roywei

"""
Exports Issues from a specified repository to a CSV file
Uses basic authentication (Github username + password) to retrieve Issues
from a repository that username has access to. Supports Github API v3.
"""
import csv
import requests
import random

GITHUB_USER = ''

roywei

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *

roywei

def runInferenceOnSingleImage(modelPathPrefix: String, inputImagePath: String,
                                context: Array[Context]):
  IndexedSeq[IndexedSeq[(String, Float)]] = {
    val dType = DType.Float32
    val inputShape = Shape(1, 3, 32, 32)

    val inputDescriptor = IndexedSeq(DataDesc("/input_11", inputShape, dType, "NCHW"))

    // Create object of ImageClassifier class
    val imgClassifier: ImageClassifier = new

roywei

"""Gluon Estimator example on MNIST dataset with simple CNN"""

import sys
import os
import time
import mxnet as mx
from mxnet import gluon, nd, autograd
from mxnet import metric
from mxnet.gluon import nn, data
from mxnet.gluon.contrib.estimator import Estimator

roywei

Epoch	1.4.1 Duration(ms)	1.5.0 Duration(ms)
0	230.757866	236.492851
1	222.984238	210.10698
2	201.988141	216.398906
3	180.265748	193.985564
4	186.75808	184.309184
5	175.55141	189.248856
6	176.789087	181.657551
7	175.327701	178.745348
8	170.667204	177.222914

roywei

python build_covid_dataset.py --covid covid-chestxray-dataset --output dataset/covid
python sample_kaggle_dataset.py --kaggle chest_xray --output dataset/normal
python train_covid19.py --dataset dataset

roywei

./gradlew run -Dai.djl.repository.zoo.location=models/saved_model

roywei

[main] INFO com.examples.PneumoniaDetection - Diagnose: [
        class: "Pneumonia", probability: 0.99310
        class: "Normal", probability: 0.00689
]

roywei

repositories {
    jcenter()
}
dependencies {
    implementation "ai.djl:api:0.5.0"
    implementation "ai.djl.tensorflow:tensorflow-api:0.5.0"
    implementation "ai.djl.tensorflow:tensorflow-engine:0.5.0"
    runtimeOnly "ai.djl.tensorflow:tensorflow-native-auto:2.1.0"
    runtimeOnly "org.slf4j:slf4j-simple:1.7.29"
    testImplementation "junit:junit:4.12"

roywei

Criteria<BufferedImage, Classifications> criteria =
                Criteria.builder()
                        .setTypes(BufferedImage.class, Classifications.class)
                        .optTranslator(new MyTranslator())
                        .build();
try (ZooModel<BufferedImage, Classifications> model = ModelZoo.loadModel(criteria)) {
    try (Predictor<BufferedImage, Classifications> predictor = model.newPredictor()) {
        Classifications result = predictor.predict(image);
            logger.info("Diagnose:" + result.toString());
    }