thetonus

import contextlib
from pathlib import PATH

from PIL import Image

def _is_valid_image(file: Path) -> bool:
    """Determine if the image is valid

    References: https://stackoverflow.com/a/53470882

thetonus

""" Create train/test/val subsets for a YOLO labeled dataset for CV projects.

This is inspired by https://github.com/akashAD98/Train_val_Test_split/blob/main/train_val_test.py.
"""
import os
import random 

from pathlib import Path
from tqdm import tqdm

thetonus

func Jsonify(input interface{}) (string, error) {
	var stringifiedJson string
	var prettyJson bytes.Buffer
	body, err := json.Marshal(input)
	if err != nil {
		return stringifiedJson, err
	}
	err = json.Indent(&prettyJson, body, "", "   ")
	if err != nil {
		return stringifiedJson, err

thetonus

"""Background tasks functionality.

Reference: https://eng.lifion.com/navigating-cpu-bound-code-in-an-async-world-a9dcdbfbab5c
"""
import asyncio
from typing import Optional, Union
from functools import wraps, partial
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor

thetonus

import logging
import re
import subprocess
import sys

import ffmpeg

logging.basicConfig(level=logging.INFO, format="%(message)s")
logger = logging.getLogger(__file__)
logger.setLevel(logging.INFO)

thetonus

import asyncio
import os
from typing import List

import aiofiles
import httpx

async def download(client: httpx.AsyncClient, link: str, filename: str) -> None:
    """ Download link """

thetonus

""" Simple timing decorator """
from functools import wraps
from time import time

__all__ = ("timer")

def timer(f):
    @wraps(f)
    def wrap(*args, **kwargs):
        start = time()

thetonus

Keras to Onyx

• https://medium.com/analytics-vidhya/how-to-convert-your-keras-model-to-onnx-8d8b092c4e4f

thetonus

""" Convert Keras .h5 model to .tflite """
from tensorflow.contrib import lite

def main(model_path: str) -> None:
  
  try:
    converter = lite.TFLiteConverter.from_keras_model_file(model_path)
    model = converter.convert()
  except Exception as e:
    print(e)

thetonus

# After install Ubuntu 18.04, run this script by the folks of Lambda Labs. 
# Better to let the pros set up your machine - they handle everything, including CUDA and NVIDIA stuff.
# https://lambdalabs.com/lambda-stack-deep-learning-software

LAMBDA_REPO=$(mktemp) && \
wget -O${LAMBDA_REPO} https://lambdalabs.com/static/misc/lambda-stack-repo.deb && \
sudo dpkg -i ${LAMBDA_REPO} && rm -f ${LAMBDA_REPO} && \
sudo apt-get update && sudo apt-get install -y lambda-stack-cuda