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deepjavalibrary · Oct 26, 2022 · dc5c328 · dc5c328
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Showing 9 changed files with 14 additions and 13 deletions.
diff --git a/api/src/main/java/ai/djl/ndarray/BaseNDManager.java b/api/src/main/java/ai/djl/ndarray/BaseNDManager.java
@@ -192,7 +192,7 @@ public NDArray randomInteger(long low, long high, Shape shape, DataType dataType
 
     /** {@inheritDoc} */
     @Override
-    public NDArray randPerm(long n) {
+    public NDArray randomPermutation(long n) {
         throw new UnsupportedOperationException("Not supported!");
     }
 

diff --git a/api/src/main/java/ai/djl/ndarray/NDManager.java b/api/src/main/java/ai/djl/ndarray/NDManager.java
@@ -1181,7 +1181,7 @@ default NDArray linspace(float start, float stop, int num, boolean endpoint, Dev
      * @param n (int) – the upper bound (exclusive)
      * @return a random permutation of integers from 0 to n - 1.
      */
-    NDArray randPerm(long n);
+    NDArray randomPermutation(long n);
 
     /**
      * Draws samples from a uniform distribution.

diff --git a/api/src/main/java/ai/djl/nn/core/Linear.java b/api/src/main/java/ai/djl/nn/core/Linear.java
@@ -35,10 +35,10 @@
  * <p>It has the following shapes:
  *
  * <ul>
- *   <li>input X: [batch_num, input_dim]
+ *   <li>input X: [x1, x2, …, xn, input_dim]
  *   <li>weight W: [units, input_dim]
  *   <li>Bias b: [units]
- *   <li>output Y: [batch_num, units]
+ *   <li>output Y: [y1, y2, …, yn, units]
  * </ul>
  *
  * <p>The Linear block should be constructed using {@link Linear.Builder}.
@@ -165,7 +165,7 @@ public void loadMetadata(byte loadVersion, DataInputStream is)
      *
      * @param input input X: [x1, x2, …, xn, input_dim]
      * @param weight weight W: [units, input_dim]
-     * @return output Y: [x1, x2, …, xn, units]
+     * @return output Y: [y1, y2, …, yn, units]
      */
     public static NDList linear(NDArray input, NDArray weight) {
         return linear(input, weight, null);
@@ -177,7 +177,7 @@ public static NDList linear(NDArray input, NDArray weight) {
      * @param input input X: [x1, x2, …, xn, input_dim]
      * @param weight weight W: [units, input_dim]
      * @param bias bias b: [units]
-     * @return output Y: [x1, x2, …, xn, units]
+     * @return output Y: [y1, y2, …, yn, units]
      */
     public static NDList linear(NDArray input, NDArray weight, NDArray bias) {
         return input.getNDArrayInternal().linear(input, weight, bias);

diff --git a/api/src/main/java/ai/djl/translate/StackBatchifier.java b/api/src/main/java/ai/djl/translate/StackBatchifier.java
@@ -77,9 +77,9 @@ public NDList batchify(NDList[] inputs) {
                     NDArray currInput = input.get(i);
                     if (!currInput.getShape().equals(kindDataShape)) {
                         throw new IllegalArgumentException(
-                                "You cannot batch data with different input shapes"
+                                "You cannot batch data with different input shapes. currInput: "
                                         + currInput.getShape()
-                                        + " vs "
+                                        + " vs kindDataShape: "
                                         + kindDataShape,
                                 e);
                     }

diff --git a/api/src/main/java/ai/djl/util/passthrough/PassthroughNDManager.java b/api/src/main/java/ai/djl/util/passthrough/PassthroughNDManager.java
@@ -143,7 +143,7 @@ public NDArray randomInteger(long low, long high, Shape shape, DataType dataType
 
     /** {@inheritDoc} */
     @Override
-    public NDArray randPerm(long n) {
+    public NDArray randomPermutation(long n) {
         throw new UnsupportedOperationException("Not supported!");
     }
 

diff --git a/engines/mxnet/mxnet-engine/src/main/java/ai/djl/mxnet/engine/MxNDManager.java b/engines/mxnet/mxnet-engine/src/main/java/ai/djl/mxnet/engine/MxNDManager.java
@@ -235,7 +235,7 @@ public NDArray randomInteger(long low, long high, Shape shape, DataType dataType
 
     /** {@inheritDoc} */
     @Override
-    public NDArray randPerm(long n) {
+    public NDArray randomPermutation(long n) {
         NDArray array = arange(0, n, 1, DataType.INT64);
         MxOpParams params = new MxOpParams();
         return invoke("_npi_shuffle", new NDList(array), params).singletonOrThrow();

diff --git a/engines/pytorch/pytorch-engine/src/main/java/ai/djl/pytorch/engine/PtNDManager.java b/engines/pytorch/pytorch-engine/src/main/java/ai/djl/pytorch/engine/PtNDManager.java
@@ -154,7 +154,7 @@ public NDArray randomInteger(long low, long high, Shape shape, DataType dataType
 
     /** {@inheritDoc} */
     @Override
-    public NDArray randPerm(long n) {
+    public NDArray randomPermutation(long n) {
         return JniUtils.randperm(this, n, DataType.INT64, device);
     }
 

diff --git a/examples/src/main/java/ai/djl/examples/training/transferlearning/TransferFreshFruit.java b/examples/src/main/java/ai/djl/examples/training/transferlearning/TransferFreshFruit.java
@@ -84,6 +84,7 @@ public static TrainingResult runExample(String[] args)
         ZooModel<NDList, NDList> embedding = criteria.loadModel();
 
         Block baseBlock = embedding.getBlock();
+        baseBlock.freezeParameters(false);
         Block blocks =
                 new SequentialBlock()
                         .add(baseBlock)

diff --git a/integration/src/main/java/ai/djl/integration/tests/ndarray/NDArrayCreationOpTest.java b/integration/src/main/java/ai/djl/integration/tests/ndarray/NDArrayCreationOpTest.java
@@ -395,10 +395,10 @@ public void testRandomInteger() {
     }
 
     @Test
-    public void testRandPerm() {
+    public void testRandomPermutation() {
         try (NDManager manager = NDManager.newBaseManager()) {
             long size = 3;
-            NDArray array = manager.randPerm(size);
+            NDArray array = manager.randomPermutation(size);
             Assert.assertTrue(
                     array.contentEquals(manager.create(new long[] {0, 1, 2}))
                             || array.contentEquals(manager.create(new long[] {0, 2, 1}))