text:
bug?: Multiple buy grid trade triggered doesn't sell even if sell is highly profitable

We're currently facing an issue with using multiple buy grid trades and a single sell grid trade.
The bot is configured to buy 10 grid trades, and trigger sell 100% of coins at +1%, as per these screenshots:

I'm currently running the latest version of your bot from the master branch.
Expected Behaviours
The bot, right after going >= +1.0% gain, should trigger sell stop limit order, but it is not doing it.
I'm questioning if it's only triggering sell stop limit order if EVERY buy grid trade step is completed or not, I hope sure it's not the case!
How to "unlock it"
Go to dedicated pair settings and click on Reset grid trade -> Delete and reset (central button); It will trigger sell stop limit order right away.
Additional case
I have a case in which I already triggered all buy grid trades, I'll wait and see if that sells on its own, I'll attach screenshot if needed:

PS: By "unlocking the coin" it will ruin history trade stats, showing the coin was bought at 0.00000 USDT and sell at it's current value, be aware!

@ilbuonmarcio
By looking at all screenshots, it looks like the Sell grid trade is executed already, but you still have enough coin to sell, hence, the bot is not removing the last buy price.

trigger sell 100% of coins at +1%, as per these screenshots:

You mentioned, you set to sell 100% of coins. Does it mean the bot didn't sell all the coins?

I'm questioning if it's only triggering sell stop limit order if EVERY buy grid trade step is completed or not, I hope sure it's not the case!

Nope, definitely not.
As soon as the sell condition is met, the bot will sell. And as mentioned, it looks like already sold. It just didn't sell all the coins.
Since I cannot see your configuration, I cannot tell whether you set to sell all or not.

Nothing is showing up for this coin in the trade history nor in Binance App, so something is clearly wrong here.
It should always sell 100% of the coins, the bot never sold those coins.

For the latest screenshot (CVCUSDT) it is still at current status, I'm still waiting for it to trigger sell stop limit

This is a current example, for me the one in the left is bugged, the one on the right is correct:

To make it clear, not every coin is doing this thing, only some coins, there are still sell transactions made and registered both in bot's history and Binance app.
I asked the user running for more info and it seems like the ones stuck are the ones which have had sell configured at +2% and later changed at +1%, can this be a configuration migration issue?

Hmm, that is strange. I never had that issue.
I just saw your last message. For the last screenshot, the bot thinks it's already sold.
Let me give you the steps to get the order information. Then we can see what is happening.

Press F12 in your browser
Click Network -> WS
Refresh the frontend
Click first in the list
Click Messages

Then you will see messages coming through.

Find one of the result message
Click stats -> symbols
Then find ONGUSDT
Right click on the symbols and click "Copy object"

The data will be pretty large for issue page, so put in gist and remove later.

https://gist.github.com/ilbuonmarcio/af1fb1f6850b2805823050a73b504ab9
All yours, thanks for your time :)
PS. Can it be that the user updated sell grid trade to 2% and to 1% in rapid succession without waiting for it to set to 2% first?

You can delete the log. I got it.
From the log, I can see the sell order is executed
"executedOrder": {
                        "symbol": "ONGUSDT",
                        "orderId": ,
                        "orderListId": -1,
                        "clientOrderId": "",
                        "price": "1.00960000",
                        "origQty": "100.00000000",
                        "executedQty": "100.00000000",
                        "cummulativeQuoteQty": "101.63000000",
                        "status": "FILLED",
                        "timeInForce": "GTC",
                        "type": "STOP_LOSS_LIMIT",
                        "side": "SELL",
                        "stopPrice": "1.01750000",
                        "icebergQty": "0.00000000",
                        "time": 1632845766750,
                        "updateTime": 1632847371175,
                        "isWorking": true,
                        "origQuoteOrderQty": "0.00000000",
                        "currentGridTradeIndex": 0,
                        "nextCheck": "2021-09-28T16:42:43+00:00"
                    }

The time is Tue 28 Sep 2021 16:42:51 UTC
You can convert to your timezone.
Can you check again in Binanace?

Actually, something is wrong. Time is not matching.
Let me dig further from your log.

Ok, here is what happened.
Your lastBuyPriceRemoveThreshold is set as 5.
And this is the timeline.

Side
Executed Quantity
Price
Remaining Quantity
Cumulative Quote Qty
Time

BUY
118.45400000
1.18940000
118.454
118.45400000
Fri 24 Sep 2021 00:51:48 UTC

SELL
100.00000000
1.00960000
18.454 => USDT 18.6311584
101.63000000
Tue 28 Sep 2021 16:16:06 UTC

BUY
19.00000000
1.01210000

19.22610000
Tue 28 Sep 2021 17:18:14 UTC

BUY
19.00000000
1.00820000

19.15200000
Tue 28 Sep 2021 17:55:53 UTC

BUY
19.00000000
1.00680000

19.12920000
Tue 28 Sep 2021 18:19:22 UTC

BUY
19.00000000
1.01120000

19.21280000
Tue 28 Sep 2021 18:33:13 UTC

BUY
19.00000000
1.01530000

19.29070000
Tue 28 Sep 2021 18:48:13 UTC

BUY
19.00000000
1.01590000

19.30210000
Tue 28 Sep 2021 19:08:56 UTC

BUY
19.00000000
1.01450000
151.454
19.26980000
Tue 28 Sep 2021 19:57:31 UTC

Final balance of ONG is "total": 153.25495. I assume you had something like 1.80095 ONG already.
Now, when the coin is sold, it didn't sell all of the coins. It sold exactly 100 quantities, which looks very suspicious. It should've sell as below.

Estimated free balance before executing 1st sell grid: 118.454+1.80095 = 120.25495
Estimated the price at that time of executing 1st sell grid: 1.0209 based on the grid sell price.

So the order must have been placed as below:
price: 1.0096,
quantity: 119,
side: 'sell',
stopPrice: 1.0175,
symbol: 'ONGUSDT',
timeInForce: 'GTC',
type: 'STOP_LOSS_LIMIT'

I just added your case into the test code and got the above expectation.
Do you have any code modifications?

On this instance it's as stock as it is, without any modifications.
So what should I do, increase the 5 USDT?

The code should work as expected. You saw the result of the test.
Can you give me the running version and commit hash on the bottom of the frontend?
And go to the docker and get the code for place-sell-order.js code.
$ docker exec -it binance-bot /bin/ash
/srv # cat /srv/app/cronjob/trailingTrade/step/place-sell-order.js

I'm currently running on 7f58b8fcaacf6fc72f74e1c64299371e6efec13e
The code for place-sell-order.js is as following:
https://gist.github.com/ilbuonmarcio/4012220022eae5d3109e514d09cc64a5

My latest released version hash is 244734f.
I can see you built the master branch code.
I just went back to your log and found I was miscalculated 1st buy grid trade.

Side
Executed Quantity
Price
Cumulative Quote Qty
Created Time
Updated Time

BUY
100.00000000
1.18940000
118.45400000
Fri 24 Sep 2021 00:49:10 UTC
Fri 24 Sep 2021 00:51:48 UTC

SELL
100.00000000
1.00960000
101.63000000
Tue 28 Sep 2021 16:16:06 UTC
Tue 28 Sep 2021 16:16:06 UTC

BUY
19.00000000
1.01210000
19.22610000
Tue 28 Sep 2021 17:17:19 UTC
Tue 28 Sep 2021 17:18:14 UTC

BUY
19.00000000
1.00820000
19.15200000

Tue 28 Sep 2021 17:55:53 UTC

BUY
19.00000000
1.00680000
19.12920000

Tue 28 Sep 2021 18:19:22 UTC

BUY
19.00000000
1.01120000
19.21280000

Tue 28 Sep 2021 18:33:13 UTC

BUY
19.00000000
1.01530000
19.29070000

Tue 28 Sep 2021 18:48:13 UTC

BUY
19.00000000
1.01590000
19.30210000

Tue 28 Sep 2021 19:08:56 UTC

BUY
19.00000000
1.01450000
19.26980000

Tue 28 Sep 2021 19:57:31 UTC

It looks like 1st grid trade bought 100, and sold 100.
Then it should've removed the last buy price, but it didn't.
It looks like some nasty bug that cannot find the exact reason.
Maybe because of the last change I made.
I suggest going back to version 0.0.79 until I find the exact reason. And let me know whether it's having the same issue.
For ONGUSDT, reset grid trade and put the recalculated last buy price.
133 quantity * total purchase after 1st sell 134.5827 = recalculated last buy price 1.0119

I'll try and let you know, thanks!

Wait, this doesn't explain this.
So at the moment, you have 153.25495 ONG. However, grid buy 2-8, which purchased 19 each, must be 133ONG.
Where is 20.2549 coming from? Did you manually purchased after?
    {
        "asset": "ONG",
        "free": "153.25495000",
        "locked": "0.00000000",
        "total": 153.25495
    },

You will need him to take a screenshot of Binance transaction.
Clearly, there are missing transactions that the bot didn't catch.
Go to Binance and get the transaction from Thu 23 Sep 2021 UTC ~ Tue 29 Sep 2021 UTC.
Note that period may need to convert to your timezone.

Another strange thing is, it triggered sell when the sell trigger price is not reached. In fact, it's much less than the sell trigger price.

Side
Executed Quantity
Price
Cumulative Quote Qty
Created Time
Updated Time
Next Check

BUY
100.00000000
1.18940000
118.45400000
Fri 24 Sep 2021 00:49:10 UTC
Fri 24 Sep 2021 00:51:48 UTC
2021-09-24T00:51:50+00:00

SELL
100.00000000
1.00960000
101.63000000
Tue 28 Sep 2021 16:16:06 UTC
Tue 28 Sep 2021 16:16:06 UTC
2021-09-28T16:42:43+00:00

BUY
19.00000000
1.01210000
19.22610000
Tue 28 Sep 2021 17:17:19 UTC
Tue 28 Sep 2021 17:18:14 UTC
2021-09-28T17:18:19+00:00

If you see the last buy price for 1st grid buy is 1.1894. It should trigger sell when the price reached 1.1894 * 1.02 = 1.213188.
but the sell is triggered when the price is 1.0209 based on the calculation of limit price 1.0096.
That is why quote qty is less than buy.
Can you check the slack message whether he manually triggered sell? You should look for around this time Tue 28 Sep 2021 16:16:06 UTC.
If he triggered sell manually, the message would be as below.
ONGUSDT Action: Queued action: sell
- Message: The sell order received by the bot. Wait for placing the order.

I just wrote him to give me screenshots of the transactions on Binance as per your request, I'll let you know once I got those.
Unfortunately I don't think he set up Slack integration for his bot, so I'm not sure I can provide you the information you asked

As you can see from my previous message, the timing is not making sense.
If there were any open orders buy or sell, then the bot will not remove the last buy price.
https://github.com/chrisleekr/binance-trading-bot/blob/master/app/cronjob/trailingTrade/step/remove-last-buy-price.js#L170
My assumption is, for some reason, he triggered buy/sell orders and there were open orders.
This scenario is making sense. We can verify by checking Binance transactions.
The screenshot must include cancelled orders.
In this case, I probably need to see only Tue 28 Sep 2021 UTC transactions for ONGUSDT.

Hey @ilbuonmarcio
Can you confirm the issue still exists?

I'm still waiting for the screenshots but yeah, it's still here

I figured out that there was effectively a race condition between configuration saves, and indeed the configuration was updated half with the first save and half with the second save.
I can't be sure about how it could happen, but the only thing I know is that there were 10 buy grid trades in both configs and there were coin pairs already purchased with a single grid trade as the previous configuration and others still free, probably that's why they bugged out like that.
My friend just resetted the bot so I can't go back and check, but thanks for your help, I'll be in touch with you again if it arises again for sure!
Of the 7 bots I see this is the only one with this problem, so that could be surely not a bug in the code, but a mere race condition between applying configs.

I'll close it for now, so you can focus on more urgent requests.
If it comes up again I'll get in touch asap
Thanks again for the time provided!

@chrisleekr is it normal that if I save buy grid trades on global configuration (10 grid trades), and there are coins which are already some of the grids triggered (3 out of 10), if I save and apply changes to all coins via global configuration, only the remaining 7 will be updated and the other initial 3 will remain stuck with the old config?

So let me sum up:

There are 3 open orders out of 10.

Are they customised configurations?

Updated global configurations for all coins.
3 coins are still old config?

The change of configuration wouldn't cancel open orders. If open orders are cancelled, it will use an updated configuration for the next order.

No, I mispelled here.
Example: there is a single coin, which has 3 already completed buy grid trades out of his 10, with global configuration applied, so no custom values provided.
If I edit and apply global settings to all coin pairs, like I edit all 10 grid trades, only the last 7 gets applied and the remaining first 3 grid trades won't be updated, thus being "half updated" with first 3 with the old status and latest 7 with new status.
Is this a feature or a bug?
I just encountered that today after a bot virtual machine complete redo, and cannot understand if that's the one or the other...

