Tensorflow Slim: TypeError: Expected int32, got list containing Tensors of type '_Message' instead
Question:
I am following this tutorial for learning TensorFlow Slim but upon running the following code for Inception:
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]))
I seem to be getting this set of errors:
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.
This is strange because all of this code is from their official guide. I am new to TF and any help would be appreciated.
Answers:
I got the same problem when using the 1.0 released and I could make it work without having to roll back on a previous version.
The problem is caused by change in the api. That discussion helped me to find the solution: Google group >
Recent API Changes in TensorFlow
You just have to update all the line with tf.concat
for example
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
should be changed to
net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
Note:
I was able to use the models without problem. But I still got error afterward when wanting to load the pretrained weight.
Seems that the slim module got several changed since they made the checkpoint file. The graph created by the code and the one present in the checkpoint file were different.
Note2:
I was able to use the pretrain weights for inception_resnet_v2 by adding to all conv2d layer biases_initializer=None
I got same error when I did the work.
I found that
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))
The output is shape=(10, 64, 64).
The code want concat outputs[0] to outputs[9] => get a new shape(640,64).
But the “tf.concat” API may not allow to do this.
(train_labels same to this)
So I write to
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))
It can run!
explicitly writing the name of the arguments solves the problem.
instead of
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
use
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
I found most people answering wrong way. Its just due to the change in the tf.concat.
It works in the following way.
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
use the following
net = tf.concat(values=[branch_0, branch_1, branch_2, branch_3],axis=3,)
Remember while passing the keyword arguments should be before the others.
I am following this tutorial for learning TensorFlow Slim but upon running the following code for Inception:
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]))
I seem to be getting this set of errors:
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.
This is strange because all of this code is from their official guide. I am new to TF and any help would be appreciated.
I got the same problem when using the 1.0 released and I could make it work without having to roll back on a previous version.
The problem is caused by change in the api. That discussion helped me to find the solution: Google group >
Recent API Changes in TensorFlow
You just have to update all the line with tf.concat
for example
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
should be changed to
net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
Note:
I was able to use the models without problem. But I still got error afterward when wanting to load the pretrained weight.
Seems that the slim module got several changed since they made the checkpoint file. The graph created by the code and the one present in the checkpoint file were different.
Note2:
I was able to use the pretrain weights for inception_resnet_v2 by adding to all conv2d layer biases_initializer=None
I got same error when I did the work.
I found that
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))
The output is shape=(10, 64, 64).
The code want concat outputs[0] to outputs[9] => get a new shape(640,64).
But the “tf.concat” API may not allow to do this.
(train_labels same to this)
So I write to
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))
It can run!
explicitly writing the name of the arguments solves the problem.
instead of
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
use
net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
I found most people answering wrong way. Its just due to the change in the tf.concat.
It works in the following way.
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
use the following
net = tf.concat(values=[branch_0, branch_1, branch_2, branch_3],axis=3,)
Remember while passing the keyword arguments should be before the others.