我有如下代码：
def yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold = .6):
"""Filters YOLO boxes by thresholding on object and class confidence.
Arguments:
box_confidence -- tensor of shape (3, 3, 5, 1)
boxes -- tensor of shape (3, 3, 5, 4)
box_class_probs -- tensor of shape (3, 3, 5, 80)
threshold -- real value, if [ highest class probability score < threshold], then get rid of the corresponding box
Returns:
scores -- tensor of shape (None,), containing the class probability score for selected boxes
boxes -- tensor of shape (None, 4), containing (b_x, b_y, b_h, b_w) coordinates of selected boxes
classes -- tensor of shape (None,), containing the index of the class detected by the selected boxes
Note: "None" is here because you don't know the exact number of selected boxes, as it depends on the threshold.
For example, the actual output size of scores would be (10,) if there are 10 boxes.
"""
# Step 1: Compute box scores
box_scores = np.multiply(box_confidence, box_class_probs)
# Step 2: Find the box_classes thanks to the max box_scores, keep track of the corresponding score
box_classes = K.argmax(box_scores, -1)
box_class_scores = K.max(box_scores, -1)
# Step 3: Create a filtering mask based on "box_class_scores" by using "threshold". The mask should have the
# same dimension as box_class_scores, and be True for the boxes you want to keep (with probability >= threshold)
filtering_mask = K.greater_equal(box_class_scores,threshold)
# Step 4: Apply the mask to scores, boxes and classes
print(filtering_mask.shape)
print(filtering_mask.eval())
print(box_class_scores.shape)
print(box_class_scores.eval())
scores = tf.boolean_mask(box_class_scores, filtering_mask)
print(scores.eval())
boxes = tf.boolean_mask(boxes, filtering_mask)
classes = tf.boolean_mask(box_classes, filtering_mask)
return scores, boxes, classes
with tf.Session() as test_a:
box_confidence = tf.random_normal([3, 3, 5, 1], mean=1, stddev=4, seed = 1)
boxes = tf.random_normal([3, 3, 5, 4], mean=1, stddev=4, seed = 1)
box_class_probs = tf.random_normal([3, 3, 5, 80], mean=1, stddev=4, seed = 1)
scores, boxes, classes = yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold = 0.5)
print("scores[2] = " + str(scores[2].eval()))
print("boxes[2] = " + str(boxes[2].eval()))
print("classes[2] = " + str(classes[2].eval()))
print("scores.shape = " + str(scores.shape))
print("boxes.shape = " + str(boxes.shape))
print("classes.shape = " + str(classes.shape))
这是输出：
(3, 3, 5)
[[[ True True True True True]
[ True True True True True]
[ True False True True True]]
[[ True True True True True]
[ True True True True True]
[ True True True True True]]
[[ True True True True False]
[ True True True True True]
[ True True True True True]]]
(3, 3, 5)
[[[ 45.00004959 21.20238304 17.39275742 26.73288918 49.47431946]
[ 22.16205978 27.96604347 12.38916492 33.66600418 62.04590225]
[ 113.03194427 2.68868852 6.33391762 45.17211914 10.5103178 ]]
[[ 8.22186852 35.88579941 48.54780579 12.48789883 32.40937042]
[ 75.73269653 17.52830696 62.99983597 29.0468502 42.82471848]
[ 72.42234039 108.19727325 36.93912888 40.9789238 36.91137314]]
[[ 1.57321405 3.35663748 16.33576775 5.16499805 19.43038177]
[ 48.13769913 68.20082092 47.06818008 1.82166731 67.30760956]
[ 33.01203537 63.93298721 9.71860027 49.06838989 60.74739456]]]
[ 22.63684464 10.29589462 58.76845551 74.67560577 20.25722504
47.24279022 6.96320772 22.59087944 86.61974335 1.05248117
57.47060394 92.50878143 16.8335762 23.29385757 78.58971405
6.95861435 65.61254883 45.47106171 43.53435135 10.0660677
60.34520721 28.5535984 15.9668026 45.14865494 5.49425364
2.35473752 29.40540886 2.5579865 46.96302032 9.39739799
45.78501892 49.42660904 34.68322754 40.72031784 58.91592407
35.39850616 56.24537277 6.80519342 9.52552414 138.54457092
14.07888412 56.37608719 69.59171295 25.83714676]
scores[2] = 62.0051
boxes[2] = [-1.89158893 0.7749185 3.57417917 -0.05729628]
classes[2] = 36
scores.shape = (?,)
boxes.shape = (?, 4)
classes.shape = (?,)
我有一个简单的问题。 scores的结果如何产生？它有44个元素，而filtering_mask和box_class_scores有45个元素（3 * 3 * 5），filtering_mask有2个假值，必须得分43个元素。即使filter_mask具有1个假值，分数中的数字均不匹配box_class_scores。
有人可以向我解释如何计算scores
答案 0 :(得分：1)
问题不在于掩盖效果是否如您所愿。问题在于您在图形中使用随机值，其行为可能会有些令人惊讶。每次您呼叫eval()实际上是在默认会话中对run的呼叫。问题在于TensorFlow中随机值的工作方式。每次在会话上调用run时，都会生成一个新的随机值。这意味着每个eval调用都基于box_confidence，boxes和box_class_probs的不同值产生结果。有一些解决方法，要么简单地不使用随机值生成器作为输入，要么在对run的同一调用中评估所有输出（而不与eval一起使用）。由于您似乎正在编写测试代码，因此解决该问题的一种简单方法是将输入替换为由NumPy随机值构成的常量。
import tensorflow as tf
import numpy as np
def yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold = .6):
# ...
with tf.Session() as test_a:
np.random.seed(1)
box_confidence = tf.constant(np.random.normal(loc=1, scale=4, size=[3, 3, 5, 1]), dtype=tf.float32)
boxes = tf.constant(np.random.normal(loc=1, scale=4, size=[3, 3, 5, 4]), dtype=tf.float32)
box_class_probs = tf.constant(np.random.normal(loc=1, scale=4, size=[3, 3, 5, 80]), dtype=tf.float32
scores, boxes, classes = yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold = 0.5)
print("scores[2] = " + str(scores[2].eval()))
print("boxes[2] = " + str(boxes[2].eval()))
print("classes[2] = " + str(classes[2].eval()))
print("scores.shape = " + str(scores.shape))
print("boxes.shape = " + str(boxes.shape))
print("classes.shape = " + str(classes.shape))
或者您仍然可以使用TensorFlow随机数，但可以使用变量作为输入。与变量的区别在于，它们仅在初始化时评估其初始值，然后在会话之间保留其值（直到再次更改它），因此您不必每次都生成新的随机值。
import tensorflow as tf
def yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold = .6):
# ...
with tf.Session() as test_a:
box_confidence = tf.Variable(tf.random_normal([3, 3, 5, 1], mean=1, stddev=4, seed = 1)))
boxes = tf.Variable(tf.random_normal([3, 3, 5, 4], mean=1, stddev=4, seed = 1))
box_class_probs = tf.Variable(tf.random_normal([3, 3, 5, 80], mean=1, stddev=4, seed = 1))
# You must initialize the variables
test_a.run(tf.global_variables_initializer())
scores, boxes, classes = yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold = 0.5)
print("scores[2] = " + str(scores[2].eval()))
print("boxes[2] = " + str(boxes[2].eval()))
print("classes[2] = " + str(classes[2].eval()))
print("scores.shape = " + str(scores.shape))
print("boxes.shape = " + str(boxes.shape))
print("classes.shape = " + str(classes.shape))