Example: there is a single coin, which has 3 already completed buy grid trades out of his 10, with global configuration applied, so no custom values provided.

So 3 out of 10 buy grid trades are executed. Then even if you update the global configuration, it won't update executed orders.
It makes sense to not update those values because those 3 buy orders are already executed.
The same rule applies when you try to update the symbol configuration which already has executed orders. You will see them as greyed out because it's already executed.
That is the expected flow.
Did I understand your problem correctly?

Yeah, but that makes me think, if I need to update every coin with that new configuration, and some are already in open trades, then I need to know what are the ones who differ from global configuration, because even after applying the global settings, in the GUI it remains set as the while "Global" settings tag is applied for each coin, and not the yellow one, even if effectively they are differing from the global configuration.

the bot will update also grid 1 and 2 after the coin is sold with the new configuration?

Yes. After the coin is sold, the grid trade will be completely reset and start using a new configuration.

@ilbuonmarcio This issue can be closed?

authors: chrisleekr ilbuonmarcio

repo: chrisleekr/binance-trading-bot


text:
GetVariableValue Helper

Description:
I'm working on a plugin right now to allow switching from one scene to another randomly or pseudo randomly, i want to build an entry for the 7 day roguelike using gb studio as the game boy is my favorite and there aren't enough games like cave noire.
the level switching seems to be working fine, basically I want to have a variable integer input (random or otherwise) select the next scene to load. the scenes are assigned slots by the user then i want to use a startup script to build the map.
the problem is if i try to pull, say input.variable.value, it breaks the script, and the compiler error really doesnt give me anything to work off of. This happens if I allocate a tmp as well, not sure if its a typecasting thing but i wouldnt know from the error message.
Example of desired function:
user triggers exit event -> script determines next room number -> launches next stage from pool of stages selected for the exit
Solution:
fairly straightforward, I need an easy way to pull the integer data out of a variable. I realize the plugin engine isnt going to be used by everyone but it adds a ton of flexibility and makes a 7drl entry possible
Alternatives:
I've considered doing a massive switch statement but this is an attempt to minimize tons of objects in the inspector. Main alternative is to create a script that handles the entire random generation sequence at the beginning of the game and assign all levels as variables, then try to use the compare function to build the map network. Another option would be to demark a set of global variables representing the possible slot values at init, then compare based on those.
Context:
I'm attempting to make this a modular set of controlled randomization tools, hopefully if the plugin engine is sticking around some other folks can get some use out of it too once I've got it acting right. I've attached my event plugin .js, its usable for fully random changes between 3 rooms atm (expandable if you know where to copy/paste)
TLDR i got random room jumping working,  how to i get a variable number value as int to compare with a "number", also i probably need to learn more JS
I'm running the v2 beta for the record.
Thank you for this tool, you're making some boyhood dreams come true.
export const id = "SWITCH_SCENE_INDEX";
export const name = "Scene: Switch To Scene By Index";
export const fields = [
  { //this is supposed to set the key variable value that selects the next stage
    key: "sn",
	label: "Scene Select",
    type: "variable",
    defaultValue: "LAST_VARIABLE"
  },
  {
    key: "s0",
	label: "Scene Index 0",
    type: "scene",
    defaultValue: "LAST_SCENE"
  },
  {
    key: "s1",
	label: "Scene Index 1",
    type: "scene",
    defaultValue: "LAST_SCENE"
  },
  {
    key: "s2",
	label: "Scene Index 2",
    type: "scene",
    defaultValue: "LAST_SCENE"
  },
  {
    key: "x",
	label: "FIELD_X",
    type: "number",
    min: 0,
    max: 255,
    defaultValue: 0,
    width: "50%"
  },
  {
    key: "y",
	label: "FIELD_Y",
    type: "number",
    min: 0,
    max: 255,
    defaultValue: 0,
    width: "50%"
  },
  {
    key: "direction",
    label: "FIELD_DIRECTION",
    type: "direction",
    width: "50%",
    defaultValue: ""
  },
  {
    key: "fadeSpeed",
    label: "FIELD_FADE_SPEED",
    type: "fadeSpeed",
    defaultValue: "2",
    width: "50%"
  }
];

export const compile = (input, helpers) => {
  const { caseVariableValue, ifVariableValue , variableCopy, sceneSwitch } = helpers;
//  const tmp1 = "tmp1";      //non working, for reference
//  variableCopy(tmp1, input.sn);
//  if (tmp1.value == 1){
//	  sceneSwitch(input.s1,input.x,input.y,input.direction,input.fadeSpeed);
// }

// Comparing values always returns as true, it just triggers whichever command comes first even if nested within eachothers default actions
// ifVariableValue(parseInt(input.sn.value), "==", 0,sceneSwitch(input.s0,input.x,input.y,input.direction,input.fadeSpeed),[]);
// ifVariableValue(parseInt(input.sn.value), "==", 1,sceneSwitch(input.s1,input.x,input.y,input.direction,input.fadeSpeed),[]);
// ifVariableValue(parseInt(input.sn.value), "==", 2,sceneSwitch(input.s2,input.x,input.y,input.direction,input.fadeSpeed),[]);

	const randSelect = Math.floor(Math.random() * Math.floor(3));
	switch (randSelect){
		case 0:
			sceneSwitch(input.s0,input.x,input.y,input.direction,input.fadeSpeed);
			break;
		case 1:
			sceneSwitch(input.s1,input.x,input.y,input.direction,input.fadeSpeed);
			break;
		case 2:
			sceneSwitch(input.s2,input.x,input.y,input.direction,input.fadeSpeed);
			break;
	}
};

``

V2beta as in 2.0beta5?
Plugins do not actually run JavaScript on the gameboy, they just let you access and compile the script command functions as part of scriptrunner_b.c.
JavaScript math is only to help pick things in the editor outside of runtime.
A large switch event is probably your best option, perhaps a blank scene that handles the random selection, so you only have to script the scene selector once.
There's a few updates to the engine being tested in #709 , one in particular tries to improve random seed so it's not reseeded each script, but instead once on first button press (each call then increments the seed).
I'd also recommend a buffer of variables to store the last random scene picked,
Set L3=L2, set L2=L1, set L1=L0,
Define rand start
Math rand L0,
If L0==L1, Goto rand start
Else if L0==L2, Goto rand start etc...
Then switch case based on L0 for the scene.

I know theres translation going on, but i'm usually a C# coder so i wondered if i was missing something basic about access permissions or typecasting. i can see examples of the value being pulled elsewhere but i think it may be comparing it with another whole variable instead of trying to compare an int against a single param.
I'm on the last beta release from itch so 2.0b4,
I intend to have a cycle at init that creates a hacky 2D array from global vars, and use it to randomly lay out a map at the beginning using a ruleset to select the stages. When the user exits a room, an event checks the matrix for the assignment in that direction, and loads it. Should hit up the discord.

source: gharchive/issue

authors: RichardULZ bradybothwell

repo: chrismaltby/gb-studio

license: MIT

license_type: permissive

license_source: github-api


text:
Possible typo?

The heading points to the documentation for #getErrorMessages but the examples shows a #getErrorMessage example.

Actually, I think that code block a child of the isValid heading? Also, getErrorMessages returns an array, not a string, so the changed example wouldn't work anyway. 😿

Something wrong :smile_cat:

source: gharchive/pull-request

authors: Semigradsky maniart twisty

repo: christianalfoni/formsy-react

license: mit

license_source: bigquery


text:
More ext4 optimizations

Hello Christian
Have a look at this link: https://blogofterje.wordpress.com/2012/01/14/optimizing-fs-on-sd-card/
There are some pretty cool things explained about how to optimize the filesystem. At the end those read/write speed optimizations mean also less cpu cycles.
There's something he does not explain there. About the block size, some cards can do bigger sizes (i.e. samsung evo https://www.reddit.com/r/retroid/comments/icsw32/i_spent_entirely_too_much_time_researching/?utm_medium=android_app&utm_source=share ).
Finally, I know it's not ideal for you but I have erased my exfat partition and replaced it for a full sized ext4 so I can have the best performance here.
I think at least removing the journal thing and looking at those raid paramters is pretty interesting.
Thank you so much.

Thanks Dreamer.  Be aware that this article is over 10 years old so many of these changes are implemented bu default already.  Also these optimizations will not lead to better emulation performance on this chipset which is the overall goal.  No problem going full ext4 on your end as it will lead to slightly better read/write performance p, however, it’s not something I eill implement as default since it would require many to have to result to loading their devices via wifi or a Linux OS which takes away from the ease of the system.

Thanks Christian.
Just to let you know that I did the change for a full ext4 and I have also disabled journalling since I don't think we need it in this device. I find the system very very fast compared to before. I don't know if it's me but even the boot time and everything is more fluid.
About journaling: https://forum.xda-developers.com/t/tools-how-to-disable-and-enable-journaling-in-internal-ext4-partitions.1252028/
It is not possible to make a patch for this since the ext4 filesystem can not be mounted to disable it , but the newest releases can have journalling off.
The rest of the parameters are specific to each sd card, so they do not apply here either. But someone can do it in his personal device using that tool to find the best way to format the sd card.
Thank you so much Christian

I'll check this out.  Thank you

Changing to full ext4 would have a performance boost in itself.  The journaling I will investigate

I understand that the full ext4 means that windows people need to be used to things like filezilla and send files over the network. Or perhaps with samba but the wifi dongle that comes with the console is really bad. I'm also using an usb-c to ethernet adapter for all the updates, which makes a super fast connection. Is working incredibly well.
Strictly, more read /writes (or a bottleneck due to the exfat use in linux), can imply more CPU cicles or load average if there are longer thread waits. So, performance can be worse at the end. It seems to me important to get ride of that exfat filesystem. Of course, I understand your point.
Thank you so much for all the support.

So I checked and journaling is already off for the primary ext4 partition for ArkOS.  So nothing to tune for that.

Getting rid of the exfat filesystem would negate one of the few ease of use features of ArkOS so I just simply won't do that.  What performance penalty there is for accommodating exfat is very minor in my opinion.  Especially since it doesn't impact the emulation performance positively or negatively.  Of course, if someone wants to rid the exfat partition totally, they can do so on their own installs fairly simply using tools like gparted on Linux machines or minitool partition wizard on Windows machines.

Nice, I see. Perhaps it was my partition.
Thank you so much Christian.

authors: christianhaitian dreamer1234 oaspiazu

repo: christianhaitian/arkos


text:
Donations

Hi, is there a way to donate to the project?
Thanks!

Hi Robin, thanks for asking. I've never actually considered it and so no option at the current moment, but really appreciate the thought!

In that case thanks a lot for the work on this awesome tool. In a case of need I will be happy to contribute in the future :)

authors: christoomey fourstepper

repo: christoomey/vim-tmux-navigator


text:
Setting margin content & page numbering

Hard to find documentation on this, but does this or Chrome headerless support setting the margin content with css using something like the following (I can't get it to work):
@media print {
     @page {
		size: A4 portrait;
		margin-top: 40px;
		margin-bottom: 40px;

		@top-left {
			content: "TOP SECRET";
			color: red
		}

		@bottom-right {
			content: "Page " counter(page);
			font-style: italic
		}
	}
}

Basically I want to set headers and footers as well as occasionally do page numbering.

