def test(self):
epoch = self.scheduler.last_epoch + 1
self.ckp.write_log('Evaluation:')
self.ckp.add_log(torch.zeros(1, len(self.scale)), False)
self.model.eval()
# We can use custom forward function
def _test_forward(x, scale):
if self.args.self_ensemble:
return utility.x8_forward(x, self.model, self.args.precision)
elif self.args.chop_forward:
return utility.chop_forward(x, self.model, scale)
else:
return self.model(x)
timer_test = utility.timer()
set_name = self.args.data_test
for idx_scale, scale in enumerate(self.scale):
eval_acc = 0
self._scale_change(idx_scale, self.loader_test)
for idx_img, (lr, hr, _) in enumerate(self.loader_test):
no_eval = isinstance(hr[0], torch._six.string_classes)
if no_eval:
lr = self.prepare([lr], volatile=True)[0]
filename = hr[0]
else:
lr, hr = self.prepare([lr, hr], volatile=True)
filename = idx_img + 1
rgb_range = self.args.rgb_range
sr = _test_forward(lr, scale)
sr = utility.quantize(sr, rgb_range)
if no_eval:
save_list = [sr]
else:
eval_acc += utility.calc_PSNR(sr, hr.div(rgb_range),
set_name, scale)
save_list = [sr, lr.div(rgb_range), hr.div(rgb_range)]
if self.args.save_results:
self.ckp.save_results(filename, save_list, scale)
self.ckp.log_test[-1, idx_scale] = eval_acc / len(self.loader_test)
best = self.ckp.log_test.max(0)
performance = 'PSNR: {:.3f}'.format(self.ckp.log_test[-1,
idx_scale])
self.ckp.write_log(
'[{} x{}]\t{} (Best: {:.3f} from epoch {})'.format(
set_name, scale, performance, best[0][idx_scale],
best[1][idx_scale] + 1))
is_best = (best[1][0] + 1 == epoch)
# self.ckp.write_log(
# 'Time: {:.2f}s\n'.format(timer_test.toc()), refresh=True
# )
self.ckp.save(self, epoch, is_best=is_best)
def test(self, test_only=False, starttime=0):
epoch = self.scheduler.last_epoch + 1
self.ckp.write_log('Evaluation:')
self.ckp.add_log(torch.zeros(1, len(self.scale)), False)
self.model.eval()
# We can use custom forward function
def _test_forward(x, scale):
if self.args.self_ensemble:
return utility.x8_forward(x, self.model, self.args.precision)
elif self.args.chop_forward:
return utility.chop_forward(x, self.model, scale)
else:
return self.model(x)
set_name = self.args.data_test
for idx_scale, scale in enumerate(self.scale):
eval_acc = 0
self._scale_change(idx_scale, self.loader_test)
for idx_img, (lr, hr, _) in enumerate(self.loader_test):
no_eval = isinstance(hr[0], torch._six.string_classes)
if no_eval:
lr = self.prepare([lr])[0]
filename = hr[0]
else:
lr, hr = self.prepare([lr, hr])
filename = idx_img + 1
rgb_range = self.args.rgb_range
timer_test = utility.timer()
sr = _test_forward(lr, scale)
self.test_time[1] += timer_test.toc()
self.test_time[0] += 1
sr = utility.quantize(sr, rgb_range)
if no_eval:
save_list = [sr]
else:
if self.args.loss == '1*SSIM':
eval_acc += pytorch_ssim.ssim(
sr, hr.div(rgb_range)).item()
else:
eval_acc += utility.calc_PSNR(sr, hr.div(rgb_range),
set_name, scale)
save_list = [sr, lr.div(rgb_range), hr.div(rgb_range)]
if self.args.save_results:
self.ckp.save_results(filename, save_list, scale)
self.ckp.log_test[-1, idx_scale] = eval_acc / len(self.loader_test)
best = self.ckp.log_test.max(0)
if self.args.loss == '1*SSIM':
performance = 'SSIM: {:.3f}'.format(
self.ckp.log_test[-1, idx_scale])
else:
performance = 'PSNR: {:.3f}'.format(
self.ckp.log_test[-1, idx_scale])
self.ckp.write_log(
'[{} x{}]\t{} (Best: {:.3f} from epoch {})'.format(
set_name, scale, performance, best[0][idx_scale],
best[1][idx_scale] + 1))
is_best = (best[1][0] + 1 == epoch)
if test_only:
self.ckp.write_log(
'Total time: {:.3f}s\r\nAvg. time: {:.3f}s\n'.format(
self.test_time[1], self.test_time[1] / self.test_time[0]),
refresh=True)
elif starttime != 0:
now = datetime.datetime.now()
elapsed = now - starttime
est = now + (elapsed / epoch) * (self.args.epochs - epoch)
self.ckp.write_log("Elapsed: {}\n".format(str(elapsed)))
print('Will finish: {}\n'.format(
est.strftime('%d-%m-%Y-%H:%M:%S')))
self.ckp.save(self, epoch, is_best=is_best)