def test_fn(model):
top1, top5, losses = classifier.test(eval_data_loader, model,
cc.criterion,
[cc.tflogger, cc.pylogger], None,
args)
# pdb.set_trace()
return top1, top5, losses
def eval_fn(model):
if memoized_data_loader:
loss = 0
for images, targets in memoized_data_loader:
outputs = model(images)
loss += criterion(outputs, targets).item()
loss = loss / len(memoized_data_loader)
else:
_, _, loss = classifier.test(eval_data_loader, model, criterion, loggers, None, args)
return loss
def calib_eval_fn(model):
classifier.test(calib_data_loader, model, cc.criterion, [], None, args)
def test_fn(model):
top1, top5, loss = classifier.test(test_data_loader, model, criterion, loggers, None, args)
return OrderedDict([('top-1', top1), ('top-5', top5), ('loss', loss)])
def test_fn(model):
return classifier.test(test_data_loader, model, cc.criterion,
[cc.tflogger, cc.pylogger], None, args)