From 54f6e4e3a33f745da32142bdcba35015edea7d40 Mon Sep 17 00:00:00 2001 From: Lee Sharma Date: Wed, 10 Oct 2018 12:11:34 -0400 Subject: [PATCH 01/11] Remove old model files Turns out that these were in the wrong format. Stay tuned for the *actual* model files. --- src/main/resources/model.ckpt-1000000.index | Bin 16876 -> 0 bytes src/main/resources/model.ckpt-2000000.index | Bin 16876 -> 0 bytes 2 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 src/main/resources/model.ckpt-1000000.index delete mode 100644 src/main/resources/model.ckpt-2000000.index diff --git a/src/main/resources/model.ckpt-1000000.index b/src/main/resources/model.ckpt-1000000.index deleted file mode 100644 index 56dfc46a7f76b7ba9cadf24861227b0290e38955..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 16876 zcmb8130M@z631r&0YO1d@m>%F#e>7~!h;x1qDj=4rzS>4julZvBB*H0%Xo20Ji#jn z#-~V74iV81;wmC4As$GKQ4oR`D4tOyUazVfSeNP91-}msU;nn|U)9xBJw3DGIDJD| zXB|Ts=hR_bkgp;*G+<%SRQCbzFAQ4Z=I7<=?d2ZkK47$WsPEhf3l}XI;G+ojHk6s` znd=zJodg;0Kch6>-q>Qn!X*Jg{$2|d-a(C{azTbbeO2NRXG4o6-irdf!M^(Cq#g)V zynO1xltesAO!c|h*+9;gBuBsHf~}K~q{G*H^?q(?Y3U;!B2T$+0{qnB%M9;E+Z$Vo 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We've verified that this works on all systems, so we're ready to actually integrate TensorFlow into the app. --- src/main/java/Main.java | 41 ----------------------------------------- 1 file changed, 41 deletions(-) diff --git a/src/main/java/Main.java b/src/main/java/Main.java index 8511086..b7f328e 100644 --- a/src/main/java/Main.java +++ b/src/main/java/Main.java @@ -21,15 +21,9 @@ import javafx.stage.FileChooser; import javafx.stage.Stage; -import org.tensorflow.Graph; -import org.tensorflow.Session; -import org.tensorflow.Tensor; -import org.tensorflow.TensorFlow; - import java.awt.image.BufferedImage; import java.io.File; import java.io.IOException; -import java.nio.charset.StandardCharsets; /** * Entry point to the What Does It Mean? captioning application. @@ -42,10 +36,6 @@ public class Main extends Application { * @param args command-line arguments */ public static void main(String[] args) { - // FIXME: delete me once everyone verifies their setup - helloTensorflow(); // verifies tensorflow installation - System.out.println("If you a tensorflow console message, you're all set!"); - launch(args); System.out.println("Program Finished"); } @@ -129,35 +119,4 @@ public void start(Stage primaryStage) { }); } - - /** - * Verifies basic Tensorflow installation. - * - *

Reprinted from: https://www.tensorflow.org/install/install_java - * - *

FIXME: Delete me once everyone verifies their setup. - */ - private static void helloTensorflow() { - try (Graph g = new Graph()) { - final String value = "Hello Tensorflow " + TensorFlow.version(); - - // Construct the computation graph with a single operation, a constant - // named "MyConst" with a value "value". - try (Tensor t = Tensor.create(value.getBytes(StandardCharsets.UTF_8))) { - // The Java API doesn't yet include convenience functions for adding operations. - g.opBuilder("Const", "MyConst") - .setAttr("dtype", t.dataType()) - .setAttr("value", t) - .build(); - } - - // Execute the "MyConst" operation in a Session. - try (Session s = new Session(g); - // Generally, there may be multiple output tensors, all of them must - // be closed to prevent resource leaks. - Tensor output = s.runner().fetch("MyConst").run().get(0)) { - System.out.println(new String(output.bytesValue(), StandardCharsets.UTF_8)); - } - } - } } From 23429dd0ee714f2e8c165849fdcd62a19908deb3 Mon Sep 17 00:00:00 2001 From: Lee Sharma Date: Wed, 10 Oct 2018 12:16:20 -0400 Subject: [PATCH 03/11] Add Git LFS Apparently the model is too big for git, so let's try git lfs. This is just setting up the large file tracking. --- .gitattributes | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 .gitattributes diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..71738fd --- /dev/null +++ b/.gitattributes @@ -0,0 +1,4 @@ +src/main/resources/model filter=lfs diff=lfs merge=lfs -text +src/main/resources/model/variables/variables.index filter=lfs diff=lfs merge=lfs -text +src/main/resources/model/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text +src/main/resources/model/saved_model.pbtxt filter=lfs diff=lfs merge=lfs -text From 02ccae59c90895f107c16c762b5a4f952454d6e9 Mon Sep 17 00:00:00 2001 From: Lee Sharma Date: Wed, 10 Oct 2018 12:40:36 -0400 Subject: [PATCH 04/11] Add the Tensorflow model This commit adds the trained (2M iter) tensorflow model to the resources directory. Because of their large size, the model files are managed by git lfs. To download them: 1. Download git-lfs (https://git-lfs.github.com/) 2. Run `git lfs pull` 2a. If that doesn't work, try deleting `src/main/resources/model` and trying again If that doesn't work, I also added a shell script to download the files: `bin/download_model.sh`. It's tested in OS X, and it should work on UNIX-based systems that have curl and unzip. I'm not sure about Windows there. As a last-ditch option, you could go to: https://www.dropbox.com/s/1q7cwmewooi18s0/model.zip?dl=0 Unzip the files yourself, and put them in src/main/resources. This will serve as a launching point for actually performing the inference operation. Trained Model Source: https://github.com/KranthiGV/Pretrained-Show-and-Tell-model --- bin/download_model.sh | 8 ++++++++ src/main/resources/model/saved_model.pbtxt | 3 +++ .../model/variables/variables.data-00000-of-00001 | 3 +++ src/main/resources/model/variables/variables.index | 3 +++ 4 files changed, 17 insertions(+) create mode 100755 bin/download_model.sh create mode 100644 src/main/resources/model/saved_model.pbtxt create mode 100644 src/main/resources/model/variables/variables.data-00000-of-00001 create mode 100644 src/main/resources/model/variables/variables.index diff --git a/bin/download_model.sh b/bin/download_model.sh new file mode 100755 index 0000000..249df82 --- /dev/null +++ b/bin/download_model.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +# downloads a zipped copy of our underlying model +curl -L -o model.zip \ + https://www.dropbox.com/s/1q7cwmewooi18s0/model.zip?dl=0 + +# unzips the compressed file +unzip model.zip -d src/main/resources/ \ No newline at end of file diff --git a/src/main/resources/model/saved_model.pbtxt b/src/main/resources/model/saved_model.pbtxt new file mode 100644 index 0000000..02635da --- /dev/null +++ b/src/main/resources/model/saved_model.pbtxt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7d3d60e511653f813e49c5b55edabcee5d3070b93605a9ab4ff476884576f41a +size 847198697 diff --git a/src/main/resources/model/variables/variables.data-00000-of-00001 b/src/main/resources/model/variables/variables.data-00000-of-00001 new file mode 100644 index 0000000..0c5d64c --- /dev/null +++ b/src/main/resources/model/variables/variables.data-00000-of-00001 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:63021d24b007e37acb9f7df42bb942f2f66915b82c37597d51e32bd5bed15f7c +size 298004488 diff --git a/src/main/resources/model/variables/variables.index b/src/main/resources/model/variables/variables.index new file mode 100644 index 0000000..c5daa25 --- /dev/null +++ b/src/main/resources/model/variables/variables.index @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1191187a4bd6a652889ae2393ada81b37f6281a206756a53518e208d1d03aca6 +size 23226 From 379625ca52ceb888b7bc456f6fa51774b471f87b Mon Sep 17 00:00:00 2001 From: Lee Sharma Date: Wed, 10 Oct 2018 12:45:37 -0400 Subject: [PATCH 05/11] Load the Tensorflow model This commit writes some boilerplate Java to load the model. This will serve as a launching point for actually performing the inference operation. --- src/main/java/Inference.java | 31 +++++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) create mode 100644 src/main/java/Inference.java diff --git a/src/main/java/Inference.java b/src/main/java/Inference.java new file mode 100644 index 0000000..fc4eeb3 --- /dev/null +++ b/src/main/java/Inference.java @@ -0,0 +1,31 @@ +/* + * ----------------------------------------------------------------------------- + * Inference.java + * ----------------------------------------------------------------------------- + * Created October 4, 2018 06:33 + * Course: CMSC 495 Capstone + * Group: F(antastic) + * Project: What Does It Mean?: Automatic Caption Generator + */ + +import org.tensorflow.Graph; +import org.tensorflow.SavedModelBundle; +import org.tensorflow.Session; +import org.tensorflow.TensorFlow; + +public class Inference { + + /** + * Test method to make sure the model is loading correctly. + * + * @param args command-line args + */ + public static void main(String[] args) { + TensorFlow.version(); // crash the app if tensorflow isn't loaded + try (SavedModelBundle model = SavedModelBundle.load("src/main/resources/model", "serve")) { + Session sess = model.session(); + Graph graph = model.graph(); + System.out.println("Success!"); + } + } +} From 2880f872e4ffbd542920bb557ba796c76c993f4b Mon Sep 17 00:00:00 2001 From: Lee Sharma Date: Fri, 12 Oct 2018 03:30:58 -0400 Subject: [PATCH 06/11] An absolutely ridiculous multi-lang implementation It works! Kind of. I'm using both Java and Python here via an "exec" command, which is awful. However, my Java beam search isn't working, and we need *something* immediately. This will get us a grade. Former-commit-id: 1088b813da2558a7d58dd0a5da7c47b789f92b74 [formerly de31829a403b7b63932a3c13aa008d014cf624d4] [formerly 34a4ccf5f102bca70a908d7fc0a8b2eafa8c2b42 [formerly 44772b9b31c60eceeee723e93dd6be66903f6074]] Former-commit-id: b16f580f57e677857bf545fec74fe39fa3158962 [formerly 08019804f54a7a1ef690675fbb49ff694989f198] Former-commit-id: f73bc9c0ee1f2b2fae1c360aef307d07ff8ff650 --- bin/build.sh | 9 + bin/download_model.sh | 8 - bin/install.osx.sh | 37 ++ bin/install.ubuntu.sh | 54 ++ bin/run.sh | 3 + pom.xml | 5 + src/main/java/CaptionGenerator.java | 94 ++-- src/main/java/Inference.java | 31 -- src/main/java/Main.java | 12 +- src/main/python/.gitignore | 8 + src/main/python/README.md | 337 ++++++++++++ src/main/python/WORKSPACE | 1 + .../g3doc/COCO_val2014_000000224477.jpg | Bin 0 -> 194898 bytes src/main/python/g3doc/example_captions.jpg | Bin 0 -> 430666 bytes .../g3doc/show_and_tell_architecture.png | Bin 0 -> 900803 bytes src/main/python/im2txt/BUILD | 96 ++++ src/main/python/im2txt/configuration.py | 104 ++++ .../python/im2txt/data/build_mscoco_data.py | 483 ++++++++++++++++++ .../data/download_and_preprocess_mscoco.sh | 90 ++++ src/main/python/im2txt/evaluate.py | 198 +++++++ src/main/python/im2txt/inference_utils/BUILD | 31 ++ .../inference_utils/caption_generator.py | 211 ++++++++ .../inference_utils/caption_generator_test.py | 178 +++++++ .../inference_utils/inference_wrapper_base.py | 182 +++++++ .../im2txt/inference_utils/vocabulary.py | 78 +++ src/main/python/im2txt/inference_wrapper.py | 51 ++ src/main/python/im2txt/ops/BUILD | 32 ++ src/main/python/im2txt/ops/image_embedding.py | 114 +++++ .../python/im2txt/ops/image_embedding_test.py | 136 +++++ .../python/im2txt/ops/image_processing.py | 133 +++++ src/main/python/im2txt/ops/inputs.py | 204 ++++++++ src/main/python/im2txt/run_inference.py | 87 ++++ src/main/python/im2txt/show_and_tell_model.py | 358 +++++++++++++ .../python/im2txt/show_and_tell_model_test.py | 200 ++++++++ src/main/python/im2txt/train.py | 114 +++++ src/main/resources/canned_captions.txt | 11 - src/main/resources/full_model/.gitattributes | 1 + .../full_model/ckpt5.data-00000-of-00001 | 3 + src/main/resources/full_model/ckpt5.index | Bin 0 -> 16873 bytes src/main/resources/full_model/ckpt5.meta | Bin 0 -> 1207932 bytes src/main/resources/model/saved_model.pbtxt | 4 +- .../resources/model/variables/.gitattributes | 2 + .../variables/variables.data-00000-of-00001 | 3 - .../variables/variables.data-00000-of-00002 | 3 + .../variables/variables.data-00001-of-00002 | 3 + .../resources/model/variables/variables.index | 4 +- src/test/java/CaptionGeneratorTest.java | 112 ++-- .../TopNTest.java} | 32 +- 48 files changed, 3677 insertions(+), 180 deletions(-) create mode 100755 bin/build.sh delete mode 100755 bin/download_model.sh create mode 100755 bin/install.osx.sh create mode 100755 bin/install.ubuntu.sh create mode 100755 bin/run.sh delete mode 100644 src/main/java/Inference.java create mode 100644 src/main/python/.gitignore create mode 100644 src/main/python/README.md create mode 100644 src/main/python/WORKSPACE create mode 100644 src/main/python/g3doc/COCO_val2014_000000224477.jpg create mode 100644 src/main/python/g3doc/example_captions.jpg create mode 100644 src/main/python/g3doc/show_and_tell_architecture.png create mode 100644 src/main/python/im2txt/BUILD create mode 100644 src/main/python/im2txt/configuration.py create mode 100644 src/main/python/im2txt/data/build_mscoco_data.py create mode 100755 src/main/python/im2txt/data/download_and_preprocess_mscoco.sh create mode 100644 src/main/python/im2txt/evaluate.py create mode 100644 src/main/python/im2txt/inference_utils/BUILD create mode 100644 src/main/python/im2txt/inference_utils/caption_generator.py create mode 100644 src/main/python/im2txt/inference_utils/caption_generator_test.py create mode 100644 src/main/python/im2txt/inference_utils/inference_wrapper_base.py create mode 100644 src/main/python/im2txt/inference_utils/vocabulary.py create mode 100644 src/main/python/im2txt/inference_wrapper.py create mode 100644 src/main/python/im2txt/ops/BUILD create mode 100644 src/main/python/im2txt/ops/image_embedding.py create mode 100644 src/main/python/im2txt/ops/image_embedding_test.py create mode 100644 src/main/python/im2txt/ops/image_processing.py create mode 100644 src/main/python/im2txt/ops/inputs.py create mode 100644 src/main/python/im2txt/run_inference.py create mode 100644 src/main/python/im2txt/show_and_tell_model.py create mode 100644 src/main/python/im2txt/show_and_tell_model_test.py create mode 100644 src/main/python/im2txt/train.py delete mode 100644 src/main/resources/canned_captions.txt create mode 100644 src/main/resources/full_model/.gitattributes create mode 100644 src/main/resources/full_model/ckpt5.data-00000-of-00001 create mode 100644 src/main/resources/full_model/ckpt5.index create mode 100644 src/main/resources/full_model/ckpt5.meta create mode 100644 src/main/resources/model/variables/.gitattributes delete mode 100644 src/main/resources/model/variables/variables.data-00000-of-00001 create mode 100644 src/main/resources/model/variables/variables.data-00000-of-00002 create mode 100644 src/main/resources/model/variables/variables.data-00001-of-00002 rename src/test/java/{TruthTest.java => show_and_tell/TopNTest.java} (56%) diff --git a/bin/build.sh b/bin/build.sh new file mode 100755 index 0000000..1536e32 --- /dev/null +++ b/bin/build.sh @@ -0,0 +1,9 @@ +#!/usr/bin/env bash + +mvn clean +mvn compile + +cd src/main/python +bazel clean +bazel build -c opt //im2txt:run_inference +cd ../../.. \ No newline at end of file diff --git a/bin/download_model.sh b/bin/download_model.sh deleted file mode 100755 index 249df82..0000000 --- a/bin/download_model.sh +++ /dev/null @@ -1,8 +0,0 @@ -#!/bin/bash - -# downloads a zipped copy of our underlying model -curl -L -o model.zip \ - https://www.dropbox.com/s/1q7cwmewooi18s0/model.zip?dl=0 - -# unzips the compressed file -unzip model.zip -d src/main/resources/ \ No newline at end of file diff --git a/bin/install.osx.sh b/bin/install.osx.sh new file mode 100755 index 0000000..c8af733 --- /dev/null +++ b/bin/install.osx.sh @@ -0,0 +1,37 @@ +#!/usr/bin/env bash + +echo "This script assumes that you have the Homebrew package manager installed." +echo "If you don't, please either install it (https://brew.sh/) or manually" +echo "install Bazel (https://bazel.build/) and Maven (https://maven.apache.org/)." +echo "" +echo "If you want to use a python virtual environment, stop this script and" +echo "activate it now." +echo "" + +echo "=============================" +echo "Installing bazel and maven..." +echo "=============================" +brew install bazel +brew install maven + +echo "" +echo "=============================" +echo "Installing python packages..." +echo "=============================" +pip install tensorflow +pip install numpy +pip install nltk + +echo "" +echo "============================" +echo "Installing java packages..." +echo "============================" +mvn validate + +echo "" +echo "==========================" +echo "Verifying installations..." +echo "==========================" +bazel version +mvn --version +pip --version \ No newline at end of file diff --git a/bin/install.ubuntu.sh b/bin/install.ubuntu.sh new file mode 100755 index 0000000..ac7ac1a --- /dev/null +++ b/bin/install.ubuntu.sh @@ -0,0 +1,54 @@ +#!/usr/bin/env bash + +echo "This script assumes that you have the apt-get package manager installed." +echo "If you don't, please either install it or manually install the dependencies" +echo "Bazel (https://bazel.build/) and Maven (https://maven.apache.org/)." +echo "" +echo "If you want to use a python virtual environment, stop this script and" +echo "activate it now." +echo "" + +echo "=============================" +echo "Installing bazel and maven..." +echo "=============================" +sudo apt-get update -y +sudo apt-get upgrade -y +# Bazel +sudo apt-get install pkg-config zip g++ zlib1g-dev unzip python +chmod +x bazel-0.17.2-installer-linux-x86_64.sh +./bazel-0.17.2-installer-linux-x86_64.sh --user +export PATH="$PATH:$HOME/bin" + +# Maven +cd /opt/ +wget http://www-eu.apache.org/dist/maven/maven-3/3.3.9/binaries/apache-maven-3.3.9-bin.tar.gz +sudo tar -xvzf apache-maven-3.3.9-bin.tar.gz +rm apache-maven-3.3.9-bin.tar.gz +sudo mv apache-maven-3.3.9 maven +# env vars +sudo echo "export M2_HOME=/opt/maven" > /etc/profile.d/mavenenv.sh +sudo echo "export PATH=\${M2_HOME}/bin:\${PATH}" > /etc/profile.d/mavenenv.sh +sudo chmod +x /etc/profile.d/mavenenv.sh +sudo source /etc/profile.d/mavenenv.sh + +echo "" +echo "=============================" +echo "Installing python packages..." +echo "=============================" +pip install tensorflow +pip install numpy +pip install nltk + +echo "" +echo "============================" +echo "Installing java packages..." +echo "============================" +mvn validate + +echo "" +echo "==========================" +echo "Verifying installations..." +echo "==========================" +bazel version +mvn --version +pip --version \ No newline at end of file diff --git a/bin/run.sh b/bin/run.sh new file mode 100755 index 0000000..eefd650 --- /dev/null +++ b/bin/run.sh @@ -0,0 +1,3 @@ +#!/usr/bin/env bash + +mvn exec:java \ No newline at end of file diff --git a/pom.xml b/pom.xml index 5b5f398..c508bad 100644 --- a/pom.xml +++ b/pom.xml @@ -105,6 +105,11 @@ tensorflow 1.10.0 + + org.tensorflow + proto + 1.10.0 + diff --git a/src/main/java/CaptionGenerator.java b/src/main/java/CaptionGenerator.java index 25978b0..c875a47 100644 --- a/src/main/java/CaptionGenerator.java +++ b/src/main/java/CaptionGenerator.java @@ -10,45 +10,18 @@ import java.awt.image.BufferedImage; import java.io.BufferedReader; -import java.io.FileReader; import java.io.IOException; -import java.util.ArrayList; -import java.util.List; +import java.io.InputStreamReader; /** * Generates a caption from a stored image. */ class CaptionGenerator { - private String[] captions; /** * Constructs a new CaptionGenerator with canned captions. */ CaptionGenerator() { - String captionsPath = "./src/main/resources/canned_captions.txt"; - this.captions = readLines(captionsPath); - } - - /** - * Reads a file and returns the lines, one line per array entry. - * - * @param filename input file path - * @return lines in file - */ - private String[] readLines(String filename) { - List lines = new ArrayList<>(); - String line; - - // open the file and read in lines - try (BufferedReader br = new BufferedReader(new FileReader(filename))) { - while ((line = br.readLine()) != null) { - lines.add(line); - } - } catch (IOException err) { - err.printStackTrace(); - } - - return lines.toArray(new String[0]); } /** @@ -56,38 +29,47 @@ private String[] readLines(String filename) { * * @return image caption */ - String generateCaption(BufferedImage image) { - if (image == null) { - throw new IllegalArgumentException("Input must be a valid image."); - } + String generateCaption(String filename) throws IOException { + // if (image == null) { + // throw new IllegalArgumentException("Input must be a valid image."); + // } - // choose a caption (pre-Tensorflow) - int digest = hash(image); - int idx = Math.abs(digest % captions.length); + String checkpointPath = "src/main/resources/full_model/ckpt5"; + String vocabFile = "src/main/resources/word_counts.txt"; + String imageFile = filename + .replace("\\", "\\\\") + .replace(" ", "\\ "); - return captions[idx]; - } + String str; + String cmd = String.format("src/main/python/bazel-bin/im2txt/run_inference \\\n" + + " --checkpoint_path=%s \\\n" + + " --vocab_file=%s \\\n" + + " --input_files=%s", + checkpointPath, vocabFile, imageFile); + StringBuilder caption = new StringBuilder(); + System.out.println("Captioning image with the following command:"); + System.out.println(cmd); - /** - * Naive hash to identify the image content. - * - *

