Utils.py 文件源码

def per_class_acc(predictions, label_tensor):
    labels = label_tensor
    size = predictions.shape[0]
    num_class = predictions.shape[3]
    hist = np.zeros((num_class, num_class))
    for i in range(size):
      hist += fast_hist(labels[i].flatten(), predictions[i].argmax(2).flatten(), num_class)
    acc_total = np.diag(hist).sum() / hist.sum()
    print ('accuracy = %f'%np.nanmean(acc_total))
    iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
    print ('mean IU  = %f'%np.nanmean(iu))
    for ii in range(num_class):
        if float(hist.sum(1)[ii]) == 0:
          acc = 0.0
        else:
          acc = np.diag(hist)[ii] / float(hist.sum(1)[ii])
        print("    class # %d accuracy = %f "%(ii,acc))