Coderx7

import cv2
import numpy as np
import matplotlib.pyplot as plt

# create a simple image
image = np.kron([[1, 0] * 4, [0, 1] * 4] * 4, np.ones((50, 50))).astype(np.uint8) * 255
# test opencv
cv2.imshow('checkboard',image)
cv2.waitKey(0)
# test matplotlib

Coderx7

Here's my experience of installing the NVIDIA CUDA kit 8.0 on a fresh install of Ubuntu Desktop 16.04.3 LTS.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install CUDA
• Install cuDNN

Table of contents generated with markdown-toc

1. Install NVIDIA Graphics Driver via apt-get

Coderx7

=> creating model 'simplenetv1_imagenet_3p'
=> Model : simplenetv1_imagenet_3p(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=[3, 3], stride=(2, 2), padding=(1, 1))
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.05, affine=True)
    (2): ReLU(inplace)
    (3): Conv2d(64, 128, kernel_size=[3, 3], stride=(2, 2), padding=(1, 1))
    (4): BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True)
    (5): ReLU(inplace)
    (6): Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))

Coderx7

#!/usr/bin/env python
#ROS Node to convert a GPS waypoint published on the topic "waypoint" into a 2D Navigation Goal in SLAM to achieve autonomous navigation to a GPS Waypoint
#Converts Decimal GPS Coordinates of waypoint to ROS Position Vector relative to the current gps position of the robot
#Accounts for curvature of the earth using haversine formula

#Depends rospy, std_msgs, geographic_msgs, sensor_msgs, numpy
#Written by Alex McClung, 2015, alex.mcclung@hotmail.com, To be Released Open Source under Creative Commons Attribution Share-Alike Licence

import roslib
import rospy

Coderx7

#!/bin/bash

## Bash script for setting up ROS Neotic (with Gazebo 9) development environment for PX4 on Ubuntu LTS (20.04).
## It installs the common dependencies for all targets (including Qt Creator)
##
## Installs:
## - Common dependencies libraries and tools as defined in `ubuntu_sim_common_deps.sh`
## - ROS Melodic (including Gazebo9)
## - MAVROS

Coderx7

#!/bin/bash

## Bash script for setting up a PX4 development environment on Ubuntu LTS (16.04 and above).
## It can be used for installing simulators (only) or for installing the preconditions for Snapdragon Flight or Raspberry Pi.
##
## Installs:
## - Common dependencies and tools for all targets (including: Ninja build system, latest versions of cmake, git, anaconda3, pyulog)
## - jMAVSim simulator dependencies
## - PX4/Firmware source (to ~/src/Firmware/)

Coderx7

#!/usr/bin/python

# Author: SeyyedHossein Hasanpour copyright 2017, license GPLv3.
# Seyyed Hossein Hasan Pour:
# Coderx7@Gmail.com
# Changelog:
# 2015:
# initial code to calculate confusionmatrix by Axel Angel
# 7/3/2016:(adding new features-by-hossein) 
# added mean subtraction so that, the accuracy can be reported accurately just like caffe when doing a mean subtraction

Coderx7

Here is a gif showing how to do this from the beginning to the very end

Coderx7

General guidelines for CPU performance on PyTorch

This file serves a BKM to get better performance on CPU for PyTorch, mostly focusing on inference or deployment. Chinese version available here.

1. Use mkldnn layout

layout refers to how data is organized in a tensor. PyTorch default layout is NCHW, from optimization perspective, MKL-DNN library (renamed as DNNL recently) may choose a different layout, sometimes refered to as internal layout or primitive layout. This is actually a normal technique for acceleration libraries, common knowledge is that NHWC runs faster than NCHW for convolution, changing the default NCHW to NHWC is called a reorder. MKL-DNN may choose different internal layouts based on the input pattern and the algorithm selected, e.g. nChw16c, a.k.a. reorder a 4-dim tensor into 5-dim by chop down dimension C by 16, for vectorization purpose (AVX512 instruction length is 16x32 bit).

By default on CPU, conv2d will ru

Coderx7

package main

import (
	"github.com/kardianos/service"
	"log"
	"flag"
)

type Service struct {}