delimiter='\t')
sys.exit(0)
split = args.split
if split == 'attributes' and args.method != 'protonet':
raise NotImplementedError
if args.method in ['maml', 'maml_approx'
]: #maml do not support testing with feature
if 'Conv' in args.model:
image_size = 84
else:
image_size = 224
datamgr = SetDataManager(image_size,
n_episode=iter_num,
n_query=15,
**few_shot_args,
args=args)
if args.dataset == 'cross':
if split == 'base':
loadfile = configs.data_dir['miniImagenet'] + 'all.json'
else:
loadfile = configs.data_dir['CUB'] + split + '.json'
else:
loadfile = configs.data_dir[args.dataset] + split + '.json'
novel_loader = datamgr.get_data_loader(loadfile, aug=False)
if args.adaptation:
model.task_update_num = 100 #We perform adaptation on MAML simply by updating more times.
model.eval()
if params.method in ['baseline++', 'S2M2_R', 'rotation']:
if params.dct_status:
base_datamgr = SimpleDataManager(image_size_dct,
batch_size=params.batch_size)
base_loader = base_datamgr.get_data_loader_dct(
base_file,
aug=params.train_aug,
filter_size=params.filter_size)
base_datamgr_test = SimpleDataManager(
image_size_dct, batch_size=params.test_batch_size)
base_loader_test = base_datamgr_test.get_data_loader_dct(
base_file, aug=False, filter_size=params.filter_size)
test_few_shot_params = dict(n_way=params.train_n_way,
n_support=params.n_shot)
val_datamgr = SetDataManager(image_size_dct,
n_query=15,
**test_few_shot_params)
val_loader = val_datamgr.get_data_loader_dct(
val_file, aug=False, filter_size=params.filter_size)
else:
base_datamgr = SimpleDataManager(image_size,
batch_size=params.batch_size)
base_loader = base_datamgr.get_data_loader(base_file,
aug=params.train_aug)
base_datamgr_test = SimpleDataManager(
image_size, batch_size=params.test_batch_size)
base_loader_test = base_datamgr_test.get_data_loader(base_file,
aug=False)
test_few_shot_params = dict(n_way=params.train_n_way,
n_support=params.n_shot)
val_datamgr = SetDataManager(image_size,
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()
rotation=params.rotation,
isAircraft=isAircraft,
grey=params.grey,
shuffle=False)
if params.dataset_unlabel is not None:
base_loader_u = base_datamgr_u.get_data_loader(
base_file_unlabel, aug=params.train_aug)
else:
base_loader_u = base_datamgr_u.get_data_loader(
base_file, aug=params.train_aug)
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)
base_datamgr_l = SetDataManager(image_size,
n_query=n_query,
**train_few_shot_params,
isAircraft=isAircraft,
grey=params.grey)
base_loader_l = base_datamgr_l.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, \
jigsaw=params.jigsaw, lbda=params.lbda, rotation=params.rotation)
val_datamgr = SetDataManager(image_size,
n_query=n_query,
**test_few_shot_params,
isAircraft=isAircraft,
grey=params.grey)
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
params.name)
if not os.path.isdir(params.checkpoint_dir):
os.makedirs(params.checkpoint_dir)
# dataloader
print('\n--- Prepare dataloader ---')
print('\ttrain with seen domain {}'.format(params.dataset))
print('\tval with seen domain {}'.format(params.testset))
base_file = os.path.join(params.data_dir, params.dataset, 'base.json')
val_file = os.path.join(params.data_dir, params.testset, 'val.json')
# model
image_size = 224
n_query = max(1, int(16 * params.test_n_way / params.train_n_way))
base_datamgr = SetDataManager(image_size,
n_query=n_query,
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,
num_layers=args.rnn_num_layers,
dropout=args.rnn_dropout,
)
l3_model = l3_model.cuda()
embedding_model = embedding_model.cuda()
lang_model = lang_model.cuda()
# if test_n_way is smaller than train_n_way, reduce n_query to keep batch
# size small
n_query = max(1, int(16 * args.test_n_way / args.train_n_way))
train_few_shot_args = dict(n_way=args.train_n_way, n_support=args.n_shot)
base_datamgr = SetDataManager("CUB",
84,
n_query=n_query,
**train_few_shot_args,
args=args)
print("Loading train data")
base_loader = base_datamgr.get_data_loader(
base_file,
aug=True,
lang_dir=constants.LANG_DIR,
normalize=True,
vocab=vocab,
# Maximum training data restrictions only apply at train time
max_class=args.max_class,
max_img_per_class=args.max_img_per_class,
max_lang_per_class=args.max_lang_per_class,
)
base_file = configs.data_dir[params.dataset] + 'base.json'
val_file = configs.data_dir[params.dataset] + 'val.json'
params.checkpoint_dir = '%s/checkpoints/%s/%s_%s' % (
configs.save_dir, params.dataset, params.model, params.method)
start_epoch = params.start_epoch
stop_epoch = params.stop_epoch
base_datamgr = SimpleDataManager(image_size, batch_size=params.batch_size)
base_loader = base_datamgr.get_data_loader(base_file, aug=params.train_aug)
base_datamgr_test = SimpleDataManager(image_size,
batch_size=params.test_batch_size)
base_loader_test = base_datamgr_test.get_data_loader(base_file, aug=False)
test_few_shot_params = dict(n_way=5, n_support=1)
val_datamgr = SetDataManager(image_size,
n_query=15,
**test_few_shot_params)
val_loader = val_datamgr.get_data_loader(val_file, aug=False)
if params.method == 'manifold_mixup':
print(params.num_classes)
model = wrn_mixup_model.wrn28_10(params.num_classes)
elif params.method == 'S2M2_R':
model = wrn_mixup_model.wrn28_10(params.num_classes)
elif params.method == 'rotation':
model = BaselineTrain(model_dict[params.model],
params.num_classes,
loss_type='dist')
if params.method == 'S2M2_R':
if use_gpu:
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
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)))
if __name__ == '__main__':
np.random.seed(10)
params = parse_args()
image_size = 224
iter_num = 2000
n_query = 16
print('Loading target dataset!')