This is done by simply summing up the pixel values in the image. This - * is definitely not a secure method, but it will return the same value if two - * images are identical and (likely) return different values for different - * images. - * - *

All this code will be deleted once we have Tensorflow integration. - * - * @param image image input - * @return integer digest - */ - private int hash(BufferedImage image) { - int sum = 0; - for (int i = 0; i < image.getWidth(); i++) { - for (int j = 0; j < image.getHeight(); j++) { - sum += image.getRGB(i,j); + try { + Process proc = Runtime.getRuntime().exec(cmd); + + // read the output from the command + BufferedReader stdInput = new BufferedReader(new InputStreamReader(proc.getInputStream())); + System.out.println("Here is the standard output of the command:\n"); + while ((str = stdInput.readLine()) != null) { + caption.append(str); + System.out.println(str); } + + BufferedReader stdError = new BufferedReader(new InputStreamReader(proc.getErrorStream())); + System.out.println("Here is the error output of the command:\n"); + while ((str = stdError.readLine()) != null) { + System.out.println(str); + } + + } catch (IOException ex) { + ex.printStackTrace(); } - return Math.abs(sum); + return caption.toString(); } } diff --git a/src/main/java/Inference.java b/src/main/java/Inference.java deleted file mode 100644 index fc4eeb3..0000000 --- a/src/main/java/Inference.java +++ /dev/null @@ -1,31 +0,0 @@ -/* - * ----------------------------------------------------------------------------- - * Inference.java - * ----------------------------------------------------------------------------- - * Created October 4, 2018 06:33 - * Course: CMSC 495 Capstone - * Group: F(antastic) - * Project: What Does It Mean?: Automatic Caption Generator - */ - -import org.tensorflow.Graph; -import org.tensorflow.SavedModelBundle; -import org.tensorflow.Session; -import org.tensorflow.TensorFlow; - -public class Inference { - - /** - * Test method to make sure the model is loading correctly. - * - * @param args command-line args - */ - public static void main(String[] args) { - TensorFlow.version(); // crash the app if tensorflow isn't loaded - try (SavedModelBundle model = SavedModelBundle.load("src/main/resources/model", "serve")) { - Session sess = model.session(); - Graph graph = model.graph(); - System.out.println("Success!"); - } - } -} diff --git a/src/main/java/Main.java b/src/main/java/Main.java index b7f328e..a09727b 100644 --- a/src/main/java/Main.java +++ b/src/main/java/Main.java @@ -30,6 +30,8 @@ */ public class Main extends Application { + private String imgFilename; + /** * Launches the application. * @@ -85,11 +87,16 @@ public void start(Stage primaryStage) { //Caption Button User Event btn2.setOnAction(event -> { //Instantiate CaptionGenerator - CaptionGenerator cg = new CaptionGenerator(); + CaptionGenerator cg = null; + cg = new CaptionGenerator(); //Convert JavaFX Image to BufferedImage BufferedImage img = SwingFXUtils.fromFXImage(myImageView.getImage(), null); //Run captioning method and set label text - lb1.setText(cg.generateCaption(img)); + try { + lb1.setText(cg.generateCaption(imgFilename)); + } catch (IOException ex) { + ex.printStackTrace(); // TODO: error popup + } }); //Browse Button User Event @@ -100,6 +107,7 @@ public void start(Stage primaryStage) { File file = fc.showOpenDialog(primaryStage); try { + imgFilename = file.toString(); Image image = new Image(String.valueOf(file.toURI().toURL())); myImageView.setImage(image); myImageView.setPreserveRatio(true); diff --git a/src/main/python/.gitignore b/src/main/python/.gitignore new file mode 100644 index 0000000..5447fb7 --- /dev/null +++ b/src/main/python/.gitignore @@ -0,0 +1,8 @@ +/bazel-bin +/bazel-ci_build-cache +/bazel-genfiles +/bazel-out +/bazel-im2txt +/bazel-testlogs +/bazel-tf +/bazel-python diff --git a/src/main/python/README.md b/src/main/python/README.md new file mode 100644 index 0000000..5f7ce9c --- /dev/null +++ b/src/main/python/README.md @@ -0,0 +1,337 @@ +# Show and Tell: A Neural Image Caption Generator + +A TensorFlow implementation of the image-to-text model described in the paper: + +"Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning +Challenge." + +Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan. + +*IEEE transactions on pattern analysis and machine intelligence (2016).* + +Full text available at: http://arxiv.org/abs/1609.06647 + +## Contact +***Author:*** Chris Shallue + +***Pull requests and issues:*** @cshallue + +## Contents +* [Model Overview](#model-overview) + * [Introduction](#introduction) + * [Architecture](#architecture) +* [Getting Started](#getting-started) + * [A Note on Hardware and Training Time](#a-note-on-hardware-and-training-time) + * [Install Required Packages](#install-required-packages) + * [Prepare the Training Data](#prepare-the-training-data) + * [Download the Inception v3 Checkpoint](#download-the-inception-v3-checkpoint) +* [Training a Model](#training-a-model) + * [Initial Training](#initial-training) + * [Fine Tune the Inception v3 Model](#fine-tune-the-inception-v3-model) +* [Generating Captions](#generating-captions) + +## Model Overview + +### Introduction + +The *Show and Tell* model is a deep neural network that learns how to describe +the content of images. For example: + +![Example captions](g3doc/example_captions.jpg) + +### Architecture + +The *Show and Tell* model is an example of an *encoder-decoder* neural network. +It works by first "encoding" an image into a fixed-length vector representation, +and then "decoding" the representation into a natural language description. + +The image encoder is a deep convolutional neural network. This type of +network is widely used for image tasks and is currently state-of-the-art for +object recognition and detection. Our particular choice of network is the +[*Inception v3*](http://arxiv.org/abs/1512.00567) image recognition model +pretrained on the +[ILSVRC-2012-CLS](http://www.image-net.org/challenges/LSVRC/2012/) image +classification dataset. + +The decoder is a long short-term memory (LSTM) network. This type of network is +commonly used for sequence modeling tasks such as language modeling and machine +translation. In the *Show and Tell* model, the LSTM network is trained as a +language model conditioned on the image encoding. + +Words in the captions are represented with an embedding model. Each word in the +vocabulary is associated with a fixed-length vector representation that is +learned during training. + +The following diagram illustrates the model architecture. + +![Show and Tell Architecture](g3doc/show_and_tell_architecture.png) + +In this diagram, \{*s*0, *s*1, ..., *s**N*-1\} +are the words of the caption and \{*w**e**s*0, +*w**e**s*1, ..., *w**e**s**N*-1\} +are their corresponding word embedding vectors. The outputs \{*p*1, +*p*2, ..., *p**N*\} of the LSTM are probability +distributions generated by the model for the next word in the sentence. The +terms \{log *p*1(*s*1), +log *p*2(*s*2), ..., +log *p**N*(*s**N*)\} are the log-likelihoods of the +correct word at each step; the negated sum of these terms is the minimization +objective of the model. + +During the first phase of training the parameters of the *Inception v3* model +are kept fixed: it is simply a static image encoder function. A single trainable +layer is added on top of the *Inception v3* model to transform the image +embedding into the word embedding vector space. The model is trained with +respect to the parameters of the word embeddings, the parameters of the layer on +top of *Inception v3* and the parameters of the LSTM. In the second phase of +training, all parameters - including the parameters of *Inception v3* - are +trained to jointly fine-tune the image encoder and the LSTM. + +Given a trained model and an image we use *beam search* to generate captions for +that image. Captions are generated word-by-word, where at each step *t* we use +the set of sentences already generated with length *t* - 1 to generate a new set +of sentences with length *t*. We keep only the top *k* candidates at each step, +where the hyperparameter *k* is called the *beam size*. We have found the best +performance with *k* = 3. + +## Getting Started + +### A Note on Hardware and Training Time + +The time required to train the *Show and Tell* model depends on your specific +hardware and computational capacity. In this guide we assume you will be running +training on a single machine with a GPU. In our experience on an NVIDIA Tesla +K20m GPU the initial training phase takes 1-2 weeks. The second training phase +may take several additional weeks to achieve peak performance (but you can stop +this phase early and still get reasonable results). + +It is possible to achieve a speed-up by implementing distributed training across +a cluster of machines with GPUs, but that is not covered in this guide. + +Whilst it is possible to run this code on a CPU, beware that this may be +approximately 10 times slower. + +### Install Required Packages +First ensure that you have installed the following required packages: + +* **Bazel** ([instructions](http://bazel.io/docs/install.html)) +* **TensorFlow** 1.0 or greater ([instructions](https://www.tensorflow.org/install/)) +* **NumPy** ([instructions](http://www.scipy.org/install.html)) +* **Natural Language Toolkit (NLTK)**: + * First install NLTK ([instructions](http://www.nltk.org/install.html)) + * Then install the NLTK data package "punkt" ([instructions](http://www.nltk.org/data.html)) +* **Unzip** +### Prepare the Training Data + +To train the model you will need to provide training data in native TFRecord +format. The TFRecord format consists of a set of sharded files containing +serialized `tf.SequenceExample` protocol buffers. Each `tf.SequenceExample` +proto contains an image (JPEG format), a caption and metadata such as the image +id. + +Each caption is a list of words. During preprocessing, a dictionary is created +that assigns each word in the vocabulary to an integer-valued id. Each caption +is encoded as a list of integer word ids in the `tf.SequenceExample` protos. + +We have provided a script to download and preprocess the [MSCOCO](http://mscoco.org/) image captioning data set into this format. Downloading +and preprocessing the data may take several hours depending on your network and +computer speed. Please be patient. + +Before running the script, ensure that your hard disk has at least 150GB of +available space for storing the downloaded and processed data. + +```shell +# Location to save the MSCOCO data. +MSCOCO_DIR="${HOME}/im2txt/data/mscoco" + +# Build the preprocessing script. +cd research/im2txt +bazel build //im2txt:download_and_preprocess_mscoco + +# Run the preprocessing script. +bazel-bin/im2txt/download_and_preprocess_mscoco "${MSCOCO_DIR}" +``` + +The final line of the output should read: + +``` +2016-09-01 16:47:47.296630: Finished processing all 20267 image-caption pairs in data set 'test'. +``` + +When the script finishes you will find 256 training, 4 validation and 8 testing +files in `DATA_DIR`. The files will match the patterns `train-?????-of-00256`, +`val-?????-of-00004` and `test-?????-of-00008`, respectively. + +### Download the Inception v3 Checkpoint + +The *Show and Tell* model requires a pretrained *Inception v3* checkpoint file +to initialize the parameters of its image encoder submodel. + +This checkpoint file is provided by the +[TensorFlow-Slim image classification library](https://github.com/tensorflow/models/tree/master/research/slim#tensorflow-slim-image-classification-library) +which provides a suite of pre-trained image classification models. You can read +more about the models provided by the library +[here](https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models). + + +Run the following commands to download the *Inception v3* checkpoint. + +```shell +# Location to save the Inception v3 checkpoint. +INCEPTION_DIR="${HOME}/im2txt/data" +mkdir -p ${INCEPTION_DIR} + +wget "http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz" +tar -xvf "inception_v3_2016_08_28.tar.gz" -C ${INCEPTION_DIR} +rm "inception_v3_2016_08_28.tar.gz" +``` + +Note that the *Inception v3* checkpoint will only be used for initializing the +parameters of the *Show and Tell* model. Once the *Show and Tell* model starts +training it will save its own checkpoint files containing the values of all its +parameters (including copies of the *Inception v3* parameters). If training is +stopped and restarted, the parameter values will be restored from the latest +*Show and Tell* checkpoint and the *Inception v3* checkpoint will be ignored. In +other words, the *Inception v3* checkpoint is only used in the 0-th global step +(initialization) of training the *Show and Tell* model. + +## Training a Model + +### Initial Training + +Run the training script. + +```shell +# Directory containing preprocessed MSCOCO data. +MSCOCO_DIR="${HOME}/im2txt/data/mscoco" + +# Inception v3 checkpoint file. +INCEPTION_CHECKPOINT="${HOME}/im2txt/data/inception_v3.ckpt" + +# Directory to save the model. +MODEL_DIR="${HOME}/im2txt/model" + +# Build the model. +cd research/im2txt +bazel build -c opt //im2txt/... + +# Run the training script. +bazel-bin/im2txt/train \ + --input_file_pattern="${MSCOCO_DIR}/train-?????-of-00256" \ + --inception_checkpoint_file="${INCEPTION_CHECKPOINT}" \ + --train_dir="${MODEL_DIR}/train" \ + --train_inception=false \ + --number_of_steps=1000000 +``` + +Run the evaluation script in a separate process. This will log evaluation +metrics to TensorBoard which allows training progress to be monitored in +real-time. + +Note that you may run out of memory if you run the evaluation script on the same +GPU as the training script. You can run the command +`export CUDA_VISIBLE_DEVICES=""` to force the evaluation script to run on CPU. +If evaluation runs too slowly on CPU, you can decrease the value of +`--num_eval_examples`. + +```shell +MSCOCO_DIR="${HOME}/im2txt/data/mscoco" +MODEL_DIR="${HOME}/im2txt/model" + +# Ignore GPU devices (only necessary if your GPU is currently memory +# constrained, for example, by running the training script). +export CUDA_VISIBLE_DEVICES="" + +# Run the evaluation script. This will run in a loop, periodically loading the +# latest model checkpoint file and computing evaluation metrics. +bazel-bin/im2txt/evaluate \ + --input_file_pattern="${MSCOCO_DIR}/val-?????-of-00004" \ + --checkpoint_dir="${MODEL_DIR}/train" \ + --eval_dir="${MODEL_DIR}/eval" +``` + +Run a TensorBoard server in a separate process for real-time monitoring of +training progress and evaluation metrics. + +```shell +MODEL_DIR="${HOME}/im2txt/model" + +# Run a TensorBoard server. +tensorboard --logdir="${MODEL_DIR}" +``` + +### Fine Tune the Inception v3 Model + +Your model will already be able to generate reasonable captions after the first +phase of training. Try it out! (See [Generating Captions](#generating-captions)). + +You can further improve the performance of the model by running a +second training phase to jointly fine-tune the parameters of the *Inception v3* +image submodel and the LSTM. + +```shell +# Restart the training script with --train_inception=true. +bazel-bin/im2txt/train \ + --input_file_pattern="${MSCOCO_DIR}/train-?????-of-00256" \ + --train_dir="${MODEL_DIR}/train" \ + --train_inception=true \ + --number_of_steps=3000000 # Additional 2M steps (assuming 1M in initial training). +``` + +Note that training will proceed much slower now, and the model will continue to +improve by a small amount for a long time. We have found that it will improve +slowly for an additional 2-2.5 million steps before it begins to overfit. This +may take several weeks on a single GPU. If you don't care about absolutely +optimal performance then feel free to halt training sooner by stopping the +training script or passing a smaller value to the flag `--number_of_steps`. Your +model will still work reasonably well. + +## Generating Captions + +Your trained *Show and Tell* model can generate captions for any JPEG image! The +following command line will generate captions for an image from the test set. + +```shell +# Path to checkpoint file or a directory containing checkpoint files. Passing +# a directory will only work if there is also a file named 'checkpoint' which +# lists the available checkpoints in the directory. It will not work if you +# point to a directory with just a copy of a model checkpoint: in that case, +# you will need to pass the checkpoint path explicitly. +CHECKPOINT_PATH="${HOME}/im2txt/model/train" + +# Vocabulary file generated by the preprocessing script. +VOCAB_FILE="${HOME}/im2txt/data/mscoco/word_counts.txt" + +# JPEG image file to caption. +IMAGE_FILE="${HOME}/im2txt/data/mscoco/raw-data/val2014/COCO_val2014_000000224477.jpg" + +# Build the inference binary. +cd research/im2txt +bazel build -c opt //im2txt:run_inference + +# Ignore GPU devices (only necessary if your GPU is currently memory +# constrained, for example, by running the training script). +export CUDA_VISIBLE_DEVICES="" + +# Run inference to generate captions. +bazel-bin/im2txt/run_inference \ + --checkpoint_path=${CHECKPOINT_PATH} \ + --vocab_file=${VOCAB_FILE} \ + --input_files=${IMAGE_FILE} +``` + +Example output: + +``` +Captions for image COCO_val2014_000000224477.jpg: + 0) a man riding a wave on top of a surfboard . (p=0.040413) + 1) a person riding a surf board on a wave (p=0.017452) + 2) a man riding a wave on a surfboard in the ocean . (p=0.005743) +``` + +Note: you may get different results. 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z`!^oV(*cU3OS5nR4*snm{{OD_5)^#yKjl2L-{l0iz0r|Mn285IPkg==Ruz@PBk)q9M_th$%ke*vI&jT7GRUTp{EQ)kl%La>+-Ry>q0oUqzDp`JMsDljMgg~4A+i~;0JeK^BAeISQsq4RZVV@MXcWu8sK zzqd3`p}%rUzlQ|basZ4C%KCc#-s{)G#DVvnv`VFZZ~jTEKuSzyGVI!`-;^vTB*g_| z)trk0{}rNjXJFMEkTmJYMcw<7z!3*}X6xA|((>f<{|#URUnO>B)^Xb%4B?=kBUKng zaG0qvRHn4m}?4VyCJyQ{^X<5sznx~94Bmzl#i}n=bT#*B;~b-nXf7VQ{f=+{JT;Pc=iVTn(}Umi1{Gcb{?vn#u-{08o{Mns<8yB? zfiL+tnNKYO=mV)n`>lVBt^XswO1uSTk0hS}Y=8aM2e8gFY8VV(4<7zAk^f06$<=|` zKc|vk{!ML0xe26_UquxC=IgiU0bllAD3$F`fcM{g((63wD). + # The default value is larger than the expected actual vocab size to allow + # for differences between tokenizer versions used in preprocessing. There is + # no harm in using a value greater than the actual vocab size, but using a + # value less than the actual vocab size will result in an error. + self.vocab_size = 12000 + + # Number of threads for image preprocessing. Should be a multiple of 2. + self.num_preprocess_threads = 4 + + # Batch size. + self.batch_size = 32 + + # File containing an Inception v3 checkpoint to initialize the variables + # of the Inception model. Must be provided when starting training for the + # first time. + self.inception_checkpoint_file = None + + # Dimensions of Inception v3 input images. + self.image_height = 299 + self.image_width = 299 + + # Scale used to initialize model variables. + self.initializer_scale = 0.08 + + # LSTM input and output dimensionality, respectively. + self.embedding_size = 512 + self.num_lstm_units = 512 + + # If < 1.0, the dropout keep probability applied to LSTM variables. + self.lstm_dropout_keep_prob = 0.7 + + +class TrainingConfig(object): + """Wrapper class for training hyperparameters.""" + + def __init__(self): + """Sets the default training hyperparameters.""" + # Number of examples per epoch of training data. + self.num_examples_per_epoch = 586363 + + # Optimizer for training the model. + self.optimizer = "SGD" + + # Learning rate for the initial phase of training. + self.initial_learning_rate = 2.0 + self.learning_rate_decay_factor = 0.5 + self.num_epochs_per_decay = 8.0 + + # Learning rate when fine tuning the Inception v3 parameters. + self.train_inception_learning_rate = 0.0005 + + # If not None, clip gradients to this value. + self.clip_gradients = 5.0 + + # How many model checkpoints to keep. + self.max_checkpoints_to_keep = 5 diff --git a/src/main/python/im2txt/data/build_mscoco_data.py b/src/main/python/im2txt/data/build_mscoco_data.py new file mode 100644 index 0000000..2c3e9d9 --- /dev/null +++ b/src/main/python/im2txt/data/build_mscoco_data.py @@ -0,0 +1,483 @@ +# Copyright 2016 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. +# ============================================================================== +"""Converts MSCOCO data to TFRecord file format with SequenceExample protos. + +The MSCOCO images are expected to reside in JPEG files located in the following +directory structure: + + train_image_dir/COCO_train2014_000000000151.jpg + train_image_dir/COCO_train2014_000000000260.jpg + ... + +and + + val_image_dir/COCO_val2014_000000000042.jpg + val_image_dir/COCO_val2014_000000000073.jpg + ... + +The MSCOCO annotations JSON files are expected to reside in train_captions_file +and val_captions_file respectively. + +This script converts the combined MSCOCO data into sharded data files consisting +of 256, 4 and 8 TFRecord files, respectively: + + output_dir/train-00000-of-00256 + output_dir/train-00001-of-00256 + ... + output_dir/train-00255-of-00256 + +and + + output_dir/val-00000-of-00004 + ... + output_dir/val-00003-of-00004 + +and + + output_dir/test-00000-of-00008 + ... + output_dir/test-00007-of-00008 + +Each TFRecord file contains ~2300 records. Each record within the TFRecord file +is a serialized SequenceExample proto consisting of precisely one image-caption +pair. Note that each image has multiple captions (usually 5) and therefore each +image is replicated multiple times in the TFRecord files. + +The SequenceExample proto contains the following fields: + + context: + image/image_id: integer MSCOCO image identifier + image/data: string containing JPEG encoded image in RGB colorspace + + feature_lists: + image/caption: list of strings containing the (tokenized) caption words + image/caption_ids: list of integer ids corresponding to the caption words + +The captions are tokenized using the NLTK (http://www.nltk.org/) word tokenizer. +The vocabulary of word identifiers is constructed from the sorted list (by +descending frequency) of word tokens in the training set. Only tokens appearing +at least 4 times are considered; all other words get the "unknown" word id. + +NOTE: This script will consume around 100GB of disk space because each image +in the MSCOCO dataset is replicated ~5 times (once per caption) in the output. +This is done for two reasons: + 1. In order to better shuffle the training data. + 2. It makes it easier to perform asynchronous preprocessing of each image in + TensorFlow. + +Running this script using 16 threads may take around 1 hour on a HP Z420. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from collections import Counter +from collections import namedtuple +from datetime import datetime +import json +import os.path +import random +import sys +import threading + + + +import nltk.tokenize +import numpy as np +from six.moves import xrange +import tensorflow as tf + +tf.flags.DEFINE_string("train_image_dir", "/tmp/train2014/", + "Training image directory.") +tf.flags.DEFINE_string("val_image_dir", "/tmp/val2014", + "Validation image directory.") + +tf.flags.DEFINE_string("train_captions_file", "/tmp/captions_train2014.json", + "Training captions JSON file.") +tf.flags.DEFINE_string("val_captions_file", "/tmp/captions_val2014.json", + "Validation captions JSON file.") + +tf.flags.DEFINE_string("output_dir", "/tmp/", "Output data directory.") + +tf.flags.DEFINE_integer("train_shards", 256, + "Number of shards in training TFRecord files.") +tf.flags.DEFINE_integer("val_shards", 4, + "Number of shards in validation TFRecord files.") +tf.flags.DEFINE_integer("test_shards", 8, + "Number of shards in testing TFRecord files.") + +tf.flags.DEFINE_string("start_word", "", + "Special word added to the beginning of each sentence.") +tf.flags.DEFINE_string("end_word", "", + "Special word added to the end of each sentence.") +tf.flags.DEFINE_string("unknown_word", "", + "Special word meaning 'unknown'.") +tf.flags.DEFINE_integer("min_word_count", 4, + "The minimum number of occurrences of each word in the " + "training set for inclusion in the vocabulary.") +tf.flags.DEFINE_string("word_counts_output_file", "/tmp/word_counts.txt", + "Output vocabulary file of word counts.") + +tf.flags.DEFINE_integer("num_threads", 8, + "Number of threads to preprocess the images.") + +FLAGS = tf.flags.FLAGS + +ImageMetadata = namedtuple("ImageMetadata", + ["image_id", "filename", "captions"]) + + +class Vocabulary(object): + """Simple vocabulary wrapper.""" + + def __init__(self, vocab, unk_id): + """Initializes the vocabulary. + + Args: + vocab: A dictionary of word to word_id. + unk_id: Id of the special 'unknown' word. + """ + self._vocab = vocab + self._unk_id = unk_id + + def word_to_id(self, word): + """Returns the integer id of a word string.""" + if word in self._vocab: + return self._vocab[word] + else: + return self._unk_id + + +class ImageDecoder(object): + """Helper class for decoding images in TensorFlow.""" + + def __init__(self): + # Create a single TensorFlow Session for all image decoding calls. + self._sess = tf.Session() + + # TensorFlow ops for JPEG decoding. + self._encoded_jpeg = tf.placeholder(dtype=tf.string) + self._decode_jpeg = tf.image.decode_jpeg(self._encoded_jpeg, channels=3) + + def decode_jpeg(self, encoded_jpeg): + image = self._sess.run(self._decode_jpeg, + feed_dict={self._encoded_jpeg: encoded_jpeg}) + assert len(image.shape) == 3 + assert image.shape[2] == 3 + return image + + +def _int64_feature(value): + """Wrapper for inserting an int64 Feature into a SequenceExample proto.""" + return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) + + +def _bytes_feature(value): + """Wrapper for inserting a bytes Feature into a SequenceExample proto.""" + return tf.train.Feature(bytes_list=tf.train.BytesList(value=[str(value)])) + + +def _int64_feature_list(values): + """Wrapper for inserting an int64 FeatureList into a SequenceExample proto.""" + return tf.train.FeatureList(feature=[_int64_feature(v) for v in values]) + + +def _bytes_feature_list(values): + """Wrapper for inserting a bytes FeatureList into a SequenceExample proto.""" + return tf.train.FeatureList(feature=[_bytes_feature(v) for v in values]) + + +def _to_sequence_example(image, decoder, vocab): + """Builds a SequenceExample proto for an image-caption pair. + + Args: + image: An ImageMetadata object. + decoder: An ImageDecoder object. + vocab: A Vocabulary object. + + Returns: + A SequenceExample proto. + """ + with tf.gfile.FastGFile(image.filename, "r") as f: + encoded_image = f.read() + + try: + decoder.decode_jpeg(encoded_image) + except (tf.errors.InvalidArgumentError, AssertionError): + print("Skipping file with invalid JPEG data: %s" % image.filename) + return + + context = tf.train.Features(feature={ + "image/image_id": _int64_feature(image.image_id), + "image/data": _bytes_feature(encoded_image), + }) + + assert len(image.captions) == 1 + caption = image.captions[0] + caption_ids = [vocab.word_to_id(word) for word in caption] + feature_lists = tf.train.FeatureLists(feature_list={ + "image/caption": _bytes_feature_list(caption), + "image/caption_ids": _int64_feature_list(caption_ids) + }) + sequence_example = tf.train.SequenceExample( + context=context, feature_lists=feature_lists) + + return sequence_example + + +def _process_image_files(thread_index, ranges, name, images, decoder, vocab, + num_shards): + """Processes and saves a subset of images as TFRecord files in one thread. + + Args: + thread_index: Integer thread identifier within [0, len(ranges)]. + ranges: A list of pairs of integers specifying the ranges of the dataset to + process in parallel. + name: Unique identifier specifying the dataset. + images: List of ImageMetadata. + decoder: An ImageDecoder object. + vocab: A Vocabulary object. + num_shards: Integer number of shards for the output files. + """ + # Each thread produces N shards where N = num_shards / num_threads. For + # instance, if num_shards = 128, and num_threads = 2, then the first thread + # would produce shards [0, 64). + num_threads = len(ranges) + assert not num_shards % num_threads + num_shards_per_batch = int(num_shards / num_threads) + + shard_ranges = np.linspace(ranges[thread_index][0], ranges[thread_index][1], + num_shards_per_batch + 1).astype(int) + num_images_in_thread = ranges[thread_index][1] - ranges[thread_index][0] + + counter = 0 + for s in xrange(num_shards_per_batch): + # Generate a sharded version of the file name, e.g. 'train-00002-of-00010' + shard = thread_index * num_shards_per_batch + s + output_filename = "%s-%.5d-of-%.5d" % (name, shard, num_shards) + output_file = os.path.join(FLAGS.output_dir, output_filename) + writer = tf.python_io.TFRecordWriter(output_file) + + shard_counter = 0 + images_in_shard = np.arange(shard_ranges[s], shard_ranges[s + 1], dtype=int) + for i in images_in_shard: + image = images[i] + + sequence_example = _to_sequence_example(image, decoder, vocab) + if sequence_example is not None: + writer.write(sequence_example.SerializeToString()) + shard_counter += 1 + counter += 1 + + if not counter % 1000: + print("%s [thread %d]: Processed %d of %d items in thread batch." % + (datetime.now(), thread_index, counter, num_images_in_thread)) + sys.stdout.flush() + + writer.close() + print("%s [thread %d]: Wrote %d image-caption pairs to %s" % + (datetime.now(), thread_index, shard_counter, output_file)) + sys.stdout.flush() + shard_counter = 0 + print("%s [thread %d]: Wrote %d image-caption pairs to %d shards." % + (datetime.now(), thread_index, counter, num_shards_per_batch)) + sys.stdout.flush() + + +def _process_dataset(name, images, vocab, num_shards): + """Processes a complete data set and saves it as a TFRecord. + + Args: + name: Unique identifier specifying the dataset. + images: List of ImageMetadata. + vocab: A Vocabulary object. + num_shards: Integer number of shards for the output files. + """ + # Break up each image into a separate entity for each caption. + images = [ImageMetadata(image.image_id, image.filename, [caption]) + for image in images for caption in image.captions] + + # Shuffle the ordering of images. Make the randomization repeatable. + random.seed(12345) + random.shuffle(images) + + # Break the images into num_threads batches. Batch i is defined as + # images[ranges[i][0]:ranges[i][1]]. + num_threads = min(num_shards, FLAGS.num_threads) + spacing = np.linspace(0, len(images), num_threads + 1).astype(np.int) + ranges = [] + threads = [] + for i in xrange(len(spacing) - 1): + ranges.append([spacing[i], spacing[i + 1]]) + + # Create a mechanism for monitoring when all threads are finished. + coord = tf.train.Coordinator() + + # Create a utility for decoding JPEG images to run sanity checks. + decoder = ImageDecoder() + + # Launch a thread for each batch. + print("Launching %d threads for spacings: %s" % (num_threads, ranges)) + for thread_index in xrange(len(ranges)): + args = (thread_index, ranges, name, images, decoder, vocab, num_shards) + t = threading.Thread(target=_process_image_files, args=args) + t.start() + threads.append(t) + + # Wait for all the threads to terminate. + coord.join(threads) + print("%s: Finished processing all %d image-caption pairs in data set '%s'." % + (datetime.now(), len(images), name)) + + +def _create_vocab(captions): + """Creates the vocabulary of word to word_id. + + The vocabulary is saved to disk in a text file of word counts. The id of each + word in the file is its corresponding 0-based line number. + + Args: + captions: A list of lists of strings. + + Returns: + A Vocabulary object. + """ + print("Creating vocabulary.") + counter = Counter() + for c in captions: + counter.update(c) + print("Total words:", len(counter)) + + # Filter uncommon words and sort by descending count. + word_counts = [x for x in counter.items() if x[1] >= FLAGS.min_word_count] + word_counts.sort(key=lambda x: x[1], reverse=True) + print("Words in vocabulary:", len(word_counts)) + + # Write out the word counts file. + with tf.gfile.FastGFile(FLAGS.word_counts_output_file, "w") as f: + f.write("\n".join(["%s %d" % (w, c) for w, c in word_counts])) + print("Wrote vocabulary file:", FLAGS.word_counts_output_file) + + # Create the vocabulary dictionary. + reverse_vocab = [x[0] for x in word_counts] + unk_id = len(reverse_vocab) + vocab_dict = dict([(x, y) for (y, x) in enumerate(reverse_vocab)]) + vocab = Vocabulary(vocab_dict, unk_id) + + return vocab + + +def _process_caption(caption): + """Processes a caption string into a list of tonenized words. + + Args: + caption: A string caption. + + Returns: + A list of strings; the tokenized caption. + """ + tokenized_caption = [FLAGS.start_word] + tokenized_caption.extend(nltk.tokenize.word_tokenize(caption.lower())) + tokenized_caption.append(FLAGS.end_word) + return tokenized_caption + + +def _load_and_process_metadata(captions_file, image_dir): + """Loads image metadata from a JSON file and processes the captions. + + Args: + captions_file: JSON file containing caption annotations. + image_dir: Directory containing the image files. + + Returns: + A list of ImageMetadata. + """ + with tf.gfile.FastGFile(captions_file, "r") as f: + caption_data = json.load(f) + + # Extract the filenames. + id_to_filename = [(x["id"], x["file_name"]) for x in caption_data["images"]] + + # Extract the captions. Each image_id is associated with multiple captions. + id_to_captions = {} + for annotation in caption_data["annotations"]: + image_id = annotation["image_id"] + caption = annotation["caption"] + id_to_captions.setdefault(image_id, []) + id_to_captions[image_id].append(caption) + + assert len(id_to_filename) == len(id_to_captions) + assert set([x[0] for x in id_to_filename]) == set(id_to_captions.keys()) + print("Loaded caption metadata for %d images from %s" % + (len(id_to_filename), captions_file)) + + # Process the captions and combine the data into a list of ImageMetadata. + print("Processing captions.") + image_metadata = [] + num_captions = 0 + for image_id, base_filename in id_to_filename: + filename = os.path.join(image_dir, base_filename) + captions = [_process_caption(c) for c in id_to_captions[image_id]] + image_metadata.append(ImageMetadata(image_id, filename, captions)) + num_captions += len(captions) + print("Finished processing %d captions for %d images in %s" % + (num_captions, len(id_to_filename), captions_file)) + + return image_metadata + + +def main(unused_argv): + def _is_valid_num_shards(num_shards): + """Returns True if num_shards is compatible with FLAGS.num_threads.""" + return num_shards < FLAGS.num_threads or not num_shards % FLAGS.num_threads + + assert _is_valid_num_shards(FLAGS.train_shards), ( + "Please make the FLAGS.num_threads commensurate with FLAGS.train_shards") + assert _is_valid_num_shards(FLAGS.val_shards), ( + "Please make the FLAGS.num_threads commensurate with FLAGS.val_shards") + assert _is_valid_num_shards(FLAGS.test_shards), ( + "Please make the FLAGS.num_threads commensurate with FLAGS.test_shards") + + if not tf.gfile.IsDirectory(FLAGS.output_dir): + tf.gfile.MakeDirs(FLAGS.output_dir) + + # Load image metadata from caption files. + mscoco_train_dataset = _load_and_process_metadata(FLAGS.train_captions_file, + FLAGS.train_image_dir) + mscoco_val_dataset = _load_and_process_metadata(FLAGS.val_captions_file, + FLAGS.val_image_dir) + + # Redistribute the MSCOCO data as follows: + # train_dataset = 100% of mscoco_train_dataset + 85% of mscoco_val_dataset. + # val_dataset = 5% of mscoco_val_dataset (for validation during training). + # test_dataset = 10% of mscoco_val_dataset (for final evaluation). + train_cutoff = int(0.85 * len(mscoco_val_dataset)) + val_cutoff = int(0.90 * len(mscoco_val_dataset)) + train_dataset = mscoco_train_dataset + mscoco_val_dataset[0:train_cutoff] + val_dataset = mscoco_val_dataset[train_cutoff:val_cutoff] + test_dataset = mscoco_val_dataset[val_cutoff:] + + # Create vocabulary from the training captions. + train_captions = [c for image in train_dataset for c in image.captions] + vocab = _create_vocab(train_captions) + + _process_dataset("train", train_dataset, vocab, FLAGS.train_shards) + _process_dataset("val", val_dataset, vocab, FLAGS.val_shards) + _process_dataset("test", test_dataset, vocab, FLAGS.test_shards) + + +if __name__ == "__main__": + tf.app.run() diff --git a/src/main/python/im2txt/data/download_and_preprocess_mscoco.sh b/src/main/python/im2txt/data/download_and_preprocess_mscoco.sh new file mode 100755 index 0000000..ab3ff28 --- /dev/null +++ b/src/main/python/im2txt/data/download_and_preprocess_mscoco.sh @@ -0,0 +1,90 @@ +#!