def test_process(self, testset):
args = self.args
record = np.zeros((args.num_test_episodes, 2)) # loss and acc
label = torch.arange(args.eval_way, dtype=torch.int16).repeat(
# args.num_tasks *
args.eval_query)
label = label.type(torch.LongTensor)
if torch.cuda.is_available():
label = label.cuda()
test_sampler = CategoriesSampler(
testset.label,
args.num_test_episodes, # args.num_eval_episodes,
args.eval_way,
args.eval_shot + args.eval_query)
test_loader = DataLoader(dataset=testset,
batch_sampler=test_sampler,
num_workers=args.num_workers,
pin_memory=True)
for i, batch in tqdm(enumerate(test_loader, 1),
total=len(test_loader)):
data = batch[0]
data = data.to(self.args.device)
logits = self.model(data)
loss = F.cross_entropy(logits, label)
acc = count_acc(logits, label)
record[i - 1, 0] = loss.item()
record[i - 1, 1] = acc
assert (i == record.shape[0])
vl, _ = compute_confidence_interval(record[:, 0])
va, vap = compute_confidence_interval(record[:, 1])
print('{} way {} shot,Test acc={:.4f} + {:.4f}\n'.format(
args.eval_way, args.eval_shot, va, vap))
return vl, va, vap
def evaluate_fsl(self, data_loader):
# restore model args
args = self.args
# evaluation mode
self.model.eval()
record = np.zeros((args.num_eval_episodes, 2)) # loss and acc
label = torch.arange(args.eval_way,
dtype=torch.int16).repeat(args.eval_query)
label = label.type(torch.LongTensor)
if torch.cuda.is_available():
label = label.cuda()
print('{} best epoch {}, best val acc={:.4f} + {:.4f}'.format(
args.test_mode, self.trlog['max_acc_epoch'], self.trlog['max_acc'],
self.trlog['max_acc_interval']))
with torch.no_grad():
for i, batch in tqdm(enumerate(data_loader, 1)):
if torch.cuda.is_available():
data, _ = [_.cuda() for _ in batch]
else:
data = batch[0]
p = args.eval_shot * args.eval_way
data_shot, data_query = data[:p], data[p:]
logits = self.model.forward_fsl(data_shot, data_query)
loss = F.cross_entropy(logits, label)
acc = count_acc(logits, label)
record[i - 1, 0] = loss.item()
record[i - 1, 1] = acc
assert (i == record.shape[0])
vl, _ = compute_confidence_interval(record[:, 0])
va, vap = compute_confidence_interval(record[:, 1])
# train mode
self.model.train()
return vl, va, vap
def evaluate_gfsl(self, fsl_loader, gfsl_loader, gfsl_dataset):
# restore model args
args = self.args
# evaluation mode
self.model.eval()
record = np.zeros((args.num_eval_episodes, 5)) # loss and acc
label_unseen_query = torch.arange(
min(args.eval_way,
self.valset.num_class)).repeat(args.eval_query).long()
if torch.cuda.is_available():
label_unseen_query = label_unseen_query.cuda()
print('{} best epoch {}, best val acc={:.4f} + {:.4f}'.format(
args.test_mode, self.trlog['max_acc_epoch'], self.trlog['max_acc'],
self.trlog['max_acc_interval']))
with torch.no_grad():
for i, batch in tqdm(enumerate(zip(gfsl_loader, fsl_loader), 1)):
if torch.cuda.is_available():
data_seen, data_unseen, seen_label, unseen_label = batch[
0][0].cuda(), batch[1][0].cuda(), batch[0][1].cuda(
), batch[1][1].cuda()
else:
data_seen, data_unseen, seen_label, unseen_label = batch[
0][0], batch[1][0], batch[0][1], batch[1][1]
p2 = args.eval_shot * args.eval_way
data_unseen_shot, data_unseen_query = data_unseen[:
p2], data_unseen[
p2:]
label_unseen_shot, _ = unseen_label[:p2], unseen_label[p2:]
whole_query = torch.cat([data_seen, data_unseen_query], 0)
whole_label = torch.cat(
[seen_label, label_unseen_query + gfsl_dataset.num_class])
logits_s, logits_u = self.model.forward_generalized(
data_unseen_shot, whole_query)
# compute un-biased accuracy
new_logits = torch.cat([logits_s, logits_u], 1)
record[i - 1, 0] = F.cross_entropy(new_logits,
whole_label).item()
record[i - 1, 1] = count_acc(new_logits, whole_label)
# compute harmonic mean
HM_nobias, SA_nobias, UA_nobias = count_acc_harmonic_low_shot_joint(
torch.cat([logits_s, logits_u], 1), whole_label,
seen_label.shape[0])
record[i - 1, 2:] = np.array([HM_nobias, SA_nobias, UA_nobias])
del logits_s, logits_u, new_logits
