Move no-top models to the separate model types (#511)

Since no-top models could not be wrapped into ImageRecognitionModelBase they should not be loaded by CV model types.
Kotlin · Jan 2, 2023 · 1b21f39 · 1b21f39
1 parent 2ab09f0
commit 1b21f39
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Showing 19 changed files with 396 additions and 186 deletions.
diff --git a/examples/src/main/kotlin/examples/onnx/cv/efficicentnet/EfficientNetB0.kt b/examples/src/main/kotlin/examples/onnx/cv/efficicentnet/EfficientNetB0.kt
@@ -23,7 +23,7 @@ import java.io.File
  */
 fun efficientNetB0Prediction() {
     val modelHub = ONNXModelHub(cacheDirectory = File("cache/pretrainedModels"))
-    val modelType = ONNXModels.CV.EfficientNetB0()
+    val modelType = ONNXModels.CV.EfficientNetB0
     val model = modelHub.loadModel(modelType)
     model.printSummary()
 

diff --git a/examples/src/main/kotlin/examples/onnx/cv/efficicentnet/lightAPI/EfficientNetB0LightAPI.kt b/examples/src/main/kotlin/examples/onnx/cv/efficicentnet/lightAPI/EfficientNetB0LightAPI.kt
@@ -20,7 +20,7 @@ fun efficientNetB0EasyPrediction() {
     val modelHub =
         ONNXModelHub(cacheDirectory = File("cache/pretrainedModels"))
 
-    val model = ONNXModels.CV.EfficientNetB0().pretrainedModel(modelHub)
+    val model = ONNXModels.CV.EfficientNetB0.pretrainedModel(modelHub)
 
     model.printSummary()
 

diff --git a/examples/src/main/kotlin/examples/onnx/cv/efficicentnet/lightAPI/EfficientNetB7LightAPI.kt b/examples/src/main/kotlin/examples/onnx/cv/efficicentnet/lightAPI/EfficientNetB7LightAPI.kt
@@ -20,7 +20,7 @@ fun efficientNetB7LightAPIPrediction() {
     val modelHub =
         ONNXModelHub(cacheDirectory = File("cache/pretrainedModels"))
 
-    val model = ONNXModels.CV.EfficientNetB7().pretrainedModel(modelHub)
+    val model = ONNXModels.CV.EfficientNetB7.pretrainedModel(modelHub)
     model.printSummary()
 
     model.use {

diff --git a/...in/examples/onnx/cv/efficicentnet/notop/EfficientNetB0additionalTrainingWithTensorFlow.kt b/...in/examples/onnx/cv/efficicentnet/notop/EfficientNetB0additionalTrainingWithTensorFlow.kt
@@ -22,7 +22,7 @@ import org.jetbrains.kotlinx.dl.dataset.generator.FromFolders
 import org.jetbrains.kotlinx.dl.onnx.dataset.preprocessor.onnx
 import org.jetbrains.kotlinx.dl.onnx.inference.ONNXModelHub
 import org.jetbrains.kotlinx.dl.onnx.inference.ONNXModels
-import org.jetbrains.kotlinx.dl.onnx.inference.ONNXModels.CV.Companion.createPreprocessing
+import org.jetbrains.kotlinx.dl.onnx.inference.ONNXModels.CVnoTop.Companion.createPreprocessing
 import java.io.File
 
 private const val EPOCHS = 1
@@ -54,7 +54,7 @@ private val topModel = Sequential.of(
  */
 fun efficientNetB0AdditionalTraining() {
     val modelHub = ONNXModelHub(cacheDirectory = File("cache/pretrainedModels"))
-    val modelType = ONNXModels.CV.EfficientNetB0(noTop = true)
+    val modelType = ONNXModels.CVnoTop.EfficientNetB0
 
     modelHub.loadModel(modelType).use { model ->
         println(model)

