org.nd4j.linalg.factory.Nd4j#toNpyByteArray

Source File: BaseMemMapTest.java From konduit-serving with Apache License 2.0

6 votes

@Override
public io.vertx.core.json.JsonObject getConfigObject() throws Exception {
    INDArray arr = Nd4j.linspace(1, 4, 4);
    File tmpFile = new File(temporary.getRoot(), "tmpfile.npy");
    byte[] save = Nd4j.toNpyByteArray(arr);
    org.apache.commons.io.FileUtils.writeByteArrayToFile(tmpFile, save);
    InferenceConfiguration inferenceConfiguration =
            InferenceConfiguration.builder()
                    .servingConfig(ServingConfig.builder()
                            .httpPort(port)
                            .build())
                    .memMapConfig(MemMapConfig.builder()
                            .arrayPath(tmpFile.getAbsolutePath())
                            .build())
                    .build();

    return new JsonObject(inferenceConfiguration.toJson());
}

Source File: MemMapSpecificVerticleTest.java From konduit-serving with Apache License 2.0

6 votes

@Override
public JsonObject getConfigObject() throws Exception {
    File unkVectorPath = temporary.newFile();
    Nd4j.writeAsNumpy(unknownVector,unkVectorPath);
    INDArray arr = Nd4j.linspace(1,8,8).reshape(2,4);
    File tmpFile = new File(temporary.getRoot(),"tmpfile.npy");
    byte[] save = Nd4j.toNpyByteArray(arr);
    FileUtils.writeByteArrayToFile(tmpFile, save);
    InferenceConfiguration inferenceConfiguration =
            InferenceConfiguration.builder()
                    .servingConfig(ServingConfig.builder()
                            .httpPort(port)
                            .build())
                    .memMapConfig(MemMapConfig.builder()
                            .unkVectorPath(unkVectorPath.getAbsolutePath())
                            .arrayPath(tmpFile.getAbsolutePath())
                            .build())
                    .build();

    return new JsonObject(inferenceConfiguration.toJson());
}

Source File: BatchNumpyInputParserTest.java From konduit-serving with Apache License 2.0

6 votes

@Test(timeout = 60000)
public void runAdd(TestContext testContext) throws Exception {
    BatchNumpyInputParserVerticle verticleRef = (BatchNumpyInputParserVerticle) verticle;
    NativeImageLoader nativeImageLoader = new NativeImageLoader();
    Image image = nativeImageLoader.asImageMatrix(new ClassPathResource("data/5.png").getFile());
    File tmpFile = temporary.newFile();
    byte[] npyContents = Nd4j.toNpyByteArray(image.getImage());
    try (FileOutputStream fileOutputStream = new FileOutputStream(tmpFile)) {
        IOUtils.write(npyContents, fileOutputStream);
        fileOutputStream.flush();
    }


    given().port(port)
            .multiPart("input1", tmpFile)
            .when().post("/")
            .then().statusCode(200);
    assertNotNull("Inputs were null. This means parsing failed.", verticleRef.getBatch());
    assertTrue(verticleRef.getBatch().length >= 1);
    assertNotNull(verticleRef.getBatch());
}

Source File: SameDiffVerticleClassificationMetricsTest.java From konduit-serving with Apache License 2.0

5 votes

@Test
public void runAdd(TestContext testContext) throws Exception {
    INDArray x = Nd4j.create(new float[]{1.0f, 2.0f});
    INDArray y = Nd4j.create(new float[]{2.0f, 3.0f});
    byte[] xNpy = Nd4j.toNpyByteArray(x);
    byte[] yNpy = Nd4j.toNpyByteArray(y);


    File xFile = temporary.newFile();
    FileUtils.writeByteArrayToFile(xFile, xNpy);

    File yFile = temporary.newFile();
    FileUtils.writeByteArrayToFile(yFile, yNpy);