torch.cuda.empty_cache()
assert (i == record.shape[0])
vl, _ = compute_confidence_interval(record[:, 0])
va, vap = compute_confidence_interval(record[:, 2])
# train mode
self.model.train()
return vl, va, vap
def evaluate_fsl(self):
args = self.args
record = np.zeros((args.num_eval_episodes, 2)) # loss and acc
label = torch.arange(args.eval_way,
dtype=torch.int16).repeat(args.eval_query)
label = label.type(torch.LongTensor)
if torch.cuda.is_available():
label = label.cuda()
for i, batch in tqdm(enumerate(self.test_fsl_loader, 1)):
if torch.cuda.is_available():
data, _ = [_.cuda() for _ in batch]
else:
data = batch[0]
p2 = args.eval_shot * args.eval_way
p = args.eval_shot * args.eval_way
data_shot, data_query = data[:p], data[p:]
with torch.no_grad():
logits = self.model.forward_fsl(data_shot, data_query)
loss = F.cross_entropy(logits, label)
acc = count_acc(logits, label)
record[i - 1, 0] = loss.item()
record[i - 1, 1] = acc
del loss, logits
torch.cuda.empty_cache()
assert (i == record.shape[0])
print('-'.join([args.model_class, args.model_path]))
self.trlog['acc_mean'], self.trlog[
'acc_interval'] = compute_confidence_interval(record[:, 1])
print('FSL {}-way Acc {:.5f} + {:.5f}'.format(
args.eval_way, self.trlog['acc_mean'], self.trlog['acc_interval']))
def evaluate_test(self):
# restore model args
args = self.args
# evaluation mode
self.model.load_state_dict(torch.load(osp.join(self.args.save_path, 'max_acc.pth'))['params'])
self.model.eval()
record = np.zeros((10000, 2)) # loss and acc
label = torch.arange(args.eval_way, dtype=torch.int16).repeat(args.eval_query)
label = label.type(torch.LongTensor)
if torch.cuda.is_available():
label = label.cuda()
print('best epoch {}, best val acc={:.4f} + {:.4f}'.format(
self.trlog['max_acc_epoch'],
self.trlog['max_acc'],
self.trlog['max_acc_interval']))
with torch.no_grad():
for i, batch in tqdm(enumerate(self.test_loader, 1)):
if torch.cuda.is_available():
data, _ = [_.cuda() for _ in batch]
else:
data = batch[0]
logits = self.model(data)
loss = F.cross_entropy(logits, label)
acc = count_acc(logits, label)
record[i-1, 0] = loss.item()
record[i-1, 1] = acc
assert(i == record.shape[0])
vl, _ = compute_confidence_interval(record[:,0])
va, vap = compute_confidence_interval(record[:,1])
self.trlog['test_acc'] = va
self.trlog['test_acc_interval'] = vap
self.trlog['test_loss'] = vl
print('best epoch {}, best val acc={:.4f} + {:.4f}\n'.format(
self.trlog['max_acc_epoch'],
self.trlog['max_acc'],
self.trlog['max_acc_interval']))
print('Test acc={:.4f} + {:.4f}\n'.format(
self.trlog['test_acc'],
self.trlog['test_acc_interval']))
return vl, va, vap
def evaluate(self, data_loader):
# restore model args
args = self.args
# evaluation mode
self.model.eval()
record = np.zeros((args.num_eval_episodes, 2)) # loss and acc
label = torch.arange(args.eval_way, dtype=torch.int16).repeat(
# args.num_tasks *
args.eval_query)
label = label.type(torch.LongTensor)
if torch.cuda.is_available():
label = label.cuda()
with torch.no_grad():
for i, batch in tqdm(enumerate(data_loader, 1),
total=len(data_loader),
desc='eval procedure'):
if torch.cuda.is_available():
data, _ = [_.cuda() for _ in batch]
else:
data = batch[0]
logits = self.model(data)
loss = F.cross_entropy(logits, label)
acc = count_acc(logits, label)
record[i - 1, 0] = loss.item()
record[i - 1, 1] = acc
assert (i == record.shape[0])
vl, _ = compute_confidence_interval(record[:, 0])
va, vap = compute_confidence_interval(record[:, 1])
# train mode
self.model.train()
if self.args.fix_BN:
self.model.encoder.eval()
return vl, va, vap
def evaluate_test(self, use_max_tst=False):
# restore model args
args = self.args
# evaluation mode
if use_max_tst:
assert args.tst_criterion != '', 'Please specify a criterion'
fname = osp.join(self.args.save_path, 'max_tst_criterion.pth')
criterion = args.tst_criterion
max_acc_epoch = 'max_tst_criterion_epoch'
max_acc = 'max_tst_criterion'
