├── keras_codes ├── cifar10 │ ├── cifar10 │ │ ├── __init__.py │ │ ├── models │ │ │ ├── tf │ │ │ │ └── cnn_cifar10.pb │ │ │ ├── cnn_cifar10_config.npy │ │ │ ├── cnn_cifar10_weights.h5 │ │ │ └── cnn_cifar10-history.npy │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-36.pyc │ │ │ └── cifar10_classifier.cpython-36.pyc │ │ ├── cifar10_tensorflow_export_pb.py │ │ ├── cnn_cifar10_train.py │ │ ├── cifar10_tensorflow_classifier.py │ │ └── cifar10_classifier.py │ ├── .gitignore │ ├── requirements.txt │ └── ReadMe.md └── .gitignore ├── ProjectSettings ├── ProjectVersion.txt ├── ClusterInputManager.asset ├── EditorBuildSettings.asset ├── NetworkManager.asset ├── TimeManager.asset ├── AudioManager.asset ├── TagManager.asset ├── EditorSettings.asset ├── UnityConnectSettings.asset ├── DynamicsManager.asset ├── Physics2DSettings.asset ├── NavMeshAreas.asset ├── GraphicsSettings.asset ├── QualitySettings.asset ├── InputManager.asset └── ProjectSettings.asset ├── UnityPackageManager └── manifest.json ├── Assets ├── Resources │ ├── images │ │ ├── cifar10 │ │ │ ├── bird1.png │ │ │ ├── bird2.png │ │ │ ├── bird3.png │ │ │ ├── cat1.png │ │ │ ├── cat2.png │ │ │ ├── cat3.png │ │ │ ├── airplane1.png │ │ │ ├── airplane2.png │ │ │ ├── airplane3.png │ │ │ ├── automobile1.png │ │ │ ├── automobile2.png │ │ │ ├── automobile3.png │ │ │ ├── cat1.png.meta │ │ │ ├── cat2.png.meta │ │ │ ├── cat3.png.meta │ │ │ ├── airplane1.png.meta │ │ │ ├── airplane2.png.meta │ │ │ ├── airplane3.png.meta │ │ │ ├── bird1.png.meta │ │ │ ├── bird2.png.meta │ │ │ ├── bird3.png.meta │ │ │ ├── automobile1.png.meta │ │ │ ├── automobile2.png.meta │ │ │ └── automobile3.png.meta │ │ ├── inception │ │ │ ├── lion.jpg │ │ │ ├── tiger.jpg │ │ │ ├── lion.jpg.meta │ │ │ └── tiger.jpg.meta │ │ ├── Thumbs.db.meta │ │ ├── cifar10.meta │ │ └── inception.meta │ ├── tf_models │ │ ├── cnn_cifar10.bytes │ │ └── cnn_cifar10.bytes.meta │ ├── images.meta │ └── tf_models.meta ├── ML-Agents.meta ├── Scenes.meta ├── Scenes │ ├── default_scene.unity.meta │ └── default_scene.unity ├── MyMachine.meta ├── Resources.meta └── MyMachine │ ├── Cifar10ImageClassifier.cs.meta │ ├── Cifar10ImageClassifierDemo.cs.meta │ ├── Cifar10ImageClassifierDemo.cs │ └── Cifar10ImageClassifier.cs ├── .gitignore ├── LICENSE └── README.md /keras_codes/cifar10/cifar10/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /keras_codes/.gitignore: -------------------------------------------------------------------------------- 1 | env/ 2 | .idea/ 3 | *.iml 4 | 5 | -------------------------------------------------------------------------------- /ProjectSettings/ProjectVersion.txt: -------------------------------------------------------------------------------- 1 | m_EditorVersion: 2017.3.0f3 2 | -------------------------------------------------------------------------------- /keras_codes/cifar10/.gitignore: -------------------------------------------------------------------------------- 1 | venv/ 2 | .idea/ 3 | *.iml 4 | 5 | -------------------------------------------------------------------------------- /UnityPackageManager/manifest.json: -------------------------------------------------------------------------------- 1 | { 2 | "dependencies": { 3 | } 4 | } 5 | -------------------------------------------------------------------------------- /keras_codes/cifar10/requirements.txt: -------------------------------------------------------------------------------- 1 | keras == 2.0.2 2 | numpy 3 | h5py 4 | pillow 5 | tensorflow == 1.2.0 -------------------------------------------------------------------------------- /Assets/Resources/images/cifar10/bird1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chen0040/unity-tensorflow-samples/HEAD/Assets/Resources/images/cifar10/bird1.png 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-------------------------------------------------------------------------------- 1 | %YAML 1.1 2 | %TAG !u! tag:unity3d.com,2011: 3 | --- !u!5 &1 4 | TimeManager: 5 | m_ObjectHideFlags: 0 6 | Fixed Timestep: 0.02 7 | Maximum Allowed Timestep: 0.33333334 8 | m_TimeScale: 1 9 | Maximum Particle Timestep: 0.03 10 | -------------------------------------------------------------------------------- /Assets/MyMachine.meta: -------------------------------------------------------------------------------- 1 | fileFormatVersion: 2 2 | guid: b6f309306c775b54eba5132a98dccde5 3 | folderAsset: yes 4 | timeCreated: 1519970005 5 | licenseType: Free 6 | DefaultImporter: 7 | externalObjects: {} 8 | userData: 9 | assetBundleName: 10 | assetBundleVariant: 11 | -------------------------------------------------------------------------------- /Assets/Resources.meta: -------------------------------------------------------------------------------- 1 | fileFormatVersion: 2 2 | guid: 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DefaultImporter: 7 | externalObjects: {} 8 | userData: 9 | assetBundleName: 10 | assetBundleVariant: 11 | -------------------------------------------------------------------------------- /Assets/Resources/tf_models/cnn_cifar10.bytes.meta: -------------------------------------------------------------------------------- 1 | fileFormatVersion: 2 2 | guid: 9cf85b14fb11b67408bfea37972ae992 3 | timeCreated: 1524707466 4 | licenseType: Free 5 | TextScriptImporter: 6 | externalObjects: {} 7 | userData: 8 | assetBundleName: 9 | assetBundleVariant: 10 | -------------------------------------------------------------------------------- /Assets/Resources/images/cifar10.meta: -------------------------------------------------------------------------------- 1 | fileFormatVersion: 2 2 | guid: 0a2da0f0c022fce4ebe6485d036e0ff1 3 | folderAsset: yes 4 | timeCreated: 1524387480 5 | licenseType: Free 6 | DefaultImporter: 7 | externalObjects: {} 8 | userData: 9 | assetBundleName: 10 | assetBundleVariant: 11 | -------------------------------------------------------------------------------- /Assets/Resources/images/inception.meta: -------------------------------------------------------------------------------- 1 | fileFormatVersion: 2 2 | guid: 351b6393ca8c20d4f90bfaf781b288a2 3 | folderAsset: yes 4 | timeCreated: 1524387480 5 | licenseType: Free 6 | DefaultImporter: 7 | externalObjects: {} 8 | userData: 9 | assetBundleName: 10 | assetBundleVariant: 11 | -------------------------------------------------------------------------------- /Assets/MyMachine/Cifar10ImageClassifier.cs.meta: -------------------------------------------------------------------------------- 1 | fileFormatVersion: 2 2 | guid: 4bef38b84683ffb4f9fc8750c4140a69 3 | timeCreated: 1520046919 4 | licenseType: Free 5 | MonoImporter: 6 | externalObjects: {} 7 | serializedVersion: 2 8 | defaultReferences: [] 9 | executionOrder: 0 10 | icon: {instanceID: 0} 11 | userData: 12 | assetBundleName: 13 | assetBundleVariant: 14 | -------------------------------------------------------------------------------- /Assets/MyMachine/Cifar10ImageClassifierDemo.cs.meta: -------------------------------------------------------------------------------- 1 | fileFormatVersion: 2 2 | guid: ac45332ba2daea74189382af46fa6527 3 | timeCreated: 1524388153 4 | licenseType: Free 5 | MonoImporter: 6 | externalObjects: {} 7 | serializedVersion: 2 8 | defaultReferences: [] 9 | executionOrder: 0 10 | icon: {instanceID: 0} 11 | userData: 12 | assetBundleName: 13 | assetBundleVariant: 14 | -------------------------------------------------------------------------------- /keras_codes/cifar10/ReadMe.md: -------------------------------------------------------------------------------- 1 | # CUDA Setup for GPU Deep Learning 2 | 3 | * Step 1: Download and install the [CUDA® Toolkit 9.0](https://developer.nvidia.com/cuda-90-download-archive) (you should download CUDA® Toolkit 9.0) 4 | * Step 2: Download and unzip the [cuDNN 7.0.4 for CUDA@ Toolkit 9.0](https://developer.nvidia.com/cudnn) and add the 5 | bin folder of the unzipped directory to the $PATH of your Windows environment -------------------------------------------------------------------------------- /ProjectSettings/AudioManager.asset: -------------------------------------------------------------------------------- 1 | %YAML 1.1 2 | %TAG !u! tag:unity3d.com,2011: 3 | --- !u!11 &1 4 | AudioManager: 5 | m_ObjectHideFlags: 0 6 | m_Volume: 1 