SSL client without certificate ?

Hi guys,
This is  not really an issue, rather a request.
I have today a piece of code that uses CFNetwork to establish an SSL connection. I would like to make that code multi-platform, using BlueSocket and BlueSSLService.
In my current code, I don't provide any certificate for the client.
With BlueSSLService, it seems I have to provide a PKCS12 certificate chain, I have tried to generate one, but with no success up to now (I'm clearly no ssl expert).
My questions are :

is the certificate-less client valid, or is it a kind of hack, made possible by CFNetwork ?
is there any chance that BlueSSLService will support  that kind of thing some day ? If not, is there a plan to support self-signed certificates on MacOS as well as Linux ?

Thanks for the work you are doing on those components !

A certificate-less client is more or less a hack.  It's really not secure.  As far as self-signed certificates support in BlueSSLService on macOS, that support is already there.  The only difference is that you need to supply your certificates in a PKCS12 package.  It's really no difference than on Linux except for the packaging.  Let me know if you have anymore questions.  Hope this helps.

Thanks for your answer Billabt.
I'll try again to generate a valid self-signed PKCS12 certificate chain then (I had a hard time trying to do so up to now).

I'm sorry to be back on the same topic Billabt, I had to pause my project for some time.
I tried again using blueSSLService, using a self-signed certificate.
Here is how I built the pkcs12 file :
openssl genrsa -out ca.key 2048
openssl req -new -x509 -days 1826 -key ca.key -out ca.crt
openssl genrsa -out client.key 2048
openssl req -new -key client.key -out client.csr -subject
openssl x509 -req -days 730 -in client.csr -CA ca.crt -CAkey ca.key -out client.crt -set_serial 1234
cat ca.crt >> client.crt
openssl pkcs12 -inkey client.key -in client.crt -export -out client.p12

This results in a p12 file that seems to be valid (I don't have an error when loading the certificate), but I have a handshake failure, with an error code that seems to be a pretty generic "invalide chain" :
ERROR: SSLHandshake, code: -9807, reason: Could not determine error reason.
If I ask openssl to read the file, here is the output (content of keys and certificates are replaced by markers to reduce the size of the message) :
MAC verified OK
Bag Attributes
    localKeyID: 00 9D 39 6B 74 AC 83 82 41 88 27 C0 E0 77 A6 03 1A 60 85 8A
subject=/C=fr/ST=idf/L=paris/O=jd/CN=client.duquennoy
issuer=/C=fr/ST=idf/L=paris/O=jd/CN=duquennoy
-----BEGIN CERTIFICATE-----
CERTIFICATE_DATA_HERE
-----END CERTIFICATE-----
Bag Attributes: <No Attributes>
subject=/C=fr/ST=idf/L=paris/O=jd/CN=duquennoy
issuer=/C=fr/ST=idf/L=paris/O=jd/CN=duquennoy
-----BEGIN CERTIFICATE-----
CERTIFICATE_DATA_HERE
-----END CERTIFICATE-----
Bag Attributes
    localKeyID: 00 9D 39 6B 74 AC 83 82 41 88 27 C0 E0 77 A6 03 1A 60 85 8A
Key Attributes: <No Attributes>
Enter PEM pass phrase:
Verifying - Enter PEM pass phrase:
-----BEGIN RSA PRIVATE KEY-----
Proc-Type: 4,ENCRYPTED
DEK-Info: DES-EDE3-CBC,B22893331941E496

RSA_KEY_HERE
-----END RSA PRIVATE KEY-----

I spent hours asking google to help me, but I got flooded by tons of non related situations, and found no solution.
Any advice or help would be incredibly appreciated :-)

@jduquennoy I am also in the same situation right now. Did you find any way to resolve this?


Decorator implementation for modern spinner

The spinner can be used as context manager - however in our recent meeting we talked about a decorator implementation which is more neat and conform to the way click itself works.

Solved by @schwobaseggl


SUS015 View Response Status for a Case (MI)

This PR add the endpoint to generate a social MI report for the SEL user
Access this endpoint by going to http://localhost:8086/response_chasing/ to download the CSV
Run alongside https://github.com/ONSdigital/rm-reporting/pull/9

 SUS015 View Response Status for a Case (MI) (13)


SPARK-11382 Replace example code in mllib-decision-tree.md using incl…

…ude_example

Can one of the admins verify this patch?

@yinxusen Could you help review?

@mengxr Could you trigger a build (including site?)? Thanks

@mengxr Yes I will review it.

@gliptak Why the code changes do not match with the title? I think the SPARK-11382 should fix mllib-decision-tree.md and mllib-ensembles.md.

I was planning to split into two commits (although I'm open to work the other file in this same commit). Thanks

@gliptak  I mean, this JIRA issue aiming to modify decision-tree.md and mllib-ensembles.md, but your pull request changes mllib-frequent-pattern-mining.md.

Can I add new JIRA covering mllib-ensembles.md?

Yes you can.

Created https://issues.apache.org/jira/browse/SPARK-11445 and updated description of https://issues.apache.org/jira/browse/SPARK-11382

ok to test

Build triggered.

Build started.

Test build #45023 has started for PR 9378 at commit 7bcdee5.

Test build #45023 has finished for PR 9378 at commit 7bcdee5.

This patch fails Scala style tests.
This patch does not merge cleanly.
This patch adds the following public classes (experimental):\n  * public class JavaAssociationRulesExample \n  * public class JavaPrefixSpanExample \n

Build finished. Test FAILed.

Test FAILed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/45023/
Test FAILed.

This became a duplicate of https://github.com/apache/spark/pull/9340
commit 820064e613609bbf7edd726d982da1de60bf417a
Author: Pravin Gadakh<EMAIL_ADDRESS>Date:   Wed Nov 4 08:32:08 2015 -0800
[SPARK-11380][DOCS] Replace example code in mllib-frequent-pattern-mining.md using include_example

Author: Pravin Gadakh<EMAIL_ADDRESS>Author: Pravin Gadakh<EMAIL_ADDRESS>
Closes #9340 from pravingadakh/SPARK-11380.

@gliptak Yes it is duplicated with #9340 . Do you plan to change example code of mllib decision tree?

@gliptak And sorry for my mistake of the mismatch between the title and content in SPARK-11382. I have already corrected its content.

https://issues.apache.org/jira/browse/SPARK-11445 is already being worked at https://github.com/apache/spark/pull/9407

@gliptak, SPARK-11445 aims to fix mllib-ensemble.md, while this one (SPARK-11382) aims to fix mllib-decision-tree.md. They are two different JIRA issues.

@yinxusen
Now I'm confused myself. Current master already have mllib-frequent-pattern-mining.md corrected:
https://raw.githubusercontent.com/apache/spark/master/docs/mllib-frequent-pattern-mining.md
so this pull request was superceded (although the title doesn't match changes in my branch).
Please confirm that I see this correctly.
Thanks


Summary list action links visually hidden text are read out without spaces in screen readers

What
This is a report from a screen reader user:

https://design-system.service.gov.uk/components/summary-list/
You use visually hidden text to give your links unique and descriptive
names. Unfortunately, you often omit the space between the words. This
can make the names harder to recognize when speech synthesizers
mispronounce the combined word. For example:
"Open this summary listexample in a new window"
"Changename"
"Changedate of birth"
"Changecontact information"
"Changecontact details"

I believe that I missed this as audibly it sounds correct to me, but I imagine depending on how you have your screen reader configured, for example how fast you have words announced, it could make it hard to understand.
I've managed to reproduce this issue in NVDA + Firefox and used the 'speech viewer' tool to see what the user has found.
The user has also let us know that the following combinations have the same issue, which suggests that it's an issue with most screen readers:

Orca 3.28.0 with Mozilla Firefox 70.0.1 on Ubuntu Mate 18.04
NVDA 2019.2.1 with Mozilla Firefox 70.0.1 on Microsoft Windows 10
NVDA with Google Chrome 78.0.39 on Microsoft Windows 10.
JAWS 2019.1809.28 with Mozilla Firefox 70.0.1 on Microsoft Windows 10
JAWS 2019.1809.28 with Google Chrome 78.0.39 on Microsoft Windows 10.

Recommend that we consider testing with tools like 'speech viewer' in the future to help catch issues like this.
I tried a quick fix by adding   instead of a space character, and that seemed to resolve this issue, but we'd want to test this across screen readers.
I will also raise an issue in the design system website for the related issues there.
Who needs to know about this
Developers

We might want to report this as an issue to the vendors as it seems that the accessibility tree has the correct content.

Next steps:

[ ] Create an example using   instead of the space character
[ ] Compare the accessibility trees in different browsers before and after
[ ] Compare assistive technologies before and after

Most probably related: #1032 and alphagov/reported-bugs#39.

Good call @selfthinker – although we fixed this in the wider 'visually-hidden' class by adding white-space: nowrap, the summary list itself uses word-wrap: break-word:
https://github.com/alphagov/govuk-frontend/blob/82811f3bb8186581bc2a6c01b000b78626b7871f/src/govuk/components/summary-list/_summary-list.scss#L59-L64
I wonder if the same approach might fix it (applying white-space: nowrap)

I've tested this in a few screen reader combinations and recorded the results in a spreadsheet: https://docs.google.com/spreadsheets/d/1Q-jTC8bcBagPHLlhyxH81FSqlNL446kyMU04st9PJxE/edit?usp=sharing
Will update with thoughts later.