max_acc_interval = 'max_tst_criterion_interval'
test_acc = 'test_acc_at_max_criterion'
test_acc_interval = 'test_acc_interval_at_max_criterion'
test_loss = 'test_loss_at_max_criterion'
else:
fname = osp.join(self.args.save_path, 'max_acc.pth')
criterion = 'SupervisedAcc'
max_acc_epoch = 'max_acc_epoch'
max_acc = 'max_acc'
max_acc_interval = 'max_acc_interval'
test_acc = 'test_acc'
test_acc_interval = 'test_acc_interval'
test_loss = 'test_loss'
print('\nCriterion selected: {}'.format(criterion))
print('Reloading model from {}'.format(fname))
self.model.load_state_dict(torch.load(fname)['params'])
self.model.eval()
record = np.zeros((10000, 2)) # loss and acc
metrics = defaultdict(list) # all other metrics
label = torch.arange(args.eval_way, dtype=torch.int16).repeat(args.eval_query)
label = label.type(torch.LongTensor)
if torch.cuda.is_available():
label = label.cuda()
all_labels = torch.arange(args.eval_way, device=label.device).repeat(args.eval_shot + args.eval_query)
max_validation_str = 'Maximum value of valid_{} {:.4f} + {:.4f} reached at Epoch {}\n'.format(
criterion,
self.trlog[max_acc],
self.trlog[max_acc_interval],
self.trlog[max_acc_epoch])
print(max_validation_str)
with torch.no_grad():
for i, batch in tqdm(enumerate(self.test_loader, 1)):
if torch.cuda.is_available():
data, _ = [_.cuda() for _ in batch]
else:
data = batch[0]
embeddings, logits = self.model(data, return_feature=True)
loss = F.cross_entropy(logits, label)
acc = count_acc(logits, label)
record[i-1, 0] = loss.item()
record[i-1, 1] = acc
if args.tst_free:
embeddings_dict = self.model.get_embeddings_dict(embeddings, all_labels)
# TST-free part
clustering_losses = tst_free.clustering_loss(embeddings_dict, args.sinkhorn_reg, 'wasserstein',
sqrt_temperature=np.sqrt(args.temperature),
normalize_by_dim=False,
clustering_iterations=20, sinkhorn_iterations=20,
sinkhorn_iterations_warmstart=4,
sanity_check=False)
for key, val in clustering_losses.items():
metrics[key].append(val)
if args.debug_fast:
print('Debug fast, breaking TEST after 1 mini-batch')
record = record[:1]
break
assert(i == record.shape[0])
vl, _ = compute_confidence_interval(record[:,0])
va, vap = compute_confidence_interval(record[:,1])
metric_summaries = {key: compute_confidence_interval(val) for key, val in metrics.items()}
self.trlog[test_acc] = va
self.trlog[test_acc_interval] = vap
self.trlog[test_loss] = vl
summary_lines = []
summary_lines.append(max_validation_str)
summary_lines.append('test_SupervisedAcc {:.4f} + {:.4f} (ep{})'.format(
self.trlog[test_acc],
self.trlog[test_acc_interval],
self.trlog[max_acc_epoch]))
for key, (mean, std) in metric_summaries.items():
summary_lines.append('test_{} {:.4f} + {:.4f} (ep{})'.format(key, mean, std, self.trlog[max_acc_epoch]))
#self.print_metric_summaries(metric_summaries, prefix='\ttest_')
#self.log_metric_summaries(metric_summaries, 0, prefix='test_')
self.trlog['TST'] = metric_summaries
summary_lines_str = '\n'.join(summary_lines)
print('\n{}'.format(summary_lines_str))
with open(osp.join(self.args.save_path, 'summary_max_{}.txt'.format(criterion)), 'w') as f:
f.write(summary_lines_str)
def evaluate(self, data_loader):
# restore model args
args = self.args
# evaluation mode
self.model.eval()
accuracies = []
losses = []
metrics = OrderedDict()
label = torch.arange(args.eval_way, dtype=torch.int16).repeat(args.eval_query)
label = label.type(torch.LongTensor)
if torch.cuda.is_available():
label = label.cuda()
all_labels = torch.arange(args.eval_way, device=label.device).repeat(args.eval_shot + args.eval_query)
#print('best epoch {}, best val acc={:.4f} + {:.4f}'.format(
# self.trlog['max_acc_epoch'],
# self.trlog['max_acc'],
# self.trlog['max_acc_interval']))
with torch.no_grad():
for i, batch in enumerate(data_loader, 1):
if torch.cuda.is_available():
data, _ = [_.cuda() for _ in batch]
else:
data = batch[0]
embeddings, logits = self.model(data, return_feature=True)
if args.tst_free:
