所以我已经在这个问题上停留了好几个星期了。我想从图像文件名列表中创建一个图像批处理。我将文件名列表插入队列并使用阅读器获取文件。然后读取器返回文件名和读取的图像文件。
我的问题是，当我使用解码后的 jpg 和来自阅读器的标签进行批处理时，tf.train.shuffle_batch() 混淆了图像和文件名，因此现在标签的图像顺序错误文件。我在 queue/shuffle_batch 上做错了什么吗？我该如何修复它，以便批处理为正确的文件提供正确的标签？
非常感谢!
import tensorflow as tf
from tensorflow.python.framework import ops
def preprocess_image_tensor(image_tf):
image = tf.image.convert_image_dtype(image_tf, dtype=tf.float32)
image = tf.image.resize_image_with_crop_or_pad(image, 300, 300)
image = tf.image.per_image_standardization(image)
return image
# original image names and labels
image_paths = ["image_0.jpg", "image_1.jpg", "image_2.jpg", "image_3.jpg", "image_4.jpg", "image_5.jpg", "image_6.jpg", "image_7.jpg", "image_8.jpg"]
labels = [0, 1, 2, 3, 4, 5, 6, 7, 8]
# converting arrays to tensors
image_paths_tf = ops.convert_to_tensor(image_paths, dtype=tf.string, name="image_paths_tf")
labels_tf = ops.convert_to_tensor(labels, dtype=tf.int32, name="labels_tf")
# getting tensor slices
image_path_tf, label_tf = tf.train.slice_input_producer([image_paths_tf, labels_tf], shuffle=False)
# getting image tensors from jpeg and performing preprocessing
image_buffer_tf = tf.read_file(image_path_tf, name="image_buffer")
image_tf = tf.image.decode_jpeg(image_buffer_tf, channels=3, name="image")
image_tf = preprocess_image_tensor(image_tf)
# creating a batch of images and labels
batch_size = 5
num_threads = 4
images_batch_tf, labels_batch_tf = tf.train.batch([image_tf, label_tf], batch_size=batch_size, num_threads=num_threads)
# running testing session to check order of images and labels
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
print image_path_tf.eval()
print label_tf.eval()
coord.request_stop()
coord.join(threads)请您参考如下方法:
等等....你的tf用法是不是有点奇怪？
您基本上是通过调用运行图表两次:
print image_path_tf.eval()
print label_tf.eval()
因为你只要求 image_path_tf 和 label_tf，所以这条线以下的任何东西都不会运行:
image_path_tf, label_tf = tf.train.slice_input_producer([image_paths_tf, labels_tf], shuffle=False)
也许试试这个？
image_paths, labels = sess.run([images_batch_tf, labels_batch_tf])
print(image_paths)
print(labels)
