def single_test(params, results_logger):
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()
acc_mean, acc_std = model.test_loop(novel_loader, return_std=True)
else:
novel_file = os.path.join(
checkpoint_dir.replace("checkpoints",
"features"), split_str + ".hdf5"
) # defaut split = novel, but you can also test base or val classes
cl_data_file = feat_loader.init_loader(novel_file)
for i in range(iter_num):
acc = feature_evaluation(cl_data_file,
model,
n_query=15,
adaptation=params.adaptation,
**few_shot_params)
acc_all.append(acc)
acc_all = np.asarray(acc_all)
acc_mean = np.mean(acc_all)
acc_std = np.std(acc_all)
print('%d Test Acc = %4.2f%% +- %4.2f%%' %
(iter_num, acc_mean, 1.96 * acc_std / np.sqrt(iter_num)))
with open('record/results.txt', 'a') as f:
timestamp = time.strftime("%Y%m%d-%H%M%S", time.localtime())
aug_str = '-aug' if params.train_aug else ''
aug_str += '-adapted' if params.adaptation else ''
if params.method in ['baseline', 'baseline++']:
exp_setting = '%s-%s-%s-%s%s %sshot %sway_test' % (
params.dataset, split_str, params.model, params.method,
aug_str, params.n_shot, params.test_n_way)
else:
exp_setting = '%s-%s-%s-%s%s %sshot %sway_train %sway_test' % (
params.dataset, split_str, params.model, params.method,
aug_str, params.n_shot, params.train_n_way, params.test_n_way)
acc_str = '%d Test Acc = %4.2f%% +- %4.2f%%' % (
iter_num, acc_mean, 1.96 * acc_std / np.sqrt(iter_num))
f.write('Time: %s, Setting: %s, Acc: %s \n' %
(timestamp, exp_setting, acc_str))
results_logger.log("single_test_acc", acc_mean)
results_logger.log("single_test_acc_std",
1.96 * acc_std / np.sqrt(iter_num))
results_logger.log("time", timestamp)
results_logger.log("exp_setting", exp_setting)
results_logger.log("acc_str", acc_str)
return acc_mean
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
datamgr = SetDataManager(image_size,
n_eposide=iter_num,
n_query=15,
**few_shot_params,
isAircraft=isAircraft,
grey=params.grey)
loadfile = os.path.join('filelists', params.dataset, 'novel.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()
acc_mean, acc_std = model.test_loop(novel_loader, return_std=True)
else:
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)
if params.save_iter != -1:
outfile = os.path.join(
checkpoint_dir.replace("checkpoints", "features"),
split + "_" + str(params.save_iter) + ".hdf5")
else:
outfile = os.path.join(
checkpoint_dir.replace("checkpoints", "features"),
split + ".hdf5")
datamgr = SimpleDataManager(image_size,
if __name__ == '__main__':
params = parse_args('test')
set_cuda(params.cuda)
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 == '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
def get_model(params, mode):
'''
Args:
params: argparse params
mode: (str), 'train', 'test'
'''
print('get_model() start...')
# few_shot_params_d = get_few_shot_params(params, None)
# few_shot_params = few_shot_params_d[mode]
few_shot_params = get_few_shot_params(params, mode)
if 'omniglot' in params.dataset or 'cross_char' in params.dataset:
# if params.dataset in ['omniglot', 'cross_char', 'cross_char_half', 'cross_char_quarter', ...]:
# assert params.model == 'Conv4' and not params.train_aug ,'omniglot only support Conv4 without augmentation'
assert 'Conv4' in params.model and not params.train_aug ,'omniglot/cross_char only support Conv4 without augmentation'
params.model = params.model.replace('Conv4', 'Conv4S') # because Conv4Drop should also be Conv4SDrop
if params.recons_decoder is not None:
if 'ConvS' not in params.recons_decoder:
raise ValueError('omniglot / cross_char should use ConvS/HiddenConvS decoder.')