novel_file = os.path.join(params.data_dir, params.dataset, 'novel.json')
datamgr = SetDataManager(image_size,
n_query=n_query,
n_way=params.test_n_way,
n_support=params.n_shot,
n_eposide=iter_num)
novel_loader = datamgr.get_data_loader(novel_file, aug=False)
evaluate(novel_loader, n_way=params.test_n_way, n_support=params.n_shot)
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)
datamgr = SetDataManager(n_eposide=iter_num, n_query=15, **few_shot_params)
novel_loader = datamgr.get_data_loader(root='./filelists/tabula_muris',
mode='test')
x_dim = novel_loader.dataset.get_dim()
go_mask = novel_loader.dataset.go_mask
if params.method == 'baseline':
model = BaselineFinetune(backbone.FCNet(x_dim), **few_shot_params)
elif params.method == 'baseline++':
model = BaselineFinetune(backbone.FCNet(x_dim),
loss_type='dist',
**few_shot_params)
elif params.method == 'protonet':
model = ProtoNet(backbone.FCNet(x_dim), **few_shot_params)
elif params.method == 'comet':
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'))
split = 'novel'
if params.save_iter != -1:
split_str = split + "_" + str(params.save_iter)
else:
split_str = split
iter_num = 600
few_shot_params = dict(n_way=params.test_n_way, n_support=params.n_shot)
acc_all = []
model = load_weight_file_for_test(model, params)
print_model_params(model, params)
if params.method in ['maml', 'maml_approx']:
datamgr = SetDataManager(params.image_size,
n_eposide=iter_num,
n_query=15,
**few_shot_params,
isAircraft=(params.dataset == 'aircrafts'))
loadfile = os.path.join('filelists', params.test_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)
params = parse_args('mytest')
loadfile = os.path.join('filelists', params.test_dataset, 'novel.json')
else:
if "recognition36" in params.test_dataset:
loadfile = os.path.join('filelists', params.test_dataset,
if params.n_shot == 1:
params.stop_epoch = 600
elif params.n_shot == 5:
params.stop_epoch = 400
else:
params.stop_epoch = 600
if params.method in ['tcmaml', 'tcmaml_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)
train_datamgr = SetDataManager(
image_size, n_query=n_query, **train_few_shot_params
) # default number of episodes (tasks) is 100 per epoch
train_loader = train_datamgr.get_data_loader(train_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)
backbone.ConvBlock.maml = True
backbone.SimpleBlock.maml = True
backbone.ResNet.maml = True
# 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)
if params.method in [
'protonet', 'matchingnet', 'relationnet', 'relationnet_softmax',
'maml', 'maml_approx'
]:
n_query = max(
1, int(params.n_query * 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, \
jigsaw=params.jigsaw, lbda=params.lbda, rotation=params.rotation)
base_datamgr = SetDataManager(image_size,
n_query=n_query,
**train_few_shot_params,
isAircraft=isAircraft)
base_loader = base_datamgr.get_data_loader(base_file,
aug=params.train_aug)
base_loader1 = copy.deepcopy(base_loader)
images = torch.empty(0, 3, 224, 224) ### [total_images, 3, 224, 224]
for i, inputs in enumerate(base_loader1):
print(i)
x = inputs[0] ### [5,21,3,224,224]
x = x.view(105, *x.size()[2:]) ### [105,3,224,224]
# print(x.size())
images = torch.cat([images, x], dim=0)
print(len(images))
dataset = JigsawDataset(images)
print(' train with single seen domain {}'.format(params.dataset))
base_file = os.path.join(params.data_dir, params.dataset, 'base.json')
val_file = os.path.join(params.data_dir, params.dataset, 'val.json')
# model
print('\n--- build model ---')
if 'Conv' in params.model:
image_size = 84
else:
image_size = 224
if params.method in ['maml_baseline'] :
print(' training the {} with backbone {}'.format(params.method, params.model))
n_query = 15
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, n_eposide=100, **train_few_shot_params)
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, n_eposide=100, **test_few_shot_params)
