def train_baseline(base_loader, base_loader_test, val_loader, model,
start_epoch, stop_epoch, params, tmp):
if params.dct_status:
channels = params.channels
else:
channels = 3
val_acc_best = 0.0
if not os.path.isdir(params.checkpoint_dir):
os.makedirs(params.checkpoint_dir)
if path.exists(params.checkpoint_dir + '/val_' + params.dataset + '.pt'):
loader = torch.load(params.checkpoint_dir + '/val_' + params.dataset +
'.pt')
else:
loader = []
for ii, (x, _) in enumerate(val_loader):
loader.append(x)
#print("head of train_dct: ", x.shape)
torch.save(loader,
params.checkpoint_dir + '/val_' + params.dataset + '.pt')
criterion = nn.CrossEntropyLoss().cuda()
optimizer = optim.Adam(model.parameters())
print("stop_epoch", start_epoch, stop_epoch)
for epoch in range(start_epoch, stop_epoch):
print('\nEpoch: %d' % epoch)
model.train()
train_loss = 0
reg_loss = 0
correct = 0
correct1 = 0.0
total = 0
for batch_idx, (input_var, target_var) in enumerate(base_loader):
if use_gpu:
input_var, target_var = input_var.cuda(), target_var.cuda()
input_dct_var, target_var = Variable(input_var), Variable(
target_var)
f, outputs = model.forward(input_dct_var)
loss = criterion(outputs, target_var)
train_loss += loss.data.item()
_, predicted = torch.max(outputs.data, 1)
total += target_var.size(0)
correct += predicted.eq(target_var.data).cpu().sum()
optimizer.zero_grad()
loss.backward()
optimizer.step()
if batch_idx % 50 == 0:
print(
'{0}/{1}'.format(batch_idx, len(base_loader)),
'Loss: %.3f | Acc: %.3f%% ' %
(train_loss / (batch_idx + 1), 100. * correct / total))
if not os.path.isdir(params.checkpoint_dir):
os.makedirs(params.checkpoint_dir)
if (epoch % params.save_freq == 0) or (epoch == stop_epoch - 1):
outfile = os.path.join(params.checkpoint_dir,
'{:d}.tar'.format(epoch))
torch.save({'epoch': epoch, 'state': model.state_dict()}, outfile)
model.eval()
with torch.no_grad():
test_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(base_loader_test):
if use_gpu:
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = Variable(inputs), Variable(targets)
f, outputs = model.forward(inputs)
loss = criterion(outputs, targets)
test_loss += loss.data.item()
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
correct += predicted.eq(targets.data).cpu().sum()
print('Loss: %.3f | Acc: %.3f%%' %
(test_loss / (batch_idx + 1), 100. * correct / total))
torch.cuda.empty_cache()
valmodel = BaselineFinetune(model_dict[params.model],
params.train_n_way,
params.n_shot,
loss_type='dist')
valmodel.n_query = 15
acc_all1, acc_all2, acc_all3 = [], [], []
for i, x in enumerate(loader):
# print("len of loader: ",len(loader))
# print("shape of x: ",x.shape)
if params.dct_status:
x = x.view(-1, channels, image_size_dct, image_size_dct)
else:
x = x.view(-1, channels, image_size, image_size)
if use_gpu:
x = x.cuda()
with torch.no_grad():
f, scores = model(x)
f = f.view(params.train_n_way, params.n_shot + valmodel.n_query,
-1)
scores = valmodel.set_forward_adaptation(f.cpu())
acc = []
for each_score in scores:
pred = each_score.data.cpu().numpy().argmax(axis=1)
y = np.repeat(range(5), 15)
acc.append(np.mean(pred == y) * 100)
acc_all1.append(acc[0])
acc_all2.append(acc[1])
acc_all3.append(acc[2])
print('Test Acc at 100= %4.2f%%' % (np.mean(acc_all1)))
print('Test Acc at 200= %4.2f%%' % (np.mean(acc_all2)))
print('Test Acc at 300= %4.2f%%' % (np.mean(acc_all3)))
if np.mean(acc_all3) > val_acc_best:
val_acc_best = np.mean(acc_all3)
bestfile = os.path.join(params.checkpoint_dir, 'best.tar')
torch.save({'epoch': epoch, 'state': model.state_dict()}, bestfile)
return model
def train_s2m2(base_loader, base_loader_test, val_loader, model, start_epoch,
stop_epoch, params, tmp):
if params.dct_status:
channels = params.channels
else:
channels = 3
val_acc_best = 0.0
if not os.path.isdir(params.checkpoint_dir):
os.makedirs(params.checkpoint_dir)
if path.exists(params.checkpoint_dir + '/val_' + params.dataset + '.pt'):
loader = torch.load(params.checkpoint_dir + '/val_' + params.dataset +
'.pt')
else:
loader = []
for _, (x, _) in enumerate(val_loader):
loader.append(x)
torch.save(loader,
params.checkpoint_dir + '/val_' + params.dataset + '.pt')
def mixup_criterion(criterion, pred, y_a, y_b, lam):
return lam * criterion(pred, y_a) + (1 - lam) * criterion(pred, y_b)
criterion = nn.CrossEntropyLoss()
if params.model == 'WideResNet28_10':
rotate_classifier = nn.Sequential(nn.Linear(640, 4))
elif params.model == 'ResNet18':
rotate_classifier = nn.Sequential(nn.Linear(512, 4))
rotate_classifier.cuda()
if 'rotate' in tmp:
print("loading rotate model")
rotate_classifier.load_state_dict(tmp['rotate'])
optimizer = torch.optim.Adam([{
'params': model.parameters()
}, {
'params': rotate_classifier.parameters()
}])
print("stop_epoch", start_epoch, stop_epoch)
for epoch in range(start_epoch, stop_epoch):
print('\nEpoch: %d' % epoch)
model.train()
train_loss = 0
rotate_loss = 0
correct = 0
total = 0
torch.cuda.empty_cache()
print("inside base_loader: ", len(base_loader))
for batch_idx, (inputs, targets) in enumerate(base_loader):
if use_gpu:
inputs, targets = inputs.cuda(), targets.cuda()
#print("shape of input: ", inputs.shape)
lam = np.random.beta(params.alpha, params.alpha)
f, outputs, target_a, target_b = model(inputs,
targets,
mixup_hidden=True,
mixup_alpha=params.alpha,
lam=lam)
loss = mixup_criterion(criterion, outputs, target_a, target_b, lam)
train_loss += loss.data.item()
optimizer.zero_grad()
loss.backward()
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
correct += (
lam * predicted.eq(target_a.data).cpu().sum().float() +
(1 - lam) * predicted.eq(target_b.data).cpu().sum().float())
bs = inputs.size(0)
inputs_ = []
targets_ = []
a_ = []
indices = np.arange(bs)
np.random.shuffle(indices)
split_size = int(bs / 4)
for j in indices[0:split_size]:
x90 = inputs[j].transpose(2, 1).flip(1)
x180 = x90.transpose(2, 1).flip(1)
x270 = x180.transpose(2, 1).flip(1)
inputs_ += [inputs[j], x90, x180, x270]
targets_ += [targets[j] for _ in range(4)]
a_ += [
torch.tensor(0),
torch.tensor(1),
torch.tensor(2),
torch.tensor(3)
]
inputs = Variable(torch.stack(inputs_, 0))
targets = Variable(torch.stack(targets_, 0))
a_ = Variable(torch.stack(a_, 0))
if use_gpu:
inputs = inputs.cuda()
targets = targets.cuda()
a_ = a_.cuda()
rf, outputs = model(inputs)
rotate_outputs = rotate_classifier(rf)
rloss = criterion(rotate_outputs, a_)
closs = criterion(outputs, targets)
loss = (rloss + closs) / 2.0
rotate_loss += rloss.data.item()
loss.backward()
optimizer.step()
if batch_idx % 50 == 0:
print(
'{0}/{1}'.format(batch_idx, len(base_loader)),
'Loss: %.3f | Acc: %.3f%% | RotLoss: %.3f ' %
(train_loss /
(batch_idx + 1), 100. * correct / total, rotate_loss /
(batch_idx + 1)))
if not os.path.isdir(params.checkpoint_dir):
os.makedirs(params.checkpoint_dir)
if (epoch % params.save_freq == 0) or (epoch == stop_epoch - 1):
