def evaluate(novel_loader, n_way=5, n_support=5):
iter_num = len(novel_loader)
acc_all = []
# Model
if params.method == 'MatchingNet':
model = MatchingNet(model_dict[params.model],
n_way=n_way,
n_support=n_support).cuda()
elif params.method == 'RelationNet':
model = RelationNet(model_dict[params.model],
n_way=n_way,
n_support=n_support).cuda()
elif params.method == 'ProtoNet':
model = ProtoNet(model_dict[params.model],
n_way=n_way,
n_support=n_support).cuda()
elif params.method == 'GNN':
model = GnnNet(model_dict[params.model],
n_way=n_way,
n_support=n_support).cuda()
elif params.method == 'TPN':
model = TPN(model_dict[params.model], n_way=n_way,
n_support=n_support).cuda()
else:
print("Please specify the method!")
assert (False)
# Update model
checkpoint_dir = '%s/checkpoints/%s/best_model.tar' % (params.save_dir,
params.name)
state = torch.load(checkpoint_dir)['state']
if 'FWT' in params.name:
model_params = model.state_dict()
pretrained_dict = {k: v for k, v in state.items() if k in model_params}
model_params.update(pretrained_dict)
model.load_state_dict(model_params)
else:
model.load_state_dict(state)
# For TPN model, we compute Batch Norm statistics from the test-time support set, not the exponential moving averages.
if params.method != 'TPN':
model.eval()
for ti, (x, _) in enumerate(novel_loader): # x:(5, 20, 3, 224, 224)
x = x.cuda()
n_query = x.size(1) - n_support
model.n_query = n_query
yq = np.repeat(range(n_way), n_query)
with torch.no_grad():
scores = model.set_forward(x) # (80, 5)
_, topk_labels = scores.data.topk(1, 1, True, True)
topk_ind = topk_labels.cpu().numpy() # (80, 1)
top1_correct = np.sum(topk_ind[:, 0] == yq)
acc = top1_correct * 100. / (n_way * n_query)
acc_all.append(acc)
print('Task %d : %4.2f%%' % (ti, acc))
acc_all = np.asarray(acc_all)
acc_mean = np.mean(acc_all)
acc_std = np.std(acc_all)
print('Test Acc = %4.2f +- %4.2f%%' %
(acc_mean, 1.96 * acc_std / np.sqrt(iter_num)))
def prepare_model(args):
if args.method == 'protonet':
from methods.protonet import ProtoNet
model = ProtoNet(args)
elif args.method == 'relationnet':
from methods.relationnet import RelationNet
model = RelationNet(args)
return model
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
n_way=params.train_n_way,
n_support=params.n_shot)
base_loader = base_datamgr.get_data_loader(base_file, aug=params.train_aug)
val_datamgr = SetDataManager(image_size,
n_query=n_query,
n_way=params.test_n_way,
n_support=params.n_shot)
val_loader = val_datamgr.get_data_loader(val_file, aug=False)
if params.method == 'MatchingNet':
model = MatchingNet(model_dict[params.model],
n_way=params.train_n_way,
n_support=params.n_shot).cuda()
elif params.method == 'RelationNet':
model = RelationNet(model_dict[params.model],
n_way=params.train_n_way,
n_support=params.n_shot).cuda()
elif params.method == 'RelationNetLRP':
model = RelationNetLRP(model_dict[params.model],
n_way=params.train_n_way,
n_support=params.n_shot).cuda()
elif params.method == 'ProtoNet':
model = ProtoNet(model_dict[params.model],
n_way=params.train_n_way,
n_support=params.n_shot).cuda()
elif params.method == 'GNN':
model = GnnNet(model_dict[params.model],
n_way=params.train_n_way,
n_support=params.n_shot).cuda()
elif params.method == 'GNNLRP':
model = GnnNetLRP(model_dict[params.model],
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 )
feature_model = model_dict[params.model]
loss_type = 'mse' if params.method == 'relationnet' else 'softmax'
if params.adversarial:
model = RelationNet(feature_model,
loss_type=loss_type,
discriminator=backbone.Disc_model(
params.train_n_way),
**train_few_shot_params)
else:
model = RelationNet(feature_model,
loss_type=loss_type,
**train_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'),
**train_few_shot_params)
if params.dataset in ['omniglot', 'cross_char'
tf_path=params.tf_dir,
**train_few_shot_params)
elif params.method == 'matchingnet':
model = MatchingNet(model_dict[params.model],
tf_path=params.tf_dir,
**train_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
else:
feature_model = model_dict[params.model]
loss_type = 'mse' if params.method == 'relationnet' else 'softmax'
model = RelationNet(feature_model,
loss_type=loss_type,
tf_path=params.tf_dir,
**train_few_shot_params)
else:
raise ValueError('Unknown method')
model = model.cuda()
# load model
start_epoch = params.start_epoch
stop_epoch = params.stop_epoch
if params.resume != '':
resume_file = get_resume_file(
'%s/checkpoints/%s' % (params.save_dir, params.resume),
params.resume_epoch)
if resume_file is not None:
