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samples/languages/java/object_detection/.gitignore 0 → 100644
View file @ d5e826e3
images
labels
models
src/main/protobuf
target
samples/languages/java/object_detection/README.md 0 → 100644
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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/programmers_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
samples/languages/java/object_detection/download.sh 0 → 100755
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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 0 → 100644
View file @ d5e826e3
<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 0 → 100644
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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.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) {
throw new IOException(
String.format(
"Expected 3-byte BGR encoding in BufferedImage, found %d (file: %s). This code could be made more robust",
img.getType(), filename));
}
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");
}
}
samples/languages/java/object_detection/src/main/java/object_detection/protos/StringIntLabelMapOuterClass.java 0 → 100644
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// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: string_int_label_map.proto
package object_detection.protos;
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* String name. The most common practice is to set this to a MID or synsets
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* </pre>
*
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*/
boolean hasName();
/**
* <pre>
* String name. The most common practice is to set this to a MID or synsets
* id.
* </pre>
*
* <code>optional string name = 1;</code>
*/
java.lang.String getName();
/**
* <pre>
* String name. The most common practice is to set this to a MID or synsets
* id.
* </pre>
*
* <code>optional string name = 1;</code>
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com.google.protobuf.ByteString
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* <pre>
* Integer id that maps to the string name above. Label ids should start from
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*
* <code>optional int32 id = 2;</code>
*/
boolean hasId();
/**
* <pre>
* Integer id that maps to the string name above. Label ids should start from
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*
* <code>optional int32 id = 2;</code>
*/
int getId();
/**
* <pre>
* Human readable string label.
* </pre>
*
* <code>optional string display_name = 3;</code>
*/
boolean hasDisplayName();
/**
* <pre>
* Human readable string label.
* </pre>
*
* <code>optional string display_name = 3;</code>
*/
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* <pre>
* Human readable string label.
* </pre>
*
* <code>optional string display_name = 3;</code>
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com.google.protobuf.ByteString
getDisplayNameBytes();
}
/**
* Protobuf type {@code object_detection.protos.StringIntLabelMapItem}
*/
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samples/languages/java/training/README.md 0 → 100644
View file @ d5e826e3
# 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.
samples/languages/java/training/model/create_graph.py 0 → 100644
View file @ d5e826e3
from __future__ import print_function
import tensorflow as tf
x = tf.placeholder(tf.float32, name='input')
y_ = tf.placeholder(tf.float32, name='target')
W = tf.Variable(5., name='W')
b = tf.Variable(3., name='b')
y = x * W + b
y = tf.identity(y, name='output')
loss = tf.reduce_mean(tf.square(y - y_))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
train_op = optimizer.minimize(loss, name='train')
init = tf.global_variables_initializer()
# Creating a tf.train.Saver adds operations to the graph to save and
# restore variables from checkpoints.
saver_def = tf.train.Saver().as_saver_def()
print('Operation to initialize variables: ', init.name)
print('Tensor to feed as input data: ', x.name)
print('Tensor to feed as training targets: ', y_.name)
print('Tensor to fetch as prediction: ', y.name)
print('Operation to train one step: ', train_op.name)
print('Tensor to be fed for checkpoint filename:', saver_def.filename_tensor_name)
print('Operation to save a checkpoint: ', saver_def.save_tensor_name)
print('Operation to restore a checkpoint: ', saver_def.restore_op_name)
print('Tensor to read value of W ', W.value().name)
print('Tensor to read value of b ', b.value().name)
with open('graph.pb', 'w') as f:
f.write(tf.get_default_graph().as_graph_def().SerializeToString())
samples/languages/java/training/pom.xml 0 → 100644
View file @ d5e826e3
<project>
<modelVersion>4.0.0</modelVersion>
<groupId>org.myorg</groupId>
<artifactId>training</artifactId>
<version>1.0-SNAPSHOT</version>
<properties>
<exec.mainClass>Train</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>
</dependencies>
</project>
samples/languages/java/training/src/main/java/Train.java 0 → 100644
View file @ d5e826e3
/* 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 java.nio.file.Files;
import java.nio.file.Paths;
import java.util.List;
import java.util.Random;
import org.tensorflow.Graph;
import org.tensorflow.Session;
import org.tensorflow.Tensor;
import org.tensorflow.Tensors;
/**
* Training a trivial linear model.
*/
public class Train {
public static void main(String[] args) throws Exception {
if (args.length != 2) {
System.err.println("Require two arguments: The GraphDef file and checkpoint directory");
System.exit(1);
}
final byte[] graphDef = Files.readAllBytes(Paths.get(args[0]));
final String checkpointDir = args[1];
final boolean checkpointExists = Files.exists(Paths.get(checkpointDir));
try (Graph graph = new Graph();
Session sess = new Session(graph);
Tensor<String> checkpointPrefix =
Tensors.create(Paths.get(checkpointDir, "ckpt").toString())) {
graph.importGraphDef(graphDef);
// Initialize or restore
if (checkpointExists) {
sess.runner().feed("save/Const", checkpointPrefix).addTarget("save/restore_all").run();
} else {
sess.runner().addTarget("init").run();
}
System.out.print("Starting from : ");
printVariables(sess);
// Train a bunch of times.
// (Will be much more efficient if we sent batches instead of individual values).
final Random r = new Random();
final int NUM_EXAMPLES = 500;
for (int i = 1; i <= 5; i++) {
for (int n = 0; n < NUM_EXAMPLES; n++) {
float in = r.nextFloat();
try (Tensor<Float> input = Tensors.create(in);
Tensor<Float> target = Tensors.create(3 * in + 2)) {
sess.runner().feed("input", input).feed("target", target).addTarget("train").run();
}
}
System.out.printf("After %5d examples: ", i*NUM_EXAMPLES);
printVariables(sess);
}
// Checkpoint
sess.runner().feed("save/Const", checkpointPrefix).addTarget("save/control_dependency").run();
// Example of "inference" in the same graph:
try (Tensor<Float> input = Tensors.create(1.0f);
Tensor<Float> output =
sess.runner().feed("input", input).fetch("output").run().get(0).expect(Float.class)) {
System.out.printf(
"For input %f, produced %f (ideally would produce 3*%f + 2)\n",
input.floatValue(), output.floatValue(), input.floatValue());
}
}
}
private static void printVariables(Session sess) {
List<Tensor<?>> values = sess.runner().fetch("W/read").fetch("b/read").run();
System.out.printf("W = %f\tb = %f\n", values.get(0).floatValue(), values.get(1).floatValue());
for (Tensor<?> t : values) {
t.close();
}
}
}
tutorials/image/cifar10_estimator/cifar10_main.py
View file @ d5e826e3
......@@ -503,6 +503,8 @@ if __name__ == '__main__':
help='Epsilon for batch norm.')
args = parser.parse_args()
if args.num_gpus > 0:
assert tf.test.is_gpu_available(), "Requested GPUs but none found."
if args.num_gpus < 0:
raise ValueError(
'Invalid GPU count: \"--num-gpus\" must be 0 or a positive integer.')
......
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