embeddings_logits_dict = self.model.get_embeddings_dict(embeddings, all_labels, logits)
for sinkhorn_reg_str in args.sinkhorn_reg: # loop over all possible regularizations
sinkhorn_reg_float = float(sinkhorn_reg_str)
transductive_losses = tst_free.transductive_from_logits(embeddings_logits_dict,
regularization=sinkhorn_reg_float)
for key, val in transductive_losses.items():
key += '_reg{}'.format(sinkhorn_reg_str)
metrics.setdefault(key, [])
metrics[key].append(val)
# data contains both support and query sets (typically 25+75 for 5-shot 5-way 15-query)
loss = F.cross_entropy(logits, label)
acc = count_acc(logits, label)
losses.append(loss.item())
accuracies.append(acc)
if args.tst_free:
# Also do the transductive based on the logits
embeddings_dict = self.model.get_embeddings_dict(embeddings, all_labels)
# TST-free part
for sinkhorn_reg_str in args.sinkhorn_reg: # loop over all possible regularizations
sinkhorn_reg_float = float(sinkhorn_reg_str)
clustering_losses = tst_free.clustering_loss(embeddings_dict, sinkhorn_reg_float, 'wasserstein',
sqrt_temperature=np.sqrt(args.temperature),
normalize_by_dim=False,
clustering_iterations=20, sinkhorn_iterations=20,
sinkhorn_iterations_warmstart=4,
sanity_check=False)
for key, val in clustering_losses.items():
key += '_reg{}'.format(sinkhorn_reg_str)
metrics.setdefault(key, [])
metrics[key].append(val)
if args.debug_fast:
print('Debug fast, breaking eval after 1 mini-batch')
break
assert(i == len(losses) and i == len(accuracies))
vl, _ = compute_confidence_interval(losses)
va, vap = compute_confidence_interval(accuracies)
metric_summaries = {key: compute_confidence_interval(val) for key, val in metrics.items()}
# train mode
self.model.train()
if self.args.fix_BN:
self.model.encoder.eval()
if args.tst_free:
return vl, va, vap, metric_summaries
else:
return vl, va, vap
def evaluate_test(self):
# restore model args
args = self.args
# evaluation mode
self.model.load_state_dict(
torch.load(osp.join(self.args.save_path, 'max_acc.pth'))['params'])
self.model.eval()
if args.test_mode == 'FSL':
record = np.zeros((10000, 2)) # loss and acc
label = torch.arange(args.eval_way).repeat(args.eval_query).type(
torch.LongTensor)
if torch.cuda.is_available():
label = label.cuda()
with torch.no_grad():
for i, batch in enumerate(self.test_fsl_loader, 1):
if torch.cuda.is_available():
data, _ = [_.cuda() for _ in batch]
else:
data = batch[0]
p = args.eval_shot * args.eval_way
data_shot, data_query = data[:p], data[p:]
logits = self.model.forward_fsl(data_shot, data_query)
loss = F.cross_entropy(logits, label)
acc = count_acc(logits, label)
record[i - 1, 0] = loss.item()
record[i - 1, 1] = acc
assert (i == record.shape[0])
vl, _ = compute_confidence_interval(record[:, 0])
va, vap = compute_confidence_interval(record[:, 1])
self.trlog['test_acc'] = va
self.trlog['test_acc_interval'] = vap
self.trlog['test_loss'] = vl
print('best epoch {}, best val acc={:.4f} + {:.4f}\n'.format(
self.trlog['max_acc_epoch'], self.trlog['max_acc'],
self.trlog['max_acc_interval']))
print('Test acc={:.4f} + {:.4f}\n'.format(
self.trlog['test_acc'], self.trlog['test_acc_interval']))
else:
record = np.zeros((10000, 5)) # loss and acc
label_unseen_query = torch.arange(
min(args.eval_way,
self.valset.num_class)).repeat(args.eval_query).long()
if torch.cuda.is_available():
label_unseen_query = label_unseen_query.cuda()
with torch.no_grad():
for i, batch in tqdm(
enumerate(
zip(self.test_gfsl_loader, self.test_fsl_loader),
1)):
if torch.cuda.is_available():
data_seen, data_unseen, seen_label, unseen_label = batch[
0][0].cuda(), batch[1][0].cuda(), batch[0][1].cuda(
), batch[1][1].cuda()
else:
data_seen, data_unseen, seen_label, unseen_label = batch[
0][0], batch[1][0], batch[0][1], batch[1][1]
p2 = args.eval_shot * args.eval_way
data_unseen_shot, data_unseen_query = data_unseen[:
p2], data_unseen[
p2:]
label_unseen_shot, _ = unseen_label[:p2], unseen_label[p2:]
whole_query = torch.cat([data_seen, data_unseen_query], 0)
