def test(model: BaseModel, dataset, device, tracker: BaseTracker, checkpoint: ModelCheckpoint, log):
model.eval()
tracker.reset("test")
loader = dataset.test_dataloader()
with Ctq(loader) as tq_test_loader:
for data in tq_test_loader:
data = data.to(device)
with torch.no_grad():
model.set_input(data)
model.forward()
tracker.track(model)
tq_test_loader.set_postfix(**tracker.get_metrics(), color=COLORS.TEST_COLOR)
metrics = tracker.publish()
tracker.print_summary()
checkpoint.save_best_models_under_current_metrics(model, metrics)
for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
epoch_start_time = time.time()
epoch_iter = 0
iter_start_time = 0
for i, data in enumerate(dataset):
total_steps += opt.batchSize
epoch_iter += opt.batchSize
model.set_input(data)
model.forward()
if total_steps % opt.display_freq == 0:
save_result = total_steps % opt.update_html_freq == 0
visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
model.optimize_parameters()
if total_steps % opt.print_freq == 0:
errors = model.get_current_errors()
t = (time.time() - iter_start_time) / opt.batchSize
visualizer.print_current_errors(epoch, epoch_iter, errors, t)
if opt.display_id > 0:
visualizer.plot_current_errors(epoch, float(epoch_iter)/dataset_size, opt, errors)
