python – TF slice_input_producer不保持张量同步

2023年5月9日 269次阅读

我正在将图像读入我的TF网络,但我也需要相关的标签.

所以我试着遵循this answer,但输出的标签实际上并不匹配我在每批中获得的图像.

我的图像名称格式为dir / 3.jpg,所以我只是从图像文件名中提取标签.

truth_filenames_np = ...
truth_filenames_tf = tf.convert_to_tensor(truth_filenames_np)

# get the labels
labels = [f.rsplit("/", 1)[1] for f in truth_filenames_np]

labels_tf = tf.convert_to_tensor(labels)

# *** This line should make sure both input tensors are synced (from my limited understanding)
# My list is also already shuffled, so I set shuffle=False
truth_image_name, truth_label = tf.train.slice_input_producer([truth_filenames_tf, labels_tf], shuffle=False)


truth_image_value = tf.read_file(truth_image_name)
truth_image = tf.image.decode_jpeg(truth_image_value)
truth_image.set_shape([IMAGE_DIM, IMAGE_DIM, 3])
truth_image = tf.cast(truth_image, tf.float32)
truth_image = truth_image/255.0

# Another key step, where I batch them together
truth_images_batch, truth_label_batch = tf.train.batch([truth_image, truth_label], batch_size=mb_size)


with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)

    for i in range(epochs):
        print "Epoch ", i
        X_truth_batch = truth_images_batch.eval()
        X_label_batch = truth_label_batch.eval()

        # Here I display all the images in this batch, and then I check which file numbers they actually are. 
        # BUT, the images that are displayed don't correspond with what is printed by X_label_batch!
        print X_label_batch
        plot_batch(X_truth_batch)



    coord.request_stop()
    coord.join(threads)

我做错了什么,或者slice_input_producer确实没有确保其输入张量同步？

在旁边：

我还注意到,当我从tf.train.batch获得批处理时,批处理中的元素在我给它的原始列表中彼此相邻,但批处理顺序不是原始顺序.
示例：如果我的数据是[“dir / 1.jpg”,“dir / 2.jpg”,“dir / 3.jpg”,“dir / 4.jpg”,“dir / 5.jpg”,dir / 6 .jpg“],然后我可以获得批次(使用batch_size = 2)[”dir / 3.jpg“,”dir / 4.jpg“],然后批处理[”dir / 1.jpg“,”dir / 2 .jpg“],然后是最后一个.
因此,由于订单与批次订单不匹配,因此很难为标签使用FIFO队列.

最佳答案 这是一个完整的可运行示例,可以重现该问题：

import tensorflow as tf

truth_filenames_np = ['dir/%d.jpg' % j for j in range(66)]
truth_filenames_tf = tf.convert_to_tensor(truth_filenames_np)
# get the labels
labels = [f.rsplit("/", 1)[1] for f in truth_filenames_np]
labels_tf = tf.convert_to_tensor(labels)

# My list is also already shuffled, so I set shuffle=False
truth_image_name, truth_label = tf.train.slice_input_producer(
    [truth_filenames_tf, labels_tf], shuffle=False)

# # Another key step, where I batch them together
# truth_images_batch, truth_label_batch = tf.train.batch(
#     [truth_image_name, truth_label], batch_size=11)

epochs = 7

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)
    for i in range(epochs):
        print("Epoch ", i)
        X_truth_batch = truth_image_name.eval()
        X_label_batch = truth_label.eval()
        # Here I display all the images in this batch, and then I check
        # which file numbers they actually are.
        # BUT, the images that are displayed don't correspond with what is
        # printed by X_label_batch!
        print(X_truth_batch)
        print(X_label_batch)
    coord.request_stop()
    coord.join(threads)

这打印的是：

Epoch  0
b'dir/0.jpg'
b'1.jpg'
Epoch  1
b'dir/2.jpg'
b'3.jpg'
Epoch  2
b'dir/4.jpg'
b'5.jpg'
Epoch  3
b'dir/6.jpg'
b'7.jpg'
Epoch  4
b'dir/8.jpg'
b'9.jpg'
Epoch  5
b'dir/10.jpg'
b'11.jpg'
Epoch  6
b'dir/12.jpg'
b'13.jpg'

所以基本上每个eval调用都会运行另一次操作！添加批处理对此没有任何影响 – 只需打印批处理(前11个文件名后跟下11个标签,依此类推)

我看到的解决方法是：

for i in range(epochs):
    print("Epoch ", i)
    pair = tf.convert_to_tensor([truth_image_name, truth_label]).eval()
    print(pair[0])
    print(pair[1])

正确打印：

Epoch  0
b'dir/0.jpg'
b'0.jpg'
Epoch  1
b'dir/1.jpg'
b'1.jpg'
# ...

但是对于违反最不突然的原则没有任何作用.

编辑：另一种方式：

import tensorflow as tf

truth_filenames_np = ['dir/%d.jpg' % j for j in range(66)]
truth_filenames_tf = tf.convert_to_tensor(truth_filenames_np)
labels = [f.rsplit("/", 1)[1] for f in truth_filenames_np]
labels_tf = tf.convert_to_tensor(labels)
truth_image_name, truth_label = tf.train.slice_input_producer(
    [truth_filenames_tf, labels_tf], shuffle=False)
epochs = 7
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    tf.train.start_queue_runners(sess=sess)
    for i in range(epochs):
        print("Epoch ", i)
        X_truth_batch, X_label_batch = sess.run(
            [truth_image_name, truth_label])
        print(X_truth_batch)
        print(X_label_batch)

这是一个更好的方法,因为tf.convert_to_tensor和co只接受相同类型/形状的张量等.

请注意,为了简单起见,我删除了协调器,但是会产生警告：

W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\kernels\queue_base.cc:294] _0_input_producer/input_producer/fraction_of_32_full/fraction_of_32_full: Skipping cancelled enqueue attempt with queue not closed

见this

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