YashasSamaga

import requests
import numpy as np
import matplotlib.pyplot as plt

r = requests.get('https://api.covid19india.org/states_daily.json')

data = r.json()

print(len(data['states_daily']))

YashasSamaga

#ifndef CUDA_COMMON_HPP
#define CUDA_COMMON_HPP

#include <iostream>

#include <cuda_runtime.h>
#include <cublas_v2.h>

#define CHECK_CUDA(cond) check_cuda(cond, __LINE__)

YashasSamaga

// https://github.com/AlexeyAB/darknet/wiki/How-to-evaluate-accuracy-and-speed-of-YOLOv4
// g++ -I/usr/local/include/opencv4/ main.cpp -lopencv_core -lopencv_imgproc -lopencv_dnn -lopencv_imgcodecs -O3 -std=c++17 -lstdc++fs

#include <iostream>
#include <queue>
#include <iterator>
#include <sstream>
#include <fstream>
#include <iomanip>
#include <chrono>

YashasSamaga

#include <cuda_runtime.h>

#include <random>
#include <iostream>

struct relu_grad
{
    __device__ float operator()(float x) { return x > 0; }
};

YashasSamaga

#include <cuda_runtime.h>

#include <iostream>
#include <algorithm>
#include <random>

__global__ void relu(float* output, const float* input, unsigned int* sign32, int n)
{
    int i = blockIdx.x * blockDim.x + threadIdx.x;

YashasSamaga

#include "mish.hpp"

#include <cuda_runtime.h>

#include <random>
#include <iostream>

template <class Activation>
__global__ void activate_vec1(float* __restrict__ output, const float* __restrict__ input, int n)
{

YashasSamaga

g++ -I/usr/local/include/opencv4/ benchmark.cpp -lopencv_core -lopencv_imgproc -lopencv_dnn -lopencv_imgcodecs -O3 -std=c++17

YashasSamaga

#!/bin/bash
# License: MIT. See license file in root directory
# Copyright(c) JetsonHacks (2017-2019)

OPENCV_VERSION=4.1.1
# Jetson Nano
ARCH_BIN=5.3
INSTALL_DIR=/usr/local
# Download the opencv_extras repository
# If you are installing the opencv testdata, ie

YashasSamaga

DISCLAIMER

This gist is unofficial. It was created for personal use but have kept it public in case it would be of use to others. This document is not updated regularly and may not reflect the current status of the CUDA backend.

YashasSamaga

import cv2
import time

CONFIDENCE_THRESHOLD = 0.2
NMS_THRESHOLD = 0.4
COLORS = [(0, 255, 255), (255, 255, 0), (0, 255, 0), (255, 0, 0)]

class_names = []
with open("classes.txt", "r") as f:
    class_names = [cname.strip() for cname in f.readlines()]