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

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Jan 2, 2023
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Move no-top models to the separate model types #511

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2 changes: 1 addition & 1 deletion examples/src/main/kotlin/examples/onnx/cv/efficicentnet/EfficientNetB0.kt
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Expand Up @@ -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()

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2 changes: 1 addition & 1 deletion examples/src/main/kotlin/examples/onnx/cv/efficicentnet/lightAPI/EfficientNetB0LightAPI.kt
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Expand Up @@ -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()

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2 changes: 1 addition & 1 deletion examples/src/main/kotlin/examples/onnx/cv/efficicentnet/lightAPI/EfficientNetB7LightAPI.kt
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Expand Up @@ -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 {
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4 changes: 2 additions & 2 deletions ...in/examples/onnx/cv/efficicentnet/notop/EfficientNetB0additionalTrainingWithTensorFlow.kt
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Expand Up @@ -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
Expand Down Expand Up @@ -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)
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4 changes: 2 additions & 2 deletions examples/src/main/kotlin/examples/onnx/cv/onnxAdditionalTraining.kt
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Expand Up @@ -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
Expand All @@ -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()
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4 changes: 2 additions & 2 deletions ...src/main/kotlin/examples/onnx/cv/resnet/notop/ResNet50additionalTrainingWithTensorFlow.kt
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Expand Up @@ -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
Expand Down Expand Up @@ -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)
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2 changes: 1 addition & 1 deletion examples/src/main/kotlin/examples/transferlearning/modelhub/nasnet/NasNetMobile.kt
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Expand Up @@ -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)
}

/** */
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4 changes: 2 additions & 2 deletions ...ansferlearning/modelhub/resnet/Example_5_ResNet50_prediction_additional_training_noTop.kt
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Expand Up @@ -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
Expand All @@ -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)
Expand Down
4 changes: 2 additions & 2 deletions ...ng/modelhub/resnet/Example_6_ResNet50_prediction_additional_training_noTop_with_helper.kt
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Expand Up @@ -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
Expand All @@ -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)
Expand Down
4 changes: 2 additions & 2 deletions ...lin/examples/transferlearning/modelhub/vgg16/Example_5_VGG16_additional_training_noTop.kt
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Expand Up @@ -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
Expand Down Expand Up @@ -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>()
Expand Down
3 changes: 2 additions & 1 deletion examples/src/main/kotlin/examples/transferlearning/transferLearningRunner.kt
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Expand Up @@ -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
Expand All @@ -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"))
Expand Down
28 changes: 14 additions & 14 deletions examples/src/test/kotlin/examples/onnx/cv/OnnxEfficientNetTestSuite.kt
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Expand Up @@ -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
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6 changes: 2 additions & 4 deletions examples/src/test/kotlin/examples/transferlearning/modelhub/inception/InceptionTestSuite.kt
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Expand Up @@ -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)
)
)
Expand All @@ -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)
)
)
Expand Down
6 changes: 2 additions & 4 deletions examples/src/test/kotlin/examples/transferlearning/modelhub/mobilenet/MobileNetTestSuite.kt
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Expand Up @@ -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)
)
)
Expand All @@ -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)
)
)
Expand Down
5 changes: 2 additions & 3 deletions examples/src/test/kotlin/examples/transferlearning/modelhub/nasnet/NasNetTestSuite.kt
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Expand Up @@ -17,7 +17,7 @@ class NasNetTestSuite {

@Test
fun nasNetMobileAdditionalTrainingNoTopTest() {
runImageRecognitionTransferLearning(modelType = TFModels.CV.NASNetMobile(noTop = true))
runImageRecognitionTransferLearning(modelType = TFModels.CVnoTop.NASNetMobile)
}

@Test
Expand All @@ -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)
)
)
Expand Down
15 changes: 5 additions & 10 deletions examples/src/test/kotlin/examples/transferlearning/modelhub/resnet/ResNetTestSuite.kt
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Expand Up @@ -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)
)
)
Expand All @@ -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)
)
)
Expand All @@ -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)
)
)
Expand All @@ -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)
)
)
Expand All @@ -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)
)
)
Expand Down
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