tmp = torch.load(resume_file)
start_epoch = tmp['epoch'] + 1
if __name__ == '__main__':
params = parse_args('test')
os.environ["CUDA_VISIBLE_DEVICES"] = str(params.gpu)
acc_all = []
iter_num = 600
few_shot_params = dict(n_way=params.test_n_way, n_support=params.n_shot)
feature_model = backbone.Conv4NP
loss_type = 'mse'
if params.method in ['relationnet']:
model = RelationNet(feature_model,
loss_type=loss_type,
**few_shot_params)
elif params.method in ['OurNet']:
model = OurNet(feature_model, loss_type=loss_type, **few_shot_params)
elif params.method in ['CosineBatch']:
model = CosineBatch(feature_model,
loss_type=loss_type,
**few_shot_params)
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:
def select_model(params):
"""
select which model to use based on params
"""
if params.method in ['baseline', 'baseline++']:
if params.dataset == 'CUB':
params.num_classes = 200
elif params.dataset == 'cars':
params.num_classes = 196
elif params.dataset == 'aircrafts':
params.num_classes = 100
elif params.dataset == 'dogs':
params.num_classes = 120
elif params.dataset == 'flowers':
params.num_classes = 102
elif params.dataset == 'miniImagenet':
params.num_classes = 100
elif params.dataset == 'tieredImagenet':
params.num_classes = 608
if params.method == 'baseline':
model = BaselineTrain( model_dict[params.model], params.num_classes, \
jigsaw=params.jigsaw, lbda=params.lbda, rotation=params.rotation, tracking=params.tracking)
elif params.method == 'baseline++':
model = BaselineTrain( model_dict[params.model], params.num_classes, \
loss_type = 'dist', jigsaw=params.jigsaw, lbda=params.lbda, rotation=params.rotation, tracking=params.tracking)
elif params.method in [
'protonet', 'matchingnet', 'relationnet', 'relationnet_softmax',
'maml', 'maml_approx'
]:
train_few_shot_params = dict(n_way = params.train_n_way, n_support = params.n_shot, \
jigsaw=params.jigsaw, lbda=params.lbda, rotation=params.rotation)
if params.method == 'protonet':
model = ProtoNet(model_dict[params.model],
**train_few_shot_params,
use_bn=(not params.no_bn),
pretrain=params.pretrain,
tracking=params.tracking)
elif params.method == 'matchingnet':
model = MatchingNet(model_dict[params.model],
**train_few_shot_params)
elif params.method in ['relationnet', 'relationnet_softmax']:
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,
**train_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
BasicBlock.maml = True
Bottleneck.maml = True
ResNet.maml = True
model = MAML(model_dict[params.model],
approx=(params.method == 'maml_approx'),
**train_few_shot_params)
else:
raise ValueError('Unknown method')
return model
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
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)
def explain_gnnnet():
params = options.parse_args('test')
feature_model = backbone.model_dict['ResNet10']
params.method = 'gnnnet'
params.dataset = 'miniImagenet' # name relationnet --testset miniImagenet
params.name = 'gnn'
params.testset = 'miniImagenet'
params.data_dir = '/home/sunjiamei/work/fewshotlearning/dataset/'
params.save_dir = '/home/sunjiamei/work/fewshotlearning/CrossDomainFewShot-master/output'
if 'Conv' in params.model:
image_size = 84
else:
image_size = 224
split = params.split
n_query = 1
loadfile = os.path.join(params.data_dir, params.testset, split + '.json')
few_shot_params = dict(n_way=params.test_n_way, n_support=params.n_shot)
data_datamgr = SetDataManager(image_size,
n_query=n_query,
**few_shot_params)
data_loader = data_datamgr.get_data_loader(loadfile, aug=False)
# model
print(' build metric-based model')
if params.method == 'protonet':
model = ProtoNet(backbone.model_dict[params.model], **few_shot_params)
elif params.method == 'matchingnet':
model = MatchingNet(backbone.model_dict[params.model],
**few_shot_params)
elif params.method == 'gnnnet':
model = GnnNet(backbone.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
else:
feature_model = backbone.model_dict[params.model]
loss_type = 'LRP'
model = RelationNet(feature_model,
loss_type=loss_type,
**few_shot_params)
else:
raise ValueError('Unknown method')
checkpoint_dir = '%s/checkpoints/%s' % (params.save_dir, params.name)
# print(checkpoint_dir)
if params.save_epoch != -1:
modelfile = get_assigned_file(checkpoint_dir, params.save_epoch)
else:
modelfile = get_best_file(checkpoint_dir)
# print(modelfile)
if modelfile is not None:
tmp = torch.load(modelfile)
try:
model.load_state_dict(tmp['state'])
print('loaded pretrained model')
except RuntimeError:
print('warning! RuntimeError when load_state_dict()!')
