evaluation

[LvGuangzu]

Hello, I see that you only have the mdice evaluation indicator in Train.py. Could you please share the code for other evaluation indicators during the training of the model?

def test(model, path, dataset):

data_path = os.path.join(path, dataset)
image_root = '{}/images/'.format(data_path)
gt_root = '{}/masks/'.format(data_path)
model.eval()
num1 = len(os.listdir(gt_root))
test_loader = test_dataset(image_root, gt_root, 352)
DSC = 0.0
for i in range(num1):
    image, gt, name = test_loader.load_data()
    gt = np.asarray(gt, np.float32)
    gt /= (gt.max() + 1e-8)
    image = image.cuda()

    res, res1  = model(image)
    # eval Dice
    res = F.upsample(res + res1 , size=gt.shape, mode='bilinear', align_corners=False)
    res = res.sigmoid().data.cpu().numpy().squeeze()
    res = (res - res.min()) / (res.max() - res.min() + 1e-8)
    input = res
    target = np.array(gt)
    N = gt.shape
    smooth = 1
    input_flat = np.reshape(input, (-1))
    target_flat = np.reshape(target, (-1))
    intersection = (input_flat * target_flat)
    dice = (2 * intersection.sum() + smooth) / (input.sum() + target.sum() + smooth)
    dice = '{:.4f}'.format(dice)
    dice = float(dice)
    DSC = DSC + dice

return DSC / num1