)
progress_bar(
batch_idx, len(trainloader),
'Loss:%.3f|Acc:%.3f%% (%d/%d)--Train' %
(train_loss / (batch_idx + 1), acc, correct, total))
if epoch == 0 or epoch == args.epoch_edge - 1 or epoch == args.epoch_edge // 2:
self.save_checkpoint_t7(
epoch,
acc,
train_loss,
'_edge_model',
path_postfix,
)
return correct / total