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samples/languages/java/README.md
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# TensorFlow for Java: Examples # TensorFlow for Java: Examples
These examples include using pre-trained models for [image These examples have moved to the [java
classification](label_image) and [object detection](object_detection), repository](https://github.com/tensorflow/java).
and driving the [training](training) of a pre-defined model - all using the
TensorFlow Java API.
The TensorFlow Java API does not have feature parity with the Python API.
The Java API is most suitable for inference using pre-trained models
and for training pre-defined models from a single Java process.
Python will be the most convenient language for defining the
numerical computation of a model.
- [Slides](https://docs.google.com/presentation/d/e/2PACX-1vQ6DzxNTBrJo7K5P8t5_rBRGnyJoPUPBVOJR4ooHCwi4TlBFnIriFmI719rDNpcQzojqsV58aUqmBBx/pub?start=false&loop=false&delayms=3000) from January 2018.
- See README.md in each subdirectory for details.
For a recent real-world example, see the use of this API to [assess microscope
image quality](https://research.googleblog.com/2018/03/using-deep-learning-to-facilitate.html)
in the image processing package [Fiji (ImageJ)](https://fiji.sc/).
samples/languages/java/docker/Dockerfile deleted 100644 → 0
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FROM tensorflow/tensorflow:1.4.0
WORKDIR /
RUN apt-get update
RUN apt-get -y install maven openjdk-8-jdk
RUN mvn dependency:get -Dartifact=org.tensorflow:tensorflow:1.4.0
RUN mvn dependency:get -Dartifact=org.tensorflow:proto:1.4.0
CMD ["/bin/bash", "-l"]
samples/languages/java/docker/README.md deleted 100644 → 0
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Dockerfile for building an image suitable for running the Java examples.
Typical usage:
```
docker build -t java-tensorflow .
docker run -it --rm -v ${PWD}/..:/examples -w /examples java-tensorflow
```
That second command will pop you into a shell which has all
the dependencies required to execute the scripts and Java
examples.
The script `sanity_test.sh` builds this container and runs a compilation
check on all the maven projects.
samples/languages/java/docker/sanity_test.sh deleted 100755 → 0
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#!/bin/bash
#
# Silly sanity test
DIR="$(cd "$(dirname "$0")" && pwd -P)"
docker build -t java-tensorflow .
docker run -it --rm -v ${PWD}/..:/examples java-tensorflow bash /examples/docker/test_inside_container.sh
samples/languages/java/docker/test_inside_container.sh deleted 100644 → 0
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#!/bin/bash
set -ex
cd /examples/label_image
mvn compile
cd /examples/object_detection
mvn compile
cd /examples/training
mvn compile
samples/languages/java/label_image/README.md deleted 100644 → 0
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# Image Classification Example
1. Download the model:
- If you have [TensorFlow 1.4+ for Python installed](https://www.tensorflow.org/install/),
run `python ./download.py`
- If not, but you have [docker](https://www.docker.com/get-docker) installed,
run `download.sh`.
2. Compile [`LabelImage.java`](src/main/java/LabelImage.java):
```
mvn compile
```
3. Download some sample images:
If you already have some images, great. Otherwise `download_sample_images.sh`
gets a few.
3. Classify!
```
mvn -q exec:java -Dexec.args="<path to image file>"
```
samples/languages/java/label_image/download.py deleted 100644 → 0
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"""Create an image classification graph.
Script to download a pre-trained image classifier and tweak it so that
the model accepts raw bytes of an encoded image.
Doing so involves some model-specific normalization of an image.
Ideally, this would have been part of the image classifier model,
but the particular model being used didn't include this normalization,
so this script does the necessary tweaking.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import urllib
import os
import zipfile
import tensorflow as tf
URL = 'https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip'
LABELS_FILE = 'imagenet_comp_graph_label_strings.txt'
GRAPH_FILE = 'tensorflow_inception_graph.pb'
GRAPH_INPUT_TENSOR = 'input:0'
GRAPH_PROBABILITIES_TENSOR = 'output:0'
IMAGE_HEIGHT = 224
IMAGE_WIDTH = 224
MEAN = 117
SCALE = 1
LOCAL_DIR = 'src/main/resources'
def download():
print('Downloading %s' % URL)
zip_filename, _ = urllib.request.urlretrieve(URL)
with zipfile.ZipFile(zip_filename) as zip:
zip.extract(LABELS_FILE)
zip.extract(GRAPH_FILE)
os.rename(LABELS_FILE, os.path.join(LOCAL_DIR, 'labels.txt'))
os.rename(GRAPH_FILE, os.path.join(LOCAL_DIR, 'graph.pb'))
def create_graph_to_decode_and_normalize_image():
"""See file docstring.
Returns:
input: The placeholder to feed the raw bytes of an encoded image.
y: A Tensor (the decoded, normalized image) to be fed to the graph.
"""
image = tf.placeholder(tf.string, shape=(), name='encoded_image_bytes')
with tf.name_scope("preprocess"):
y = tf.image.decode_image(image, channels=3)
y = tf.cast(y, tf.float32)
y = tf.expand_dims(y, axis=0)
y = tf.image.resize_bilinear(y, (IMAGE_HEIGHT, IMAGE_WIDTH))
y = (y - MEAN) / SCALE
return (image, y)
def patch_graph():
"""Create graph.pb that applies the model in URL to raw image bytes."""
with tf.Graph().as_default() as g:
input_image, image_normalized = create_graph_to_decode_and_normalize_image()
original_graph_def = tf.GraphDef()
with open(os.path.join(LOCAL_DIR, 'graph.pb')) as f:
original_graph_def.ParseFromString(f.read())
softmax = tf.import_graph_def(
original_graph_def,
name='inception',
input_map={GRAPH_INPUT_TENSOR: image_normalized},
return_elements=[GRAPH_PROBABILITIES_TENSOR])
# We're constructing a graph that accepts a single image (as opposed to a
# batch of images), so might as well make the output be a vector of
# probabilities, instead of a batch of vectors with batch size 1.
output_probabilities = tf.squeeze(softmax, name='probabilities')
# Overwrite the graph.
with open(os.path.join(LOCAL_DIR, 'graph.pb'), 'w') as f:
f.write(g.as_graph_def().SerializeToString())
print('------------------------------------------------------------')
print('MODEL GRAPH : graph.pb')
print('LABELS : labels.txt')
print('INPUT TENSOR : %s' % input_image.op.name)
print('OUTPUT TENSOR: %s' % output_probabilities.op.name)
if __name__ == '__main__':
if not os.path.exists(LOCAL_DIR):
os.makedirs(LOCAL_DIR)
download()
patch_graph()
samples/languages/java/label_image/download.sh deleted 100755 → 0
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#!/bin/bash
DIR="$(cd "$(dirname "$0")" && pwd -P)"
docker run -it -v ${DIR}:/x -w /x --rm tensorflow/tensorflow:1.4.0 python download.py
samples/languages/java/label_image/download_sample_images.sh deleted 100755 → 0
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#!/bin/bash
DIR=$(dirname $0)
mkdir -p ${DIR}/images
cd ${DIR}/images
# Some random images
curl -o "porcupine.jpg" -L "https://cdn.pixabay.com/photo/2014/11/06/12/46/porcupines-519145_960_720.jpg"
curl -o "whale.jpg" -L "https://static.pexels.com/photos/417196/pexels-photo-417196.jpeg"
curl -o "terrier1u.jpg" -L "https://upload.wikimedia.org/wikipedia/commons/3/34/Australian_Terrier_Melly_%282%29.JPG"
curl -o "terrier2.jpg" -L "https://cdn.pixabay.com/photo/2014/05/13/07/44/yorkshire-terrier-343198_960_720.jpg"
samples/languages/java/label_image/pom.xml deleted 100644 → 0
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<project>
<modelVersion>4.0.0</modelVersion>
<groupId>org.myorg</groupId>
<artifactId>label-image</artifactId>
<version>1.0-SNAPSHOT</version>
<properties>
<exec.mainClass>LabelImage</exec.mainClass>
<!-- The sample code requires at least JDK 1.7. -->
<!-- The maven compiler plugin defaults to a lower version -->
<maven.compiler.source>1.7</maven.compiler.source>
<maven.compiler.target>1.7</maven.compiler.target>
</properties>
<dependencies>
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>tensorflow</artifactId>
<version>1.4.0</version>
</dependency>
<!-- For ByteStreams.toByteArray: https://google.github.io/guava/releases/23.0/api/docs/com/google/common/io/ByteStreams.html -->
<dependency>
<groupId>com.google.guava</groupId>
<artifactId>guava</artifactId>
<version>23.6-jre</version>
</dependency>
</dependencies>
</project>
samples/languages/java/label_image/src/main/java/LabelImage.java deleted 100644 → 0
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/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
import com.google.common.io.ByteStreams;
import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.ArrayList;
import java.util.List;
import org.tensorflow.Graph;