I added a few new options to Page->pdf.  Extra code here to help get page numbering:
        // option headerTemplate
        if (array_key_exists('headerTemplate', $options)) {
            // headerTemplate requires type to be a string
            if (!is_string($options['headerTemplate'])) {
                throw new \InvalidArgumentException(
                    'Invalid options "headerTemplate" for print to pdf. Must be a string'
                );
            }
            $pdfOptions['headerTemplate'] = $options['headerTemplate'];
        }

		// option footerTemplate
        if (array_key_exists('footerTemplate', $options)) {
            // footerTemplate requires type to be a string
            if (!is_string($options['footerTemplate'])) {
                throw new \InvalidArgumentException(
                    'Invalid options "footerTemplate" for print to pdf. Must be a string'
                );
            }
            $pdfOptions['footerTemplate'] = $options['footerTemplate'];
        }

		// option pageRanges
        if (array_key_exists('pageRanges', $options)) {
            // pageRanges requires type to be a string
            if (!is_string($options['pageRanges'])) {
                throw new \InvalidArgumentException(
                    'Invalid options "pageRanges" for print to pdf. Must be a string'
                );
            }
            $pdfOptions['pageRanges'] = $options['pageRanges'];
        }

		// option ignoreInvalidPageRanges
        if (array_key_exists('ignoreInvalidPageRanges', $options)) {
            // ignoreInvalidPageRanges requires type to be boolean
            if (!is_bool($options['ignoreInvalidPageRanges'])) {
                throw new \InvalidArgumentException(
                    'Invalid options "ignoreInvalidPageRanges" for print to pdf. Must be true or false'
                );
            }
            $pdfOptions['ignoreInvalidPageRanges'] = $options['ignoreInvalidPageRanges'];
        }

		// option scale
        if (array_key_exists('scale', $options)) {
            // scale requires type to be a float
            if (!is_float($options['scale'])) {
                throw new \InvalidArgumentException(
                    'Invalid options "scale" for print to pdf. Must be a number'
                );
            }
            $pdfOptions['scale'] = $options['scale'];
        }

		// option format
        if (array_key_exists('format', $options)) {
            // format requires type to be a string
            if (!is_string($options['format'])) {
                throw new \InvalidArgumentException(
                    'Invalid options "format" for print to pdf. Must be a string'
                );
            }
            $pdfOptions['format'] = $options['format'];
        }

Added a few more features in the pdf function in Page.php.  The previous one I posted above had a bug, being "format".  This works and has been tested and matches https://chromedevtools.github.io/devtools-protocol/tot/Page/#method-printToPDF
public function pdf(array $options = []): PagePdf
    {
        $this->assertNotClosed();

        $pdfOptions = $options; // []

		$pdfOptions = [];

        // is landscape?
        if (array_key_exists('landscape', $options)) {
            // landscape requires type to be boolean
            if (!is_bool($options['landscape'])) {
                throw new \InvalidArgumentException(
                    'Invalid options "landscape" for print to pdf. Must be true or false'
                );
            }
            $pdfOptions['landscape'] = $options['landscape'];
        }

		// option displayHeaderFooter
        if (array_key_exists('displayHeaderFooter', $options)) {
            // printBackground requires type to be boolean
            if (!is_bool($options['displayHeaderFooter'])) {
                throw new \InvalidArgumentException(
                    'Invalid options "displayHeaderFooter" for print to pdf. Must be true or false'
                );
            }
            $pdfOptions['displayHeaderFooter'] = $options['displayHeaderFooter'];
        }

        // should print background?
        if (array_key_exists('printBackground', $options)) {
            // printBackground requires type to be boolean
            if (!is_bool($options['printBackground'])) {
                throw new \InvalidArgumentException(
                    'Invalid options "printBackground" for print to pdf. Must be true or false'
                );
            }
            $pdfOptions['printBackground'] = $options['printBackground'];
        }

		// option scale
        if (array_key_exists('scale', $options)) {
            // scale requires type to be a float
            if (!is_float($options['scale'])) {
                throw new \InvalidArgumentException(
                    'Invalid options "scale" for print to pdf. Must be a number'
                );
            }
            $pdfOptions['scale'] = $options['scale'];
        }

        // option paperWidth
        if (array_key_exists('paperWidth', $options)) {
            // paperWidth requires type to be float
            if (gettype($options['paperWidth']) !== 'double') {
                throw new \InvalidArgumentException(
                    'Invalid options "paperWidth" for print to pdf. Must be float like 1.0 or 5.4'
                );
            }
            $pdfOptions['paperWidth'] = $options['paperWidth'];
        }

        // option paperHeight
        if (array_key_exists('paperHeight', $options)) {
            // paperHeight requires type to be float
            if (gettype($options['paperHeight']) !== 'double') {
                throw new \InvalidArgumentException(
                    'Invalid options "paperHeight" for print to pdf. Must be float like 1.0 or 5.4'
                );
            }
            $pdfOptions['paperHeight'] = $options['paperHeight'];
        }

        // option marginTop
        if (array_key_exists('marginTop', $options)) {
            // marginTop requires type to be float
            if (gettype($options['marginTop']) !== 'double') {
                throw new \InvalidArgumentException(
                    'Invalid options "marginTop" for print to pdf. Must be float like 1.0 or 5.4'
                );
            }
            $pdfOptions['marginTop'] = $options['marginTop'];
        }

        // option marginBottom
        if (array_key_exists('marginBottom', $options)) {
            // marginBottom requires type to be float
            if (gettype($options['marginBottom']) !== 'double') {
                throw new \InvalidArgumentException(
                    'Invalid options "marginBottom" for print to pdf. Must be float like 1.0 or 5.4'
                );
            }
            $pdfOptions['marginBottom'] = $options['marginBottom'];
        }

        // option marginLeft
        if (array_key_exists('marginLeft', $options)) {
            // marginBottom requires type to be float
            if (gettype($options['marginLeft']) !== 'double') {
                throw new \InvalidArgumentException(
                    'Invalid options "marginLeft" for print to pdf. Must be float like 1.0 or 5.4'
                );
            }
            $pdfOptions['marginLeft'] = $options['marginLeft'];
        }

        // option marginRight
        if (array_key_exists('marginRight', $options)) {
            // marginBottom requires type to be float
            if (gettype($options['marginRight']) !== 'double') {
                throw new \InvalidArgumentException(
                    'Invalid options "marginRight" for print to pdf. Must be float like 1.0 or 5.4'
                );
            }
            $pdfOptions['marginRight'] = $options['marginRight'];
        }

		// option pageRanges
        if (array_key_exists('pageRanges', $options)) {
            // pageRanges requires type to be a string
            if (!is_string($options['pageRanges'])) {
                throw new \InvalidArgumentException(
                    'Invalid options "pageRanges" for print to pdf. Must be a string'
                );
            }
            $pdfOptions['pageRanges'] = $options['pageRanges'];
        }

		// option ignoreInvalidPageRanges
        if (array_key_exists('ignoreInvalidPageRanges', $options)) {
            // ignoreInvalidPageRanges requires type to be boolean
            if (!is_bool($options['ignoreInvalidPageRanges'])) {
                throw new \InvalidArgumentException(
                    'Invalid options "ignoreInvalidPageRanges" for print to pdf. Must be true or false'
                );
            }
            $pdfOptions['ignoreInvalidPageRanges'] = $options['ignoreInvalidPageRanges'];
        }

		// option headerTemplate
        if (array_key_exists('headerTemplate', $options)) {
            // headerTemplate requires type to be a string
            if (!is_string($options['headerTemplate'])) {
                throw new \InvalidArgumentException(
                    'Invalid options "headerTemplate" for print to pdf. Must be a string'
                );
            }
            $pdfOptions['headerTemplate'] = $options['headerTemplate'];
        }

		// option footerTemplate
        if (array_key_exists('footerTemplate', $options)) {
            // footerTemplate requires type to be a string
            if (!is_string($options['footerTemplate'])) {
                throw new \InvalidArgumentException(
                    'Invalid options "footerTemplate" for print to pdf. Must be a string'
                );
            }
            $pdfOptions['footerTemplate'] = $options['footerTemplate'];
        }

        // option preferCSSPageSize
        if (array_key_exists('preferCSSPageSize', $options)) {
            // printBackground requires type to be boolean
            if (!is_bool($options['preferCSSPageSize'])) {
                throw new \InvalidArgumentException(
                    'Invalid options "preferCSSPageSize" for print to pdf. Must be true or false'
                );
            }
            $pdfOptions['preferCSSPageSize'] = $options['preferCSSPageSize'];
        }

        // request screenshot
        $responseReader = $this->getSession()
            ->sendMessage(new Message('Page.printToPDF', $pdfOptions));

        return new PagePdf($responseReader);
    }

HEllo @jplevene margin and header/footer for pdf have been merged

authors: gsouf jplevene

repo: chrome-php/headless-chromium-php


text:
[FR] Add support for JPEG XL

The reasoning is simple:
JPEG XL can losslessly reduce the size of an image in any popular image format, it's important for already lossy .jpg's in existing comic books.
Sadly it's not supported in image crate, but jpegxl-sys exists.

I can't use jpegxl-sys due to a licensing issue (comiconv is mit/apache and jpegxl is gpl), but zune-jpegxl for encoding and jxl-oxide for decoding might work (and it would be pure rust).

try building from master

Thanks!
I've checked it:
cargo install --git <repo_url>

# Installed package `comiconv v0.3.3 (https://github.com/chrontax/comiconv#e2a9ed36)` (executable `comiconv`)

comiconv -f jxl -q 100 comics.zip

The comics (130 MiB, 163 JPEG images = 134 MiB uncompressed) became 4x larger (405 MiB) and it took ~7 minutes to do the job. The progress bar shows that all 163 files are processed very fast (a few seconds), then the app "hangs" (i.e. prints no output) until it actually finishes.
Compressing all 163 images via cjxl takes 11 seconds:
parallel cjxl -q 100 --lossless_jpeg=1 {} {.}.jxl ::: *.jpg

# or

fd . -e jpg -x cjxl -q 100 --lossless_jpeg=1 {} {.}.jxl

And the final size is ~110 MiB, with lossless jpeg -> jxl conversion.

So, yeah, it works, but the result is definitely not what was expected.

Then it must be that zune-jpegxl is quite a poor implementation. Guess I'll have to look for something else.

For now I'll leave jpegxl encoding as is, as I can't seem to find a better library that would be compatible licnese-wise, and it is not of utmost priority to me.

authors: chrontax murlakatamenka

repo: chrontax/comiconv


text:
leveldb shared library doesn't list snappy as a dependency.

What steps will reproduce the problem?
1. from a fresh clone, `make`
2. `ldd libleveldb.so.1`

What is the expected output? What do you see instead?
snappy should be listed, but it is not.

What version of the product are you using? On what operating system?
i'm using leveldb 1.5 on ubuntu 12.04

Please provide any additional information below.
the problem is that the '-lsnappy' option (from the generated build_config.mk's
PLATFORM_LDFLAGS) is being provided before the list of source files. if it is
provided after, then the dependency is properly listed in the generated so file.
This can be fixed by moving $(SOURCES) to be a sooner option in the $(SHARED3)
target.

Original issue reported on code.google.com by jtolds on 5 Jun 2012 at 6:05

This is critical to use shared leveldb with python ctypes (see
http://code.google.com/p/leveldb-py/) so, please fix this. I attached a patch.

Original comment by carlopi...@gmail.com on 31 Aug 2012 at 2:07
Attachments:

leveldb-shared-fix.patch

Fixed in 1.6.0.

Original comment by san...@google.com on 12 Oct 2012 at 7:58

Changed state: Fixed

For anyone waiting on this issue, I have a fork of LevelDB that both fixes this
and adds Debian packaging: http://code.google.com/r/jtolds-leveldb/

Original comment by jtolds on 31 Aug 2012 at 4:05

repo: chrsmithdemos/leveldb


text:
SEO Optimisations

WIP

ToDo: Lighthouse still complains about header sequence / missing usage of H2 before H3

repo: chrultrabook/docs


text:
Resolves #136

#ISSUE-NO #136
What does this PR do?
Fetches files from the src folder in index.html
Description of Task to be completed?
Replacement of src by ./src in index.html
How can this be manually tested?
Running the website in any suitable server.
What is the relevant issue link?
https://github.com/chryz-hub/chryz-hub.github.io/issues/136
Screenshots (if appropriate)

Thank you very much for contributing

authors: jomefavourite tanishghosh


text:
Could you please explain about the obtained results on the RAP and PETA datasets?

