Question

我正在关注学习TensorFlow Slim的this教程，但是在为Inception运行以下代码时：

import numpy as np
import os
import tensorflow as tf
import urllib2

from datasets import imagenet
from nets import inception
from preprocessing import inception_preprocessing

slim = tf.contrib.slim

batch_size = 3
image_size = inception.inception_v1.default_image_size
checkpoints_dir = '/tmp/checkpoints/'
with tf.Graph().as_default():
    url = 'https://upload.wikimedia.org/wikipedia/commons/7/70/EnglishCockerSpaniel_simon.jpg'
    image_string = urllib2.urlopen(url).read()
    image = tf.image.decode_jpeg(image_string, channels=3)
    processed_image = inception_preprocessing.preprocess_image(image, image_size, image_size, is_training=False)
    processed_images  = tf.expand_dims(processed_image, 0)

    # Create the model, use the default arg scope to configure the batch norm parameters.
    with slim.arg_scope(inception.inception_v1_arg_scope()):
        logits, _ = inception.inception_v1(processed_images, num_classes=1001, is_training=False)
    probabilities = tf.nn.softmax(logits)

    init_fn = slim.assign_from_checkpoint_fn(
        os.path.join(checkpoints_dir, 'inception_v1.ckpt'),
        slim.get_model_variables('InceptionV1'))

    with tf.Session() as sess:
        init_fn(sess)
        np_image, probabilities = sess.run([image, probabilities])
        probabilities = probabilities[0, 0:]
        sorted_inds = [i[0] for i in sorted(enumerate(-probabilities), key=lambda x:x[1])]

    plt.figure()
    plt.imshow(np_image.astype(np.uint8))
    plt.axis('off')
    plt.show()

    names = imagenet.create_readable_names_for_imagenet_labels()
    for i in range(5):
        index = sorted_inds[i]
        print('Probability %0.2f%% => [%s]' % (probabilities[index], names[index]))

我似乎得到了这组错误：

Traceback (most recent call last):
  File "DA_test_pred.py", line 24, in <module>
    logits, _ = inception.inception_v1(processed_images, num_classes=1001, is_training=False)
  File "/home/deepankar1994/Desktop/MTP/TensorFlowEx/TFSlim/models/slim/nets/inception_v1.py", line 290, in inception_v1
    net, end_points = inception_v1_base(inputs, scope=scope)
  File "/home/deepankar1994/Desktop/MTP/TensorFlowEx/TFSlim/models/slim/nets/inception_v1.py", line 96, in inception_v1_base
    net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/array_ops.py", line 1053, in concat
    dtype=dtypes.int32).get_shape(
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 651, in convert_to_tensor
    as_ref=False)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 716, in internal_convert_to_tensor
    ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/constant_op.py", line 176, in _constant_tensor_conversion_function
    return constant(v, dtype=dtype, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/constant_op.py", line 165, in constant
    tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/tensor_util.py", line 367, in make_tensor_proto
    _AssertCompatible(values, dtype)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/tensor_util.py", line 302, in _AssertCompatible
    (dtype.name, repr(mismatch), type(mismatch).__name__))
TypeError: Expected int32, got list containing Tensors of type '_Message' instead.

这很奇怪，因为所有这些代码都来自他们的官方指南。我是TF的新手，我们将不胜感激。

Answer 1

我在使用1.0发布时遇到了同样的问题，我可以让它工作而不必回滚以前的版本。

问题是由api的变化引起的。那次讨论帮助我找到了解决方案：Google group > Recent API Changes in TensorFlow

您只需使用tf.concat

更新所有行

例如

content

应改为

net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])

注意：

我能够毫无问题地使用这些模型。但是当我想要加载预训练的重量时我仍然有错误。似乎自从制作检查点文件后，slim模块有了几处变化。代码创建的图表和检查点文件中的图表是不同的。

注2：

我可以通过添加到所有conv2d图层net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)

来使用preception重量来启动inception_resnet_v2

Answer 2

显式写入参数名称可以解决问题。

而不是

net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])

使用

net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])

Answer 3

当我完成工作时，我得到了同样的错误。

我找到了

logits = tf.nn.xw_plus_b(tf.concat(outputs, 0), w, b)
loss = tf.reduce_mean(
  tf.nn.softmax_cross_entropy_with_logits(
    labels=tf.concat(train_labels, 0), logits=logits))

输出为shape=(10, 64, 64)。

代码需要concat输出[0]到输出[9] =＆gt;得到一个新的形状（640,64）。

但＆＃34; tf.concat＆＃34; API可能不允许这样做。

（train_labels与此相同）

所以我写信给

A = tf.concat(0,[outputs[0],outputs[1]])
A = tf.concat(0,[A,outputs[2]])
A = tf.concat(0,[A,outputs[3]])
A = tf.concat(0,[A,outputs[4]])
A = tf.concat(0,[A,outputs[5]])
A = tf.concat(0,[A,outputs[6]])
A = tf.concat(0,[A,outputs[7]])
A = tf.concat(0,[A,outputs[8]])
A = tf.concat(0,[A,outputs[9]])
B = tf.concat(0,[train_labels[0],train_labels[1]])
B = tf.concat(0,[B,train_labels[2]])
B = tf.concat(0,[B,train_labels[3]])
B = tf.concat(0,[B,train_labels[4]])
B = tf.concat(0,[B,train_labels[5]])
B = tf.concat(0,[B,train_labels[6]])
B = tf.concat(0,[B,train_labels[7]])
B = tf.concat(0,[B,train_labels[8]])
B = tf.concat(0,[B,train_labels[9]])

logits = tf.nn.xw_plus_b(tf.concat(0, A), w, b)
loss = tf.reduce_mean(
  tf.nn.softmax_cross_entropy_with_logits(
    labels=tf.concat(0, B), logits=logits))

它可以运行！

Answer 4

我发现大多数人回答错误的方式。这只是由于tf.concat中的更改。它以以下方式工作。

net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])

使用以下内容

net = tf.concat(values=[branch_0, branch_1, branch_2, branch_3],axis=3,)

记住，在传递关键字参数时，应该先于其他参数。

Tensorflow Slim：TypeError：预期int32，得到的列表包含类型为'_Message'的张量

4 个答案: