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[examples] Fix ImageClassification invalid probability #1575

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merged 3 commits into from
Apr 16, 2022
Merged

[examples] Fix ImageClassification invalid probability #1575

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a002d07
[examples] Fix ImageClassification invalid probability
dandansamax Apr 16, 2022
fdb26ba
Delete unnecessary Test
dandansamax Apr 16, 2022
2d82a8d
Add unitest to check probability less than 1
frankfliu Apr 16, 2022
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1 change: 1 addition & 0 deletions examples/src/main/java/ai/djl/examples/inference/ImageClassification.java
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Original file line number Diff line number Diff line change
Expand Up @@ -68,6 +68,7 @@ public static Classifications predict() throws IOException, ModelException, Tran
ImageClassificationTranslator.builder()
.addTransform(new ToTensor())
.optSynset(classes)
.optApplySoftmax(true)
.build();

try (Predictor<Image, Classifications> predictor = model.newPredictor(translator)) {
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13 changes: 9 additions & 4 deletions examples/src/test/java/ai/djl/examples/training/TrainMnistTest.java
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Expand Up @@ -28,6 +28,7 @@ public class TrainMnistTest {
public void testTrainMnist() throws ModelException, TranslateException, IOException {
TestRequirements.engine("MXNet", "PyTorch");

double expectedProb;
if (Boolean.getBoolean("nightly")) {
String[] args = new String[] {"-g", "1"};

Expand All @@ -39,14 +40,18 @@ public void testTrainMnist() throws ModelException, TranslateException, IOExcept
Assert.assertTrue(accuracy > 0.9f, "Accuracy: " + accuracy);
Assert.assertTrue(loss < 0.35f, "Loss: " + loss);

Classifications classifications = ImageClassification.predict();
Classifications.Classification best = classifications.best();
Assert.assertEquals(best.getClassName(), "0");
Assert.assertTrue(Double.compare(best.getProbability(), 0.9) > 0);
expectedProb = 0.9;
} else {
String[] args = new String[] {"-g", "1", "-m", "2"};

TrainMnist.runExample(args);
expectedProb = 0;
}

Classifications classifications = ImageClassification.predict();
Classifications.Classification best = classifications.best();
Assert.assertEquals(best.getClassName(), "0");
double probability = best.getProbability();
Assert.assertTrue(probability > expectedProb && probability <= 1);
}
}