It seems adding an extra spacing does 'work' but in some assistive technologies now there is double spacing which could impact pronunciation too.
One thing that's interesting to me is that the accessibility trees are correct but then the screenreader can read something different to the tree, which means either:

the screen reader uses additional information to determine the announcement.
the output of the screen reader is actually correct but the 'speech viewer' is not.

It's hard to know if it's the latter without real user feedback, we could consider messaging the original issue raiser to ask them if they ran into this from being confused or from a testing perspective.
Through doing it I did wonder if there was a way to tweak the CSS itself to see if that's the issue, but this is one of those really gnarly ones that also takes a really long testing.

Things we could do to go forwards with this issue:

explore changing the visually-hidden class properties (previous change here: https://github.com/alphagov/govuk-frontend/pull/1109)
adopt the &nbps; approach.
start promoting the use of aria-label where possible.
look at the recent user research session with a participant who uses a screenreader to see if they ran into issue.
go back to the original issue reporter and see if we can learn more if this is a barrier for people or just a oddity from a testing perspective.

Had a chat with Ollie.
We're going to try to make reduced test cases to raise bugs with the assistive technology vendors as the accessibility tree is correct, this process may also help us learn more about the underlying issues and also the vendors themselves may be able to give us advice if they've seen this sort of issue before.

NVDA: https://github.com/nvaccess/nvda/issues/10208
VoiceOver: TODO
JAWs: TODO

Closing this out as raised issues, hopefully they get resolved! We can re-open if this becomes a more serious issue for our users.

This could be fixed by providing two seperate blocks of content rather than concatenating them, such as this:

  Delete
  Delete name

The only downside of this is duplication of content if CSS is disabled, but that can be fixed by adding a hidden attribute to the first span which is overridden with CSS when available.
This also fixes any odd grammatical issues which arise from concatenating words when translating to other languages.

There are some other issues with the current implementation
Voiceover iOS
In Voiceover on iOS the span in the middle of the accessible name means that if a user is navigating by links they hear:

“link: Change your name” (swipe)
“link: Change” (swipe)
“link: Your name”

Android TalkBack
With Android TalkBack when a user is navigating normally by swiping the effect is the same as with VO navigating by links

We've had this issue reported in an accessibility audit, though I don't know for what AT combinations.

Digital Accessibility Centre (DAC) raised this issue in a recent audit for a New Style Employment and Support Allowance. I can share the full report if useful.

Hi @ladine-cook, thank you for letting us know this issue has been raised by DAC. If you are able to share the full report with is that would be amazing, we would especially like to know which browser/assistive tech combination this issue was found in (including versions).

Thanks for sending that over @ladine-cook, that was very useful.
It looks like again they were seeing this issue with JAWS (versions 18 and 19), although I'm not sure whether it was with Firefox or IE11.
I can reproduce the issue using the check your answers pattern example in AssistivLabs with JAWS 2021.2103.174 and Firefox or Chrome, but not with JAWS and IE11.
It's a bit of a shame that nothing has moved on the issue we raised with JAWS https://github.com/FreedomScientific/VFO-standards-support/issues/372, I've leave a message on the issue to see if we can't get this fixed upstream.


Make oldest source Default by default

The oldest source for any species should, once the sources are all added, be its original description. This should usually be a good description of the gall so if no user has checked "Default" on a different description, this one should be Default by default. That way any gall that has at least one source mapping will load it when you open the page or when it comes up in search results.

I'm not sure this is working at the moment, unless it's on a lag or something? Eg I just added a new species and one description and it isn't showing up as default.

I just made a new species and added two sources, and it worked there. So the issue might be that it's only working when it can select the oldest of two or more options?


Updated mame games list

Updated the game display list for the latest stable 0.214 version of mame.
Maintained backward compatibility with name2003plus, mame2010 and FBANeo.

Should we add those files to the lr-mame package?

Not! It is designed to display a friendly name on the Mame and NeoGeo list of roms on Emulationstation.
Example: The rom folder has a rom named mk.zip and the emulationstation list shows Mortal Kombat.

Where those files come from?

I created it.
I exported the dat files from all versions of mame supported by batocera and FBA, then I learned how the original file structure worked and rebuilt the files with the most up to date information.


Timeframe for a new release

Dear developpers,
Thanks a lot for this pretty useful library.
I would like to add simde to conda-forge. Would you have in mind a date for a future release?
It would be nice to provide one of the latest available version of simde within conda, as the latest release is more than one-year old.
Thanks in advance for your feedback,
Best,
Justin

Dear developpers,
Any news on this issue?
Thanks in advance,
Justin

@mr-c Kind reminders for this question.
Having a fresh release will help in the development of software rooted in SIMDE.

I'll be working towards a release this weekend as part of the Debian-Med spirit https://wiki.debian.org/Sprints/2022/Debian Med Sprint 2023%2C Berlin

Thanks a lot @mr-c for this update.
Have a nice weekend.

I anticipate releasing this weekend

Super ! Thanks a lot @mr-c

https://github.com/simd-everywhere/simde/releases/tag/v0.7.4 is released!


Sort by implementation in uv python list

Summary
With #6917, there are a lot more PyPy downloads in uv python list --all-versions. I find it clearer to have all the CPython downloads listed, then all the PyPy downloads, rather than interspersing them. But this is subjective, feel free to push back!

Can you show a brief before and after?

Before:

After:


Select and Date Component Style Issue

Reported version v2.1.0
Select Component label and undeline color is at 42 to 54% opacity of black and we dont have control of it. like it mentioned I have imported default material styles ><EMAIL_ADDRESS>Select Component only support this style token -> selectedOptionBackgroundColor
Date Component only support these style token -> pickerSelectedDateBackgroundColor & pickerSelectedDateColor
There is no way to create dark version of select and date component using theme API. I do need to manualy override those.
Just like Input field we do need seperate fontColor field for select and date. Right now for a time being we are overriding select font color using --inputText-fontColor token.
Expected Token Example
Select component style -> we do need select-font-color
.mat-form-field-appearance-legacy .mat-form-field-label {   color: var(--select-fontColor); }
.mat-form-field-appearance-legacy .mat-form-field-underline {   background-color: var(--select-fontColor); }
.mat-select-arrow {   color: var(--select-fontColor); }
Image showing Select Component styles without override

If you see it looks odd when compare to input field.

Correction
Please correct me, I think Date component will be fine because internally it is using dxc-input. so if i do change --inputText-fontColor token it will reflect on Date component also

Hi,
Yes, some components are related between them. In this case dxc-date is consuming dxc-input. In regards of the token, we will discuss it internally, this is a design change, not a bug.
Please, take a look at this repository: https://github.com/dxc-technology/halstack-style-guide
You can open design issues there.
Regards.

Sure. I will create there. thanks for the quick response. @Lisa159


Versionbot should comment on a PR should its status check fail

It should specifically ping the committer to let them know that a relevant Change-Type: tag should be included.
[hq edit] connects to https://github.com/resin-io/hq/pull/443

In and working.


Possible to make HasMany an Iterable again?

Issue was always with @freezed classes equality. It might be possible with Freezed 2.0+?

How does Isar achieve that with IsarLinks being an Iterable if it supposedly is Freezed-compatible?


Can we use X.509 instead of TPM

[Enter feedback here]

We are having some questions on Zero touch provisioning for Edge SDK, will DPS work at Gateway level or it can go all the way till leaf nodes. Hope we can still use X.509 certificates instead of TPM.
We would need more information/links on creating modules of Python and C language in Gateway, We have already worked with .Net modules.

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: a2113dfd-71ce-f982-35d2-528ec73c6a66
Version Independent ID: 5f043c8f-03ed-5d95-ec2d-4a02d80ecaec
Content: Auto-provision Azure IoT Edge device with DPS - Linux
Content Source: articles/iot-edge/how-to-auto-provision-simulated-device-linux.md
Service: iot-edge
GitHub Login: @kgremban
Microsoft Alias: kgremban

Since we have not heard back from you we will now proceed to close this thread. If there are further questions regarding this matter, please tag me in your reply. We will gladly continue the discussion and we will reopen the issue.

DPS works at the (edge gateway level today)[https://docs.microsoft.com/en-us/azure/iot-edge/how-to-auto-provision-simulated-device-linux]. Edge doesn't yet support X.509 certificates but that work is planned, and when that support goes live in Edge you will also be able to provision Edge devices via DPS using X.509 certs.

Hi, was this functionality ever added?

@toddgauntgs No, X.509 support hasn't been added to IoT Edge yet. There is a request on the IoT Edge feedback forum that you can upvote to show your interest in the feature: https://feedback.azure.com/forums/907045-azure-iot-edge/suggestions/34812739-support-x-509-certificates-in-iot-edge-for-raspbia

@kgremban Thanks for the follow-up. Good to know, definitely interested.


Feature - Add "Hidden Values"

If we could pass hidden values as a prop to the form component, it would make it viable to share most if not all form schemas with trpc-like backends which could make synchronization between front end and backend very easy in cases where the backend is using zod schemas for input.
For example, imagine we had a "comment" form that created a comment for a post. The form only needs to include a text field in the UI, but the backend expects both the text of the comment and the ID of the post:
export const MyCommentSchema = z.object({
  text: z.string(),
  postId: z.string(),
})

import { MyCommentSchema } from 'schema'; // import schema from shared package / file
// trpc router
const trpcRouter = t.router({
  postComment: z.procedure.input(MyCommentSchema).mutation(/* do stuff */)
})

In our next.js page for example:
import { MyCommentSchema } from 'schema'; // import schema from shared package / file
// front end

const MyPage = ()=>{
  const mutation = api.postComment.useMutation()
  const {query: {postId}} = useRouter()
  return (
    <Form
      schema={MyCommentSchema}
      onSubmit={mutation.mutate}
      hiddenValues={{
        postId: postId,
      }}
    />
  )
}

No component would be rendered for the postId field (since we don't want users to have to input hidden values), and the hidden value would get passed to the onSubmit callback.
Any additional changes to our shared schema would both add the field to the input of the trpc router, and also render a new field on the frontend. Enables fullstack typesafe form submission with a single source of truth.

This could be very nice!


adding custom latency to the tcp connections for MConnection benchmark tests

          I wonder if we could add latency accurately using a library such as [speedbump](https://github.com/kffl/speedbump/blob/master/lib/README.md)

Originally posted by @evan-forbes in https://github.com/celestiaorg/celestia-core/pull/1189#discussion_r1468195032

Reassigning myself once I actively begin working on it.


pretrained swin transformer model weight size does not match weight size of model after fine tuning