I managed to run the codes for RAP and PETA datasets. However, I get confusing results and really appreciate any hint from your side to justify them.
In the first epoch, I expect that the results on the test set be near random. However, I get very good results. I know that transfer learning can improve the results, but not mA:  0.72, Accuracy:  0.61, Precision: 0.69, Recall:  0.83, F1_Score:  0.75, which are competitive to ACN (f1: 75.98), DeepMar(f1: 75.56), and JRL(f1: 74.62). I got these values from your paper.
The results of the first and last epoch are as follows:
On RAP:
After first epoch:
Epoch: [0][0/2124]	Time 1.167 (1.167)	Loss 3.7118 (3.7118)	Accu 0.210 (0.210)
Epoch: [0][100/2124]	Time 0.878 (0.886)	Loss 1.5951 (2.0204)	Accu 0.866 (0.785)
Epoch: [0][200/2124]	Time 0.889 (0.885)	Loss 1.5291 (1.8360)	Accu 0.878 (0.829)
Epoch: [0][300/2124]	Time 0.904 (0.897)	Loss 1.4167 (1.7277)	Accu 0.888 (0.848)
Epoch: [0][400/2124]	Time 0.882 (0.901)	Loss 1.2797 (1.6585)	Accu 0.895 (0.858)
Epoch: [0][500/2124]	Time 0.887 (0.898)	Loss 1.4562 (1.6092)	Accu 0.889 (0.865)
Epoch: [0][600/2124]	Time 0.887 (0.896)	Loss 1.3090 (1.5669)	Accu 0.893 (0.870)
Epoch: [0][700/2124]	Time 0.892 (0.896)	Loss 1.3036 (1.5345)	Accu 0.902 (0.874)
Epoch: [0][800/2124]	Time 0.881 (0.897)	Loss 1.1357 (1.5079)	Accu 0.909 (0.877)
Epoch: [0][900/2124]	Time 0.889 (0.900)	Loss 1.2629 (1.4862)	Accu 0.905 (0.879)
Epoch: [0][1000/2124]	Time 0.887 (0.901)	Loss 1.3137 (1.4677)	Accu 0.895 (0.881)
Epoch: [0][1100/2124]	Time 0.888 (0.902)	Loss 1.1418 (1.4485)	Accu 0.907 (0.883)
Epoch: [0][1200/2124]	Time 0.893 (0.904)	Loss 1.0898 (1.4303)	Accu 0.906 (0.885)
Epoch: [0][1300/2124]	Time 0.901 (0.906)	Loss 1.1799 (1.4150)	Accu 0.907 (0.886)
Epoch: [0][1400/2124]	Time 0.917 (0.906)	Loss 1.2728 (1.4011)	Accu 0.908 (0.888)
Epoch: [0][1500/2124]	Time 0.890 (0.908)	Loss 1.1938 (1.3896)	Accu 0.913 (0.889)
Epoch: [0][1600/2124]	Time 0.890 (0.909)	Loss 1.3497 (1.3785)	Accu 0.899 (0.890)
Epoch: [0][1700/2124]	Time 0.886 (0.909)	Loss 1.1483 (1.3673)	Accu 0.902 (0.891)
Epoch: [0][1800/2124]	Time 0.885 (0.909)	Loss 1.1885 (1.3576)	Accu 0.913 (0.892)
Epoch: [0][1900/2124]	Time 0.889 (0.908)	Loss 1.1853 (1.3481)	Accu 0.903 (0.892)
Epoch: [0][2000/2124]	Time 0.979 (0.908)	Loss 1.2094 (1.3386)	Accu 0.906 (0.893)
Epoch: [0][2100/2124]	Time 0.917 (0.909)	Loss 1.2271 (1.3301)	Accu 0.914 (0.894)
Test: [0/531]	Time 0.525 (0.525)	Loss 1.2188 (1.2188)	Accu 0.913 (0.913)
Test: [100/531]	Time 0.252 (0.271)	Loss 1.0559 (1.1692)	Accu 0.921 (0.910)
Test: [200/531]	Time 0.255 (0.271)	Loss 1.1272 (1.1629)	Accu 0.917 (0.910)
Test: [300/531]	Time 0.290 (0.273)	Loss 1.1043 (1.1609)	Accu 0.911 (0.911)
Test: [400/531]	Time 0.268 (0.272)	Loss 0.9677 (1.1629)	Accu 0.924 (0.911)
Test: [500/531]	Time 0.254 (0.272)	Loss 1.2490 (1.1570)	Accu 0.906 (0.911)
 * Accu 0.911
====================================================================================================
	     Attr              	p_true/n_true	p_tol/n_tol	p_pred/n_pred	cur_mA
	# 0: Female            	   5106\  10169	   5284\  11667	   6604\  10347	0.91896
	# 1: AgeLess16         	     87\  16697	    150\  16835	    225\  16760	0.78590
	# 2: Age17-30          	   5990\   4586	   6790\  10195	  11599\   5386	0.66600
	# 3: Age31-45          	   8456\   2630	   9402\   7583	  13409\   3576	0.62311
	# 4: BodyFat           	     39\  16329	    570\  16415	    125\  16860	0.53159
	# 5: BodyNormal        	    141\  14567	   2305\  14680	    254\  16731	0.52674
	# 6: BodyThin          	  13161\      0	  13161\   3824	  16985\      0	0.50000
	# 7: Customer          	      1\  15671	   1314\  15671	      1\  16984	0.50038
	# 8: Clerk             	  15931\    352	  15961\   1024	  16603\    382	0.67094
	# 9: BaldHead          	    623\  15888	    903\  16082	    817\  16168	0.83893
	#10: LongHair          	     37\  16871	     92\  16893	     59\  16926	0.70044
	#11: BlackHair         	   2932\  12778	   3276\  13709	   3863\  13122	0.91354
	#12: Hat               	  15831\    187	  15860\   1125	  16769\    216	0.58220
	#13: Glasses           	    114\  16688	    282\  16703	    129\  16856	0.70168
	#14: Muffler           	    508\  15730	   1125\  15860	    638\  16347	0.72168
	#15: Shirt             	   2447\  12632	   3645\  13340	   3155\  13830	0.80913
	#16: Sweater           	    267\  15178	   1303\  15682	    771\  16214	0.58639
	#17: Vest              	    610\  15865	    779\  16206	    951\  16034	0.88101
	#18: TShirt            	   3171\   9689	   3957\  13028	   6510\  10475	0.77254
	#19: Cotton            	   1394\  13636	   1863\  15122	   2880\  14105	0.82499
	#20: Jacket            	   4192\   9205	   4889\  12096	   7083\   9902	0.80922
	#21: Suit-Up           	    217\  16382	    525\  16460	    295\  16690	0.70430
	#22: Tight             	    319\  15665	    746\  16239	    893\  16092	0.69613
	#23: ShortSleeve       	   1450\  14480	   1633\  15352	   2322\  14663	0.91557
	#24: LongTrousers      	      0\  16555	    430\  16555	      0\  16985	0.50000
	#25: Skirt             	   9426\   4510	   9840\   7145	  12061\   4924	0.79457
	#26: ShortSkirt        	    208\  16337	    488\  16497	    368\  16617	0.70827
	#27: Dress             	    165\  16513	    354\  16631	    283\  16702	0.72950
	#28: Jeans             	    362\  15940	    506\  16479	    901\  16084	0.84135
	#29: TightTrousers     	   4124\  11403	   4442\  12543	   5264\  11721	0.91876
	#30: LeatherShoes      	   1796\  13848	   1977\  15008	   2956\  14029	0.91558
	#31: SportShoes        	   3536\  10519	   4449\  12536	   5553\  11432	0.81694
	#32: Boots             	   2718\  10602	   3401\  13584	   5700\  11285	0.78983
	#33: ClothShoes        	   1282\  14757	   1378\  15607	   2132\  14853	0.93794
	#34: CasualShoes       	    108\  16590	    262\  16723	    241\  16744	0.70213
	#35: Backpack          	   3161\   8336	   3895\  13090	   7915\   9070	0.72419
	#36: SSBag             	    737\  13721	   1436\  15549	   2565\  14420	0.69783
	#37: HandBag           	    195\  16351	    367\  16618	    462\  16523	0.75763
	#38: Box               	    763\  15079	   1188\  15797	   1481\  15504	0.79840
	#39: PlasticBag        	    202\  16246	    443\  16542	    498\  16487	0.71904
	#40: PaperBag          	    257\  16177	    661\  16324	    404\  16581	0.68990
	#41: HandTrunk         	    169\  16350	    428\  16557	    376\  16609	0.69118
	#42: OtherAttchment    	      1\  16804	    181\  16804	      1\  16984	0.50276
	#43: Calling           	    298\  16335	    420\  16565	    528\  16457	0.84782
	#44: Talking           	   3354\   9232	   5013\  11972	   6094\  10891	0.72010
	#45: Gathering         	    388\  16148	    542\  16443	    683\  16302	0.84896
	#46: Holding           	     23\  16444	    460\  16525	    104\  16881	0.52255
	#47: Pusing            	     46\  16080	    829\  16156	    122\  16863	0.52539
	#48: Pulling           	     65\  16504	    430\  16555	    116\  16869	0.57404
	#49: CarryingbyArm     	    118\  16650	    166\  16819	    287\  16698	0.85040
	#50: CarryingbyHand    	    152\  16472	    321\  16664	    344\  16641	0.73100
	mA:        0.7262236883133811
	Accuracy:  0.6180353370625473
	Precision: 0.6933766020171329
	Recall:    0.8369415628364497
	F1_Score:  0.7584248952334295
====================================================================================================

Last epoch:
Learning Rate: 1.0000000000000002e-07

Epoch: [60][0/2124]	Time 1.221 (1.221)	Loss 0.0951 (0.0951)	Accu 0.994 (0.994)
Epoch: [60][100/2124]	Time 0.942 (0.945)	Loss 0.0676 (0.0749)	Accu 0.994 (0.995)
Epoch: [60][200/2124]	Time 0.948 (0.943)	Loss 0.0711 (0.0776)	Accu 0.997 (0.995)
Epoch: [60][300/2124]	Time 0.937 (0.943)	Loss 0.0808 (0.0778)	Accu 0.993 (0.994)
Epoch: [60][400/2124]	Time 0.945 (0.942)	Loss 0.0667 (0.0791)	Accu 0.996 (0.995)
Epoch: [60][500/2124]	Time 0.948 (0.942)	Loss 0.0914 (0.0791)	Accu 0.995 (0.995)
Epoch: [60][600/2124]	Time 0.937 (0.942)	Loss 0.1048 (0.0791)	Accu 0.994 (0.995)
Epoch: [60][700/2124]	Time 0.943 (0.943)	Loss 0.0667 (0.0790)	Accu 0.996 (0.995)
Epoch: [60][800/2124]	Time 0.938 (0.942)	Loss 0.0862 (0.0786)	Accu 0.994 (0.995)
Epoch: [60][900/2124]	Time 0.945 (0.942)	Loss 0.0577 (0.0782)	Accu 0.996 (0.995)
Epoch: [60][1000/2124]	Time 0.945 (0.942)	Loss 0.0878 (0.0785)	Accu 0.995 (0.995)
Epoch: [60][1100/2124]	Time 0.939 (0.942)	Loss 0.0633 (0.0784)	Accu 0.997 (0.995)
Epoch: [60][1200/2124]	Time 0.939 (0.942)	Loss 0.0653 (0.0784)	Accu 0.998 (0.995)
Epoch: [60][1300/2124]	Time 0.936 (0.942)	Loss 0.0223 (0.0779)	Accu 0.993 (0.995)
Epoch: [60][1400/2124]	Time 0.946 (0.942)	Loss 0.0895 (0.0776)	Accu 0.996 (0.995)
Epoch: [60][1500/2124]	Time 0.935 (0.942)	Loss 0.0688 (0.0776)	Accu 0.994 (0.995)
Epoch: [60][1600/2124]	Time 0.940 (0.942)	Loss 0.1036 (0.0775)	Accu 0.995 (0.995)
Epoch: [60][1700/2124]	Time 0.942 (0.942)	Loss 0.0848 (0.0776)	Accu 0.996 (0.995)
Epoch: [60][1800/2124]	Time 0.943 (0.942)	Loss 0.0623 (0.0777)	Accu 0.998 (0.995)
Epoch: [60][1900/2124]	Time 0.939 (0.942)	Loss 0.0829 (0.0775)	Accu 0.994 (0.995)
Epoch: [60][2000/2124]	Time 0.941 (0.942)	Loss 0.0599 (0.0776)	Accu 0.999 (0.995)
Epoch: [60][2100/2124]	Time 0.949 (0.942)	Loss 0.0891 (0.0777)	Accu 0.995 (0.995)
Test: [0/531]	Time 0.506 (0.506)	Loss 2.9564 (2.9564)	Accu 0.920 (0.920)
Test: [100/531]	Time 0.244 (0.249)	Loss 2.0963 (2.4719)	Accu 0.929 (0.924)
Test: [200/531]	Time 0.245 (0.247)	Loss 2.2751 (2.4434)	Accu 0.929 (0.924)
Test: [300/531]	Time 0.248 (0.246)	Loss 1.8800 (2.4323)	Accu 0.935 (0.924)
Test: [400/531]	Time 0.245 (0.246)	Loss 1.9830 (2.4411)	Accu 0.938 (0.924)
Test: [500/531]	Time 0.245 (0.246)	Loss 2.3173 (2.4350)	Accu 0.920 (0.924)
 * Accu 0.924
====================================================================================================
	     Attr              	p_true/n_true	p_tol/n_tol	p_pred/n_pred	cur_mA
	# 0: Female            	   5058\  11188	   5284\  11667	   5537\  11414	0.95809
	# 1: AgeLess16         	     91\  16806	    150\  16835	    120\  16865	0.80247
	# 2: Age17-30          	   4506\   7401	   6790\  10195	   7300\   9685	0.69478
	# 3: Age31-45          	   7356\   4045	   9402\   7583	  10894\   6091	0.65791
	# 4: BodyFat           	    124\  16119	    570\  16415	    420\  16565	0.59976
	# 5: BodyNormal        	    696\  13723	   2305\  14680	   1653\  15332	0.61838
	# 6: BodyThin          	  12348\    744	  13161\   3824	  15428\   1557	0.56639
	# 7: Customer          	    267\  14980	   1314\  15671	    958\  16027	0.57955
	# 8: Clerk             	  15885\    561	  15961\   1024	  16348\    637	0.77154
	# 9: BaldHead          	    632\  15939	    903\  16082	    775\  16210	0.84550
	#10: LongHair          	     54\  16858	     92\  16893	     89\  16896	0.79244
	#11: BlackHair         	   2870\  13193	   3276\  13709	   3386\  13599	0.91921
	#12: Hat               	  15706\    516	  15860\   1125	  16315\    670	0.72448
	#13: Glasses           	    190\  16660	    282\  16703	    233\  16752	0.83559
	#14: Muffler           	    783\  15572	   1125\  15860	   1071\  15914	0.83892
	#15: Shirt             	   2878\  12508	   3645\  13340	   3710\  13275	0.86360
	#16: Sweater           	    400\  15032	   1303\  15682	   1050\  15935	0.63277
	#17: Vest              	    630\  16060	    779\  16206	    776\  16209	0.89986
	#18: TShirt            	   2666\  11162	   3957\  13028	   4532\  12453	0.76526
	#19: Cotton            	   1211\  14245	   1863\  15122	   2088\  14897	0.79602
	#20: Jacket            	   3642\  10395	   4889\  12096	   5343\  11642	0.80216
	#21: Suit-Up           	    272\  16317	    525\  16460	    415\  16570	0.75470
	#22: Tight             	    301\  15919	    746\  16239	    621\  16364	0.69189
	#23: ShortSleeve       	   1325\  15059	   1633\  15352	   1618\  15367	0.89615
	#24: LongTrousers      	     80\  16025	    430\  16555	    610\  16375	0.57702
	#25: Skirt             	   8901\   5804	   9840\   7145	  10242\   6743	0.85844
	#26: ShortSkirt        	    236\  16283	    488\  16497	    450\  16535	0.73532
	#27: Dress             	    168\  16529	    354\  16631	    270\  16715	0.73422
	#28: Jeans             	    257\  16267	    506\  16479	    469\  16516	0.74752
	#29: TightTrousers     	   3824\  11749	   4442\  12543	   4618\  12367	0.89879
	#30: LeatherShoes      	   1571\  14450	   1977\  15008	   2129\  14856	0.87873
	#31: SportShoes        	   3315\  11015	   4449\  12536	   4836\  12149	0.81189
	#32: Boots             	   1929\  11835	   3401\  13584	   3678\  13307	0.71922
	#33: ClothShoes        	   1166\  15311	   1378\  15607	   1462\  15523	0.91359
	#34: CasualShoes       	    115\  16571	    262\  16723	    267\  16718	0.71492
	#35: Backpack          	   1928\  10731	   3895\  13090	   4287\  12698	0.65739
	#36: SSBag             	    447\  14624	   1436\  15549	   1372\  15613	0.62590
	#37: HandBag           	    215\  16545	    367\  16618	    288\  16697	0.79072
	#38: Box               	    818\  15392	   1188\  15797	   1223\  15762	0.83146
	#39: PlasticBag        	    235\  16345	    443\  16542	    432\  16553	0.75928
	#40: PaperBag          	    336\  16106	    661\  16324	    554\  16431	0.74748
	#41: HandTrunk         	    224\  16339	    428\  16557	    442\  16543	0.75510
	#42: OtherAttchment    	     52\  16663	    181\  16804	    193\  16792	0.63945
	#43: Calling           	    285\  16509	    420\  16565	    341\  16644	0.83760
	#44: Talking           	   3333\  10114	   5013\  11972	   5191\  11794	0.75484
	#45: Gathering         	    380\  16319	    542\  16443	    504\  16481	0.84678
	#46: Holding           	     65\  16206	    460\  16525	    384\  16601	0.56100
	#47: Pusing            	    188\  15633	    829\  16156	    711\  16274	0.59720
	#48: Pulling           	    126\  16426	    430\  16555	    255\  16730	0.64262
	#49: CarryingbyArm     	    119\  16767	    166\  16819	    171\  16814	0.85689
	#50: CarryingbyHand    	    169\  16577	    321\  16664	    256\  16729	0.76063
	mA:        0.7561061703050626
	Accuracy:  0.6462470962802557
	Precision: 0.7595584857564548
	Recall:    0.7902205159181761
	F1_Score:  0.7745861801404217
====================================================================================================