val_loader = val_datamgr.get_data_loader(val_file, aug=False)
# 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)
else:
raise ValueError('Unknown method')
model = MAMLBaseline(params, tf_path=params.tf_dir)
model.cuda()
def main():
timer = Timer()
args, writer = init()
train_file = args.dataset_dir + 'train.json'
val_file = args.dataset_dir + 'val.json'
few_shot_params = dict(n_way=args.n_way, n_support=args.n_shot, n_query=args.n_query)
n_episode = 10 if args.debug else 100
if args.method_type is Method_type.baseline:
train_datamgr = SimpleDataManager(train_file, args.dataset_dir, args.image_size, batch_size=64)
train_loader = train_datamgr.get_data_loader(aug = True)
else:
train_datamgr = SetDataManager(train_file, args.dataset_dir, args.image_size,
n_episode=n_episode, mode='train', **few_shot_params)
train_loader = train_datamgr.get_data_loader(aug=True)
val_datamgr = SetDataManager(val_file, args.dataset_dir, args.image_size,
n_episode=n_episode, mode='val', **few_shot_params)
val_loader = val_datamgr.get_data_loader(aug=False)
if args.model_type is Model_type.ConvNet:
pass
elif args.model_type is Model_type.ResNet12:
from methods.backbone import ResNet12
encoder = ResNet12()
else:
raise ValueError('')
if args.method_type is Method_type.baseline:
from methods.baselinetrain import BaselineTrain
model = BaselineTrain(encoder, args)
elif args.method_type is Method_type.protonet:
from methods.protonet import ProtoNet
model = ProtoNet(encoder, args)
else:
raise ValueError('')
from torch.optim import SGD,lr_scheduler
if args.method_type is Method_type.baseline:
optimizer = SGD(model.encoder.parameters(), lr=args.lr, momentum=0.9, weight_decay=args.weight_decay)
scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.max_epoch, eta_min=0, last_epoch=-1)
else:
optimizer = torch.optim.SGD(model.encoder.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4,
nesterov=True)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.step_size, gamma=0.5)
args.ngpu = torch.cuda.device_count()
torch.backends.cudnn.benchmark = True
model = model.cuda()
label = torch.from_numpy(np.repeat(range(args.n_way), args.n_query))
label = label.cuda()
if args.test:
test(model, label, args, few_shot_params)
return
if args.resume:
resume_OK = resume_model(model, optimizer, args, scheduler)
else:
resume_OK = False
if (not resume_OK) and (args.warmup is not None):
load_pretrained_weights(model, args)
if args.debug:
args.max_epoch = args.start_epoch + 1
for epoch in range(args.start_epoch, args.max_epoch):
train_one_epoch(model, optimizer, args, train_loader, label, writer, epoch)
scheduler.step()
vl, va = val(model, args, val_loader, label)
if writer is not None:
writer.add_scalar('data/val_acc', float(va), epoch)
print('epoch {}, val, loss={:.4f} acc={:.4f}'.format(epoch, vl, va))
if va >= args.max_acc:
args.max_acc = va
print('saving the best model! acc={:.4f}'.format(va))
save_model(model, optimizer, args, epoch, args.max_acc, 'max_acc', scheduler)
save_model(model, optimizer, args, epoch, args.max_acc, 'epoch-last', scheduler)
if epoch != 0:
print('ETA:{}/{}'.format(timer.measure(), timer.measure(epoch / args.max_epoch)))
if writer is not None:
writer.close()
test(model, label, args, few_shot_params)
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 [
'protonet', 'matchingnet', 'relationnet', 'relationnet_softmax',
'maml', 'maml_approx'
]:
base_datamgr = SetDataManager(image_size, params.batchsize)
base_loader = base_datamgr.get_data_loader(base_file,
aug=params.train_aug,
ifshuffle=True)
#val_datamgr = SetDataManager(image_size, params.batchsize)
#val_loader = val_datamgr.get_data_loader( val_file, aug = False,ifshuffle = False)
#a batch for SetDataManager: a [n_way, n_support + n_query, dim, w, h] tensor
if 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':
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)
vocab_srt = [v[0] for v in vocab_srt]