    Response response = given().port(port)
            .multiPart("x", xFile)
            .multiPart("y", yFile)
            .post("/nd4j/numpy")
            .andReturn();

    assertEquals("Response failed", 200, response.getStatusCode());

    INDArray bodyResult = BinarySerde.toArray(ByteBuffer.wrap(response.getBody().asByteArray()));
    assertArrayEquals(new long[]{2}, bodyResult.shape());
    assertEquals(Nd4j.create(new float[]{3.0f, 5.0f}), bodyResult);

    given().port(port).get("/metrics")
            .then()
            .statusCode(200).and()
            .contentType("text/plain");

    Response response1 = given().port(port).get("/metrics")
            .andReturn();

    System.out.println(response1.asString());

}

Source File: SameDiffVerticleNumpyTest.java From konduit-serving with Apache License 2.0

5 votes

@Test
public void runAdd(TestContext testContext) throws Exception {
    INDArray x = Nd4j.create(new float[]{1.0f, 2.0f});
    INDArray y = Nd4j.create(new float[]{2.0f, 3.0f});
    byte[] xNpy = Nd4j.toNpyByteArray(x);
    byte[] yNpy = Nd4j.toNpyByteArray(y);


    File xFile = temporary.newFile();
    FileUtils.writeByteArrayToFile(xFile, xNpy);

    File yFile = temporary.newFile();
    FileUtils.writeByteArrayToFile(yFile, yNpy);


    Response response = given().port(port)
            .multiPart("x", xFile)
            .multiPart("y", yFile)
            .post("/numpy/numpy")
            .andReturn();

    assertEquals("Response failed", 200, response.getStatusCode());

    INDArray bodyResult = Nd4j.createNpyFromByteArray(response.getBody().asByteArray());
    assertArrayEquals(new long[]{2}, bodyResult.shape());
    assertEquals(Nd4j.create(new float[]{3.0f, 5.0f}), bodyResult);
}

Source File: SameDiffVerticleNd4jTest.java From konduit-serving with Apache License 2.0

5 votes

@Test
public void runAdd(TestContext testContext) throws Exception {
    INDArray x = Nd4j.create(new float[]{1.0f, 2.0f});
    INDArray y = Nd4j.create(new float[]{2.0f, 3.0f});
    byte[] xNpy = Nd4j.toNpyByteArray(x);
    byte[] yNpy = Nd4j.toNpyByteArray(y);


    File xFile = temporary.newFile();
    FileUtils.writeByteArrayToFile(xFile, xNpy);

    File yFile = temporary.newFile();
    FileUtils.writeByteArrayToFile(yFile, yNpy);


    Response response = given().port(port)
            .multiPart("x", xFile)
            .multiPart("y", yFile)
            .post("/nd4j/numpy")
            .andReturn();

    assertEquals("Response failed", 200, response.getStatusCode());

    INDArray bodyResult = BinarySerde.toArray(ByteBuffer.wrap(response.getBody().asByteArray()));
    assertArrayEquals(new long[]{2}, bodyResult.shape());
    assertEquals(Nd4j.create(new float[]{3.0f, 5.0f}), bodyResult);


}

Source File: OnnxTest.java From konduit-serving with Apache License 2.0

5 votes

@Test
public void runSqueezenet(TestContext testContext) throws Exception {

    long inputTensorSize = 224 * 224 * 3;

    FloatPointer inputTensorValues = new FloatPointer(inputTensorSize);
    FloatIndexer idx = FloatIndexer.create(inputTensorValues);
    for (long i = 0; i < inputTensorSize; i++)
        idx.put(i, (float) i / (inputTensorSize + 1));

    DataBuffer buffer = Nd4j.createBuffer(inputTensorValues, DataType.FLOAT, inputTensorSize, idx);

    INDArray contents = Nd4j.create(buffer);

    byte[] npyContents = Nd4j.toNpyByteArray(contents);

