johnolafenwa

checkpoint = torch.load("pathtosavemodel")
model = SimpleNet(num_classes=10)


model.load_state_dict(checkpoint)
model.eval()

johnolafenwa

from torchvision.datasets import CIFAR10
from torchvision.transforms import transforms
from torch.utils.data import DataLoader

johnolafenwa

import torch
import torch.nn as nn
from torch.autograd import Variable
import keras.backend as K
from keras.models import *
from keras.layers import *

import torch
from torchvision.models import squeezenet1_1

johnolafenwa

from keras.models import Model
from keras.layers import *
import os
import tensorflow as tf


def keras_to_tensorflow(keras_model, output_dir, model_name,out_prefix="output_", log_tensorboard=True):

    if os.path.exists(output_dir) == False:
        os.mkdir(output_dir)

johnolafenwa

package com.specpal.mobileai;
import android.content.res.AssetManager;
import android.graphics.Bitmap;
import android.graphics.Canvas;
import android.graphics.Matrix;
import android.os.Environment;
import java.io.File;
import java.io.FileOutputStream;
import java.io.InputStream;
import org.json.*;

johnolafenwa

 //Load the tensorflow inference library
    static {
        System.loadLibrary("tensorflow_inference");
    }

    //PATH TO OUR MODEL FILE AND NAMES OF THE INPUT AND OUTPUT NODES
    private String MODEL_PATH = "file:///android_asset/squeezenet.pb";
    private String INPUT_NAME = "input_1";
    private String OUTPUT_NAME = "output_1";
    private TensorFlowInferenceInterface tf;

johnolafenwa

//FUNCTION TO COMPUTE THE MAXIMUM PREDICTION AND ITS CONFIDENCE
    public Object[] argmax(float[] array){


        int best = -1;
        float best_confidence = 0.0f;

        for(int i = 0;i < array.length;i++){

            float value = array[i];

johnolafenwa

public void predict(final Bitmap bitmap){


        //Runs inference in background thread
        new AsyncTask<Integer,Integer,Integer>(){

            @Override

            protected Integer doInBackground(Integer ...params){

johnolafenwa

 @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_main);


        Toolbar toolbar = (Toolbar) findViewById(R.id.toolbar);
        setSupportActionBar(toolbar);

johnolafenwa

from imageai.Detection import ObjectDetection
import os

execution_path = os.getcwd()

detector = ObjectDetection()
detector.setModelTypeAsYOLOv3()
detector.setModelPath( os.path.join(execution_path , "yolo.h5"))
detector.loadModel()
detections = detector.detectObjectsFromImage(input_image=os.path.join(execution_path , "image.jpg"), output_image_path=os.path.join(execution_path , "imagenew.jpg"), minimum_percentage_probability=30)