{% extends "nominations_portal.html" %} {% load bootstrap3 crispy_forms_tags django_bootstrap_breadcrumbs %} {% crispy form form.helper %} {% block page_title %}Our Revolution - Application Status{% endblock %} {% block breadcrumbs %} {{ block.super }} {% breadcrumb "Application Status" request.get_full_path %} {% endblock %} {% block content %} {{ block.super }}
To submit your group’s candidate for nomination, complete the following steps. Don’t worry if you can’t do it all in one sitting - we’ll save your progress after each step.
You've completed your nomination application. Thank you for being a part of the political revolution. It is because of dedicated individuals like you and those in your group that our movement continues to grow and our shared mission is succeeding.
Your endorsement nomination request will be processed. The staff at Our Revolution looks forward to being in touch with you soon.
{% endif %}


What's in a Story by Dan North
edited to use Behat style
A tool to help devs, QA, and stakeholders communicate better
using simple sentences to describe requirements
in a structured way,
which can then be automatically executed as tests.
What if we were on the Wikipedia team, introducing multilingual support for the first time.
Given I am on "http://es.wikipedia.org/"
When I type "United States" in the search box
And I press enter
Then I should see the heading "Estados Unidos"
And I should see the text "(Redirigido desde «United States»)"
Feature: Account Holder withdraws cash
I want to withdraw cash from an ATM
As an Account Holder
So that I can get money when the bank is closed
Scenario: Account has sufficient funds
Given the account balance is $100
And the card is valid
And the machine contains enough money
When the Account Holder requests $20
Then the ATM should dispense $20
And the account balance should be $80
And the card should be returned
Scenario: Account has insufficient funds
...
Feature: Account Holder withdraws cash
describes an action taken by a user
I want to withdraw cash from an ATM
As an Account Holder
So that I can get money when the bank is closed
Scenario: Account has sufficient funds
...
Scenario: Account has insufficient funds
...
indicate what's different between the scenarios
Given the account balance is $100
And the card is valid
And the machine contains enough money
When the Account Holder requests $20
Then the ATM should dispense $20
And the account balance should be $80
And the card should be returned
(aka Givens, Whens, and Thens)
Given the account balance is $100
And the card is valid
And the machine contains enough money
should define all of, and no more than, the required context
When the Account Holder requests $20
simple, one line if possible
Then the ATM should dispense $20
And the account balance should be $80
And the card should be returned


| 041:005 Khan | : |
And they say: "Our hearts are under coverings (screened) from that to which you invite us, and in our ears is deafness, and between us and you is a screen, so work you (on your way); verily, we are working (on our way)." |
| 041:005 Maulana | : |
And they say: Our hearts are under coverings from that to which thou callest us, and there is a deafness in our ears, and there is a veil between us and thee, so act, we too are acting. |
| 041:005 Pickthal | : |
And they say: Our hearts are protected from that unto which thou (O Muhammad) callest us, and in our ears there is a deafness, and between us and thee there is a veil. Act, then. Lo! we also shall be acting. |
| 041:005 Rashad | : |
They said, "Our minds are made up, our ears are deaf to your message, and a barrier separates us from you. Do what you want, and so will we." |
| 041:005 Sarwar | : |
They say, "Our hearts are covered against and our ears are deaf to whatever you (Muhammad) invite us to. There is a barrier between us and you. So act as you please and we shall act as we please". |
| 041:005 Shakir | : |
And they say: Our hearts are under coverings from that to which you call us, and there is a heaviness in our ears, and a veil hangs between us and you, so work, we too are working. |
| 041:005 Sherali | : |
And they say, 'Our hearts are secure under coverings against that which thou callest us, and in our ears there is a heaviness, and between us and thee is a veil. So carry on thy work; we, too, are working.' |
| 041:005 Yusufali | : |
They say: "Our hearts are under veils, (concealed) from that to which thou dost invite us, and in our ears in a deafness, and between us and thee is a screen: so do thou (what thou wilt); for us, we shall do (what we will!)" |
Concepts/Themes Covered: أركان الإسلام > التوحيد > الشرك والمشركون > عبادة غير الله تعالى Pillars of Islam> Islamic> Polytheism and the Polytheists> Worshiping Otherwise His Glory أركان الإسلام > التوحيد > الكافرون > إعراضهم عن آيات الله Pillars of Islam> Islamic> The Disbelievers> Their Turning away from Allah's Signs أركان الإسلام > (محمد (صلى الله عليه وسلم > أقوال الكافرين فيه Pillars of Islam> The Blessed Muhammad(PBUH)> The Disbelievers' Tales About Him(PBUH) القرآن الكريم > حقيقته وتصديقه للكتب الأوائل The Holy Quran> The Quran's Reality and its confirmation of the Previous Books
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


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Image Stitching In Computer Vision
Ever wondered how image stitching is done for panorama, here's an algorithm using RANSAC.
Table of Contents
1 Pairwise stitching
In this part of the assignment, the task is to stitch together two images. The general setup is described as the following:
- Preprocessing images
- The two input images were converted to double and turned to gray scale
- Detecting interesting spots
- Harris corner detector was used and applied to both images
- Extracting features from detected blobs
- SIFT feature was extracted from each detected blob
- Putative matching
- A similarity matrix between 2 sets of features was build using pairwise squared Euclidean distance
- Nearest Neighbor algorithm was applied on the similarity matrix to match features in one image to those of the other
- Model selection
- RANSAC was implemented
- s = minimum points was chosen to be 4
- t = inlier threshold was chosen to be 30
- N = number of iteration was chosen to be 8000
- d = consensus size was chosen to be 0.1 of number of features (I expect the inlier ratio to be 0.1)
- 4 pairs of feature matching was selected in each iteration of the algorithm
- A homogeneous least square equation was solved using SVD
- using the found transformation, the sum of square error was calculated
- inliers were selected by accepting all features with SSE < t
- if the number of inliers is larger than d, the homogeneous equation for homography is recalculated by fitting all points, and the number of inliers is updated
- Thae final output is the homography transformation with the largest number of inliers
- RANSAC was implemented
- Image stitching
- images are stitched together by applying the best transformation on the first image and superimpose the transformed image on the second image (the overlapping region used the second image for color output, and took average for the grayscale output)
Outputs:
Number of homography inliers is 371
Average residual is<PHONE_NUMBER>30
2 Multiple image stitching
The second part of the assignment is to stitch multiple images together. My solution is outlined as the following:
- Image preprocessing
- images were read in and stored in a data structure called imCell
- Image stitching
- while imCell has more than 1 element
for each element in imCell except the first one
compute the number of inliers for the stitch between this element and imCell{1} using code from part1 (the order of stitching is important because I used nearest neighbor to match the features from the first image to the second image which means the transformation is unlikely to be one to one. I used the current element as the first argument of stitch because it doesn’t have overlapping features so that most of its distinctive points are preserved. I cannot use imCell{1} because it contains too much irrelevant details after several iteration inside the while loop)
check that the max number of inliers is greater than 50 (arbitrarily chosen so as to assure that the found transformation is reliable) and proceed to the next step. Otherwise, go back to the previous for loop if less than 10 times going back tried. If more than 10 times tried, restart from step 2 by replacing imCell with the original imCell
update imCell{1} with the color image output from the stitch(imCell{chosen}, imCell{1}) where ‘chosen’ is the id of the element that gives the most inliers
delete the imCell(chosen)
Output:
The order of stitching for Pier is:
1.JPG stitched with 2.JPG
the combined JPG was stitched with 3.JPG
Graph 2.1 Pier stitched image
The order of stitching for Hill is: 1.JPG stitched with 2.JPG the combined JPG was stitched with 3.JPG
Graph 2.2 Hill stitched image
The order of stitching for Ledge is: 1.JPG stitched with 2.JPG the combined JPG was stitched with 3.JPG
Graph 2.3 Ledge stitched image
Discussion: There are some artifact in stitching multiple images.
- Stitched image has inconsistent colors
Since images comes from different sources, when stitching them together, the overall color is inconsistent as well. This could be addressed by using one image as a baseline and reconstruct the other image using its gradient. - Stitched image has obvious separating lines between images
In performing transformation, some of the pixels on the boundary get assigned no colors. It could be an artifact of matlab. - On rare occasions the algorithm didn’t succeed
There are several reasons that could induce this problem- no iteration reached the desired inlier ratio (addressed by lowering inlier ratio and increasing number of iterations)
- too little inliers (addressed in my implementation by either reentering the for loop or restarting the algorithm)
- matlab artifact in applying transformation (in rare cases, matlab imtransform function rescales the transformed image, can be addressed by setting ‘XYScale’ to 1)


{% extends 'home/base.html' %} {% load static %} {% block title %}Home{% endblock %} {% block topblock %}
Profile
We are here to help organize your kitchen and make grocery shopping for people with a shared kitchen more convenient!


-
JavaScript tells the browser how to change the web page in response to events that happen, HTML marks the content up into different structural types, like paragraphs, blocks, lists, images etc. CSS tells the browser how each type of element should be displayed depending on the media type being used.
Control flow is the order (sequencing) of things to occur to get the desired behaviour, whereas loops perform the same task over and over again until a set of criteria are met. Taking the 'brushing' your teeth analogy at a high level - you normally make your way to the bathroon, then open open the vanity removing the dental floss, after flossing you then take the toothbrush and toothpaste out, apply the toothpaste to the brush, then brush. Looping would be repeated brushing until the teeth are clean.
Object literal values are accessed using dot notation (console.log(person.location);), Array values are accesssed using numerical index (console.log(array[2]);).
A function is a block of code designed to perform a particular task; very useful when you need to use the same code many times, you only need to define it once.


You see the on-screen-console at to bottom of the page.
Click any button to see a random output.
We also have a demo object here that you can inspect. It's called "myObj". Just type myObj
in the console input box .
Note: When console loses focus (e.g. when you click outside it), it minimizes.


This component consists of a spherical joint 1 at frame_a, a revolute joint at frame_b and a spherical joint 2 which is connected via rod 1 to the spherical joint 1 and via rod 2 to the revolute joint, see the default animation in the following figure (the axes vectors are not part of the default animation):
Besides an optional point mass in the middle of rod 1, this joint aggregation has no mass and no inertia, and introduces neither constraints nor potential state variables. It should be used in kinematic loops whenever possible since the non-linear system of equations introduced by this joint aggregation is solved analytically (i.e., a solution is always computed, if a unique solution exists).
An additional frame_ib is present. It is fixed in rod 2 connecting the revolute and the spherical joint at the side of the revolute joint that is connected to this rod (= rod2.frame_a = revolute.frame_a).
An additional frame_im is present. It is fixed in rod 2 connecting the revolute and the spherical joint at the side of spherical joint 2 that is connected to this rod (= rod2.frame_b). It is always parallel to frame_ib.
The easiest way to define the parameters of this joint is by moving the MultiBody system in a reference configuration where all frames of all components are parallel to each other (alternatively, at least frame_b and frame_ib of the JointSSR joint should be parallel to each other when defining an instance of this component).


Jul 27, 2021
4 mins read
Imagine millenia ago there was a particular set of humans who maintain a connection to something higher than this place.
They came from a plane of being higher than the one they were currently living on.
This often manifested in spiritual beings. They would experience things such as vision from worlds far away in time and destination. They would hear about prophecy and secret knowledge that was *kept from the minds of rational men.
*Knowledge that when someone was conscious of, would slip from their grasp.
With this secret knowledge and spiritual connection they were capable of traveling into themselves and perhaps even across, through each other.
They would contain countries, continents and rarely entire planets inside themselves.
Some deeper levels of contained alternate realities such as different histories, futures at different times and different potentials.
This opened passages across space and time for them to transit with ease. They could access and experience in their single life-time than others could optain in generations.
These being with bodies that contained realities for them to traverse would share knowledge through spirituality while also creating points of cultural and societal import - acting as milestones for them to use in their travels.
Could it be that 5 millenia ago confuscious brought back knowledge to a country he held dearly? Did Buddha go to his home 2.5 millenia ago to share his noble truths with his kingdom? Did a group of knowledgable Jewish men - perhaps preparing for an event - have a more radical friend who fell back into his body to be born imacualately an outsider, then experience a more loving and personal God than he studied? Perhaps Zoroastra dove through a body to experience a similar connection around the same time.
What if that Jewish man who fell into his body to experience a more loving God had never made it back out? He had spent his time traveling, giving out too much of himself, healing, always teaching and giving. At the end he was not bright enough to find himself and raise himself up at the appropriate time. An end he forsaw, as most who fall into their bodies do, but one he may have expected to end differently.
Next to his wrapped body was a shoe, wresting atop his breast. Aside him a wine glass, partially filled with the ones who build this all.
1000 years later he awakens again to be questioned by a sourcerer who happened to have learned from and used power from his next carnation which happened to be “the most powerful sourcerer in this lake” and maybe the most powerful “All-Time” ..
Using the next carnates future offsprings soul he had some upperhand maybe trust maybe son to kill the father, maybe speed, in any case the sourcerer decided to do his friend and completely unaware collegue a solid and vote for his kind leaving the seat.
How did his body get so large as to possibly be the first and only to contain an entire planet? He already gave up a ticket to his eternal spot among his ancient ancestors that came from someplace much older than this planet. He had chosen to protect the planet instead. To stay and hold it together for all those he loved and loved him. At the end of his long tenue protecting the planet, something happened.
Perhaps he was awarded time, perhaps he switched cubes, or perhaps he could not accept the terminal condition of his land..
Maybe it perfectly met the 850 years lived by the oldest man in the book and stopped when the patent office nearly closed in the 1850’s. A 1903 solar storm may have cleaned up his footprints.
*To Check Out 1904 Haunting T-Shirt or Tattoo
Now, in present day, one has the position to live his bright life - A Victory Lap - A life that could add something beautiful to those who spiritually walked before him. To understand and treasure the spiritual gift and this connection to all those in his body, and to also hold close to his heart romance, love and commitment to his brothers and sisters.
Sharing is caring!


STORAGR covers some of the biggest Bulgarian cities, giving our users fast and quality delivery.
Our storages:


| Creation time | {{row.creationTime | date:'short'}} | Phase | {{row.phase | enumeration}} | ID | {{row.id}} | Name | {{row.name}} | Author | {{row.author.firstName}} {{row.author.lastName}} |
|---|
| Creation time | {{row.creationTime | date:'short'}} | Phase | {{row.phase | enumeration}} | ID | {{row.id}} | Name | {{row.name}} | Author | {{row.author.firstName}} {{row.author.lastName}} |
|---|


Filters
Information
Data obtained from AEC.
We're now loading the dataset. This is quite large, so it may take a little while depending on your connection.
Using this website is relatively straight forward. The main display shows a force directed graph of political parties and the information they have reported to the AEC declaring the receipts that they have received. Political parties are required by Australian law to provide this information on a yearly basis for all amounts greater than $12,400 (current for the 2013-2014 year). Donors to political parties in Australia must also declare their donations.
This information is required to be provided to the AEC by 20 October each year and is subsequently made public.
All periodic disclosures published by the AEC are available for analysis on this site. Filters can help you extract meaningful information. The filters can be accessed by clicking on the icon on the upper left of the chart. This will open the filter controls, allowing you to search for a party or donor, select the year, parties or receipt types that interest you and also change the type of the view to a number of different options.
It is possible that by selecting certain combinations of filters that the amount of data displayed could result in slow performance. If this happens you can limit your filters to return a more restrictive set of data.
It is also possible to zoom in and out of the chart by using your mouse scroll wheel, track pad or the slider control under the filter controls toggle button.
Additional information regarding any party or entity on the main chart is displayed if you click on a node. This will open the information panel on the right of the main display. This panel can be toggled by clicking on the button, located in the upper right of the main display. You can also click on an entry in the displayed table to select another political party or payer.
We love receiving feedback, and you can reach us at<EMAIL_ADDRESS>We do our best to read and reply to all messages.
The Political Donations visualisation website takes publically available data from the AEC and presents it in a graphical view to make visual analysis of the donations to all political parties in Australia easier and more intuitive.
The data is presented as a force directed layout showing both links and magnitudes of donations to all major parties. The data can be filtered and detailed information is available by clicking on any node in the layout.
For questions or to provide feedback, please send email to<EMAIL_ADDRESS>We love to hear about users who are extracting useful information or educating themselves about the level of donations that political parties in Australia receive.
With politicaldonations.info it’s easy to get all sorts of interesting and useful information regarding donations to political parites. To get you started here are some examples of ways you can use the site to answer simple questions.
How much did the Coalition receive in donations each year between 1998-2015?
Who donated to both ALP and Coalition in 2013-2014?
Who gave donations over a $1 million in 2014-2015?
Searching for a specific donor to see how much they gave ín a specific year?


|
Connected Home over IP
672cf7684
|
#include <system/SystemConfig.h>
#include <stddef.h>
#include <stdint.h>
#include <unistd.h>
#include <support/DLLUtil.h>
#include <system/SystemError.h>
#include <system/SystemStats.h>
Go to the source code of this file.
Classes | |
| class | chip::System::ObjectPool< T, N > |
| A class template used for allocating Object subclass objects from an ObjectArena<> template union. More... | |
| class | chip::System::Object |
| This represents a reference-counted object allocated from space contained in an ObjectPool<T, N> object. More... | |
| union | chip::System::ObjectArena< ALIGN, SIZE > |
| A union template used for representing a well-aligned block of memory. More... | |
| class | chip::System::ObjectPool< T, N > |
| A class template used for allocating Object subclass objects from an ObjectArena<> template union. More... | |
Namespaces | |
| chip | |
| Adaptation for LwIP ip4_addr_t type. | |
| chip::System | |
Macros | |
| #define | SYSTEM_OBJECT_HWM_TEST_HOOK() |
This file contains declarations of the following classes and templates:
| #define SYSTEM_OBJECT_HWM_TEST_HOOK | ( | ) |


Practiced creating rows and collumns using Bootstrap.
Utilized a standalone css stylesheet to make style adjustments to classes.
Used basic style formatting in order to show emphasize pricing, etc.
Languages Utilized: HTML, CSS, Bootstrap and Git
Co-Authors
Chelsea Becker
Eric Endsley
Background Information
About Me
Born and raised in Arizona I spent a lot of my time enjoying the outdoors. Moved to Oregon in 2019 to pursue work with ODFW and decided to stay in the PNW after the work concluded. Currently awaiting arrival of my first child!


