def validate(self, valLoader, lr=None, mode='val'):
if mode == 'test':
nEpisode = self.nEpisode
self.logger.info(
'\n\nTest mode: randomly sample {:d} episodes...'.format(
nEpisode))
elif mode == 'val':
nEpisode = len(valLoader)
self.logger.info(
'\n\nValidation mode: pre-defined {:d} episodes...'.format(
nEpisode))
valLoader = iter(valLoader)
else:
raise ValueError('mode is wrong!')
episodeAccLog = []
top1 = AverageMeter()
self.netFeat.eval()
#self.netSIB.eval() # set train mode, since updating bn helps to estimate better gradient
if lr is None:
lr = self.optimizer.param_groups[0]['lr']
#for batchIdx, data in enumerate(valLoader):
for batchIdx in range(nEpisode):
data = valLoader.getEpisode() if mode == 'test' else next(
valLoader)
data = to_device(data, self.device)
SupportTensor, SupportLabel, QueryTensor, QueryLabel = \
data['SupportTensor'].squeeze(0), data['SupportLabel'].squeeze(0), \
data['QueryTensor'].squeeze(0), data['QueryLabel'].squeeze(0)
with torch.no_grad():
SupportFeat, QueryFeat = self.netFeat(
SupportTensor), self.netFeat(QueryTensor)
SupportFeat, QueryFeat, SupportLabel = \
SupportFeat.unsqueeze(0), QueryFeat.unsqueeze(0), SupportLabel.unsqueeze(0)
clsScore = self.netSIB(lr, SupportFeat, SupportLabel, QueryFeat)
clsScore = clsScore.view(QueryFeat.size()[0] * QueryFeat.size()[1],
-1)
QueryLabel = QueryLabel.view(-1)
acc1 = accuracy(clsScore, QueryLabel, topk=(1, ))
top1.update(acc1[0].item(), clsScore.size()[0])
msg = 'Top1: {:.3f}%'.format(top1.avg)
progress_bar(batchIdx, nEpisode, msg)
episodeAccLog.append(acc1[0].item())
mean, ci95 = getCi(episodeAccLog)
self.logger.info(
'Final Perf with 95% confidence intervals: {:.3f}%, {:.3f}%'.
format(mean, ci95))
return mean, ci95
def validate(self, valLoader, lr=None):
nEpisode = self.nEpisode
self.logger.info(
'\n\nTest mode: randomly sample {:d} episodes...'.format(nEpisode))
episodeAccLog = []
top1 = AverageMeter()
self.netFeat.eval()
if lr is None:
lr = self.optimizer.param_groups[0]['lr']
for batchIdx in range(nEpisode):
data = valLoader.getEpisode()
data = to_device(data, self.device)
SupportTensor, SupportLabel, QueryTensor, QueryLabel = \
data['SupportTensor'].squeeze(0), data['SupportLabel'].squeeze(0), \
data['QueryTensor'].squeeze(0), data['QueryLabel'].squeeze(0)
with torch.no_grad():
SupportFeat, QueryFeat = self.netFeat(
SupportTensor), self.netFeat(QueryTensor)
SupportFeat, QueryFeat, SupportLabel = \
SupportFeat.unsqueeze(0), QueryFeat.unsqueeze(0), SupportLabel.unsqueeze(0)
clsScore = self.netSIB(SupportFeat, SupportLabel, QueryFeat, lr)
clsScore = clsScore.view(QueryFeat.shape[0] * QueryFeat.shape[1],
-1)
QueryLabel = QueryLabel.view(-1)
acc1 = accuracy(clsScore, QueryLabel, topk=(1, ))
top1.update(acc1[0].item(), clsScore.shape[0])
msg = 'Top1: {:.3f}%'.format(top1.avg)
progress_bar(batchIdx, nEpisode, msg)
episodeAccLog.append(acc1[0].item())
mean, ci95 = getCi(episodeAccLog)
self.logger.info(
'Final Perf with 95% confidence intervals: {:.3f}%, {:.3f}%'.
format(mean, ci95))
return mean, ci95
def validate(self, valLoader, lr=None, mode='val'):
"""
Run one epoch on val-set.
:param valLoader: the dataloader of val-set
:type valLoader: class `ValLoader`
:param float lr: learning rate for synthetic GD
:param string mode: 'val' or 'train'
"""
if mode == 'test':
nEpisode = self.nEpisode
self.logger.info(
'\n\nTest mode: randomly sample {:d} episodes...'.format(
nEpisode))
elif mode == 'val':
nEpisode = len(valLoader)
self.logger.info(
'\n\nValidation mode: pre-defined {:d} episodes...'.format(
nEpisode))
valLoader = iter(valLoader)
else:
raise ValueError('mode is wrong!')
