def run_test(dataset=None, epoch=-1, phase="test"):
print('Running Test')
opt = TestOptions().parse()
opt.serial_batches = True # no shuffle
if dataset is None:
dataset = CreateDataset(opt)
# be consistent with training
dataset = torch.utils.data.Subset(dataset, range(len(dataset)))
dataset = DataLoader(dataset, opt)
else:
opt.nclasses = len(dataset.dataset.dataset.classes)
opt.input_nc = dataset.dataset.dataset.opt.input_nc
dataset.dataset.dataset.opt.num_aug = 1
# dataset.dataset.dataset.opt.is_train = False
model = ClassifierModel(opt)
writer = Writer(opt)
# test
writer.reset_counter()
for i, data in enumerate(dataset):
model.set_input(data, epoch)
loss, (prec1, prec5), y_pred, y_true = model.test()
writer.update_counter(loss, prec1, prec5, y_pred, y_true)
if epoch == -1:
writer.plot_summary("val", dataset.dataset.dataset.classes)
else:
writer.plot(epoch, phase, dataset.dataset.dataset.classes)
return writer.statistics.top1.avg