model.load_state_dict(tmp['state'], strict=False)
except KeyError:
for k in tmp['model_state']: ##### revise latter
if 'running' in k:
tmp['model_state'][k] = tmp['model_state'][k].squeeze()
model.load_state_dict(tmp['model_state'], strict=False)
except:
raise
model = model.cuda()
model.eval()
model.n_query = n_query
# for module in model.modules():
# print(type(module))
lrp_preset = lrp_presets.SequentialPresetA()
feature_model = model.feature
fc_encoder = model.fc
gnn_net = model.gnn
lrp_wrapper.add_lrp(fc_encoder, lrp_preset)
# lrp_wrapper.add_lrp(feature_model, lrp_preset)
# lrp_wrapper.add_lrp(fc_encoder,lrp_preset)
# lrp_wrapper.add_lrp(feature_model, lrp_preset)
# acc = 0
# count = 0
# tested the forward pass is correct by observing the accuracy
# for i, (x, _, _) in enumerate(data_loader):
# x = x.cuda()
# support_label = torch.from_numpy(np.repeat(range(model.n_way), model.n_support)).unsqueeze(1)
# support_label = torch.zeros(model.n_way*model.n_support, model.n_way).scatter(1, support_label, 1).view(model.n_way, model.n_support, model.n_way)
# support_label = torch.cat([support_label, torch.zeros(model.n_way, 1, model.n_way)], dim=1)
# support_label = support_label.view(1, -1, model.n_way)
# support_label = support_label.cuda()
# x = x.view(-1, *x.size()[2:])
#
# x_feature = feature_model(x)
# x_fc_encoded = fc_encoder(x_feature)
# z = x_fc_encoded.view(model.n_way, -1, x_fc_encoded.size(1))
# gnn_feature = [
# torch.cat([z[:, :model.n_support], z[:, model.n_support + i:model.n_support + i + 1]], dim=1).view(1, -1, z.size(2))
# for i in range(model.n_query)]
# gnn_nodes = torch.cat([torch.cat([z, support_label], dim=2) for z in gnn_feature], dim=0)
# scores = gnn_net(gnn_nodes)
# scores = scores.view(model.n_query, model.n_way, model.n_support + 1, model.n_way)[:, :, -1].permute(1, 0,
# 2).contiguous().view(
# -1, model.n_way)
# pred = scores.data.cpu().numpy().argmax(axis=1)
# y = np.repeat(range(model.n_way), n_query)
# acc += np.sum(pred == y)
# count += len(y)
# # print(1.0*acc/count)
# print(1.0*acc/count)
with open(
'/home/sunjiamei/work/fewshotlearning/dataset/miniImagenet/class_to_readablelabel.json',
'r') as f:
class_to_readable = json.load(f)
explanation_save_dir = os.path.join(params.save_dir, 'explanations',
params.name)
if not os.path.isdir(explanation_save_dir):
os.makedirs(explanation_save_dir)
for batch_idx, (x, y, p) in enumerate(data_loader):
print(p)
label_to_readableclass, query_img_path, query_gt_class = LRPutil.get_class_label(
p, class_to_readable, model.n_query)
x = x.cuda()
support_label = torch.from_numpy(
np.repeat(range(model.n_way),
model.n_support)).unsqueeze(1) #torch.Size([25, 1])
support_label = torch.zeros(model.n_way * model.n_support,
model.n_way).scatter(1, support_label,
1).view(
model.n_way,
model.n_support,
model.n_way)
support_label = torch.cat(
[support_label,
torch.zeros(model.n_way, 1, model.n_way)], dim=1)
support_label = support_label.view(1, -1, model.n_way)
support_label = support_label.cuda() #torch.Size([1, 30, 5])
x = x.contiguous()
x = x.view(-1, *x.size()[2:]) #torch.Size([30, 3, 224, 224])
x_feature = feature_model(x) #torch.Size([30, 512])
x_fc_encoded = fc_encoder(x_feature) #torch.Size([30, 128])
z = x_fc_encoded.view(model.n_way, -1,
x_fc_encoded.size(1)) # (5,6,128)
gnn_feature = [
torch.cat([
z[:, :model.n_support],
z[:, model.n_support + i:model.n_support + i + 1]
],
dim=1).view(1, -1, z.size(2))
for i in range(model.n_query)
] # model.n_query is the number of query images for each class
# gnn_feature is grouped into n_query groups. each group contains the support image features concatenated with one query image features.