diff --git a/examples/src/main/kotlin/examples/onnx/cv/onnxAdditionalTraining.kt b/examples/src/main/kotlin/examples/onnx/cv/onnxAdditionalTraining.kt
@@ -22,7 +22,7 @@ import org.jetbrains.kotlinx.dl.dataset.generator.FromFolders
 import org.jetbrains.kotlinx.dl.onnx.dataset.preprocessor.onnx
 import org.jetbrains.kotlinx.dl.onnx.inference.ONNXModelHub
 import org.jetbrains.kotlinx.dl.onnx.inference.ONNXModels
-import org.jetbrains.kotlinx.dl.onnx.inference.ONNXModels.CV.Companion.createPreprocessing
+import org.jetbrains.kotlinx.dl.onnx.inference.ONNXModels.CVnoTop.Companion.createPreprocessing
 import java.io.File
 
 private const val EPOCHS = 1
@@ -43,7 +43,7 @@ private const val TRAIN_TEST_SPLIT_RATIO = 0.8
  * We use the preprocessing DSL to describe the dataset generation pipeline.
  * We demonstrate the workflow on the subset of Kaggle Cats vs Dogs binary classification dataset.
  */
-fun runONNXAdditionalTraining(modelType: ONNXModels.CV) {
+fun runONNXAdditionalTraining(modelType: ONNXModels.CVnoTop) {
     val modelHub = ONNXModelHub(cacheDirectory = File("cache/pretrainedModels"))
 
     val dogsVsCatsDatasetPath = dogsCatsSmallDatasetPath()

diff --git a/...src/main/kotlin/examples/onnx/cv/resnet/notop/ResNet50additionalTrainingWithTensorFlow.kt b/...src/main/kotlin/examples/onnx/cv/resnet/notop/ResNet50additionalTrainingWithTensorFlow.kt
@@ -22,7 +22,7 @@ import org.jetbrains.kotlinx.dl.dataset.generator.FromFolders
 import org.jetbrains.kotlinx.dl.onnx.dataset.preprocessor.onnx
 import org.jetbrains.kotlinx.dl.onnx.inference.ONNXModelHub
 import org.jetbrains.kotlinx.dl.onnx.inference.ONNXModels
-import org.jetbrains.kotlinx.dl.onnx.inference.ONNXModels.CV.Companion.createPreprocessing
+import org.jetbrains.kotlinx.dl.onnx.inference.ONNXModels.CVnoTop.Companion.createPreprocessing
 import java.io.File
 
 private const val EPOCHS = 3
@@ -56,7 +56,7 @@ fun resnet50additionalTraining() {
     val modelHub = ONNXModelHub(
         cacheDirectory = File("cache/pretrainedModels")
     )
-    val modelType = ONNXModels.CV.ResNet50noTopCustom
+    val modelType = ONNXModels.CVnoTop.ResNet50Custom
 
     modelHub.loadModel(modelType).use { model ->
         println(model)

diff --git a/examples/src/main/kotlin/examples/transferlearning/modelhub/nasnet/NasNetMobile.kt b/examples/src/main/kotlin/examples/transferlearning/modelhub/nasnet/NasNetMobile.kt
@@ -20,7 +20,7 @@ import org.jetbrains.kotlinx.dl.api.inference.loaders.TFModels
  * NOTE: Input resolution is 224*224
  */
 fun nasNetMobilePrediction() {
-    runImageRecognitionPrediction(modelType = TFModels.CV.NASNetMobile())
+    runImageRecognitionPrediction(modelType = TFModels.CV.NASNetMobile)
 }
 