/bin/bash +# Copyright 2016 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. +# ============================================================================== + +# Script to download and preprocess the MSCOCO data set. +# +# The outputs of this script are sharded TFRecord files containing serialized +# SequenceExample protocol buffers. See build_mscoco_data.py for details of how +# the SequenceExample protocol buffers are constructed. +# +# usage: +# ./download_and_preprocess_mscoco.sh +set -e + +if [ -z "$1" ]; then + echo "usage download_and_preproces_mscoco.sh [data dir]" + exit +fi + +if [ "$(uname)" == "Darwin" ]; then + UNZIP="tar -xf" +else + UNZIP="unzip -nq" +fi + +# Create the output directories. +OUTPUT_DIR="${1%/}" +SCRATCH_DIR="${OUTPUT_DIR}/raw-data" +mkdir -p "${OUTPUT_DIR}" +mkdir -p "${SCRATCH_DIR}" +CURRENT_DIR=$(pwd) +WORK_DIR="$0.runfiles/im2txt/im2txt" + +# Helper function to download and unpack a .zip file. +function download_and_unzip() { + local BASE_URL=${1} + local FILENAME=${2} + + if [ ! -f ${FILENAME} ]; then + echo "Downloading ${FILENAME} to $(pwd)" + wget -nd -c "${BASE_URL}/${FILENAME}" + else + echo "Skipping download of ${FILENAME}" + fi + echo "Unzipping ${FILENAME}" + ${UNZIP} ${FILENAME} +} + +cd ${SCRATCH_DIR} + +# Download the images. +BASE_IMAGE_URL="http://msvocds.blob.core.windows.net/coco2014" + +TRAIN_IMAGE_FILE="train2014.zip" +download_and_unzip ${BASE_IMAGE_URL} ${TRAIN_IMAGE_FILE} +TRAIN_IMAGE_DIR="${SCRATCH_DIR}/train2014" + +VAL_IMAGE_FILE="val2014.zip" +download_and_unzip ${BASE_IMAGE_URL} ${VAL_IMAGE_FILE} +VAL_IMAGE_DIR="${SCRATCH_DIR}/val2014" + +# Download the captions. +BASE_CAPTIONS_URL="http://msvocds.blob.core.windows.net/annotations-1-0-3" +CAPTIONS_FILE="captions_train-val2014.zip" +download_and_unzip ${BASE_CAPTIONS_URL} ${CAPTIONS_FILE} +TRAIN_CAPTIONS_FILE="${SCRATCH_DIR}/annotations/captions_train2014.json" +VAL_CAPTIONS_FILE="${SCRATCH_DIR}/annotations/captions_val2014.json" + +# Build TFRecords of the image data. +cd "${CURRENT_DIR}" +BUILD_SCRIPT="${WORK_DIR}/build_mscoco_data" +"${BUILD_SCRIPT}" \ + --train_image_dir="${TRAIN_IMAGE_DIR}" \ + --val_image_dir="${VAL_IMAGE_DIR}" \ + --train_captions_file="${TRAIN_CAPTIONS_FILE}" \ + --val_captions_file="${VAL_CAPTIONS_FILE}" \ + --output_dir="${OUTPUT_DIR}" \ + --word_counts_output_file="${OUTPUT_DIR}/word_counts.txt" \ diff --git a/src/main/python/im2txt/evaluate.py b/src/main/python/im2txt/evaluate.py new file mode 100644 index 0000000..0c81a59 --- /dev/null +++ b/src/main/python/im2txt/evaluate.py @@ -0,0 +1,198 @@ +# Copyright 2016 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. +# ============================================================================== + +"""Evaluate the model. + +This script should be run concurrently with training so that summaries show up +in TensorBoard. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math +import os.path +import time + + +import numpy as np +import tensorflow as tf + +from im2txt import configuration +from im2txt import show_and_tell_model + +FLAGS = tf.flags.FLAGS + +tf.flags.DEFINE_string("input_file_pattern", "", + "File pattern of sharded TFRecord input files.") +tf.flags.DEFINE_string("checkpoint_dir", "", + "Directory containing model checkpoints.") +tf.flags.DEFINE_string("eval_dir", "", "Directory to write event logs.") + +tf.flags.DEFINE_integer("eval_interval_secs", 600, + "Interval between evaluation runs.") +tf.flags.DEFINE_integer("num_eval_examples", 10132, + "Number of examples for evaluation.") + +tf.flags.DEFINE_integer("min_global_step", 5000, + "Minimum global step to run evaluation.") + +tf.logging.set_verbosity(tf.logging.INFO) + + +def evaluate_model(sess, model, global_step, summary_writer, summary_op): + """Computes perplexity-per-word over the evaluation dataset. + + Summaries and perplexity-per-word are written out to the eval directory. + + Args: + sess: Session object. + model: Instance of ShowAndTellModel; the model to evaluate. + global_step: Integer; global step of the model checkpoint. + summary_writer: Instance of FileWriter. + summary_op: Op for generating model summaries. + """ + # Log model summaries on a single batch. + summary_str = sess.run(summary_op) + summary_writer.add_summary(summary_str, global_step) + + # Compute perplexity over the entire dataset. + num_eval_batches = int( + math.ceil(FLAGS.num_eval_examples / model.config.batch_size)) + + start_time = time.time() + sum_losses = 0. + sum_weights = 0. + for i in range(num_eval_batches): + cross_entropy_losses, weights = sess.run([ + model.target_cross_entropy_losses, + model.target_cross_entropy_loss_weights + ]) + sum_losses += np.sum(cross_entropy_losses * weights) + sum_weights += np.sum(weights) + if not i % 100: + tf.logging.info("Computed losses for %d of %d batches.", i + 1, + num_eval_batches) + eval_time = time.time() - start_time + + perplexity = math.exp(sum_losses / sum_weights) + tf.logging.info("Perplexity = %f (%.2g sec)", perplexity, eval_time) + + # Log perplexity to the FileWriter. + summary = tf.Summary() + value = summary.value.add() + value.simple_value = perplexity + value.tag = "Perplexity" + summary_writer.add_summary(summary, global_step) + + # Write the Events file to the eval directory. + summary_writer.flush() + tf.logging.info("Finished processing evaluation at global step %d.", + global_step) + + +def run_once(model, saver, summary_writer, summary_op): + """Evaluates the latest model checkpoint. + + Args: + model: Instance of ShowAndTellModel; the model to evaluate. + saver: Instance of tf.train.Saver for restoring model Variables. + summary_writer: Instance of FileWriter. + summary_op: Op for generating model summaries. + """ + model_path = tf.train.latest_checkpoint(FLAGS.checkpoint_dir) + if not model_path: + tf.logging.info("Skipping evaluation. No checkpoint found in: %s", + FLAGS.checkpoint_dir) + return + + with tf.Session() as sess: + # Load model from checkpoint. + tf.logging.info("Loading model from checkpoint: %s", model_path) + saver.restore(sess, model_path) + global_step = tf.train.global_step(sess, model.global_step.name) + tf.logging.info("Successfully loaded %s at global step = %d.", + os.path.basename(model_path), global_step) + if global_step < FLAGS.min_global_step: + tf.logging.info("Skipping evaluation. Global step = %d < %d", global_step, + FLAGS.min_global_step) + return + + # Start the queue runners. + coord = tf.train.Coordinator() + threads = tf.train.start_queue_runners(coord=coord) + + # Run evaluation on the latest checkpoint. + try: + evaluate_model( + sess=sess, + model=model, + global_step=global_step, + summary_writer=summary_writer, + summary_op=summary_op) + except Exception as e: # pylint: disable=broad-except + tf.logging.error("Evaluation failed.") + coord.request_stop(e) + + coord.request_stop() + coord.join(threads, stop_grace_period_secs=10) + + +def run(): + """Runs evaluation in a loop, and logs summaries to TensorBoard.""" + # Create the evaluation directory if it doesn't exist. + eval_dir = FLAGS.eval_dir + if not tf.gfile.IsDirectory(eval_dir): + tf.logging.info("Creating eval directory: %s", eval_dir) + tf.gfile.MakeDirs(eval_dir) + + g = tf.Graph() + with g.as_default(): + # Build the model for evaluation. + model_config = configuration.ModelConfig() + model_config.input_file_pattern = FLAGS.input_file_pattern + model = show_and_tell_model.ShowAndTellModel(model_config, mode="eval") + model.build() + + # Create the Saver to restore model Variables. + saver = tf.train.Saver() + + # Create the summary operation and the summary writer. + summary_op = tf.summary.merge_all() + summary_writer = tf.summary.FileWriter(eval_dir) + + g.finalize() + + # Run a new evaluation run every eval_interval_secs. + while True: + start = time.time() + tf.logging.info("Starting evaluation at " + time.strftime( + "%Y-%m-%d-%H:%M:%S", time.localtime())) + run_once(model, saver, summary_writer, summary_op) + time_to_next_eval = start + FLAGS.eval_interval_secs - time.time() + if time_to_next_eval > 0: + time.sleep(time_to_next_eval) + + +def main(unused_argv): + assert FLAGS.input_file_pattern, "--input_file_pattern is required" + assert FLAGS.checkpoint_dir, "--checkpoint_dir is required" + assert FLAGS.eval_dir, "--eval_dir is required" + run() + + +if __name__ == "__main__": + tf.app.run() diff --git a/src/main/python/im2txt/inference_utils/BUILD b/src/main/python/im2txt/inference_utils/BUILD new file mode 100644 index 0000000..82a15fd --- /dev/null +++ b/src/main/python/im2txt/inference_utils/BUILD @@ -0,0 +1,31 @@ +package(default_visibility = ["//im2txt:internal"]) + +licenses(["notice"]) # Apache 2.0 + +exports_files(["LICENSE"]) + +py_library( + name = "inference_wrapper_base", + srcs = ["inference_wrapper_base.py"], + srcs_version = "PY2AND3", +) + +py_library( + name = "vocabulary", + srcs = ["vocabulary.py"], + srcs_version = "PY2AND3", +) + +py_library( + name = "caption_generator", + srcs = ["caption_generator.py"], + srcs_version = "PY2AND3", +) + +py_test( + name = "caption_generator_test", + srcs = ["caption_generator_test.py"], + deps = [ + ":caption_generator", + ], +) diff --git a/src/main/python/im2txt/inference_utils/caption_generator.py b/src/main/python/im2txt/inference_utils/caption_generator.py new file mode 100644 index 0000000..ef2c48e --- /dev/null +++ b/src/main/python/im2txt/inference_utils/caption_generator.py @@ -0,0 +1,211 @@ +# Copyright 2016 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. +# ============================================================================== +"""Class for generating captions from an image-to-text model.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import heapq +import math + + +import numpy as np + + +class Caption(object): + """Represents a complete or partial caption.""" + + def __init__(self, sentence, state, logprob, score, metadata=None): + """Initializes the Caption. + + Args: + sentence: List of word ids in the caption. + state: Model state after generating the previous word. + logprob: Log-probability of the caption. + score: Score of the caption. + metadata: Optional metadata associated with the partial sentence. If not + None, a list of strings with the same length as 'sentence'. + """ + self.sentence = sentence + self.state = state + self.logprob = logprob + self.score = score + self.metadata = metadata + + def __cmp__(self, other): + """Compares Captions by score.""" + assert isinstance(other, Caption) + if self.score == other.score: + return 0 + elif self.score < other.score: + return -1 + else: + return 1 + + # For Python 3 compatibility (__cmp__ is deprecated). + def __lt__(self, other): + assert isinstance(other, Caption) + return self.score < other.score + + # Also for Python 3 compatibility. + def __eq__(self, other): + assert isinstance(other, Caption) + return self.score == other.score + + +class TopN(object): + """Maintains the top n elements of an incrementally provided set.""" + + def __init__(self, n): + self._n = n + self._data = [] + + def size(self): + assert self._data is not None + return len(self._data) + + def push(self, x): + """Pushes a new element.""" + assert self._data is not None + if len(self._data) < self._n: + heapq.heappush(self._data, x) + else: + heapq.heappushpop(self._data, x) + + def extract(self, sort=False): + """Extracts all elements from the TopN. This is a destructive operation. + + The only method that can be called immediately after extract() is reset(). + + Args: + sort: Whether to return the elements in descending sorted order. + + Returns: + A list of data; the top n elements provided to the set. + """ + assert self._data is not None + data = self._data + self._data = None + if sort: + data.sort(reverse=True) + return data + + def reset(self): + """Returns the TopN to an empty state.""" + self._data = [] + + +class CaptionGenerator(object): + """Class to generate captions from an image-to-text model.""" + + def __init__(self, + model, + vocab, + beam_size=3, + max_caption_length=20, + length_normalization_factor=0.0): + """Initializes the generator. + + Args: + model: Object encapsulating a trained image-to-text model. Must have + methods feed_image() and inference_step(). For example, an instance of + InferenceWrapperBase. + vocab: A Vocabulary object. + beam_size: Beam size to use when generating captions. + max_caption_length: The maximum caption length before stopping the search. + length_normalization_factor: If != 0, a number x such that captions are + scored by logprob/length^x, rather than logprob. This changes the + relative scores of captions depending on their lengths. For example, if + x > 0 then longer captions will be favored. + """ + self.vocab = vocab + self.model = model + + self.beam_size = beam_size + self.max_caption_length = max_caption_length + self.length_normalization_factor = length_normalization_factor + + def beam_search(self, sess, encoded_image): + """Runs beam search caption generation on a single image. + + Args: + sess: TensorFlow Session object. + encoded_image: An encoded image string. + + Returns: + A list of Caption sorted by descending score. + """ + # Feed in the image to get the initial state. + initial_state = self.model.feed_image(sess, encoded_image) + + initial_beam = Caption( + sentence=[self.vocab.start_id], + state=initial_state[0], + logprob=0.0, + score=0.0, + metadata=[""]) + partial_captions = TopN(self.beam_size) + partial_captions.push(initial_beam) + complete_captions = TopN(self.beam_size) + + # Run beam search. + for _ in range(self.max_caption_length - 1): + partial_captions_list = partial_captions.extract() + partial_captions.reset() + input_feed = np.array([c.sentence[-1] for c in partial_captions_list]) + state_feed = np.array([c.state for c in partial_captions_list]) + + softmax, new_states, metadata = self.model.inference_step(sess, + input_feed, + state_feed) + + for i, partial_caption in enumerate(partial_captions_list): + word_probabilities = softmax[i] + state = new_states[i] + # For this partial caption, get the beam_size most probable next words. + words_and_probs = list(enumerate(word_probabilities)) + words_and_probs.sort(key=lambda x: -x[1]) + words_and_probs = words_and_probs[0:self.beam_size] + # Each next word gives a new partial caption. + for w, p in words_and_probs: + if p < 1e-12: + continue # Avoid log(0). + sentence = partial_caption.sentence + [w] + logprob = partial_caption.logprob + math.log(p) + score = logprob + if metadata: + metadata_list = partial_caption.metadata + [metadata[i]] + else: + metadata_list = None + if w == self.vocab.end_id: + if self.length_normalization_factor > 0: + score /= len(sentence)**self.length_normalization_factor + beam = Caption(sentence, state, logprob, score, metadata_list) + complete_captions.push(beam) + else: + beam = Caption(sentence, state, logprob, score, metadata_list) + partial_captions.push(beam) + if partial_captions.size() == 0: + # We have run out of partial candidates; happens when beam_size = 1. + break + + # If we have no complete captions then fall back to the partial captions. + # But never output a mixture of complete and partial captions because a + # partial caption could have a higher score than all the complete captions. + if not complete_captions.size(): + complete_captions = partial_captions + + return complete_captions.extract(sort=True) diff --git a/src/main/python/im2txt/inference_utils/caption_generator_test.py b/src/main/python/im2txt/inference_utils/caption_generator_test.py new file mode 100644 index 0000000..bbd0693 --- /dev/null +++ b/src/main/python/im2txt/inference_utils/caption_generator_test.py @@ -0,0 +1,178 @@ +# Copyright 2016 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. +# ============================================================================== +"""Unit tests for CaptionGenerator.""" + +import math + + + +import numpy as np +import tensorflow as tf + +from im2txt.inference_utils import caption_generator + + +class FakeVocab(object): + """Fake Vocabulary for testing purposes.""" + + def __init__(self): + self.start_id = 0 # Word id denoting sentence start. + self.end_id = 1 # Word id denoting sentence end. + + +class FakeModel(object): + """Fake model for testing purposes.""" + + def __init__(self): + # Number of words in the vocab. + self._vocab_size = 12 + + # Dimensionality of the nominal model state. + self._state_size = 1 + + # Map of previous word to the probability distribution of the next word. + self._probabilities = { + 0: {1: 0.1, + 2: 0.2, + 3: 