[docs] Fixes dataset document.

Change-Id: Ia84cc5faf2ebb955188ec995fedb7fa8a596f6de
deepjavalibrary · Mar 14, 2022 · b8ccd0e · b8ccd0e
1 parent d9fb99f
commit b8ccd0e
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Showing 2 changed files with 9 additions and 12 deletions.
diff --git a/basicdataset/src/test/java/ai/djl/basicdataset/ImageFolderTest.java b/basicdataset/src/test/java/ai/djl/basicdataset/ImageFolderTest.java
@@ -29,7 +29,6 @@
 import ai.djl.training.dataset.Batch;
 import ai.djl.training.dataset.RandomAccessDataset;
 import ai.djl.training.loss.Loss;
-import ai.djl.translate.Pipeline;
 import ai.djl.translate.TranslateException;
 import java.io.IOException;
 import java.nio.file.Paths;
@@ -52,8 +51,8 @@ public void testImageFolder() throws IOException, TranslateException {
             ImageFolder dataset =
                     ImageFolder.builder()
                             .setRepository(repository)
-                            .optPipeline(
-                                    new Pipeline().add(new Resize(100, 100)).add(new ToTensor()))
+                            .addTransform(new Resize(100, 100))
+                            .addTransform(new ToTensor())
                             .setSampling(1, false)
                             .build();
 

diff --git a/docs/development/how_to_use_dataset.md b/docs/development/how_to_use_dataset.md
@@ -64,17 +64,15 @@ The dataset will take the folder name e.g. `boots`, `pumps`, `shoes` in sorted o
 Add the following code snippet to your project to use the ImageFolder dataset.
 
 ```java
+// set the image folder path
+Repository repository = Repository.newInstance("folder", Paths.get("/imagefolder");
 ImageFolder dataset =
     new ImageFolder.Builder()
-    // input the root string
-    .setRepository(new SimpleRepository(Paths.get("root")))
-    .optPipeline(
-        // create preprocess pipeline you want
-        new Pipeline()
-            .add(new Resize(width, height))
-            .add(new ToTensor()))
-    .setSampling(batchSize, true)
-    .build();
+        .setRepository(repository)
+        .addTransform(new Resize(100, 100))
+        .addTransform(new ToTensor())
+        .setSampling(batchSize, true)
+        .build();
 // call prepare before using
 dataset.prepare();