Fixes java format

deepjavalibrary · Nov 26, 2023 · 3eb3231 · 3eb3231
1 parent d4d6e48
commit 3eb3231
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Showing 5 changed files with 28 additions and 21 deletions.
diff --git a/api/src/main/java/ai/djl/modality/Classifications.java b/api/src/main/java/ai/djl/modality/Classifications.java
@@ -88,11 +88,12 @@ public Classifications(List<String> classNames, NDArray probabilities) {
      */
     public Classifications(List<String> classNames, NDArray probabilities, int topK) {
         this.classNames = classNames;
-        if (probabilities.getDataType().equals(DataType.FLOAT32)) {
+        if (probabilities.getDataType() == DataType.FLOAT32) {
             // Avoid converting float32 to float64 as this is not supported on MPS device
             this.probabilities = new ArrayList<>();
-            for (float prob : probabilities.toFloatArray())
+            for (float prob : probabilities.toFloatArray()) {
                 this.probabilities.add((double) prob);
+            }
         } else {
             NDArray array = probabilities.toType(DataType.FLOAT64, false);
             this.probabilities =

diff --git a/api/src/main/java/ai/djl/ndarray/internal/NDArrayEx.java b/api/src/main/java/ai/djl/ndarray/internal/NDArrayEx.java
@@ -435,10 +435,11 @@ default NDArray toTensor() {
                 result = result.expandDims(0);
             }
             // For Apple Silicon MPS it is important not to switch to 64-bit float here
-            if (result.getDataType().equals(DataType.FLOAT32))
+            if (result.getDataType() == DataType.FLOAT32) {
                 result = result.div(255.0f).transpose(0, 3, 1, 2);
-            else
+            } else {
                 result = result.div(255.0).transpose(0, 3, 1, 2);
+            }
             if (dim == 3) {
                 result = result.squeeze(0);
             }

diff --git a/basicdataset/src/main/java/ai/djl/basicdataset/cv/classification/FashionMnist.java b/basicdataset/src/main/java/ai/djl/basicdataset/cv/classification/FashionMnist.java
@@ -117,8 +117,11 @@ private NDArray readData(Artifact.Item item, long length) throws IOException {
             }
 
             byte[] buf = Utils.toByteArray(is);
-            try (NDArray array = manager.create(ByteBuffer.wrap(buf),
-                    new Shape(length, IMAGE_WIDTH, IMAGE_HEIGHT, 1), DataType.UINT8)) {
+            try (NDArray array =
+                    manager.create(
+                            ByteBuffer.wrap(buf),
+                            new Shape(length, IMAGE_WIDTH, IMAGE_HEIGHT, 1),
+                            DataType.UINT8)) {
                 return array.toType(DataType.FLOAT32, false);
             }
         }
@@ -131,7 +134,8 @@ private NDArray readLabel(Artifact.Item item) throws IOException {
             }
 
             byte[] buf = Utils.toByteArray(is);
-            try (NDArray array = manager.create(ByteBuffer.wrap(buf), new Shape(buf.length), DataType.UINT8)) {
+            try (NDArray array =
+                    manager.create(ByteBuffer.wrap(buf), new Shape(buf.length), DataType.UINT8)) {
                 return array.toType(DataType.FLOAT32, false);
             }
         }

diff --git a/basicdataset/src/main/java/ai/djl/basicdataset/cv/classification/Mnist.java b/basicdataset/src/main/java/ai/djl/basicdataset/cv/classification/Mnist.java
@@ -112,7 +112,9 @@ private NDArray readData(Artifact.Item item, long length) throws IOException {
             }
 
             byte[] buf = Utils.toByteArray(is);
-            try (NDArray array = manager.create(ByteBuffer.wrap(buf), new Shape(length, 28, 28, 1), DataType.UINT8)) {
+            try (NDArray array =
+                    manager.create(
+                            ByteBuffer.wrap(buf), new Shape(length, 28, 28, 1), DataType.UINT8)) {
                 return array.toType(DataType.FLOAT32, false);
             }
         }
@@ -124,7 +126,8 @@ private NDArray readLabel(Artifact.Item item) throws IOException {
                 throw new AssertionError("Failed skip data.");
             }
             byte[] buf = Utils.toByteArray(is);
-            try (NDArray array = manager.create(ByteBuffer.wrap(buf), new Shape(buf.length), DataType.UINT8)) {
+            try (NDArray array =
+                    manager.create(ByteBuffer.wrap(buf), new Shape(buf.length), DataType.UINT8)) {
                 return array.toType(DataType.FLOAT32, false);
             }
         }

diff --git a/engines/pytorch/pytorch-engine/src/test/java/ai/djl/pytorch/integration/MpsTest.java b/engines/pytorch/pytorch-engine/src/test/java/ai/djl/pytorch/integration/MpsTest.java
@@ -42,8 +42,8 @@ public void testMps() {
     }
 
     private static boolean checkMpsCompatible() {
-        return "aarch64".equals(System.getProperty("os.arch")) &&
-                System.getProperty("os.name").startsWith("Mac");
+        return "aarch64".equals(System.getProperty("os.arch"))
+                && System.getProperty("os.name").startsWith("Mac");
     }
 
     @Test
@@ -54,9 +54,10 @@ public void testToTensorMPS() {
 
         // Test that toTensor does not fail on MPS (e.g. due to use of float64 for division)
         try (NDManager manager = NDManager.newBaseManager(Device.fromName("mps"))) {
-            NDArray array = manager.create(127f).reshape(1, 1, 1, 1);;
+            NDArray array = manager.create(127f).reshape(1, 1, 1, 1);
+            ;
             NDArray tensor = array.getNDArrayInternal().toTensor();
-            Assert.assertEquals(tensor.toFloatArray(), new float[]{127f/255f});
+            Assert.assertEquals(tensor.toFloatArray(), new float[] {127f / 255f});
         }
     }
 
@@ -66,16 +67,13 @@ public void testClassificationsMPS() {
             throw new SkipException("MPS classification test requires Apple Silicon macOS.");
         }
 
-        // Test that classifications do not fail on MPS (e.g. due to conversion of probabilities to float64)
-       try (NDManager manager = NDManager.newBaseManager(Device.fromName("mps"))) {
+        // Test that classifications do not fail on MPS (e.g. due to conversion of probabilities to
+        // float64)
+        try (NDManager manager = NDManager.newBaseManager(Device.fromName("mps"))) {
             List<String> names = Arrays.asList("First", "Second", "Third", "Fourth", "Fifth");
-            NDArray tensor = manager.create(new float[]{0f, 0.125f, 1f, 0.5f, 0.25f});
-            Classifications classifications = new Classifications(
-                    names,
-                    tensor
-            );
+            NDArray tensor = manager.create(new float[] {0f, 0.125f, 1f, 0.5f, 0.25f});
+            Classifications classifications = new Classifications(names, tensor);
             Assert.assertNotNull(classifications.topK(1).equals(Arrays.asList("Third")));
         }
     }
-
 }