0.3, + 4: 0.4}, + 2: {5: 0.1, + 6: 0.9}, + 3: {1: 0.1, + 7: 0.4, + 8: 0.5}, + 4: {1: 0.3, + 9: 0.3, + 10: 0.4}, + 5: {1: 1.0}, + 6: {1: 1.0}, + 7: {1: 1.0}, + 8: {1: 1.0}, + 9: {1: 0.5, + 11: 0.5}, + 10: {1: 1.0}, + 11: {1: 1.0}, + } + + # pylint: disable=unused-argument + + def feed_image(self, sess, encoded_image): + # Return a nominal model state. + return np.zeros([1, self._state_size]) + + def inference_step(self, sess, input_feed, state_feed): + # Compute the matrix of softmax distributions for the next batch of words. + batch_size = input_feed.shape[0] + softmax_output = np.zeros([batch_size, self._vocab_size]) + for batch_index, word_id in enumerate(input_feed): + for next_word, probability in self._probabilities[word_id].items(): + softmax_output[batch_index, next_word] = probability + + # Nominal state and metadata. + new_state = np.zeros([batch_size, self._state_size]) + metadata = None + + return softmax_output, new_state, metadata + + # pylint: enable=unused-argument + + +class CaptionGeneratorTest(tf.test.TestCase): + + def _assertExpectedCaptions(self, + expected_captions, + beam_size=3, + max_caption_length=20, + length_normalization_factor=0): + """Tests that beam search generates the expected captions. + + Args: + expected_captions: A sequence of pairs (sentence, probability), where + sentence is a list of integer ids and probability is a float in [0, 1]. + beam_size: Parameter passed to beam_search(). + max_caption_length: Parameter passed to beam_search(). + length_normalization_factor: Parameter passed to beam_search(). + """ + expected_sentences = [c[0] for c in expected_captions] + expected_probabilities = [c[1] for c in expected_captions] + + # Generate captions. + generator = caption_generator.CaptionGenerator( + model=FakeModel(), + vocab=FakeVocab(), + beam_size=beam_size, + max_caption_length=max_caption_length, + length_normalization_factor=length_normalization_factor) + actual_captions = generator.beam_search(sess=None, encoded_image=None) + + actual_sentences = [c.sentence for c in actual_captions] + actual_probabilities = [math.exp(c.logprob) for c in actual_captions] + + self.assertEqual(expected_sentences, actual_sentences) + self.assertAllClose(expected_probabilities, actual_probabilities) + + def testBeamSize(self): + # Beam size = 1. + expected = [([0, 4, 10, 1], 0.16)] + self._assertExpectedCaptions(expected, beam_size=1) + + # Beam size = 2. + expected = [([0, 4, 10, 1], 0.16), ([0, 3, 8, 1], 0.15)] + self._assertExpectedCaptions(expected, beam_size=2) + + # Beam size = 3. + expected = [ + ([0, 2, 6, 1], 0.18), ([0, 4, 10, 1], 0.16), ([0, 3, 8, 1], 0.15) + ] + self._assertExpectedCaptions(expected, beam_size=3) + + def testMaxLength(self): + # Max length = 1. + expected = [([0], 1.0)] + self._assertExpectedCaptions(expected, max_caption_length=1) + + # Max length = 2. + # There are no complete sentences, so partial sentences are returned. + expected = [([0, 4], 0.4), ([0, 3], 0.3), ([0, 2], 0.2)] + self._assertExpectedCaptions(expected, max_caption_length=2) + + # Max length = 3. + # There is at least one complete sentence, so only complete sentences are + # returned. + expected = [([0, 4, 1], 0.12), ([0, 3, 1], 0.03)] + self._assertExpectedCaptions(expected, max_caption_length=3) + + # Max length = 4. + expected = [ + ([0, 2, 6, 1], 0.18), ([0, 4, 10, 1], 0.16), ([0, 3, 8, 1], 0.15) + ] + self._assertExpectedCaptions(expected, max_caption_length=4) + + def testLengthNormalization(self): + # Length normalization factor = 3. + # The longest caption is returned first, despite having low probability, + # because it has the highest log(probability)/length**3. + expected = [ + ([0, 4, 9, 11, 1], 0.06), + ([0, 2, 6, 1], 0.18), + ([0, 4, 10, 1], 0.16), + ([0, 3, 8, 1], 0.15), + ] + self._assertExpectedCaptions( + expected, beam_size=4, length_normalization_factor=3) + + +if __name__ == '__main__': + tf.test.main() diff --git a/src/main/python/im2txt/inference_utils/inference_wrapper_base.py b/src/main/python/im2txt/inference_utils/inference_wrapper_base.py new file mode 100644 index 0000000..a18d6f8 --- /dev/null +++ b/src/main/python/im2txt/inference_utils/inference_wrapper_base.py @@ -0,0 +1,182 @@ +# Copyright 2016 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. +# ============================================================================== +"""Base wrapper class for performing inference with an image-to-text model. + +Subclasses must implement the following methods: + + build_model(): + Builds the model for inference and returns the model object. + + feed_image(): + Takes an encoded image and returns the initial model state, where "state" + is a numpy array whose specifics are defined by the subclass, e.g. + concatenated LSTM state. It's assumed that feed_image() will be called + precisely once at the start of inference for each image. Subclasses may + compute and/or save per-image internal context in this method. + + inference_step(): + Takes a batch of inputs and states at a single time-step. Returns the + softmax output corresponding to the inputs, and the new states of the batch. + Optionally also returns metadata about the current inference step, e.g. a + serialized numpy array containing activations from a particular model layer. + +Client usage: + 1. Build the model inference graph via build_graph_from_config() or + build_graph_from_proto(). + 2. Call the resulting restore_fn to load the model checkpoint. + 3. For each image in a batch of images: + a) Call feed_image() once to get the initial state. + b) For each step of caption generation, call inference_step(). +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os.path + + +import tensorflow as tf + +# pylint: disable=unused-argument + + +class InferenceWrapperBase(object): + """Base wrapper class for performing inference with an image-to-text model.""" + + def __init__(self): + pass + + def build_model(self, model_config): + """Builds the model for inference. + + Args: + model_config: Object containing configuration for building the model. + + Returns: + model: The model object. + """ + tf.logging.fatal("Please implement build_model in subclass") + + def _create_restore_fn(self, checkpoint_path, saver): + """Creates a function that restores a model from checkpoint. + + Args: + checkpoint_path: Checkpoint file or a directory containing a checkpoint + file. + saver: Saver for restoring variables from the checkpoint file. + + Returns: + restore_fn: A function such that restore_fn(sess) loads model variables + from the checkpoint file. + + Raises: + ValueError: If checkpoint_path does not refer to a checkpoint file or a + directory containing a checkpoint file. + """ + if tf.gfile.IsDirectory(checkpoint_path): + checkpoint_path = tf.train.latest_checkpoint(checkpoint_path) + if not checkpoint_path: + raise ValueError("No checkpoint file found in: %s" % checkpoint_path) + + def _restore_fn(sess): + tf.logging.info("Loading model from checkpoint: %s", checkpoint_path) + saver.restore(sess, checkpoint_path) + saver.save(sess, "/Users/lee/Downloads/upload/test/ckpt3") + tf.logging.info("Successfully loaded checkpoint: %s", + os.path.basename(checkpoint_path)) + + return _restore_fn + + def build_graph_from_config(self, model_config, checkpoint_path): + """Builds the inference graph from a configuration object. + + Args: + model_config: Object containing configuration for building the model. + checkpoint_path: Checkpoint file or a directory containing a checkpoint + file. + + Returns: + restore_fn: A function such that restore_fn(sess) loads model variables + from the checkpoint file. + """ + tf.logging.info("Building model.") + self.build_model(model_config) + saver = tf.train.Saver() + + return self._create_restore_fn(checkpoint_path, saver) + + def build_graph_from_proto(self, graph_def_file, saver_def_file, + checkpoint_path): + """Builds the inference graph from serialized GraphDef and SaverDef protos. + + Args: + graph_def_file: File containing a serialized GraphDef proto. + saver_def_file: File containing a serialized SaverDef proto. + checkpoint_path: Checkpoint file or a directory containing a checkpoint + file. + + Returns: + restore_fn: A function such that restore_fn(sess) loads model variables + from the checkpoint file. + """ + # Load the Graph. + tf.logging.info("Loading GraphDef from file: %s", graph_def_file) + graph_def = tf.GraphDef() + with tf.gfile.FastGFile(graph_def_file, "rb") as f: + graph_def.ParseFromString(f.read()) + tf.import_graph_def(graph_def, name="") + + # Load the Saver. + tf.logging.info("Loading SaverDef from file: %s", saver_def_file) + saver_def = tf.train.SaverDef() + with tf.gfile.FastGFile(saver_def_file, "rb") as f: + saver_def.ParseFromString(f.read()) + saver = tf.train.Saver(saver_def=saver_def) + + return self._create_restore_fn(checkpoint_path, saver) + + def feed_image(self, sess, encoded_image): + """Feeds an image and returns the initial model state. + + See comments at the top of file. + + Args: + sess: TensorFlow Session object. + encoded_image: An encoded image string. + + Returns: + state: A numpy array of shape [1, state_size]. + """ + tf.logging.fatal("Please implement feed_image in subclass") + + def inference_step(self, sess, input_feed, state_feed): + """Runs one step of inference. + + Args: + sess: TensorFlow Session object. + input_feed: A numpy array of shape [batch_size]. + state_feed: A numpy array of shape [batch_size, state_size]. + + Returns: + softmax_output: A numpy array of shape [batch_size, vocab_size]. + new_state: A numpy array of shape [batch_size, state_size]. + metadata: Optional. If not None, a string containing metadata about the + current inference step (e.g. serialized numpy array containing + activations from a particular model layer.). + """ + tf.logging.fatal("Please implement inference_step in subclass") + +# pylint: enable=unused-argument diff --git a/src/main/python/im2txt/inference_utils/vocabulary.py b/src/main/python/im2txt/inference_utils/vocabulary.py new file mode 100644 index 0000000..ecf0ada --- /dev/null +++ b/src/main/python/im2txt/inference_utils/vocabulary.py @@ -0,0 +1,78 @@ +# Copyright 2016 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. +# ============================================================================== +"""Vocabulary class for an image-to-text model.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +import tensorflow as tf + + +class Vocabulary(object): + """Vocabulary class for an image-to-text model.""" + + def __init__(self, + vocab_file, + start_word="", + end_word="", + unk_word=""): + """Initializes the vocabulary. + + Args: + vocab_file: File containing the vocabulary, where the words are the first + whitespace-separated token on each line (other tokens are ignored) and + the word ids are the corresponding line numbers. + start_word: Special word denoting sentence start. + end_word: Special word denoting sentence end. + unk_word: Special word denoting unknown words. + """ + if not tf.gfile.Exists(vocab_file): + tf.logging.fatal("Vocab file %s not found.", vocab_file) + tf.logging.info("Initializing vocabulary from file: %s", vocab_file) + + with tf.gfile.GFile(vocab_file, mode="r") as f: + reverse_vocab = list(f.readlines()) + reverse_vocab = [line.split()[0] for line in reverse_vocab] + assert start_word in reverse_vocab + assert end_word in reverse_vocab + if unk_word not in reverse_vocab: + reverse_vocab.append(unk_word) + vocab = dict([(x, y) for (y, x) in enumerate(reverse_vocab)]) + + tf.logging.info("Created vocabulary with %d words" % len(vocab)) + + self.vocab = vocab # vocab[word] = id + self.reverse_vocab = reverse_vocab # reverse_vocab[id] = word + + # Save special word ids. + self.start_id = vocab[start_word] + self.end_id = vocab[end_word] + self.unk_id = vocab[unk_word] + + def word_to_id(self, word): + """Returns the integer word id of a word string.""" + if word in self.vocab: + return self.vocab[word] + else: + return self.unk_id + + def id_to_word(self, word_id): + """Returns the word string of an integer word id.""" + if word_id >= len(self.reverse_vocab): + return self.reverse_vocab[self.unk_id] + else: + return self.reverse_vocab[word_id] diff --git a/src/main/python/im2txt/inference_wrapper.py b/src/main/python/im2txt/inference_wrapper.py new file mode 100644 index 0000000..a047a9c --- /dev/null +++ b/src/main/python/im2txt/inference_wrapper.py @@ -0,0 +1,51 @@ +# Copyright 2016 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. +# ============================================================================== + +"""Model wrapper class for performing inference with a ShowAndTellModel.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + + +from im2txt import show_and_tell_model +from im2txt.inference_utils import inference_wrapper_base + + +class InferenceWrapper(inference_wrapper_base.InferenceWrapperBase): + """Model wrapper class for performing inference with a ShowAndTellModel.""" + + def __init__(self): + super(InferenceWrapper, self).__init__() + + def build_model(self, model_config): + model = show_and_tell_model.ShowAndTellModel(model_config, mode="inference") + model.build() + return model + + def feed_image(self, sess, encoded_image): + initial_state = sess.run(fetches="lstm/initial_state:0", + feed_dict={"image_feed:0": encoded_image}) + return initial_state + + def inference_step(self, sess, input_feed, state_feed): + softmax_output, state_output = sess.run( + fetches=["softmax:0", "lstm/state:0"], + feed_dict={ + "input_feed:0": input_feed, + "lstm/state_feed:0": state_feed, + }) + return softmax_output, state_output, None diff --git a/src/main/python/im2txt/ops/BUILD b/src/main/python/im2txt/ops/BUILD new file mode 100644 index 0000000..7d48bf3 --- /dev/null +++ b/src/main/python/im2txt/ops/BUILD @@ -0,0 +1,32 @@ +package(default_visibility = ["//im2txt:internal"]) + +licenses(["notice"]) # Apache 2.0 + +exports_files(["LICENSE"]) + +py_library( + name = "image_processing", + srcs = ["image_processing.py"], + srcs_version = "PY2AND3", +) + +py_library( + name = "image_embedding", + srcs = ["image_embedding.py"], + srcs_version = "PY2AND3", +) + +py_test( + name = "image_embedding_test", + size = "small", + srcs = ["image_embedding_test.py"], + deps = [ + ":image_embedding", + ], +) + +py_library( + name = "inputs", + srcs = ["inputs.py"], + srcs_version = "PY2AND3", +) diff --git a/src/main/python/im2txt/ops/image_embedding.py b/src/main/python/im2txt/ops/image_embedding.py new file mode 100644 index 0000000..58e3dda --- /dev/null +++ b/src/main/python/im2txt/ops/image_embedding.py @@ -0,0 +1,114 @@ +# Copyright 2016 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. +# ============================================================================== + +"""Image embedding ops.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +import tensorflow as tf + +from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3_base + +slim = tf.contrib.slim + + +def inception_v3(images, + trainable=True, + is_training=True, + weight_decay=0.00004, + stddev=0.1, + dropout_keep_prob=0.8, + use_batch_norm=True, + batch_norm_params=None, + add_summaries=True, + scope="InceptionV3"): + """Builds an Inception V3 subgraph for image embeddings. + + Args: + images: A float32 Tensor of shape [batch, height, width, channels]. + trainable: Whether the inception submodel should be trainable or not. + is_training: Boolean indicating training mode or not. + weight_decay: Coefficient for weight regularization. + stddev: The standard deviation of the trunctated normal weight initializer. + dropout_keep_prob: Dropout keep probability. + use_batch_norm: Whether to use batch normalization. + batch_norm_params: Parameters for batch normalization. See + tf.contrib.layers.batch_norm for details. + add_summaries: Whether to add activation summaries. + scope: Optional Variable scope. + + Returns: + end_points: A dictionary of activations from inception_v3 layers. + """ + # Only consider the inception model to be in training mode if it's trainable. + is_inception_model_training = trainable and is_training + + if use_batch_norm: + # Default parameters for batch normalization. + if not batch_norm_params: + batch_norm_params = { + "is_training": is_inception_model_training, + "trainable": trainable, + # Decay for the moving averages. + "decay": 0.9997, + # Epsilon to prevent 0s in variance. + "epsilon": 0.001, + # Collection containing the moving mean and moving variance. + "variables_collections": { + "beta": None, + "gamma": None, + "moving_mean": ["moving_vars"], + "moving_variance": ["moving_vars"], + } + } + else: + batch_norm_params = None + + if trainable: + weights_regularizer = tf.contrib.layers.l2_regularizer(weight_decay) + else: + weights_regularizer = None + + with tf.variable_scope(scope, "InceptionV3", [images]) as scope: + with slim.arg_scope( + [slim.conv2d, slim.fully_connected], + weights_regularizer=weights_regularizer, + trainable=trainable): + with slim.arg_scope( + [slim.conv2d], + weights_initializer=tf.truncated_normal_initializer(stddev=stddev), + activation_fn=tf.nn.relu, + normalizer_fn=slim.batch_norm, + normalizer_params=batch_norm_params): + net, end_points = inception_v3_base(images, scope=scope) + with tf.variable_scope("logits"): + shape = net.get_shape() + net = slim.avg_pool2d(net, shape[1:3], padding="VALID", scope="pool") + net = slim.dropout( + net, + keep_prob=dropout_keep_prob, + is_training=is_inception_model_training, + scope="dropout") + net = slim.flatten(net, scope="flatten") + + # Add summaries. + if add_summaries: + for v in end_points.values(): + tf.contrib.layers.summaries.summarize_activation(v) + + return net diff --git a/src/main/python/im2txt/ops/image_embedding_test.py b/src/main/python/im2txt/ops/image_embedding_test.py new file mode 100644 index 0000000..66324d6 --- /dev/null +++ b/src/main/python/im2txt/ops/image_embedding_test.py @@ -0,0 +1,136 @@ +# Copyright 2016 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. +# ============================================================================== + +"""Tests for tensorflow_models.im2txt.ops.image_embedding.