7 | Rolloff Scale: 1 8 | Doppler Factor: 1 9 | Default Speaker Mode: 2 10 | m_SampleRate: 0 11 | m_DSPBufferSize: 0 12 | m_VirtualVoiceCount: 512 13 | m_RealVoiceCount: 32 14 | m_SpatializerPlugin: 15 | m_AmbisonicDecoderPlugin: 16 | m_DisableAudio: 0 17 | m_VirtualizeEffects: 1 18 | -------------------------------------------------------------------------------- /ProjectSettings/TagManager.asset: -------------------------------------------------------------------------------- 1 | %YAML 1.1 2 | %TAG !u! tag:unity3d.com,2011: 3 | --- !u!78 &1 4 | TagManager: 5 | serializedVersion: 2 6 | tags: [] 7 | layers: 8 | - Default 9 | - TransparentFX 10 | - Ignore Raycast 11 | - 12 | - Water 13 | - UI 14 | - 15 | - 16 | - 17 | - 18 | - 19 | - 20 | - 21 | - 22 | - 23 | - 24 | - 25 | - 26 | - 27 | - 28 | - 29 | - 30 | - 31 | - 32 | - 33 | - 34 | - 35 | - 36 | - 37 | - 38 | - 39 | - 40 | m_SortingLayers: 41 | - name: Default 42 | uniqueID: 0 43 | locked: 0 44 | -------------------------------------------------------------------------------- /keras_codes/cifar10/cifar10/cifar10_tensorflow_export_pb.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | 4 | 5 | def main(): 6 | current_dir = os.path.dirname(__file__) 7 | sys.path.append(os.path.join(current_dir, '..')) 8 | current_dir = current_dir if current_dir is not '' else '.' 9 | 10 | from cifar10.cifar10_classifier import Cifar10Classifier 11 | 12 | classifier = Cifar10Classifier() 13 | classifier.load_model(current_dir + '/models') 14 | classifier.export_tensorflow_model(output_fld=os.path.join(current_dir, 'models', 'tf')) 15 | 16 | 17 | if __name__ == '__main__': 18 | main() 19 | 20 | -------------------------------------------------------------------------------- /ProjectSettings/EditorSettings.asset: -------------------------------------------------------------------------------- 1 | %YAML 1.1 2 | %TAG !u! tag:unity3d.com,2011: 3 | --- !u!159 &1 4 | EditorSettings: 5 | m_ObjectHideFlags: 0 6 | serializedVersion: 7 7 | m_ExternalVersionControlSupport: Hidden Meta Files 8 | m_SerializationMode: 2 9 | m_LineEndingsForNewScripts: 2 10 | m_DefaultBehaviorMode: 0 11 | m_SpritePackerMode: 0 12 | m_SpritePackerPaddingPower: 1 13 | m_EtcTextureCompressorBehavior: 1 14 | m_EtcTextureFastCompressor: 1 15 | m_EtcTextureNormalCompressor: 2 16 | m_EtcTextureBestCompressor: 4 17 | m_ProjectGenerationIncludedExtensions: txt;xml;fnt;cd;asmdef;rsp 18 | m_ProjectGenerationRootNamespace: 19 | m_UserGeneratedProjectSuffix: 20 | m_CollabEditorSettings: 21 | inProgressEnabled: 1 22 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # =============== # 2 | # Unity generated # 3 | # =============== # 4 | Temp/ 5 | Library/ 6 | TestSample/ 7 | Assets/ML-Agents/ 8 | 9 | # ===================================== # 10 | # Visual Studio / MonoDevelop generated # 11 | # ===================================== # 12 | ExportedObj/ 13 | obj/ 14 | *.svd 15 | *.userprefs 16 | /*.csproj 17 | *.pidb 18 | *.suo 19 | /*.sln 20 | *.user 21 | *.unityproj 22 | *.booproj 23 | .vs/ 24 | 25 | # ============ # 26 | # OS generated # 27 | # ============ # 28 | .DS_Store 29 | .DS_Store? 30 | ._* 31 | .Spotlight-V100 32 | .Trashes 33 | ehthumbs.db 34 | Thumbs.db 35 | 36 | # Packages # 37 | ############ 38 | # it's better to unpack these files and commit the raw source 39 | # git has its own built in compression methods 40 | *.7z 41 | *.dmg 42 | *.gz 43 | *.iso 44 | *.rar 45 | *.tar 46 | *.zip 47 | 48 | # ============ # 49 | # Project Specific # 50 | # ============ # 51 | -------------------------------------------------------------------------------- /ProjectSettings/UnityConnectSettings.asset: -------------------------------------------------------------------------------- 1 | %YAML 1.1 2 | %TAG !u! tag:unity3d.com,2011: 3 | --- !u!310 &1 4 | UnityConnectSettings: 5 | m_ObjectHideFlags: 0 6 | m_Enabled: 0 7 | m_TestMode: 0 8 | m_TestEventUrl: 9 | m_TestConfigUrl: 10 | m_TestInitMode: 0 11 | CrashReportingSettings: 12 | m_EventUrl: https://perf-events.cloud.unity3d.com/api/events/crashes 13 | m_NativeEventUrl: https://perf-events.cloud.unity3d.com/symbolicate 14 | m_Enabled: 0 15 | m_CaptureEditorExceptions: 1 16 | UnityPurchasingSettings: 17 | m_Enabled: 0 18 | m_TestMode: 0 19 | UnityAnalyticsSettings: 20 | m_Enabled: 0 21 | m_InitializeOnStartup: 1 22 | m_TestMode: 0 23 | m_TestEventUrl: 24 | m_TestConfigUrl: 25 | UnityAdsSettings: 26 | m_Enabled: 0 27 | m_InitializeOnStartup: 1 28 | m_TestMode: 0 29 | m_IosGameId: 30 | m_AndroidGameId: 31 | m_GameIds: {} 32 | m_GameId: 33 | PerformanceReportingSettings: 34 | m_Enabled: 0 35 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Xianshun Chen 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /Assets/MyMachine/Cifar10ImageClassifierDemo.cs: -------------------------------------------------------------------------------- 1 | using System.Collections; 2 | using System.Collections.Generic; 3 | using UnityEngine; 4 | using TensorFlow; 5 | 6 | public class Cifar10ImageClassifierDemo : MonoBehaviour { 7 | 8 | private Cifar10ImageClassifier classifier = new Cifar10ImageClassifier(); 9 | 10 | // Use this for initialization 11 | void Start () { 12 | Debug.Log(TensorFlow.TFCore.Version); 13 | classifier.LoadModel("tf_models/cnn_cifar10"); 14 | 15 | string[] image_names = new string[9]; 16 | int index = 0; 17 | for(int i=1; i <= 3; ++i) 18 | { 19 | image_names[index++] = "cat" + i; 20 | image_names[index++] = "bird" + i; 21 | image_names[index++] = "automobile" + i; 22 | } 23 | foreach(string image_name in image_names) 24 | { 25 | Debug.Log(image_name); 26 | Texture2D img = Resources.Load("images/cifar10/" + image_name); 27 | Debug.Log("Predicted: " + classifier.PredictLabel(img)); 28 | } 29 | 30 | } 31 | 32 | // Update is called once per frame 33 | void Update () { 34 | 35 | } 36 | } 37 | -------------------------------------------------------------------------------- /ProjectSettings/DynamicsManager.asset: -------------------------------------------------------------------------------- 1 | %YAML 1.1 2 | %TAG !u! tag:unity3d.com,2011: 3 | --- !u!55 &1 4 | PhysicsManager: 5 | m_ObjectHideFlags: 0 6 | serializedVersion: 7 7 | m_Gravity: {x: 0, y: -9.81, z: 0} 8 | m_DefaultMaterial: {fileID: 0} 9 | m_BounceThreshold: 2 10 | m_SleepThreshold: 0.005 11 | m_DefaultContactOffset: 0.01 12 | m_DefaultSolverIterations: 6 13 | m_DefaultSolverVelocityIterations: 1 14 | m_QueriesHitBackfaces: 0 15 | m_QueriesHitTriggers: 1 16 | m_EnableAdaptiveForce: 0 17 | m_ClothInterCollisionDistance: 0 18 | m_ClothInterCollisionStiffness: 0 19 | m_ContactsGeneration: 1 20 | m_LayerCollisionMatrix: ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff 21 | m_AutoSimulation: 1 22 | m_AutoSyncTransforms: 1 23 | m_ClothInterCollisionSettingsToggle: 0 24 | m_ContactPairsMode: 0 25 | m_BroadphaseType: 0 26 | m_WorldBounds: 27 | m_Center: {x: 0, y: 0, z: 0} 28 | m_Extent: {x: 250, y: 250, z: 250} 29 | m_WorldSubdivisions: 8 30 | -------------------------------------------------------------------------------- /keras_codes/cifar10/cifar10/cnn_cifar10_train.py: -------------------------------------------------------------------------------- 1 | from keras.datasets import cifar10 2 | import keras.backend as K 3 | import os 4 | import sys 5 | 6 | 7 | def main(): 8 | current_dir = os.path.dirname(__file__) 9 | sys.path.append(os.path.join(current_dir, '..')) 10 | current_dir = current_dir if current_dir is not '' else '.' 11 | 12 | from cifar10.cifar10_classifier import Cifar10Classifier 13 | 14 | img_width, img_height = 32, 32 15 | batch_size = 128 16 | epochs = 20 17 | nb_classes = 10 18 | 19 | output_dir_path = os.path.join(current_dir, 'models') 20 | 21 | (Xtrain, Ytrain), (Xtest, Ytest) = cifar10.load_data() 22 | 23 | if K.image_data_format() == 'channels_first': 24 | input_shape = (3, img_width, img_height) 25 | else: 26 | input_shape = (img_width, img_height, 3) 27 | 28 | classifier = Cifar10Classifier() 29 | 30 | classifier.fit(Xtrain, Ytrain, model_dir_path=output_dir_path, 31 | batch_size=batch_size, 32 | epochs=epochs, 33 | input_shape=input_shape, nb_classes=nb_classes) 34 | 35 | score = classifier.evaluate(Xtest, Ytest, batch_size=batch_size) 36 | 37 | print('score: ', score[0]) 38 | print('accurarcy: ', score[1]) 39 | 40 | 41 | if __name__ == '__main__': 42 | main() 43 | 44 | -------------------------------------------------------------------------------- /keras_codes/cifar10/cifar10/cifar10_tensorflow_classifier.