Google News for Android and iOS now supports bilingual feeds
by Abner LiIn recent years, Google has strived to make its products work better internationally. Google News for Android and iOS now supports bilingual feeds to show stories for up to two “languages & regions of interest.”
Today, more than 60% of people around the world speak and consume news across two or more languages. Finding articles in these languages can be challenging, since it requires you to search for topics across various apps and websites.
This new capability allows your News feed to be populated with articles from an additional language, as well as what the app calls “region of interest.” This is touted as allowing you to “learn more about a culture or to catch up on what’s going on elsewhere.”
For example, this means you can stay up to date on the news in English and Hindi at the same time, and enjoy articles from local news outlets in both languages alongside one another. You’ll be able to connect with any of your favorite publishers and topics from anywhere around the world.
The new language will benefit from your “previous personalization preferences,” so all content surfaced should be relevant for you.
Bilingual feed support is already rolled out to Google News for Android and iOS in 141 countries and 41 language options. Start by tapping your profile image in the upper-right corner > Settings > Languages & regions of interest > Add a language and region. One pairing can be set as the “primary” in order to “see more from here.”
More about Google News:
- Google News now has in-depth UEFA Champions League highlights, data, and more
- Google News 5.10 hints at in-app crosswords, more sports coverage [APK Insight]
- Google News Initiative releases real-time Analytics dashboard for newsrooms
FTC: We use income earning auto affiliate links. More.


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Tests for, and describe differences in event count profiles in groups of reconstructed cophylogenies
| Version: | 0.1 |
| Depends: | R (≥ 3.0), dirmult |
| Published: | 2015-01-12 |
| Author: | Mark T Merilo |
| Maintainer: | Mark T Merilo <mmerilo at uoguelph.ca> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | DEEPR results |
| Reference manual: | DEEPR.pdf |
| Package source: | DEEPR_0.1.tar.gz |
| Windows binaries: | r-devel: DEEPR_0.1.zip, r-release: DEEPR_0.1.zip, r-oldrel: DEEPR_0.1.zip |
| OS X binaries: | r-release: DEEPR_0.1.tgz, r-oldrel: DEEPR_0.1.tgz |
Please use the canonical form https://CRAN.R-project.org/package=DEEPR to link to this page.


Welcome!
I hope that after going through these tutorials you will feel comfortable drawing from your imagination
without the need for references. I know the frustration of looking at professional art and wondering how the artists managed to pull
complete pictures out of their heads. After many years of struggling, I have come to a place where I feel comfortable drawing realistic figures
and landscapes without reference, and I believe anyone can use the same formulas that I do to construct imagery with good results.
The site is currently a work in progress, so if you follow me on my
facebook page, you can keep track of when I upload
new material. Good luck, and be patient with yourself!
Drawing people:
Head: Front
Head: Profile


Anshuman Srivastava
Also known as
Ravenclaw
CSE Undergrad at
GLA University Mathura
My Hobbies
Coding
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WITNESS LIST Finance Committee February 8, 2005 -12:00P SB 1 Article III - School for the Blind and Visually Impaired FOR: Bethea, Deanna Homemaker (School for the Blind and Visually Impaired), Dickinson, TX Sewell, Denise mother of deaf and blind child (School for the Blind and Visually Impaired), Tyler, TX SB 1 Article III - School for the Deaf FOR: Craig, Joel Self Employer (School for the Deaf), Austin, TX Sefak - Kennedy, Connie (Parent and President of TSD - PTA), Austin, TX SB 1 Article III - State Board for Educator Certification FOR: Smith, Herman Executive Director (State Board for Educator Certification), Bryan, TX SB 1 Article III ON: McGeady, John LBB Staff (LBB), Austin, TX Newton, John LBB Staff (LBB), Austin, TX Parks, Ursula LBB Staff (LBB), Austin, TX Sherman, Susan LBB Staff (LBB), Austin, TX Weilmaker, John LBB Staff (LBB), Austin, TX SB 1 Article III - Community Colleges ON: Daniel, David President of Midland College and President of TACC (Community Colleges), San Antonio, TX Garcia, Rey Executive Director, TACC (Community Colleges), San Antonio, TX Pickelman, John Chancellor North Harris Montgomery Community College Dist (Community Colleges), Houston, TX Walker, Gloria Vice Chancellor, Finance and Administration (Community Colleges - Special Item Funding Request), Missouri City, TX SB 1 Article III - School for the Blind and Visually Impaired ON: Brooks, Gene Board Member - TSBVI (School for the Blind and Visually Impaired), Austin, TX Hatlen, Phil Superintendent (School for the Blind and Visually Impaired), Austin, TX Schulz, Barney Business Administrator (School for the Blind and Visually Impaired), Round Rock, TX SB 1 Article III - School for the Deaf ON: Bego, Gary Director of Business and Operations (School for the Deaf), Austin, TX Bugen, Claire Superintendent (School for the Deaf), Austin, TX Fernandez, Rogelo Consumer Relations Manager (School for the Deaf), Austin, TX Henry, Karen Parent (School for the Deaf), Austin, TX SB 1 Article III - State Board for Educator Certification ON: Schiess, Jennifer LBB Staff (LBB), Austin, TX SB 1 Article III - Texas Education Agency ON: Barnes, Susan Association Commissioner Standards and Programs (Texas Education Agency), Austin, TX Bradford, Ronald Superintendent (Windham School District), Austin, TX Henandez, Maria Legislative Budget Board/Analyst (Texas Education Agency), Austin, TX Hollinsworth, Lonnie Attorney (Texas Classroom Teachers Association), Austin, TX Jones, Adam Associate Commissioner (Texas Education Agency), Austin, TX Kelly, Jack Legislative Analyst (Texas State Teachers Association), Dripping Springs, TX Lora, Rene Legislative Liason (Texas Federation of Teachers), Austin, TX Martin, Christi Policy Advisor (Texas Education Agency), Round Rock, TX Neeley, Shirley Commissioner of Education (Texas Education Agency), Austin, TX Noland, Christa Develeopment Director (Texas Unit of Recording for the Blind and Dyslexis), Austin, TX Norris, Robert Senior Analyst (Legislative Budget Board), Austin, TX SB 1 Article III - Texas State Technical College System ON: Leal, J. Gilbert President (Texas State Technical College System), Austin, TX Reeser, Michael President (Texas State Technical College System), Austin, TX Segura, Bill Chancellor (Texas State Technical College System), McGregor, TX Travis, Rick LBB Manager (LBB), Austin, TX Vonder Hoya, Susan C.F.U. (Texas State Technical College System, Waco), Waco, TX SB 1 Article III - School for the Deaf Registering, but not testifying: on: Estes, Charles President of TSD Governing Board (School for the Deaf), Denton, TX SB 1 Article III - Texas State Technical College System on: Hendricks, J. Gary President (Texas State Technical College System), Marshall, TX Stuckly, Elton President (Texas State Technical College System), Waco, TX


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Go to the source code of this file.
◆ DESCRIPTORS
2 segments descriptors for kernel mode; 2 segments descriptors for user mode.
◆ __attribute__()
The size is the size of the table subtracted by 1. The offset is the linear address of the table itself.
◆ gdt_load()
| void gdt_load |
( |
uint32_t |
gdt_ptr | ) |
|
◆ gdt_setEntry()
| void gdt_setEntry |
( |
int |
index, |
|
|
uint32_t |
base, |
|
|
uint64_t |
limit, |
|
|
uint8_t |
access, |
|
|
uint8_t |
flags |
|
) |
| |
This function sets the bits of a gdt entry.
◆ init_gdt()
Kernel CS (from 0 to 4GB). Access = 0x9A:
- 1 present
- 00 ring 0
- 1 always 1
- 1 code segment
- 0 can be executed by ring lower or equal to DPL,
- 1 code segment is readable
- 0 access bit, always 0, cpu set this to 1 when accessing this sector Flags 0xCF:
- 1 If 0 the limit is in 1 B blocks (byte granularity), if 1 the limit is in 4 KiB blocks (page granularity).
- 1 If 0 the selector defines 16 bit protected mode. If 1 it defines 32 bit protected mode. You can have both 16 bit and 32 bit selectors at once.
- 0 0
- 0 0
- 1 1 1 1 – ignored
Kernel DS (from 0 to 4GB). Access = 0x92:
- Only differ at the fifth bit(counting from least insignificant bit), 0 means it's a data segment.
User CS and DS. Only differ in ring number (ring 3)
◆ access
◆ base_high
◆ base_low
◆ base_middle
◆ granularity
◆ limit_low
◆ offset
◆ size


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#include <stdio.h>
#include <stdlib.h>
#include <time.h>
|
|
#define | HUMAN 0 |
| |
|
#define | COMPUTER 1 |
| |
|
#define | GIVEUP -1 |
| |
|
| int | x (int n, int p) |
| |
| int | win (int n, int p) |
| | return the result of the game More...
|
| |
| int | best_choice (int n, int p) |
| | the function carry out the best way to win if there are no best way return 0 More...
|
| |
| int | random_choice (int n, int x) |
| | select random number for restrict the number of apple at first turn More...
|
| |
| int | computer_choice (int n, int p) |
| | the function make computer can carry out a way to win More...
|
| |
| int | human_choice (int n, int p) |
| | the function that user take the apple More...
|
| |
| int | get_num (void) |
| | input the number of the apples before starting a game. if the number is not between 10 to 50 then try again More...
|
| |
| int | get_turn (void) |
| | if the value of turn is 0 then user start first. if turn is 1, computer will start More...
|
| |
| int | is_over (int n, int p) |
| | the function control the number of apples can be taken More...
|
| |
|
int | main (void) |
| | control the whole game
|
| |
| int best_choice |
( |
int |
n, |
|
|
int |
p |
|
) |
| |
the function carry out the best way to win if there are no best way return 0
- Parameters
-
- Returns
- int
| int computer_choice |
( |
int |
n, |
|
|
int |
p |
|
) |
| |
the function make computer can carry out a way to win
- Parameters
-
- Returns
- int
input the number of the apples before starting a game. if the number is not between 10 to 50 then try again
- Returns
- int
if the value of turn is 0 then user start first. if turn is 1, computer will start
- Returns
- int
| int human_choice |
( |
int |
n, |
|
|
int |
p |
|
) |
| |
the function that user take the apple
- Parameters
-
- Returns
- int
| int is_over |
( |
int |
n, |
|
|
int |
p |
|
) |
| |
the function control the number of apples can be taken
- Parameters
-
- Returns
- int
| int random_choice |
( |
int |
n, |
|
|
int |
x |
|
) |
| |
select random number for restrict the number of apple at first turn
- Parameters
-
- Returns
- int
return the result of the game
- Parameters
-
- Returns
- int
return the number of apple which are not chosen now
- Parameters
-
- Returns
- int


Deploying Quantization Aware Trained models in INT8 using TRTorch ¶
Overview ¶
Quantization Aware training (QAT) simulates quantization during training by quantizing weights and activation layers. This will help to reduce the loss in accuracy when we convert the network trained in FP32 to INT8 for faster inference. QAT introduces additional nodes in the graph which will be used to learn the dynamic ranges of weights and activation layers. In this notebook, we illustrate the following steps from training to inference of a QAT model in TRTorch.
## 1. Requirements Please install the required dependencies and import these libraries accordingly
[2]:
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import trtorch
from torch.utils.tensorboard import SummaryWriter
from pytorch_quantization import nn as quant_nn
from pytorch_quantization import quant_modules
from pytorch_quantization.tensor_quant import QuantDescriptor
from pytorch_quantization import calib
from tqdm import tqdm
import os
import sys
sys.path.insert(0, "../examples/int8/training/vgg16")
from vgg16 import vgg16
WARNING: Logging before flag parsing goes to stderr.
E0831 15:09:13.151450<PHONE_NUMBER>76192 amp_wrapper.py:31] AMP is not avaialble.
## 2. VGG16 Overview ### Very Deep Convolutional Networks for Large-Scale Image Recognition VGG is one of the earliest family of image classification networks that first used small (3x3) convolution filters and achieved significant improvements on ImageNet recognition challenge. The network architecture looks as follows
## 3. Training a baseline VGG16 model We train VGG16 on CIFAR10 dataset. Define training and testing datasets and dataloaders. This will download the CIFAR 10 data in your
data
directory. Data preprocessing is performed using
torchvision
transforms.
[3]:
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
# ========== Define Training dataset and dataloaders =============#
training_dataset = datasets.CIFAR10(root='./data',
train=True,
download=True,
transform=transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
]))
training_dataloader = torch.utils.data.DataLoader(training_dataset,
batch_size=32,
shuffle=True,
num_workers=2)
# ========== Define Testing dataset and dataloaders =============#
testing_dataset = datasets.CIFAR10(root='./data',
train=False,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
]))
testing_dataloader = torch.utils.data.DataLoader(testing_dataset,
batch_size=16,
shuffle=False,
num_workers=2)
Files already downloaded and verified
Files already downloaded and verified
[4]:
def train(model, dataloader, crit, opt, epoch):
# global writer
model.train()
running_loss = 0.0
for batch, (data, labels) in enumerate(dataloader):
data, labels = data.cuda(), labels.cuda(non_blocking=True)
opt.zero_grad()
out = model(data)
loss = crit(out, labels)
loss.backward()
opt.step()
running_loss += loss.item()
if batch % 500 == 499:
print("Batch: [%5d | %5d] loss: %.3f" % (batch + 1, len(dataloader), running_loss / 100))
running_loss = 0.0
def test(model, dataloader, crit, epoch):
global writer
global classes
total = 0
correct = 0
loss = 0.0
class_probs = []
class_preds = []
model.eval()
with torch.no_grad():
for data, labels in dataloader:
data, labels = data.cuda(), labels.cuda(non_blocking=True)
out = model(data)
loss += crit(out, labels)
preds = torch.max(out, 1)[1]
class_probs.append([F.softmax(i, dim=0) for i in out])
class_preds.append(preds)
total += labels.size(0)
correct += (preds == labels).sum().item()
test_probs = torch.cat([torch.stack(batch) for batch in class_probs])
test_preds = torch.cat(class_preds)
return loss / total, correct / total
def save_checkpoint(state, ckpt_path="checkpoint.pth"):
torch.save(state, ckpt_path)
print("Checkpoint saved")
Define the VGG model that we are going to perfom QAT on.
[5]:
# CIFAR 10 has 10 classes
model = vgg16(num_classes=len(classes), init_weights=False)
model = model.cuda()
[6]:
# Declare Learning rate
lr = 0.1
state = {}
state["lr"] = lr
# Use cross entropy loss for classification and SGD optimizer
crit = nn.CrossEntropyLoss()
opt = optim.SGD(model.parameters(), lr=state["lr"], momentum=0.9, weight_decay=1e-4)
# Adjust learning rate based on epoch number
def adjust_lr(optimizer, epoch):
global state
new_lr = lr * (0.5**(epoch // 12)) if state["lr"] > 1e-7 else state["lr"]
if new_lr != state["lr"]:
state["lr"] = new_lr
print("Updating learning rate: {}".format(state["lr"]))
for param_group in optimizer.param_groups:
param_group["lr"] = state["lr"]
[7]:
# Train the model for 25 epochs to get ~80% accuracy.
num_epochs=25
for epoch in range(num_epochs):
adjust_lr(opt, epoch)
print('Epoch: [%5d / %5d] LR: %f' % (epoch + 1, num_epochs, state["lr"]))
train(model, training_dataloader, crit, opt, epoch)
test_loss, test_acc = test(model, testing_dataloader, crit, epoch)
print("Test Loss: {:.5f} Test Acc: {:.2f}%".format(test_loss, 100 * test_acc))
save_checkpoint({'epoch': epoch + 1,
'model_state_dict': model.state_dict(),
'acc': test_acc,
'opt_state_dict': opt.state_dict(),
'state': state},
ckpt_path="vgg16_base_ckpt")
Epoch: [ 1 / 25] LR: 0.100000
/home/dperi/Downloads/py3/lib/python3.6/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)
return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
Batch: [ 500 | 1563] loss: 12.466
Batch: [ 1000 | 1563] loss: 10.726
Batch: [ 1500 | 1563] loss: 10.289
Test Loss: 0.12190 Test Acc: 19.86%
Epoch: [ 2 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 10.107
Batch: [ 1000 | 1563] loss: 9.986
Batch: [ 1500 | 1563] loss: 9.994
Test Loss: 0.12230 Test Acc: 21.54%
Epoch: [ 3 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 9.826
Batch: [ 1000 | 1563] loss: 9.904
Batch: [ 1500 | 1563] loss: 9.771
Test Loss: 0.11709 Test Acc: 22.71%
Epoch: [ 4 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 9.760
Batch: [ 1000 | 1563] loss: 9.629
Batch: [ 1500 | 1563] loss: 9.642
Test Loss: 0.11945 Test Acc: 23.89%
Epoch: [ 5 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 9.590
Batch: [ 1000 | 1563] loss: 9.489
Batch: [ 1500 | 1563] loss: 9.468
Test Loss: 0.11180 Test Acc: 30.01%
Epoch: [ 6 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 9.281
Batch: [ 1000 | 1563] loss: 9.057
Batch: [ 1500 | 1563] loss: 8.957
Test Loss: 0.11106 Test Acc: 28.03%
Epoch: [ 7 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 8.799
Batch: [ 1000 | 1563] loss: 8.808
Batch: [ 1500 | 1563] loss: 8.647
Test Loss: 0.10456 Test Acc: 32.25%
Epoch: [ 8 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 8.672
Batch: [ 1000 | 1563] loss: 8.478
Batch: [ 1500 | 1563] loss: 8.522
Test Loss: 0.10404 Test Acc: 32.40%
Epoch: [ 9 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 8.422
Batch: [ 1000 | 1563] loss: 8.290
Batch: [ 1500 | 1563] loss: 8.474
Test Loss: 0.10282 Test Acc: 41.11%
Epoch: [ 10 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 8.131
Batch: [ 1000 | 1563] loss: 8.005
Batch: [ 1500 | 1563] loss: 8.074
Test Loss: 0.09473 Test Acc: 38.91%
Epoch: [ 11 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 8.132
Batch: [ 1000 | 1563] loss: 8.047
Batch: [ 1500 | 1563] loss: 7.941
Test Loss: 0.09928 Test Acc: 41.69%
Epoch: [ 12 / 25] LR: 0.100000
Batch: [ 500 | 1563] loss: 7.911
Batch: [ 1000 | 1563] loss: 7.974
Batch: [ 1500 | 1563] loss: 7.871
Test Loss: 0.10598 Test Acc: 38.90%
Updating learning rate: 0.05
Epoch: [ 13 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 6.981
Batch: [ 1000 | 1563] loss: 6.543
Batch: [ 1500 | 1563] loss: 6.377
Test Loss: 0.07362 Test Acc: 53.72%
Epoch: [ 14 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 6.208
Batch: [ 1000 | 1563] loss: 6.113
Batch: [ 1500 | 1563] loss: 6.016
Test Loss: 0.07922 Test Acc: 55.78%
Epoch: [ 15 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 5.945
Batch: [ 1000 | 1563] loss: 5.726
Batch: [ 1500 | 1563] loss: 5.568
Test Loss: 0.05914 Test Acc: 65.33%
Epoch: [ 16 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 5.412
Batch: [ 1000 | 1563] loss: 5.356
Batch: [ 1500 | 1563] loss: 5.143
Test Loss: 0.05833 Test Acc: 68.91%
Epoch: [ 17 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 5.096
Batch: [ 1000 | 1563] loss: 5.064
Batch: [ 1500 | 1563] loss: 4.962
Test Loss: 0.05291 Test Acc: 71.72%
Epoch: [ 18 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 4.958
Batch: [ 1000 | 1563] loss: 4.887
Batch: [ 1500 | 1563] loss: 4.711
Test Loss: 0.05003 Test Acc: 73.61%
Epoch: [ 19 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 4.651
Batch: [ 1000 | 1563] loss: 4.567
Batch: [ 1500 | 1563] loss: 4.603
Test Loss: 0.05046 Test Acc: 73.80%
Epoch: [ 20 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 4.467
Batch: [ 1000 | 1563] loss: 4.399
Batch: [ 1500 | 1563] loss: 4.310
Test Loss: 0.05038 Test Acc: 74.45%
Epoch: [ 21 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 4.226
Batch: [ 1000 | 1563] loss: 4.196
Batch: [ 1500 | 1563] loss: 4.169
Test Loss: 0.05287 Test Acc: 71.18%
Epoch: [ 22 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 4.120
Batch: [ 1000 | 1563] loss: 4.035
Batch: [ 1500 | 1563] loss: 4.018
Test Loss: 0.06157 Test Acc: 70.29%
Epoch: [ 23 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 3.915
Batch: [ 1000 | 1563] loss: 3.968
Batch: [ 1500 | 1563] loss: 3.989
Test Loss: 0.04128 Test Acc: 79.01%
Epoch: [ 24 / 25] LR: 0.050000
Batch: [ 500 | 1563] loss: 3.871
Batch: [ 1000 | 1563] loss: 3.800
Batch: [ 1500 | 1563] loss: 3.871
Test Loss: 0.04785 Test Acc: 75.77%
Updating learning rate: 0.025
Epoch: [ 25 / 25] LR: 0.025000
Batch: [ 500 | 1563] loss: 3.141
Batch: [ 1000 | 1563] loss: 2.979
Batch: [ 1500 | 1563] loss: 2.874
Test Loss: 0.03345 Test Acc: 83.15%
Checkpoint saved
## 4. Apply Quantization
quant_modules.initialize()
will ensure quantized version of modules will be called instead of original modules. For example, when you define a model with convolution, linear, pooling layers,
QuantConv2d
,
QuantLinear
and
QuantPooling
will be called.
QuantConv2d
basically wraps quantizer nodes around inputs and weights of regular
Conv2d
. Please refer to all the quantized modules in pytorch-quantization toolkit for more information. A
QuantConv2d
is represented in
pytorch-quantization
toolkit as follows.
def forward(self, input):
# the actual quantization happens in the next level of the class hierarchy
quant_input, quant_weight = self._quant(input)
if self.padding_mode == 'circular':
expanded_padding = ((self.padding[1] + 1) // 2, self.padding[1] // 2,
(self.padding[0] + 1) // 2, self.padding[0] // 2)
output = F.conv2d(F.pad(quant_input, expanded_padding, mode='circular'),
quant_weight, self.bias, self.stride,
_pair(0), self.dilation, self.groups)
else:
output = F.conv2d(quant_input, quant_weight, self.bias, self.stride, self.padding, self.dilation,
self.groups)
return output
[8]:
quant_modules.initialize()
[9]:
# All the regular conv, FC layers will be converted to their quantozed counterparts due to quant_modules.initialize()
qat_model = vgg16(num_classes=len(classes), init_weights=False)
qat_model = qat_model.cuda()
[10]:
# vgg16_base_ckpt is the checkpoint generated from Step 3 : Training a baseline VGG16 model.
ckpt = torch.load("./vgg16_base_ckpt")
modified_state_dict={}
for key, val in ckpt["model_state_dict"].items():
# Remove 'module.' from the key names
if key.startswith('module'):
modified_state_dict[key[7:]] = val
else:
modified_state_dict[key] = val
# Load the pre-trained checkpoint
qat_model.load_state_dict(modified_state_dict)
opt.load_state_dict(ckpt["opt_state_dict"])
## 5. Model Calibration
The quantizer nodes introduced in the model around desired layers capture the dynamic range (min_value, max_value) that is observed by the layer. Calibration is the process of computing the dynamic range of these layers by passing calibration data, which is usually a subset of training or validation data. There are different ways of calibration:
max
,
histogram
and
entropy
. We use
max
calibration technique as it is simple and effective.
[11]:
def compute_amax(model, **kwargs):
# Load calib result
for name, module in model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer):
if module._calibrator is not None:
if isinstance(module._calibrator, calib.MaxCalibrator):
module.load_calib_amax()
else:
module.load_calib_amax(**kwargs)
print(F"{name:40}: {module}")
model.cuda()
def collect_stats(model, data_loader, num_batches):
"""Feed data to the network and collect statistics"""
# Enable calibrators
for name, module in model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
# Feed data to the network for collecting stats
for i, (image, _) in tqdm(enumerate(data_loader), total=num_batches):
model(image.cuda())
if i >= num_batches:
break
# Disable calibrators
for name, module in model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer):
if module._calibrator is not None:
module.enable_quant()
module.disable_calib()
else:
module.enable()
def calibrate_model(model, model_name, data_loader, num_calib_batch, calibrator, hist_percentile, out_dir):
"""
Feed data to the network and calibrate.
Arguments:
model: classification model
model_name: name to use when creating state files
data_loader: calibration data set
num_calib_batch: amount of calibration passes to perform
calibrator: type of calibration to use (max/histogram)
hist_percentile: percentiles to be used for historgram calibration
out_dir: dir to save state files in
"""
if num_calib_batch > 0:
print("Calibrating model")
with torch.no_grad():
collect_stats(model, data_loader, num_calib_batch)
if not calibrator == "histogram":
compute_amax(model, method="max")
calib_output = os.path.join(
out_dir,
F"{model_name}-max-{num_calib_batch*data_loader.batch_size}.pth")
torch.save(model.state_dict(), calib_output)
else:
for percentile in hist_percentile:
print(F"{percentile} percentile calibration")
compute_amax(model, method="percentile")
calib_output = os.path.join(
out_dir,
F"{model_name}-percentile-{percentile}-{num_calib_batch*data_loader.batch_size}.pth")
torch.save(model.state_dict(), calib_output)
for method in ["mse", "entropy"]:
print(F"{method} calibration")
compute_amax(model, method=method)
calib_output = os.path.join(
out_dir,
F"{model_name}-{method}-{num_calib_batch*data_loader.batch_size}.pth")
torch.save(model.state_dict(), calib_output)
[12]:
#Calibrate the model using max calibration technique.
with torch.no_grad():
calibrate_model(
model=qat_model,
model_name="vgg16",
data_loader=training_dataloader,
num_calib_batch=32,
calibrator="max",
hist_percentile=[99.9, 99.99, 99.999, 99.9999],
out_dir="./")

W0831 15:32:46.979588<PHONE_NUMBER>76192 tensor_quantizer.py:239] Call .cuda() if running on GPU after loading calibrated amax.
W0831 15:32:46.980288<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([64, 1, 1, 1]).
W0831 15:32:46.987690<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
W0831 15:32:57.002152<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([64, 1, 1, 1]).
features.0._input_quantizer : TensorQuantizer(8bit narrow fake per-tensor amax=2.7537 calibrator=MaxCalibrator scale=1.0 quant)
features.0._weight_quantizer : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0287, 4.4272](64) calibrator=MaxCalibrator scale=1.0 quant)
features.3._input_quantizer : TensorQuantizer(8bit narrow fake per-tensor amax=30.1997 calibrator=MaxCalibrator scale=1.0 quant)
W0831 15:32:57.006651<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
W0831 15:32:57.009306<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([128, 1, 1, 1]).
W0831 15:32:57.011739<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
W0831 15:32:57.014180<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([128, 1, 1, 1]).
W0831 15:32:57.016433<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
W0831 15:32:57.018157<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([256, 1, 1, 1]).
W0831 15:32:57.019830<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
W0831 15:32:57.021619<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([256, 1, 1, 1]).
W0831 15:32:57.023381<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
W0831 15:32:57.024606<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([256, 1, 1, 1]).
W0831 15:32:57.026464<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
W0831 15:32:57.027716<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).
W0831 15:32:57.029010<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
W0831 15:32:57.030247<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).
W0831 15:32:57.031455<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
W0831 15:32:57.032716<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).
W0831 15:32:57.034027<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
W0831 15:32:57.035287<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).
W0831 15:32:57.036572<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
W0831 15:32:57.037535<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).
W0831 15:32:57.038545<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
W0831 15:32:57.039479<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).
W0831 15:32:57.040493<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
W0831 15:32:57.041564<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([4096, 1]).
W0831 15:32:57.042597<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
W0831 15:32:57.043280<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([4096, 1]).
W0831 15:32:57.044521<PHONE_NUMBER>76192 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
 Quantization Aware Training
In this phase, we finetune the model weights and leave the quantizer node values frozen. The dynamic ranges for each layer obtained from the calibration are kept constant while the weights of the model are finetuned to be close to the accuracy of original FP32 model (model without quantizer nodes) is preserved. Usually the finetuning of QAT model should be quick compared to the full training of the original model. Use QAT to fine-tune for around 10% of the original training schedule with an
annealing learning-rate. Please refer to Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT for detailed recommendations. For this VGG model, it is enough to finetune for 1 epoch to get acceptable accuracy. During finetuning with QAT, the quantization is applied as a composition of
max
,
clamp
,
round
and
mul
ops.
# amax is absolute maximum value for an input
# The upper bound for integer quantization (127 for int8)
max_bound = torch.tensor((2.0**(num_bits - 1 + int(unsigned))) - 1.0, device=amax.device)
scale = max_bound / amax
outputs = torch.clamp((inputs * scale).round_(), min_bound, max_bound)
tensor_quant function in
pytorch_quantization
toolkit is responsible for the above tensor quantization. Usually, per channel quantization is recommended for weights, while per tensor quantization is recommended for activations in a network. During inference, we use
torch.fake_quantize_per_tensor_affine
and
torch.fake_quantize_per_channel_affine
to perform quantization as this is easier to convert into corresponding TensorRT operators. Please refer to next sections for more details on
how these operators are exported in torchscript and converted in TRTorch.
[13]:
# Finetune the QAT model for 1 epoch
num_epochs=1
for epoch in range(num_epochs):
adjust_lr(opt, epoch)
print('Epoch: [%5d / %5d] LR: %f' % (epoch + 1, num_epochs, state["lr"]))
train(qat_model, training_dataloader, crit, opt, epoch)
test_loss, test_acc = test(qat_model, testing_dataloader, crit, epoch)
print("Test Loss: {:.5f} Test Acc: {:.2f}%".format(test_loss, 100 * test_acc))
save_checkpoint({'epoch': epoch + 1,
'model_state_dict': qat_model.state_dict(),
'acc': test_acc,
'opt_state_dict': opt.state_dict(),
'state': state},
ckpt_path="vgg16_qat_ckpt")
Updating learning rate: 0.1
Epoch: [ 1 / 1] LR: 0.100000
Batch: [ 500 | 1563] loss: 2.694
Batch: [ 1000 | 1563] loss: 2.682
Batch: [ 1500 | 1563] loss: 2.624
Test Loss: 0.03277 Test Acc: 83.58%
Checkpoint saved
## 7. Export to Torchscript Export the model to Torch script. Trace the model and convert it into torchscript for deployment. To learn more about Torchscript, please refer to
https://pytorch.org/docs/stable/jit.html
. Setting
quant_nn.TensorQuantizer.use_fb_fake_quant
=
True
enables the QAT model to use
torch.fake_quantize_per_tensor_affine
and
torch.fake_quantize_per_channel_affine
operators instead of
tensor_quant
function to export quantization operators. In torchscript, they
are represented as
aten::fake_quantize_per_tensor_affine
and
aten::fake_quantize_per_channel_affine
.
[16]:
quant_nn.TensorQuantizer.use_fb_fake_quant = True
with torch.no_grad():
data = iter(testing_dataloader)
images, _ = data.next()
jit_model = torch.jit.trace(qat_model, images.to("cuda"))
torch.jit.save(jit_model, "trained_vgg16_qat.jit.pt")
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E0831 15:41:35.086258<PHONE_NUMBER>76192 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!
E0831 15:41:35.089163<PHONE_NUMBER>76192 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!
E0831 15:41:35.089860<PHONE_NUMBER>76192 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!
E0831 15:41:35.103728<PHONE_NUMBER>76192 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!
E0831 15:41:35.104618<PHONE_NUMBER>76192 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!
E0831 15:41:35.107046<PHONE_NUMBER>76192 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!
E0831 15:41:35.107893<PHONE_NUMBER>76192 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!
E0831 15:41:35.116841<PHONE_NUMBER>76192 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!
E0831 15:41:35.117565<PHONE_NUMBER>76192 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!
E0831 15:41:35.120490<PHONE_NUMBER>76192 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!
E0831 15:41:35.121185<PHONE_NUMBER>76192 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!
E0831 15:41:35.128972<PHONE_NUMBER>76192 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!
E0831 15:41:35.129700<PHONE_NUMBER>76192 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!
E0831 15:41:35.132617<PHONE_NUMBER>76192 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!
E0831 15:41:35.133241<PHONE_NUMBER>76192 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!
## 8. Inference using TRTorch In this phase, we run the exported torchscript graph of VGG QAT using TRTorch. TRTorch is a Pytorch-TensorRT compiler which converts Torchscript graphs into TensorRT. TensorRT 8.0 supports inference of quantization aware trained models and introduces new APIs;
QuantizeLayer
and
DequantizeLayer
. We can observe the entire VGG QAT graph quantization nodes from the debug log of TRTorch. To enable debug logging, you can set
trtorch.logging.set_reportable_log_level(trtorch.logging.Level.Debug)
. For example,
QuantConv2d
layer from
pytorch_quantization
toolkit is represented as follows in Torchscript
%quant_input : Tensor = aten::fake_quantize_per_tensor_affine(%x, %636, %637, %638, %639)
%quant_weight : Tensor = aten::fake_quantize_per_channel_affine(%394, %640, %641, %637, %638, %639)
%input.2 : Tensor = aten::_convolution(%quant_input, %quant_weight, %395, %687, %688, %689, %643, %690, %642, %643, %643, %644, %644)
aten::fake_quantize_per_*_affine
is converted into
QuantizeLayer
+
DequantizeLayer
in TRTorch internally. Please refer to quantization op converters in TRTorch.
[17]:
qat_model = torch.jit.load("trained_vgg16_qat.jit.pt").eval()
compile_spec = {"inputs": [trtorch.Input([16, 3, 32, 32])],
"op_precision": torch.int8,
}
trt_mod = trtorch.compile(qat_model, compile_spec)
test_loss, test_acc = test(trt_mod, testing_dataloader, crit, 0)
print("VGG QAT accuracy using TensorRT: {:.2f}%".format(100 * test_acc))
VGG QAT accuracy using TensorRT: 83.59%
## 9. References * Very Deep Convolution Networks for large scale Image Recognition * Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT * QAT workflow for VGG16 * Deploying VGG QAT model in C++ using TRTorch * Pytorch-quantization toolkit from NVIDIA * Pytorch quantization toolkit userguide * Quantization basics


