metric.py

#https://www.kaggle.com/c/tgs-salt-identification-challenge/discussion/67693
def iou_metric(outputs, labels,logits=True):
    outputs,labels= size_correct(outputs,labels)
    outputs= (outputs>0).detach().cpu().numpy() if logits else (outputs>.5).detach().cpu().numpy() 
    labels= labels.detach().cpu().numpy()
    batch_size = outputs.shape[0]
    metric = 0.0
    for batch in range(batch_size):
        t, p = labels[batch], outputs[batch]
        true = np.sum(t)
        pred = np.sum(p)
        
        # deal with empty mask first
        if true == 0:
            metric += (pred == 0)
            continue
        
        # non empty mask case.  Union is never empty 
        # hence it is safe to divide by its number of pixels
        intersection = np.sum(t * p)
        union = true + pred - intersection
        iou = intersection / union
        
        # iou metrric is a stepwise approximation of the real iou over 0.5
        iou = np.floor(max(0, (iou - 0.45)*20)) / 10
        iou= np.clip(iou,0,1.0)
        
        metric += iou
        
    # teake the average over all images in batch
    metric /= batch_size
    return metric