whole_label = torch.cat([
seen_label,
label_unseen_query + self.traintestset.num_class
])
logits_s, logits_u = self.model.forward_generalized(
data_unseen_shot, whole_query)
# compute un-biased accuracy
new_logits = torch.cat([logits_s, logits_u], 1)
record[i - 1, 0] = F.cross_entropy(new_logits,
whole_label).item()
record[i - 1, 1] = count_acc(new_logits, whole_label)
# compute harmonic mean
HM_nobias, SA_nobias, UA_nobias = count_acc_harmonic_low_shot_joint(
torch.cat([logits_s, logits_u], 1), whole_label,
seen_label.shape[0])
record[i - 1,
2:] = np.array([HM_nobias, SA_nobias, UA_nobias])
del logits_s, logits_u, new_logits
torch.cuda.empty_cache()
m_list = []
p_list = []
for i in range(5):
m1, p1 = compute_confidence_interval(record[:, i])
m_list.append(m1)
p_list.append(p1)
self.trlog['test_loss'] = m_list[0]
self.trlog['test_acc'] = m_list[1]
self.trlog['test_acc_interval'] = p_list[1]
self.trlog['test_HM_acc'] = m_list[2]
self.trlog['test_HM_acc_interval'] = p_list[2]
self.trlog['test_HMSeen_acc'] = m_list[3]
self.trlog['test_HMSeen_acc_interval'] = p_list[3]
self.trlog['test_HMUnseen_acc'] = m_list[4]
self.trlog['test_HMUnseen_acc_interval'] = p_list[4]
print('best epoch {}, best val acc={:.4f} + {:.4f}\n'.format(
self.trlog['max_acc_epoch'], self.trlog['max_acc'],
self.trlog['max_acc_interval']))
print('Test HM acc={:.4f} + {:.4f}\n'.format(
self.trlog['test_HM_acc'], self.trlog['test_HM_acc_interval']))
print('GFSL {}-way Acc w/o Bias {:.5f} + {:.5f}'.format(
args.eval_way, m_list[1], p_list[1]))
print('GFSL {}-way HM w/o Bias {:.5f} + {:.5f}'.format(
args.eval_way, m_list[2], p_list[2]))
print('GFSL {}-way HMSeen w/o Bias {:.5f} + {:.5f}'.format(
args.eval_way, m_list[3], p_list[3]))
print('GFSL {}-way HMUnseen w/o Bias {:.5f} + {:.5f}'.format(
args.eval_way, m_list[4], p_list[4]))
def evaluate_gfsl(self):
args = self.args
label_unseen_query = torch.arange(args.eval_way).repeat(
args.eval_query).long()
if torch.cuda.is_available():
label_unseen_query = label_unseen_query.cuda()
generalized_few_shot_acc = np.zeros((args.num_eval_episodes, 2))
generalized_few_shot_delta = np.zeros((args.num_eval_episodes, 4))
generalized_few_shot_hmeanacc = np.zeros((args.num_eval_episodes, 6))
generalized_few_shot_hmeanmap = np.zeros((args.num_eval_episodes, 6))
generalized_few_shot_ausuc = np.zeros((args.num_eval_episodes, 1))
AUC_record = []
for i, batch in tqdm(
enumerate(zip(self.test_gfsl_loader, self.test_fsl_loader),
1)):
if torch.cuda.is_available():
data_seen, data_unseen, seen_label, unseen_label = batch[0][
0].cuda(), batch[1][0].cuda(), batch[0][1].cuda(
), batch[1][1].cuda()
else:
data_seen, data_unseen, seen_label, unseen_label = batch[0][
0], batch[1][0], batch[0][1], batch[1][1]
p2 = args.eval_shot * args.eval_way
data_unseen_shot, data_unseen_query = data_unseen[:
p2], data_unseen[
p2:]
label_unseen_shot, _ = unseen_label[:p2], unseen_label[p2:]
whole_query = torch.cat([data_seen, data_unseen_query], 0)
whole_label = torch.cat(
[seen_label, label_unseen_query + self.trainset.num_class])
if args.model_class in ['CLS', 'Castle', 'ACastle']:
with torch.no_grad():
logits_s, logits_u = self.model.forward_generalized(
data_unseen_shot, whole_query)
elif args.model_class in ['ProtoNet']:
with torch.no_grad():
logits_s, logits_u = self.model.forward_generalized(
data_unseen_shot, whole_query, self.model.seen_proto)
# compute un-biased accuracy
new_logits = torch.cat([logits_s, logits_u], 1)
if 'acc' in self.criteria or 'hmeanacc' in self.criteria or 'delta' in self.criteria:
new_logits_acc_biased = torch.cat(
[logits_s - self.best_bias_acc, logits_u], 1)
if 'hmeanmap' in self.criteria:
new_logits_map_biased = torch.cat(
[logits_s - self.best_bias_map, logits_u], 1)
# Criterion: Acc
if 'acc' in self.criteria:
generalized_few_shot_acc[i - 1, 0] = count_acc(
new_logits, whole_label)
# compute biased accuracy
generalized_few_shot_acc[i - 1, 1] = count_acc(
new_logits_acc_biased, whole_label)
if 'delta' in self.criteria:
# compute delta value for un-biased logits
unbiased_detla1, unbiased_detla2 = count_delta_value(
new_logits, whole_label, seen_label.shape[0],
self.trainset.num_class)
# compute delta value
biased_detla1, biased_detla2 = count_delta_value(
new_logits_acc_biased, whole_label, seen_label.shape[0],
self.trainset.num_class)
generalized_few_shot_delta[i - 1, :] = np.array([
unbiased_detla1, unbiased_detla2, biased_detla1,
biased_detla2
])
if 'hmeanacc' in self.criteria:
# compute harmonic mean
HM_nobias, SA_nobias, UA_nobias = count_acc_harmonic_low_shot_joint(
new_logits, whole_label, seen_label.shape[0])
HM, SA, UA = count_acc_harmonic_low_shot_joint(
new_logits_acc_biased, whole_label, seen_label.shape[0])
generalized_few_shot_hmeanacc[i - 1, :] = np.array(
[HM_nobias, SA_nobias, UA_nobias, HM, SA, UA])
if 'hmeanmap' in self.criteria:
# compute harmonic mean
HM_nobias, SA_nobias, UA_nobias = count_acc_harmonic_MAP(
new_logits, whole_label, seen_label.shape[0], 'macro')
HM, SA, UA = count_acc_harmonic_MAP(new_logits_map_biased,
whole_label,
seen_label.shape[0],
'macro')
generalized_few_shot_hmeanmap[i - 1, :] = np.array(
[HM_nobias, SA_nobias, UA_nobias, HM, SA, UA])
if 'ausuc' in self.criteria:
# compute AUSUC
generalized_few_shot_ausuc[i - 1,
0], temp_auc_record = Compute_AUSUC(
logits_s.detach().cpu().numpy(),
logits_u.detach().cpu().numpy(),
whole_label.cpu().numpy(),
np.arange(
self.trainset.num_class),
self.trainset.num_class +
np.arange(args.eval_way))
AUC_record.append(temp_auc_record)
del logits_s, logits_u, new_logits
torch.cuda.empty_cache()
self.AUC_record = AUC_record
print('-'.join([args.model_class, args.model_path]))
if 'acc' in self.criteria:
self.trlog['acc_mean'], self.trlog[
'acc_interval'] = compute_confidence_interval(
generalized_few_shot_acc[:, 0])
self.trlog['acc_biased_mean'], self.trlog[
'acc_biased_interval'] = compute_confidence_interval(
generalized_few_shot_acc[:, 1])
print('GFSL {}-way Acc w/o Bias {:.5f} + {:.5f}'.format(
args.eval_way, self.trlog['acc_mean'],
self.trlog['acc_interval']))
print('GFSL {}-way Acc w/ Bias {:.5f} + {:.5f}'.format(
args.eval_way, self.trlog['acc_biased_mean'],
self.trlog['acc_biased_interval']))
if 'delta' in self.criteria:
self.trlog['detla1_mean'], self.trlog[
'detla1_interval'] = compute_confidence_interval(
generalized_few_shot_delta[:, 0])
self.trlog['detla2_mean'], self.trlog[
'detla2_interval'] = compute_confidence_interval(
generalized_few_shot_delta[:, 1])
self.trlog['detla1_biased_mean'], self.trlog[
'detla1_biased_interval'] = compute_confidence_interval(
generalized_few_shot_delta[:, 2])
self.trlog['detla2_biased_mean'], self.trlog[
'detla2_biased_interval'] = compute_confidence_interval(
generalized_few_shot_delta[:, 3])
print('GFSL {}-way Detla1 w/o Bias {:.5f} + {:.5f}'.format(
args.eval_way, self.trlog['detla1_mean'],
self.trlog['detla1_interval']))
print('GFSL {}-way Detla1 w/ Bias {:.5f} + {:.5f}'.format(
args.eval_way, self.trlog['detla1_biased_mean'],
self.trlog['detla1_biased_interval']))
print('GFSL {}-way Detla2 w/o Bias {:.5f} + {:.5f}'.format(
args.eval_way, self.trlog['detla2_mean'],
self.trlog['detla2_interval']))
print('GFSL {}-way Detla2 w/ Bias {:.5f} + {:.5f}'.format(
args.eval_way, self.trlog['detla2_biased_mean'],
self.trlog['detla2_biased_interval']))
if 'hmeanacc' in self.criteria:
self.trlog['HM_mean'], self.trlog[
'HM_interval'] = compute_confidence_interval(
generalized_few_shot_hmeanacc[:, 0])
self.trlog['S2All_mean'], self.trlog[
'S2All_interval'] = compute_confidence_interval(
generalized_few_shot_hmeanacc[:, 1])
self.trlog['U2All_mean'], self.trlog[
'U2All_interval'] = compute_confidence_interval(
generalized_few_shot_hmeanacc[:, 2])
self.trlog['HM_biased_mean'], self.trlog[
'HM_biased_nterval'] = compute_confidence_interval(
generalized_few_shot_hmeanacc[:, 3])
self.trlog['S2All_biased_mean'], self.trlog[
