denguir

How to install CUDA / cuDNN / TensorRT on Ubuntu

Install NVIDIA drivers

Update & upgrade

sudo apt update && sudo apt upgrade

Remove previous NVIDIA installation

docPhil99

The code for this tutorial is here

Opencv provides are useful, but limited, method of building a GUI. A much more complete system could be acheived using pyqt. The question is, how do we display images. There are quite a few possible routes but perhaps the easiest is to use QLabel since it has a setPixmap function. Below is some code that creates two labels. It then creates a grey pixmap and displays it one of the labels. code: staticLabel1.py

from PyQt5.QtWidgets import QWidget, QApplication, QLabel, QVBoxLayout
from PyQt5.QtGui import QPixmap, QColor
import sys

japrescott

sudo apt-get update
sudo apt-get install -y libevent-dev libncurses-dev make
cd /tmp
wget https://github.com/tmux/tmux/releases/download/2.7/tmux-2.7.tar.gz
tar xvzf tmux-2.7.tar.gz
cd tmux-2.7/
./configure && make
sudo make install
cd ..
rm -rf ./tmux-2.7*

matthiassb

#! /bin/bash
### BEGIN INIT INFO
# Provides:          dns-sync
# Required-Start:
# Required-Stop:
# Default-Start:     S
# Default-Stop:
# Short-Description: Synchronizes /etc/resolv.conf in WLS with Windows DNS - Matthias Brooks
### END INIT INFO

hscspring

- a 形容词  
	- ad 副形词  
	- ag 形容词性语素  
	- an 名形词  
- b 区别词  
- c 连词  
- d 副词  
	- df   
	- dg 副语素  
- e 叹词  

nasimrahaman

import torch
import torch.nn as nn


def log_sum_exp(x):
    # See implementation detail in
    # http://timvieira.github.io/blog/post/2014/02/11/exp-normalize-trick/
    # b is a shift factor. see link.
    # x.size() = [N, C]:
    b, _ = torch.max(x, 1)

oshea00

import numpy as np
from math import sqrt

scaling = False

# Implements Kabsch algorithm - best fit.
# Supports scaling (umeyama)
# Compares well to SA results for the same data.
# Input:
#     Nominal  A Nx3 matrix of points

jihunchoi

def _sequence_mask(sequence_length, max_len=None):
    if max_len is None:
        max_len = sequence_length.data.max()
    batch_size = sequence_length.size(0)
    seq_range = torch.range(0, max_len - 1).long()
    seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
    seq_range_expand = Variable(seq_range_expand)
    if sequence_length.is_cuda:
        seq_range_expand = seq_range_expand.cuda()
    seq_length_expand = (sequence_length.unsqueeze(1)

panovr

import argparse
import os
import shutil
import time

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim

farrajota

function mpii_convert_json(  )
% convert mpii annotations .mat file to .json


%% load annotation file
fprintf('Load annotations... ')
data = load('/media/HDD2/Datasets/Human_Pose/mpii/mpii_human_pose_v1_u12_2/mpii_human_pose_v1_u12_1.mat');
fprintf('Done.\n')
%% open file
fprintf('Open file mpii_human_pose_annotations.json\n')