# if mode == 'train':
# params.num_classes = n_base_class_map[params.dataset]
if params.method in ['baseline', 'baseline++'] and mode=='train':
assert params.num_classes >= n_base_classes[params.dataset]
# if params.dataset == 'omniglot': # 4112/688/1692
# assert params.num_classes >= 4112, 'class number need to be larger than max label id in base class'
# if params.dataset == 'cross_char': # 1597/31/31
# assert params.num_classes >= 1597, 'class number need to be larger than max label id in base class'
# if params.dataset == 'cross_char_half': # 758/31/31
# assert params.num_classes >= 758, 'class number need to be larger than max label id in base class'
# if params.dataset in ['cross_char_quarter', 'cross_char_quarter_10shot']: # 350/31/31
# assert params.num_classes >= 350, 'class number need to be larger than max label id in base class'
# if params.dataset == 'cross_char_base3lang': # 69/31/31
# assert params.num_classes >= 69, 'class number need to be larger than max label id in base class'
# if params.dataset == 'miniImagenet': # 64/16/20
# assert params.num_classes >= 64, 'class number need to be larger than max label id in base class'
# if params.dataset == 'CUB': # 100/50/50
# assert params.num_classes >= 100, 'class number need to be larger than max label id in base class'
# if params.dataset == 'cross': # 64+16+20/50/50
# assert params.num_classes >= 100, 'class number need to be larger than max label id in base class'
# if params.dataset == 'cross_base80cl': # 80/50/50
# assert params.num_classes >= 100, 'class number need to be larger than max label id in base class'
if params.recons_decoder == None:
print('params.recons_decoder == None')
recons_decoder = None
else:
recons_decoder = decoder_dict[params.recons_decoder]
print('recons_decoder:\n',recons_decoder)
backbone_func = get_backbone_func(params)
if 'baseline' in params.method:
loss_types = {
'baseline':'softmax',
'baseline++':'dist',
}
loss_type = loss_types[params.method]
if recons_decoder is None and params.min_gram is None: # default baseline/baseline++
if mode == 'train':
model = BaselineTrain(
model_func = backbone_func, loss_type = loss_type,
num_class = params.num_classes, **few_shot_params)
elif mode == 'test':
model = BaselineFinetune(
model_func = backbone_func, loss_type = loss_type,
**few_shot_params, finetune_dropout_p = params.finetune_dropout_p)
else: # other settings for baseline
if params.min_gram is not None:
min_gram_params = {
'min_gram':params.min_gram,
'lambda_gram':params.lambda_gram,
}
if mode == 'train':
model = BaselineTrainMinGram(
model_func = backbone_func, loss_type = loss_type,
num_class = params.num_classes, **few_shot_params, **min_gram_params)
elif mode == 'test':
model = BaselineFinetune(
model_func = backbone_func, loss_type = loss_type,
**few_shot_params, finetune_dropout_p = params.finetune_dropout_p)
# model = BaselineFinetuneMinGram(backbone_func, loss_type = loss_type, **few_shot_params, **min_gram_params)
elif params.method == 'protonet':
# default ProtoNet
if recons_decoder is None and params.min_gram is None:
model = ProtoNet( backbone_func, **few_shot_params )
else: # other settings
if params.min_gram is not None:
min_gram_params = {
'min_gram':params.min_gram,
'lambda_gram':params.lambda_gram,
}
model = ProtoNetMinGram(backbone_func, **few_shot_params, **min_gram_params)
if params.recons_decoder is not None:
if 'Hidden' in params.recons_decoder:
if params.recons_decoder == 'HiddenConv': # 'HiddenConv', 'HiddenConvS'
model = ProtoNetAE2(backbone_func, **few_shot_params, recons_func=recons_decoder, lambda_d=params.recons_lambda, extract_layer = 2)
elif params.recons_decoder == 'HiddenConvS': # 'HiddenConv', 'HiddenConvS'
model = ProtoNetAE2(backbone_func, **few_shot_params, recons_func=recons_decoder, lambda_d=params.recons_lambda, extract_layer = 2, is_color=False)
elif params.recons_decoder == 'HiddenRes10':
model = ProtoNetAE2(backbone_func, **few_shot_params, recons_func=recons_decoder, lambda_d=params.recons_lambda, extract_layer = 6)
elif params.recons_decoder == 'HiddenRes18':
model = ProtoNetAE2(backbone_func, **few_shot_params, recons_func=recons_decoder, lambda_d=params.recons_lambda, extract_layer = 8)
else:
if 'ConvS' in params.recons_decoder:
model = ProtoNetAE(backbone_func, **few_shot_params, recons_func=recons_decoder, lambda_d=params.recons_lambda, is_color=False)
else:
model = ProtoNetAE(backbone_func, **few_shot_params, recons_func=recons_decoder, lambda_d=params.recons_lambda, is_color=True)
elif params.method == 'matchingnet':
model = MatchingNet( backbone_func, **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( backbone_func, 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( backbone_func, approx = (params.method == 'maml_approx') , **few_shot_params )
if 'omniglot' in params.dataset or 'cross_char' in params.dataset:
# if params.dataset in ['omniglot', 'cross_char', 'cross_char_half']: #maml use different parameter in omniglot
model.n_task = 32
model.task_update_num = 1
model.train_lr = 0.1
else:
raise ValueError('Unexpected params.method: %s'%(params.method))
print('get_model() finished.')