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import numpy as np 3 | from keras.datasets import cifar10 4 | import os 5 | 6 | 7 | def main(): 8 | 9 | current_dir = os.path.dirname(__file__) 10 | current_dir = current_dir if current_dir is not '' else '.' 11 | 12 | (Xtrain, Ytrain), (Xtest, Ytest) = cifar10.load_data() 13 | 14 | Xtest = Xtest.astype('float32') / 255 15 | 16 | with tf.gfile.FastGFile(current_dir + '/models/tf/cnn_cifar10.pb', 'rb') as f: 17 | graph_def = tf.GraphDef() 18 | graph_def.ParseFromString(f.read()) 19 | _ = tf.import_graph_def(graph_def, name='') 20 | 21 | with tf.Session() as sess: 22 | [print(n.name) for n in sess.graph.as_graph_def().node] 23 | predict_op = sess.graph.get_tensor_by_name('output_node0:0') 24 | 25 | for i in range(Xtest.shape[0]): 26 | x = Xtest[i] 27 | x = np.expand_dims(x, axis=0) 28 | y = Ytest[i] 29 | predicted = sess.run(predict_op, feed_dict={"conv2d_1_input:0": x, 30 | 'dropout_1/keras_learning_phase:0': 0}) 31 | 32 | predicted_y = np.argmax(predicted, axis=1) 33 | print('actual: ', y, '\tpredicted: ', predicted_y) 34 | 35 | 36 | if __name__ == '__main__': 37 | main() 38 | -------------------------------------------------------------------------------- /ProjectSettings/Physics2DSettings.asset: -------------------------------------------------------------------------------- 1 | %YAML 1.1 2 | %TAG !u! tag:unity3d.com,2011: 3 | --- !u!19 &1 4 | Physics2DSettings: 5 | m_ObjectHideFlags: 0 6 | serializedVersion: 3 7 | m_Gravity: {x: 0, y: -9.81} 8 | 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serializedVersion: 2 71 | sprites: [] 72 | outline: [] 73 | physicsShape: [] 74 | spritePackingTag: 75 | userData: 76 | assetBundleName: 77 | assetBundleVariant: 78 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # unity-tensorflow-samples 2 | 3 | Unity project that loads pretrained tensorflow pb model files and use them to predict 4 | 5 | # Important 6 | 7 | * In order to run the samples, your unity must be at least version 2017.3.0f3 and must be 64 bit 8 | * The pre-trained tensorflow *.pb graph models in keras_codes should be trained using tensorflow 1.2 as the version of "Unity TensorFlow Plugin" that I was using has tensorflow 1.2 only. 9 | * If your graph model is named cifar10.pb, it should be changed to cifar10.bytes and put under "Resources" folder (which is under Asset folder) 10 | * If your graph model has the full path "Asset/Resources/tf_models/cifar10.bytes", you should call Resources.load("tf_models/cifar10") to load it. 11 | 12 | 13 | # Usage 14 | 15 | Below is the sample code on how to use the Cifar10ImageClassifier to classify images stored in the "Resources/images/cifar10" folder: 16 | 17 | ```cs 18 | using System.Collections; 19 | using System.Collections.Generic; 20 | using UnityEngine; 21 | using TensorFlow; 22 | 23 | public class Cifar10ImageClassifierDemo : MonoBehaviour { 24 | 25 | private Cifar10ImageClassifier classifier = new Cifar10ImageClassifier(); 26 | 27 | // Use this for initialization 28 | void Start () { 29 | Debug.Log(TensorFlow.TFCore.Version); 30 | classifier.LoadModel("tf_models/cnn_cifar10"); 31 | 32 | string[] image_names = new string[9]; 33 | int index = 0; 34 | for(int i=1; i <= 3; ++i) 35 | { 36 | image_names[index++] = "cat" + i; 37 | image_names[index++] = "bird" + i; 38 | image_names[index++] = "automobile" + i; 39 | } 40 | foreach(string image_name in image_names) 41 | { 42 | Debug.Log(image_name); 43 | Texture2D img = Resources.Load("images/cifar10/" + image_name); 44 | Debug.Log("Predicted: " + classifier.PredictLabel(img)); 45 | } 46 | 47 | } 48 | 49 | // Update is called once per frame 50 | void Update () { 51 | 52 | } 53 | } 54 | ``` 55 | 56 | # Note: 57 | 58 | * Cifar10ImageClassifier takes image having width = 32, height = 32, channels = 3 59 | * In order for the Cifar10ImageClassifier to read the images in the "Resources/images/cifar10" folder, you make make the images readable in their Import Settings. 