# print(len(gnn_feature), gnn_feature[0].shape)
gnn_nodes = torch.cat(
[torch.cat([z, support_label], dim=2) for z in gnn_feature], dim=0
) # the features are concatenated with the one hot label. for the unknow image the one hot label is all zero
# perform gnn_net step by step
# the first iteration
print('x', gnn_nodes.shape)
W_init = torch.eye(
gnn_nodes.size(1), device=gnn_nodes.device
).unsqueeze(0).repeat(gnn_nodes.size(0), 1, 1).unsqueeze(
3
) # (n_querry, n_way*(num_support + 1), n_way*(num_support + 1), 1)
# print(W_init.shape)
W1 = gnn_net._modules['layer_w{}'.format(0)](
gnn_nodes, W_init
) # (n_querry, n_way*(num_support + 1), n_way*(num_support + 1), 2)
# print(Wi.shape)
x_new1 = F.leaky_relu(gnn_net._modules['layer_l{}'.format(0)](
[W1, gnn_nodes])[1]) # (num_querry, num_support + 1, num_outputs)
# print(x_new1.shape) #torch.Size([1, 30, 48])
gnn_nodes_1 = torch.cat([gnn_nodes, x_new1],
2) # (concat more features)
# print('gn1',gnn_nodes_1.shape) #torch.Size([1, 30, 181])
# the second iteration
W2 = gnn_net._modules['layer_w{}'.format(1)](
gnn_nodes_1, W_init
) # (n_querry, n_way*(num_support + 1), n_way*(num_support + 1), 2)
x_new2 = F.leaky_relu(gnn_net._modules['layer_l{}'.format(1)](
[W2,
gnn_nodes_1])[1]) # (num_querry, num_support + 1, num_outputs)
# print(x_new2.shape)
gnn_nodes_2 = torch.cat([gnn_nodes_1, x_new2],
2) # (concat more features)
# print('gn2', gnn_nodes_2.shape) #torch.Size([1, 30, 229])
Wl = gnn_net.w_comp_last(gnn_nodes_2, W_init)
# print(Wl.shape) #torch.Size([1, 30, 30, 2])
scores = gnn_net.layer_last(
[Wl, gnn_nodes_2])[1] # (num_querry, num_support + 1, num_way)
print(scores.shape)
scores_sf = torch.softmax(scores, dim=-1)
# print(scores_sf)
gnn_logits = torch.log(LRPutil.LOGIT_BETA * scores_sf /
(1 - scores_sf))
gnn_logits = gnn_logits.view(-1, model.n_way,
model.n_support + n_query, model.n_way)
# print(gnn_logits)
query_scores = scores.view(
model.n_query, model.n_way, model.n_support + 1,
model.n_way)[:, :,
-1].permute(1, 0,
2).contiguous().view(-1, model.n_way)
preds = query_scores.data.cpu().numpy().argmax(axis=-1)
# print(preds.shape)
for k in range(model.n_way):
mask = torch.zeros(5).cuda()
mask[k] = 1
gnn_logits_cls = gnn_logits.clone()
gnn_logits_cls[:, :, -1] = gnn_logits_cls[:, :, -1] * mask
# print(gnn_logits_cls)
# print(gnn_logits_cls.shape)
gnn_logits_cls = gnn_logits_cls.view(-1, model.n_way)
relevance_gnn_nodes_2 = explain_Gconv(gnn_logits_cls,
gnn_net.layer_last, Wl,
gnn_nodes_2)
relevance_x_new2 = relevance_gnn_nodes_2.narrow(-1, 181, 48)
# relevance_gnn_nodes = relevance_gnn_nodes_2
relevance_gnn_nodes_1 = explain_Gconv(
relevance_x_new2, gnn_net._modules['layer_l{}'.format(1)], W2,
gnn_nodes_1)
relevance_x_new1 = relevance_gnn_nodes_1.narrow(-1, 133, 48)
relevance_gnn_nodes = explain_Gconv(
relevance_x_new1, gnn_net._modules['layer_l{}'.format(0)], W1,
gnn_nodes)
relevance_gnn_features = relevance_gnn_nodes.narrow(-1, 0, 128)
print(relevance_gnn_features.shape)
relevance_gnn_features += relevance_gnn_nodes_1.narrow(-1, 0, 128)
relevance_gnn_features += relevance_gnn_nodes_2.narrow(
-1, 0, 128) #[2, 30, 128]
relevance_gnn_features = relevance_gnn_features.view(
n_query, model.n_way, model.n_support + 1, 128)
for i in range(n_query):
query_i = relevance_gnn_features[i][:, model.
n_support:model.n_support +
1]
if i == 0:
relevance_z = query_i
else:
relevance_z = torch.cat((relevance_z, query_i), 1)
relevance_z = relevance_z.view(-1, 128)
query_feature = x_feature.view(model.n_way, -1,
512)[:, model.n_support:]
# print(query_feature.shape)
query_feature = query_feature.contiguous()
query_feature = query_feature.view(n_query * model.n_way, 512)
# print(query_feature.shape)
relevance_query_features = fc_encoder.compute_lrp(
query_feature, target=relevance_z)
# print(relevance_query_features.shape)
# print(relevance_gnn_features.shape)
# explain the fc layer and the image encoder
query_images = x.view(model.n_way, -1,
*x.size()[1:])[:, model.n_support:]
query_images = query_images.contiguous()
query_images = query_images.view(-1, *x.size()[1:]).detach()
# print(query_images.shape)
lrp_wrapper.add_lrp(feature_model, lrp_preset)
relevance_query_images = feature_model.compute_lrp(
query_images, target=relevance_query_features)
print(relevance_query_images.shape)
for j in range(n_query * model.n_way):
predict_class = label_to_readableclass[preds[j]]
true_class = query_gt_class[int(j % model.n_way)][int(
j // model.n_way)]
explain_class = label_to_readableclass[k]
img_name = query_img_path[int(j % model.n_way)][int(
