Tensorflow Slim:TypeError:预期为int32,获取的列表包含类型为'_Message'的Tensor
computer-vision
deep-learning
machine-learning
python
8
0

我正在按照教程学习TensorFlow Slim,但是在运行以下代码进行Inception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]))

我似乎收到这组错误:

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,因为所有这些代码均来自其官方指南。我是TF的新手,任何帮助将不胜感激。

参考资料:
Stack Overflow
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共 2 个回答
高赞 时间 活跃

使用1.0发行版时,我遇到了同样的问题,我可以使其工作而不必回滚到以前的版本。

问题是由api的更改引起的。这次讨论帮助我找到了解决方案: Google组> TensorFlow中的最近API更改

您只需要使用tf.concat更新所有行

例如

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

应该更改为

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

注意:

我能够毫无问题地使用模型。但是后来我想加载预训练的重量时仍然出现错误。自从他们制作了检查点文件以来,slim模块似乎发生了一些变化。由代码创建的图形与检查点文件中存在的图形不同。

笔记2:

通过添加到所有conv2d层中,我能够对inception_resnet_v2使用预训练权重biases_initializer=None

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显式地编写参数名称即可解决问题。

代替

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

采用

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