episodeAccLog = []
top1 = AverageMeter()
self.netFeat.eval()
#self.netSIB.eval() # set train mode, since updating bn helps to estimate better gradient
if lr is None:
lr = self.optimizer.param_groups[0]['lr']
#for batchIdx, data in enumerate(valLoader):
# nEpisode = 1
for batchIdx in range(nEpisode):
data = valLoader.getEpisode() if mode == 'test' else next(
valLoader)
data = to_device(data, self.device)
SupportTensor, SupportLabel, QueryTensor, QueryLabel = \
data['SupportTensor'].squeeze(0), data['SupportLabel'].squeeze(0), \
data['QueryTensor'].squeeze(0), data['QueryLabel'].squeeze(0)
with torch.no_grad():
# SupportFeat, QueryFeat = self.netFeat(SupportTensor), self.netFeat(QueryTensor)
SupportFeat, QueryFeat = self.pretrain.get_features(
SupportTensor), self.pretrain.get_features(QueryTensor)
SupportFeat, QueryFeat, SupportLabel = \
SupportFeat.unsqueeze(0), QueryFeat.unsqueeze(0), SupportLabel.unsqueeze(0)
clsScore = self.netSIB(SupportFeat, SupportLabel, QueryFeat, lr)
clsScore = clsScore.view(QueryFeat.shape[0] * QueryFeat.shape[1],
-1)
# Inductive
'''
clsScore = torch.zeros(QueryFeat.shape[1], 5).cuda()
for i in range(QueryFeat.shape[1]):
singleScore = self.netSIB(SupportFeat, SupportLabel, QueryFeat[:, i, :].unsqueeze(1), lr)
clsScore[i] = singleScore[0][0]
'''
QueryLabel = QueryLabel.view(-1)
if self.davg:
# diff_scores = self.calc_diff_scores(self.pretrain, SupportFeat.squeeze(0), QueryFeat.squeeze(0), SupportLabel.squeeze(0), QueryLabel) # cosine similarity
diff_scores = self._evaluate_hardness_logodd(
self.pretrain,
SupportFeat.squeeze(0), QueryFeat.squeeze(0),
SupportLabel.squeeze(0), QueryLabel) # logodd
else:
diff_scores = None
acc1 = accuracy(clsScore,
QueryLabel,
topk=(1, ),
diff_scores=diff_scores)
top1.update(acc1[0].item(), clsScore.shape[0])
msg = 'Top1: {:.3f}%'.format(top1.avg)
progress_bar(batchIdx, nEpisode, msg)
episodeAccLog.append(acc1[0].item())
mean, ci95 = getCi(episodeAccLog)
msg = 'Final Perf with 95% confidence intervals: {:.3f}%, {:.3f}%'.format(
mean, ci95)
self.logger.info(msg)
self.write_output_message(msg)
return mean, ci95
def validate(self, valLoader, mode='val'):
if mode == 'test':
nEpisode = self.nEpisode
self.logger.info(
'\n\nTest mode: randomly sample {:d} episodes...'.format(
nEpisode))
elif mode == 'val':
nEpisode = len(valLoader)
self.logger.info(
'\n\nValidation mode: pre-defined {:d} episodes...'.format(
nEpisode))
valLoader = iter(valLoader)
else:
raise ValueError('mode is wrong!')
episodeAccLog = []
top1 = AverageMeter()
self.netFeat.eval()
self.netRefine.eval()
self.netClassifier.eval()
#for batchIdx, data in enumerate(valLoader):
for batchIdx in range(nEpisode):
data = valLoader.getEpisode() if mode == 'test' else next(
valLoader)
data = to_device(data, self.device)
SupportTensor, SupportLabel, QueryTensor, QueryLabel = \
data['SupportTensor'].squeeze(0), data['SupportLabel'].squeeze(0), \
data['QueryTensor'].squeeze(0), data['QueryLabel'].squeeze(0)
with torch.no_grad():
SupportFeat, QueryFeat = self.netFeat(
SupportTensor), self.netFeat(QueryTensor)
SupportFeat, QueryFeat, SupportLabel = \
SupportFeat.unsqueeze(0), QueryFeat.unsqueeze(0), SupportLabel.unsqueeze(0)
nbSupport, nbQuery = SupportFeat.size()[1], QueryFeat.size()[1]
feat = torch.cat((SupportFeat, QueryFeat), dim=1)
refine_feat = self.netRefine(feat)
refine_feat = feat + refine_feat
refine_support, refine_query = refine_feat.narrow(
1, 0,
nbSupport), refine_feat.narrow(1, nbSupport, nbQuery)
clsScore = self.netClassifier(refine_support, SupportLabel,
refine_query)
clsScore = clsScore.squeeze(0)
QueryLabel = QueryLabel.view(-1)
acc1 = accuracy(clsScore, QueryLabel, topk=(1, ))
top1.update(acc1[0].item(), clsScore.size()[0])
msg = 'Top1: {:.3f}%'.format(top1.avg)
progress_bar(batchIdx, nEpisode, msg)
episodeAccLog.append(acc1[0].item())
mean, ci95 = getCi(episodeAccLog)
self.logger.info(
'Final Perf with 95% confidence intervals: {:.3f}%, {:.3f}%'.
format(mean, ci95))
self.netRefine.train()
self.netClassifier.train()
return mean, ci95