with open(args.embeddings_file, "w") as fout:
fout.write("\n".join(vocab_srt))
fout.write("\n")
np.savetxt(args.embeddings_metadata, weights, fmt="%f", delimiter="\t")
sys.exit(0)
# Run the test loop for 600 iterations
ITER_NUM = 600
N_QUERY = 15
test_datamgr = SetDataManager(
"CUB",
84,
n_query=N_QUERY,
n_way=args.test_n_way,
n_support=args.n_shot,
n_episode=ITER_NUM,
args=args,
)
test_loader = test_datamgr.get_data_loader(
os.path.join(constants.DATA_DIR, f"{args.split}.json"),
aug=False,
lang_dir=constants.LANG_DIR,
normalize=False,
vocab=vocab,
)
normalizer = TransformLoader(84).get_normalize()
model.eval()
def meta_train(self,
config,
method,
descriptor_str,
debug=True,
use_test=False,
require_pretrain=False,
metric="acc"):
config["meta_training"] = True
params = self.params
params.save_freq = 10
params.n_query = max(1,
int(16 * params.test_n_way / params.train_n_way))
params.dataset = config["dataset"]
params.model = config["model"]
params.method = config["method"]
params.n_shot = config["n_shot"]
train_episodes = config["train_episodes"]
val_episodes = config["val_episodes"]
end_epoch = config["end_epoch"]
if "weight_decay" in config:
weight_decay = config["weight_decay"]
else:
weight_decay = 0
result_dir = "results/meta/%s" % (params.dataset)
if not os.path.isdir(result_dir):
os.makedirs(result_dir)
result_file = os.path.join(
result_dir,
"%s_%s_%s.txt" % (params.method, params.model, descriptor_str))
self.few_shot_params = dict(n_way=params.test_n_way,
n_support=params.n_shot,
n_query=self.params.n_query)
params.checkpoint_dir = '%s/checkpoints/%s/%s/%s_%s_%s' % (
configs.save_dir, params.dataset, params.method, params.model,
descriptor_str, params.n_shot)
params.stop_epoch = 100
self.initialize(params, False)
image_size = self.image_size
pretrain = PretrainedModel(self.params)
if use_test:
file_name = "novel.json"
else:
file_name = "val.json"
if params.dataset == 'cross':
base_file = configs.data_dir['miniImagenet'] + 'base.json'
val_file = configs.data_dir['CUB'] + file_name
else:
base_file = configs.data_dir[params.dataset] + 'base.json'
val_file = configs.data_dir[params.dataset] + file_name
n_query = max(1, int(16 * params.test_n_way / params.train_n_way))
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=train_episodes)
base_loader = base_datamgr.get_data_loader(base_file,
aug=params.train_aug,
debug=debug)
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,
n_eposide=val_episodes)
val_loader = val_datamgr.get_data_loader(val_file,
aug=False,
debug=debug)
backbone = self.get_backbone()
if "params" in config:
model_params = config["params"]
else:
model_params = {}
if require_pretrain:
model_params["pretrain"] = pretrain
model = method(backbone, **train_few_shot_params, **model_params)
model = model.cuda()
# Freeze backbone
model.feature = None
if not require_pretrain:
model.pretrain = pretrain
optimizer = torch.optim.Adam(model.parameters(),
weight_decay=weight_decay)
max_acc = 0
for epoch in range(0, end_epoch):
model.epoch = epoch
model.train()
model.train_loop(
epoch, base_loader,
optimizer) # model are called by reference, no need to return
model.eval()
if not os.path.isdir(params.checkpoint_dir):
os.makedirs(params.checkpoint_dir)
acc = model.test_loop(val_loader, metric=metric)
message = "Epoch: %d, Validation accuracy: %.3f, Best validation accuracy: %.3f" % (
epoch, acc, max_acc)
print(message)
append_to_file(result_file, message)
if acc > max_acc:
print("best model! save...")
max_acc = acc
outfile = os.path.join(params.checkpoint_dir, 'best_model.tar')
torch.save({
'epoch': epoch,
'state': model.state_dict()
}, outfile)
if (epoch % params.save_freq == 0) or (epoch
== params.stop_epoch - 1):
outfile = os.path.join(params.checkpoint_dir,
'{:d}.tar'.format(epoch))
torch.save({
'epoch': epoch,
'state': model.state_dict()
}, outfile)
self.meta_test(config, method, descriptor_str, debug, require_pretrain)