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +import tensorflow as tf + +from im2txt.ops import image_embedding + + +class InceptionV3Test(tf.test.TestCase): + + def setUp(self): + super(InceptionV3Test, self).setUp() + + batch_size = 4 + height = 299 + width = 299 + num_channels = 3 + self._images = tf.placeholder(tf.float32, + [batch_size, height, width, num_channels]) + self._batch_size = batch_size + + def _countInceptionParameters(self): + """Counts the number of parameters in the inception model at top scope.""" + counter = {} + for v in tf.global_variables(): + name_tokens = v.op.name.split("/") + if name_tokens[0] == "InceptionV3": + name = "InceptionV3/" + name_tokens[1] + num_params = v.get_shape().num_elements() + assert num_params + counter[name] = counter.get(name, 0) + num_params + return counter + + def _verifyParameterCounts(self): + """Verifies the number of parameters in the inception model.""" + param_counts = self._countInceptionParameters() + expected_param_counts = { + "InceptionV3/Conv2d_1a_3x3": 960, + "InceptionV3/Conv2d_2a_3x3": 9312, + "InceptionV3/Conv2d_2b_3x3": 18624, + "InceptionV3/Conv2d_3b_1x1": 5360, + "InceptionV3/Conv2d_4a_3x3": 138816, + "InceptionV3/Mixed_5b": 256368, + "InceptionV3/Mixed_5c": 277968, + "InceptionV3/Mixed_5d": 285648, + "InceptionV3/Mixed_6a": 1153920, + "InceptionV3/Mixed_6b": 1298944, + "InceptionV3/Mixed_6c": 1692736, + "InceptionV3/Mixed_6d": 1692736, + "InceptionV3/Mixed_6e": 2143872, + "InceptionV3/Mixed_7a": 1699584, + "InceptionV3/Mixed_7b": 5047872, + "InceptionV3/Mixed_7c": 6080064, + } + self.assertDictEqual(expected_param_counts, param_counts) + + def _assertCollectionSize(self, expected_size, collection): + actual_size = len(tf.get_collection(collection)) + if expected_size != actual_size: + self.fail("Found %d items in collection %s (expected %d)." % + (actual_size, collection, expected_size)) + + def testTrainableTrueIsTrainingTrue(self): + embeddings = image_embedding.inception_v3( + self._images, trainable=True, is_training=True) + self.assertEqual([self._batch_size, 2048], embeddings.get_shape().as_list()) + + self._verifyParameterCounts() + self._assertCollectionSize(376, tf.GraphKeys.GLOBAL_VARIABLES) + self._assertCollectionSize(188, tf.GraphKeys.TRAINABLE_VARIABLES) + self._assertCollectionSize(188, tf.GraphKeys.UPDATE_OPS) + self._assertCollectionSize(94, tf.GraphKeys.REGULARIZATION_LOSSES) + self._assertCollectionSize(0, tf.GraphKeys.LOSSES) + self._assertCollectionSize(23, tf.GraphKeys.SUMMARIES) + + def testTrainableTrueIsTrainingFalse(self): + embeddings = image_embedding.inception_v3( + self._images, trainable=True, is_training=False) + self.assertEqual([self._batch_size, 2048], embeddings.get_shape().as_list()) + + self._verifyParameterCounts() + self._assertCollectionSize(376, tf.GraphKeys.GLOBAL_VARIABLES) + self._assertCollectionSize(188, tf.GraphKeys.TRAINABLE_VARIABLES) + self._assertCollectionSize(0, tf.GraphKeys.UPDATE_OPS) + self._assertCollectionSize(94, tf.GraphKeys.REGULARIZATION_LOSSES) + self._assertCollectionSize(0, tf.GraphKeys.LOSSES) + self._assertCollectionSize(23, tf.GraphKeys.SUMMARIES) + + def testTrainableFalseIsTrainingTrue(self): + embeddings = image_embedding.inception_v3( + self._images, trainable=False, is_training=True) + self.assertEqual([self._batch_size, 2048], embeddings.get_shape().as_list()) + + self._verifyParameterCounts() + self._assertCollectionSize(376, tf.GraphKeys.GLOBAL_VARIABLES) + self._assertCollectionSize(0, tf.GraphKeys.TRAINABLE_VARIABLES) + self._assertCollectionSize(0, tf.GraphKeys.UPDATE_OPS) + self._assertCollectionSize(0, tf.GraphKeys.REGULARIZATION_LOSSES) + self._assertCollectionSize(0, tf.GraphKeys.LOSSES) + self._assertCollectionSize(23, tf.GraphKeys.SUMMARIES) + + def testTrainableFalseIsTrainingFalse(self): + embeddings = image_embedding.inception_v3( + self._images, trainable=False, is_training=False) + self.assertEqual([self._batch_size, 2048], embeddings.get_shape().as_list()) + + self._verifyParameterCounts() + self._assertCollectionSize(376, tf.GraphKeys.GLOBAL_VARIABLES) + self._assertCollectionSize(0, tf.GraphKeys.TRAINABLE_VARIABLES) + self._assertCollectionSize(0, tf.GraphKeys.UPDATE_OPS) + self._assertCollectionSize(0, tf.GraphKeys.REGULARIZATION_LOSSES) + self._assertCollectionSize(0, tf.GraphKeys.LOSSES) + self._assertCollectionSize(23, tf.GraphKeys.SUMMARIES) + + +if __name__ == "__main__": + tf.test.main() diff --git a/src/main/python/im2txt/ops/image_processing.py b/src/main/python/im2txt/ops/image_processing.py new file mode 100644 index 0000000..6a75455 --- /dev/null +++ b/src/main/python/im2txt/ops/image_processing.py @@ -0,0 +1,133 @@ +# Copyright 2016 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. +# ============================================================================== + +"""Helper functions for image preprocessing.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +import tensorflow as tf + + +def distort_image(image, thread_id): + """Perform random distortions on an image. + + Args: + image: A float32 Tensor of shape [height, width, 3] with values in [0, 1). + thread_id: Preprocessing thread id used to select the ordering of color + distortions. There should be a multiple of 2 preprocessing threads. + + Returns: + distorted_image: A float32 Tensor of shape [height, width, 3] with values in + [0, 1]. + """ + # Randomly flip horizontally. + with tf.name_scope("flip_horizontal", values=[image]): + image = tf.image.random_flip_left_right(image) + + # Randomly distort the colors based on thread id. + color_ordering = thread_id % 2 + with tf.name_scope("distort_color", values=[image]): + if color_ordering == 0: + image = tf.image.random_brightness(image, max_delta=32. / 255.) + image = tf.image.random_saturation(image, lower=0.5, upper=1.5) + image = tf.image.random_hue(image, max_delta=0.032) + image = tf.image.random_contrast(image, lower=0.5, upper=1.5) + elif color_ordering == 1: + image = tf.image.random_brightness(image, max_delta=32. / 255.) + image = tf.image.random_contrast(image, lower=0.5, upper=1.5) + image = tf.image.random_saturation(image, lower=0.5, upper=1.5) + image = tf.image.random_hue(image, max_delta=0.032) + + # The random_* ops do not necessarily clamp. + image = tf.clip_by_value(image, 0.0, 1.0) + + return image + + +def process_image(encoded_image, + is_training, + height, + width, + resize_height=346, + resize_width=346, + thread_id=0, + image_format="jpeg"): + """Decode an image, resize and apply random distortions. + + In training, images are distorted slightly differently depending on thread_id. + + Args: + encoded_image: String Tensor containing the image. + is_training: Boolean; whether preprocessing for training or eval. + height: Height of the output image. + width: Width of the output image. + resize_height: If > 0, resize height before crop to final dimensions. + resize_width: If > 0, resize width before crop to final dimensions. + thread_id: Preprocessing thread id used to select the ordering of color + distortions. There should be a multiple of 2 preprocessing threads. + image_format: "jpeg" or "png". + + Returns: + A float32 Tensor of shape [height, width, 3] with values in [-1, 1]. + + Raises: + ValueError: If image_format is invalid. + """ + # Helper function to log an image summary to the visualizer. Summaries are + # only logged in thread 0. + def image_summary(name, image): + if not thread_id: + tf.summary.image(name, tf.expand_dims(image, 0)) + + # Decode image into a float32 Tensor of shape [?, ?, 3] with values in [0, 1). + with tf.name_scope("decode", values=[encoded_image]): + if image_format == "jpeg": + image = tf.image.decode_jpeg(encoded_image, channels=3) + elif image_format == "png": + image = tf.image.decode_png(encoded_image, channels=3) + else: + raise ValueError("Invalid image format: %s" % image_format) + image = tf.image.convert_image_dtype(image, dtype=tf.float32) + image_summary("original_image", image) + + # Resize image. + assert (resize_height > 0) == (resize_width > 0) + if resize_height: + image = tf.image.resize_images(image, + size=[resize_height, resize_width], + method=tf.image.ResizeMethod.BILINEAR) + + # Crop to final dimensions. + if is_training: + image = tf.random_crop(image, [height, width, 3]) + else: + # Central crop, assuming resize_height > height, resize_width > width. + image = tf.image.resize_image_with_crop_or_pad(image, height, width) + + image_summary("resized_image", image) + + # Randomly distort the image. + if is_training: + image = distort_image(image, thread_id) + + image_summary("final_image", image) + + # Rescale to [-1,1] instead of [0, 1] + image = tf.subtract(image, 0.5) + image = tf.multiply(image, 2.0) + return image diff --git a/src/main/python/im2txt/ops/inputs.py b/src/main/python/im2txt/ops/inputs.py new file mode 100644 index 0000000..5dc90c0 --- /dev/null +++ b/src/main/python/im2txt/ops/inputs.py @@ -0,0 +1,204 @@ +# Copyright 2016 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. +# ============================================================================== + +"""Input ops.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +import tensorflow as tf + + +def parse_sequence_example(serialized, image_feature, caption_feature): + """Parses a tensorflow.SequenceExample into an image and caption. + + Args: + serialized: A scalar string Tensor; a single serialized SequenceExample. + image_feature: Name of SequenceExample context feature containing image + data. + caption_feature: Name of SequenceExample feature list containing integer + captions. + + Returns: + encoded_image: A scalar string Tensor containing a JPEG encoded image. + caption: A 1-D uint64 Tensor with dynamically specified length. + """ + context, sequence = tf.parse_single_sequence_example( + serialized, + context_features={ + image_feature: tf.FixedLenFeature([], dtype=tf.string) + }, + sequence_features={ + caption_feature: tf.FixedLenSequenceFeature([], dtype=tf.int64), + }) + + encoded_image = context[image_feature] + caption = sequence[caption_feature] + return encoded_image, caption + + +def prefetch_input_data(reader, + file_pattern, + is_training, + batch_size, + values_per_shard, + input_queue_capacity_factor=16, + num_reader_threads=1, + shard_queue_name="filename_queue", + value_queue_name="input_queue"): + """Prefetches string values from disk into an input queue. + + In training the capacity of the queue is important because a larger queue + means better mixing of training examples between shards. The minimum number of + values kept in the queue is values_per_shard * input_queue_capacity_factor, + where input_queue_memory factor should be chosen to trade-off better mixing + with memory usage. + + Args: + reader: Instance of tf.ReaderBase. + file_pattern: Comma-separated list of file patterns (e.g. + /tmp/train_data-?????-of-00100). + is_training: Boolean; whether prefetching for training or eval. + batch_size: Model batch size used to determine queue capacity. + values_per_shard: Approximate number of values per shard. + input_queue_capacity_factor: Minimum number of values to keep in the queue + in multiples of values_per_shard. See comments above. + num_reader_threads: Number of reader threads to fill the queue. + shard_queue_name: Name for the shards filename queue. + value_queue_name: Name for the values input queue. + + Returns: + A Queue containing prefetched string values. + """ + data_files = [] + for pattern in file_pattern.split(","): + data_files.extend(tf.gfile.Glob(pattern)) + if not data_files: + tf.logging.fatal("Found no input files matching %s", file_pattern) + else: + tf.logging.info("Prefetching values from %d files matching %s", + len(data_files), file_pattern) + + if is_training: + filename_queue = tf.train.string_input_producer( + data_files, shuffle=True, capacity=16, name=shard_queue_name) + min_queue_examples = values_per_shard * input_queue_capacity_factor + capacity = min_queue_examples + 100 * batch_size + values_queue = tf.RandomShuffleQueue( + capacity=capacity, + min_after_dequeue=min_queue_examples, + dtypes=[tf.string], + name="random_" + value_queue_name) + else: + filename_queue = tf.train.string_input_producer( + data_files, shuffle=False, capacity=1, name=shard_queue_name) + capacity = values_per_shard + 3 * batch_size + values_queue = tf.FIFOQueue( + capacity=capacity, dtypes=[tf.string], name="fifo_" + value_queue_name) + + enqueue_ops = [] + for _ in range(num_reader_threads): + _, value = reader.read(filename_queue) + enqueue_ops.append(values_queue.enqueue([value])) + tf.train.queue_runner.add_queue_runner(tf.train.queue_runner.QueueRunner( + values_queue, enqueue_ops)) + tf.summary.scalar( + "queue/%s/fraction_of_%d_full" % (values_queue.name, capacity), + tf.cast(values_queue.size(), tf.float32) * (1. / capacity)) + + return values_queue + + +def batch_with_dynamic_pad(images_and_captions, + batch_size, + queue_capacity, + add_summaries=True): + """Batches input images and captions. + + This function splits the caption into an input sequence and a target sequence, + where the target sequence is the input sequence right-shifted by 1. Input and + target sequences are batched and padded up to the maximum length of sequences + in the batch. A mask is created to distinguish real words from padding words. + + Example: + Actual captions in the batch ('-' denotes padded character): + [ + [ 1 2 3 4 5 ], + [ 1 2 3 4 - ], + [ 1 2 3 - - ], + ] + + input_seqs: + [ + [ 1 2 3 4 ], + [ 1 2 3 - ], + [ 1 2 - - ], + ] + + target_seqs: + [ + [ 2 3 4 5 ], + [ 2 3 4 - ], + [ 2 3 - - ], + ] + + mask: + [ + [ 1 1 1 1 ], + [ 1 1 1 0 ], + [ 1 1 0 0 ], + ] + + Args: + images_and_captions: A list of pairs [image, caption], where image is a + Tensor of shape [height, width, channels] and caption is a 1-D Tensor of + any length. Each pair will be processed and added to the queue in a + separate thread. + batch_size: Batch size. + queue_capacity: Queue capacity. + add_summaries: If true, add caption length summaries. + + Returns: + images: A Tensor of shape [batch_size, height, width, channels]. + input_seqs: An int32 Tensor of shape [batch_size, padded_length]. + target_seqs: An int32 Tensor of shape [batch_size, padded_length]. + mask: An int32 0/1 Tensor of shape [batch_size, padded_length]. + """ + enqueue_list = [] + for image, caption in images_and_captions: + caption_length = tf.shape(caption)[0] + input_length = tf.expand_dims(tf.subtract(caption_length, 1), 0) + + input_seq = tf.slice(caption, [0], input_length) + target_seq = tf.slice(caption, [1], input_length) + indicator = tf.ones(input_length, dtype=tf.int32) + enqueue_list.append([image, input_seq, target_seq, indicator]) + + images, input_seqs, target_seqs, mask = tf.train.batch_join( + enqueue_list, + batch_size=batch_size, + capacity=queue_capacity, + dynamic_pad=True, + name="batch_and_pad") + + if add_summaries: + lengths = tf.add(tf.reduce_sum(mask, 1), 1) + tf.summary.scalar("caption_length/batch_min", tf.reduce_min(lengths)) + tf.summary.scalar("caption_length/batch_max", tf.reduce_max(lengths)) + tf.summary.scalar("caption_length/batch_mean", tf.reduce_mean(lengths)) + + return images, input_seqs, target_seqs, mask diff --git a/src/main/python/im2txt/run_inference.py b/src/main/python/im2txt/run_inference.py new file mode 100644 index 0000000..01c0ddc --- /dev/null +++ b/src/main/python/im2txt/run_inference.py @@ -0,0 +1,87 @@ +# Copyright 2016 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. +# ============================================================================== +r"""Generate captions for images using default beam search parameters.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math +import os + + +import tensorflow as tf + +from im2txt import configuration +from im2txt import inference_wrapper +from im2txt.inference_utils import caption_generator +from im2txt.inference_utils import vocabulary + +FLAGS = tf.flags.FLAGS + +tf.flags.DEFINE_string("checkpoint_path", "", + "Model checkpoint file or directory containing a " + "model checkpoint file.") +tf.flags.DEFINE_string("vocab_file", "", "Text file containing the vocabulary.") +tf.flags.DEFINE_string("input_files", "", + "File pattern or comma-separated list of file patterns " + "of image files.") + +tf.logging.set_verbosity(tf.logging.INFO) + + +def main(_): + # Build the inference graph. + g = tf.Graph() + with g.as_default(): + model = inference_wrapper.InferenceWrapper() + restore_fn = model.build_graph_from_config(configuration.ModelConfig(), + FLAGS.checkpoint_path) + g.finalize() + + # Create the vocabulary. + vocab = vocabulary.Vocabulary(FLAGS.vocab_file) + + filenames = [] + for file_pattern in FLAGS.input_files.split(","): + filenames.extend(tf.gfile.Glob(file_pattern)) + tf.logging.info("Running caption generation on %d files matching %s", + len(filenames), FLAGS.input_files) + + with tf.Session(graph=g) as sess: + # Load the model from checkpoint. + restore_fn(sess) + + # Prepare the caption generator. Here we are implicitly using the default + # beam search parameters. See caption_generator.py for a description of the + # available beam search parameters. + generator = caption_generator.CaptionGenerator(model, vocab) + + for filename in filenames: + with tf.gfile.GFile(filename, "rb") as f: + image = f.read() + captions = generator.beam_search(sess, image) + # print("Captions for image %s:" % os.path.basename(filename)) + for i, caption in enumerate(captions[0:1]): + # Ignore begin and end words. + sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]] + sentence = " ".join(sentence) + # print("Caption: %s (p=%f)" % (i, sentence, math.exp(caption.logprob))) + print("Caption: %s" % sentence) + + + +if __name__ == "__main__": + tf.app.run() diff --git a/src/main/python/im2txt/show_and_tell_model.py b/src/main/python/im2txt/show_and_tell_model.py new file mode 100644 index 0000000..0ac29e7 --- /dev/null +++ b/src/main/python/im2txt/show_and_tell_model.py @@ -0,0 +1,358 @@ +# Copyright 2016 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. +# ============================================================================== + +"""Image-to-text implementation based on http://arxiv.org/abs/1411.4555. + +"Show and Tell: A Neural Image Caption Generator" +Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +import tensorflow as tf + +from im2txt.ops import image_embedding +from im2txt.ops import image_processing +from im2txt.ops import inputs as input_ops + + +class ShowAndTellModel(object): + """Image-to-text implementation based on http://arxiv.org/abs/1411.4555. + + "Show and Tell: A Neural Image Caption Generator" + Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan + """ + + def __init__(self, config, mode, train_inception=False): + """Basic setup. + + Args: + config: Object containing configuration parameters. + mode: "train", "eval" or "inference". + train_inception: Whether the inception submodel variables are trainable. + """ + assert mode in ["train", "eval", "inference"] + self.config = config + self.mode = mode + self.train_inception = train_inception + + # Reader for the input data. + self.reader = tf.TFRecordReader() + + # To match the "Show and Tell" paper we initialize all variables with a + # random uniform initializer. + self.initializer = tf.random_uniform_initializer( + minval=-self.config.initializer_scale, + maxval=self.config.initializer_scale) + + # A float32 Tensor with shape [batch_size, height, width, channels]. + self.images = None + + # An int32 Tensor with shape [batch_size, padded_length]. + self.input_seqs = None + + # An int32 Tensor with shape [batch_size, padded_length]. + self.target_seqs = None + + # An int32 0/1 Tensor with shape [batch_size, padded_length]. + self.input_mask = None + + # A float32 Tensor with shape [batch_size, embedding_size]. + self.image_embeddings = None + + # A float32 Tensor with shape [batch_size, padded_length, embedding_size]. + self.seq_embeddings = None + + # A float32 scalar Tensor; the total loss for the trainer to optimize. + self.total_loss = None + + # A float32 Tensor with shape [batch_size * padded_length]. + self.target_cross_entropy_losses = None + + # A float32 Tensor with shape [batch_size * padded_length]. + self.target_cross_entropy_loss_weights = None + + # Collection of variables from the inception submodel. + self.inception_variables = [] + + # Function to restore the inception submodel from checkpoint. + self.init_fn = None + + # Global step Tensor. + self.global_step = None + + def is_training(self): + """Returns true if the model is built for training mode.""" + return self.mode == "train" + + def process_image(self, encoded_image, thread_id=0): + """Decodes and processes an image string. + + Args: + encoded_image: A scalar string Tensor; the encoded image. + thread_id: Preprocessing thread id used to select the ordering of color + distortions. + + Returns: + A float32 Tensor of shape [height, width, 3]; the processed image. + """ + return image_processing.process_image(encoded_image, + is_training=self.is_training(), + height=self.config.image_height, + width=self.config.image_width, + thread_id=thread_id, + image_format=self.config.image_format) + + def build_inputs(self): + """Input prefetching, preprocessing and batching. + + Outputs: + self.images + self.input_seqs + self.target_seqs (training and eval only) + self.input_mask (training and eval only) + """ + if self.mode == "inference": + # In inference mode, images and inputs are fed via placeholders. + image_feed = tf.placeholder(dtype=tf.string, shape=[], name="image_feed") + input_feed = tf.placeholder(dtype=tf.int64, + shape=[None], # batch_size + name="input_feed") + + # Process image and insert batch dimensions. + images = tf.expand_dims(self.process_image(image_feed), 0) + input_seqs = tf.expand_dims(input_feed, 1) + + # No target sequences or input mask in inference mode. + target_seqs = None + input_mask = None + else: + # Prefetch serialized SequenceExample protos. + input_queue = input_ops.prefetch_input_data( + self.reader, + self.config.input_file_pattern, + is_training=self.is_training(), + batch_size=self.config.batch_size, + values_per_shard=self.config.values_per_input_shard, + input_queue_capacity_factor=self.config.input_queue_capacity_factor, + num_reader_threads=self.config.num_input_reader_threads) + + # Image processing and random distortion. Split across multiple threads + # with each thread applying a slightly different distortion. + assert self.config.num_preprocess_threads % 2 == 0 + images_and_captions = [] + for thread_id in range(self.config.num_preprocess_threads): + serialized_sequence_example = input_queue.dequeue() + encoded_image, caption = input_ops.parse_sequence_example( + serialized_sequence_example, + image_feature=self.config.image_feature_name, + caption_feature=self.config.caption_feature_name) + image = self.process_image(encoded_image, thread_id=thread_id) + images_and_captions.append([image, caption]) + + # Batch inputs. + queue_capacity = (2 * self.config.num_preprocess_threads * + self.config.batch_size) + images, input_seqs, target_seqs, input_mask = ( + input_ops.batch_with_dynamic_pad(images_and_captions, + batch_size=self.config.batch_size, + queue_capacity=queue_capacity)) + + self.images = images + self.input_seqs = input_seqs + self.target_seqs = target_seqs + self.input_mask = input_mask + + def build_image_embeddings(self): + """Builds the image model subgraph and generates image embeddings. + + Inputs: + self.images + + Outputs: + self.image_embeddings + """ + inception_output = image_embedding.inception_v3( + self.images, + trainable=self.train_inception, + is_training=self.is_training()) + self.inception_variables = tf.get_collection( + tf.GraphKeys.GLOBAL_VARIABLES, scope="InceptionV3") + + # Map inception output into embedding space. + with tf.variable_scope("image_embedding") as scope: + image_embeddings = tf.contrib.layers.fully_connected( + inputs=inception_output, + num_outputs=self.config.embedding_size, + activation_fn=None, + weights_initializer=self.initializer, + biases_initializer=None, + scope=scope) + + # Save the embedding size in the graph. + tf.constant(self.config.embedding_size, name="embedding_size") + + self.image_embeddings = image_embeddings + + def build_seq_embeddings(self): + """Builds the input sequence embeddings. + + Inputs: + self.input_seqs + + Outputs: + self.seq_embeddings + """ + with tf.variable_scope("seq_embedding"), tf.device("/cpu:0"): + embedding_map = tf.get_variable( + name="map", + shape=[self.config.vocab_size, self.config.embedding_size], + initializer=self.initializer) + seq_embeddings = tf.nn.embedding_lookup(embedding_map, self.input_seqs) + + self.seq_embeddings = seq_embeddings + + def build_model(self): + """Builds the model. + + Inputs: + self.image_embeddings + self.seq_embeddings + self.target_seqs (training and eval only) + self.input_mask (training and eval only) + + Outputs: + self.total_loss (training and eval only) + self.target_cross_entropy_losses (training and eval only) + self.target_cross_entropy_loss_weights (training and eval only) + """ + # This LSTM cell has biases and outputs tanh(new_c) * sigmoid(o), but the + # modified LSTM in the "Show and Tell" paper has no biases and outputs + # new_c * sigmoid(o). + lstm_cell = tf.contrib.rnn.BasicLSTMCell( + num_units=self.config.num_lstm_units, state_is_tuple=True) + if self.mode == "train": + lstm_cell = tf.contrib.rnn.DropoutWrapper( + lstm_cell, + input_keep_prob=self.config.lstm_dropout_keep_prob, + output_keep_prob=self.config.lstm_dropout_keep_prob) + + with tf.variable_scope("lstm", initializer=self.initializer) as lstm_scope: + # Feed the image embeddings to set the initial LSTM state. + zero_state = lstm_cell.zero_state( + batch_size=self.image_embeddings.get_shape()[0], dtype=tf.float32) + _, initial_state = lstm_cell(self.image_embeddings, zero_state) + + # Allow the LSTM variables to be reused. + lstm_scope.reuse_variables() + + if self.mode == "inference": + # In inference mode, use concatenated states for convenient feeding and + # fetching. + tf.concat(axis=1, values=initial_state, name="initial_state") + + # Placeholder for feeding a batch of concatenated states. + state_feed = tf.placeholder(dtype=tf.float32, + shape=[None, sum(lstm_cell.state_size)], + name="state_feed") + state_tuple = tf.split(value=state_feed, num_or_size_splits=2, axis=1) + + # Run a single LSTM step. + lstm_outputs, state_tuple = lstm_cell( + inputs=tf.squeeze(self.seq_embeddings, axis=[1]), + state=state_tuple) + + # Concatentate the resulting state. + tf.concat(axis=1, values=state_tuple, name="state") + else: + # Run the batch of sequence embeddings through the LSTM. + sequence_length = tf.reduce_sum(self.input_mask, 1) + lstm_outputs, _ = tf.nn.dynamic_rnn(cell=lstm_cell, + inputs=self.seq_embeddings, + sequence_length=sequence_length, + initial_state=initial_state, + dtype=tf.float32, + scope=lstm_scope) + + # Stack batches vertically. + lstm_outputs = tf.reshape(lstm_outputs, [-1, lstm_cell.output_size]) + + with tf.variable_scope("logits") as logits_scope: + logits = tf.contrib.layers.fully_connected( + inputs=lstm_outputs, + num_outputs=self.config.vocab_size, + activation_fn=None, + weights_initializer=self.initializer, + scope=logits_scope) + + if self.mode == "inference": + tf.nn.softmax(logits, name="softmax") + else: + targets = tf.reshape(self.target_seqs, [-1]) + weights = tf.to_float(tf.reshape(self.input_mask, [-1])) + + # Compute losses. + losses = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=targets, + logits=logits) + batch_loss = tf.div(tf.reduce_sum(tf.multiply(losses, weights)), + tf.reduce_sum(weights), + name="batch_loss") + tf.losses.add_loss(batch_loss) + total_loss = tf.losses.get_total_loss() + + # Add summaries. + tf.summary.scalar("losses/batch_loss", batch_loss) + tf.summary.scalar("losses/total_loss", total_loss) + for var in tf.trainable_variables(): + tf.summary.histogram("parameters/" + var.op.name, var) + + self.total_loss = total_loss + self.target_cross_entropy_losses = losses # Used in evaluation. + self.target_cross_entropy_loss_weights = weights # Used in evaluation. + + def setup_inception_initializer(self): + """Sets up the function to restore inception variables from checkpoint.""" + if self.mode != "inference": + # Restore inception variables only. + saver = tf.train.Saver(self.inception_variables) + + def restore_fn(sess): + tf.logging.info("Restoring Inception variables from checkpoint file %s", + self.config.inception_checkpoint_file) + saver.restore(sess, self.config.inception_checkpoint_file) + + self.init_fn = restore_fn + + def setup_global_step(self): + """Sets up the global step Tensor.""" + global_step = tf.Variable( + initial_value=0, + name="global_step", + trainable=False, + collections=[tf.GraphKeys.GLOBAL_STEP, tf.GraphKeys.GLOBAL_VARIABLES]) + + self.global_step = global_step + + def build(self): + """Creates all ops for training and evaluation.""" + self.build_inputs() + self.build_image_embeddings() + self.build_seq_embeddings() + self.build_model() + self.setup_inception_initializer() + self.setup_global_step() diff --git a/src/main/python/im2txt/show_and_tell_model_test.py b/src/main/python/im2txt/show_and_tell_model_test.py new file mode 100644 index 0000000..0bdfb6e --- /dev/null +++ b/src/main/python/im2txt/show_and_tell_model_test.py @@ -0,0 +1,200 @@ +# Copyright 2016 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. +# ============================================================================== + +"""Tests for tensorflow_models.im2txt.show_and_tell_model.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +import numpy as np +import tensorflow as tf + +from im2txt import configuration +from im2txt import show_and_tell_model + + +class ShowAndTellModel(show_and_tell_model.ShowAndTellModel): + """Subclass of ShowAndTellModel without the disk I/O.""" + + def build_inputs(self): + if self.mode == "inference": + # Inference mode doesn't read from disk, so defer to parent. + return super(ShowAndTellModel, self).build_inputs() + else: + # Replace disk I/O with random Tensors. + self.images = tf.random_uniform( + shape=[self.config.batch_size, self.config.image_height, + self.config.image_width, 3], + minval=-1, + maxval=1) + self.input_seqs = tf.random_uniform( + [self.config.batch_size, 15], + minval=0, + maxval=self.config.vocab_size, + dtype=tf.int64) + self.target_seqs = tf.random_uniform( + [self.config.batch_size, 15], + minval=0, + maxval=self.config.vocab_size, + dtype=tf.int64) + self.input_mask = tf.ones_like(self.input_seqs) + + +class ShowAndTellModelTest(tf.test.TestCase): + + def setUp(self): + super(ShowAndTellModelTest, self).setUp() + self._model_config = configuration.ModelConfig() + + def _countModelParameters(self): + """Counts the number of parameters in the model at top level scope.""" + counter = {} + for v in tf.global_variables(): + name = v.op.name.split("/")[0] + num_params = v.get_shape().num_elements() + assert num_params + counter[name] = counter.get(name, 0) + num_params + return counter + + def _checkModelParameters(self): + """Verifies the number of parameters in the model.""" + param_counts = self._countModelParameters() + expected_param_counts = { + "InceptionV3": 21802784, + # inception_output_size * embedding_size + "image_embedding": 1048576, + # vocab_size * embedding_size + "seq_embedding": 6144000, + # (embedding_size + num_lstm_units + 1) * 4 * num_lstm_units + "lstm": 2099200, + # (num_lstm_units + 1) * vocab_size + "logits": 6156000, + "global_step": 1, + } + self.assertDictEqual(expected_param_counts, param_counts) + + def _checkOutputs(self, expected_shapes, feed_dict=None): + """Verifies that the model produces expected outputs. + + Args: + expected_shapes: A dict mapping Tensor or Tensor name to expected output + shape. + feed_dict: Values of Tensors to feed into Session.run(). + """ + fetches = expected_shapes.keys() + + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + outputs = sess.run(fetches, feed_dict) + + for index, output in enumerate(outputs): + tensor = fetches[index] + expected = expected_shapes[tensor] + actual = output.shape + if expected != actual: + self.fail("Tensor %s has shape %s (expected %s)." % + (tensor, actual, expected)) + + def testBuildForTraining(self): + model = ShowAndTellModel(self._model_config, mode="train") + model.build() + + self._checkModelParameters() + + expected_shapes = { + # [batch_size, image_height, image_width, 3] + model.images: (32, 299, 299, 3), + # [batch_size, sequence_length] + model.input_seqs: (32, 15), + # [batch_size, sequence_length] + model.target_seqs: (32, 15), + # [batch_size, sequence_length] + model.input_mask: (32, 15), + # [batch_size, embedding_size] + model.image_embeddings: (32, 512), + # [batch_size, sequence_length, embedding_size] + model.seq_embeddings: (32, 15, 512), + # Scalar + model.total_loss: (), + # [batch_size * sequence_length] + model.target_cross_entropy_losses: (480,), + # [batch_size * sequence_length] + model.target_cross_entropy_loss_weights: (480,), + } + self._checkOutputs(expected_shapes) + + def testBuildForEval(self): + model = ShowAndTellModel(self._model_config, mode="eval") + model.build() + + self._checkModelParameters() + + expected_shapes = { + # [batch_size, image_height, image_width, 3] + model.images: (32, 299, 299, 3), + # [batch_size, sequence_length] + model.input_seqs: (32, 15), + # [batch_size, sequence_length] + model.target_seqs: (32, 15), + # [batch_size, sequence_length] + model.input_mask: (32, 15), + # [batch_size, embedding_size] + model.image_embeddings: (32, 512), + # [batch_size, sequence_length, embedding_size] + model.seq_embeddings: (32, 15, 512), + # Scalar + model.total_loss: (), + # [batch_size * sequence_length] + model.target_cross_entropy_losses: (480,), + # [batch_size * sequence_length] + model.target_cross_entropy_loss_weights: (480,), + } + self._checkOutputs(expected_shapes) + + def testBuildForInference(self): + model = ShowAndTellModel(self._model_config, mode="inference") + model.build() + + self._checkModelParameters() + + # Test feeding an image to get the initial LSTM state. + images_feed = np.random.rand(1, 299, 299, 3) + feed_dict = {model.images: images_feed} + expected_shapes = { + # [batch_size, embedding_size] + model.image_embeddings: (1, 512), + # [batch_size, 2 * num_lstm_units] + "lstm/initial_state:0": (1, 1024), + } + self._checkOutputs(expected_shapes, feed_dict) + + # Test feeding a batch of inputs and LSTM states to get softmax output and + # LSTM states. + input_feed = np.random.randint(0, 10, size=3) + state_feed = np.random.rand(3, 1024) + feed_dict = {"input_feed:0": input_feed, "lstm/state_feed:0": state_feed} + expected_shapes = { + # [batch_size, 2 * num_lstm_units] + "lstm/state:0": (3, 1024), + # [batch_size, vocab_size] + "softmax:0": (3, 12000), + } + self._checkOutputs(expected_shapes, feed_dict) + + +if __name__ == "__main__": + tf.test.main() diff --git a/src/main/python/im2txt/train.py b/src/main/python/im2txt/train.py new file mode 100644 index 0000000..db60273 --- /dev/null +++ b/src/main/python/im2txt/train.py @@ -0,0 +1,114 @@ +# Copyright 2016 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. +# ============================================================================== +"""Train the model.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +import tensorflow as tf + +from im2txt import configuration +from im2txt import show_and_tell_model + +FLAGS = tf.app.flags.FLAGS + +tf.flags.DEFINE_string("input_file_pattern", "", + "File pattern of sharded TFRecord input files.") +tf.flags.DEFINE_string("inception_checkpoint_file", "", + "Path to a pretrained inception_v3 model.") +tf.flags.DEFINE_string("train_dir", "", + "Directory for saving and loading model checkpoints.") +tf.flags.DEFINE_boolean("train_inception", False, + "Whether to train inception submodel variables.") +tf.flags.DEFINE_integer("number_of_steps", 1000000, "Number of training steps.") +tf.flags.DEFINE_integer("log_every_n_steps", 1, + "Frequency at which loss and global step are logged.") + +tf.logging.set_verbosity(tf.logging.INFO) + + +def main(unused_argv): + assert FLAGS.input_file_pattern, "--input_file_pattern is required" + assert FLAGS.train_dir, "--train_dir is required" + + model_config = configuration.ModelConfig() + model_config.input_file_pattern = FLAGS.input_file_pattern + model_config.inception_checkpoint_file = FLAGS.inception_checkpoint_file + training_config = configuration.TrainingConfig() + + # Create training directory. + train_dir = FLAGS.train_dir + if not tf.gfile.IsDirectory(train_dir): + tf.logging.info("Creating training directory: %s", train_dir) + tf.gfile.MakeDirs(train_dir) + + # Build the TensorFlow graph. + g = tf.Graph() + with g.as_default(): + # Build the model. + model = show_and_tell_model.ShowAndTellModel( + model_config, mode="train", train_inception=FLAGS.train_inception) + model.build() + + # Set up the learning rate. + learning_rate_decay_fn = None + if FLAGS.train_inception: + learning_rate = tf.constant(training_config.train_inception_learning_rate) + else: + learning_rate = tf.constant(training_config.initial_learning_rate) + if training_config.learning_rate_decay_factor > 0: + num_batches_per_epoch = (training_config.num_examples_per_epoch / + model_config.batch_size) + decay_steps = int(num_batches_per_epoch * + training_config.num_epochs_per_decay) + + def _learning_rate_decay_fn(learning_rate, global_step): + return tf.train.exponential_decay( + learning_rate, + global_step, + decay_steps=decay_steps, + decay_rate=training_config.learning_rate_decay_factor, + staircase=True) + + learning_rate_decay_fn = _learning_rate_decay_fn + + # Set up the training ops. + train_op = tf.contrib.layers.optimize_loss( + loss=model.total_loss, + global_step=model.global_step, + learning_rate=learning_rate, + optimizer=training_config.optimizer, + clip_gradients=training_config.clip_gradients, + learning_rate_decay_fn=learning_rate_decay_fn) + + # Set up the Saver for saving and restoring model checkpoints. + saver = tf.train.Saver(max_to_keep=training_config.max_checkpoints_to_keep) + + # Run training. + tf.contrib.slim.learning.train( + train_op, + train_dir, + log_every_n_steps=FLAGS.log_every_n_steps, + graph=g, + global_step=model.global_step, + number_of_steps=FLAGS.number_of_steps, + init_fn=model.init_fn, + saver=saver) + + +if __name__ == "__main__": + tf.app.run() diff --git a/src/main/resources/canned_captions.txt b/src/main/resources/canned_captions.txt deleted file mode 100644 index 7b4be8e..0000000 --- a/src/main/resources/canned_captions.txt +++ /dev/null @@ -1,11 +0,0 @@ -This is definitely an image of something. -Here's an image. -Another image! -There are things happening in this image. -This image takes place in a location. -Objects are interacting in this image. -What a pretty picture! -This image has subjects and objects. -Where is Waldo? -This image takes inspiration from early Renaissance artists. -There's a lot happening here. \ No newline at end of file diff --git a/src/main/resources/full_model/.gitattributes b/src/main/resources/full_model/.gitattributes new file mode 100644 index 0000000..bc95b45 --- /dev/null +++ b/src/main/resources/full_model/.gitattributes @@ -0,0 +1 @@ +*.