GuillaumeDua

13 valuable things I learned using CMake

Author : Dua Guillaume
Date : 04-26-2020

Requirement : A first experience with CMake

Intro

As a modern C++ specialist, my job is to focus on software development, from a performance and quality perspective.

raulqf

Install OpenCV 4.10 with CUDA 12.6 and CUDNN 8.9 in Ubuntu 24.04

First of all install update and upgrade your system:

    $ sudo apt update
    $ sudo apt upgrade

Then, install required libraries:

Mahedi-61

#!/bin/bash

### steps ####
# Verify the system has a cuda-capable gpu
# Download and install the nvidia cuda toolkit and cudnn
# Setup environmental variables
# Verify the installation
###

### to verify your gpu is cuda enable check

peteflorence

Here's a simple implementation of bilinear interpolation on tensors using PyTorch.

I wrote this up since I ended up learning a lot about options for interpolation in both the numpy and PyTorch ecosystems. More generally than just interpolation, too, it's also a nice case study in how PyTorch magically can put very numpy-like code on the GPU (and by the way, do autodiff for you too).

For interpolation in PyTorch, this open issue calls for more interpolation features. There is now a nn.functional.grid_sample() feature but at least at first this didn't look like what I needed (but we'll come back to this later).

In particular I wanted to take an image, W x H x C, and sample it many times at different random locations. Note also that this is different than upsampling which exhaustively samples and also doesn't give us fle

mbinna

Effective Modern CMake

Getting Started

For a brief user-level introduction to CMake, watch C++ Weekly, Episode 78, Intro to CMake by Jason Turner. LLVM’s CMake Primer provides a good high-level introduction to the CMake syntax. Go read it now.

After that, watch Mathieu Ropert’s CppCon 2017 talk Using Modern CMake Patterns to Enforce a Good Modular Design (slides). It provides a thorough explanation of what modern CMake is and why it is so much better than “old school” CMake. The modular design ideas in this talk are based on the book [Large-Scale C++ Software Design](https://www.amazon.de/Large-Scale-Soft

bartolsthoorn

import torch
import torch.nn as nn
import numpy as np
import torch.optim as optim
from torch.autograd import Variable

# (1, 0) => target labels 0+2
# (0, 1) => target labels 1
# (1, 1) => target labels 3
train = []

oeway

import numpy as np
import scipy
import scipy.ndimage
from scipy.ndimage.filters import gaussian_filter
from scipy.ndimage.interpolation import map_coordinates
import collections
from PIL import Image
import numbers

__author__ = "Wei OUYANG"

felixgwu

import torch
from torch import nn

__all__ = ['FCDenseNet', 'fcdensenet_tiny', 'fcdensenet56_nodrop',
           'fcdensenet56', 'fcdensenet67', 'fcdensenet103',
           'fcdensenet103_nodrop']


class DenseBlock(nn.Module):

Starl1ght

#include <vector>
#include <sstream>
#include <iostream>
#include <iterator>
#include <tuple>

template <typename T>
std::vector<std::string> Split(T&& input) {
	std::stringstream ss{ std::forward<T>(input) };
	return std::vector<std::string> {std::istream_iterator<std::string>(ss), std::istream_iterator<std::string>{} };

meetps

import numpy as np

def computeIoU(y_pred_batch, y_true_batch):
    return np.mean(np.asarray([pixelAccuracy(y_pred_batch[i], y_true_batch[i]) for i in range(len(y_true_batch))])) 

def pixelAccuracy(y_pred, y_true):
    y_pred = np.argmax(np.reshape(y_pred,[N_CLASSES_PASCAL,img_rows,img_cols]),axis=0)
    y_true = np.argmax(np.reshape(y_true,[N_CLASSES_PASCAL,img_rows,img_cols]),axis=0)
    y_pred = y_pred * (y_true>0)