Dear developer,
When I fine tuned a swin-transfromer model for an object detection task using default pretrained swin_tiny_patch4_window7_224.pth, I noticed that the latest.pth model's  architecture is not same as pretrained model. The latest.pth and swin_tiny_patch4_window7_224.pth have different number of layer (231 vs 190) and some layers' parameter shapes are not the same. I wonder how pretrained parameters are inherited ? Thanks !
I just modified num_classes in config files and added my customed classes here
Initialized by user-defined init_weights` in SwinTransformer
backbone.stages.2.blocks.1.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.1.attn.w_msa.qkv.weight - torch.Size([1152, 384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.1.attn.w_msa.qkv.bias - torch.Size([1152]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.1.attn.w_msa.proj.weight - torch.Size([384, 384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.1.attn.w_msa.proj.bias - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.1.norm2.weight - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.1.norm2.bias - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.1.ffn.layers.0.0.weight - torch.Size([1536, 384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.1.ffn.layers.0.0.bias - torch.Size([1536]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.1.ffn.layers.1.weight - torch.Size([384, 1536]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.1.ffn.layers.1.bias - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.2.norm1.weight - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.2.norm1.bias - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.2.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.2.attn.w_msa.qkv.weight - torch.Size([1152, 384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.2.attn.w_msa.qkv.bias - torch.Size([1152]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.2.attn.w_msa.proj.weight - torch.Size([384, 384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.2.attn.w_msa.proj.bias - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.2.norm2.weight - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.2.norm2.bias - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.2.ffn.layers.0.0.weight - torch.Size([1536, 384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.2.ffn.layers.0.0.bias - torch.Size([1536]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.2.ffn.layers.1.weight - torch.Size([384, 1536]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.2.ffn.layers.1.bias - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.3.norm1.weight - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.3.norm1.bias - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.3.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.3.attn.w_msa.qkv.weight - torch.Size([1152, 384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.3.attn.w_msa.qkv.bias - torch.Size([1152]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.3.attn.w_msa.proj.weight - torch.Size([384, 384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.3.attn.w_msa.proj.bias - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.3.norm2.weight - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.3.norm2.bias - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.3.ffn.layers.0.0.weight - torch.Size([1536, 384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.3.ffn.layers.0.0.bias - torch.Size([1536]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.3.ffn.layers.1.weight - torch.Size([384, 1536]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.3.ffn.layers.1.bias - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.4.norm1.weight - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.4.norm1.bias - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.4.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.4.attn.w_msa.qkv.weight - torch.Size([1152, 384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.4.attn.w_msa.qkv.bias - torch.Size([1152]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.4.attn.w_msa.proj.weight - torch.Size([384, 384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.4.attn.w_msa.proj.bias - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.4.norm2.weight - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.4.norm2.bias - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.4.ffn.layers.0.0.weight - torch.Size([1536, 384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.4.ffn.layers.0.0.bias - torch.Size([1536]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.4.ffn.layers.1.weight - torch.Size([384, 1536]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.4.ffn.layers.1.bias - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.5.norm1.weight - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.5.norm1.bias - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.5.attn.w_msa.relative_position_bias_table - torch.Size([169, 12]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.5.attn.w_msa.qkv.weight - torch.Size([1152, 384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.5.attn.w_msa.qkv.bias - torch.Size([1152]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.5.attn.w_msa.proj.weight - torch.Size([384, 384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.5.attn.w_msa.proj.bias - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.5.norm2.weight - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.5.norm2.bias - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.5.ffn.layers.0.0.weight - torch.Size([1536, 384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.5.ffn.layers.0.0.bias - torch.Size([1536]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.5.ffn.layers.1.weight - torch.Size([384, 1536]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.blocks.5.ffn.layers.1.bias - torch.Size([384]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.downsample.norm.weight - torch.Size([1536]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.downsample.norm.bias - torch.Size([1536]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.2.downsample.reduction.weight - torch.Size([768, 1536]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.0.norm1.weight - torch.Size([768]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.0.norm1.bias - torch.Size([768]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.0.attn.w_msa.relative_position_bias_table - torch.Size([169, 24]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.0.attn.w_msa.qkv.weight - torch.Size([2304, 768]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.0.attn.w_msa.qkv.bias - torch.Size([2304]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.0.attn.w_msa.proj.weight - torch.Size([768, 768]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.0.attn.w_msa.proj.bias - torch.Size([768]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.0.norm2.weight - torch.Size([768]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.0.norm2.bias - torch.Size([768]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.0.ffn.layers.0.0.weight - torch.Size([3072, 768]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.0.ffn.layers.0.0.bias - torch.Size([3072]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.0.ffn.layers.1.weight - torch.Size([768, 3072]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.0.ffn.layers.1.bias - torch.Size([768]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.1.norm1.weight - torch.Size([768]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.1.norm1.bias - torch.Size([768]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.1.attn.w_msa.relative_position_bias_table - torch.Size([169, 24]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.1.attn.w_msa.qkv.weight - torch.Size([2304, 768]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.1.attn.w_msa.qkv.bias - torch.Size([2304]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.1.attn.w_msa.proj.weight - torch.Size([768, 768]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.1.attn.w_msa.proj.bias - torch.Size([768]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.1.norm2.weight - torch.Size([768]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.1.norm2.bias - torch.Size([768]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.1.ffn.layers.0.0.weight - torch.Size([3072, 768]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.1.ffn.layers.0.0.bias - torch.Size([3072]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.1.ffn.layers.1.weight - torch.Size([768, 3072]):
Initialized by user-defined init_weights in SwinTransformer
backbone.stages.3.blocks.1.ffn.layers.1.bias - torch.Size([768]):
Initialized by user-defined init_weights in SwinTransformer
backbone.norm0.weight - torch.Size([96]):
The value is the same before and after calling init_weights of MaskRCNN
backbone.norm0.bias - torch.Size([96]):
The value is the same before and after calling init_weights of MaskRCNN
backbone.norm1.weight - torch.Size([192]):
The value is the same before and after calling init_weights of MaskRCNN
backbone.norm1.bias - torch.Size([192]):
The value is the same before and after calling init_weights of MaskRCNN
backbone.norm2.weight - torch.Size([384]):
The value is the same before and after calling init_weights of MaskRCNN
backbone.norm2.bias - torch.Size([384]):
The value is the same before and after calling init_weights of MaskRCNN
backbone.norm3.weight - torch.Size([768]):
The value is the same before and after calling init_weights of MaskRCNN
backbone.norm3.bias - torch.Size([768]):
The value is the same before and after calling init_weights of MaskRCNN
neck.lateral_convs.0.conv.weight - torch.Size([256, 96, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.lateral_convs.0.conv.bias - torch.Size([256]):
The value is the same before and after calling init_weights of MaskRCNN
neck.lateral_convs.1.conv.weight - torch.Size([256, 192, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.lateral_convs.1.conv.bias - torch.Size([256]):
The value is the same before and after calling init_weights of MaskRCNN
neck.lateral_convs.2.conv.weight - torch.Size([256, 384, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.lateral_convs.2.conv.bias - torch.Size([256]):
The value is the same before and after calling init_weights of MaskRCNN
neck.lateral_convs.3.conv.weight - torch.Size([256, 768, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.lateral_convs.3.conv.bias - torch.Size([256]):
The value is the same before and after calling init_weights of MaskRCNN
neck.fpn_convs.0.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.fpn_convs.0.conv.bias - torch.Size([256]):
The value is the same before and after calling init_weights of MaskRCNN
neck.fpn_convs.1.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.fpn_convs.1.conv.bias - torch.Size([256]):
The value is the same before and after calling init_weights of MaskRCNN
neck.fpn_convs.2.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.fpn_convs.2.conv.bias - torch.Size([256]):
The value is the same before and after calling init_weights of MaskRCNN
neck.fpn_convs.3.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.fpn_convs.3.conv.bias - torch.Size([256]):
The value is the same before and after calling init_weights of MaskRCNN
rpn_head.rpn_conv.weight - torch.Size([256, 256, 3, 3]):
NormalInit: mean=0, std=0.01, bias=0
rpn_head.rpn_conv.bias - torch.Size([256]):
NormalInit: mean=0, std=0.01, bias=0
rpn_head.rpn_cls.weight - torch.Size([3, 256, 1, 1]):
NormalInit: mean=0, std=0.01, bias=0
rpn_head.rpn_cls.bias - torch.Size([3]):
NormalInit: mean=0, std=0.01, bias=0
rpn_head.rpn_reg.weight - torch.Size([12, 256, 1, 1]):
NormalInit: mean=0, std=0.01, bias=0
rpn_head.rpn_reg.bias - torch.Size([12]):
NormalInit: mean=0, std=0.01, bias=0
roi_head.bbox_head.fc_cls.weight - torch.Size([12, 1024]):
NormalInit: mean=0, std=0.01, bias=0
roi_head.bbox_head.fc_cls.bias - torch.Size([12]):
NormalInit: mean=0, std=0.01, bias=0
roi_head.bbox_head.fc_reg.weight - torch.Size([44, 1024]):
NormalInit: mean=0, std=0.001, bias=0
roi_head.bbox_head.fc_reg.bias - torch.Size([44]):
NormalInit: mean=0, std=0.001, bias=0
roi_head.bbox_head.shared_fcs.0.weight - torch.Size([1024, 12544]):
XavierInit: gain=1, distribution=uniform, bias=0
roi_head.bbox_head.shared_fcs.0.bias - torch.Size([1024]):
XavierInit: gain=1, distribution=uniform, bias=0
roi_head.bbox_head.shared_fcs.1.weight - torch.Size([1024, 1024]):
XavierInit: gain=1, distribution=uniform, bias=0
roi_head.bbox_head.shared_fcs.1.bias - torch.Size([1024]):
XavierInit: gain=1, distribution=uniform, bias=0
roi_head.mask_head.convs.0.conv.weight - torch.Size([256, 256, 3, 3]):
Initialized by user-defined init_weights in ConvModule
roi_head.mask_head.convs.0.conv.bias - torch.Size([256]):
The value is the same before and after calling init_weights of MaskRCNN
roi_head.mask_head.convs.1.conv.weight - torch.Size([256, 256, 3, 3]):
Initialized by user-defined init_weights in ConvModule
roi_head.mask_head.convs.1.conv.bias - torch.Size([256]):
The value is the same before and after calling init_weights of MaskRCNN
roi_head.mask_head.convs.2.conv.weight - torch.Size([256, 256, 3, 3]):
Initialized by user-defined init_weights in ConvModule
roi_head.mask_head.convs.2.conv.bias - torch.Size([256]):
The value is the same before and after calling init_weights of MaskRCNN
roi_head.mask_head.convs.3.conv.weight - torch.Size([256, 256, 3, 3]):
Initialized by user-defined init_weights in ConvModule
roi_head.mask_head.convs.3.conv.bias - torch.Size([256]):
The value is the same before and after calling init_weights of MaskRCNN
roi_head.mask_head.upsample.weight - torch.Size([256, 256, 2, 2]):
Initialized by user-defined init_weights in FCNMaskHead
roi_head.mask_head.upsample.bias - torch.Size([256]):
Initialized by user-defined init_weights in FCNMaskHead
roi_head.mask_head.conv_logits.weight - torch.Size([11, 256, 1, 1]):
Initialized by user-defined init_weights in FCNMaskHead
roi_head.mask_head.conv_logits.bias - torch.Size([11]):
Initialized by user-defined init_weights in FCNMaskHead
roi_head.mask_head.conv_logits.bias - torch.Size([11]):
Initialized by user-defined init_weights in FCNMaskHead
2022-03-10 15:31:50,696 - mmdet - INFO - Start running, host: wanghm@ubuntu-SYS-4028GR-TR2, work_dir: /data5/data/wanghm_data/0pycharm_project/InterGPS/diagram_parser/detection/mmdetection/tools/work_dirs/mask_rcnn_swin-t-p4-w7_fpn_1x_coco
2022-03-10 15:31:50,696 - mmdet - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH   ) StepLrUpdaterHook
(NORMAL      ) CheckpointHook
(LOW         ) EvalHook
(VERY_LOW    ) TextLoggerHook
before_train_epoch:
(VERY_HIGH   ) StepLrUpdaterHook
(NORMAL      ) NumClassCheckHook
(LOW         ) IterTimerHook
(LOW         ) EvalHook
(VERY_LOW    ) TextLoggerHook
before_train_iter:
(VERY_HIGH   ) StepLrUpdaterHook
(LOW         ) IterTimerHook
(LOW         ) EvalHook
after_train_iter:
(ABOVE_NORMAL) OptimizerHook
(NORMAL      ) CheckpointHook
(LOW         ) IterTimerHook
(LOW         ) EvalHook
(VERY_LOW    ) TextLoggerHook
after_train_epoch:
(NORMAL      ) CheckpointHook
(LOW         ) EvalHook
(VERY_LOW    ) TextLoggerHook
before_val_epoch:
(NORMAL      ) NumClassCheckHook
(LOW         ) IterTimerHook
(VERY_LOW    ) TextLoggerHook
before_val_iter:
(LOW         ) IterTimerHook
after_val_iter:
(LOW         ) IterTimerHook
after_val_epoch:
(VERY_LOW    ) TextLoggerHook
after_run:
(VERY_LOW    ) TextLoggerHook
2022-03-10 15:31:50,697 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs
2022-03-10 15:31:50,697 - mmdet - INFO - Checkpoints will be saved to /data5/data/wanghm_data/0pycharm_project/InterGPS/diagram_parser/detection/mmdetection/tools/work_dirs/mask_rcnn_swin-t-p4-w7_fpn_1x_coco by HardDiskBackend.
2022-03-10 15:32:28,198 - mmdet - INFO - Epoch [1][50/803]      lr: 4.995e-06, eta: 1:59:48, time: 0.750, data_time: 0.078, memory: 7132, loss_rpn_cls: 0.6708, loss_rpn_bbox: 0.0572, loss_cls: 1.6349, acc: 57.7012, loss_bbox: 0.0457, loss_mask: 0.5419, loss: 2.9505
2022-03-10 15:33:05,542 - mmdet - INFO - Epoch [1][100/803]     lr: 9.990e-06, eta: 1:58:56, time: 0.747, data_time: 0.031, memory: 7132, loss_rpn_cls: 0.4914, loss_rpn_bbox: 0.0581, loss_cls: 0.4062, acc: 92.4297, loss_bbox: 0.2542, loss_mask: 0.5392, loss: 1.7491
2022-03-10 15:33:43,444 - mmdet - INFO - Epoch [1][150/803]     lr: 1.499e-05, eta: 1:58:49, time: 0.758, data_time: 0.030, memory: 7389, loss_rpn_cls: 0.2595, loss_rpn_bbox: 0.0512, loss_cls: 0.3023, acc: 90.0801, loss_bbox: 0.3970, loss_mask: 0.4285, loss: 1.4385