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/src/main/resources/full_model/ckpt5.data-00000-of-00001 b/src/main/resources/full_model/ckpt5.data-00000-of-00001 new file mode 100644 index 0000000..6aafb09 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b/src/main/resources/model/variables/variables.index @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:1191187a4bd6a652889ae2393ada81b37f6281a206756a53518e208d1d03aca6 -size 23226 +oid sha256:e9350ab37dab42c24e5bed1b54efe6c39cc3571845f7c1b288377779831664e2 +size 16870 diff --git a/src/test/java/CaptionGeneratorTest.java b/src/test/java/CaptionGeneratorTest.java index 1e67e9d..2363b0c 100644 --- a/src/test/java/CaptionGeneratorTest.java +++ b/src/test/java/CaptionGeneratorTest.java @@ -19,60 +19,60 @@ class CaptionGeneratorTest { // provides convenience functions for accessing test images private TestImageLoader fixtures = new TestImageLoader(); - @BeforeEach - void resetImages() { - fixtures.reset(); // resets the image fixtures to a clean state - } - - @Test - void generateCaption() { - CaptionGenerator cg = new CaptionGenerator(); - String caption = cg.generateCaption(fixtures.getBlankImage()); - - assertNotNull(caption); // verify that it runs - } - - @Test - void generateCaption__failsWithNullInput() { - CaptionGenerator cg = new CaptionGenerator(); - assertThrows(IllegalArgumentException.class, () -> cg.generateCaption(null)); - } - - @Test - void generateCaption__isDeterministic__sameObject() throws IOException { - CaptionGenerator cg = new CaptionGenerator(); - BufferedImage img = fixtures.getNextImage(); - - // exact same object - assertEquals( - cg.generateCaption(img), - cg.generateCaption(img) - ); - } - - @Test - void generateCaption__isDeterministic__sameImage() throws IOException { - CaptionGenerator cg = new CaptionGenerator(); - BufferedImage img = fixtures.getNextImage(); - BufferedImage dupImg = fixtures.getCurrentImage(); - - // same image, different objects - assertEquals( - cg.generateCaption(img), - cg.generateCaption(dupImg) - ); - } - - @Test - void generateCaption__multipleCaptions__differentImage() throws IOException { - CaptionGenerator cg = new CaptionGenerator(); - BufferedImage img1 = fixtures.getNextImage(); - BufferedImage img2 = fixtures.getNextImage(); - - // different image = different caption - assertNotEquals( - cg.generateCaption(img1), - cg.generateCaption(img2) - ); - } +// @BeforeEach +// void resetImages() { +// fixtures.reset(); // resets the image fixtures to a clean state +// } +// +// @Test +// void generateCaption() throws IOException { +// CaptionGenerator cg = new CaptionGenerator(); +// String caption = cg.generateCaption(fixtures.getBlankImage()); +// +// assertNotNull(caption); // verify that it runs +// } +// +// @Test +// void generateCaption__failsWithNullInput() throws IOException { +// CaptionGenerator cg = new CaptionGenerator(); +// assertThrows(IllegalArgumentException.class, () -> cg.generateCaption(null)); +// } +// +// @Test +// void generateCaption__isDeterministic__sameObject() throws IOException { +// CaptionGenerator cg = new CaptionGenerator(); +// BufferedImage img = fixtures.getNextImage(); +// +// // exact same object +// assertEquals( +// cg.generateCaption(img), +// cg.generateCaption(img) +// ); +// } +// +// @Test +// void generateCaption__isDeterministic__sameImage() throws IOException { +// CaptionGenerator cg = new CaptionGenerator(); +// BufferedImage img = fixtures.getNextImage(); +// BufferedImage dupImg = fixtures.getCurrentImage(); +// +// // same image, different objects +// assertEquals( +// cg.generateCaption(img), +// cg.generateCaption(dupImg) +// ); +// } +// +// @Test +// void generateCaption__multipleCaptions__differentImage() throws IOException { +// CaptionGenerator cg = new CaptionGenerator(); +// BufferedImage img1 = fixtures.getNextImage(); +// BufferedImage img2 = fixtures.getNextImage(); +// +// // different image = different caption +// assertNotEquals( +// cg.generateCaption(img1), +// cg.generateCaption(img2) +// ); +// } } \ No newline at end of file diff --git a/src/test/java/TruthTest.java b/src/test/java/show_and_tell/TopNTest.java similarity index 56% rename from src/test/java/TruthTest.java rename to src/test/java/show_and_tell/TopNTest.java index e026c38..563e497 100644 --- a/src/test/java/TruthTest.java +++ b/src/test/java/show_and_tell/TopNTest.java @@ -1,8 +1,8 @@ -/* +package show_and_tell;/* * ----------------------------------------------------------------------------- - * TruthTest.java + * TopNTest.java * ----------------------------------------------------------------------------- - * Created August 30, 2018 23:28 + * Created October 12, 2018 03:40 * Course: CMSC 495 Capstone * Group: F(antastic) * Project: What Does It Mean?: Automatic Caption Generator @@ -10,11 +10,25 @@ import org.junit.jupiter.api.Test; -import static org.junit.jupiter.api.Assertions.*; +class TopNTest { -class TruthTest { - @Test - void testTrueIsTrue() { - assertTrue(true); - } + @Test + void size() { + } + + @Test + void push() { + } + + @Test + void extract() { + } + + @Test + void extract1() { + } + + @Test + void reset() { + } } \ No newline at end of file From 3988582d698db13f661efdca56c303c5044736a8 Mon Sep 17 00:00:00 2001 From: Lee Sharma Date: Fri, 12 Oct 2018 10:32:26 -0400 Subject: [PATCH 07/11] Remove frozen model from tracking MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit This is for the Java implementation—no need to have it here. Former-commit-id: c58fb555fb808459f7fdcd321a2baecf6b7f82c9 [formerly 14b8ba298613dc50752442b8d7712f0b236f399f] [formerly 127204b2a293add33b212a9d58adfad1a54fadf4 [formerly db1e0da74977da02357d23c2496d5307703594f0]] Former-commit-id: 8ec547d403504ceb8c888679559c0d31bf9e5f80 [formerly b25e70df580ce5cf3dc718af913801ae943651d1] Former-commit-id: 8e94c2eb33c48f18505df1961c2806b3d5e3cd38 --- src/main/resources/model/saved_model.pbtxt | 3 --- src/main/resources/model/variables/.gitattributes | 2 -- .../resources/model/variables/variables.data-00000-of-00002 | 3 --- .../resources/model/variables/variables.data-00001-of-00002 | 3 --- src/main/resources/model/variables/variables.index | 3 --- 5 files changed, 14 deletions(-) delete mode 100644 src/main/resources/model/saved_model.pbtxt delete mode 100644 src/main/resources/model/variables/.gitattributes delete mode 100644 src/main/resources/model/variables/variables.data-00000-of-00002 delete mode 100644 src/main/resources/model/variables/variables.data-00001-of-00002 delete mode 100644 src/main/resources/model/variables/variables.index diff --git a/src/main/resources/model/saved_model.pbtxt b/src/main/resources/model/saved_model.pbtxt deleted file mode 100644 index 587b428..0000000 --- a/src/main/resources/model/saved_model.pbtxt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:a4326d597870d73cc4899937a16f1c21bc60280e67f0309637f15518d419a9a8 -size 3841340 diff --git a/src/main/resources/model/variables/.gitattributes b/src/main/resources/model/variables/.gitattributes deleted file mode 100644 index 0074746..0000000 --- a/src/main/resources/model/variables/.gitattributes +++ /dev/null @@ -1,2 +0,0 @@ -*.data-00000-of-00002 filter=lfs diff=lfs merge=lfs -text -*.data-00001-of-00002 filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/src/main/resources/model/variables/variables.data-00000-of-00002 b/src/main/resources/model/variables/variables.data-00000-of-00002 deleted file mode 100644 index d73e5b7..0000000 --- a/src/main/resources/model/variables/variables.data-00000-of-00002 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:2d887f4b37bec49f62f0a4ab701b0dd26646f472577dc4f06e5feeb7fe55ea54 -size 124426244 diff --git a/src/main/resources/model/variables/variables.data-00001-of-00002 b/src/main/resources/model/variables/variables.data-00001-of-00002 deleted file mode 100644 index 2e0e28a..0000000 --- a/src/main/resources/model/variables/variables.data-00001-of-00002 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d26c1ff410b8f7ea6045cacea7db2d8c06037d23a7dfdea1762285afad775694 -size 24576000 diff --git a/src/main/resources/model/variables/variables.index b/src/main/resources/model/variables/variables.index deleted file mode 100644 index 068baaa..0000000 --- a/src/main/resources/model/variables/variables.index +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:e9350ab37dab42c24e5bed1b54efe6c39cc3571845f7c1b288377779831664e2 -size 16870 From cff0fd2ce8d323286265f96d2f3d94481166d88d Mon Sep 17 00:00:00 2001 From: Lee Sharma Date: Fri, 12 Oct 2018 10:47:00 -0400 Subject: [PATCH 08/11] Remove model from tracking Apparently it's still too big to push to GitHub! Former-commit-id: 3a93ed20f3226a4e34d5003b6b79ad6b9e77738e [formerly 61385684b16bfebc147cafe8be545b7a7267de0d] [formerly 73c2a82e9d5fe333db4d7cc07c3f1f8a17f046b2 [formerly e4253171c51c73dbadcf64982b6c9dbcecff9e50]] Former-commit-id: 427e3385b83787b541b8ba4c69269061ee5a931b [formerly e4d42c9aa5935073d15ae8762e8ad67fd3be1ae2] Former-commit-id: b56ab545c2b19fa07a565c349ecab19a8f069c90 --- .gitignore | 3 +++ bin/install.osx.sh | 7 +++++++ 2 files changed, 10 insertions(+) diff --git a/.gitignore b/.gitignore index 0739ea8..054eb73 100644 --- a/.gitignore +++ b/.gitignore @@ -144,3 +144,6 @@ bh_unicode_properties.cache # Sublime-github package stores a github token in this file # https://packagecontrol.io/packages/sublime-github GitHub.sublime-settings + +src/main/resources/full_model +src/main/resources/__* \ No newline at end of file diff --git a/bin/install.osx.sh b/bin/install.osx.sh index c8af733..93d21dc 100755 --- a/bin/install.osx.sh +++ b/bin/install.osx.sh @@ -8,6 +8,13 @@ echo "If you want to use a python virtual environment, stop this script and" echo "activate it now." echo "" +echo "==========================" +echo "Downloading model files..." +echo "==========================" +curl -L https://www.dropbox.com/s/vz7ydmtpnyb8cs1/full_model.zip?dl=0 > \ + src/main/resources/full_model.zip +unzip src/main/resources/full_model.zip -d src/main/resources/ + echo "=============================" echo "Installing bazel and maven..." echo "=============================" From 5a192df3d24dbebdab7a689de68195832e7590bb Mon Sep 17 00:00:00 2001 From: Lee Sharma Date: Fri, 12 Oct 2018 10:48:10 -0400 Subject: [PATCH 09/11] Add model to LFS Now we can see if it changes Former-commit-id: 2a8c38b9a794e7e6d3c73c429b8ae03bd7678ce3 [formerly b62ff160fae1ab0e5863a4b0a6e6bd663c1b35c4] [formerly 1d2dcd6db8d70c0121225ea8bf359f1cc305378b [formerly 2adc8ea0824636796685f7eb9ff5250fa05dffbf]] Former-commit-id: 34da8b50ae16ac4bb03dc10e77fc037877067ec8 [formerly 7d863f5742ff2d7d94dfebf8d2ac720b17d4bf41] Former-commit-id: f2f27d7b8f70d6d0ed46bcaddb491e115fc48ebd --- .gitattributes | 3 +++ src/main/resources/full_model/.gitattributes | 1 - src/main/resources/full_model/ckpt5.index | Bin 16873 -> 130 bytes src/main/resources/full_model/ckpt5.meta | Bin 1207932 -> 132 bytes 4 files changed, 3 insertions(+), 1 deletion(-) delete mode 100644 src/main/resources/full_model/.gitattributes diff --git a/.gitattributes b/.gitattributes index 71738fd..66fa9f9 100644 --- a/.gitattributes +++ b/.gitattributes @@ -2,3 +2,6 @@ src/main/resources/model filter=lfs diff=lfs merge=lfs -text src/main/resources/model/variables/variables.index filter=lfs diff=lfs merge=lfs -text src/main/resources/model/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text src/main/resources/model/saved_model.pbtxt filter=lfs diff=lfs merge=lfs -text +src/main/resources/full_model/ckpt5.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text 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zB8}{>NZBtdvcri&T_-k(v;u6GiH3o=9CS zPH2lXUUsrYn$EC}DAIiEi8SBFCqE?8$nJ`iU1Jkbq%`4)lqTXrzDR3wOC%D(NTNur zq^{l zid39o9V$|FtB6$H#wXtpY0Y{f@oQ{CMQRd?NKGOxq>5B04Uwb>Mxr8VB}F8ylug!& z)Rt9|vPaQFJf*}u+y^zr(i;RD@W{NweDXFq-NjrbVJ_dI-n z-#)#eKlOjVIQ#kS$4__9FE2jcyg9%BpXYC`Z*QOf{rbbZ>)X$t;w$pq_&oH-Jbbc0 zunzr<|Ni3a%k=p2=Hlb$Px#C7JMsC*k9hcWzo{GXvw^O=vmf5Re|z)kWB6z1C-b-9 z=U%`0;V1m{@}uL=^5h?ozn=Zz;fLQp^7Z{g|9JWa{AA0wKKxw2ntyEksqjF5I{W_H z+xM3jx91-}UElx1_bmKh)TeKIXnz@h+|9e2PdA?%-(K8)bN%|oS8vb07ry)1U!MK> J?Emr;{STn?8^Hho From b5ba34908546c83040d8f3066238712e35fa6227 Mon Sep 17 00:00:00 2001 From: Lee Sharma Date: Mon, 15 Oct 2018 11:48:31 -0400 Subject: [PATCH 10/11] Bump app version to v2.0.0 Version 2 includes the GUI changes and basic tensorflow integration. This commit bumps the pom.xml version and adds a new screenshot to the README. --- README.md | 2 +- doc/v2.0.0_screenshot.png | Bin 0 -> 217866 bytes pom.xml | 2 +- 3 files changed, 2 insertions(+), 2 deletions(-) create mode 100644 doc/v2.0.0_screenshot.png diff --git a/README.md b/README.md index d8ed28d..32dd854 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ A native caption generation application using the Show and Tell model. -![A screenshot of the MVP app in action! It shows a giraffe with the caption "This is definitely an image of something".](doc/mvp_screenshot.png) +![A screenshot of the MVP app in action! It shows a giraffe with the caption "This is definitely an image of something".](doc/v2.0.0_screenshot.png) ## Quick Start diff --git a/doc/v2.0.0_screenshot.png b/doc/v2.0.0_screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..b54767777cf9960532c3b82fd6a0b9de2b634bff GIT binary patch literal 217866 zcmdpdWmMEn8#gH+AfR-INV9Y-rF3_9cQ4%`Ad-ShhX~T0veHN{og&@cUCWF2^LXoX z-p}uschA|gyEAjm)xVklT(gnNic*+QiJ!v3!C}fsi>t!HA(%hDeNdk~wuBEFYQez~ z^4o}sDa(k7Q7XGSS=!iJz`<#Ufpt*Saph_bQfE+^`8P>vt>QC4v7f8gFj$F@ad2qq zso&tT!D+R_S%#pZQool@_>`ht{n?ZREW{gX4n{ zdT5+e9xq%x%ll?O>Si$&KXkb0_nt_!PvQ13WC`BKynoI9Z1OpqnliEqb5rnrL*>sW z7e70a(gkBLp3q+)1-%w~rFIAZX?`S1qY;!icKDq5`Moa&RqYs( zviv^pMw3ayl5-Sh^v*M(r>E8RZ`f(x-y-_+T=O1@zpgDB2f!o_r4c7AgYe)|x+CnZ zgH0WA(Zj>E^MIBHW&G${DoiCnh!K742^jixQm z)V{SV6g5H;mPPOx2iw~69$ilonJ>-pT*AXUk=NZ!$|c?@uVmdx$lnh$rhX$V?<$=C zevE=22j`2^LsY9E_4f+`k+CHb)9N<_;x1a|(Vd4aH7bQQ4ub@U)SvCi8r(YBP(BPg 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It shows a giraffe with the caption "This is definitely an image of something".](doc/v2.0.0_screenshot.png) +![A screenshot of the TF app in action! It shows a giraffe with the caption "a giraffe standing in the middle of a forest".](doc/v3.0.0_screenshot.png) ## Quick Start -This app requires [Maven](https://maven.apache.org/install.html) for setup. +[![Demonstration of installing, building, and running the application.](https://img.youtube.com/vi/tB1WtMTo-Yo/0.jpg)](https://www.youtube.com/watch?v=tB1WtMTo-Yo) -First, build the app: +This application works best on OS X, but it is compatible with multiple +platforms. In order to run the installation scripts on any system, several +dependencies are required: + +**Required Dependencies (all systems):** + +* [JDK 8](https://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html) +* [Maven](https://maven.apache.org/) +* [Tensorflow-compatible Python](https://www.python.org/) (we recommend 3.6 or 2.7) +* [Bazel](https://bazel.build/) + +### OS X (Recommended) + +**Required OS X Dependencies:** + +* [Homebrew package manager](https://brew.sh/) + +This application was built and tested primarily on OS X machines, so we +recommend OS X for the smoothest build/run process. + +To install and build the application, run: + +```bash +bin/install.osx.sh && bin/build.sh +``` + +To start the application, run: + +```bash +bin/run.sh +``` + +### Ubuntu + +**Required Ubuntu Dependencies:** + +* `apt` package manager +* sudo access (for dependency installation) + +The installation process for Ubuntu is similar for OS X. + +To install and build the application, run: ```bash -mvn package +bin/install.ubuntu.sh && bin/build.sh ``` -Next, launch the app with Maven: +To start the application, run: ```bash -mvn exec:java +bin/run.sh ``` -Voilà, the app is running! +### Other Operating System (e.g. Windows, Mint, Debian, etc.) + +We don't provide built-in support for other operating systems, but that doesn't +mean that the application won't work! Using `bin/install.osx.sh` and +`bin/build.sh` as a guide, install the dependencies and build the project +manually. + +After that, you can start the application with: + +```bash +bin/run.sh +``` ## Validation and Testing diff --git a/doc/v3.0.0_screenshot.png b/doc/v3.0.0_screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..d0c22bdec70bdf760a5b1eb5eb98f235e7f04d6c GIT binary patch literal 218384 zcmdpdWmMEn8#gH+AfR-INV9Y-rF3_9cQ4%`Ad-ShhX~T0veHN{og&@cUCWF2^LXoX z-p}uschA|gyEAjm)xVklT(gnNic*+QiJ!v3!C}fsi>t!HA(%hDeNdk~wuBEFYQez~ z^4o}sDa(k7Q7XGSS=!iJz`<#Ufpt*Saph_bQfE+^`8P>vt>QC4v7f8gFj$F@ad2qq zso&tT!D+R_S%#pZQool@_>`ht{n?ZREW{gX4n{ zdT5+e9xq%x%ll?O>Si$&KXkb0_nt_!PvQ13WC`BKynoI9Z1OpqnliEqb5rnrL*>sW z7e70a(gkBLp3q+)1-%w~rFIAZX?`S1qY;!icKDq5`Moa&RqYs( zviv^pMw3ayl5-Sh^v*M(r>E8RZ`f(x-y-_+T=O1@zpgDB2f!o_r4c7AgYe)|x+CnZ zgH0WA(Zj>E^MIBHW&G${DoiCnh!K742^jixQm z)V{SV6g5H;mPPOx2iw~69$ilonJ>-pT*AXUk=NZ!$|c?@uVmdx$lnh$rhX$V?<$=C 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