import org.tensorflow.Session;
import org.tensorflow.Tensor;
import org.tensorflow.Tensors;
/**
* Simplified version of
* https://github.com/tensorflow/tensorflow/blob/r1.4/tensorflow/java/src/main/java/org/tensorflow/examples/LabelImage.java
*/
public class LabelImage {
public static void main(String[] args) throws Exception {
if (args.length < 1) {
System.err.println("USAGE: Provide a list of image filenames");
System.exit(1);
}
final List<String> labels = loadLabels();
try (Graph graph = new Graph();
Session session = new Session(graph)) {
graph.importGraphDef(loadGraphDef());
float[] probabilities = null;
for (String filename : args) {
byte[] bytes = Files.readAllBytes(Paths.get(filename));
try (Tensor<String> input = Tensors.create(bytes);
Tensor<Float> output =
session
.runner()
.feed("encoded_image_bytes", input)
.fetch("probabilities")
.run()
.get(0)
.expect(Float.class)) {
if (probabilities == null) {
probabilities = new float[(int) output.shape()[0]];
}
output.copyTo(probabilities);
int label = argmax(probabilities);
System.out.printf(
"%-30s --> %-15s (%.2f%% likely)\n",
filename, labels.get(label), probabilities[label] * 100.0);
}
}
}
}
private static byte[] loadGraphDef() throws IOException {
try (InputStream is = LabelImage.class.getClassLoader().getResourceAsStream("graph.pb")) {
return ByteStreams.toByteArray(is);
}
}
private static ArrayList<String> loadLabels() throws IOException {
ArrayList<String> labels = new ArrayList<String>();
String line;
final InputStream is = LabelImage.class.getClassLoader().getResourceAsStream("labels.txt");
try (BufferedReader reader = new BufferedReader(new InputStreamReader(is))) {
while ((line = reader.readLine()) != null) {
labels.add(line);
}
}
return labels;
}
private static int argmax(float[] probabilities) {
int best = 0;
for (int i = 1; i < probabilities.length; ++i) {
if (probabilities[i] > probabilities[best]) {
best = i;
}
}
return best;
}
}
samples/languages/java/object_detection/.gitignore deleted 100644 → 0
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images
labels
models
src/main/protobuf
target
samples/languages/java/object_detection/README.md deleted 100644 → 0
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# Object Detection in Java
Example of using pre-trained models of the [TensorFlow Object Detection
API](https://github.com/tensorflow/models/tree/master/research/object_detection)
in Java.
## Quickstart
1. Download some metadata files:
```
./download.sh
```
2. Download a model from the [object detection API model
zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md).
For example:
```
mkdir -p models
curl -L \
http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2017_11_17.tar.gz \
| tar -xz -C models/
```
3. Have some test images handy. For example:
```
mkdir -p images
curl -L -o images/test.jpg \
https://pixnio.com/free-images/people/mother-father-and-children-washing-dog-labrador-retriever-outside-in-the-fresh-air-725x483.jpg
```
4. Compile and run!
```
mvn -q compile exec:java \
-Dexec.args="models/ssd_inception_v2_coco_2017_11_17/saved_model labels/mscoco_label_map.pbtxt images/test.jpg"
```
## Notes
- This example demonstrates the use of the TensorFlow [SavedModel
format](https://www.tensorflow.org/guide/saved_model). If you have
TensorFlow for Python installed, you could explore the model to get the names
of the tensors using `saved_model_cli` command. For example:
```
saved_model_cli show --dir models/ssd_inception_v2_coco_2017_11_17/saved_model/ --all
```
- The file in `src/main/object_detection/protos/` was generated using:
```
./download.sh
protoc -Isrc/main/protobuf --java_out=src/main/java src/main/protobuf/string_int_label_map.proto
```
Where `protoc` was downloaded from
https://github.com/google/protobuf/releases/tag/v3.5.1
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#!/bin/bash
set -ex
DIR="$(cd "$(dirname "$0")" && pwd -P)"
cd "${DIR}"
