我想将函数 tf.metrics.mean_iou 用于 FCN 以进行语义分割。它仅在混淆矩阵在 IoU 之前计算时有效，否则返回 0。
这里是我的例子:
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)
如果有一个函数可以计算平均 IoU 而无需初始化其中的所有变量，那就太好了。有什么办法可以修复第二个示例吗？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，即通过 Session() 分别运行它们。
谢谢
请您参考如下方法:
在从张量中获取 iou 值之前，您需要运行 tf.metrics.mean_iou 返回的更新操作。
固定代码如下:
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)
返回正确值:0.66071427