Spark is a really cool dual-themed static blog site generator. It's still being developed, but it's a fully functional system! I use it to power my site, thinkingincode.ninja.
Check out the project at its GitHub repository for more details.


#include "cudpp.h"
#include "cudpp_util.h"
#include "cudpp_plan.h"
#include <cutil.h>
#include <cstdlib>
#include <cstdio>
#include <assert.h>
#include "cta/rand_cta.cu"
#include "kernel/rand_kernel.cu"
Functions | |
Rand Functions | |
| void | launchRandMD5Kernel (unsigned int *d_out, unsigned int seed, size_t numElements) |
| Launches the MD5 Random number generator kernel. | |
| void | cudppRandDispatch (void *d_out, size_t numElements, const CUDPPRandPlan *plan) |
| Dispatches the rand function based on the plan. |
rand_md5_app.cu
| void launchRandMD5Kernel | ( | unsigned int * | d_out, | |
| unsigned int | seed, | |||
| size_t | numElements | |||
| ) |
Launches the MD5 Random number generator kernel.
The MD5 Random number generator works by generating 128 bit digests which are then broken down into 32 bit chunks and stored inside d_out. d_out is expected to be of type unsigned int and can hold numElements elements.
An analysis of the stastical distribution of the MD5 random number generator can be found in the original paper Parallel white noise generation on a GPU via cryptographic hash. The optimizations mentioned in the paper are also present in the CUDPP version of the MD5 Random number generator.
It is also worth pointing out that the GPU version will not generate the same output * as the CPU version. This is due to the difference in the floating point accuracy and several optimizations that have been used (i.e. calculating sin using device hardware rather than storing it in an array that the original implementation does). However, the distribution of the numbers is well suited for random number generation, even without the CPU-GPU invariance.
| [out] | d_out | the array of unsigned integers allocated on device memory |
| [in] | seed | the random seed used to vary the output |
| [in] | numElements | the number of elements in d_out |
| void cudppRandDispatch | ( | void * | d_out, | |
| size_t | numElements, | |||
| const CUDPPRandPlan * | plan | |||
| ) |
Dispatches the rand function based on the plan.
This is the dispatch call which looks at the algorithm specified in plan and calls the appropriate random number generation algorithm.
| [out] | d_out | the array allocated on device memory where the random numbers will be stored must be of type unsigned int |
| [in] | numElements | the number of elements in the array d_out |
| [in] | plan | pointer to CUDPPRandPlan which contains the algorithm to run |


