chore(components/djl): removed main classes used in tests
diff --git a/components/camel-ai/camel-djl/src/test/java/org/apache/camel/component/djl/training/MnistTraining.java b/components/camel-ai/camel-djl/src/test/java/org/apache/camel/component/djl/training/MnistTraining.java
deleted file mode 100644
index ef665aa..0000000
--- a/components/camel-ai/camel-djl/src/test/java/org/apache/camel/component/djl/training/MnistTraining.java
+++ /dev/null
@@ -1,86 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements. See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You 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.
- */
-
-package org.apache.camel.component.djl.training;
-
-import java.io.IOException;
-import java.nio.file.Paths;
-
-import ai.djl.Model;
-import ai.djl.basicdataset.cv.classification.Mnist;
-import ai.djl.basicmodelzoo.basic.Mlp;
-import ai.djl.engine.Engine;
-import ai.djl.metric.Metrics;
-import ai.djl.ndarray.types.Shape;
-import ai.djl.nn.Block;
-import ai.djl.training.DefaultTrainingConfig;
-import ai.djl.training.EasyTrain;
-import ai.djl.training.Trainer;
-import ai.djl.training.dataset.Dataset;
-import ai.djl.training.dataset.RandomAccessDataset;
-import ai.djl.training.evaluator.Accuracy;
-import ai.djl.training.listener.TrainingListener;
-import ai.djl.training.loss.Loss;
-import ai.djl.training.util.ProgressBar;
-import ai.djl.translate.TranslateException;
-
-// Helper to train mnist model for tests
-public final class MnistTraining {
- private static final String MODEL_DIR = "src/test/resources/models/mnist";
- private static final String MODEL_NAME = "mlp";
-
- private MnistTraining() {
- // No-op; won't be called
- }
-
- public static void main(String[] args) throws IOException, TranslateException {
- // Construct neural network
- Block block = new Mlp(Mnist.IMAGE_HEIGHT * Mnist.IMAGE_WIDTH, Mnist.NUM_CLASSES, new int[] { 128, 64 });
-
- try (Model model = Model.newInstance(MODEL_NAME)) {
- model.setBlock(block);
-
- // get training and validation dataset
- RandomAccessDataset trainingSet = prepareDataset(Dataset.Usage.TRAIN, 64, Long.MAX_VALUE);
- RandomAccessDataset validateSet = prepareDataset(Dataset.Usage.TEST, 64, Long.MAX_VALUE);
-
- final Engine engine = Engine.getInstance();
- // setup training configuration
- DefaultTrainingConfig config = new DefaultTrainingConfig(Loss.softmaxCrossEntropyLoss())
- .addEvaluator(new Accuracy()).optDevices(engine.getDevices(engine.getGpuCount()))
- .addTrainingListeners(TrainingListener.Defaults.logging());
-
- try (Trainer trainer = model.newTrainer(config)) {
- trainer.setMetrics(new Metrics());
-
- Shape inputShape = new Shape(1, Mnist.IMAGE_HEIGHT * Mnist.IMAGE_WIDTH);
-
- // initialize trainer with proper input shape
- trainer.initialize(inputShape);
- EasyTrain.fit(trainer, 20, trainingSet, validateSet);
- }
- model.save(Paths.get(MODEL_DIR), MODEL_NAME);
- }
- }
-
- private static RandomAccessDataset prepareDataset(Dataset.Usage usage, int batchSize, long limit) throws IOException {
- Mnist mnist = Mnist.builder().optUsage(usage).setSampling(batchSize, true).optLimit(limit).build();
- mnist.prepare(new ProgressBar());
- return mnist;
- }
-
-}