outfile = os.path.join(params.checkpoint_dir,
'{:d}.tar'.format(epoch))
torch.save({'epoch': epoch, 'state': model.state_dict()}, outfile)
model.eval()
with torch.no_grad():
test_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(base_loader_test):
if use_gpu:
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = Variable(inputs), Variable(targets)
f, outputs = model.forward(inputs)
loss = criterion(outputs, targets)
test_loss += loss.data.item()
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
correct += predicted.eq(targets.data).cpu().sum()
print('Loss: %.3f | Acc: %.3f%%' %
(test_loss / (batch_idx + 1), 100. * correct / total))
if params.dct_status:
valmodel = BaselineFinetune(model_dict[params.model + '_dct'],
params.train_n_way,
params.n_shot,
loss_type='dist')
else:
valmodel = BaselineFinetune(model_dict[params.model],
params.train_n_way,
params.n_shot,
loss_type='dist')
valmodel.n_query = 15
acc_all1, acc_all2, acc_all3 = [], [], []
for i, x in enumerate(loader):
if params.dct_status:
x = x.view(-1, channels, image_size_dct, image_size_dct)
else:
x = x.view(-1, channels, image_size, image_size)
if use_gpu:
x = x.cuda()
with torch.no_grad():
f, scores = model(x)
f = f.view(params.train_n_way, params.n_shot + valmodel.n_query,
-1)
scores = valmodel.set_forward_adaptation(f.cpu())
acc = []
for each_score in scores:
pred = each_score.data.cpu().numpy().argmax(axis=1)
y = np.repeat(range(5), 15)
acc.append(np.mean(pred == y) * 100)
acc_all1.append(acc[0])
acc_all2.append(acc[1])
acc_all3.append(acc[2])
print('Test Acc at 100= %4.2f%%' % (np.mean(acc_all1)))
print('Test Acc at 200= %4.2f%%' % (np.mean(acc_all2)))
print('Test Acc at 300= %4.2f%%' % (np.mean(acc_all3)))
if np.mean(acc_all3) > val_acc_best:
val_acc_best = np.mean(acc_all3)
bestfile = os.path.join(params.checkpoint_dir, 'best.tar')
torch.save(
{
'epoch': epoch,
'state': model.state_dict(),
'rotate': rotate_classifier.state_dict()
}, bestfile)
return model
def get_logits_targets(params):
acc_all = []
iter_num = 600
few_shot_params = dict(n_way = params.test_n_way , n_support = params.n_shot)
if params.dataset in ['omniglot', 'cross_char']:
assert params.model == 'Conv4' and not params.train_aug ,'omniglot only support Conv4 without augmentation'
params.model = 'Conv4S'
if params.method == 'baseline':
model = BaselineFinetune( model_dict[params.model], **few_shot_params )
elif params.method == 'baseline++':
model = BaselineFinetune( model_dict[params.model], loss_type = 'dist', **few_shot_params )
elif params.method == 'protonet':
model = ProtoNet( model_dict[params.model], **few_shot_params )
elif params.method == 'DKT':
model = DKT(model_dict[params.model], **few_shot_params)
elif params.method == 'matchingnet':
model = MatchingNet( model_dict[params.model], **few_shot_params )
elif params.method in ['relationnet', 'relationnet_softmax']:
if params.model == 'Conv4':
feature_model = backbone.Conv4NP
elif params.model == 'Conv6':
feature_model = backbone.Conv6NP
elif params.model == 'Conv4S':
feature_model = backbone.Conv4SNP
else:
feature_model = lambda: model_dict[params.model]( flatten = False )
loss_type = 'mse' if params.method == 'relationnet' else 'softmax'
model = RelationNet( feature_model, loss_type = loss_type , **few_shot_params )
elif params.method in ['maml' , 'maml_approx']:
backbone.ConvBlock.maml = True
backbone.SimpleBlock.maml = True