Process finished with exit code 0

On PETA dataset:
First epoch:
Epoch: [0][0/357]	Time 0.908 (0.908)	Loss 3.5823 (3.5823)	Accu 0.267 (0.267)
Epoch: [0][100/357]	Time 0.627 (0.620)	Loss 2.3207 (2.4436)	Accu 0.779 (0.728)
Epoch: [0][200/357]	Time 0.638 (0.624)	Loss 1.7467 (2.2292)	Accu 0.829 (0.768)
Epoch: [0][300/357]	Time 0.635 (0.628)	Loss 1.6015 (2.0944)	Accu 0.845 (0.789)
Test: [0/238]	Time 0.394 (0.394)	Loss 1.6004 (1.6004)	Accu 0.858 (0.858)
Test: [100/238]	Time 0.182 (0.184)	Loss 1.6022 (1.6593)	Accu 0.860 (0.848)
Test: [200/238]	Time 0.183 (0.184)	Loss 1.6460 (1.6428)	Accu 0.856 (0.848)
 * Accu 0.848
====================================================================================================
	     Attr              	p_true/n_true	p_tol/n_tol	p_pred/n_pred	cur_mA
	# 0: Age16-30          	3449\2471	3831\3769	4747\2853	0.77795
	# 1: Age31-45          	2150\3038	2441\5159	4271\3329	0.73483
	# 2: Age46-60          	 502\6041	 779\6821	1282\6318	0.76503
	# 3: AgeAbove61        	 381\6673	 461\7139	 847\6753	0.88059
	# 4: Backpack          	1189\5128	1497\6103	2164\5436	0.81725
	# 5: CarryingOther     	 849\4346	1531\6069	2572\5028	0.63532
	# 6: Casual lower      	6426\ 457	6546\1054	7023\ 577	0.70763
	# 7: Casual upper      	6389\ 430	6495\1105	7064\ 536	0.68641
	# 8: Formal lower      	 859\5343	1043\6557	2073\5527	0.81922
	# 9: Formal upper      	 848\5410	1007\6593	2031\5569	0.83134
	#10: Hat               	 603\6176	 725\6875	1302\6298	0.86503
	#11: Jacket            	 152\6875	 534\7066	 343\7257	0.62881
	#12: Jeans             	1830\4260	2315\5285	2855\4745	0.79828
	#13: Leather Shoes     	1944\3570	2215\5385	3759\3841	0.77030
	#14: Logo              	  14\7288	 296\7304	  30\7570	0.52255
	#15: Long hair         	1595\4835	1804\5796	2556\5044	0.85917
	#16: Male              	3983\2330	4147\3453	5106\2494	0.81761
	#17: Messenger Bag     	1631\3943	2275\5325	3013\4587	0.72870
	#18: Muffler           	 527\6543	 632\6968	 952\6648	0.88643
	#19: No accessory      	5676\ 729	5742\1858	6805\ 795	0.69043
	#20: No carrying       	1647\4041	2091\5509	3115\4485	0.76059
	#21: Plaid             	 138\7293	 203\7397	 242\7358	0.83287
	#22: PlasticBags       	 305\6472	 585\7015	 848\6752	0.72198
	#23: Sandals           	  28\7384	 144\7456	 100\7500	0.59239
	#24: Shoes             	2372\2281	2774\4826	4917\2683	0.66387
	#25: Shorts            	 149\7037	 261\7339	 451\7149	0.76487
	#26: Short Sleeve      	 936\5636	1090\6510	1810\5790	0.86223
	#27: Skirt             	 194\6966	 351\7249	 477\7123	0.75683
	#28: Sneaker           	1172\4689	1675\5925	2408\5192	0.74555
	#29: Stripes           	  25\7408	 126\7474	  91\7509	0.59479
	#30: Sunglasses        	  15\7359	 218\7382	  38\7562	0.53285
	#31: Trousers          	3698\1478	3949\3651	5871\1729	0.67063
	#32: Tshirt            	 414\6497	 640\6960	 877\6723	0.79018
	#33: UpperOther        	3065\2477	3456\4144	4732\2868	0.74230
	#34: V-Neck            	   0\7520	  80\7520	   0\7600	0.50000
	mA:        0.7358514624787084
	Accuracy:  0.595082002485614
	Precision: 0.645127954842694
	Recall:    0.8638696694387276
	F1_Score:  0.7386445997368981
====================================================================================================

Last epoch:
Learning Rate: 1.0000000000000002e-06
Epoch: [59][0/357]	Time 0.870 (0.870)	Loss 0.0104 (0.0104)	Accu 1.000 (1.000)
Epoch: [59][100/357]	Time 0.671 (0.675)	Loss 0.0078 (0.0112)	Accu 1.000 (1.000)
Epoch: [59][200/357]	Time 0.671 (0.674)	Loss 0.0105 (0.0110)	Accu 0.998 (1.000)
Epoch: [59][300/357]	Time 0.673 (0.674)	Loss 0.0089 (0.0107)	Accu 0.999 (1.000)
Test: [0/238]	Time 0.399 (0.399)	Loss 1.8962 (1.8962)	Accu 0.940 (0.940)
Test: [100/238]	Time 0.179 (0.181)	Loss 1.7418 (2.2203)	Accu 0.952 (0.931)
Test: [200/238]	Time 0.181 (0.180)	Loss 2.4142 (2.2189)	Accu 0.923 (0.932)
 * Accu 0.932
====================================================================================================
	     Attr              	p_true/n_true	p_tol/n_tol	p_pred/n_pred	cur_mA
	# 0: Age16-30          	3364\3309	3831\3769	3824\3776	0.87803
	# 1: Age31-45          	2057\4430	2441\5159	2786\4814	0.85069
	# 2: Age46-60          	 599\6678	 779\6821	 742\6858	0.87398
	# 3: AgeAbove61        	 421\7126	 461\7139	 434\7166	0.95571
	# 4: Backpack          	1180\5786	1497\6103	1497\6103	0.86815
	# 5: CarryingOther     	1002\5591	1531\6069	1480\6120	0.78786
	# 6: Casual lower      	6400\ 810	6546\1054	6644\ 956	0.87310
	# 7: Casual upper      	6335\ 833	6495\1105	6607\ 993	0.86461
	# 8: Formal lower      	 861\6309	1043\6557	1109\6491	0.89384
	# 9: Formal upper      	 835\6364	1007\6593	1064\6536	0.89723
	#10: Hat               	 629\6833	 725\6875	 671\6929	0.93074
	#11: Jacket            	 322\6957	 534\7066	 431\7169	0.79379
	#12: Jeans             	1970\4841	2315\5285	2414\5186	0.88348
	#13: Leather Shoes     	1913\4953	2215\5385	2345\5255	0.89172
	#14: Logo              	 167\7196	 296\7304	 275\7325	0.77470
	#15: Long hair         	1581\5527	1804\5796	1850\5750	0.91499
	#16: Male              	3918\3103	4147\3453	4268\3332	0.92171
	#17: Messenger Bag     	1830\4807	2275\5325	2348\5252	0.85356
	#18: Muffler           	 588\6921	 632\6968	 635\6965	0.96182
	#19: No accessory      	5540\1406	5742\1858	5992\1608	0.86077
	#20: No carrying       	1683\5024	2091\5509	2168\5432	0.85842
	#21: Plaid             	 151\7359	 203\7397	 189\7411	0.86935
	#22: PlasticBags       	 426\6949	 585\7015	 492\7108	0.85940
	#23: Sandals           	  68\7370	 144\7456	 154\7446	0.73034
	#24: Shoes             	2182\3994	2774\4826	3014\4586	0.80710
	#25: Shorts            	 160\7270	 261\7339	 229\7371	0.80181
	#26: Short Sleeve      	 917\6220	1090\6510	1207\6393	0.89837
	#27: Skirt             	 243\7164	 351\7249	 328\7272	0.84029
	#28: Sneaker           	1123\5555	1675\5925	1493\6107	0.80400
	#29: Stripes           	  57\7433	 126\7474	  98\7502	0.72345
	#30: Sunglasses        	  93\7235	 218\7382	 240\7360	0.70335
	#31: Trousers          	3502\3059	3949\3651	4094\3506	0.86233
	#32: Tshirt            	 430\6757	 640\6960	 633\6967	0.82135
	#33: UpperOther        	2900\3690	3456\4144	3354\4246	0.86478
	#34: V-Neck            	  35\7464	  80\7520	  91\7509	0.71503
	mA:        0.8482808189594254
	Accuracy:  0.7842523445229501
	Precision: 0.8555594485923317
	Recall:    0.8686840206576898
	F1_Score:  0.8620717839089516

Yes, I also observed that. I conjecture that this is because the backbone and training strategy (Adam, Weight loss) is really powerful, but the dataset is relatively small.
Anyway, training more epochs can further improve the results (mA 73->85 for PETA).