'S2All_biased_interval'] = compute_confidence_interval(
generalized_few_shot_hmeanacc[:, 4])
self.trlog['U2All_biased_mean'], self.trlog[
'U2All_biased_interval'] = compute_confidence_interval(
generalized_few_shot_hmeanacc[:, 5])
print('GFSL {}-way HM_mean w/o Bias {:.5f} + {:.5f}'.format(
args.eval_way, self.trlog['HM_mean'],
self.trlog['HM_interval']))
print('GFSL {}-way HM_mean w/ Bias {:.5f} + {:.5f}'.format(
args.eval_way, self.trlog['HM_biased_mean'],
self.trlog['HM_biased_nterval']))
print('GFSL {}-way S2All_mean w/o Bias {:.5f} + {:.5f}'.format(
args.eval_way, self.trlog['S2All_mean'],
self.trlog['S2All_interval']))
print('GFSL {}-way S2All_mean w/ Bias {:.5f} + {:.5f}'.format(
args.eval_way, self.trlog['S2All_biased_mean'],
self.trlog['S2All_biased_interval']))
print('GFSL {}-way U2All_mean w/o Bias {:.5f} + {:.5f}'.format(
args.eval_way, self.trlog['U2All_mean'],
self.trlog['U2All_interval']))
print('GFSL {}-way U2All_mean w/ Bias {:.5f} + {:.5f}'.format(
args.eval_way, self.trlog['U2All_biased_mean'],
self.trlog['U2All_biased_interval']))
if 'hmeanmap' in self.criteria:
self.trlog['HM_map_mean'], self.trlog[
'HM_map_interval'] = compute_confidence_interval(
generalized_few_shot_hmeanmap[:, 0])
self.trlog['S2All_map_mean'], self.trlog[
'S2All_map_interval'] = compute_confidence_interval(
generalized_few_shot_hmeanmap[:, 1])
self.trlog['U2All_map_mean'], self.trlog[
'U2All_map_interval'] = compute_confidence_interval(
generalized_few_shot_hmeanmap[:, 2])
self.trlog['HM_map_biased_mean'], self.trlog[
'HM_map_biased_nterval'] = compute_confidence_interval(
generalized_few_shot_hmeanmap[:, 3])
self.trlog['S2All_map_biased_mean'], self.trlog[
'S2All_map_biased_interval'] = compute_confidence_interval(
generalized_few_shot_hmeanmap[:, 4])
self.trlog['U2All_map_biased_mean'], self.trlog[
'U2All_map_biased_interval'] = compute_confidence_interval(
generalized_few_shot_hmeanmap[:, 5])
print('GFSL {}-way HM_map_mean w/o Bias {:.5f} + {:.5f}'.format(
args.eval_way, self.trlog['HM_map_mean'],
self.trlog['HM_map_interval']))
print('GFSL {}-way HM_map_mean w/ Bias {:.5f} + {:.5f}'.format(
args.eval_way, self.trlog['HM_map_biased_mean'],
self.trlog['HM_map_biased_nterval']))
print('GFSL {}-way S2All_map_mean w/o Bias {:.5f} + {:.5f}'.format(
args.eval_way, self.trlog['S2All_map_mean'],
self.trlog['S2All_map_interval']))
print('GFSL {}-way S2All_map_mean w/ Bias {:.5f} + {:.5f}'.format(
args.eval_way, self.trlog['S2All_map_biased_mean'],
self.trlog['S2All_map_biased_interval']))
print('GFSL {}-way U2All_map_mean w/o Bias {:.5f} + {:.5f}'.format(
args.eval_way, self.trlog['U2All_map_mean'],
self.trlog['U2All_map_interval']))
print('GFSL {}-way U2All_map_mean w/ Bias {:.5f} + {:.5f}'.format(
args.eval_way, self.trlog['U2All_map_biased_mean'],
self.trlog['U2All_map_biased_interval']))
if 'ausuc' in self.criteria:
self.trlog['AUSUC_mean'], self.trlog[
'AUSUC_interval'] = compute_confidence_interval(
generalized_few_shot_ausuc[:, 0])
print('GFSL {}-way AUSUC {:.5f} + {:.5f}'.format(
args.eval_way, self.trlog['AUSUC_mean'],
self.trlog['AUSUC_interval']))
if torch.cuda.is_available():
data, _ = [_.cuda() for _ in batch]
else:
data = batch[0]
data_emb = model(data, is_emb=True)
if args.centralize:
data_emb = data_emb - class_mean
if args.normalize:
data_emb = F.normalize(data_emb, dim=1, p=2)
split_index = args.way * args.shot
data_shot, data_query = data_emb[:split_index], data_emb[
split_index:]
SVM = LinearSVC(C=best_c,
multi_class='crammer_singer',
dual=False,
max_iter=5000).fit(data_shot.cpu().numpy(),
shot_label)
prediction = SVM.predict(data_query.cpu().numpy())
acc = np.mean(prediction == query_label)
test_acc_record[i - 1] = acc
# print('batch {}: {:.2f}({:.2f})'.format(i, ave_acc.item() * 100, acc * 100))
m, pm = compute_confidence_interval(test_acc_record)
ensemble_result.append('{:.4f} + {:.4f}'.format(m, pm))
print('{} way {} shot,Test acc={:.4f} + {:.4f}, best_gamma:{}'.