# The protobuf file needed for mapping labels to human readable names.
# From:
# https://github.com/tensorflow/models/blob/f87a58c/research/object_detection/protos/string_int_label_map.proto
mkdir -p src/main/protobuf
curl -L -o src/main/protobuf/string_int_label_map.proto "https://raw.githubusercontent.com/tensorflow/models/f87a58cd96d45de73c9a8330a06b2ab56749a7fa/research/object_detection/protos/string_int_label_map.proto"
# Labels from:
# https://github.com/tensorflow/models/tree/865c14c/research/object_detection/data
mkdir -p labels
curl -L -o labels/mscoco_label_map.pbtxt "https://raw.githubusercontent.com/tensorflow/models/865c14c1209cb9ae188b2a1b5f0883c72e050d4c/research/object_detection/data/mscoco_label_map.pbtxt"
curl -L -o labels/oid_bbox_trainable_label_map.pbtxt "https://raw.githubusercontent.com/tensorflow/models/865c14c1209cb9ae188b2a1b5f0883c72e050d4c/research/object_detection/data/oid_bbox_trainable_label_map.pbtxt"
samples/languages/java/object_detection/pom.xml deleted 100644 → 0
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<project>
<modelVersion>4.0.0</modelVersion>
<groupId>org.myorg</groupId>
<artifactId>detect-objects</artifactId>
<version>1.0-SNAPSHOT</version>
<properties>
<exec.mainClass>DetectObjects</exec.mainClass>
<!-- The sample code requires at least JDK 1.7. -->
<!-- The maven compiler plugin defaults to a lower version -->
<maven.compiler.source>1.7</maven.compiler.source>
<maven.compiler.target>1.7</maven.compiler.target>
</properties>
<dependencies>
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>tensorflow</artifactId>
<version>1.4.0</version>
</dependency>
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>proto</artifactId>
<version>1.4.0</version>
</dependency>
</dependencies>
</project>
samples/languages/java/object_detection/src/main/java/DetectObjects.java deleted 100644 → 0
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/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
import static object_detection.protos.StringIntLabelMapOuterClass.StringIntLabelMap;
import static object_detection.protos.StringIntLabelMapOuterClass.StringIntLabelMapItem;
import com.google.protobuf.TextFormat;
import java.awt.Graphics2D;
import java.awt.image.BufferedImage;
import java.awt.image.DataBufferByte;
import java.io.File;
import java.io.IOException;
import java.io.PrintStream;
import java.nio.ByteBuffer;
import java.nio.charset.StandardCharsets;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.List;
import java.util.Map;
import javax.imageio.ImageIO;
import org.tensorflow.SavedModelBundle;
import org.tensorflow.Tensor;
import org.tensorflow.framework.MetaGraphDef;
import org.tensorflow.framework.SignatureDef;
import org.tensorflow.framework.TensorInfo;
import org.tensorflow.types.UInt8;
/**
* Java inference for the Object Detection API at:
* https://github.com/tensorflow/models/blob/master/research/object_detection/
*/
public class DetectObjects {
public static void main(String[] args) throws Exception {
if (args.length < 3) {
printUsage(System.err);
System.exit(1);
}
final String[] labels = loadLabels(args[1]);
try (SavedModelBundle model = SavedModelBundle.load(args[0], "serve")) {
printSignature(model);
for (int arg = 2; arg < args.length; arg++) {
final String filename = args[arg];
List<Tensor<?>> outputs = null;
try (Tensor<UInt8> input = makeImageTensor(filename)) {
outputs =
model
.session()
.runner()
.feed("image_tensor", input)
.fetch("detection_scores")
.fetch("detection_classes")
.fetch("detection_boxes")
.run();
}
try (Tensor<Float> scoresT = outputs.get(0).expect(Float.class);
Tensor<Float> classesT = outputs.get(1).expect(Float.class);
Tensor<Float> boxesT = outputs.get(2).expect(Float.class)) {
// All these tensors have:
// - 1 as the first dimension
// - maxObjects as the second dimension
// While boxesT will have 4 as the third dimension (2 sets of (x, y) coordinates).
// This can be verified by looking at scoresT.shape() etc.
int maxObjects = (int) scoresT.shape()[1];
float[] scores = scoresT.copyTo(new float[1][maxObjects])[0];
float[] classes = classesT.copyTo(new float[1][maxObjects])[0];
float[][] boxes = boxesT.copyTo(new float[1][maxObjects][4])[0];
// Print all objects whose score is at least 0.5.