 /** */

diff --git a/...ansferlearning/modelhub/resnet/Example_5_ResNet50_prediction_additional_training_noTop.kt b/...ansferlearning/modelhub/resnet/Example_5_ResNet50_prediction_additional_training_noTop.kt
@@ -17,7 +17,7 @@ import org.jetbrains.kotlinx.dl.api.core.optimizer.Adam
 import org.jetbrains.kotlinx.dl.api.inference.keras.loadWeightsForFrozenLayers
 import org.jetbrains.kotlinx.dl.api.inference.loaders.TFModelHub
 import org.jetbrains.kotlinx.dl.api.inference.loaders.TFModels
-import org.jetbrains.kotlinx.dl.api.inference.loaders.TFModels.CV.Companion.createPreprocessing
+import org.jetbrains.kotlinx.dl.api.inference.loaders.TFModels.CVnoTop.Companion.createPreprocessing
 import org.jetbrains.kotlinx.dl.dataset.OnFlyImageDataset
 import org.jetbrains.kotlinx.dl.dataset.embedded.dogsCatsSmallDatasetPath
 import org.jetbrains.kotlinx.dl.dataset.generator.FromFolders
@@ -43,7 +43,7 @@ private const val TRAIN_TEST_SPLIT_RATIO = 0.7
  */
 fun resnet50noTopAdditionalTraining() {
     val modelHub = TFModelHub(cacheDirectory = File("cache/pretrainedModels"))
-    val modelType = TFModels.CV.ResNet50(noTop = true, inputShape = intArrayOf(IMAGE_SIZE, IMAGE_SIZE, 3))
+    val modelType = TFModels.CVnoTop.ResNet50(inputShape = intArrayOf(IMAGE_SIZE, IMAGE_SIZE, 3))
     val model = modelHub.loadModel(modelType)
 
     val hdfFile = modelHub.loadWeights(modelType)

diff --git a/...ng/modelhub/resnet/Example_6_ResNet50_prediction_additional_training_noTop_with_helper.kt b/...ng/modelhub/resnet/Example_6_ResNet50_prediction_additional_training_noTop_with_helper.kt
@@ -17,7 +17,7 @@ import org.jetbrains.kotlinx.dl.api.core.optimizer.Adam
 import org.jetbrains.kotlinx.dl.api.inference.keras.loadWeightsForFrozenLayers
 import org.jetbrains.kotlinx.dl.api.inference.loaders.TFModelHub
 import org.jetbrains.kotlinx.dl.api.inference.loaders.TFModels
-import org.jetbrains.kotlinx.dl.api.inference.loaders.TFModels.CV.Companion.createPreprocessing
+import org.jetbrains.kotlinx.dl.api.inference.loaders.TFModels.CVnoTop.Companion.createPreprocessing
 import org.jetbrains.kotlinx.dl.dataset.OnFlyImageDataset
 import org.jetbrains.kotlinx.dl.dataset.embedded.dogsCatsSmallDatasetPath
 import org.jetbrains.kotlinx.dl.dataset.generator.FromFolders
@@ -44,7 +44,7 @@ private const val TRAIN_TEST_SPLIT_RATIO = 0.7
  */
 fun resnet50additionalTrainingNoTopWithHelper() {
     val modelHub = TFModelHub(cacheDirectory = File("cache/pretrainedModels"))
-    val modelType = TFModels.CV.ResNet50(noTop = true, inputShape = intArrayOf(IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))
+    val modelType = TFModels.CVnoTop.ResNet50(inputShape = intArrayOf(IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))
     val noTopModel = modelHub.loadModel(modelType)
 
     val hdfFile = modelHub.loadWeights(modelType)

diff --git a/...lin/examples/transferlearning/modelhub/vgg16/Example_5_VGG16_additional_training_noTop.kt b/...lin/examples/transferlearning/modelhub/vgg16/Example_5_VGG16_additional_training_noTop.kt
@@ -18,7 +18,7 @@ import org.jetbrains.kotlinx.dl.api.core.optimizer.Adam
 import org.jetbrains.kotlinx.dl.api.inference.keras.loadWeightsForFrozenLayers
 import org.jetbrains.kotlinx.dl.api.inference.loaders.TFModelHub
 import org.jetbrains.kotlinx.dl.api.inference.loaders.TFModels
-import org.jetbrains.kotlinx.dl.api.inference.loaders.TFModels.CV.Companion.createPreprocessing
+import org.jetbrains.kotlinx.dl.api.inference.loaders.TFModels.CVnoTop.Companion.createPreprocessing
 import org.jetbrains.kotlinx.dl.api.summary.printSummary
 import org.jetbrains.kotlinx.dl.dataset.OnFlyImageDataset
 import org.jetbrains.kotlinx.dl.dataset.embedded.dogsCatsSmallDatasetPath
@@ -49,7 +49,7 @@ private const val EPOCHS = 2
  */
 fun vgg16noTopAdditionalTraining() {
     val modelHub = TFModelHub(cacheDirectory = File("cache/pretrainedModels"))
-    val modelType = TFModels.CV.VGG16(noTop = true, inputShape = intArrayOf(IMAGE_SIZE, IMAGE_SIZE, 3))
+    val modelType = TFModels.CVnoTop.VGG16(inputShape = intArrayOf(IMAGE_SIZE, IMAGE_SIZE, 3))
     val model = modelHub.loadModel(modelType)
 