j // model.n_way)].split('/')[-1]
if not os.path.isdir(
os.path.join(explanation_save_dir, 'episode' +
str(batch_idx), img_name.strip('.jpg'))):
os.makedirs(
os.path.join(explanation_save_dir,
'episode' + str(batch_idx),
img_name.strip('.jpg')))
save_path = os.path.join(explanation_save_dir,
'episode' + str(batch_idx),
img_name.strip('.jpg'))
if not os.path.exists(
os.path.join(
save_path,
true_class + '_' + predict_class + img_name)):
original_img = Image.fromarray(
np.uint8(
project(query_images[j].permute(
1, 2, 0).detach().cpu().numpy())))
original_img.save(
os.path.join(
save_path,
true_class + '_' + predict_class + img_name))
img_relevance = relevance_query_images.narrow(0, j, 1)
print(predict_class, true_class, explain_class)
# assert relevance_querry_cls[j].sum() != 0
# assert img_relevance.sum()!=0
hm = img_relevance.permute(0, 2, 3, 1).cpu().detach().numpy()
hm = LRPutil.gamma(hm)
hm = LRPutil.heatmap(hm)[0]
hm = project(hm)
hp_img = Image.fromarray(np.uint8(hm))
hp_img.save(
os.path.join(
save_path,
true_class + '_' + explain_class + '_lrp_hm.jpg'))
break
def explain_relationnet():
# print(sys.path)
params = options.parse_args('test')
feature_model = backbone.model_dict['ResNet10']
params.method = 'relationnet'
params.dataset = 'miniImagenet' # name relationnet --testset miniImagenet
params.name = 'relationnet'
params.testset = 'miniImagenet'
params.data_dir = '/home/sunjiamei/work/fewshotlearning/dataset/'
params.save_dir = '/home/sunjiamei/work/fewshotlearning/CrossDomainFewShot-master/output'
if 'Conv' in params.model:
image_size = 84
else:
image_size = 224
split = params.split
n_query = 1
loadfile = os.path.join(params.data_dir, params.testset, split + '.json')
few_shot_params = dict(n_way=params.test_n_way, n_support=params.n_shot)
data_datamgr = SetDataManager(image_size,
n_query=n_query,
**few_shot_params)
data_loader = data_datamgr.get_data_loader(loadfile, aug=False)
acc_all = []
iter_num = 1000
# model
print(' build metric-based model')
if params.method == 'protonet':
model = ProtoNet(backbone.model_dict[params.model], **few_shot_params)
elif params.method == 'matchingnet':
model = MatchingNet(backbone.model_dict[params.model],
**few_shot_params)
elif params.method == 'gnnnet':
model = GnnNet(backbone.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
else:
feature_model = backbone.model_dict[params.model]
loss_type = 'LRPmse'
model = RelationNet(feature_model,
loss_type=loss_type,
**few_shot_params)
else:
raise ValueError('Unknown method')
checkpoint_dir = '%s/checkpoints/%s' % (params.save_dir, params.name)
# print(checkpoint_dir)
if params.save_epoch != -1:
modelfile = get_assigned_file(checkpoint_dir, params.save_epoch)
else:
modelfile = get_best_file(checkpoint_dir)
# print(modelfile)
if modelfile is not None:
tmp = torch.load(modelfile)
try:
model.load_state_dict(tmp['state'])
except RuntimeError:
print('warning! RuntimeError when load_state_dict()!')
model.load_state_dict(tmp['state'], strict=False)
except KeyError:
for k in tmp['model_state']: ##### revise latter
if 'running' in k:
tmp['model_state'][k] = tmp['model_state'][k].squeeze()
model.load_state_dict(tmp['model_state'], strict=False)
except:
raise
model = model.cuda()
model.eval()
model.n_query = n_query
# ---test the accuracy on the test set to verify the model is loaded----
acc = 0
count = 0
# for i, (x, y) in enumerate(data_loader):
# scores = model.set_forward(x)
# pred = scores.data.cpu().numpy().argmax(axis=1)
# y = np.repeat(range(model.n_way), n_query)
# acc += np.sum(pred == y)
# count += len(y)
# # print(1.0*acc/count)
# print(1.0*acc/count)
preset = lrp_presets.SequentialPresetA()
feature_model = copy.deepcopy(model.feature)
lrp_wrapper.add_lrp(feature_model, preset=preset)
relation_model = copy.deepcopy(model.relation_module)
# print(relation_model)
lrp_wrapper.add_lrp(relation_model, preset=preset)
with open(
'/home/sunjiamei/work/fewshotlearning/dataset/miniImagenet/class_to_readablelabel.json',
'r') as f:
class_to_readable = json.load(f)
explanation_save_dir = os.path.join(params.save_dir, 'explanations',
params.name)
if not os.path.isdir(explanation_save_dir):
os.makedirs(explanation_save_dir)
for i, (x, y, p) in enumerate(data_loader):
'''x is the images with shape as n_way, n_support + n_querry, 3, img_size, img_size
y is the global labels of the images with shape as (n_way, n_support + n_query)
p is the image path as a list of tuples, length is n_query+n_support, each tuple element is with length n_way'''
if i >= 3:
break
