def validate(val_loader, net, epoch, print_pr=False):
"""
validation for one epoch on the val set
"""
batch_time = meter.TimeMeter(True)
data_time = meter.TimeMeter(True)
prec = meter.ClassErrorMeter(topk=[1], accuracy=True)
retrieval_map = meter.RetrievalMAPMeter()
# testing mode
net.eval()
total_seen_class = [0 for _ in range(40)]
total_right_class = [0 for _ in range(40)]
for i, (views, pcs, labels) in enumerate(val_loader):
batch_time.reset()
views = views.to(device=config.device)
pcs = pcs.to(device=config.device)
labels = labels.to(device=config.device)
preds, fts = net(pcs, views, get_fea=True) # bz x C x H x W
# prec.add(preds.data, labels.data)
prec.add(preds.data, labels.data)
retrieval_map.add(fts.detach() / torch.norm(fts.detach(), 2, 1, True),
labels.detach())
for j in range(views.size(0)):
total_seen_class[labels.data[j]] += 1
total_right_class[labels.data[j]] += (np.argmax(
preds.data, 1)[j] == labels.cpu()[j])
if i % config.print_freq == 0:
print(
f'Epoch: [{epoch}][{i}/{len(val_loader)}]\t'
f'Batch Time {batch_time.value():.3f}\t'
f'Epoch Time {data_time.value():.3f}\t'
f'Prec@1 {prec.value(1):.3f}\t'
f'Mean Class accuracy {(np.mean(np.array(total_right_class)/np.array(total_seen_class,dtype=np.float))):.3f}'
)
mAP = retrieval_map.mAP()
print(f' instance accuracy at epoch {epoch}: {prec.value(1)} ')
print(
f' mean class accuracy at epoch {epoch}: {(np.mean(np.array(total_right_class)/np.array(total_seen_class,dtype=np.float)))} '
)
print(f' map at epoch {epoch}: {mAP} ')
if print_pr:
print(f'pr: {retrieval_map.pr()}')
return prec.value(1), mAP
def validate(val_loader, net):
"""
validation for one epoch on the val set
"""
batch_time = meter.TimeMeter(True)
data_time = meter.TimeMeter(True)
prec = meter.ClassErrorMeter(topk=[1], accuracy=True)
retrieval = meter.RetrievalMAPMeter()
ft_all, lbl_all = None, None
# testing mode
net.eval()
for i, (views, labels) in enumerate(val_loader):
batch_time.reset()
# bz x 12 x 3 x 224 x 224
views = views.to(device=config.device)
labels = labels.to(device=config.device)
preds, fts = net(views, get_ft=True) # bz x C x H x W
prec.add(preds.detach(), labels.detach())
retrieval.add(fts.detach(), labels.detach())
# ft_all = append(ft_all, fts.detach())
# lbl_all = append(lbl_all, labels.detach(), flaten=True)
if i % config.print_freq == 0:
print(f'[{i}/{len(val_loader)}]\t'
f'Batch Time {batch_time.value():.3f}\t'
f'Epoch Time {data_time.value():.3f}\t'
f'Prec@1 {prec.value(1):.3f}\t')
# mAP = cal_map(ft_all, lbl_all)
mAP = retrieval.mAP()
print(f'mean class accuracy : {prec.value(1)} ')
print(f'Retrieval mAP : {mAP} ')
return prec.value(1), mAP