     val layers = mutableListOf<Layer>()

diff --git a/examples/src/main/kotlin/examples/transferlearning/transferLearningRunner.kt b/examples/src/main/kotlin/examples/transferlearning/transferLearningRunner.kt
@@ -20,6 +20,7 @@ import org.jetbrains.kotlinx.dl.api.inference.keras.*
 import org.jetbrains.kotlinx.dl.api.inference.loaders.TFModelHub
 import org.jetbrains.kotlinx.dl.api.inference.loaders.TFModels
 import org.jetbrains.kotlinx.dl.api.inference.loaders.TFModels.CV.Companion.createPreprocessing
+import org.jetbrains.kotlinx.dl.api.inference.loaders.TFModels.CVnoTop.Companion.createPreprocessing
 import org.jetbrains.kotlinx.dl.api.summary.printSummary
 import org.jetbrains.kotlinx.dl.dataset.OnFlyImageDataset
 import org.jetbrains.kotlinx.dl.dataset.embedded.dogsCatsSmallDatasetPath
@@ -33,7 +34,7 @@ private const val NUM_CLASSES = 2
 private const val TRAIN_TEST_SPLIT_RATIO = 0.7
 
 fun runImageRecognitionTransferLearning(
-    modelType: TFModels.CV<out GraphTrainableModel>,
+    modelType: TFModels.CVnoTop<out GraphTrainableModel>,
     epochs: Int = 2
 ) {
     val modelHub = TFModelHub(cacheDirectory = File("cache/pretrainedModels"))

diff --git a/examples/src/test/kotlin/examples/onnx/cv/OnnxEfficientNetTestSuite.kt b/examples/src/test/kotlin/examples/onnx/cv/OnnxEfficientNetTestSuite.kt
@@ -30,72 +30,72 @@ class OnnxEfficientNetTestSuite {
 
     @Test
     fun efficientNetB1PredictionTest() {
-        runONNXImageRecognitionPrediction(ONNXModels.CV.EfficientNetB1())
+        runONNXImageRecognitionPrediction(ONNXModels.CV.EfficientNetB1)
     }
 
     @Test
     fun efficientNetB1AdditionalTrainingTest() {
-        runONNXAdditionalTraining(ONNXModels.CV.EfficientNetB1(noTop = true))
+        runONNXAdditionalTraining(ONNXModels.CVnoTop.EfficientNetB1)
     }
 
     @Test
     fun efficientNetB2PredictionTest() {
-        runONNXImageRecognitionPrediction(ONNXModels.CV.EfficientNetB2())
+        runONNXImageRecognitionPrediction(ONNXModels.CV.EfficientNetB2)
     }
 
     @Test
     fun efficientNetB2AdditionalTrainingTest() {
-        runONNXAdditionalTraining(ONNXModels.CV.EfficientNetB2(noTop = true))
+        runONNXAdditionalTraining(ONNXModels.CVnoTop.EfficientNetB2)
     }
 
     @Test
     fun efficientNetB3PredictionTest() {
-        runONNXImageRecognitionPrediction(ONNXModels.CV.EfficientNetB3())
+        runONNXImageRecognitionPrediction(ONNXModels.CV.EfficientNetB3)
     }
 