REGRESSIONS IN THE NEWS MEDIA
10.12.22
MIT: REGRESSIONS
by Ankita Devasia @ MIT Admissions
10.12.22
‘MIT: REGRESSIONS’ sparks debate over the Institute’s past and future
by Cindy Xie @ The Tech
07.24.22
MIT: Progressions & Regressions
by Jessica Dai @ Reboot
04.28.22
Behind the scenes of ‘MIT: Regressions (a history of the Institute)'
by Srinidhi Narayanan @ The Tech
home


{{< layout}} {{$propositionHeader}}{{/propositionHeader}} {{$pageTitle}}Information about urgent passport{{/pageTitle}} {{$header}}
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With each version of iTunes, Apple tries to prevent people reverse-engineering it, in order to protect some sensitive stuff, involving DRMs, device management, etc.
Different levels of protection are used in the iTunes binary, as well as in some private frameworks.
Machine code for sensitive procedures is heavily obfuscated, a few tricks are used to fool disassembly softwares and debuggers, and usually, iTunes doesn’t run out of the box in a debugger like LLDB or GDB.
Previous versions of iTunes used a very simple protection mechanism in order to prevent it running in a debugger.
At startup, iTunes called the ptrace
function, with the PT_DENY_ATTACH
argument.
So it would quit almost immediately, when run inside a debugger.
The workaround was also very simple: a breakpoint on ptrace
followed by an immediate return when the breakpoint is hit.
On LLDB, that means:
lldb /Applications/iTunes.app/Contents/MacOS/iTunes Current executable set to '/Applications/iTunes.app/Contents/MacOS/iTunes' (x86_64). (lldb) b ptrace Breakpoint 1: where = libsystem_kernel.dylib`__ptrace, address = 0x00000000000163b8 (lldb) br command add 1 Enter your debugger command(s). Type 'DONE' to end. > thread return > c > DONE
That’s it… iTunes now runs fine in LLDB.
With OS X 10.10 Yosemite, a few things have changed…
iTunes no longer calls ptrace
at startup, but still quits when running inside LLDB:
lldb /Applications/iTunes.app/Contents/MacOS/iTunes Current executable set to '/Applications/iTunes.app/Contents/MacOS/iTunes' (x86_64). (lldb) r Process 48470 launched: '/Applications/iTunes.app/Contents/MacOS/iTunes' (x86_64) Process 48470 exited with status = 1 (0x00000001) (lldb)
As we can see, the process exits almost immediately with status code 1.
Let’s see if we can find a workaround.
As the application exits cleanly, we’ll simply break on the system exit
function, in order to see where exactly the application exits:
(lldb) b _Exit Breakpoint 1: where = libsystem_c.dylib`_Exit, address = 0x00007fff8b16b042
When we run iTunes again, it eventually hit the breakpoint, so we can ask for a backtrace:
* thread #1: tid = 0x8a841, 0x00007fff8b16b042 libsystem_c.dylib`_Exit, name = 'iTunes main', queue = 'com.apple.main-thread', stop reason = breakpoint 1.1 frame #0: 0x00007fff8b16b042 libsystem_c.dylib`_Exit libsystem_c.dylib`_Exit: -> 0x7fff8b16b042: pushq %rbp 0x7fff8b16b043: movq %rsp, %rbp 0x7fff8b16b046: callq 0x7fff8b1914c0 ; symbol stub for: _exit (lldb) bt * thread #1: tid = 0x8a841, 0x00007fff8b16b042 libsystem_c.dylib`_Exit, name = 'iTunes main', queue = 'com.apple.main-thread', stop reason = breakpoint 1.1 * frame #0: 0x00007fff8b16b042 libsystem_c.dylib`_Exit frame #1: 0x00000001007762b8 iTunes`___lldb_unnamed_function35905$$iTunes + 230 frame #2: 0x0000000100006262 iTunes`___lldb_unnamed_function115$$iTunes + 196 frame #3: 0x000000010000616a iTunes`___lldb_unnamed_function114$$iTunes + 42 frame #4: 0x0000000100005454 iTunes`___lldb_unnamed_function83$$iTunes + 50 frame #5: 0x0000000100005320 iTunes`___lldb_unnamed_function79$$iTunes + 56 frame #6: 0x0000000100002ac6 iTunes`___lldb_unnamed_function11$$iTunes + 231 frame #7: 0x00007fff8c0247f4 CoreFoundation`__CFRUNLOOP_IS_CALLING_OUT_TO_A_TIMER_CALLBACK_FUNCTION__ + 20 frame #8: 0x00007fff8c024483 CoreFoundation`__CFRunLoopDoTimer + 1059 frame #9: 0x00007fff8c09915d CoreFoundation`__CFRunLoopDoTimers + 301 frame #10: 0x00007fff8bfe05c2 CoreFoundation`__CFRunLoopRun + 2018 frame #11: 0x00007fff8bfdfb98 CoreFoundation`CFRunLoopRunSpecific + 296 frame #12: 0x00007fff8fb868ff HIToolbox`RunCurrentEventLoopInMode + 235 frame #13: 0x00007fff8fb86672 HIToolbox`ReceiveNextEventCommon + 431 frame #14: 0x00007fff8fb864b3 HIToolbox`_BlockUntilNextEventMatchingListInModeWithFilter + 71 frame #15: 0x00007fff852495a5 AppKit`_DPSNextEvent + 1000 frame #16: 0x00007fff85248d79 AppKit`-[NSApplication nextEventMatchingMask:untilDate:inMode:dequeue:] + 139 frame #17: 0x00007fff8523cd53 AppKit`-[NSApplication run] + 594 frame #18: 0x000000010093cad0 iTunes`___lldb_unnamed_function48166$$iTunes + 360 frame #19: 0x000000010037c7e0 iTunes`___lldb_unnamed_function14308$$iTunes + 52
Now we know that the function iTunes`___lldb_unnamed_function35905$$iTunes
is the one that calls exit
.
So we’ll just prevent this, by adding another breakpoint:
(lldb) b iTunes`___lldb_unnamed_function35905$$iTunes Breakpoint 2: where = iTunes`___lldb_unnamed_function35905$$iTunes, address = 0x00000001007761d2
The breakpoint is hit when iTunes is run again, and we'll simply return immediately, without executing the code that leads to the exit call:
(lldb) thread return (lldb) c
Unfortunately, the breakpoint is hit again and again.
Looking closer at the backtrace, we can see:
__CFRunLoopDoTimer
__CFRUNLOOP_IS_CALLING_OUT_TO_A_TIMER_CALLBACK_FUNCTION__
It means that the function that checks if iTunes is running inside a debugger is repeatedly called at a specific time interval, using a CoreFoundation timer.
Fortunately, we can add a command to our breakpoint, so it will automatically returns when the breakpoint is hit:
(lldb) br command add 2 Enter your debugger command(s). Type 'DONE' to end. > thread return > c > DONE
Now we can run iTunes again.
The breakpoint will be hit quite a few times, but our command will just return and continues the program’s execution normally.
But we’re not done yet…
At some point, iTunes will crash:
Process 49840 stopped * thread #1: tid = 0x8b7a4, 0x00007fff8c024483 CoreFoundation`__CFRunLoopDoTimer + 1059, name = 'iTunes main', queue = 'com.apple.main-thread', stop reason = EXC_BAD_ACCESS (code=1, address=0x0) frame #0: 0x00007fff8c024483 CoreFoundation`__CFRunLoopDoTimer + 1059 CoreFoundation`__CFRunLoopDoTimer + 1059: -> 0x7fff8c024483: leaq -0x163c94f7(%rip), %rax ; __CF120290 0x7fff8c02448a: movb $0x1, (%rax) 0x7fff8c02448d: leaq -0x163c94ff(%rip), %rax ; __CF120293 0x7fff8c024494: cmpb $0x0, (%rax)
EXC_BAD_ACCESS
, meaning we’ve got a segmentation fault somewhere...
So what can we do now? Looks like there’s now way to have iTunes running from here…
Well, actually there is.
A segmentation fault means that the software is trying to access a portion of memory it doesn’t own.
Usually, this results in a crash, but it doesn’t mean the software and the system can’t sometimes recover from it.
So let’s try to ignore the segmentation fault, by returning from where we are and continuing the program’s execution as if nothing happened:
(lldb) thread return (lldb) c Process 49840 resuming
That’s it…
iTunes resumes its execution normally, and never calls again the function that prevented it to run in LLDB.
You are now free to work with iTunes in LLDB as usual... : )


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Use F11 for full screen.
| PROPELLER BOARD | Heading: | LEFT MOTOR -Relay: | N/A | -Power: | N/A | |||||
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On VAX/VMS and ALPHA/VMS systems the make_gfloat.com command file may be executed to build the cfitsio.olb object library using the default G-floating point option for double variables. The make_dfloat.com and make_ieee.com files may be used instead to build the library with the other floating point options. Note that the getcwd function that is used in the group.c module may require that programs using CFITSIO be linked with the ALPHA$LIBRARY:VAXCRTL.OLB library. See the example link line in the next section of this document.