#code from mvcnn, add logging later
#parse_args
# num_models = 1000 #max number of models to use per class, add this functionality later
# n_models_train = num_models*num_views
# if params.num_views and params.num_views >=5:
# n_query = max(1, int(8* params.test_n_way/params.train_n_way)) #why is this required?
# else:
# n_query = max(1, int(16* params.test_n_way/params.train_n_way)) #why is this required?
train_few_shot_params = dict(n_way=params.train_n_way,
n_support=params.n_shot)
base_datamgr = SetDataManager(image_size,
n_query=params.n_query,
**train_few_shot_params,
num_views=params.num_views)
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=params.n_query,
**test_few_shot_params,
num_views=params.num_views)
val_loader = val_datamgr.get_data_loader(val_file, aug=False)
backbone = model_dict[params.model]
model = ProtoNet(backbone, params.num_views, **train_few_shot_params)
model = model.cuda()
# model = torch.nn.DataParallel(model).cuda()
def meta_test(self,
config,
method,
descriptor_str,
debug=True,
require_pretrain=False,
metric="acc"):
config["meta_training"] = True
params = self.params
params.save_freq = 50
params.n_query = max(1,
int(16 * params.test_n_way / params.train_n_way))
params.dataset = config["dataset"]
params.model = config["model"]
params.method = config["method"]
params.n_shot = config["n_shot"]
self.few_shot_params = dict(n_way=params.test_n_way,
n_support=params.n_shot,
n_query=self.params.n_query)
params.checkpoint_dir = '%s/checkpoints/%s/%s/%s_%s_%s' % (
configs.save_dir, params.dataset, params.method, params.model,
descriptor_str, params.n_shot)
params.stop_epoch = 2000
self.initialize(params, False)
image_size = self.image_size
pretrain = PretrainedModel(self.params)
if params.dataset == 'cross':
test_file = configs.data_dir['CUB'] + 'novel.json'
else:
test_file = configs.data_dir[params.dataset] + 'novel.json'
n_query = 15
few_shot_params = dict(n_way=params.test_n_way,
n_support=params.n_shot)
datamgr = SetDataManager(image_size,
n_query=n_query,
**few_shot_params,
n_eposide=params.stop_epoch)
loader = datamgr.get_data_loader(test_file, aug=False, debug=debug)
if "params" in config:
model_params = config["params"]
else:
model_params = {}
if require_pretrain:
model_params["pretrain"] = pretrain
backbone = self.get_backbone()
model = method(backbone, **few_shot_params, **model_params)
model = model.cuda()
model_file = os.path.join(params.checkpoint_dir, "best_model.tar")
# Load model
state_dict = model.state_dict()
saved_states = torch.load(model_file)["state"]
state_dict.update(saved_states)
model.load_state_dict(state_dict)
# Freeze backbone
model.feature = None
model.pretrain = pretrain
model.eval()
acc = model.test_loop(loader, metric=metric)
print(acc)
model = BaselineTrain(model_dict[params.model],
params.num_classes,
loss_type='dist')
elif params.method in [
'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)
base_loader = base_datamgr.get_data_loader(base_file,
aug=params.train_aug)
if params.n_shot_test == -1: # modify val loader support
params.n_shot_test = params.n_shot
else: # modify target loader support
train_few_shot_params['n_support'] = params.n_shot_test
if params.adversarial or params.adaptFinetune:
target_datamgr = SetDataManager(image_size,
n_query=n_query,
**train_few_shot_params)
target_loader = target_datamgr.get_data_loader(novel_file,
aug=False)
# ipdb.set_trace()
# bl, tl = iter(base_loader), iter(target_loader)
else:
raise print('train_aug is wrong')
print('Testing! {} shots on {} dataset with {} epochs of {}({})'.format(
params.n_shot, params.testset, params.save_epoch, name, params.method))
# dataset
print(' build dataset')
if 'Conv' in params.model:
image_size = 84
else:
image_size = 224
split = params.split
loadfile = os.path.join(params.data_dir, params.testset, split + '.json')
test_few_shot_params = dict(n_way=params.test_n_way,
n_support=params.n_shot)
val_datamgr = SetDataManager(image_size,
n_query=params.n_query,
**test_few_shot_params)
val_loader = val_datamgr.get_data_loader(loadfile, aug=False)
datasets = params.dataset
datasets.remove(params.testset)
base_loaders = [
val_datamgr.get_data_loader(os.path.join(params.data_dir, dataset,
'base.json'),
aug=False) for dataset in datasets
]
print(' build feature encoder')
# feature encoder
checkpoint_dir = '%s/checkmodels/%s' % (params.save_dir, name)
if params.save_iter != -1:
split_str = split + "_" + str(params.save_iter)
else:
split_str = split
if params.method in ['maml', 'maml_approx'
]: #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'
image_size = 224
base_datamgr = SimpleDataManager(image_size, batch_size=batch_size)
base_loader = base_datamgr.get_data_loader(base_file, aug=True)
base_jigsaw_datamgr = JigsawDataManger(
image_size,
batch_size=batch_size,
max_replace_block_num=params.jig_replace_num_train)
base_jigsaw_loader = base_jigsaw_datamgr.get_data_loader(base_file,
aug=False)
extra_data = 15 # extra_unlabeled data
val_datamgr = SetDataManager(image_size,
n_way=params.test_n_way,
n_support=params.n_shot,
n_query=params.n_query + extra_data,
n_eposide=50)
val_loader = val_datamgr.get_data_loader(val_file, aug=False)
if params.dataset == "miniImagenet":
num_class = 64
elif params.dataset == "tieredImagenet":
num_class = 351
elif params.dataset == "caltech256":
num_class = 257
elif params.dataset == "CUB":
num_class = 200 # set to 200 since the label range 0~199 even though there are only 100 classes to be trained
else:
raise ValueError('Unknown dataset')
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 get_train_val_loader(params, source_val):
# to prevent circular import
from data.datamgr import SimpleDataManager, SetDataManager, AugSetDataManager, VAESetDataManager
image_size = get_img_size(params)
base_file, val_file = get_train_val_filename(params)
if source_val:
source_val_file = get_source_val_filename(params)
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)
# to do fine-tune when validation
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
val_few_shot_params = get_few_shot_params(params, 'val')
val_datamgr = SetDataManager(image_size,
n_query=n_query,
**val_few_shot_params)
val_loader = val_datamgr.get_data_loader(val_file, aug=False)
if source_val:
source_val_datamgr = SetDataManager(image_size,
n_query=n_query,
**val_few_shot_params)
source_val_loader = val_datamgr.get_data_loader(source_val_file,
aug=False)
elif params.method in [
'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)
# val_few_shot_params = dict(n_way = params.test_n_way, n_support = params.n_shot)
train_few_shot_params = get_few_shot_params(params, 'train')
val_few_shot_params = get_few_shot_params(params, 'val')
if params.vaegan_exp is not None:
# TODO
is_training = False
vaegan = restore_vaegan(params.dataset,
params.vaegan_exp,
params.vaegan_step,
is_training=is_training)
base_datamgr = VAESetDataManager(
image_size,
n_query=n_query,
vaegan_exp=params.vaegan_exp,
vaegan_step=params.vaegan_step,
vaegan_is_train=params.vaegan_is_train,
lambda_zlogvar=params.zvar_lambda,
fake_prob=params.fake_prob,
**train_few_shot_params)
# train_val or val???
val_datamgr = SetDataManager(image_size,
n_query=n_query,
**val_few_shot_params)
elif params.aug_target is None: # Common Case
assert params.aug_type is None
base_datamgr = SetDataManager(image_size,
n_query=n_query,
**train_few_shot_params)
val_datamgr = SetDataManager(image_size,
n_query=n_query,
**val_few_shot_params)
if source_val:
source_val_datamgr = SetDataManager(image_size,
n_query=n_query,
**val_few_shot_params)
else:
aug_type = params.aug_type
assert aug_type is not None
base_datamgr = AugSetDataManager(image_size,
n_query=n_query,
aug_type=aug_type,
aug_target=params.aug_target,
**train_few_shot_params)
val_datamgr = AugSetDataManager(image_size,
n_query=n_query,
aug_type=aug_type,
aug_target='test-sample',
**val_few_shot_params)
base_loader = base_datamgr.get_data_loader(base_file,
aug=params.train_aug)
val_loader = val_datamgr.get_data_loader(val_file, aug=False)
if source_val:
source_val_loader = val_datamgr.get_data_loader(source_val_file,
aug=False)
#a batch for SetDataManager: a [n_way, n_support + n_query, n_channel, w, h] tensor
else:
raise ValueError('Unknown method')
if source_val:
return base_loader, val_loader, source_val_loader
else:
return base_loader, val_loader
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.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 ['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)
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 == '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':
def exp_test(params, n_episodes, should_del_features=False):#, show_data=False):
start_time = datetime.datetime.now()
print('exp_test() started at',start_time)
set_random_seed(0) # successfully reproduce "normal" testing.
if params.gpu_id:
set_gpu_id(params.gpu_id)
# acc_all = []
model = get_model(params, 'test')
########## get settings ##########
n_shot = params.test_n_shot if params.test_n_shot is not None else params.n_shot
few_shot_params = dict(n_way = params.test_n_way , n_support = n_shot)
if params.gpu_id:
model = model.cuda()
else:
model = to_device(model)
checkpoint_dir = get_checkpoint_dir(params)
print('loading from:',checkpoint_dir)
if params.save_iter != -1:
modelfile = get_assigned_file(checkpoint_dir, params.save_iter)
else:
modelfile = get_best_file(checkpoint_dir)
########## load model ##########
if modelfile is not None:
if params.gpu_id is None:
tmp = torch.load(modelfile)
else: # TODO: figure out WTF is going on here
print('params.gpu_id =', params.gpu_id)
map_location = 'cuda:0'
# gpu_str = 'cuda:' + '0'#str(params.gpu_id)
# map_location = {'cuda:1':gpu_str, 'cuda:0':gpu_str} # see here: https://hackmd.io/koKAo6kURn2YBqjoXXDhaw#RuntimeError-CUDA-error-invalid-device-ordinal
tmp = torch.load(modelfile, map_location=map_location)
# tmp = torch.load(modelfile)
if not params.method in ['baseline', 'baseline++'] :
# if 'baseline' or 'baseline++' then NO NEED to load model !!!
model.load_state_dict(tmp['state'])
print('Model successfully loaded.')