I managed to run the codes for RAP and PETA datasets. However, I get confusing results and really appreciate any hint from your side to justify them.
In the first epoch, I expect that the results on the test set be near random. However, I get very good results. I know that transfer learning can improve the results, but not mA: 0.72, Accuracy: 0.61, Precision: 0.69, Recall: 0.83, F1_Score: 0.75, which are competitive to ACN (f1: 75.98), DeepMar(f1: 75.56), and JRL(f1: 74.62). I got these values from your paper.
The results of the first and last epoch are as follows:
On RAP:
After first epoch:
Epoch: [0][0/2124]	Time 1.167 (1.167)	Loss 3.7118 (3.7118)	Accu 0.210 (0.210)
Epoch: [0][100/2124]	Time 0.878 (0.886)	Loss 1.5951 (2.0204)	Accu 0.866 (0.785)
Epoch: [0][200/2124]	Time 0.889 (0.885)	Loss 1.5291 (1.8360)	Accu 0.878 (0.829)
Epoch: [0][300/2124]	Time 0.904 (0.897)	Loss 1.4167 (1.7277)	Accu 0.888 (0.848)
Epoch: [0][400/2124]	Time 0.882 (0.901)	Loss 1.2797 (1.6585)	Accu 0.895 (0.858)
Epoch: [0][500/2124]	Time 0.887 (0.898)	Loss 1.4562 (1.6092)	Accu 0.889 (0.865)
Epoch: [0][600/2124]	Time 0.887 (0.896)	Loss 1.3090 (1.5669)	Accu 0.893 (0.870)
Epoch: [0][700/2124]	Time 0.892 (0.896)	Loss 1.3036 (1.5345)	Accu 0.902 (0.874)
Epoch: [0][800/2124]	Time 0.881 (0.897)	Loss 1.1357 (1.5079)	Accu 0.909 (0.877)
Epoch: [0][900/2124]	Time 0.889 (0.900)	Loss 1.2629 (1.4862)	Accu 0.905 (0.879)
Epoch: [0][1000/2124]	Time 0.887 (0.901)	Loss 1.3137 (1.4677)	Accu 0.895 (0.881)
Epoch: [0][1100/2124]	Time 0.888 (0.902)	Loss 1.1418 (1.4485)	Accu 0.907 (0.883)
Epoch: [0][1200/2124]	Time 0.893 (0.904)	Loss 1.0898 (1.4303)	Accu 0.906 (0.885)
Epoch: [0][1300/2124]	Time 0.901 (0.906)	Loss 1.1799 (1.4150)	Accu 0.907 (0.886)
Epoch: [0][1400/2124]	Time 0.917 (0.906)	Loss 1.2728 (1.4011)	Accu 0.908 (0.888)
Epoch: [0][1500/2124]	Time 0.890 (0.908)	Loss 1.1938 (1.3896)	Accu 0.913 (0.889)
Epoch: [0][1600/2124]	Time 0.890 (0.909)	Loss 1.3497 (1.3785)	Accu 0.899 (0.890)
Epoch: [0][1700/2124]	Time 0.886 (0.909)	Loss 1.1483 (1.3673)	Accu 0.902 (0.891)
Epoch: [0][1800/2124]	Time 0.885 (0.909)	Loss 1.1885 (1.3576)	Accu 0.913 (0.892)
Epoch: [0][1900/2124]	Time 0.889 (0.908)	Loss 1.1853 (1.3481)	Accu 0.903 (0.892)
Epoch: [0][2000/2124]	Time 0.979 (0.908)	Loss 1.2094 (1.3386)	Accu 0.906 (0.893)
Epoch: [0][2100/2124]	Time 0.917 (0.909)	Loss 1.2271 (1.3301)	Accu 0.914 (0.894)
Test: [0/531]	Time 0.525 (0.525)	Loss 1.2188 (1.2188)	Accu 0.913 (0.913)
Test: [100/531]	Time 0.252 (0.271)	Loss 1.0559 (1.1692)	Accu 0.921 (0.910)
Test: [200/531]	Time 0.255 (0.271)	Loss 1.1272 (1.1629)	Accu 0.917 (0.910)
Test: [300/531]	Time 0.290 (0.273)	Loss 1.1043 (1.1609)	Accu 0.911 (0.911)
Test: [400/531]	Time 0.268 (0.272)	Loss 0.9677 (1.1629)	Accu 0.924 (0.911)
Test: [500/531]	Time 0.254 (0.272)	Loss 1.2490 (1.1570)	Accu 0.906 (0.911)
 * Accu 0.911
====================================================================================================
	     Attr              	p_true/n_true	p_tol/n_tol	p_pred/n_pred	cur_mA
	# 0: Female            	   5106\  10169	   5284\  11667	   6604\  10347	0.91896
	# 1: AgeLess16         	     87\  16697	    150\  16835	    225\  16760	0.78590
	# 2: Age17-30          	   5990\   4586	   6790\  10195	  11599\   5386	0.66600
	# 3: Age31-45          	   8456\   2630	   9402\   7583	  13409\   3576	0.62311
	# 4: BodyFat           	     39\  16329	    570\  16415	    125\  16860	0.53159
	# 5: BodyNormal        	    141\  14567	   2305\  14680	    254\  16731	0.52674
	# 6: BodyThin          	  13161\      0	  13161\   3824	  16985\      0	0.50000
	# 7: Customer          	      1\  15671	   1314\  15671	      1\  16984	0.50038
	# 8: Clerk             	  15931\    352	  15961\   1024	  16603\    382	0.67094
	# 9: BaldHead          	    623\  15888	    903\  16082	    817\  16168	0.83893
	#10: LongHair          	     37\  16871	     92\  16893	     59\  16926	0.70044
	#11: BlackHair         	   2932\  12778	   3276\  13709	   3863\  13122	0.91354
	#12: Hat               	  15831\    187	  15860\   1125	  16769\    216	0.58220
	#13: Glasses           	    114\  16688	    282\  16703	    129\  16856	0.70168
	#14: Muffler           	    508\  15730	   1125\  15860	    638\  16347	0.72168
	#15: Shirt             	   2447\  12632	   3645\  13340	   3155\  13830	0.80913
	#16: Sweater           	    267\  15178	   1303\  15682	    771\  16214	0.58639
	#17: Vest              	    610\  15865	    779\  16206	    951\  16034	0.88101
	#18: TShirt            	   3171\   9689	   3957\  13028	   6510\  10475	0.77254
	#19: Cotton            	   1394\  13636	   1863\  15122	   2880\  14105	0.82499
	#20: Jacket            	   4192\   9205	   4889\  12096	   7083\   9902	0.80922
	#21: Suit-Up           	    217\  16382	    525\  16460	    295\  16690	0.70430
	#22: Tight             	    319\  15665	    746\  16239	    893\  16092	0.69613
	#23: ShortSleeve       	   1450\  14480	   1633\  15352	   2322\  14663	0.91557
	#24: LongTrousers      	      0\  16555	    430\  16555	      0\  16985	0.50000
	#25: Skirt             	   9426\   4510	   9840\   7145	  12061\   4924	0.79457
	#26: ShortSkirt        	    208\  16337	    488\  16497	    368\  16617	0.70827
	#27: Dress             	    165\  16513	    354\  16631	    283\  16702	0.72950
	#28: Jeans             	    362\  15940	    506\  16479	    901\  16084	0.84135
	#29: TightTrousers     	   4124\  11403	   4442\  12543	   5264\  11721	0.91876
	#30: LeatherShoes      	   1796\  13848	   1977\  15008	   2956\  14029	0.91558
	#31: SportShoes        	   3536\  10519	   4449\  12536	   5553\  11432	0.81694
	#32: Boots             	   2718\  10602	   3401\  13584	   5700\  11285	0.78983
	#33: ClothShoes        	   1282\  14757	   1378\  15607	   2132\  14853	0.93794
	#34: CasualShoes       	    108\  16590	    262\  16723	    241\  16744	0.70213
	#35: Backpack          	   3161\   8336	   3895\  13090	   7915\   9070	0.72419
	#36: SSBag             	    737\  13721	   1436\  15549	   2565\  14420	0.69783
	#37: HandBag           	    195\  16351	    367\  16618	    462\  16523	0.75763
	#38: Box               	    763\  15079	   1188\  15797	   1481\  15504	0.79840
	#39: PlasticBag        	    202\  16246	    443\  16542	    498\  16487	0.71904
	#40: PaperBag          	    257\  16177	    661\  16324	    404\  16581	0.68990
	#41: HandTrunk         	    169\  16350	    428\  16557	    376\  16609	0.69118
	#42: OtherAttchment    	      1\  16804	    181\  16804	      1\  16984	0.50276
	#43: Calling           	    298\  16335	    420\  16565	    528\  16457	0.84782
	#44: Talking           	   3354\   9232	   5013\  11972	   6094\  10891	0.72010
	#45: Gathering         	    388\  16148	    542\  16443	    683\  16302	0.84896
	#46: Holding           	     23\  16444	    460\  16525	    104\  16881	0.52255
	#47: Pusing            	     46\  16080	    829\  16156	    122\  16863	0.52539
	#48: Pulling           	     65\  16504	    430\  16555	    116\  16869	0.57404
	#49: CarryingbyArm     	    118\  16650	    166\  16819	    287\  16698	0.85040
	#50: CarryingbyHand    	    152\  16472	    321\  16664	    344\  16641	0.73100
	mA:        0.7262236883133811
	Accuracy:  0.6180353370625473
	Precision: 0.6933766020171329
	Recall:    0.8369415628364497
	F1_Score:  0.7584248952334295
====================================================================================================

