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Keras训练网络过程中需要实时观察性能，mean iou不是keras自带的评估函数，tf的又觉得不好用，自己写了一个，经过测试没有问题，本文记录自定义keras mean iou评估的实现方法。
计算 IoU
用numpy计算的，作为IoU的ground truth用作测试使用：
def iou_numpy(y_true,y_pred):
intersection = np.sum(np.multiply(y_true.astype('bool'),y_pred == 1))
union = np.sum((y_true.astype('bool')+y_pred.astype('bool'))>0)
return intersection/union
复制代码keras metric IoU
def iou_keras(y_true, y_pred):
"""
Return the Intersection over Union (IoU).
Args:
y_true: the expected y values as a one-hot
y_pred: the predicted y values as a one-hot or softmax output
Returns:
the IoU for the given label
"""
label = 1
# extract the label values using the argmax operator then
# calculate equality of the predictions and truths to the label
y_true = K.cast(K.equal(y_true, label), K.floatx())
y_pred = K.cast(K.equal(y_pred, label), K.floatx())
# calculate the |intersection| (AND) of the labels
intersection = K.sum(y_true * y_pred)
# calculate the |union| (OR) of the labels
union = K.sum(y_true) + K.sum(y_pred) - intersection
# avoid divide by zero - if the union is zero, return 1
# otherwise, return the intersection over union
return K.switch(K.equal(union, 0), 1.0, intersection / union)
复制代码计算 mean IoU
mean IoU 简便起见，选取 (0,1,0.05) 作为不同的IoU阈值，计算平均IoU
numpy 真实值计算
def mean_iou_numpy(y_true,y_pred):
iou_list = []
for thre in list(np.arange(0.0000001,0.99,0.05)):
y_pred_temp = y_pred >= thre
iou = iou_numpy(y_true, y_pred_temp)
iou_list.append(iou)
return np.mean(iou_list)
复制代码Keras mean IoU
def mean_iou_keras(y_true, y_pred):
"""
Return the mean Intersection over Union (IoU).
Args:
y_true: the expected y values as a one-hot
y_pred: the predicted y values as a one-hot or softmax output
Returns:
the mean IoU
"""
label = 1
# extract the label values using the argmax operator then
# calculate equality of the predictions and truths to the label
y_true = K.cast(K.equal(y_true, label), K.floatx())
mean_iou = K.variable(0)
thre_list = list(np.arange(0.0000001,0.99,0.05))
for thre in thre_list:
y_pred_temp = K.cast(y_pred >= thre, K.floatx())
y_pred_temp = K.cast(K.equal(y_pred_temp, label), K.floatx())
# calculate the |intersection| (AND) of the labels
intersection = K.sum(y_true * y_pred_temp)
# calculate the |union| (OR) of the labels
union = K.sum(y_true) + K.sum(y_pred_temp) - intersection
iou = K.switch(K.equal(union, 0), 1.0, intersection / union)
mean_iou = mean_iou + iou
return mean_iou / len(thre_list)
复制代码测试
## 随机生成预测值
y_true_np = np.ones([10,10])
y_pred_np = np.random.rand(10,10)
## 真实IoU值
print(f' iou : {iou_numpy(y_true_np, y_pred_np)}')
print(f' mean_iou_numpy : {mean_iou_numpy(y_true_np, y_pred_np)}')
y_true = tf.Variable(y_true_np)
y_pred = tf.Variable(y_pred_np)
## 计算节点
iou_res = iou_keras (y_true, y_pred)
m_iou_res = mean_iou_keras (y_true, y_pred)
## 变量初始化
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init_op)
## 由于存在误差，结果在0.0000001范围内即可认为相同
result = sess.run(iou_res)
print(f'result : {result} \nsame with ground truth: {abs(iou_numpy(y_true_np, y_pred_np) - result)< 0.0000001}')
result = sess.run(m_iou_res)
print(f'result : {result} \nsame with ground truth: {abs(mean_iou_numpy(y_true_np, y_pred_np) - result) < 0.0000001}')
复制代码输出：
iou : 0.0
mean_iou_numpy : 0.5295
result : 0.0
same with ground truth: True
result : 0.5295000076293945
same with ground truth: True
复制代码