else:
print('No need to load model for baseline/baseline++ when testing.')
load_epoch = int(tmp['epoch'])
########## testing ##########
if params.method in ['maml', 'maml_approx']: #maml do not support testing with feature
image_size = get_img_size(params)
load_file = get_loadfile_path(params, params.split)
datamgr = SetDataManager(image_size, n_episode = n_episodes, n_query = 15 , **few_shot_params)
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)
########## last record and post-process ##########
torch.cuda.empty_cache()
timestamp = time.strftime("%Y%m%d-%H%M%S", time.localtime())
# TODO afterward: compute this
acc_str = '%4.2f%% +- %4.2f%%' % (acc_mean, 1.96* acc_std/np.sqrt(n_episodes))
# writing settings into csv
acc_mean_str = '%4.2f' % (acc_mean)
acc_std_str = '%4.2f' %(acc_std)
# record beyond params
extra_record = {'time':timestamp, 'acc_mean':acc_mean_str, 'acc_std':acc_std_str, 'epoch':load_epoch}
if should_del_features:
del_features(params)
end_time = datetime.datetime.now()
print('exp_test() start at', start_time, ', end at', end_time, '.\n')
print('exp_test() totally took:', end_time-start_time)
return extra_record, task_datas
else: # not MAML
acc_all = []
# # draw_task: initialize task acc(actually can replace acc_all), img_path, img_is_correct, etc.
# task_datas = [None]*n_episodes # list of dict
# directly use extracted features
all_feature_files = get_all_feature_files(params)
if params.n_test_candidates is None: # common setting (no candidate)
# draw_task: initialize task acc(actually can replace acc_all), img_path, img_is_correct, etc.
task_datas = [None]*n_episodes # list of dict
feature_file = all_feature_files[0]
cl_feature, cl_filepath = feat_loader.init_loader(feature_file, return_path=True)
cl_feature_single = [cl_feature]
for i in tqdm(range(n_episodes)):
# TODO afterward: fix data list? can only fix class list?
task_data = feature_evaluation(
cl_feature_single, model, params=params, n_query=15, **few_shot_params,
cl_filepath=cl_filepath,
)
acc = task_data['acc']
acc_all.append(acc)
task_datas[i] = task_data
acc_all = np.asarray(acc_all)
acc_mean = np.mean(acc_all)
acc_std = np.std(acc_all)
print('loaded from %d epoch model.' %(load_epoch))
print('%d episodes, Test Acc = %4.2f%% +- %4.2f%%' %(n_episodes, acc_mean, 1.96* acc_std/np.sqrt(n_episodes)))
########## last record and post-process ##########
torch.cuda.empty_cache()
timestamp = time.strftime("%Y%m%d-%H%M%S", time.localtime())
# TODO afterward: compute this
acc_str = '%4.2f%% +- %4.2f%%' % (acc_mean, 1.96* acc_std/np.sqrt(n_episodes))
# writing settings into csv
acc_mean_str = '%4.2f' % (acc_mean)
acc_std_str = '%4.2f' %(acc_std)
# record beyond params
extra_record = {'time':timestamp, 'acc_mean':acc_mean_str, 'acc_std':acc_std_str, 'epoch':load_epoch}
if should_del_features:
del_features(params)
end_time = datetime.datetime.now()
print('exp_test() start at', start_time, ', end at', end_time, '.\n')
print('exp_test() totally took:', end_time-start_time)
return extra_record, task_datas
else: # n_test_candidates settings
candidate_cl_feature = [] # features of each class of each candidates
print('Loading features of %s candidates into dictionaries...' %(params.n_test_candidates))
for n in tqdm(range(params.n_test_candidates)):
nth_feature_file = all_feature_files[n]
cl_feature, cl_filepath = feat_loader.init_loader(nth_feature_file, return_path=True)
candidate_cl_feature.append(cl_feature)
print('Evaluating...')