Last epoch:
Learning Rate: 1.0000000000000002e-07

Epoch: [60][0/2124]	Time 1.221 (1.221)	Loss 0.0951 (0.0951)	Accu 0.994 (0.994)
Epoch: [60][100/2124]	Time 0.942 (0.945)	Loss 0.0676 (0.0749)	Accu 0.994 (0.995)
Epoch: [60][200/2124]	Time 0.948 (0.943)	Loss 0.0711 (0.0776)	Accu 0.997 (0.995)
Epoch: [60][300/2124]	Time 0.937 (0.943)	Loss 0.0808 (0.0778)	Accu 0.993 (0.994)
Epoch: [60][400/2124]	Time 0.945 (0.942)	Loss 0.0667 (0.0791)	Accu 0.996 (0.995)
Epoch: [60][500/2124]	Time 0.948 (0.942)	Loss 0.0914 (0.0791)	Accu 0.995 (0.995)
Epoch: [60][600/2124]	Time 0.937 (0.942)	Loss 0.1048 (0.0791)	Accu 0.994 (0.995)
Epoch: [60][700/2124]	Time 0.943 (0.943)	Loss 0.0667 (0.0790)	Accu 0.996 (0.995)
Epoch: [60][800/2124]	Time 0.938 (0.942)	Loss 0.0862 (0.0786)	Accu 0.994 (0.995)
Epoch: [60][900/2124]	Time 0.945 (0.942)	Loss 0.0577 (0.0782)	Accu 0.996 (0.995)
Epoch: [60][1000/2124]	Time 0.945 (0.942)	Loss 0.0878 (0.0785)	Accu 0.995 (0.995)
Epoch: [60][1100/2124]	Time 0.939 (0.942)	Loss 0.0633 (0.0784)	Accu 0.997 (0.995)
Epoch: [60][1200/2124]	Time 0.939 (0.942)	Loss 0.0653 (0.0784)	Accu 0.998 (0.995)
Epoch: [60][1300/2124]	Time 0.936 (0.942)	Loss 0.0223 (0.0779)	Accu 0.993 (0.995)
Epoch: [60][1400/2124]	Time 0.946 (0.942)	Loss 0.0895 (0.0776)	Accu 0.996 (0.995)
Epoch: [60][1500/2124]	Time 0.935 (0.942)	Loss 0.0688 (0.0776)	Accu 0.994 (0.995)
Epoch: [60][1600/2124]	Time 0.940 (0.942)	Loss 0.1036 (0.0775)	Accu 0.995 (0.995)
Epoch: [60][1700/2124]	Time 0.942 (0.942)	Loss 0.0848 (0.0776)	Accu 0.996 (0.995)
Epoch: [60][1800/2124]	Time 0.943 (0.942)	Loss 0.0623 (0.0777)	Accu 0.998 (0.995)
Epoch: [60][1900/2124]	Time 0.939 (0.942)	Loss 0.0829 (0.0775)	Accu 0.994 (0.995)
Epoch: [60][2000/2124]	Time 0.941 (0.942)	Loss 0.0599 (0.0776)	Accu 0.999 (0.995)
Epoch: [60][2100/2124]	Time 0.949 (0.942)	Loss 0.0891 (0.0777)	Accu 0.995 (0.995)
Test: [0/531]	Time 0.506 (0.506)	Loss 2.9564 (2.9564)	Accu 0.920 (0.920)
Test: [100/531]	Time 0.244 (0.249)	Loss 2.0963 (2.4719)	Accu 0.929 (0.924)
Test: [200/531]	Time 0.245 (0.247)	Loss 2.2751 (2.4434)	Accu 0.929 (0.924)
Test: [300/531]	Time 0.248 (0.246)	Loss 1.8800 (2.4323)	Accu 0.935 (0.924)
Test: [400/531]	Time 0.245 (0.246)	Loss 1.9830 (2.4411)	Accu 0.938 (0.924)
Test: [500/531]	Time 0.245 (0.246)	Loss 2.3173 (2.4350)	Accu 0.920 (0.924)
 * Accu 0.924
====================================================================================================
	     Attr              	p_true/n_true	p_tol/n_tol	p_pred/n_pred	cur_mA
	# 0: Female            	   5058\  11188	   5284\  11667	   5537\  11414	0.95809
	# 1: AgeLess16         	     91\  16806	    150\  16835	    120\  16865	0.80247
	# 2: Age17-30          	   4506\   7401	   6790\  10195	   7300\   9685	0.69478
	# 3: Age31-45          	   7356\   4045	   9402\   7583	  10894\   6091	0.65791
	# 4: BodyFat           	    124\  16119	    570\  16415	    420\  16565	0.59976
	# 5: BodyNormal        	    696\  13723	   2305\  14680	   1653\  15332	0.61838
	# 6: BodyThin          	  12348\    744	  13161\   3824	  15428\   1557	0.56639
	# 7: Customer          	    267\  14980	   1314\  15671	    958\  16027	0.57955
	# 8: Clerk             	  15885\    561	  15961\   1024	  16348\    637	0.77154
	# 9: BaldHead          	    632\  15939	    903\  16082	    775\  16210	0.84550
	#10: LongHair          	     54\  16858	     92\  16893	     89\  16896	0.79244
	#11: BlackHair         	   2870\  13193	   3276\  13709	   3386\  13599	0.91921
	#12: Hat               	  15706\    516	  15860\   1125	  16315\    670	0.72448
	#13: Glasses           	    190\  16660	    282\  16703	    233\  16752	0.83559
	#14: Muffler           	    783\  15572	   1125\  15860	   1071\  15914	0.83892
	#15: Shirt             	   2878\  12508	   3645\  13340	   3710\  13275	0.86360
	#16: Sweater           	    400\  15032	   1303\  15682	   1050\  15935	0.63277
	#17: Vest              	    630\  16060	    779\  16206	    776\  16209	0.89986
	#18: TShirt            	   2666\  11162	   3957\  13028	   4532\  12453	0.76526
	#19: Cotton            	   1211\  14245	   1863\  15122	   2088\  14897	0.79602
	#20: Jacket            	   3642\  10395	   4889\  12096	   5343\  11642	0.80216
	#21: Suit-Up           	    272\  16317	    525\  16460	    415\  16570	0.75470
	#22: Tight             	    301\  15919	    746\  16239	    621\  16364	0.69189
	#23: ShortSleeve       	   1325\  15059	   1633\  15352	   1618\  15367	0.89615
	#24: LongTrousers      	     80\  16025	    430\  16555	    610\  16375	0.57702
	#25: Skirt             	   8901\   5804	   9840\   7145	  10242\   6743	0.85844
	#26: ShortSkirt        	    236\  16283	    488\  16497	    450\  16535	0.73532
	#27: Dress             	    168\  16529	    354\  16631	    270\  16715	0.73422
	#28: Jeans             	    257\  16267	    506\  16479	    469\  16516	0.74752
	#29: TightTrousers     	   3824\  11749	   4442\  12543	   4618\  12367	0.89879
	#30: LeatherShoes      	   1571\  14450	   1977\  15008	   2129\  14856	0.87873
	#31: SportShoes        	   3315\  11015	   4449\  12536	   4836\  12149	0.81189
	#32: Boots             	   1929\  11835	   3401\  13584	   3678\  13307	0.71922
	#33: ClothShoes        	   1166\  15311	   1378\  15607	   1462\  15523	0.91359
	#34: CasualShoes       	    115\  16571	    262\  16723	    267\  16718	0.71492
	#35: Backpack          	   1928\  10731	   3895\  13090	   4287\  12698	0.65739
	#36: SSBag             	    447\  14624	   1436\  15549	   1372\  15613	0.62590
	#37: HandBag           	    215\  16545	    367\  16618	    288\  16697	0.79072
	#38: Box               	    818\  15392	   1188\  15797	   1223\  15762	0.83146
	#39: PlasticBag        	    235\  16345	    443\  16542	    432\  16553	0.75928
	#40: PaperBag          	    336\  16106	    661\  16324	    554\  16431	0.74748
	#41: HandTrunk         	    224\  16339	    428\  16557	    442\  16543	0.75510
	#42: OtherAttchment    	     52\  16663	    181\  16804	    193\  16792	0.63945
	#43: Calling           	    285\  16509	    420\  16565	    341\  16644	0.83760
	#44: Talking           	   3333\  10114	   5013\  11972	   5191\  11794	0.75484
	#45: Gathering         	    380\  16319	    542\  16443	    504\  16481	0.84678
	#46: Holding           	     65\  16206	    460\  16525	    384\  16601	0.56100
	#47: Pusing            	    188\  15633	    829\  16156	    711\  16274	0.59720
	#48: Pulling           	    126\  16426	    430\  16555	    255\  16730	0.64262
	#49: CarryingbyArm     	    119\  16767	    166\  16819	    171\  16814	0.85689
	#50: CarryingbyHand    	    169\  16577	    321\  16664	    256\  16729	0.76063
	mA:        0.7561061703050626
	Accuracy:  0.6462470962802557
	Precision: 0.7595584857564548
	Recall:    0.7902205159181761
	F1_Score:  0.7745861801404217
====================================================================================================

Process finished with exit code 0

On PETA dataset:
First epoch:
Epoch: [0][0/357]	Time 0.908 (0.908)	Loss 3.5823 (3.5823)	Accu 0.267 (0.267)
Epoch: [0][100/357]	Time 0.627 (0.620)	Loss 2.3207 (2.4436)	Accu 0.779 (0.728)
Epoch: [0][200/357]	Time 0.638 (0.624)	Loss 1.7467 (2.2292)	Accu 0.829 (0.768)
Epoch: [0][300/357]	Time 0.635 (0.628)	Loss 1.6015 (2.0944)	Accu 0.845 (0.789)
Test: [0/238]	Time 0.394 (0.394)	Loss 1.6004 (1.6004)	Accu 0.858 (0.858)
Test: [100/238]	Time 0.182 (0.184)	Loss 1.6022 (1.6593)	Accu 0.860 (0.848)
Test: [200/238]	Time 0.183 (0.184)	Loss 1.6460 (1.6428)	Accu 0.856 (0.848)
 * Accu 0.848
====================================================================================================
	     Attr              	p_true/n_true	p_tol/n_tol	p_pred/n_pred	cur_mA
	# 0: Age16-30          	3449\2471	3831\3769	4747\2853	0.77795
	# 1: Age31-45          	2150\3038	2441\5159	4271\3329	0.73483
	# 2: Age46-60          	 502\6041	 779\6821	1282\6318	0.76503
	# 3: AgeAbove61        	 381\6673	 461\7139	 847\6753	0.88059
	# 4: Backpack          	1189\5128	1497\6103	2164\5436	0.81725
	# 5: CarryingOther     	 849\4346	1531\6069	2572\5028	0.63532
	# 6: Casual lower      	6426\ 457	6546\1054	7023\ 577	0.70763
	# 7: Casual upper      	6389\ 430	6495\1105	7064\ 536	0.68641
	# 8: Formal lower      	 859\5343	1043\6557	2073\5527	0.81922
	# 9: Formal upper      	 848\5410	1007\6593	2031\5569	0.83134
	#10: Hat               	 603\6176	 725\6875	1302\6298	0.86503
	#11: Jacket            	 152\6875	 534\7066	 343\7257	0.62881
	#12: Jeans             	1830\4260	2315\5285	2855\4745	0.79828
	#13: Leather Shoes     	1944\3570	2215\5385	3759\3841	0.77030
	#14: Logo              	  14\7288	 296\7304	  30\7570	0.52255
	#15: Long hair         	1595\4835	1804\5796	2556\5044	0.85917
	#16: Male              	3983\2330	4147\3453	5106\2494	0.81761
	#17: Messenger Bag     	1631\3943	2275\5325	3013\4587	0.72870
	#18: Muffler           	 527\6543	 632\6968	 952\6648	0.88643
	#19: No accessory      	5676\ 729	5742\1858	6805\ 795	0.69043
	#20: No carrying       	1647\4041	2091\5509	3115\4485	0.76059
	#21: Plaid             	 138\7293	 203\7397	 242\7358	0.83287
	#22: PlasticBags       	 305\6472	 585\7015	 848\6752	0.72198
	#23: Sandals           	  28\7384	 144\7456	 100\7500	0.59239
	#24: Shoes             	2372\2281	2774\4826	4917\2683	0.66387
	#25: Shorts            	 149\7037	 261\7339	 451\7149	0.76487
	#26: Short Sleeve      	 936\5636	1090\6510	1810\5790	0.86223
	#27: Skirt             	 194\6966	 351\7249	 477\7123	0.75683
	#28: Sneaker           	1172\4689	1675\5925	2408\5192	0.74555
	#29: Stripes           	  25\7408	 126\7474	  91\7509	0.59479
	#30: Sunglasses        	  15\7359	 218\7382	  38\7562	0.53285
	#31: Trousers          	3698\1478	3949\3651	5871\1729	0.67063
	#32: Tshirt            	 414\6497	 640\6960	 877\6723	0.79018
	#33: UpperOther        	3065\2477	3456\4144	4732\2868	0.74230
	#34: V-Neck            	   0\7520	  80\7520	   0\7600	0.50000
	mA:        0.7358514624787084
	Accuracy:  0.595082002485614
	Precision: 0.645127954842694
	Recall:    0.8638696694387276
	F1_Score:  0.7386445997368981
====================================================================================================

Last epoch:
Learning Rate: 1.0000000000000002e-06
Epoch: [59][0/357]	Time 0.870 (0.870)	Loss 0.0104 (0.0104)	Accu 1.000 (1.000)
Epoch: [59][100/357]	Time 0.671 (0.675)	Loss 0.0078 (0.0112)	Accu 1.000 (1.000)
Epoch: [59][200/357]	Time 0.671 (0.674)	Loss 0.0105 (0.0110)	Accu 0.998 (1.000)
Epoch: [59][300/357]	Time 0.673 (0.674)	Loss 0.0089 (0.0107)	Accu 0.999 (1.000)
Test: [0/238]	Time 0.399 (0.399)	Loss 1.8962 (1.8962)	Accu 0.940 (0.940)
Test: [100/238]	Time 0.179 (0.181)	Loss 1.7418 (2.2203)	Accu 0.952 (0.931)
Test: [200/238]	Time 0.181 (0.180)	Loss 2.4142 (2.2189)	Accu 0.923 (0.932)
 * Accu 0.932
====================================================================================================
	     Attr              	p_true/n_true	p_tol/n_tol	p_pred/n_pred	cur_mA
	# 0: Age16-30          	3364\3309	3831\3769	3824\3776	0.87803
	# 1: Age31-45          	2057\4430	2441\5159	2786\4814	0.85069
	# 2: Age46-60          	 599\6678	 779\6821	 742\6858	0.87398
	# 3: AgeAbove61        	 421\7126	 461\7139	 434\7166	0.95571
	# 4: Backpack          	1180\5786	1497\6103	1497\6103	0.86815
	# 5: CarryingOther     	1002\5591	1531\6069	1480\6120	0.78786
	# 6: Casual lower      	6400\ 810	6546\1054	6644\ 956	0.87310
	# 7: Casual upper      	6335\ 833	6495\1105	6607\ 993	0.86461
	# 8: Formal lower      	 861\6309	1043\6557	1109\6491	0.89384
	# 9: Formal upper      	 835\6364	1007\6593	1064\6536	0.89723
	#10: Hat               	 629\6833	 725\6875	 671\6929	0.93074
	#11: Jacket            	 322\6957	 534\7066	 431\7169	0.79379
	#12: Jeans             	1970\4841	2315\5285	2414\5186	0.88348
	#13: Leather Shoes     	1913\4953	2215\5385	2345\5255	0.89172
	#14: Logo              	 167\7196	 296\7304	 275\7325	0.77470
	#15: Long hair         	1581\5527	1804\5796	1850\5750	0.91499
	#16: Male              	3918\3103	4147\3453	4268\3332	0.92171
	#17: Messenger Bag     	1830\4807	2275\5325	2348\5252	0.85356
	#18: Muffler           	 588\6921	 632\6968	 635\6965	0.96182
	#19: No accessory      	5540\1406	5742\1858	5992\1608	0.86077
	#20: No carrying       	1683\5024	2091\5509	2168\5432	0.85842
	#21: Plaid             	 151\7359	 203\7397	 189\7411	0.86935
	#22: PlasticBags       	 426\6949	 585\7015	 492\7108	0.85940
	#23: Sandals           	  68\7370	 144\7456	 154\7446	0.73034
	#24: Shoes             	2182\3994	2774\4826	3014\4586	0.80710
	#25: Shorts            	 160\7270	 261\7339	 229\7371	0.80181
	#26: Short Sleeve      	 917\6220	1090\6510	1207\6393	0.89837
	#27: Skirt             	 243\7164	 351\7249	 328\7272	0.84029
	#28: Sneaker           	1123\5555	1675\5925	1493\6107	0.80400
	#29: Stripes           	  57\7433	 126\7474	  98\7502	0.72345
	#30: Sunglasses        	  93\7235	 218\7382	 240\7360	0.70335
	#31: Trousers          	3502\3059	3949\3651	4094\3506	0.86233
	#32: Tshirt            	 430\6757	 640\6960	 633\6967	0.82135
	#33: UpperOther        	2900\3690	3456\4144	3354\4246	0.86478
	#34: V-Neck            	  35\7464	  80\7520	  91\7509	0.71503
	mA:        0.8482808189594254
	Accuracy:  0.7842523445229501
	Precision: 0.8555594485923317
	Recall:    0.8686840206576898
	F1_Score:  0.8620717839089516

could you please share your networks settings on peta dataset? i just cannot converge my network anyway...