     @Test
     fun efficientNetB3AdditionalTrainingTest() {
-        runONNXAdditionalTraining(ONNXModels.CV.EfficientNetB3(noTop = true))
+        runONNXAdditionalTraining(ONNXModels.CVnoTop.EfficientNetB3)
     }
 
     @Test
     fun efficientNetB4PredictionTest() {
-        runONNXImageRecognitionPrediction(ONNXModels.CV.EfficientNetB4())
+        runONNXImageRecognitionPrediction(ONNXModels.CV.EfficientNetB4)
     }
 
     @Test
     fun efficientNetB4AdditionalTrainingTest() {
-        runONNXAdditionalTraining(ONNXModels.CV.EfficientNetB4(noTop = true))
+        runONNXAdditionalTraining(ONNXModels.CVnoTop.EfficientNetB4)
     }
 
     @Test
     fun efficientNetB5PredictionTest() {
-        runONNXImageRecognitionPrediction(ONNXModels.CV.EfficientNetB5())
+        runONNXImageRecognitionPrediction(ONNXModels.CV.EfficientNetB5)
     }
 
     @Test
     fun efficientNetB5AdditionalTrainingTest() {
-        runONNXAdditionalTraining(ONNXModels.CV.EfficientNetB5(noTop = true))
+        runONNXAdditionalTraining(ONNXModels.CVnoTop.EfficientNetB5)
     }
 
     @Test
     fun efficientNetB6PredictionTest() {
-        runONNXImageRecognitionPrediction(ONNXModels.CV.EfficientNetB6())
+        runONNXImageRecognitionPrediction(ONNXModels.CV.EfficientNetB6)
     }
 
     @Test
     fun efficientNetB6AdditionalTrainingTest() {
-        runONNXAdditionalTraining(ONNXModels.CV.EfficientNetB6(noTop = true))
+        runONNXAdditionalTraining(ONNXModels.CVnoTop.EfficientNetB6)
     }
 
     @Test
     fun efficientNetB7PredictionTest() {
-        runONNXImageRecognitionPrediction(ONNXModels.CV.EfficientNetB7())
+        runONNXImageRecognitionPrediction(ONNXModels.CV.EfficientNetB7)
     }
 
     @Test
     fun efficientNetB7AdditionalTrainingTest() {
-        runONNXAdditionalTraining(ONNXModels.CV.EfficientNetB7(noTop = true))
+        runONNXAdditionalTraining(ONNXModels.CVnoTop.EfficientNetB7)
     }
 
     @Test

diff --git a/examples/src/test/kotlin/examples/transferlearning/modelhub/inception/InceptionTestSuite.kt b/examples/src/test/kotlin/examples/transferlearning/modelhub/inception/InceptionTestSuite.kt
@@ -18,8 +18,7 @@ class InceptionTestSuite {
     @Test
     fun inceptionV3dditionalTrainingNoTopTest() {
         runImageRecognitionTransferLearning(
-            modelType = TFModels.CV.Inception(
-                noTop = true,
+            modelType = TFModels.CVnoTop.Inception(
                 inputShape = intArrayOf(99, 99, 3)
             )
         )
@@ -33,8 +32,7 @@ class InceptionTestSuite {
     @Test
     fun xceptionAdditionalTrainingNoTopTest() {
         runImageRecognitionTransferLearning(
-            modelType = TFModels.CV.Xception(
-                noTop = true,
+            modelType = TFModels.CVnoTop.Xception(
                 inputShape = intArrayOf(90, 90, 3)
             )
         )