# TODO: frac_acc_all
is_single_exp = not isinstance(params.frac_ensemble, list)
if is_single_exp:
# draw_task: initialize task acc(actually can replace acc_all), img_path, img_is_correct, etc.
task_datas = [None]*n_episodes # list of dict
########## test and record acc ##########
for i in tqdm(range(n_episodes)):
# TODO afterward: fix data list? can only fix class list?
task_data = feature_evaluation(
candidate_cl_feature, model, params=params, n_query=15, **few_shot_params,
cl_filepath=cl_filepath,
)
acc = task_data['acc']
acc_all.append(acc)
task_datas[i] = task_data
collected = gc.collect()
# print("Garbage collector: collected %d objects." % (collected))
acc_all = np.asarray(acc_all)
acc_mean = np.mean(acc_all)
acc_std = np.std(acc_all)
print('loaded from %d epoch model.' %(load_epoch))
print('%d episodes, Test Acc = %4.2f%% +- %4.2f%%' %(n_episodes, acc_mean, 1.96* acc_std/np.sqrt(n_episodes)))
collected = gc.collect()
print("garbage collector: collected %d objects." % (collected))
########## last record and post-process ##########
torch.cuda.empty_cache()
timestamp = time.strftime("%Y%m%d-%H%M%S", time.localtime())
# TODO afterward: compute this
acc_str = '%4.2f%% +- %4.2f%%' % (acc_mean, 1.96* acc_std/np.sqrt(n_episodes))
# writing settings into csv
acc_mean_str = '%4.2f' % (acc_mean)
acc_std_str = '%4.2f' %(acc_std)
# record beyond params
extra_record = {'time':timestamp, 'acc_mean':acc_mean_str, 'acc_std':acc_std_str, 'epoch':load_epoch}
if should_del_features:
del_features(params)
end_time = datetime.datetime.now()
print('exp_test() start at', start_time, ', end at', end_time, '.\n')
print('exp_test() totally took:', end_time-start_time)
return extra_record, task_datas
else: ########## multi-frac_ensemble exps ##########
########## (haven't modified) test and record acc ##########
n_fracs = len(params.frac_ensemble)
##### initialize frac_data #####
frac_acc_alls = [[0]*n_episodes for _ in range(n_fracs)]
frac_acc_means = [None]*n_fracs
frac_acc_stds = [None]*n_fracs
# draw_task: initialize task acc(actually can replace acc_all), img_path, img_is_correct, etc.
ep_task_data_each_frac = [[None]*n_episodes for _ in range(n_fracs)] # list of list of dict
for ep_id in tqdm(range(n_episodes)):
# TODO afterward: fix data list? can only fix class list?
# TODO my_utils.py: feature_eval return frac_task_data
frac_task_data = feature_evaluation(
candidate_cl_feature, model, params=params, n_query=15, **few_shot_params,
cl_filepath=cl_filepath,
)
for frac_id in range(n_fracs):
task_data = frac_task_data[frac_id]
# TODO: i think here's the problem???
acc = task_data['acc']
frac_acc_alls[frac_id][ep_id] = acc
ep_task_data_each_frac[frac_id][ep_id] = task_data
collected = gc.collect()
# print("Garbage collector: collected %d objects." % (collected))
collected = gc.collect()
# print("Garbage collector: collected %d objects." % (collected))
### debug
# print('frac_acc_alls:', frac_acc_alls)
# yee
for frac_id in range(n_fracs):
frac_acc_alls[frac_id] = np.asarray(frac_acc_alls[frac_id])
acc_all = frac_acc_alls[frac_id]
acc_mean = np.mean(acc_all)
acc_std = np.std(acc_all)
frac_acc_means[frac_id] = acc_mean
frac_acc_stds[frac_id] = acc_std
print('loaded from %d epoch model, frac_ensemble:'%(load_epoch), params.frac_ensemble[frac_id])
print('%d episodes, Test Acc = %4.2f%% +- %4.2f%%' %(n_episodes, acc_mean, 1.96* acc_std/np.sqrt(n_episodes)))
########## (haven't modified) last record and post-process ##########
torch.cuda.empty_cache()
timestamp = time.strftime("%Y%m%d-%H%M%S", time.localtime())
# TODO afterward: compute this
# acc_str = '%4.2f%% +- %4.2f%%' % (acc_mean, 1.96* acc_std/np.sqrt(n_episodes))
frac_extra_records = []
for frac_id in range(n_fracs):
# writing settings into csv
acc_mean = frac_acc_means[frac_id]
acc_std = frac_acc_stds[frac_id]
acc_mean_str = '%4.2f' % (acc_mean)
acc_std_str = '%4.2f' %(acc_std)
# record beyond params
extra_record = {'time':timestamp, 'acc_mean':acc_mean_str, 'acc_std':acc_std_str, 'epoch':load_epoch}
frac_extra_records.append(extra_record)
if should_del_features:
del_features(params)
end_time = datetime.datetime.now()
print('exp_test() start at', start_time, ', end at', end_time, '.\n')
print('exp_test() totally took:', end_time-start_time)
return frac_extra_records, ep_task_data_each_frac
