I am using the following code and I got the iou_score, but I want to evaluate it by:
Precision & Recall
such as this one:
FP, FN, TP, TN = numeric_score(prediction, groundtruth)
N = FP + FN + TP + TN
accuracy = np.divide(TP + TN, N)
return accuracy * 100.0`
how can I add them in , or just print the Dice?
def iou_score(output, target):
smooth = 1e-5
if torch.is_tensor(output):
output = torch.sigmoid(output).data.cpu().numpy()
if torch.is_tensor(target):
target = target.data.cpu().numpy()
output_ = output > 0.5
target_ = target > 0.5
intersection = (output_ & target_).sum()
union = (output_ | target_).sum()
return (intersection + smooth) / (union + smooth)
def dice_coef(output, target):
smooth = 1e-5
output = torch.sigmoid(output).view(-1).data.cpu().numpy()
target = target.view(-1).data.cpu().numpy()
intersection = (output * target).sum()
return (2. * intersection + smooth) / \
(output.sum() + target.sum() + smooth)
iou = iou_score(output, target)
avg_meter.update(iou, input.size(0))
output = torch.sigmoid(output).cpu().numpy()
for i in range(len(output)):
for c in range(config['num_classes']):
cv2.imwrite(os.path.join('outputs', config['name'], str(c), meta['img_id'][i] + '.jpg'),
(output[i, c] * 255).astype('uint8'))
print('IoU: %.4f' % avg_meter.avg)
torch.cuda.empty_cache()
https://github.com/4uiiurz1/pytorch-nested-unet
With reagrds, any help would be so grateful!