label_to_readableclass, query_img_path, query_gt_class = LRPutil.get_class_label(
p, class_to_readable, model.n_query)
z_support, z_query = model.parse_feature(x, is_feature=False)
z_support = z_support.contiguous()
z_proto = z_support.view(model.n_way, model.n_support,
*model.feat_dim).mean(1)
# print(z_proto.shape)
z_query = z_query.contiguous().view(model.n_way * model.n_query,
*model.feat_dim)
# print(z_query.shape)
# get relations with metric function
z_proto_ext = z_proto.unsqueeze(0).repeat(model.n_query * model.n_way,
1, 1, 1, 1)
# print(z_proto_ext.shape)
z_query_ext = z_query.unsqueeze(0).repeat(model.n_way, 1, 1, 1, 1)
z_query_ext = torch.transpose(z_query_ext, 0, 1)
# print(z_query_ext.shape)
extend_final_feat_dim = model.feat_dim.copy()
extend_final_feat_dim[0] *= 2
relation_pairs = torch.cat((z_proto_ext, z_query_ext),
2).view(-1, *extend_final_feat_dim)
# print(relation_pairs.shape)
relations = relation_model(relation_pairs)
# print(relations)
scores = relations.view(-1, model.n_way)
preds = scores.data.cpu().numpy().argmax(axis=1)
# print(preds.shape)
relations = relations.view(-1, model.n_way)
# print(relations)
relations_sf = torch.softmax(relations, dim=-1)
# print(relations_sf)
relations_logits = torch.log(LRPutil.LOGIT_BETA * relations_sf /
(1 - relations_sf))
# print(relations_logits)
# print(preds)
relations_logits = relations_logits.view(-1, 1)
relevance_relations = relation_model.compute_lrp(
relation_pairs, target=relations_logits)
# print(relevance_relations.shape)
# print(model.feat_dim)
relevance_z_query = torch.narrow(relevance_relations, 1,
model.feat_dim[0], model.feat_dim[0])
# print(relevance_z_query.shape)
relevance_z_query = relevance_z_query.view(
model.n_query * model.n_way, model.n_way,
*relevance_z_query.size()[1:])
# print(relevance_z_query.shape)
query_img = x.narrow(1, model.n_support,
model.n_query).view(model.n_way * model.n_query,
*x.size()[2:])
# query_img_copy = query_img.view(model.n_way, model.n_query, *x.size()[2:])
# print(query_img.shape)
for k in range(model.n_way):
relevance_querry_cls = torch.narrow(relevance_z_query, 1, k,
1).squeeze(1)
# print(relevance_querry_cls.shape)
relevance_querry_img = feature_model.compute_lrp(
query_img.cuda(), target=relevance_querry_cls)
# print(relevance_querry_img.max(), relevance_querry_img.min())
# print(relevance_querry_img.shape)
for j in range(model.n_query * model.n_way):
predict_class = label_to_readableclass[preds[j]]
true_class = query_gt_class[int(j % model.n_way)][int(
j // model.n_way)]
explain_class = label_to_readableclass[k]
img_name = query_img_path[int(j % model.n_way)][int(
j // model.n_way)].split('/')[-1]
if not os.path.isdir(
os.path.join(explanation_save_dir, 'episode' + str(i),
img_name.strip('.jpg'))):
os.makedirs(
os.path.join(explanation_save_dir, 'episode' + str(i),
img_name.strip('.jpg')))
save_path = os.path.join(explanation_save_dir,
'episode' + str(i),
img_name.strip('.jpg'))
if not os.path.exists(
os.path.join(
save_path,
true_class + '_' + predict_class + img_name)):
original_img = Image.fromarray(
np.uint8(
project(query_img[j].permute(1, 2,
0).cpu().numpy())))
original_img.save(
os.path.join(
save_path,
true_class + '_' + predict_class + img_name))
img_relevance = relevance_querry_img.narrow(0, j, 1)
print(predict_class, true_class, explain_class)
# assert relevance_querry_cls[j].sum() != 0
# assert img_relevance.sum()!=0
hm = img_relevance.permute(0, 2, 3, 1).cpu().detach().numpy()
hm = LRPutil.gamma(hm)
hm = LRPutil.heatmap(hm)[0]
hm = project(hm)
hp_img = Image.fromarray(np.uint8(hm))
hp_img.save(
os.path.join(
save_path,
true_class + '_' + explain_class + '_lrp_hm.jpg'))
go_mask = base_loader.dataset.go_mask
x_dim = base_loader.dataset.get_dim()
if params.method == 'protonet':
model = ProtoNet(backbone.FCNet(x_dim), **train_few_shot_params)
elif params.method == 'comet':
model = COMET(backbone.EnFCNet(x_dim, go_mask),
**train_few_shot_params)
elif params.method == 'matchingnet':
model = MatchingNet(backbone.FCNet(x_dim), **train_few_shot_params)
elif params.method in ['relationnet', 'relationnet_softmax']:
loss_type = 'mse' if params.method == 'relationnet' else 'softmax'
model = RelationNet(backbone.FCNet(x_dim),
loss_type=loss_type,
**train_few_shot_params)
elif params.method in ['maml', 'maml_approx']:
model = MAML(backbone.FCNet(x_dim),
approx=(params.method == 'maml_approx'),
**train_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()
l3_n_infer=args.l3_n_infer,
l3_infer_scoring=args.l3_infer_scoring,
l3_eval=args.l3_eval,
moe_weight=args.moe_weight)