2022-03-10 15:34:22,367 - mmdet - INFO - Epoch [1][200/803]     lr: 1.998e-05, eta: 1:59:15, time: 0.778, data_time: 0.029, memory: 7495, loss_rpn_cls: 0.1275, loss_rpn_bbox: 0.0476, loss_cls: 0.3131, acc: 88.4062, loss_bbox: 0.5572, loss_mask: 0.3414, loss: 1.3868
2022-03-10 15:35:00,705 - mmdet - INFO - Epoch [1][250/803]     lr: 2.498e-05, eta: 1:58:53, time: 0.767, data_time: 0.027, memory: 7495, loss_rpn_cls: 0.0658, loss_rpn_bbox: 0.0413, loss_cls: 0.3322, acc: 87.2402, loss_bbox: 0.5324, loss_mask: 0.3199, loss: 1.2916
2022-03-10 15:35:40,476 - mmdet - INFO - Epoch [1][300/803]     lr: 2.997e-05, eta: 1:59:10, time: 0.795, data_time: 0.034, memory: 7495, loss_rpn_cls: 0.0411, loss_rpn_bbox: 0.0451, loss_cls: 0.3540, acc: 86.6562, loss_bbox: 0.5388, loss_mask: 0.3139, loss: 1.2929
2022-03-10 15:36:20,367 - mmdet - INFO - Epoch [1][350/803]     lr: 3.497e-05, eta: 1:59:14, time: 0.798, data_time: 0.029, memory: 7495, loss_rpn_cls: 0.0323, loss_rpn_bbox: 0.0437, loss_cls: 0.2928, acc: 88.5781, loss_bbox: 0.4647, loss_mask: 0.2937, loss: 1.1272
2022-03-10 15:36:59,574 - mmdet - INFO - Epoch [1][400/803]     lr: 3.996e-05, eta: 1:58:51, time: 0.784, data_time: 0.029, memory: 7495, loss_rpn_cls: 0.0251, loss_rpn_bbox: 0.0453, loss_cls: 0.2816, acc: 88.3262, loss_bbox: 0.4802, loss_mask: 0.2819, loss: 1.1141
2022-03-10 15:37:39,111 - mmdet - INFO - Epoch [1][450/803]     lr: 4.496e-05, eta: 1:58:32, time: 0.791, data_time: 0.029, memory: 7495, loss_rpn_cls: 0.0249, loss_rpn_bbox: 0.0438, loss_cls: 0.2864, acc: 88.9961, loss_bbox: 0.4666, loss_mask: 0.2756, loss: 1.0973
2022-03-10 15:38:17,069 - mmdet - INFO - Epoch [1][500/803]     lr: 4.995e-05, eta: 1:57:39, time: 0.759, data_time: 0.026, memory: 7495, loss_rpn_cls: 0.0230, loss_rpn_bbox: 0.0399, loss_cls: 0.2578, acc: 89.8340, loss_bbox: 0.4046, loss_mask: 0.2726, loss: 0.9978
2022-03-10 15:38:56,701 - mmdet - INFO - Epoch [1][550/803]     lr: 5.495e-05, eta: 1:57:17, time: 0.793, data_time: 0.026, memory: 7495, loss_rpn_cls: 0.0187, loss_rpn_bbox: 0.0381, loss_cls: 0.2535, acc: 89.9141, loss_bbox: 0.4316, loss_mask: 0.2656, loss: 1.0075
2022-03-10 15:39:35,816 - mmdet - INFO - Epoch [1][600/803]     lr: 5.994e-05, eta: 1:56:44, time: 0.782, data_time: 0.030, memory: 7495, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0365, loss_cls: 0.2314, acc: 90.5137, loss_bbox: 0.4030, loss_mask: 0.2458, loss: 0.9311
2022-03-10 15:40:15,131 - mmdet - INFO - Epoch [1][650/803]     lr: 6.494e-05, eta: 1:56:13, time: 0.786, data_time: 0.028, memory: 7495, loss_rpn_cls: 0.0203, loss_rpn_bbox: 0.0393, loss_cls: 0.2508, acc: 90.2070, loss_bbox: 0.3899, loss_mask: 0.2416, loss: 0.9419
2022-03-10 15:40:55,037 - mmdet - INFO - Epoch [1][700/803]     lr: 6.993e-05, eta: 1:55:48, time: 0.798, data_time: 0.029, memory: 7495, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0396, loss_cls: 0.2285, acc: 90.6582, loss_bbox: 0.3895, loss_mask: 0.2335, loss: 0.9077
2022-03-10 15:41:34,019 - mmdet - INFO - Epoch [1][750/803]     lr: 7.493e-05, eta: 1:55:11, time: 0.780, data_time: 0.026, memory: 7495, loss_rpn_cls: 0.0119, loss_rpn_bbox: 0.0354, loss_cls: 0.2225, acc: 91.0449, loss_bbox: 0.3980, loss_mask: 0.2275, loss: 0.8953
2022-03-10 15:42:13,594 - mmdet - INFO - Epoch [1][800/803]     lr: 7.992e-05, eta: 1:54:39, time: 0.792, data_time: 0.028, memory: 7495, loss_rpn_cls: 0.0176, loss_rpn_bbox: 0.0399, loss_cls: 0.2521, acc: 89.9141, loss_bbox: 0.4153, loss_mask: 0.2452, loss: 0.9701
2022-03-10 15:42:15,916 - mmdet - INFO - Saving checkpoint at 1 epochs
2022-03-10 15:43:33,865 - mmdet - INFO - Evaluating bbox...
2022-03-10 15:43:35,826 - mmdet - INFO -
Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.187
Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.423
Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.123
Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.232
Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.265
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.265
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.265
Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.265
Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
2022-03-10 15:43:35,826 - mmdet - INFO - Evaluating segm...
2022-03-10 15:43:38,113 - mmdet - INFO -
Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.203
Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.436
Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.152
Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.241
Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.278
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.278
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.278
Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.278
Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
2022-03-10 15:43:38,138 - mmdet - INFO - Exp name: mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py
2022-03-10 15:43:38,138 - mmdet - INFO - Epoch(val) [1][599]    bbox_mAP: 0.1870, bbox_mAP_50: 0.4230, bbox_mAP_75: 0.1230, bbox_mAP_s: 0.2320, bbox_mAP_m: -1.0000, bbox_mAP_l: -1.0000, bbox_mAP_copypaste: 0.187 0.423 0.123 0.232 -1.000 -1.000, segm_mAP: 0.2030, segm_mAP_50: 0.4360, segm_mAP_75: 0.1520, segm_mAP_s: 0.2410, segm_mAP_m: -1.0000, segm_mAP_l: -1.0000, segm_mAP_copypaste: 0.203 0.436 0.152 0.241 -1.000 -1.000
2022-03-10 15:44:18,597 - mmdet - INFO - Epoch [2][50/803]      lr: 8.521e-05, eta: 1:53:50, time: 0.809, data_time: 0.076, memory: 7495, loss_rpn_cls: 0.0118, loss_rpn_bbox: 0.0381, loss_cls: 0.2092, acc: 91.8379, loss_bbox: 0.3945, loss_mask: 0.2423, loss: 0.8959
2022-03-10 15:44:57,565 - mmdet - INFO - Epoch [2][100/803]     lr: 9.021e-05, eta: 1:53:12, time: 0.779, data_time: 0.028, memory: 7495, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0404, loss_cls: 0.2116, acc: 91.3555, loss_bbox: 0.3963, loss_mask: 0.2317, loss: 0.8927
2022-03-10 15:45:36,073 - mmdet - INFO - Epoch [2][150/803]     lr: 9.520e-05, eta: 1:52:29, time: 0.770, data_time: 0.028, memory: 7495, loss_rpn_cls: 0.0107, loss_rpn_bbox: 0.0377, loss_cls: 0.2233, acc: 91.3203, loss_bbox: 0.3844, loss_mask: 0.2421, loss: 0.8983
2022-03-10 15:46:15,058 - mmdet - INFO - Epoch [2][200/803]     lr: 1.000e-04, eta: 1:51:51, time: 0.780, data_time: 0.028, memory: 7495, loss_rpn_cls: 0.0104, loss_rpn_bbox: 0.0389, loss_cls: 0.2182, acc: 91.2500, loss_bbox: 0.3929, loss_mask: 0.2431, loss: 0.9035
2022-03-10 15:46:53,718 - mmdet - INFO - Epoch [2][250/803]     lr: 1.000e-04, eta: 1:51:11, time: 0.773, data_time: 0.026, memory: 7495, loss_rpn_cls: 0.0070, loss_rpn_bbox: 0.0394, loss_cls: 0.1916, acc: 92.4961, loss_bbox: 0.3645, loss_mask: 0.2155, loss: 0.8181
2022-03-10 15:47:32,778 - mmdet - INFO - Epoch [2][300/803]     lr: 1.000e-04, eta: 1:50:33, time: 0.781, data_time: 0.027, memory: 7495, loss_rpn_cls: 0.0078, loss_rpn_bbox: 0.0361, loss_cls: 0.1924, acc: 92.1484, loss_bbox: 0.3783, loss_mask: 0.2237, loss: 0.8383
2022-03-10 15:48:11,607 - mmdet - INFO - Epoch [2][350/803]     lr: 1.000e-04, eta: 1:49:54, time: 0.777, data_time: 0.026, memory: 7495, loss_rpn_cls: 0.0091, loss_rpn_bbox: 0.0318, loss_cls: 0.2031, acc: 91.5977, loss_bbox: 0.3793, loss_mask: 0.2202, loss: 0.8435
2022-03-10 15:48:51,498 - mmdet - INFO - Epoch [2][400/803]     lr: 1.000e-04, eta: 1:49:22, time: 0.798, data_time: 0.028, memory: 7495, loss_rpn_cls: 0.0080, loss_rpn_bbox: 0.0332, loss_cls: 0.1900, acc: 92.2422, loss_bbox: 0.3890, loss_mask: 0.2216, loss: 0.8419
2022-03-10 15:49:29,812 - mmdet - INFO - Epoch [2][450/803]     lr: 1.000e-04, eta: 1:48:40, time: 0.766, data_time: 0.027, memory: 7495, loss_rpn_cls: 0.0092, loss_rpn_bbox: 0.0338, loss_cls: 0.1860, acc: 92.2012, loss_bbox: 0.3601, loss_mask: 0.2173, loss: 0.8063
2022-03-10 15:50:08,749 - mmdet - INFO - Epoch [2][500/803]     lr: 1.000e-04, eta: 1:48:01, time: 0.779, data_time: 0.028, memory: 7495, loss_rpn_cls: 0.0096, loss_rpn_bbox: 0.0338, loss_cls: 0.1876, acc: 92.3867, loss_bbox: 0.3627, loss_mask: 0.2145, loss: 0.8083
2022-03-10 15:50:48,368 - mmdet - INFO - Epoch [2][550/803]     lr: 1.000e-04, eta: 1:47:27, time: 0.792, data_time: 0.028, memory: 7495, loss_rpn_cls: 0.0073, loss_rpn_bbox: 0.0319, loss_cls: 0.1772, acc: 92.6953, loss_bbox: 0.3676, loss_mask: 0.2116, loss: 0.7955
2022-03-10 15:51:27,833 - mmdet - INFO - Epoch [2][600/803]     lr: 1.000e-04, eta: 1:46:51, time: 0.789, data_time: 0.028, memory: 7495, loss_rpn_cls: 0.0072, loss_rpn_bbox: 0.0309, loss_cls: 0.1743, acc: 92.7461, loss_bbox: 0.3564, loss_mask: 0.2091, loss: 0.7780
2022-03-10 15:52:06,637 - mmdet - INFO - Epoch [2][650/803]     lr: 1.000e-04, eta: 1:46:11, time: 0.776, data_time: 0.027, memory: 7495, loss_rpn_cls: 0.0065, loss_rpn_bbox: 0.0285, loss_cls: 0.1605, acc: 93.4004, loss_bbox: 0.3243, loss_mask: 0.2011, loss: 0.7209
2022-03-10 15:52:45,414 - mmdet - INFO - Epoch [2][700/803]     lr: 1.000e-04, eta: 1:45:31, time: 0.776, data_time: 0.026, memory: 7495, loss_rpn_cls: 0.0101, loss_rpn_bbox: 0.0336, loss_cls: 0.1673, acc: 93.0059, loss_bbox: 0.3700, loss_mask: 0.2116, loss: 0.7926
2022-03-10 15:53:25,397 - mmdet - INFO - Epoch [2][750/803]     lr: 1.000e-04, eta: 1:44:58, time: 0.800, data_time: 0.028, memory: 7495, loss_rpn_cls: 0.0126, loss_rpn_bbox: 0.0310, loss_cls: 0.1664, acc: 93.2051, loss_bbox: 0.3459, loss_mask: 0.2114, loss: 0.7672
2022-03-10 15:54:05,928 - mmdet - INFO - Epoch [2][800/803]     lr: 1.000e-04, eta: 1:44:27, time: 0.811, data_time: 0.030, memory: 7495, loss_rpn_cls: 0.0087, loss_rpn_bbox: 0.0335, loss_cls: 0.1771, acc: 92.9512, loss_bbox: 0.3539, loss_mask: 0.2021, loss: 0.7754
2022-03-10 15:54:08,251 - mmdet - INFO - Saving checkpoint at 2 epochs
2022-03-10 15:55:26,173 - mmdet - INFO - Evaluating bbox...
2022-03-10 15:55:27,931 - mmdet - INFO -
Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.262
Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.631
Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.192
Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.343
Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.394
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.394
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.394
Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.394
Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
2022-03-10 15:55:27,932 - mmdet - INFO - Evaluating segm...
2022-03-10 15:55:29,947 - mmdet - INFO -
Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.274
Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.637
Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.204
Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.346
Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.412
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.412
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.412
Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.412
Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
2022-03-10 15:55:29,966 - mmdet - INFO - Exp name: mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py
2022-03-10 15:55:29,966 - mmdet - INFO - Epoch(val) [2][599]    bbox_mAP: 0.2620, bbox_mAP_50: 0.6310, bbox_mAP_75: 0.1920, bbox_mAP_s: 0.3430, bbox_mAP_m: -1.0000, bbox_mAP_l: -1.0000, bbox_mAP_copypaste: 0.262 0.631 0.192 0.343 -1.000 -1.000, segm_mAP: 0.2740, segm_mAP_50: 0.6370, segm_mAP_75: 0.2040, segm_mAP_s: 0.3460, segm_mAP_m: -1.0000, segm_mAP_l: -1.0000, segm_mAP_copypaste: 0.274 0.637 0.204 0.346 -1.000 -1.000
2022-03-10 15:56:11,191 - mmdet - INFO - Epoch [3][50/803]      lr: 1.000e-04, eta: 1:43:45, time: 0.824, data_time: 0.076, memory: 7495, loss_rpn_cls: 0.0088, loss_rpn_bbox: 0.0329, loss_cls: 0.1668, acc: 93.1504, loss_bbox: 0.3496, loss_mask: 0.2063, loss: 0.7643
2022-03-10 15:56:51,015 - mmdet - INFO - Epoch [3][100/803]     lr: 1.000e-04, eta: 1:43:10, time: 0.796, data_time: 0.029, memory: 7495, loss_rpn_cls: 0.0049, loss_rpn_bbox: 0.0310, loss_cls: 0.1556, acc: 93.4590, loss_bbox: 0.3405, loss_mask: 0.2049, loss: 0.7368
2022-03-10 15:57:30,086 - mmdet - INFO - Epoch [3][150/803]     lr: 1.000e-04, eta: 1:42:31, time: 0.781, data_time: 0.029, memory: 7495, loss_rpn_cls: 0.0072, loss_rpn_bbox: 0.0309, loss_cls: 0.1644, acc: 93.2324, loss_bbox: 0.3560, loss_mask: 0.2075, loss: 0.7660
2022-03-10 15:58:08,773 - mmdet - INFO - Epoch [3][200/803]     lr: 1.000e-04, eta: 1:41:50, time: 0.774, data_time: 0.026, memory: 7495, loss_rpn_cls: 0.0055, loss_rpn_bbox: 0.0277, loss_cls: 0.1616, acc: 93.4277, loss_bbox: 0.3395, loss_mask: 0.2017, loss: 0.7359
2022-03-10 15:58:47,662 - mmdet - INFO - Epoch [3][250/803]     lr: 1.000e-04, eta: 1:41:11, time: 0.778, data_time: 0.026, memory: 7495, loss_rpn_cls: 0.0076, loss_rpn_bbox: 0.0287, loss_cls: 0.1645, acc: 93.2051, loss_bbox: 0.3482, loss_mask: 0.1972, loss: 0.7462
2022-03-10 15:59:26,180 - mmdet - INFO - Epoch [3][300/803]     lr: 1.000e-04, eta: 1:40:30, time: 0.770, data_time: 0.025, memory: 7495, loss_rpn_cls: 0.0071, loss_rpn_bbox: 0.0273, loss_cls: 0.1586, acc: 93.4902, loss_bbox: 0.3450, loss_mask: 0.2083, loss: 0.7463
2022-03-10 16:00:06,070 - mmdet - INFO - Epoch [3][350/803]     lr: 1.000e-04, eta: 1:39:54, time: 0.798, data_time: 0.029, memory: 7495, loss_rpn_cls: 0.0048, loss_rpn_bbox: 0.0282, loss_cls: 0.1731, acc: 93.1992, loss_bbox: 0.3365, loss_mask: 0.2001, loss: 0.7428
2022-03-10 16:00:45,468 - mmdet - INFO - Epoch [3][400/803]     lr: 1.000e-04, eta: 1:39:16, time: 0.788, data_time: 0.028, memory: 7495, loss_rpn_cls: 0.0060, loss_rpn_bbox: 0.0283, loss_cls: 0.1658, acc: 93.2637, loss_bbox: 0.3533, loss_mask: 0.2000, loss: 0.7533
2022-03-10 16:01:25,025 - mmdet - INFO - Epoch [3][450/803]     lr: 1.000e-04, eta: 1:38:39, time: 0.791, data_time: 0.030, memory: 7495, loss_rpn_cls: 0.0038, loss_rpn_bbox: 0.0316, loss_cls: 0.1601, acc: 93.3105, loss_bbox: 0.3452, loss_mask: 0.1987, loss: 0.7395
2022-03-10 16:02:04,563 - mmdet - INFO - Epoch [3][500/803]     lr: 1.000e-04, eta: 1:38:02, time: 0.791, data_time: 0.028, memory: 7495, loss_rpn_cls: 0.0086, loss_rpn_bbox: 0.0301, loss_cls: 0.1728, acc: 92.9277, loss_bbox: 0.3518, loss_mask: 0.2007, loss: 0.7640
2022-03-10 16:02:43,989 - mmdet - INFO - Epoch [3][550/803]     lr: 1.000e-04, eta: 1:37:24, time: 0.788, data_time: 0.025, memory: 7495, loss_rpn_cls: 0.0061, loss_rpn_bbox: 0.0277, loss_cls: 0.1472, acc: 94.1523, loss_bbox: 0.3134, loss_mask: 0.2082, loss: 0.7026
2022-03-10 16:03:23,504 - mmdet - INFO - Epoch [3][600/803]     lr: 1.000e-04, eta: 1:36:47, time: 0.790, data_time: 0.030, memory: 7495, loss_rpn_cls: 0.0044, loss_rpn_bbox: 0.0304, loss_cls: 0.1581, acc: 93.2910, loss_bbox: 0.3458, loss_mask: 0.2030, loss: 0.7416
2022-03-10 16:04:02,984 - mmdet - INFO - Epoch [3][650/803]     lr: 1.000e-04, eta: 1:36:09, time: 0.790, data_time: 0.028, memory: 7495, loss_rpn_cls: 0.0046, loss_rpn_bbox: 0.0317, loss_cls: 0.1704, acc: 92.8418, loss_bbox: 0.3672, loss_mask: 0.2033, loss: 0.7771
2022-03-10 16:04:43,003 - mmdet - INFO - Epoch [3][700/803]     lr: 1.000e-04, eta: 1:35:33, time: 0.800, data_time: 0.028, memory: 7495, loss_rpn_cls: 0.0051, loss_rpn_bbox: 0.0288, loss_cls: 0.1607, acc: 93.3066, loss_bbox: 0.3578, loss_mask: 0.2027, loss: 0.7551
2022-03-10 16:05:23,457 - mmdet - INFO - Epoch [3][750/803]     lr: 1.000e-04, eta: 1:34:58, time: 0.809, data_time: 0.028, memory: 7495, loss_rpn_cls: 0.0042, loss_rpn_bbox: 0.0306, loss_cls: 0.1688, acc: 92.8848, loss_bbox: 0.3585, loss_mask: 0.1982, loss: 0.7603
2022-03-10 16:06:02,712 - mmdet - INFO - Epoch [3][800/803]     lr: 1.000e-04, eta: 1:34:19, time: 0.785, data_time: 0.027, memory: 7495, loss_rpn_cls: 0.0051, loss_rpn_bbox: 0.0300, loss_cls: 0.1592, acc: 93.2988, loss_bbox: 0.3447, loss_mask: 0.2029, loss: 0.7420
2022-03-10 16:06:05,016 - mmdet - INFO - Saving checkpoint at 3 epochs
2022-03-10 16:07:20,843 - mmdet - INFO - Evaluating bbox...
2022-03-10 16:07:22,424 - mmdet - INFO -
Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.293
Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.659
Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.195
Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.372
Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.431
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.431
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.431
Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.431
Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
2022-03-10 16:07:22,424 - mmdet - INFO - Evaluating segm...
2022-03-10 15:31:44,244 - mmdet - INFO - Environment info:
sys.platform: linux
Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]
CUDA available: True
GPU 0,1,2,3,4,5,6,7: Tesla P40
GPU 8: GeForce GT 710
CUDA_HOME: /usr/local/cuda-10.2
NVCC: Cuda compilation tools, release 10.2, V10.2.89
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.7.0
PyTorch compiling details: PyTorch built with:

GCC 7.3
C++ Version: 201402
Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
OpenMP 201511 (a.k.a. OpenMP 4.5)
NNPACK is enabled
CPU capability usage: AVX2
CUDA Runtime 10.2
NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=
sm_70;-gencode;arch=compute_75,code=sm_75
CuDNN 7.6.5
Magma 2.5.2
Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK
-DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -
Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -
Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error
=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-s
tringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE
_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.8.0
OpenCV: 4.5.4-dev
MMCV: 1.4.6
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 11.0
MMDetection: 2.22.0+6ddbff8
2022-03-10 15:31:45,608 - mmdet - INFO - Distributed training: False
2022-03-10 15:31:46,930 - mmdet - INFO - Config:
model = dict(
type='MaskRCNN',
backbone=dict(
type='SwinTransformer',
embed_dims=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.2,
patch_norm=True,
out_indices=(0, 1, 2, 3),
with_cp=False,
convert_weights=True,
init_cfg=dict(
type='Pretrained',
checkpoint=
'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth'
)),
neck=dict(
type='FPN',
in_channels=[96, 192, 384, 768],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
roi_head=dict(
type='StandardRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=11,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
mask_head=dict(
type='FCNMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=11,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100,
mask_thr_binary=0.5)))
dataset_type = 'CocoDataset'
data_root = '/data5/data/wanghm_data/0pycharm_project/InterGPS/diagram_parser/detection/coco/'
classes = ('angle', 'double angle', 'triple angle', 'bar', 'double bar',
'triple bar', 'parallel', 'double parallel', 'triple parallel',
'perpendicular', 'text')
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type='CocoDataset',
ann_file=
'/data5/data/wanghm_data/0pycharm_project/InterGPS/diagram_parser/detection/coco/annotations/instances_train.json',
img_prefix=
'/data5/data/wanghm_data/0pycharm_project/InterGPS/diagram_parser/detection/coco/train/',
pipeline=[
2022-03-10 15:31:44,244 - mmdet - INFO - Environment info:
sys.platform: linux
Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]
CUDA available: True
GPU 0,1,2,3,4,5,6,7: Tesla P40
GPU 8: GeForce GT 710
CUDA_HOME: /usr/local/cuda-10.2
NVCC: Cuda compilation tools, release 10.2, V10.2.89
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.7.0
PyTorch compiling details: PyTorch built with:

