Question

我想将函数tf.metrics.mean_iou用于FCN进行语义分段。它只有在IoU之前计算混淆矩阵时才有效，否则返回0.

这是我的例子：

此示例返回正确的值0.66071427

import tensorflow as tf
import numpy as np

y_pred0 = np.array([   [ [[0.9,0.1],[0.9,0.1],[0.9,0.1],[0.9,0.1]], [[0.2,0.8],[0.2,0.8],[0.2,0.8],[0.9,0.1]], [[0.9,0.1],[0.9,0.1],[0.2,0.8],[0.9,0.1]], [[0.9,0.1],[0.9,0.1],[0.2,0.8],[0.9,0.1]] ],   [ [[0.9,0.1],[0.9,0.1],[0.9,0.1],[0.9,0.1]], [[0.2,0.8],[0.2,0.8],[0.2,0.8],[0.9,0.1]], [[0.9,0.1],[0.9,0.1],[0.2,0.8],[0.9,0.1]], [[0.9,0.1],[0.9,0.1],[0.2,0.8],[0.9,0.1]] ]    ])
y_pred1 = tf.constant(y_pred0)
y_pred2 = tf.argmax(y_pred1, axis=3)

y_label = np.array([[[1,0,1,0],[1,0,1,0],[0,0,1,0],[0,0,1,0]], [[1,0,1,0],[1,0,1,0],[0,0,1,0],[0,0,1,0]]])
y_label2 = tf.constant(y_label)

iou, conf_mat = tf.metrics.mean_iou(y_label2, y_pred2, num_classes=2)

sess = tf.Session()
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())

sess.run(conf_mat)
res = sess.run(iou)

print(res)

此示例返回0

import tensorflow as tf
import numpy as np

def intersection_over_union(prediction, labels):
    pred = tf.argmax(prediction, axis=3)
    labl = tf.constant(labels)
    iou, conf_mat = tf.metrics.mean_iou(labl, pred, num_classes=2)
    return iou

y_pred0 = np.array([   [ [[0.9,0.1],[0.9,0.1],[0.9,0.1],[0.9,0.1]], [[0.2,0.8],[0.2,0.8],[0.2,0.8],[0.9,0.1]], [[0.9,0.1],[0.9,0.1],[0.2,0.8],[0.9,0.1]], [[0.9,0.1],[0.9,0.1],[0.2,0.8],[0.9,0.1]] ],   [ [[0.9,0.1],[0.9,0.1],[0.9,0.1],[0.9,0.1]], [[0.2,0.8],[0.2,0.8],[0.2,0.8],[0.9,0.1]], [[0.9,0.1],[0.9,0.1],[0.2,0.8],[0.9,0.1]], [[0.9,0.1],[0.9,0.1],[0.2,0.8],[0.9,0.1]] ]    ])
y_pred1 = tf.constant(y_pred0)

y_label = np.array([[[1,0,1,0],[1,0,1,0],[0,0,1,0],[0,0,1,0]], [[1,0,1,0],[1,0,1,0],[0,0,1,0],[0,0,1,0]]])

mean__iou = intersection_over_union(y_pred1, y_label)

sess = tf.Session()
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())

res = sess.run(mean__iou)

print(res)

有一个函数计算平均IoU而不初始化其中的所有变量将是非常好的。有没有办法解决第二个例子？我认为问题在于同时计算IoU和混淆矩阵，我没有找到另一种方法，因为通过Session（）单独运行它们。

由于

Answer 1

在从张量获取iou值之前，您需要运行tf.metrics.mean_iou返回的更新操作。

这是固定代码：

import tensorflow as tf
import numpy as np

def intersection_over_union(prediction, labels):
    pred = tf.argmax(prediction, axis=3)
    labl = tf.constant(labels)
    iou, conf_mat = tf.metrics.mean_iou(labl, pred, num_classes=2)
    return iou, conf_mat

y_pred0 = np.array([   [ [[0.9,0.1],[0.9,0.1],[0.9,0.1],[0.9,0.1]], [[0.2,0.8],[0.2,0.8],[0.2,0.8],[0.9,0.1]], [[0.9,0.1],[0.9,0.1],[0.2,0.8],[0.9,0.1]], [[0.9,0.1],[0.9,0.1],[0.2,0.8],[0.9,0.1]] ],   [ [[0.9,0.1],[0.9,0.1],[0.9,0.1],[0.9,0.1]], [[0.2,0.8],[0.2,0.8],[0.2,0.8],[0.9,0.1]], [[0.9,0.1],[0.9,0.1],[0.2,0.8],[0.9,0.1]], [[0.9,0.1],[0.9,0.1],[0.2,0.8],[0.9,0.1]] ]    ])
y_pred1 = tf.constant(y_pred0)

y_label = np.array([[[1,0,1,0],[1,0,1,0],[0,0,1,0],[0,0,1,0]], [[1,0,1,0],[1,0,1,0],[0,0,1,0],[0,0,1,0]]])

mean__iou, conf_mat = intersection_over_union(y_pred1, y_label)

sess = tf.Session()
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())

sess.run([conf_mat])
res = sess.run(mean__iou)

print(res)

哪个返回正确的值：0.66071427

Tensorflow tf.metrics.mean_iou返回0

1 个答案: