jkjung-avt

from pathlib import Path
import struct

lines = Path('calib_yolov4-int8-608.bin').read_text().splitlines()
for line in lines:
    pair = line.split(':')
    if len(pair) != 2:
        continue
    assert len(pair[1]) == 9
    bstr = bytes.fromhex(pair[1][1:])    # convert to byte string

jkjung-avt

s#!/bin/bash

set -e

# tensorflow version
version=2.6.0

if [[ ! $(head -1 /etc/nv_tegra_release) =~ R32.*6\.1 ]] ; then
  echo "ERROR: not JetPack-4.6"
  exit 1

jkjung-avt

"""crawl_salaries.py

For crawling average salaries of all public listed companies on Taiwan
Stock Exchange.  Data source: https://mops.twse.com.tw/mops/web/t100sb15

Source code reference:
https://blog.techbridge.cc/2019/07/26/how-to-use-taiwan-salary-data-to-do-python-data-analytics-and-data-visualization/

Usage:
$ python3 crawl_salaries.py

jkjung-avt

# Reference: https://michhar.github.io/how-i-built-pytorch-gpu/
# I have a GTX-1080 and a GTX-2080 Ti, thus CUDA_ARCH "6.1" and "7.5".

# Patch cmake if necessary: https://github.com/arrayfire/arrayfire/issues/2330

git clone https://github.com/pytorch/pytorch.git pytorch-v1.3.1
cd pytorch-v1.3.1
git checkout v1.3.1
git submodule update --init --recursive 

jkjung-avt

"""disjoint_set.py
"""


import random


class DisjointSet(object):
    """DisjointSet

jkjung-avt

"""caffemodel_nv_to_bvlc.py

1. compile the nv-caffe's caffe.proto to caffe_pb2.py
2. use caffemodel_nv_to_bvlc.py to convert the nv-caffe caffemodel file
   into bvlc-caffe compatible format.

Example usage:
$ cd ${HOME}/project/nv-caffe
$ protoc src/caffe/proto/caffe.proto --python_out=.
$ python3 ./caffemodel_nv_to_bvlc.py <in.caffemodel> <out.caffemodel>

jkjung-avt

"""dcgan_mnist.py

This script was orginally written by Rowel Atienza (see below), and
was modified by JK Jung <jkjung13@gmail.com>.

------

DCGAN on MNIST using Keras
Author: Rowel Atienza
Project: https://github.com/roatienza/Deep-Learning-Experiments

jkjung-avt

# export YOUR_GCS_BUCKET=oxford-iiit-pets-dataset

gcloud ml-engine jobs submit training `whoami`_object_detection_pets_`date +%m_%d_%Y_%H_%M_%S` \
    --runtime-version 1.9 \
    --job-dir=gs://${YOUR_GCS_BUCKET}/model_dir \
    --packages /home/jkjung/src/tensorflow/models/research/dist/object_detection-0.1.tar.gz,/home/jkjung/src/tensorflow/models/research/slim/dist/slim-0.1.tar.gz,/tmp/pycocotools/pycocotools-2.0.tar.gz \
    --module-name object_detection.model_main \
    --region asia-east1 \
    --config cloud-ssd_mobilenet_v1_pets.yml \
    -- \

jkjung-avt

import numpy as np
import keras

class DataGenerator(keras.utils.Sequence):
    'Generates data for Keras'
    def __init__(self, list_IDs, labels, batch_size=32, dim=(32,32,32), n_channels=1,
                 n_classes=10, shuffle=True):
        'Initialization'
        self.dim = dim
        self.batch_size = batch_size

jkjung-avt

# --------------------------------------------------------
# Camera Single-Shot Multibox Detector (SSD) sample code
# for Tegra X2/X1
#
# This program captures and displays video from IP CAM,
# USB webcam, or the Tegra onboard camera, and do real-time
# object detection with Single-Shot Multibox Detector (SSD)
# in Caffe. Refer to the following blog post for how to set
# up and run the code:
#