GCC 7.3
C++ Version: 201402
Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
OpenMP 201511 (a.k.a. OpenMP 4.5)
NNPACK is enabled
CPU capability usage: AVX2
CUDA Runtime 10.2
NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=
sm_70;-gencode;arch=compute_75,code=sm_75
CuDNN 7.6.5
Magma 2.5.2
Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK
-DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -
Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -
Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error
=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-s
tringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE
_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.8.0
OpenCV: 4.5.4-dev
MMCV: 1.4.6
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 11.0
MMDetection: 2.22.0+6ddbff8
2022-03-10 15:31:45,608 - mmdet - INFO - Distributed training: False
2022-03-10 15:31:46,930 - mmdet - INFO - Config:
model = dict(
type='MaskRCNN',
backbone=dict(
type='SwinTransformer',
embed_dims=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.2,
patch_norm=True,
out_indices=(0, 1, 2, 3),
with_cp=False,
convert_weights=True,
init_cfg=dict(
type='Pretrained',
checkpoint=
'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth'
)),
neck=dict(
type='FPN',
in_channels=[96, 192, 384, 768],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
roi_head=dict(
type='StandardRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=11,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
mask_head=dict(
type='FCNMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=11,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
:`

@AlexWanghaoming I'm guessing the reason may be that there are adaptive operations in https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/backbones/swin.py#L670

I have the same question“Initialized by user-defined init_weights in SwinTransformer".How to solve this problem


RemoteMirror: Project some multi-payload enums

This builds on #30161 by adding support for the most basic form of multi-payload enum.
As before, the swift_reflection_projectEnum call should return false for any enum that it is unable to project, so adopters should continue to fall back to whatever mechanism they were using before if it fails.
This is one part of rdar://31666592

@swift-ci Please test

Basically, if all payload cases have a fixed size known at compile time in all resilience domains where the enum is visible (this is a bit tricky), you want to use spare bits. Since the compiler makes the decision it should also be the one to emit the descriptor that tells you where the spare bits are. Otherwise, you use the much simpler dynamic layout algorithm that the runtime uses in swift_getEnumCaseMultiPayload().
Also do we have a way to project single payload enums yet? The interesting thing to do here is to know which non-payload case is active.

Build failed
Swift Test OS X Platform
Git Sha - 13853c164142e56ca5c698a4ec3c2803f0c7c34a

@slavapestov -- PR #30161 added the projectEnum API and implemented the no-payload and single-payload cases, with some follow-up fixes in #30281 and #30301.

@slavapestov -- If spare bits handling requires associated compiler changes, any concerns about merging this partial support and tackling that in a separate PR?  Users of this API just need to be confident that this code will either project the enum correctly or return false indicating it was unable to decode the enum.

Swift-CI failure looks entirely unrelated, so I'll retry.

@swift-ci Please test macOS

Windows CI caught a typo.  Restarting CI bots...

@swift-ci Please test

Build failed
Swift Test Linux Platform
Git Sha - 13853c164142e56ca5c698a4ec3c2803f0c7c34a

Build failed
Swift Test OS X Platform
Git Sha - 13853c164142e56ca5c698a4ec3c2803f0c7c34a

I added a few more comments just to summarize our chat discussion for other reviewers. Nothing new for you here.

It might be fun to deploy the utils/type-layout-fuzzer.py script to generate random enums and see if we can generate test cases against them.


Add Mute/Unmute of local video and audio

Enhancement.
Add the possibility to toggle local video/audio on and off.
It's a simple boolean on the MediaStreamTrack object.

That looks easy enough. I will have a look.

Added PeerConnection.SetLocalVideoTrackEnabled() and PeerConnection.IsLocalVideoTrackEnabled().

And 8101f4ecca3fdd2dd6b8545699907deaaf0ec67f adds support for muting the local audio track. Closing as fixed.


English Block

When will the english version be availbe or how can one translate the blocks to english?

Hello enemali,
Good to know you are interested.
I am currently working on translating everything to English. I recently finished the translation of the website & documentation. Next step is the software itself.
Depending on how much of my spare time I can spend on this project, an English translation will probably be available in a month or two. Would that be ok for you?
After I finished the translation to English, translating to more languages should also be a lot easier.

English translation will probably be available in a month or two. Would that be ok for you?
Yes I'm OK with the time and i will patiently wait for it . Kindly update us here when you're done with the English translation of the software .
Thank you for this wonderful software

Hi, I've also discovered this software and it is so awesome. I hope the english version will be available soon ! You've made such a wonderful work !

Hello @HerelAdrastel, @enemali,
Thanks!! Posts like this really help to keep me motivated! And so...
Today I released version 0.5.0 of VubbiScript which is available in English!
You can find it on the website: https://vubbiscript.github.io/en/getting-started/.
Enjoy!

wow , keep it  up


Arm64 Preview: Bug Report

I found a problem while using the Preview version of the AWS Toolkit for Arm64 Visual Studio.
The Mock Lambda Test Tool option in the run/debugger dropdown does not currently function. There is an executable that is not being included with the installation of the AWS Toolkit that is needed to run the Mock Lambda Test Tool "dotnet-lambda-test-tool-6.0.exe" is the name of the file the debugger is looking for.
After installing AWS ToolKit for Visual Studio, I opened a new project "AWS Serverless Application with Tests (.NET Core - C#)"  and chose "ASP.NET Core Web API" as the blueprint. The project builds fine and runs locally, but when changing to the Mock Lambda Test Tool in the debugger dropdown, it cannot fine the above executable. I verified the file does not exist by following the file path.

