Javacpp-presets: MXNet embedding extraction issue

[going-deeper]

Hello everyone. I've slightly modified the example which is located in the samples folder in order to extract embeddings from an image. I'm using MXNet models which can be derived from here https://github.com/deepinsight/insightface/wiki/Model-Zoo. The problem is running the model inference always outputs the same embedding for different images. So for aligned faces of completely different people the model outputs the same embedding. Could you please give me some guidance, how to fix this?

import java.io.Closeable;
import java.io.IOException;
import java.nio.ByteBuffer;
import java.nio.FloatBuffer;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.ArrayList;
import java.util.List;
import java.util.ListIterator;

import org.apache.mxnet.javaapi.*;
import org.bytedeco.javacpp.*;

import org.bytedeco.mxnet.*;
import static org.bytedeco.mxnet.global.mxnet.*;

import org.bytedeco.opencv.opencv_core.*;
import org.bytedeco.opencv.opencv_imgproc.*;
import static org.bytedeco.opencv.global.opencv_core.*;
import static org.bytedeco.opencv.global.opencv_imgcodecs.*;
import static org.bytedeco.opencv.global.opencv_imgproc.*;

public class ImageClassificationPredict {

    static final float DEFAULT_MEAN = 117.0f;

    // Read file to buffer
    static class BufferFile implements Closeable {
        public String file_path_;
        public int length_ = 0;
        public BytePointer buffer_;

        public BufferFile(String file_path) {
            file_path_ = file_path;
            try {
                byte[] bytes = Files.readAllBytes(Paths.get(file_path));
                length_ = bytes.length;
                System.out.println(file_path + " ... " + length_ + " bytes");
                buffer_ = new BytePointer(bytes);
            } catch (IOException e) {
                System.err.println("Can't open the file: " + e + ". Please check " + file_path + ".");
                assert false;
            }
        }

        public int GetLength() {
            return length_;
        }

        public BytePointer GetBuffer() {
            return buffer_;
        }

        public void close() throws IOException {
            buffer_.deallocate();
            buffer_ = null;
        }
    }

    static void GetImageFile(String image_file, FloatPointer image_data,
                             int channels, Size resize_size, FloatPointer mean_data) {
        // Read all kinds of file into a BGR color 3 channels image
        Mat im_ori = imread(image_file, IMREAD_COLOR);

        if (im_ori.empty()) {
            System.err.println("Can't open the image. Please check " + image_file + ".");
            assert false;
        }

        Mat im = new Mat();

        resize(im_ori, im, resize_size);

        int rows = im.rows();
        int cols = im.cols();
        int size = rows * cols * channels;

        FloatBuffer ptr_image_r = image_data.position(0).asBuffer();
        FloatBuffer ptr_image_g = image_data.position(size / 3).asBuffer();
        FloatBuffer ptr_image_b = image_data.position(size / 3 * 2).asBuffer();

        FloatBuffer ptr_mean_r, ptr_mean_g, ptr_mean_b;
        ptr_mean_r = ptr_mean_g = ptr_mean_b = null;
        if (mean_data != null && !mean_data.isNull()) {
            ptr_mean_r = mean_data.position(0).asBuffer();
            ptr_mean_g = mean_data.position(size / 3).asBuffer();
            ptr_mean_b = mean_data.position(size / 3 * 2).asBuffer();
        }

        float mean_b, mean_g, mean_r;
        mean_b = mean_g = mean_r = DEFAULT_MEAN;

        for (int i = 0; i < rows; i++) {
            ByteBuffer data = im.ptr(i).capacity(3 * cols).asBuffer();

            for (int j = 0; j < cols; j++) {
                if (mean_data != null && !mean_data.isNull()) {
                    mean_r = ptr_mean_r.get();
                    if (channels > 1) {
                        mean_g = ptr_mean_g.get();
                        mean_b = ptr_mean_b.get();
                    }
                }
                if (channels > 1) {
                    ptr_image_b.put((float)(data.get() & 0xFF) - mean_b);
                    ptr_image_g.put((float)(data.get() & 0xFF) - mean_g);
                }

                ptr_image_r.put((float)(data.get() & 0xFF) - mean_r);
            }
        }
    }


    static void PrintOutputResult(FloatPointer data) {

        for (int i = 0; i < data.limit(); i++) {
            System.out.print(data.get(i) + " ");
        }