60 | 61 | 62 | 63 | 64 | -------------------------------------------------------------------------------- /ProjectSettings/GraphicsSettings.asset: -------------------------------------------------------------------------------- 1 | %YAML 1.1 2 | %TAG !u! tag:unity3d.com,2011: 3 | --- !u!30 &1 4 | GraphicsSettings: 5 | m_ObjectHideFlags: 0 6 | serializedVersion: 12 7 | m_Deferred: 8 | m_Mode: 1 9 | m_Shader: {fileID: 69, guid: 0000000000000000f000000000000000, type: 0} 10 | m_DeferredReflections: 11 | m_Mode: 1 12 | m_Shader: {fileID: 74, guid: 0000000000000000f000000000000000, type: 0} 13 | m_ScreenSpaceShadows: 14 | m_Mode: 1 15 | m_Shader: {fileID: 64, guid: 0000000000000000f000000000000000, type: 0} 16 | m_LegacyDeferred: 17 | m_Mode: 1 18 | m_Shader: {fileID: 63, guid: 0000000000000000f000000000000000, type: 0} 19 | m_DepthNormals: 20 | m_Mode: 1 21 | m_Shader: {fileID: 62, guid: 0000000000000000f000000000000000, type: 0} 22 | m_MotionVectors: 23 | m_Mode: 1 24 | m_Shader: {fileID: 75, guid: 0000000000000000f000000000000000, type: 0} 25 | m_LightHalo: 26 | m_Mode: 1 27 | m_Shader: {fileID: 105, guid: 0000000000000000f000000000000000, type: 0} 28 | m_LensFlare: 29 | m_Mode: 1 30 | m_Shader: {fileID: 102, guid: 0000000000000000f000000000000000, type: 0} 31 | m_AlwaysIncludedShaders: 32 | - {fileID: 7, guid: 0000000000000000f000000000000000, type: 0} 33 | - {fileID: 15104, guid: 0000000000000000f000000000000000, type: 0} 34 | - {fileID: 15105, guid: 0000000000000000f000000000000000, type: 0} 35 | - {fileID: 15106, guid: 0000000000000000f000000000000000, type: 0} 36 | - {fileID: 10753, guid: 0000000000000000f000000000000000, type: 0} 37 | - {fileID: 10770, guid: 0000000000000000f000000000000000, type: 0} 38 | m_PreloadedShaders: [] 39 | m_SpritesDefaultMaterial: {fileID: 10754, guid: 0000000000000000f000000000000000, 40 | type: 0} 41 | m_CustomRenderPipeline: {fileID: 0} 42 | m_TransparencySortMode: 0 43 | m_TransparencySortAxis: {x: 0, y: 0, z: 1} 44 | m_DefaultRenderingPath: 1 45 | m_DefaultMobileRenderingPath: 1 46 | m_TierSettings: [] 47 | m_LightmapStripping: 0 48 | m_FogStripping: 0 49 | m_InstancingStripping: 0 50 | m_LightmapKeepPlain: 1 51 | m_LightmapKeepDirCombined: 1 52 | m_LightmapKeepDynamicPlain: 1 53 | m_LightmapKeepDynamicDirCombined: 1 54 | m_LightmapKeepShadowMask: 1 55 | m_LightmapKeepSubtractive: 1 56 | m_FogKeepLinear: 1 57 | m_FogKeepExp: 1 58 | m_FogKeepExp2: 1 59 | m_AlbedoSwatchInfos: [] 60 | m_LightsUseLinearIntensity: 0 61 | m_LightsUseColorTemperature: 0 62 | -------------------------------------------------------------------------------- /Assets/MyMachine/Cifar10ImageClassifier.cs: -------------------------------------------------------------------------------- 1 | using System.Collections; 2 | using System.Collections.Generic; 3 | using UnityEngine; 4 | using TensorFlow; 5 | 6 | public class Cifar10ImageClassifier { 7 | 8 | private TFGraph graph; 9 | private TFSession session; 10 | 11 | private static string[] labels = new string[] { 12 | "airplane", 13 | "automobile", 14 | "bird", 15 | "cat", 16 | "deer", 17 | "dog", 18 | "frog", 19 | "horse", 20 | "ship", 21 | "truck" 22 | }; 23 | 24 | public void LoadModel(string your_name_graph) 25 | { 26 | if (graph == null) 27 | { 28 | Debug.Log("Loading tensor graph " + your_name_graph); 29 | TextAsset graphModel = Resources.Load(your_name_graph); 30 | 31 | if(graphModel == null) 32 | { 33 | Debug.LogError("Failed to load tensor graph " + your_name_graph); 34 | } 35 | 36 | graph = new TFGraph(); 37 | graph.Import(graphModel.bytes); 38 | session = new TFSession(graph); 39 | } 40 | } 41 | 42 | public int PredictClass(Texture2D image) 43 | { 44 | Debug.Log(image); 45 | 46 | 47 | float[] imageBytes = new float[image.width * image.height * 3]; 48 | 49 | int idx = 0; 50 | for (int i = 0; i < image.width; i++) 51 | { 52 | for (int j = 0; j < image.height; j++) 53 | { 54 | Color pixel = image.GetPixel(i, j); 55 | 56 | imageBytes[idx++] = pixel.r / 255.0f; 57 | imageBytes[idx++] = pixel.g / 255.0f; 58 | imageBytes[idx++] = pixel.b / 255.0f; 59 | } 60 | } 61 | 62 | 63 | var runner = session.GetRunner(); 64 | runner 65 | .AddInput(graph["conv2d_1_input"][0], TFTensor.FromBuffer(new TFShape(new long[] { 1, 32, 32, 3 }), imageBytes, 0, 3072)) 66 | .AddInput(graph["dropout_1/keras_learning_phase"][0], new TFTensor(TFDataType.Bool, new long[0], 1)); 67 | 68 | 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/keras_codes/cifar10/cifar10/cifar10_classifier.py: -------------------------------------------------------------------------------- 1 | from keras.callbacks import ModelCheckpoint 2 | from keras.models import model_from_json 3 | from PIL import Image 4 | import numpy as np 5 | import os 6 | from keras.models import Sequential 7 | from keras.layers import Conv2D, MaxPooling2D, Dense, Activation, Flatten, Dropout 8 | from keras.utils import np_utils 9 | import tensorflow as tf 10 | import keras.backend as K 11 | 12 | 13 | class Cifar10Classifier: 14 | model_name = 'cnn_cifar10' 15 | 16 | def __init__(self): 17 | self.model = None 18 | self.input_shape = None 19 | self.nb_classes = None 20 | 21 | @staticmethod 22 | def get_architecture_file_path(model_dir_path): 23 | return os.path.join(model_dir_path, Cifar10Classifier.model_name + '_architecture.json') 24 | 25 | @staticmethod 26 | def get_weight_file_path(model_dir_path): 27 | return os.path.join(model_dir_path, Cifar10Classifier.model_name + '_weights.h5') 28 | 29 | @staticmethod 30 | def get_config_file_path(model_dir_path): 31 | return os.path.join(model_dir_path, Cifar10Classifier.model_name + '_config.npy') 32 | 33 | def load_model(self, model_dir_path): 34 | 35 | config_file_path = self.get_config_file_path(model_dir_path) 36 | weight_file_path = self.get_weight_file_path(model_dir_path) 37 | 38 | if not os.path.exists(config_file_path): 39 | return 40 | if not os.path.exists(weight_file_path): 41 | return 42 | 43 | config = np.load(config_file_path).item() 44 | 45 | self.input_shape = config['input_shape'] 46 | self.nb_classes = config['nb_classes'] 47 | 48 | self.model = self.create_model(self.input_shape, self.nb_classes) 49 | self.model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) 50 | self.model.load_weights(weight_file_path) 51 | 52 | def predict_label(self, filename): 53 | img = Image.open(filename) 54 | 55 | if K.image_data_format() == 'channels_first': 56 | _, img_width, img_height = self.input_shape 57 | else: 58 | img_width, img_height, _ = self.input_shape 59 | 60 | img = img.resize((img_width, img_height), Image.ANTIALIAS) 61 | 62 | input = np.asarray(img) 63 | input = input.astype('float32') / 255 64 | input = np.expand_dims(input, axis=0) 65 | 66 | print(input.shape) 67 | 68 | predicted_class = self.model.predict_classes(input)[0] 69 | 70 | labels = [ 71 | "airplane", 72 | "automobile", 73 | "bird", 74 | "cat", 75 | "deer", 76 | "dog", 77 | "frog", 78 | "horse", 79 | "ship", 80 | "truck" 81 | ] 82 | return predicted_class, labels[predicted_class] 83 | 84 | @staticmethod 85 | def create_model(input_shape, nb_classes): 86 | model = Sequential() 87 | model.add(Conv2D(filters=32, input_shape=input_shape, padding='same', kernel_size=(3, 3))) 88 | model.add(Activation('relu')) 89 | model.add(MaxPooling2D(pool_size=(2, 2))) 90 | 91 | model.add(Conv2D(filters=32, padding='same', kernel_size=(3, 3))) 92 | model.add(Activation('relu')) 93 | model.add(MaxPooling2D(pool_size=(2, 2))) 94 | 95 | model.add(Dropout(rate=0.25)) 96 | 97 | model.add(Conv2D(filters=64, kernel_size=(3, 3), padding='same', input_shape=input_shape)) 98 | model.add(Activation('relu')) 99 | model.add(MaxPooling2D(pool_size=(2, 2))) 100 | 101 | model.add(Conv2D(filters=64, padding='same', kernel_size=(3, 3))) 102 | model.add(Activation('relu')) 103 | model.add(MaxPooling2D(pool_size=(2, 2))) 104 | 105 | model.add(Dropout(rate=0.25)) 106 | 107 | model.add(Flatten()) 108 | model.add(Dense(units=512)) 109 | model.add(Activation('relu')) 110 | model.add(Dropout(rate=0.5)) 111 | model.add(Dense(units=nb_classes)) 112 | model.add(Activation('softmax')) 113 | 114 | model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) 115 | 116 | return model 117 | 118 | def run_test(self, current_dir): 119 | print(self.predict_label(current_dir + '/../training/bi_classifier_data/training/cat/cat.2.jpg')) 120 | 121 | def fit(self, Xtrain, Ytrain, model_dir_path, input_shape=None, nb_classes=None, test_size=None, batch_size=None, 122 | epochs=None): 123 | 124 | if batch_size is None: 125 | batch_size = 64 126 | if epochs is None: 127 | epochs = 20 128 | if test_size is None: 129 | test_size = 0.2 130 | 131 | if input_shape is None: 132 | input_shape = (32, 32, 3) 133 | 134 | if nb_classes is None: 135 | nb_classes = 10 136 | 137 | Xtrain = Xtrain.astype('float32') / 255 138 | Ytrain = np_utils.to_categorical(Ytrain, nb_classes) 139 | 140 | self.input_shape = input_shape 141 | self.nb_classes = nb_classes 142 | 143 | config_file_path = self.get_config_file_path(model_dir_path) 144 | 145 | config = dict() 146 | config['input_shape'] = input_shape 147 | config['nb_classes'] = nb_classes 148 | 149 | np.save(config_file_path, config) 150 | 151 | weight_file_path = self.get_weight_file_path(model_dir_path) 152 | 153 | self.model = self.create_model(input_shape, nb_classes) 154 | 155 | checkpoint = ModelCheckpoint(filepath=weight_file_path, save_best_only=True) 156 | history = self.model.fit(x=Xtrain, y=Ytrain, batch_size=batch_size, epochs=epochs, verbose=1, 157 | validation_split=test_size, 158 | callbacks=[checkpoint]) 159 | self.model.save_weights(weight_file_path) 160 | 161 | np.save(os.path.join(model_dir_path, Cifar10Classifier.model_name + '-history.npy'), history.history) 162 | 163 | return history 164 | 165 | def evaluate(self, Xtest, Ytest, batch_size=None): 166 | 167 | if batch_size is None: 168 | batch_size = 64 169 | 170 | Xtest = Xtest.astype('float32') / 255 171 | Ytest = np_utils.to_categorical(Ytest, self.nb_classes) 172 | 173 | return self.model.evaluate(x=Xtest, y=Ytest, batch_size=batch_size, verbose=1) 174 | 175 | def export_tensorflow_model(self, output_fld, output_model_file=None, 176 | output_graphdef_file=None, 177 | num_output=None, 178 | quantize=False, 179 | save_output_graphdef_file=False, 180 | output_node_prefix=None): 181 | 182 | K.set_learning_phase(0) 183 | 184 | if output_model_file is None: 185 | output_model_file = Cifar10Classifier.model_name + '.pb' 186 | 187 | if output_graphdef_file is None: 188 | output_graphdef_file = 'model.ascii' 189 | if num_output is None: 190 | num_output = 1 191 | if output_node_prefix is None: 192 | output_node_prefix = 'output_node' 193 | 194 | pred = [None] * num_output 195 | pred_node_names = [None] * num_output 196 | for i in range(num_output): 197 | pred_node_names[i] = output_node_prefix + str(i) 198 | pred[i] = tf.identity(self.model.outputs[i], name=pred_node_names[i]) 199 | print('output nodes names are: ', pred_node_names) 200 | 201 | sess = K.get_session() 202 | 203 | if save_output_graphdef_file: 204 | tf.train.write_graph(sess.graph.as_graph_def(), output_fld, output_graphdef_file, as_text=True) 205 | print('saved the graph definition in ascii format at: ', output_graphdef_file) 206 | 207 | from tensorflow.python.framework import graph_util 208 | from tensorflow.python.framework import graph_io 209 | # from tensorflow.tools.graph_transforms import TransformGraph 210 | constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), pred_node_names) 211 | graph_io.write_graph(constant_graph, output_fld, output_model_file, as_text=False) 212 | print('saved the freezed graph (ready for inference) at: ', output_model_file) 213 | -------------------------------------------------------------------------------- /Assets/Scenes/default_scene.unity: -------------------------------------------------------------------------------- 1 | %YAML 1.1 2 | %TAG !u! tag:unity3d.com,2011: 3 | --- !u!29 &1 4 | OcclusionCullingSettings: 5 | m_ObjectHideFlags: 0 6 | serializedVersion: 2 7 | m_OcclusionBakeSettings: 8 | smallestOccluder: 5 9 | smallestHole: 0.25 10 | backfaceThreshold: 100 11 | m_SceneGUID: 00000000000000000000000000000000 12 | m_OcclusionCullingData: {fileID: 0} 13 | --- !u!104 &2 14 | RenderSettings: 15 | 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