Toolkit: <IP_ADDRESS>
Visual Studio: 17.4.3

Hi @mattibosAhead , thanks for reaching out.
Would you be able to share the Toolkit log from your session in case it has some additional details?
You can get to the logs from the "Extensions" menu, then "AWS Toolkit", "View Toolkit Logs". There is a log file per IDE session, so you'll want to find the one that related to when you took the screenshots above.
As a workaround, can you try running the following from a command line, to install the testing tool? This should allow you to debug using the Toolkit afterwards.
dotnet tool install amazon.lambda.testtool-6.0 -g

Yes absolutely. I'll include it with this comment.
I tried running the commend manually, but it looks to be getting the same error that is in the logs.
"C:\Users\matthew.ibos\AppData\Local\Temp\5068aece-6328-44ce-8d28-7dc101e4be0f\restore.csproj : error NU1301: Unable to
load the service index for source https://pkgs.dev.azure.com/iRecordModernization/a2e250b1-683b-4e5a-87e9-739178d5960f/
_packaging/wsi.libraries/nuget/v3/index.json.
The tool package could not be restored.
Tool 'amazon.lambda.testtool-6.0' failed to install. This failure may have been caused by:

You are attempting to install a preview release and did not use the --version option to specify the version.
A package by this name was found, but it was not a .NET tool.
The required NuGet feed cannot be accessed, perhaps because of an Internet connection problem.
You mistyped the name of the tool.

For more reasons, including package naming enforcement, visit https://aka.ms/failure-installing-tool"
log_2023-08-02_16-59-07_12668.txt

It looks like your system doesn't know how to access the main NuGet repository. Can run dotnet nuget list source from the command line to see what NuGet sources you have configured? This is what mine looks like for example:
Registered Sources:
  1.  nuget.org [Enabled]
      https://api.nuget.org/v3/index.json
  2.  Microsoft Visual Studio Offline Packages [Enabled]
      C:\Program Files (x86)\Microsoft SDKs\NuGetPackages\

You'd want to have a nuget.org source added and enabled. Then, the Toolkit and the "dotnet tool install" command should be able to obtain the test tool.

Turns out I had a third party nuget package source that was blocking me. I removed it and was able to debug with the Mock Lambda Test Tool selected. I have the same two Registered Sources as you do now. Thank you for helping me sort this out!


Documentation unclear/incomplete on Salesforce connector index page

In the "Before You Begin" Section regarding the last point, "If you are using OAuth authentication, the namespace and schema location", seems incomplete.
Page URI: https://docs.mulesoft.com/salesforce-connector/10.18/

Thanks for reporting this @jasalltime. We will look into this.

I believe the idea is that before creating an app, you must have the namespace and schema location (if you are using OAuth authentication). Let me verify if this is accurate, and if so, we will reword for clarity

It'd be great if you verify. Thank you for the reply.

Hey @jasalltime - we verified the information and updated the docs accordingly. You can see the changes in this PR or on the docs site as it is now updated. The issue was that we had the accurate info in the 10.6 version but it never got carried over to 10.7 and later. The info is accurate now for all versions. Thank you for reporting this!

Thanks @sathya0 for your efforts. Now I am closing the issue.


FIX BOFT device error after PR 1742

Fixes failing CI.
PR #1742 introduced the feature that adapters of the same layer can be on different devices. A new method was introduced that is responsible for moving the parameters related to a specific adapter in a consistent way.
In BOFT, however, one parameter was overlooked, boft_P. This parameter is not stored inside a ParameterDict or ModuleDict, hence it was not moved. The reason is (presumably) that this parameter is shared between all BOFT adapters, as it's always identical. However, this clashes with having different adapters on different devices.
To solve this, the parameter is now moved on the fly to the correct device during the forward pass.
Alternatively, we could store this parameter inside a ParameterDict or BufferDict and have one copy per adapter. This would probably be faster at the cost of more memory.
I ran the BOFT tests locally with GPU and they passed, so this should fix the CI.

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Ping @yfeng95, @Zeju1997, @YuliangXiu


Removing the user name and the Host name

As for me and many other developers, without the username and the
hostname is much much better and cleaner bash screen, any one can have
them back by just uncommenting them by removing the “#” from the
begining of the line.

You can just add DEFAULT_USER=$USER in your zshrc file. Many others like having that.


Selectable DiffResult::Left text

Sometimes you need to reinsert some parts of the original code into the new code. Selection and copy of the original (DiffResult::Left) code from the diff view would make this straight-forward. Currently, these lines are invisible for selection.
The behavior for selections starting on DiffResult::Both or DiffResult::Right should remain. When placing the cursor or starting selection on DiffResult::Left, the other side (DiffResult::Right) should become invisible for this specific selection.
Additionally, when pressing the Down or Up keys (without selection), I think all lines should be considered. But this isn't really important as long as the selection can be started on DiffResult::Left with the mouse.
This will make it impossible to select fragments of the original code that do not start or end inside modified lines. This limitation could be fixed intuitively when there is an option to enable side-by-side diffs.

side-by-side diffs is necessary


[keycloak role] Allow creating updating and deleting client registration policies

As requested in this ticket

@nliam We also need the role changes for the client updates. We have the variables in the inventory, but we need also the tasks to apply the configuration.


Kerberos auth doesn't work when the host is an alias

Hi
I'm using the request module in combination with request-kerberos. Since the server i'm querying is behind a load balancer it often happens that the authentication is done with host1 and the query on host2, leading to error 401 Unauthenticated.
I manage to workaround this problem this way:
host = socket.gethostbyaddr(random.choice(socket.gethostbyname_ex(host)[2]))[0]

Don't know if i should open this issue in requests-kerberos as well.

requests-kerberos should authenticate every connection so long as you attach it as an auth handler to the session, not to individual requests. Ideally that should avoid this problem.

Do you mean something like this?
s = requests.Session()
s.auth = HTTPKerberosAuth()
s.get('http://my.beautiful.alias')

Because this example (on their project page) doesn't work in my case:
requests.get("http://my.beautiful.alias", auth=HTTPKerberosAuth())

That is exactly what I meant. =) Does that work?

In my case it doesn't...
If it connects to the same server behind the load balancer it works, if it connects to a different one then i have authentication failed.

Interesting. Do all the endpoints share the same hostname?

The reason I ask is that Kerberos with loadbalancers is...tricky.

That's my case:
➜  host judy.my.com
judy.my.com is an alias for foremanlb.my.com.
foremanlb.my.com has address X
foremanlb.my.com has address Y

It would be interesting to see if any other kerberos client implementation functions here. I think Chrome has a Kerberos implementation...

So HTTPKerberosAuth requires the same IP each time for authentication to continue working? I'm not sure that's the problem of requests and I'm very sure we don't want to randomly choose a value to give the authenticator if in fact that's a potential solution.

I didn't investigate deeply tbh...
Also requests need to pick up a random value and not the alias, otherwise it doesn't work.
This is the only way afaik:
host = socket.gethostbyaddr(random.choice(socket.gethostbyname_ex(host)[2]))[0]
requests.get(host, auth=HTTPKerberosAuth())

The thing is that both requests and HTTPKerberosAuth need to make their calls to the same server behind the alias.

not sure if this might help, but I had a similar problem and it seems to have gone after setting SPNs for the alias names. In my case, I had a webserver bound to an Active Directory domain with a specific URL asking for Kerberos auth. Without SPNs for the alias name in place, auth didn't work. I ran this with DomainAdmin privileges to register the SPNs:
setspn /A host/myaliashostname realhostname
setspn /A http/myaliashostname realhostname
In my case, I also had to reboot the (Windows) client machine on which I'm running the python/requests client (had problems before reboot).

Why this has been closed? It's still an issue for me

This issue is not really appropriate on this repository, it should be on requests-kerberos. =)


New Speed View

Thank you for your great work , I relly apriciate if you could help me to implement this view.

you can use ImageSpeedometer with ImageIndicator.
and don't forget to change Start and End Degree like this:
app:sv_startDegree="180"
app:sv_endDegree="450"


Using the createElement override with Typescript

This isn't really an issue, just thought it's worth making a note in case this helps anyone else. Feel free to close.
I'm a fan of using the createElement override and I spent a bit of time figuring how to get this working nicely with Typescript.
Here's what I came up with:
Re-export createElement with module agumentation  to add the css prop:
// createElement.ts
import { createElement } from 'glamor/react'

declare module 'react' {
  interface HTMLAttributes<T> {
    css?: any
  }
}

export default createElement

Tell TS to use our createElement for JSX with this tsconfig:
{
  "compilerOptions": {
    "jsx": "react",
    "jsxFactory": "createElement"
  }
}

Usage:
// MyComponent.tsx
import createElement from './createElement'

export default function MyComponent() {
  return <div css={{ color: 'red' }} />
}

Reference: https://stackoverflow.com/questions/40093655/how-do-i-add-attributes-to-existing-html-elements-in-typescript-jsx/44931039#44931039

This is awesome, thank you. I'll leave this open for now. Maybe we can add it to the docs later!

thanks for this! added a link to this issue from the docs


Research: Responsive Layout

A general item to invest some time looking at our current page layouts and how they function/respond to smaller screen sizes and mobile.
In addition, this research issue should be used to document approaches for updating existing flows to account for smaller screens, which may include but is not limited to: technical requirements, best practices, design ideas etc.

Closing for now, de-prioritized

Keeping this open so I don't forget

Closing as #1916 should address this.