"Table not initialized" when loading model in Java
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Description
- TensorFlow version (use command below):2.3.0
- Python version:3.7
I am trying to use the tensorflow model in java,I convert a text classification model (with tf.lookup) to fomat .pb and want to load it in JAVA.But got "Table not initialized" error.
2021-01-04 14:00:10.713588: W tensorflow/core/framework/op_kernel.cc:1651] OP_REQUIRES failed at lookup_table_op.cc:809 : Failed precondition: Table not initialized.
Exception in thread "main" java.lang.IllegalStateException: Table not initialized.
[[{{node graph/hash_table_Lookup/LookupTableFindV2}}]]
at org.tensorflow.Session.run(Native Method)
at org.tensorflow.Session.access$100(Session.java:48)
at org.tensorflow.Session$Runner.runHelper(Session.java:326)
at org.tensorflow.Session$Runner.run(Session.java:276)
at ctest.Ttest.predict(Ttest.java:32)
at ctest.Ttest.main(Ttest.java:13)
here is my code:
In PYTHON
import os
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
from tensorflow.python.framework.graph_util import convert_variables_to_constants
from tensorflow.python.ops.lookup_ops import HashTable, KeyValueTensorInitializer
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
OUTPUT_FOLDER = ''
OUTPUT_NAME = 'hash_table.pb'
OUTPUT_NAMES = ['graph/output', 'init_all_tables']
def build_graph():
d = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
init = KeyValueTensorInitializer(list(d.keys()), list(d.values()))
hash_table = HashTable(init, default_value=-1)
data = tf.placeholder(tf.string, (None,), name='data')
values = hash_table.lookup(data)
output = tf.identity(values * 2, 'output')
def freeze_graph():
with tf.Graph().as_default() as graph:
with tf.name_scope('graph'):
build_graph()
with tf.Session(graph=graph) as sess:
sess.run(tf.tables_initializer())
print(sess.run('graph/output:0', feed_dict={'graph/data:0': ['a', 'b', 'c', 'd', 'e']}))
frozen_graph = convert_variables_to_constants(sess, sess.graph_def, OUTPUT_NAMES)
tf.train.write_graph(frozen_graph, OUTPUT_FOLDER, OUTPUT_NAME, as_text=False)
def load_frozen_graph():
with open(os.path.join(OUTPUT_FOLDER, OUTPUT_NAME), 'rb') as f:
output_graph_def = tf.GraphDef()
output_graph_def.ParseFromString(f.read())
with tf.Graph().as_default() as graph:
tf.import_graph_def(output_graph_def, name='')
with tf.Session(graph=graph) as sess:
try:
sess.run(graph.get_operation_by_name('init_all_tables'))
except KeyError:
pass
print(sess.run('graph/output:0', feed_dict={'graph/data:0': ['a', 'b', 'c', 'd', 'e']}))
if __name__ == '__main__':
freeze_graph()
load_frozen_graph()
In JAVA
package ctest;
import org.tensorflow.Graph;
import org.tensorflow.Session;
import org.tensorflow.Tensor;
import java.nio.file.Files;
import java.nio.file.Paths;
public class Ttest {
public static void main(String[] args) throws Exception {
predict();
}
public static void predict() throws Exception {
try (Graph graph = new Graph()) {
graph.importGraphDef(Files.readAllBytes(Paths.get(
"/opt/resources/hash_table.pb"
)));
try (Session sess = new Session(graph)) {
byte[][] matrix = new byte[1][];
matrix[0] = "a".getBytes("UTF-8");
Tensor< ? > out = sess.runner()
.feed("graph/data:0", Tensor.create(matrix)).fetch("graph/output:0").run().get(0);
float[][] output = new float[1][(int) out.shape()[1]];
out.copyTo(output);
for(float i:output[0])
System.out.println(i);
}
}
}
}
Any suggestions would be greatly appreciated.
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the Python graph export in freeze_graph(), including the init_all_tables operation and hash_table.pb, then compare it with graph.importGraphDef and the Session runner in Ttest.java. Reproduce the Java lookup failure using the shown model and inputs; done means the exported model loads and returns lookup results without the "Table not initialized" exception.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- java, python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100