elif args.method == 'matchingnet':
model = MatchingNet(model_dict[args.model], **few_shot_args)
elif args.method in ['relationnet', 'relationnet_softmax']:
if args.model == 'Conv4':
feature_model = backbone.Conv4NP
elif args.model == 'Conv6':
feature_model = backbone.Conv6NP
else:
feature_model = lambda: model_dict[args.model](flatten=False)
loss_type = 'mse' if args.method == 'relationnet' else 'softmax'
model = RelationNet(feature_model,
loss_type=loss_type,
**few_shot_args)
elif args.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[args.model],
approx=(args.method == 'maml_approx'),
**few_shot_args)
else:
raise ValueError('Unknown method')
model = model.cuda()
if args.checkpoint_dir is None:
def main_train(params):
_set_seed(params)
results_logger = ResultsLogger(params)
if params.dataset == 'cross':
base_file = configs.data_dir['miniImagenet'] + 'all.json'
val_file = configs.data_dir['CUB'] + 'val.json'
elif params.dataset == 'cross_char':
base_file = configs.data_dir['omniglot'] + 'noLatin.json'
val_file = configs.data_dir['emnist'] + 'val.json'
else:
base_file = configs.data_dir[params.dataset] + 'base.json'
val_file = configs.data_dir[params.dataset] + 'val.json'
if 'Conv' in params.model:
if params.dataset in ['omniglot', 'cross_char']:
image_size = 28
else:
image_size = 84
else:
image_size = 224
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'
optimization = 'Adam'
if params.stop_epoch == -1:
if params.method in ['baseline', 'baseline++']:
if params.dataset in ['omniglot', 'cross_char']:
params.stop_epoch = 5
elif params.dataset in ['CUB']:
params.stop_epoch = 200 # This is different as stated in the open-review paper. However, using 400 epoch in baseline actually lead to over-fitting
elif params.dataset in ['miniImagenet', 'cross']:
params.stop_epoch = 400
else:
params.stop_epoch = 400 # default
else: # meta-learning methods
if params.n_shot == 1:
params.stop_epoch = 600
elif params.n_shot == 5:
params.stop_epoch = 400
else:
params.stop_epoch = 600 # default
if params.method in ['baseline', 'baseline++']:
base_datamgr = SimpleDataManager(image_size, batch_size=16)
base_loader = base_datamgr.get_data_loader(base_file,
aug=params.train_aug)
val_datamgr = SimpleDataManager(image_size, batch_size=64)
val_loader = val_datamgr.get_data_loader(val_file, aug=False)
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':
model = BaselineTrain(model_dict[params.model], params.num_classes)
elif params.method == 'baseline++':
model = BaselineTrain(model_dict[params.model],
params.num_classes,
loss_type='dist')
elif params.method in [
'DKT', '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,
n_query=n_query,
**train_few_shot_params) # n_eposide=100
base_loader = base_datamgr.get_data_loader(base_file,
aug=params.train_aug)
test_few_shot_params = dict(n_way=params.test_n_way,
n_support=params.n_shot)
val_datamgr = SetDataManager(image_size,
n_query=n_query,
**test_few_shot_params)
val_loader = val_datamgr.get_data_loader(val_file, aug=False)
# a batch for SetDataManager: a [n_way, n_support + n_query, dim, w, h] tensor
if (params.method == 'DKT'):
model = DKT(model_dict[params.model], **train_few_shot_params)
model.init_summary()
elif params.method == 'protonet':
model = ProtoNet(model_dict[params.model], **train_few_shot_params)
elif params.method == 'matchingnet':
model = MatchingNet(model_dict[params.model],
**train_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,
**train_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'),
**train_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()
params.checkpoint_dir = '%s/checkpoints/%s/%s_%s' % (
configs.save_dir, params.dataset, params.model, params.method)
if params.train_aug:
params.checkpoint_dir += '_aug'
if not params.method in ['baseline', 'baseline++']:
params.checkpoint_dir += '_%dway_%dshot' % (params.train_n_way,
params.n_shot)
if not os.path.isdir(params.checkpoint_dir):
os.makedirs(params.checkpoint_dir)
start_epoch = params.start_epoch
stop_epoch = params.stop_epoch
if params.method == 'maml' or params.method == 'maml_approx':
stop_epoch = params.stop_epoch * model.n_task # maml use multiple tasks in one update
if params.resume:
resume_file = get_resume_file(params.checkpoint_dir)
if resume_file is not None:
tmp = torch.load(resume_file)
start_epoch = tmp['epoch'] + 1
model.load_state_dict(tmp['state'])
elif params.warmup: # We also support warmup from pretrained baseline feature, but we never used in our paper
baseline_checkpoint_dir = '%s/checkpoints/%s/%s_%s' % (
configs.save_dir, params.dataset, params.model, 'baseline')
if params.train_aug:
baseline_checkpoint_dir += '_aug'
warmup_resume_file = get_resume_file(baseline_checkpoint_dir)
tmp = torch.load(warmup_resume_file)
if tmp is not None:
state = tmp['state']
state_keys = list(state.keys())
for i, key in enumerate(state_keys):
if "feature." in key:
newkey = key.replace(
"feature.", ""
) # an architecture model has attribute 'feature', load architecture feature to backbone by casting name from 'feature.trunk.xx' to 'trunk.xx'
state[newkey] = state.pop(key)
else:
state.pop(key)
model.feature.load_state_dict(state)
else:
raise ValueError('No warm_up file')
model = train(base_loader, val_loader, model, optimization, start_epoch,
stop_epoch, params, results_logger)
results_logger.save()
elif params.method in [
'relationnet', 'relationnet_softmax', 'relationnetlrp'
]:
if params.model == 'Conv4':
feature_model = backbone.Conv4NP
elif params.model == 'Conv6':
feature_model = backbone.Conv6NP
else:
feature_model = model_dict[params.model]
loss_type = 'mse' if params.method in [
'relationnet', 'relationnetlrp'
] else 'softmax'
if params.method == 'relationnetlrp':
model = RelationNetLRP(feature_model, **few_shot_params)
else:
model = RelationNet(feature_model, **few_shot_params)
model.lrptraining = False
model = model.cuda()
model.eval()
# load model
checkpoint_dir = '%s/checkpoints/%s' % (params.save_dir, params.name)
if params.save_epoch != -1:
modelfile = get_assigned_file(checkpoint_dir, params.save_epoch)
else:
modelfile = get_best_file(checkpoint_dir)
print(' loading the best model')
if modelfile is not None:
tmp = torch.load(modelfile)
try:
model.load_state_dict(tmp['state'])
elif params.method == 'matchingnet':
model = MatchingNet(model_dict[params.model],
**train_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,
**train_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'),
**train_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:
def finetune(novel_loader, n_pseudo=75, n_way=5, n_support=5):
iter_num = len(novel_loader)
acc_all = []
checkpoint_dir = '%s/checkpoints/%s/best_model.tar' % (params.save_dir,
params.name)
state = torch.load(checkpoint_dir)['state']
for ti, (x, _) in enumerate(novel_loader): # x:(5, 20, 3, 224, 224)
# Model
if params.method == 'MatchingNet':
model = MatchingNet(model_dict[params.model],
n_way=n_way,
n_support=n_support).cuda()
elif params.method == 'RelationNet':
model = RelationNet(model_dict[params.model],
n_way=n_way,
n_support=n_support).cuda()
elif params.method == 'ProtoNet':
model = ProtoNet(model_dict[params.model],
n_way=n_way,
n_support=n_support).cuda()
elif params.method == 'GNN':
model = GnnNet(model_dict[params.model],
n_way=n_way,
n_support=n_support).cuda()
elif params.method == 'TPN':
model = TPN(model_dict[params.model],
n_way=n_way,
n_support=n_support).cuda()
else:
print("Please specify the method!")
assert (False)
# Update model
if 'FWT' in params.name:
model_params = model.state_dict()
pretrained_dict = {
k: v
for k, v in state.items() if k in model_params
}
model_params.update(pretrained_dict)
model.load_state_dict(model_params)
else:
model.load_state_dict(state)
x = x.cuda()
# Finetune components initialization
xs = x[:, :n_support].reshape(-1, *x.size()[2:]) # (25, 3, 224, 224)
pseudo_q_genrator = PseudoSampleGenerator(n_way, n_support, n_pseudo)
loss_fun = nn.CrossEntropyLoss().cuda()
opt = torch.optim.Adam(model.parameters())
# Finetune process
n_query = n_pseudo // n_way
pseudo_set_y = torch.from_numpy(np.repeat(range(n_way),
n_query)).cuda()
model.n_query = n_query
model.train()
for epoch in range(params.finetune_epoch):
opt.zero_grad()
pseudo_set = pseudo_q_genrator.generate(
xs) # (5, n_support+n_query, 3, 224, 224)
scores = model.set_forward(pseudo_set) # (5*n_query, 5)
loss = loss_fun(scores, pseudo_set_y)
loss.backward()
opt.step()
del pseudo_set, scores, loss
torch.cuda.empty_cache()
# Inference process
n_query = x.size(1) - n_support
model.n_query = n_query
yq = np.repeat(range(n_way), n_query)
with torch.no_grad():
scores = model.set_forward(x) # (80, 5)
_, topk_labels = scores.data.topk(1, 1, True, True)
topk_ind = topk_labels.cpu().numpy() # (80, 1)
top1_correct = np.sum(topk_ind[:, 0] == yq)
acc = top1_correct * 100. / (n_way * n_query)
acc_all.append(acc)
del scores, topk_labels
torch.cuda.empty_cache()
print('Task %d : %4.2f%%' % (ti, acc))
acc_all = np.asarray(acc_all)
acc_mean = np.mean(acc_all)
acc_std = np.std(acc_all)
print('Test Acc = %4.2f +- %4.2f%%' %
(acc_mean, 1.96 * acc_std / np.sqrt(iter_num)))