我也是，求pretrained的weights

我也是，求pretrained的weights

I will upload the pre-trained weights after cvpr ddl, sorry for that.

我也是，求pretrained的weights

I will upload the pre-trained weights after cvpr ddl, sorry for that.

Updated.

authors: Ehsan-Yaghoubi WooXinyi chufengt kickingass


text:
Javascript error seen in console

Uncaught (in promise) TypeError: Error in invocation of scripting.executeScript(scripting.ScriptInjection injection, optional function callback): Error at parameter 'injection': Error at property 'args': Error at index 1: Value is unserializable.
    at createDocument (background.js:58:26)

Can't reproduce this easily.

I think it's fixed.

repo: chung-leong/conradish-x


text:
How to add extra-plugins

Hi,
I'm using this plugin in an Ionic 2 app.
I would like to add a extra plugin like for example this Youtube Plugin http://ckeditor.com/addon/youtube.
I've read this link about enabling local plugin but I'm a bit a lost.

Thanks for the help

I downloaded the ckeditor lib locally and it works easily now


text:
支持rest风格的url请求

希望能添加支持rest风格的ajax请求.

如果不能直接请求文件或接口返回文件数据，可以单独使用 ajax 获取，然后再调用 cxSelect 时将参数 data 的值设置为处理后的数据。

authors: ciaoca fefine

repo: ciaoca/cxSelect


text:
infinite scroll implementation for messages

Merhabalar,
Tüm listeyi tek seferde renderlamak yerine en azından 50şer 50şer renderlamak yavaş ciharlarda bir yarara sebep olabilir diye düşünüyorum.

Merhaba, güzel düşünmüşsünüz. Elinize sağlık, teşekkürler!

authors: cemjotform cihatislamdede

repo: cihatislamdede/deprem-yardim


text:
Silence LLVM warning: 'probe' is not a recognized processor for this target (ignoring processor)

During the build of the BPF code, LLVM repeatedly prints this extraneous warning:

'probe' is not a recognized processor for this target (ignoring processor)

This seems to indicate that target probing doesn't work,s however we are specifying this we could probably remove it to make the build output more useful.

This is basically to probe the kernel to use recently added BPF insns for newer LLVM versions (see ffa74b1f733dfac71a33013). The warning itself is harmless, and LLVM will just ignore it when it doesn't support the flag. There's unfortunately no option from LLVM to turn off the warning itself. Redirecting the output to /dev/null might omit other compiler output we don't want to have omitted. If so, we could add something like this (https://git.kernel.org/pub/scm/linux/kernel/git/davem/net-next.git/tree/tools/testing/selftests/bpf/Makefile#n40), which is kind of ugly, but if we really want to.

Hmm. Completely losing the output sounds not ideal. I guess the easiest solutions are either "| grep -v ..." or just dropping this.

Yeah, so we could do as in the Makefile which is PROBE := $(shell llc -march=bpf -mcpu=probe -filetype=null /dev/null 2>&1). What this tries is to compile an empty unit, and then based on the outcome we could either set probe or generic, it would solve the silencing problem, although still a bit fragile in case LLVM has some other output for some reason and we switch to a suboptimal mcpu, so I don't like it too much; we could do regex though.

authors: borkmann joestringer

repo: cilium/cilium


text:
Add a guide for configuring Cilium in direct routing mode

We have getting started guides for various features and modes in Cilium, but no single clear guide on how to configure Cilium for direct routing. This is a documentation request for such a mode.
This probably involves providing different steps depending on whether nodes in the cluster are all directly connected via L2 (where --auto-direct-node-routes can help) vs. L3-connected, in which case perhaps the user needs to configure BGP or another route sharing mechanism.

@joestringer is GCE using vpc-native (alias ip) in the direct routing mode scenarios?
(I am interesting in replicating the gke dataplane)

@joestringer yes, but I can't use GKE :( just plain GCE VMs (and using k3s)

Based on this doc, I think it's possible: https://docs.cilium.io/en/stable/concepts/networking/routing/#google-cloud
It looks to me like even though the setting is marked as gke, if you use that setting in google cloud, it will still use the native google cloud networking layer. But I think that the best way to find out would be to try it. If you're running k3s, then it's also worth looking at which helm options are in the Cilium docs for installing on k3s and see which of those may be relevant to you.

I would like a guide to deploying cilium bgp mode on bare metal, but I don't know if it works and where I can get.

I would like a guide to deploying cilium bgp mode on bare metal, but I don't know if it works and where I can get.

Same here, possibly including dsr

Today, this page (https://docs.cilium.io/en/stable/network/concepts/routing/#native-routing) has some pointers for running on AWS ENI and self-managed GCP, so I think the original demand of this issue is covered.
For bare metal deployments, there isn't a written guide, but our perf benchmarking suite has automation code for Cilium 1.10 (https://docs.cilium.io/en/stable/operations/performance/benchmark/#how-to-reproduce) that could be used as inspiration. It's built on Packet.net machines.
I'm afraid even accounting for L2 vs. L3-connected clusters, there are so many variables in physical deployments that any bare metal guide is bound to omit many crucial details. Also, maintaining this sort of document will be expensive because of the need for automation and hardware availability. We tend to validate all our getting started guides on every major release.

authors: Duanzhiwei aledbf damiendawreck joestringer ti-mo


text:
[v1.11] CI: Multicluster / Cluster mesh: allow-all-except-world: failed to curl between pods on different nodes

CI failure
Hit during backport #20183
📋 Test Report
❌ 1/11 tests failed (3/84 actions), 0 tests skipped, 3 scenarios skipped:
Test [allow-all-except-world]:
  ❌ allow-all-except-world/pod-to-pod/curl-1: cilium-test/client-6488dcf5d4-lmkc7 (10.120.0.162) -> cilium-test/echo-other-node-f4d46f75b-szxtb (10.104.1.43:8080)
  ❌ allow-all-except-world/pod-to-pod/curl-3: cilium-test/client2-5998d566b4-mp7zr (10.120.0.167) -> cilium-test/echo-other-node-f4d46f75b-szxtb (10.104.1.43:8080)
connectivity test failed: 1 tests failed
  ❌ allow-all-except-world/pod-to-service/curl-0: cilium-test/client-6488dcf5d4-lmkc7 (10.120.0.162) -> cilium-test/echo-other-node (echo-other-node:8080)

cilium-sysdump-out.zip (15).zip
https://github.com/cilium/cilium/runs/6867539777?check_suite_focus=true

Hello, i am using Hetnzer Cloud with k8s and cilium. After running tests i have this errors.
📋 Test Report
❌ 2/32 tests failed (16/311 actions), 2 tests skipped, 1 scenarios skipped:
Test [allow-all-except-world]:
  ❌ allow-all-except-world/pod-to-host/ping-ipv4-1: cilium-test/client-6f6788d7cc-qvfnh (10.0.4.196) -> 10.98.0.5 (10.98.0.5:0)
  ❌ allow-all-except-world/pod-to-host/ping-ipv4-3: cilium-test/client-6f6788d7cc-qvfnh (10.0.4.196) -> 10.98.0.7 (10.98.0.7:0)
  ❌ allow-all-except-world/pod-to-host/ping-ipv4-5: cilium-test/client-6f6788d7cc-qvfnh (10.0.4.196) -> 10.98.0.6 (10.98.0.6:0)
  ❌ allow-all-except-world/pod-to-host/ping-ipv4-9: cilium-test/client-6f6788d7cc-qvfnh (10.0.4.196) -> 10.98.0.4 (10.98.0.4:0)
  ❌ allow-all-except-world/pod-to-host/ping-ipv4-11: cilium-test/client2-bc59f56d5-qdrfm (10.0.4.88) -> 10.98.0.4 (10.98.0.4:0)
  ❌ allow-all-except-world/pod-to-host/ping-ipv4-13: cilium-test/client2-bc59f56d5-qdrfm (10.0.4.88) -> 10.98.0.5 (10.98.0.5:0)
  ❌ allow-all-except-world/pod-to-host/ping-ipv4-15: cilium-test/client2-bc59f56d5-qdrfm (10.0.4.88) -> 10.98.0.7 (10.98.0.7:0)
  ❌ allow-all-except-world/pod-to-host/ping-ipv4-17: cilium-test/client2-bc59f56d5-qdrfm (10.0.4.88) -> 10.98.0.6 (10.98.0.6:0)
Test [host-entity]:
  ❌ host-entity/pod-to-host/ping-ipv4-3: cilium-test/client-6f6788d7cc-qvfnh (10.0.4.196) -> 10.98.0.4 (10.98.0.4:0)
  ❌ host-entity/pod-to-host/ping-ipv4-5: cilium-test/client-6f6788d7cc-qvfnh (10.0.4.196) -> 10.98.0.5 (10.98.0.5:0)
  ❌ host-entity/pod-to-host/ping-ipv4-7: cilium-test/client-6f6788d7cc-qvfnh (10.0.4.196) -> 10.98.0.7 (10.98.0.7:0)
  ❌ host-entity/pod-to-host/ping-ipv4-9: cilium-test/client-6f6788d7cc-qvfnh (10.0.4.196) -> 10.98.0.6 (10.98.0.6:0)
  ❌ host-entity/pod-to-host/ping-ipv4-11: cilium-test/client2-bc59f56d5-qdrfm (10.0.4.88) -> 10.98.0.5 (10.98.0.5:0)
  ❌ host-entity/pod-to-host/ping-ipv4-13: cilium-test/client2-bc59f56d5-qdrfm (10.0.4.88) -> 10.98.0.7 (10.98.0.7:0)
  ❌ host-entity/pod-to-host/ping-ipv4-15: cilium-test/client2-bc59f56d5-qdrfm (10.0.4.88) -> 10.98.0.6 (10.98.0.6:0)
  ❌ host-entity/pod-to-host/ping-ipv4-19: cilium-test/client2-bc59f56d5-qdrfm (10.0.4.88) -> 10.98.0.4 (10.98.0.4:0)
connectivity test failed: 2 tests failed

kubernetes version: v1.26.4
suc@entrance-nbg1:~$ cilium status --wait
    /¯¯\
 /¯¯\__/¯¯\    Cilium:          OK
 \__/¯¯\__/    Operator:        OK
 /¯¯\__/¯¯\    Hubble Relay:    OK
 \__/¯¯\__/    ClusterMesh:     disabled
    \__/

DaemonSet         cilium             Desired: 5, Ready: 5/5, Available: 5/5
Deployment        hubble-ui          Desired: 1, Ready: 1/1, Available: 1/1
Deployment        hubble-relay       Desired: 1, Ready: 1/1, Available: 1/1
Deployment        cilium-operator    Desired: 2, Ready: 2/2, Available: 2/2
Containers:       cilium             Running: 5
                  hubble-ui          Running: 1
                  hubble-relay       Running: 1
                  cilium-operator    Running: 2
Cluster Pods:     9/9 managed by Cilium
Image versions    cilium             quay.io/cilium/cilium:v1.13.2@sha256:85708b11d45647c35b9288e0de0706d24a5ce8a378166cadc700f756cc1a38d6: 5
                  hubble-ui          quay.io/cilium/hubble-ui:v0.11.0@sha256:bcb369c47cada2d4257d63d3749f7f87c91dde32e010b223597306de95d1ecc8: 1
                  hubble-ui          quay.io/cilium/hubble-ui-backend:v0.11.0@sha256:14c04d11f78da5c363f88592abae8d2ecee3cbe009f443ef11df6ac5f692d839: 1
                  hubble-relay       quay.io/cilium/hubble-relay:v1.13.2@sha256:51b772cab0724511583c3da3286439791dc67d7c35077fa30eaba3b5d555f8f4: 1
                  cilium-operator    quay.io/cilium/operator-generic:v1.13.2@sha256:a1982c0a22297aaac3563e428c330e17668305a41865a842dec53d241c5490ab: 2

authors: joestringer wondertalik