    }

    static void predict(PredictorHandle pred_hnd, FloatPointer image_data,
                        NDListHandle nd_hnd, int n) {
        int image_size = (int)image_data.limit();
        // Set Input Image
        MXPredSetInput(pred_hnd, "data", image_data.position(0), image_size);
        // Do Predict Forward
        MXPredForward(pred_hnd);

        int output_index = 0;

        IntPointer shape = new IntPointer((IntPointer)null);
        IntPointer shape_len = new IntPointer(1);

        // Get Output Result
        MXPredGetOutputShape(pred_hnd, output_index, shape, shape_len);

        int size = 1;
        for (int i = 0; i < shape_len.get(0); i++) { size *= shape.get(i); }

        FloatPointer data = new FloatPointer(size);

        MXPredGetOutput(pred_hnd, output_index, data.position(0), size);

        // Release NDList
        if (nd_hnd != null) {
            MXNDListFree(nd_hnd);
        }

        // Release Predictor
        MXPredFree(pred_hnd);

        // Synset path for your model, you have to modify it
        // Print Output Data
        PrintOutputResult(data.position(0));
    }

    public static void main(String[] args) throws Exception {
        // Preload required by JavaCPP
        Loader.load(org.bytedeco.mxnet.global.mxnet.class);

        if (args.length < 1) {
            System.out.println("No test image here.");
            System.out.println("Usage: java ImageClassificationPredict apple.jpg [num_threads]");
            return;
        }

        final String test_file = args[0];
        int num_threads = 1;
        if (args.length == 2) {
            num_threads = Integer.parseInt(args[1]);
        }

        // Models path for your model, you have to modify it
        final String json_file = "/home/adileg/JavaProjects/javacpp-presets-release/mxnet/samples/feature_model/model-r100-ii/model-symbol.json";
        final String param_file = "/home/adileg/JavaProjects/javacpp-presets-release/mxnet/samples/feature_model/model-r100-ii/model-0000.params";

        BufferFile json_data = new BufferFile(json_file);
        BufferFile param_data = new BufferFile(param_file);

        // Parameters
        int dev_type = 1;  // 1: cpu, 2: gpu
        int dev_id = 0;  // arbitrary.
        int num_input_nodes = 1;  // 1 for feedforward
        String[] input_keys = { "data" };

        // Image size and channels
        int width = 112;
        int height = 112;
        int channels = 3;

        int[] input_shape_indptr = { 0, 4 };
        int[] input_shape_data = { 1, channels, height, width };


        if (json_data.GetLength() == 0 || param_data.GetLength() == 0) {
            System.exit(1 /* EXIT_FAILURE */);
        }


        final int image_size = width * height * channels;

        // Read Mean Data
        final FloatPointer nd_data = new FloatPointer((Pointer)null);
        final NDListHandle nd_hnd = new NDListHandle((Pointer)null);

        // Read Image Data
        final FloatPointer image_data = new FloatPointer(image_size);

        GetImageFile(test_file, image_data, channels, new Size(width, height), nd_data);


        if (num_threads == 1) {
            // Create Predictor
            final PointerPointer<PredictorHandle> pred_hnd = new PointerPointer<PredictorHandle>(1);
            MXPredCreate(json_data.GetBuffer(),
                    param_data.GetBuffer(),
                    param_data.GetLength(),
                    dev_type,
                    dev_id,
                    num_input_nodes,
                    new PointerPointer(input_keys),
                    new IntPointer(input_shape_indptr),
                    new IntPointer(input_shape_data),
                    pred_hnd);

            assert !pred_hnd.get().isNull();
            predict(pred_hnd.get(PredictorHandle.class), image_data, nd_hnd, 0);

        }

        System.out.println("run successfully");

        System.exit(0 /* EXIT_SUCCESS */);
    }
}

[saudet]

I'm not an expert in MXNet, but I'm sure someone over there would know.
If you have a working example in C/C++ though, please provide it!