    File inputFile = temporary.newFile();
    FileUtils.writeByteArrayToFile(inputFile, npyContents);

    for (int i = 0; i < 5; i++) {
        Response response = given().port(port)
                .multiPart("data_0", inputFile)
                .post("nd4j/numpy")
                .andReturn();
        //TODO: report memory leak in DNNL execution provider to ORT
        assertEquals("Response failed", 200, response.getStatusCode());

        INDArray bodyResult = Nd4j.createNpyFromByteArray(response.getBody().asByteArray());

        assertEquals(1.99018, bodyResult.getFloat(0), 1e-4);

        assertArrayEquals(new long[]{1, 1000}, bodyResult.shape());
    }
}

Source File: OnnxMultipleOutputsTest.java From konduit-serving with Apache License 2.0

5 votes

@Test
public void runFaceDetector(TestContext testContext) throws Exception {
    File imageFile = Paths.get(new ClassPathResource(".").getFile().getAbsolutePath(), "inference/onnx/data/1.jpg").toFile();

    if (!imageFile.exists()) {
        FileUtils.copyURLToFile(new URL("https://github.com/KonduitAI/konduit-serving-examples/raw/master/data/facedetector/1.jpg"), imageFile);
    }
    NativeImageLoader nativeImageLoader = new NativeImageLoader(240, 320);
    Image image = nativeImageLoader.asImageMatrix(imageFile);

    INDArray contents = image.getImage();

    byte[] npyContents = Nd4j.toNpyByteArray(contents);

    File inputFile = temporary.newFile();
    FileUtils.writeByteArrayToFile(inputFile, npyContents);

    Response response = given().port(port)
            .multiPart("input", inputFile)
            .body(npyContents)
            .post("nd4j/numpy")
            .andReturn();

    assertEquals("Response failed", 200, response.getStatusCode());

    JsonObject output = new JsonObject(response.asString());

    assertTrue(output.containsKey("scores"));
    assertTrue(output.containsKey("boxes"));

    INDArray scores = ObjectMappers.fromJson(output.getJsonObject("scores").encode(), NDArrayOutput.class).getNdArray();
    assertEquals(0.9539676, scores.getFloat(0), 1e-6);
    assertArrayEquals(new long[]{1, 8840}, scores.shape());

    INDArray boxes = ObjectMappers.fromJson(output.getJsonObject("boxes").encode(), NDArrayOutput.class).getNdArray();
    assertEquals(0.002913665, boxes.getFloat(0), 1e-6);
    assertArrayEquals(new long[]{1, 17680}, boxes.shape());
}

Source File: JsonSerdeUtilsTest.java From konduit-serving with Apache License 2.0

5 votes

@Test
public void testDeSerializeBase64Numpy() {
    INDArray numpyBinary = Nd4j.scalar(1.0);
    byte[] npy = Nd4j.toNpyByteArray(numpyBinary);
    JsonObject value = new JsonObject();
    value.put("npy", npy);
    JsonObject valuesWrapper = new JsonObject();
    valuesWrapper.put("values", value);
    // Run the test
    final INDArray result = JsonSerdeUtils.deSerializeBase64Numpy(valuesWrapper, "npy");
    assertEquals(numpyBinary, result);
}

Source File: NumpyFormatTests.java From deeplearning4j with Apache License 2.0

4 votes

@Test
    public void testToNpyFormat() throws Exception {

        val dir = testDir.newFolder();
        new ClassPathResource("numpy_arrays/").copyDirectory(dir);

        File[] files = dir.listFiles();
        int cnt = 0;

        for(File f : files){
            if(!f.getPath().endsWith(".npy")){
                log.warn("Skipping: {}", f);
                continue;
            }

            String path = f.getAbsolutePath();
            int lastDot = path.lastIndexOf('.');
            int lastUnderscore = path.lastIndexOf('_');
            String dtype = path.substring(lastUnderscore+1, lastDot);
//            System.out.println(path + " : " + dtype);

            DataType dt = DataType.fromNumpy(dtype);
            //System.out.println(dt);