backbone.BottleneckBlock.maml = True
backbone.ResNet.maml = True
model = MAML( model_dict[params.model], approx = (params.method == 'maml_approx') , **few_shot_params )
if params.dataset in ['omniglot', 'cross_char']: #maml use different parameter in omniglot
model.n_task = 32
model.task_update_num = 1
model.train_lr = 0.1
else:
raise ValueError('Unknown method')
model = model.cuda()
checkpoint_dir = '%s/checkpoints/%s/%s_%s' %(configs.save_dir, params.dataset, params.model, params.method)
if params.train_aug:
checkpoint_dir += '_aug'
if not params.method in ['baseline', 'baseline++'] :
checkpoint_dir += '_%dway_%dshot' %( params.train_n_way, params.n_shot)
#modelfile = get_resume_file(checkpoint_dir)
if not params.method in ['baseline', 'baseline++'] :
if params.save_iter != -1:
modelfile = get_assigned_file(checkpoint_dir,params.save_iter)
else:
modelfile = get_best_file(checkpoint_dir)
if modelfile is not None:
tmp = torch.load(modelfile)
model.load_state_dict(tmp['state'])
else:
print("[WARNING] Cannot find 'best_file.tar' in: " + str(checkpoint_dir))
split = params.split
if params.save_iter != -1:
split_str = split + "_" +str(params.save_iter)
else:
split_str = split
if params.method in ['maml', 'maml_approx', 'DKT']: #maml do not support testing with feature
if 'Conv' in params.model:
if params.dataset in ['omniglot', 'cross_char']:
image_size = 28
else:
image_size = 84
else:
image_size = 224
datamgr = SetDataManager(image_size, n_eposide = iter_num, n_query = 15 , **few_shot_params)
if params.dataset == 'cross':
if split == 'base':
loadfile = configs.data_dir['miniImagenet'] + 'all.json'
else:
loadfile = configs.data_dir['CUB'] + split +'.json'
elif params.dataset == 'cross_char':
if split == 'base':
loadfile = configs.data_dir['omniglot'] + 'noLatin.json'
else:
loadfile = configs.data_dir['emnist'] + split +'.json'
else:
loadfile = configs.data_dir[params.dataset] + split + '.json'
novel_loader = datamgr.get_data_loader( loadfile, aug = False)
if params.adaptation:
model.task_update_num = 100 #We perform adaptation on MAML simply by updating more times.
model.eval()
logits_list = list()
targets_list = list()
for i, (x,_) in enumerate(novel_loader):
logits = model.get_logits(x).detach()
targets = torch.tensor(np.repeat(range(params.test_n_way), model.n_query)).cuda()
logits_list.append(logits) #.cpu().detach().numpy())
targets_list.append(targets) #.cpu().detach().numpy())
else:
novel_file = os.path.join( checkpoint_dir.replace("checkpoints","features"), split_str +".hdf5")
cl_data_file = feat_loader.init_loader(novel_file)
logits_list = list()
targets_list = list()
n_query = 15
n_way = few_shot_params['n_way']
n_support = few_shot_params['n_support']
class_list = cl_data_file.keys()
for i in range(iter_num):
#----------------------
select_class = random.sample(class_list,n_way)
z_all = []
for cl in select_class:
img_feat = cl_data_file[cl]
perm_ids = np.random.permutation(len(img_feat)).tolist()
z_all.append( [ np.squeeze( img_feat[perm_ids[i]]) for i in range(n_support+n_query) ] ) # stack each batch
z_all = torch.from_numpy(np.array(z_all))
model.n_query = n_query
logits = model.set_forward(z_all, is_feature = True).detach()
targets = torch.tensor(np.repeat(range(n_way), n_query)).cuda()
logits_list.append(logits)
targets_list.append(targets)
#----------------------
return torch.cat(logits_list, 0), torch.cat(targets_list, 0)