@frankfliu Would you know how to do that with the C API?

[going-deeper]

I'm not an expert in MXNet, but I'm sure someone over there would know.
If you have a working example in C/C++ though, please provide it!

@frankfliu Would you know how to do that with the C API?

Here is working example using C++ api. This line extracts the embeddings for the aligned face image. Generally, everything works as expected in C api, for the each face image the model outputs unique embedding.
The something weird happens when it comes to JNI. I've also tried to implement the simple JNI bridge using C++ api like here but unfortunately when I call the native functions it always outputs the same embedding for the completely different images. Im trying to figure out whats the problem, because everything works fine for the different type of problems like predicting the class of an object or the face detection (both C++ and Java).

[saudet]

Here is working example using C++ api. This line extracts the embeddings for the aligned face image. Generally, everything works as expected in C api, for the each face image the model outputs unique embedding.

I don't see them subtracting the mean. You'll need to give it exactly the same input to have a chance of getting the same output.

[frankfliu]

the c_predict_api in MXNet has not been maintained, and is removed in 2.0.

Both the code you pointed to are in MXNet repo. I didn't see an obvious
issue there. But I won't be surprised if it has a bug since it's not been
maintained for a while.

Frank

On Mon, Jul 20, 2020 at 3:09 AM Adika [e-mail] wrote:

I'm not an expert in MXNet, but I'm sure someone over there would know.
If you have a working example in C/C++ though, please provide it!

@frankfliu https://github.com/frankfliu Would you know how to do that
with the C API?

Here
https://github.com/solevarr/mxnet-insightface-cpp/blob/master/src/main.cpp
is working example using C++ api. This
https://github.com/solevarr/mxnet-insightface-cpp/blob/8c6a39ace9d5d6726f2cbc121ecac5db60a8701e/src/main.cpp#L91
line extracts the embeddings for the aligned face image. Generally,
everything works as expected in C api, for the each face image the model
outputs unique embedding.
The something weird happens when it comes to JNI. I've also tried to
implement the simple JNI bridge using C++ api like here
https://github.com/apache/incubator-mxnet-test/tree/master/amalgamation/jni
but unfortunately when I call the native functions it always outputs the
same embedding for the completely different images. Im trying to figure out
whats the problem, because everything works fine for the different type of
problems like predicting the class of an object or the face detection (both
C++ and Java).

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[saudet]

the c_predict_api in MXNet has not been maintained, and is removed in 2.0.

Which API do you recommend to use?

[saudet]

@frankfliu According to https://github.com/apache/incubator-mxnet/issues/17676, it doesn't sound like c_predict_api is going away?

[frankfliu]

https://github.com/apache/incubator-mxnet/blob/master/include/mxnet/c_api.h
is official MXNet API, you might need include:
https://github.com/apache/incubator-tvm/blob/9bd2c7b44208ed992061f8c2688e1137357f1db1/nnvm/include/nnvm/c_api.h
for
some functionality.

c_predict_api is using old module API, and module API is removed in 2.0.
You can see c_predict_api.h is removed in master branch.

Frank

On Thu, Jul 23, 2020 at 3:05 AM Samuel Audet [e-mail]
wrote:

@frankfliu https://github.com/frankfliu According to
apache/incubator-mxnet#17676
https://github.com/apache/incubator-mxnet/issues/17676, it doesn't
sound like c_predict_api is going away?

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[saudet]

mxnet/c_api.h was already included, but I've just added nnvm/c_api.h. Thanks for the tip!

[szha]

c predict API was temporarily removed as it relied on the old graph executor instead of the new cached op. in 2.0, we will maintain cached op only. if you need the c predict API soon, we can provide some pointers on how to use the cached op as executor.

cc @eric-haibin-lin

[going-deeper]

Thanks to all. I solved this issue by implementing JNI bridge but instead of using MXNet C api, I've compiled and executed my model using TVM stack.