format(args.way, args.shot, m, pm, best_c))
print('ensemble result: {}'.format(','.join(ensemble_result)))
def open_evaluate(self, data_loader):
# restore model args
args = self.args
# evaluation mode
self.model.eval()
record = np.zeros((args.num_test_episodes, 4)) # loss and acc
label = torch.arange(args.eval_way, dtype=torch.int16).repeat(args.eval_query)
label = label.type(torch.LongTensor)
if torch.cuda.is_available():
label = label.cuda()
print('Evaluating ... best epoch {}, SnaTCHer={:.4f} + {:.4f} acc={:.4f} + {:.4f}'.format(
self.trlog['max_auc_epoch'],
self.trlog['max_auc'],
self.trlog['max_auc_interval'],
self.trlog['acc'],
self.trlog['acc_interval']))
with torch.no_grad():
for i, batch in enumerate(tqdm(data_loader)):
data, _ = [_.cuda() for _ in batch]
logits = self.para_model(data)
logits = logits.reshape([-1, args.eval_way + args.open_eval_way, args.way])
klogits = logits[:, :args.eval_way, :].reshape(-1, args.way)
ulogits = logits[:, args.eval_way:, :].reshape(-1, args.way)
loss = F.cross_entropy(klogits, label)
acc = count_acc(klogits, label)
""" Distance """
kdist = -(klogits.max(1)[0])
udist = -(ulogits.max(1)[0])
kdist = kdist.cpu().detach().numpy()
udist = udist.cpu().detach().numpy()
dist_auroc = calc_auroc(kdist, udist)
""" Snatcher """
with torch.no_grad():
instance_embs = self.para_model.instance_embs
support_idx = self.para_model.support_idx
query_idx = self.para_model.query_idx
support = instance_embs[support_idx.flatten()].view(*(support_idx.shape + (-1,)))
query = instance_embs[query_idx.flatten()].view(*(query_idx.shape + (-1,)))
emb_dim = support.shape[-1]
support = support[:, :, :args.way].contiguous()
# get mean of the support
bproto = support.mean(dim=1) # Ntask x NK x d
proto = self.para_model.slf_attn(bproto, bproto, bproto)
kquery = query[:, :, :args.way].contiguous()
uquery = query[:, :, args.way:].contiguous()
snatch_known = []
for j in range(75):
pproto = bproto.clone().detach()
""" Algorithm 1 Line 1 """
c = klogits.argmax(1)[j]
""" Algorithm 1 Line 2 """
pproto[0][c] = kquery.reshape(-1, emb_dim)[j]
""" Algorithm 1 Line 3 """
pproto = self.para_model.slf_attn(pproto, pproto, pproto)[0]
pdiff = (pproto - proto).pow(2).sum(-1).sum() / 64.0
""" pdiff: d_SnaTCHer in Algorithm 1 """
snatch_known.append(pdiff)
snatch_unknown = []
for j in range(ulogits.shape[0]):
pproto = bproto.clone().detach()
""" Algorithm 1 Line 1 """
c = ulogits.argmax(1)[j]
""" Algorithm 1 Line 2 """
pproto[0][c] = uquery.reshape(-1, emb_dim)[j]
""" Algorithm 1 Line 3 """
pproto = self.para_model.slf_attn(pproto, pproto, pproto)[0]
pdiff = (pproto - proto).pow(2).sum(-1).sum() / 64.0
""" pdiff: d_SnaTCHer in Algorithm 1 """
snatch_unknown.append(pdiff)
pkdiff = torch.stack(snatch_known)
pudiff = torch.stack(snatch_unknown)
pkdiff = pkdiff.cpu().detach().numpy()
pudiff = pudiff.cpu().detach().numpy()
snatch_auroc = calc_auroc(pkdiff, pudiff)
record[i - 1, 0] = loss.item()
record[i - 1, 1] = acc
record[i - 1, 2] = snatch_auroc
record[i - 1, 3] = dist_auroc
vl, _ = compute_confidence_interval(record[:, 0])
va, vap = compute_confidence_interval(record[:, 1])
auc_sna, auc_sna_p = compute_confidence_interval(record[:, 2])
auc_dist, auc_dist_p = compute_confidence_interval(record[:, 3])
print("acc: {:.4f} + {:.4f} Dist: {:.4f} + {:.4f} SnaTCHer: {:.4f} + {:.4f}" \
.format(va, vap, auc_dist, auc_dist_p, auc_sna, auc_sna_p))
# train mode
self.model.train()
if self.args.fix_BN:
self.model.encoder.eval()
return vl, va, vap, auc_sna, auc_sna_p
