def test(exp_name):
print('loading data......')
test_data = getattr(datasets, opt.dataset)(opt.root,
opt.test_data_dir,
mode='test',
size=opt.testsize)
test_dataloader = DataLoader(test_data,
batch_size=1,
shuffle=False,
num_workers=opt.num_workers)
total_batch = int(len(test_data) / 1)
model, _, _ = generate_model(opt)
model.eval()
# metrics_logger initialization
metrics = Metrics([
'recall', 'specificity', 'precision', 'F1', 'F2', 'ACC_overall',
'IoU_poly', 'IoU_bg', 'IoU_mean'
])
logger = get_logger('./results/' + exp_name + '.log')
with torch.no_grad():
for i, data in enumerate(test_dataloader):
img, gt = data['image'], data['label']
if opt.use_gpu:
img = img.cuda()
gt = gt.cuda()
output = model(img)
_recall, _specificity, _precision, _F1, _F2, \
_ACC_overall, _IoU_poly, _IoU_bg, _IoU_mean = evaluate(output, gt)
metrics.update(recall=_recall,
specificity=_specificity,
precision=_precision,
F1=_F1,
F2=_F2,
ACC_overall=_ACC_overall,
IoU_poly=_IoU_poly,
IoU_bg=_IoU_bg,
IoU_mean=_IoU_mean)
metrics_result = metrics.mean(total_batch)
print("Test Result:")
logger.info(
'recall: %.4f, specificity: %.4f, precision: %.4f, F1: %.4f, F2: %.4f, '
'ACC_overall: %.4f, IoU_poly: %.4f, IoU_bg: %.4f, IoU_mean: %.4f' %
(metrics_result['recall'], metrics_result['specificity'],
metrics_result['precision'], metrics_result['F1'],
metrics_result['F2'], metrics_result['ACC_overall'],
metrics_result['IoU_poly'], metrics_result['IoU_bg'],
metrics_result['IoU_mean']))
def valid(model, valid_dataloader, total_batch):
model.eval()
# Metrics_logger initialization
metrics = Metrics([
'recall', 'specificity', 'precision', 'F1', 'F2', 'ACC_overall',
'IoU_poly', 'IoU_bg', 'IoU_mean'
])
with torch.no_grad():
bar = tqdm(enumerate(valid_dataloader), total=total_batch)
for i, data in bar:
img, gt = data['image'], data['label']
if opt.use_gpu:
img = img.cuda()
gt = gt.cuda()
output = model(img)
_recall, _specificity, _precision, _F1, _F2, \
_ACC_overall, _IoU_poly, _IoU_bg, _IoU_mean = evaluate(output, gt)
metrics.update(recall=_recall,
specificity=_specificity,
precision=_precision,
F1=_F1,
F2=_F2,
ACC_overall=_ACC_overall,
IoU_poly=_IoU_poly,
IoU_bg=_IoU_bg,
IoU_mean=_IoU_mean)
metrics_result = metrics.mean(total_batch)
model.train()
return metrics_result