return model
return acc
if __name__ == '__main__':
params = parse_args('test')
acc_all = []
if params.dataset == 'CUB':
iter_num = 600
else:
iter_num = 10000
few_shot_params = dict(n_way=params.test_n_way, n_support=params.n_shot)
model = BaselineFinetune(model_dict[params.model], **few_shot_params)
if torch.cuda.is_available():
model = model.cuda()
if params.dct_status == False:
params.channels = 3
checkpoint_dir = '%s/checkpoints/%s/%s_%s_%sway_%sshot' % (
configs.save_dir, params.dataset, params.model, params.method,
params.test_n_way, params.n_shot)
if params.train_aug:
checkpoint_dir += '_aug'
if params.dct_status:
checkpoint_dir += '_dct'
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)
iter_num = 600
few_shot_args = dict(n_way=args.test_n_way, n_support=args.n_shot)
l3_model = None
# Always load vocab for compatibility
if args.dataset == 'CUB':
vocab = lang_utils.load_vocab(configs.lang_dir)
else:
vocab, *_ = lang_utils.load_scenes_vocab(
'./filelists/scenes/AbstractScenes_v1.1/Sentences_1002.txt')
if args.method == 'baseline':
model = BaselineFinetune(model_dict[args.model], **few_shot_args)
elif args.method == 'baseline++':
model = BaselineFinetune(model_dict[args.model],
loss_type='dist',
**few_shot_args)
elif args.method == 'protonet':
lang_model = None
if args.ml2 or args.l3:
# Add sos/eos tokens to vocabulary
embedding_model = nn.Embedding(len(vocab), args.lang_emb_size)
lang_input_size = 1600 if args.model.startswith('Conv') else 512
if args.language_task == 'decode':
# FIXME: This won't work for all models
lang_model_func = lambda inpsize: TextProposal(
embedding_model,
input_size=inpsize,
if params.dataset == 'omniglot':
assert params.num_classes >= 4112, 'class number need to be larger than max label id in base class'
if params.dataset == 'cross_char':
assert params.num_classes >= 1597, 'class number need to be larger than max label id in base class'
if params.method == 'baseline':
train_model = BaselineTrain(model_dict[params.model],
params.num_classes,
loss_type=params.loss_type,
margin=params.margin,
centered=params.centered,
temperature=params.temperature)
test_model = BaselineFinetune(model_dict[params.model],
params.num_classes,
params.n_shot,
loss_type=params.loss_type,
margin=params.margin,
centered=params.centered,
temperature=params.temperature)
elif params.method in [
'simplenet', 'protonet', 'matchingnet', 'relationnet',
'relationnet_softmax', 'maml', 'maml_approx'
]:
n_query = max(
1, int(16 * params.test_n_way / params.train_n_way)
) # if test_n_way is smaller than train_n_way, reduce n_query to keep batch size small
train_few_shot_params = dict(n_way=params.train_n_way,
n_support=params.n_shot)
base_datamgr = SetDataManager(image_size,
pred = scores.data.cpu().numpy().argmax(axis=1)
y = np.repeat(range(n_way), n_query)
acc = np.mean(pred == y) * 100
return acc
if __name__ == '__main__':
params = parse_args('test')
acc_all = []
iter_num = 600
few_shot_params = dict(n_way=params.test_n_way, n_support=params.n_shot)
if params.method == 'baseline':
model = BaselineFinetune(model_dict[params.model], **few_shot_params)
elif params.method == 'protonet':
model = ProtoNet(model_dict[params.model], **few_shot_params)
else:
raise ValueError('Unknown method')
model = model.cuda()
checkpoint_dir = '%s/checkpoints/%s/%s_%s' % (
configs.save_dir, 'miniImageNet', params.model, params.method)
if params.train_aug:
checkpoint_dir += '_aug'
if not params.method in ['baseline']:
checkpoint_dir += '_%dway_%dshot' % (params.train_n_way, params.n_shot)
if not params.method in ['baseline']:
iter_num = 600
few_shot_params = dict(n_way=params.test_n_way, n_support=params.n_shot)
params.reminder = "%s_TempR-%s_%s_Margin-%s_%s" % (
params.model, params.ratio, params.loss_type, params.large_margin,
time.time())
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],
ratio=params.ratio,
loss_type=params.loss_type,
margin=params.large_margin,
centered=params.centered,
**few_shot_params)
elif params.method == 'simplenet':
model = SimpleNetFinetune(model_dict[params.model], **few_shot_params)
elif params.method == 'protonet':
model = ProtoNet(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