--- layout: paper title: Publications --- Designing an IDE for Probabilistic Programming: Challenges and a Prototype, , NIPS Workshop on Probabilistic Programming 2014
@inproceedings{singh2014designing, title={Designing an IDE for Probabilistic Programming: Challenges and a Prototype}, booktitle={NIPS Workshop on Probabilistic Programming}, year={2014}, author={Singh, Sameer and Riedel, Sebastian and Hewitt, Luke and Rocktaschel, Tim}, }


--- layout: splash-no-container author_profile: false header: overlay_image: assets/images/home-hero-big.jpg overlay_filter: 0.3 caption: "Photo credit: [Sean O'Mahoney](http://seanomahoney.com/)" cta_label: " I need help " cta_url: "https://streetsupport.net/find-help/" cta_id: "need-help" cta_label_2: "I want to help" cta_url_2: "/get-involved" cta_id_2: "get-involved" feature_row: - image_path: /assets/images/food-donation-mark.jpg alt: "Donate food" title: "Donate food" excerpt: "We collect food that has been donated by various supermarkets, restaurants and other establishments" url: "https://www.paypal.me/spinmcr/" btn_class: "btn--primary" btn_label: "Donate" - image_path: /assets/images/food-the-well.jpg alt: "Hot meals" title: "Hot meals" excerpt: "With the help of our volunteers, we provide hot meals and tasty treats to our homeless friends" url: "/get-involved" btn_class: "btn--primary" btn_label: "Volunteer" - image_path: /assets/images/milan-at-work.jpg alt: "Providing shelter" title: "Provide shelter" excerpt: "We provide shelter for rough-sleepers and support them on their journey back into society" url: "https://www.paypal.me/spinmcr/" btn_class: "btn--primary" btn_label: "Donate" ---
We are a charitable organisation based in Manchester, tackling the ongoing issue of homelessness and poverty. With us you can find support if you're experiencing homelessness or poverty, or you can volunteer and support people in need.
Our mission is to help and support vulnerable and socially-excluded people living in poverty and rough-sleeping in the streets of Manchester
SPIN keeps growing, but so does the number of people that seek our help and support. We rely entirely on donations. With our entire managing team working for free and all of the money donated going into refurbishing our center and operating around Manchester, we put every penny into good use.
Follow us on Facebook, we post regularly and we keep you up to date with our operations.
Whether you are an academic who is skilled at writing grant applications, an excellent cook, or someone with a drive to help the less fortunate, we could really use your help in order to achieve our goals. Get involved as a volunteer, find out more about us or donate to our cause.


This is some text and this is a help tip. Here is another one . Here is one with a link inside it.
select sucks
View source


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| Packages that use IoEventType | |
|---|---|
| org.apache.mina.core.filterchain | |
| org.apache.mina.core.session | |
| org.apache.mina.filter.executor | IoFilters that provide flexible thread model and event queue monitoring interface. |
| org.apache.mina.filter.statistic | Classes that implement IoFilter and provide the ability for filters to be timed on their performance. |
| Uses of IoEventType in org.apache.mina.core.filterchain |
|---|
| Constructors in org.apache.mina.core.filterchain with parameters of type IoEventType | |
|---|---|
IoFilterEvent(IoFilter.NextFilter nextFilter,
IoEventType type,
IoSession session,
Object parameter)
|
| Uses of IoEventType in org.apache.mina.core.session |
|---|
| Methods in org.apache.mina.core.session that return IoEventType | |
|---|---|
IoEventType |
IoEvent.getType()
|
static IoEventType |
IoEventType.valueOf(String name)
Returns the enum constant of this type with the specified name. |
static IoEventType[] |
IoEventType.values()
Returns an array containing the constants of this enum type, in the order they are declared. |
| Constructors in org.apache.mina.core.session with parameters of type IoEventType | |
|---|---|
IoEvent(IoEventType type,
IoSession session,
Object parameter)
|
| Uses of IoEventType in org.apache.mina.filter.executor |
|---|
| Constructors in org.apache.mina.filter.executor with parameters of type IoEventType | |
|---|---|
ExecutorFilter(Executor executor,
IoEventType... eventTypes)
Creates a new instance with the specified Executor . |
|
ExecutorFilter(int corePoolSize,
int maximumPoolSize,
IoEventType... eventTypes)
(Convenience constructor) Creates a new instance with a new OrderedThreadPoolExecutor . |
|
ExecutorFilter(int corePoolSize,
int maximumPoolSize,
long keepAliveTime,
TimeUnit unit,
IoEventQueueHandler queueHandler,
IoEventType... eventTypes)
(Convenience constructor) Creates a new instance with a new OrderedThreadPoolExecutor . |
|
ExecutorFilter(int corePoolSize,
int maximumPoolSize,
long keepAliveTime,
TimeUnit unit,
IoEventType... eventTypes)
(Convenience constructor) Creates a new instance with a new OrderedThreadPoolExecutor . |
|
ExecutorFilter(int corePoolSize,
int maximumPoolSize,
long keepAliveTime,
TimeUnit unit,
ThreadFactory threadFactory,
IoEventQueueHandler queueHandler,
IoEventType... eventTypes)
(Convenience constructor) Creates a new instance with a new OrderedThreadPoolExecutor . |
|
ExecutorFilter(int corePoolSize,
int maximumPoolSize,
long keepAliveTime,
TimeUnit unit,
ThreadFactory threadFactory,
IoEventType... eventTypes)
(Convenience constructor) Creates a new instance with a new OrderedThreadPoolExecutor . |
|
ExecutorFilter(int maximumPoolSize,
IoEventType... eventTypes)
(Convenience constructor) Creates a new instance with a new OrderedThreadPoolExecutor . |
|
ExecutorFilter(IoEventType... eventTypes)
(Convenience constructor) Creates a new instance with a new OrderedThreadPoolExecutor . |
| Uses of IoEventType in org.apache.mina.filter.statistic |
|---|
| Methods in org.apache.mina.filter.statistic that return types with arguments of type IoEventType | |
|---|---|
Set<IoEventType> |
ProfilerTimerFilter.getEventsToProfile()
Return the set of IoEventType which are profiled. |
| Methods in org.apache.mina.filter.statistic with parameters of type IoEventType | |
|---|---|
double |
ProfilerTimerFilter.getAverageTime(IoEventType type)
Get the average time for the specified method represented by the IoEventType |
long |
ProfilerTimerFilter.getMaximumTime(IoEventType type)
The maximum time the method represented by IoEventType has executed |
long |
ProfilerTimerFilter.getMinimumTime(IoEventType type)
The minimum time the method represented by IoEventType has executed |
long |
ProfilerTimerFilter.getTotalCalls(IoEventType type)
Gets the total number of times the method has been called that is represented by the IoEventType |
long |
ProfilerTimerFilter.getTotalTime(IoEventType type)
The total time this method has been executing |
void |
ProfilerTimerFilter.profile(IoEventType type)
Set the IoEventType to be profiled |
void |
ProfilerTimerFilter.setEventsToProfile(IoEventType... eventTypes)
Set the profilers for a list of IoEventType |
void |
ProfilerTimerFilter.stopProfile(IoEventType type)
Stop profiling an IoEventType |
| Constructors in org.apache.mina.filter.statistic with parameters of type IoEventType | |
|---|---|
ProfilerTimerFilter(TimeUnit timeUnit,
IoEventType... eventTypes)
Creates a new instance of ProfilerFilter. |
|
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{% extends "qabase.html" %} {% block body %} {# Each plot is wrapped in if/endif for robustness against missing plots #} {% if READNOISE %}
Integrated Raw Counts


Tool for quantitative research in scientometrics and bibliometrics. It provides various routines for importing bibliographic data from SCOPUS (<http://scopus.com>), Clarivate Analytics Web of Science (<http://www.webofknowledge.com/>), Cochrane Library (<http://www.cochranelibrary.com/>) and PubMed (<https://www.ncbi.nlm.nih.gov/pubmed/>) databases, performing bibliometric analysis and building networks for co-citation, coupling, scientific collaboration and co-word analysis.
| Version: | 2.0.2 |
| Depends: | R (≥ 3.3.0) |
| Imports: | stats, dplyr, DT, factoextra, FactoMineR, ggraph, ggplot2, ggrepel, igraph, Matrix, networkD3, RColorBrewer, RISmed, rscopus, shiny, shinycssloaders, shinythemes, SnowballC, stringdist, stringr |
| Suggests: | knitr, rmarkdown |
| Published: | 2018-11-16 |
| Author: | Massimo Aria [cre, aut], Corrado Cuccurullo [aut] |
| Maintainer: | Massimo Aria <aria at unina.it> |
| License: | GPL-3 |
| URL: | http://www.bibliometrix.org |
| NeedsCompilation: | no |
| Citation: | bibliometrix citation info |
| Materials: | NEWS |
| CRAN checks: | bibliometrix results |
| Reference manual: | bibliometrix.pdf |
| Vignettes: |
A brief introduction to bibliometrix |
| Package source: | bibliometrix_2.0.2.tar.gz |
| Windows binaries: | r-devel: bibliometrix_2.0.2.zip, r-release: bibliometrix_2.0.2.zip, r-oldrel: bibliometrix_2.0.2.zip |
| OS X binaries: | r-release: bibliometrix_2.0.2.tgz, r-oldrel: bibliometrix_2.0.2.tgz |
| Old sources: | bibliometrix archive |
Please use the canonical form https://CRAN.R-project.org/package=bibliometrix to link to this page.