yptheangel

import org.bytedeco.ffmpeg.global.avcodec;
import org.bytedeco.ffmpeg.global.avutil;
import org.bytedeco.javacv.*;
import org.bytedeco.opencv.opencv_core.*;
import static org.bytedeco.opencv.global.opencv_imgproc.*;
import static org.bytedeco.opencv.helper.opencv_core.RGB;
import org.bytedeco.opencv.opencv_videoio.VideoWriter;
import org.datavec.image.loader.NativeImageLoader;
import org.datavec.image.transform.ColorConversionTransform;
import org.deeplearning4j.nn.graph.ComputationGraph;

yptheangel

class myCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs={}):
    if(logs.get('acc')>0.6):
      print("\nReached 60% accuracy so cancelling training!")
      self.model.stop_training = True

yptheangel

#Original source from https://github.com/opencv/opencv/blob/master/samples/python/tutorial_code/ShapeDescriptors/hull/hull_demo.py

from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng

rng.seed(12345)

yptheangel

(base) D:\Efficient-PyTorch\tools>python folder2lmdb.py -f "C:\Users\ChooWilson\.fastai\data\kaggle-cat-dog" -s train
Loading dataset from C:\Users\ChooWilson\.fastai\data\kaggle-cat-dog\train
Generate LMDB to C:\Users\ChooWilson\.fastai\data\kaggle-cat-dog\train.lmdb
2000 2000
Traceback (most recent call last):
  File "folder2lmdb.py", line 181, in <module>
    folder2lmdb(args.folder, num_workers=args.procs, name=args.split)
  File "folder2lmdb.py", line 150, in folder2lmdb
    for idx, data in enumerate(data_loader):
  File "C:\Users\ChooWilson\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 279, in __iter__

yptheangel

// Train your model and save the model if the training loss is better than the previous. Set saveUpdater boolean as "true" if you plan to continue training. Needed for updaters like Adam.
for (int i = 1; i < epochs + 1; i++) {
    tunedModel.fit(trainIterator);
    if (tunedModel.score() < lowest) {
        lowest = tunedModel.score();
        String modelFilename = new File(".").getAbsolutePath() + "/ImageClassifier_loss" + lowest + "_ep" + i + ".zip";
        ModelSerializer.writeModel(tunedModel, modelFilename, true);
    }
    System.out.println(String.format("Completed epoch %d.", i));
}

yptheangel

// Take note this config is just an example
// Take note that my input is a Rank 4 tensor, a DataSetIterator that uses ImageRecordReader 
// Method 1: using .setInputTypes(InputType.convolutional(height,width,channels))
ComputationGraphConfiguration config = new NeuralNetConfiguration.Builder()
        .seed(seed)
        .updater(new RmsProp(1e-3))
        .weightInit(WeightInit.XAVIER)
        .l2(1e-4)
        .graphBuilder()
        .addInputs("input")

yptheangel

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <groupId>org.certifai</groupId>
    <artifactId>MyDL4JProject</artifactId>
    <version>0.1-SNAPSHOT</version>
    <name>Computer Vision</name>

yptheangel

/*
 *
 *  * ******************************************************************************
 *  *  * Copyright (c) 2019 Skymind AI Bhd.
 *  *  * Copyright (c) 2020 CertifAI Sdn. Bhd.
 *  *  *
 *  *  * This program and the accompanying materials are made available under the
 *  *  * terms of the Apache License, Version 2.0 which is available at
 *  *  * https://www.apache.org/licenses/LICENSE-2.0.
 *  *  *

yptheangel

package global.skymind.solution.convolution.objectdetection;

import org.bytedeco.opencv.opencv_core.Mat;
import org.bytedeco.opencv.opencv_core.Point;
import org.bytedeco.opencv.opencv_core.Scalar;
import org.bytedeco.opencv.opencv_core.Size;
import org.datavec.image.loader.NativeImageLoader;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.layers.objdetect.DetectedObject;
import org.deeplearning4j.nn.layers.objdetect.YoloUtils;

yptheangel

import org.datavec.api.io.filters.BalancedPathFilter;
import org.datavec.api.io.labels.ParentPathLabelGenerator;
import org.datavec.api.split.FileSplit;
import org.datavec.api.split.InputSplit;
import org.datavec.image.loader.BaseImageLoader;
import org.datavec.image.recordreader.ImageRecordReader;
import org.deeplearning4j.api.storage.StatsStorage;
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;