Fixes java format

frankfliu · frankfliu · commit 3eb32312c841 · 2023-11-25T23:26:39.000-08:00
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")));
         }
     }
-
 }

Original file line number	Diff line number	Diff line change
`@@ -435,10 +435,11 @@ default NDArray toTensor() {`
`435`	`435`	`result = result.expandDims(0);`
`436`	`436`	`}`
`437`	`437`	`// For Apple Silicon MPS it is important not to switch to 64-bit float here`
`438`		`- if (result.getDataType().equals(DataType.FLOAT32))`
	`438`	`+ if (result.getDataType() == DataType.FLOAT32) {`
`439`	`439`	`result = result.div(255.0f).transpose(0, 3, 1, 2);`
`440`		`- else`
	`440`	`+ } else {`
`441`	`441`	`result = result.div(255.0).transpose(0, 3, 1, 2);`
	`442`	`+ }`
`442`	`443`	`if (dim == 3) {`
`443`	`444`	`result = result.squeeze(0);`
`444`	`445`	`}`
Original file line number	Diff line number	Diff line change
`@@ -117,8 +117,11 @@ private NDArray readData(Artifact.Item item, long length) throws IOException {`
`117`	`117`	`}`
`118`	`118`
`119`	`119`	`byte[] buf = Utils.toByteArray(is);`
`120`		`- try (NDArray array = manager.create(ByteBuffer.wrap(buf),`
`121`		`- new Shape(length, IMAGE_WIDTH, IMAGE_HEIGHT, 1), DataType.UINT8)) {`
	`120`	`+ try (NDArray array =`
	`121`	`+ manager.create(`
	`122`	`+ ByteBuffer.wrap(buf),`
	`123`	`+ new Shape(length, IMAGE_WIDTH, IMAGE_HEIGHT, 1),`
	`124`	`+ DataType.UINT8)) {`
`122`	`125`	`return array.toType(DataType.FLOAT32, false);`
`123`	`126`	`}`
`124`	`127`	`}`
`@@ -131,7 +134,8 @@ private NDArray readLabel(Artifact.Item item) throws IOException {`
`131`	`134`	`}`
`132`	`135`
`133`	`136`	`byte[] buf = Utils.toByteArray(is);`
`134`		`- try (NDArray array = manager.create(ByteBuffer.wrap(buf), new Shape(buf.length), DataType.UINT8)) {`
	`137`	`+ try (NDArray array =`
	`138`	`+ manager.create(ByteBuffer.wrap(buf), new Shape(buf.length), DataType.UINT8)) {`
`135`	`139`	`return array.toType(DataType.FLOAT32, false);`
`136`	`140`	`}`
`137`	`141`	`}`
Original file line number	Diff line number	Diff line change
`@@ -112,7 +112,9 @@ private NDArray readData(Artifact.Item item, long length) throws IOException {`
`112`	`112`	`}`
`113`	`113`
`114`	`114`	`byte[] buf = Utils.toByteArray(is);`
`115`		`- try (NDArray array = manager.create(ByteBuffer.wrap(buf), new Shape(length, 28, 28, 1), DataType.UINT8)) {`
	`115`	`+ try (NDArray array =`
	`116`	`+ manager.create(`
	`117`	`+ ByteBuffer.wrap(buf), new Shape(length, 28, 28, 1), DataType.UINT8)) {`
`116`	`118`	`return array.toType(DataType.FLOAT32, false);`
`117`	`119`	`}`
`118`	`120`	`}`
`@@ -124,7 +126,8 @@ private NDArray readLabel(Artifact.Item item) throws IOException {`
`124`	`126`	`throw new AssertionError("Failed skip data.");`
`125`	`127`	`}`
`126`	`128`	`byte[] buf = Utils.toByteArray(is);`
`127`		`- try (NDArray array = manager.create(ByteBuffer.wrap(buf), new Shape(buf.length), DataType.UINT8)) {`
	`129`	`+ try (NDArray array =`
	`130`	`+ manager.create(ByteBuffer.wrap(buf), new Shape(buf.length), DataType.UINT8)) {`
`128`	`131`	`return array.toType(DataType.FLOAT32, false);`
`129`	`132`	`}`
`130`	`133`	`}`
Original file line number	Diff line number	Diff line change
`@@ -42,8 +42,8 @@ public void testMps() {`
`42`	`42`	`}`
`43`	`43`
`44`	`44`	`private static boolean checkMpsCompatible() {`
`45`		`- return "aarch64".equals(System.getProperty("os.arch")) &&`
`46`		`- System.getProperty("os.name").startsWith("Mac");`
	`45`	`+ return "aarch64".equals(System.getProperty("os.arch"))`
	`46`	`+ && System.getProperty("os.name").startsWith("Mac");`
`47`	`47`	`}`
`48`	`48`
`49`	`49`	`@Test`
`@@ -54,9 +54,10 @@ public void testToTensorMPS() {`
`54`	`54`
`55`	`55`	`// Test that toTensor does not fail on MPS (e.g. due to use of float64 for division)`
`56`	`56`	`try (NDManager manager = NDManager.newBaseManager(Device.fromName("mps"))) {`
`57`		`- NDArray array = manager.create(127f).reshape(1, 1, 1, 1);;`
	`57`	`+ NDArray array = manager.create(127f).reshape(1, 1, 1, 1);`
	`58`	`+ ;`
`58`	`59`	`NDArray tensor = array.getNDArrayInternal().toTensor();`
`59`		`- Assert.assertEquals(tensor.toFloatArray(), new float[]{127f/255f});`
	`60`	`+ Assert.assertEquals(tensor.toFloatArray(), new float[] {127f / 255f});`
`60`	`61`	`}`
`61`	`62`	`}`
`62`	`63`
`@@ -66,16 +67,13 @@ public void testClassificationsMPS() {`
`66`	`67`	`throw new SkipException("MPS classification test requires Apple Silicon macOS.");`
`67`	`68`	`}`
`68`	`69`
`69`		`- // Test that classifications do not fail on MPS (e.g. due to conversion of probabilities to float64)`
`70`		`- try (NDManager manager = NDManager.newBaseManager(Device.fromName("mps"))) {`
	`70`	`+ // Test that classifications do not fail on MPS (e.g. due to conversion of probabilities to`
	`71`	`+ // float64)`
	`72`	`+ try (NDManager manager = NDManager.newBaseManager(Device.fromName("mps"))) {`
`71`	`73`	`List<String> names = Arrays.asList("First", "Second", "Third", "Fourth", "Fifth");`
`72`		`- NDArray tensor = manager.create(new float[]{0f, 0.125f, 1f, 0.5f, 0.25f});`
`73`		`- Classifications classifications = new Classifications(`
`74`		`- names,`
`75`		`- tensor`
`76`		`- );`
	`74`	`+ NDArray tensor = manager.create(new float[] {0f, 0.125f, 1f, 0.5f, 0.25f});`
	`75`	`+ Classifications classifications = new Classifications(names, tensor);`
`77`	`76`	`Assert.assertNotNull(classifications.topK(1).equals(Arrays.asList("Third")));`
`78`	`77`	`}`
`79`	`78`	`}`
`80`		`-`
`81`	`79`	`}`