System.out.printf("* %s\n", filename);
boolean foundSomething = false;
for (int i = 0; i < scores.length; ++i) {
if (scores[i] < 0.5) {
continue;
}
foundSomething = true;
System.out.printf("\tFound %-20s (score: %.4f)\n", labels[(int) classes[i]], scores[i]);
}
if (!foundSomething) {
System.out.println("No objects detected with a high enough score.");
}
}
}
}
}
private static void printSignature(SavedModelBundle model) throws Exception {
MetaGraphDef m = MetaGraphDef.parseFrom(model.metaGraphDef());
SignatureDef sig = m.getSignatureDefOrThrow("serving_default");
int numInputs = sig.getInputsCount();
int i = 1;
System.out.println("MODEL SIGNATURE");
System.out.println("Inputs:");
for (Map.Entry<String, TensorInfo> entry : sig.getInputsMap().entrySet()) {
TensorInfo t = entry.getValue();
System.out.printf(
"%d of %d: %-20s (Node name in graph: %-20s, type: %s)\n",
i++, numInputs, entry.getKey(), t.getName(), t.getDtype());
}
int numOutputs = sig.getOutputsCount();
i = 1;
System.out.println("Outputs:");
for (Map.Entry<String, TensorInfo> entry : sig.getOutputsMap().entrySet()) {
TensorInfo t = entry.getValue();
System.out.printf(
"%d of %d: %-20s (Node name in graph: %-20s, type: %s)\n",
i++, numOutputs, entry.getKey(), t.getName(), t.getDtype());
}
System.out.println("-----------------------------------------------");
}
private static String[] loadLabels(String filename) throws Exception {
String text = new String(Files.readAllBytes(Paths.get(filename)), StandardCharsets.UTF_8);
StringIntLabelMap.Builder builder = StringIntLabelMap.newBuilder();
TextFormat.merge(text, builder);
StringIntLabelMap proto = builder.build();
int maxId = 0;
for (StringIntLabelMapItem item : proto.getItemList()) {
if (item.getId() > maxId) {
maxId = item.getId();
}
}
String[] ret = new String[maxId + 1];
for (StringIntLabelMapItem item : proto.getItemList()) {
ret[item.getId()] = item.getDisplayName();
}
return ret;
}
private static void bgr2rgb(byte[] data) {
for (int i = 0; i < data.length; i += 3) {
byte tmp = data[i];
data[i] = data[i + 2];
data[i + 2] = tmp;
}
}
private static Tensor<UInt8> makeImageTensor(String filename) throws IOException {
BufferedImage img = ImageIO.read(new File(filename));
if (img.getType() != BufferedImage.TYPE_3BYTE_BGR) {
BufferedImage newImage = new BufferedImage(
img.getWidth(), img.getHeight(), BufferedImage.TYPE_3BYTE_BGR);
Graphics2D g = newImage.createGraphics();
g.drawImage(img, 0, 0, img.getWidth(), img.getHeight(), null);
g.dispose();
img = newImage;
}
byte[] data = ((DataBufferByte) img.getData().getDataBuffer()).getData();
// ImageIO.read seems to produce BGR-encoded images, but the model expects RGB.
bgr2rgb(data);
final long BATCH_SIZE = 1;
final long CHANNELS = 3;
long[] shape = new long[] {BATCH_SIZE, img.getHeight(), img.getWidth(), CHANNELS};
return Tensor.create(UInt8.class, shape, ByteBuffer.wrap(data));
}
private static void printUsage(PrintStream s) {
s.println("USAGE: <model> <label_map> <image> [<image>] [<image>]");
s.println("");
s.println("Where");
s.println("<model> is the path to the SavedModel directory of the model to use.");
s.println(" For example, the saved_model directory in tarballs from ");
s.println(
" https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md)");
s.println("");
s.println(
"<label_map> is the path to a file containing information about the labels detected by the model.");
s.println(" For example, one of the .pbtxt files from ");
s.println(
" https://github.com/tensorflow/models/tree/master/research/object_detection/data");
s.println("");
s.println("<image> is the path to an image file.");
s.println(" Sample images can be found from the COCO, Kitti, or Open Images dataset.");
s.println(
" See: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md");
}
}
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# Training models in Java
Example of training a model (and saving and restoring checkpoints) using the
TensorFlow Java API.
## Quickstart
1. Train for a few steps:
```
mvn -q compile exec:java -Dexec.args="model/graph.pb checkpoint"
```
2. Resume training from previous checkpoint and train some more:
```
mvn -q exec:java -Dexec.args="model/graph.pb checkpoint"
```
3. Delete checkpoint:
```
rm -rf checkpoint
```
## Details
The model in `model/graph.pb` represents a very simple linear model:
```
y = x * W + b
```
The `graph.pb` file is generated by executing `create_graph.py` in Python.
The training is orchestrated by `src/main/java/Train.java`, which generates
training data of the form `y = 3.0 * x + 2.0` and over time, using gradient
descent, the model should "learn" and the value of `W` should converge to 3.0,
and `b` to 2.0.
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