diff --git a/examples/src/test/kotlin/examples/transferlearning/modelhub/mobilenet/MobileNetTestSuite.kt b/examples/src/test/kotlin/examples/transferlearning/modelhub/mobilenet/MobileNetTestSuite.kt
@@ -23,8 +23,7 @@ class MobileNetTestSuite {
     @Test
     fun mobilenetAdditionalTrainingNoTopTest() {
         runImageRecognitionTransferLearning(
-            modelType = TFModels.CV.MobileNet(
-                noTop = true,
+            modelType = TFModels.CVnoTop.MobileNet(
                 inputShape = intArrayOf(100, 100, 3)
             )
         )
@@ -38,8 +37,7 @@ class MobileNetTestSuite {
     @Test
     fun mobilenetv2AdditionalTrainingNoTopTest() {
         runImageRecognitionTransferLearning(
-            modelType = TFModels.CV.MobileNetV2(
-                noTop = true,
+            modelType = TFModels.CVnoTop.MobileNetV2(
                 inputShape = intArrayOf(120, 120, 3)
             )
         )

diff --git a/examples/src/test/kotlin/examples/transferlearning/modelhub/nasnet/NasNetTestSuite.kt b/examples/src/test/kotlin/examples/transferlearning/modelhub/nasnet/NasNetTestSuite.kt
@@ -17,7 +17,7 @@ class NasNetTestSuite {
 
     @Test
     fun nasNetMobileAdditionalTrainingNoTopTest() {
-        runImageRecognitionTransferLearning(modelType = TFModels.CV.NASNetMobile(noTop = true))
+        runImageRecognitionTransferLearning(modelType = TFModels.CVnoTop.NASNetMobile)
     }
 
     @Test
@@ -28,8 +28,7 @@ class NasNetTestSuite {
     @Test
     fun nasNetLargeAdditionalTrainingNoTopTest() {
         runImageRecognitionTransferLearning(
-            modelType = TFModels.CV.NASNetLarge(
-                noTop = true,
+            modelType = TFModels.CVnoTop.NASNetLarge(
                 inputShape = intArrayOf(370, 370, 3)
             )
         )

diff --git a/examples/src/test/kotlin/examples/transferlearning/modelhub/resnet/ResNetTestSuite.kt b/examples/src/test/kotlin/examples/transferlearning/modelhub/resnet/ResNetTestSuite.kt
@@ -59,8 +59,7 @@ class ResNetTestSuite {
     @Test
     fun resnet50v2additionalTrainingNoTopTest() {
         runImageRecognitionTransferLearning(
-            modelType = TFModels.CV.ResNet50v2(
-                noTop = true,
+            modelType = TFModels.CVnoTop.ResNet50v2(
                 inputShape = intArrayOf(150, 150, 3)
             )
         )
@@ -74,8 +73,7 @@ class ResNetTestSuite {
     @Test
     fun resnet101additionalTrainingNoTopTest() {
         runImageRecognitionTransferLearning(
-            modelType = TFModels.CV.ResNet101(
-                noTop = true,
+            modelType = TFModels.CVnoTop.ResNet101(
                 inputShape = intArrayOf(100, 100, 3)
             )
         )
@@ -89,8 +87,7 @@ class ResNetTestSuite {
     @Test
     fun resnet101v2additionalTrainingNoTopTest() {
         runImageRecognitionTransferLearning(
-            modelType = TFModels.CV.ResNet101v2(
-                noTop = true,
+            modelType = TFModels.CVnoTop.ResNet101v2(
                 inputShape = intArrayOf(120, 120, 3)
             )
         )
@@ -104,8 +101,7 @@ class ResNetTestSuite {
     @Test
     fun resnet152additionalTrainingNoTopTest() {
         runImageRecognitionTransferLearning(
-            modelType = TFModels.CV.ResNet152(
-                noTop = true,
+            modelType = TFModels.CVnoTop.ResNet152(
                 inputShape = intArrayOf(152, 152, 3)
             )
         )
@@ -119,8 +115,7 @@ class ResNetTestSuite {
     @Test
     fun resnet152v2additionalTrainingNoTopTest() {
         runImageRecognitionTransferLearning(
-            modelType = TFModels.CV.ResNet152v2(
-                noTop = true,
+            modelType = TFModels.CVnoTop.ResNet152v2(
                 inputShape = intArrayOf(90, 90, 3)
             )
         )