            INDArray arr = Nd4j.arange(12).castTo(dt).reshape(3,4);
            byte[] bytes = Nd4j.toNpyByteArray(arr);
            byte[] expected = FileUtils.readFileToByteArray(f);
/*
            log.info("E: {}", Arrays.toString(expected));
            for( int i=0; i<expected.length; i++ ){
                System.out.print((char)expected[i]);
            }

            System.out.println();System.out.println();

            log.info("A: {}", Arrays.toString(bytes));
            for( int i=0; i<bytes.length; i++ ){
                System.out.print((char)bytes[i]);
            }
            System.out.println();
*/

            assertArrayEquals("Failed with file [" + f.getName() + "]", expected, bytes);
            cnt++;
        }

        assertTrue(cnt > 0);
    }

Source File: NumpyFormatTests.java From deeplearning4j with Apache License 2.0

4 votes

@Test
    public void testToNpyFormatScalars() throws Exception {
//        File dir = new File("C:\\DL4J\\Git\\dl4j-test-resources\\src\\main\\resources\\numpy_arrays\\scalar");

        val dir = testDir.newFolder();
        new ClassPathResource("numpy_arrays/scalar/").copyDirectory(dir);

        File[] files = dir.listFiles();
        int cnt = 0;

        for(File f : files){
            if(!f.getPath().endsWith(".npy")){
                log.warn("Skipping: {}", f);
                continue;
            }

            String path = f.getAbsolutePath();
            int lastDot = path.lastIndexOf('.');
            int lastUnderscore = path.lastIndexOf('_');
            String dtype = path.substring(lastUnderscore+1, lastDot);
//            System.out.println(path + " : " + dtype);

            DataType dt = DataType.fromNumpy(dtype);
            //System.out.println(dt);

            INDArray arr = Nd4j.scalar(dt, 1);
            byte[] bytes = Nd4j.toNpyByteArray(arr);
            byte[] expected = FileUtils.readFileToByteArray(f);

            /*
            log.info("E: {}", Arrays.toString(expected));
            for( int i=0; i<expected.length; i++ ){
                System.out.print((char)expected[i]);
            }

            System.out.println();System.out.println();

            log.info("A: {}", Arrays.toString(bytes));
            for( int i=0; i<bytes.length; i++ ){
                System.out.print((char)bytes[i]);
            }
            System.out.println();
            */

            assertArrayEquals("Failed with file [" + f.getName() + "]", expected, bytes);
            cnt++;

            System.out.println();
        }

        assertTrue(cnt > 0);
    }

Source File: OnnxMultipleInputsTest.java From konduit-serving with Apache License 2.0

3 votes

@Test
public void runAdd(TestContext testContext) throws Exception {

    long inputTensorSize = 3 * 4 * 5;

    FloatPointer inputTensorValues = new FloatPointer(inputTensorSize);
    FloatIndexer idx = FloatIndexer.create(inputTensorValues);
    for (long i = 0; i < inputTensorSize; i++)
        idx.put(i, (float) i / (inputTensorSize + 1));

    DataBuffer buffer = Nd4j.createBuffer(inputTensorValues, DataType.FLOAT, inputTensorSize, idx);

    INDArray contents = Nd4j.create(buffer);

    byte[] npyContents = Nd4j.toNpyByteArray(contents);

    File inputFile = temporary.newFile();
    FileUtils.writeByteArrayToFile(inputFile, npyContents);

    Response response = given().port(port)
            .multiPart("x", inputFile)
            .multiPart("y", inputFile)
            .post("nd4j/numpy")
            .andReturn();

    assertEquals("Response failed", 200, response.getStatusCode());

    INDArray bodyResult = Nd4j.createNpyFromByteArray(response.getBody().asByteArray());

    assertEquals(0.032786883, bodyResult.getFloat(1), 1e-6);

    assertArrayEquals(new long[]{1, 60}, bodyResult.shape());
}

Java Code Examples for org.nd4j.linalg.factory.Nd4j#toNpyByteArray()