Forums - Crash log when executing Android MNIST app

Hi,

I got crash log when I executed MNIST application using SNPE on LG V30.

Could you let me know the reason of crash log?

com.qualcomm.qti.snpe.NeuralNetwork$InvalidInput: Tensor not amongst network input tensors: input

I proceeded sequentially as follows.

[ 1. minist tensorflow source code ] 

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

# === prepare tensorflow network === #

 

#config setting

imageDim = 784

outputDim = 10

 

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

# None means that a dimension can be any of any length

x = tf.placeholder(tf.float32, [None, imageDim], name="input")

 

# 784-dimensional image vectors by it to produce 10-dimensional vectors

W = tf.Variable(tf.zeros([imageDim, outputDim]), dtype=tf.float32, name="Weight")

# a shape of [10]

b = tf.Variable(tf.zeros([outputDim]), dtype=tf.float32, name="bias")

 

# softmax

y = tf.nn.softmax(tf.matmul(x, W) + b, name="softmax")

print(x, W, b, y)

 

# input correct answers

y_ = tf.placeholder(tf.float32, [None, outputDim])

 

# cross-entropy

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

 

# Add ops to save and restore all the variables

saver = tf.train.Saver()

 

print("tensorflow network already prepare done...")

print("session start run...")

with tf.Session() as sess:

    sess.run(tf.initialize_all_variables())

#Training

    for i in range(1000):

        if i % 100 == 0:

            print("iteration num :", i)

        batch_xs, batch_ys = mnist.train.next_batch(100)

        sess.run(train_step,feed_dict={x: batch_xs, y_: batch_ys})

 

    # Save checkpoint, graph.pb and tensorboard

    saver.save(sess, "models/model.ckpt")

    tf.train.write_graph(sess.graph.as_graph_def(), "models/", "graph.pb")

    tf.summary.FileWriter("board", sess.graph)

    print("ckpt model save in the file : models/model.ckpt")

    print("graph model save in the file : models/graph.pb")

    print("board model save in the directory : board/")

 

#Testing

    correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))

    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    print(

        "accuracy : ",

        sess.run(

            accuracy,

            feed_dict={x: mnist.test.images, y_:mnist.test.labels}

        )

     )

[ 2. create frozen graph and dlc file ]

$ python freeze_graph.py --input_graph models/graph.pb --input_checkpoint models/model.ckpt --output_graph models/frozen_graph.pb --output_node_names softmax

$ snpe-tensorflow-to-dlc --graph models/frozen_graph.pb --input_dim input 784 --dlc mnist.dlc --out_node softmax
$ snpe-dlc-quantize --input_dlc mnist.dlc --input_list image_list.txt --output_dlc mnist_quantized.dlc

[ 3. mnist application source code ]

package cmc.snpe_mnist;

 

import android.app.Activity;

import android.app.Application;

import android.content.Context;

 

import java.io.File;

import java.io.IOException;

import java.io.InputStream;

import java.util.HashMap;

import java.util.Map;

 

import com.qualcomm.qti.snpe.FloatTensor;

import com.qualcomm.qti.snpe.NeuralNetwork;

import com.qualcomm.qti.snpe.SNPE;

 

public class DigitDetector {

 

    private SNPE.NeuralNetworkBuilder builder = null;

    private NeuralNetwork network = null;

    private File file = null;

    private int[] results;

 

    public int detectDigit(int[] pixels) {

        final FloatTensor tensor = network.createFloatTensor(784);

        float[] floatArray = new float[pixels.length];

        for (int i = 0; i < pixels.length; i++) {

            floatArray[i] = (float)pixels[i];

        }

        tensor.write(floatArray, 0, floatArray.length);

 

        final Map<String, FloatTensor> inputs = new HashMap<>();

        inputs.put("input", tensor);

        final Map<String, FloatTensor> outputs = network.execute(inputs);

        for (Map.Entry<String, FloatTensor> output : outputs.entrySet()) {

            final FloatTensor out_tensor = output.getValue();

            final float[] values = new float[out_tensor.getSize()];

            tensor.read(values, 0, values.length);

 

            for (int j = 0; j < values.length; j++) {

                results[j] = (int)values[j];

            }

        }

        return results[0];

    }

 

    public boolean setup(Context context) {

        Activity activity = (Activity)context;

        Application application = (Application)activity.getApplication();

        try {

 

            InputStream inputStream = context.getResources().openRawResource(R.raw.mnist);

            builder = new SNPE.NeuralNetworkBuilder(application)

                    .setRuntimeOrder(/*NeuralNetwork.Runtime.DSP,*/ NeuralNetwork.Runtime.GPU, NeuralNetwork.Runtime.CPU)

                    .setModel(inputStream, inputStream.available());

            //file = new File(context.getDataDir(), "models/mnist.dlc");

            //builder = new SNPE.NeuralNetworkBuilder(application)

            //        .setRuntimeOrder(NeuralNetwork.Runtime.DSP, NeuralNetwork.Runtime.GPU, NeuralNetwork.Runtime.CPU)

            //        .setModel(file);

 

        } catch (IOException e) {

            e.printStackTrace();

            return false;

        }

 

        network = builder.build();

 

        return true;

    }

 

    public void unset() {

        network.release();

    }

}