WhereIsAI/github-issue-similarity huggingface dataset:
Issue 1:
URL(s) with the issue:.

https://keras.io/api/applications/

Description of issue (what needs to be changed):.

Models starting from EfficientNetV2B0 and ending at EfficientNetV2L are duplicated in the table.

Are you planning to also submit a pull request to fix the issue? See the docs contributor guide:

I could but I don't know how to update docs.

Issue 2:
If you open a GitHub issue, here is our policy:

It must be a bug, a feature request, or a significant problem with the documentation (for small docs fixes please send a PR instead).

The form below must be filled out.

Here's why we have that policy:.

Keras developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.

System information.

TensorFlow version (you are using): 2.6.0

Are you willing to contribute it (Yes/No): Yes

Describe the feature and the current behavior/state. Addition of a new pre-trained model: Resnet18 in the application section.

Describe the feature clearly here. Be sure to convey here why the requested feature is needed. Any brief description of the use-case would help.

Will this change the current API? How? Yes. Addition of a new Resnet18 pre-trained model in the application section.

Who will benefit from this feature? Researchers as well as individuals who want to fine-tune or train Resnet18 in Tensorflow without switching to PyTorch

Contributing

Do you want to contribute a PR? (yes/no): Yes

If yes, please read this page for instructions

Briefly describe your candidate solution(if contributing):

Addition of following code to the api:

tf.keras.applications.ResNet18( include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax', **kwargs )

Use Case: This would allow individuals to fine-tune the ResNet18 model pre-trained on the imagenet dataset to be fine-tuned on their custom dataset. As the implementation of ResNet18 is already available in PyTorch and not in Tensorflow, sometimes it becomes difficult for developers with knowledge of Tensorflow and no prior knowledge of Pytorch to implement the following. Hopefully, this will allow better transfer learning for developers and reduce time in writing the ResNet18 model from scratch, then training it on the imagenet dataset and then fine-tuning it on their custom dataset. Let me know if this resolves the doubt or I need to provide more explanation.

Paper for ResNet18 introduction and architecture
Issue 1:
What / Why

NPM failed to install some package dependencies

It failed during installation of fsevents package

When

today?

Where

n/a

How

Current Behavior

Fails in middle of npm install

Steps to Reproduce

try installing a package that depends on fsevents

Expected Behavior

should install just fine

Who

n/a

References

n/a

Logs

ananta@debian:~/PROJECTS/unit3d-test$ npm install --verbose --no-optional
npm info it worked if it ends with ok
npm verb cli [
npm verb cli '/home/ananta/.nvm/versions/node/v13.11.0/bin/node',
npm verb cli '/home/ananta/.nvm/versions/node/v13.11.0/bin/npm',
npm verb cli 'install',
npm verb cli '--verbose',
npm verb cli '--no-optional'
npm verb cli ]
npm info using npm@6.13.7
npm info using node@v13.11.0
npm verb npm-session 98f4aa45dbec2486
npm info lifecycle @~preinstall: @
npm timing stage:loadCurrentTree Completed in 16ms
npm timing stage:loadIdealTree:cloneCurrentTree Completed in 0ms
npm timing stage:loadIdealTree:loadShrinkwrap Completed in 1061ms
npm timing stage:loadIdealTree:loadAllDepsIntoIdealTree Completed in 1609ms
npm timing stage:loadIdealTree Completed in 3122ms
npm timing stage:generateActionsToTake Completed in 846ms
npm verb correctMkdir /home/ananta/.npm/_locks correctMkdir not in flight; initializing
npm verb lock using /home/ananta/.npm/_locks/staging-f8b184b98acc8f8d.lock for /home/ananta/PROJECTS/unit3d-test/node_modules/.staging
npm http fetch GET 304 http://registry.npmjs.org/emojione-assets 1976ms (from cache)
npm timing npm Completed in 562914ms
npm ERR! cb() never called!

npm ERR! This is an error with npm itself. Please report this error at:
npm ERR! <https://npm.community>

npm ERR! A complete log of this run can be found in:
npm ERR! /home/ananta/.npm/_logs/2020-04-10T08_01_52_782Z-debug.log
ananta@debian:~/PROJECTS/unit3d-test$

Last few lines of debug.log

16995 silly extract es-abstract@1.17.5 extracted to /home/ananta/PROJECTS/unit3d-test/node_modules/.staging/es-abstract-fadff1e1 (5857ms)
16996 silly extract collect.js@4.20.3 extracted to /home/ananta/PROJECTS/unit3d-test/node_modules/.staging/collect.js-584c6c61 (1565ms)
16997 silly extract es5-ext@0.10.52 extracted to /home/ananta/PROJECTS/unit3d-test/node_modules/.staging/es5-ext-7071914d (7239ms)
16998 silly extract lodash@4.17.15 extracted to /home/ananta/PROJECTS/unit3d-test/node_modules/.staging/lodash-f9e1c64d (3434ms)
16999 silly extract caniuse-lite@1.0.30001038 extracted to /home/ananta/PROJECTS/unit3d-test/node_modules/.staging/caniuse-lite-5af91014 (1916ms)
17000 silly extract fsevents@1.2.11 extracted to /home/ananta/PROJECTS/unit3d-test/node_modules/.staging/fsevents-c428c5f0 (2638ms)
17001 timing npm Completed in 562914ms
17002 error cb() never called!
17003 error This is an error with npm itself. Please report this error at:
17004 error <https://npm.community>

Issue 2:
*Updated* as of 01/15/2021

Note: Please read this doc before filing a new issue.
Issue 1:
There should be a way to safely rename / revoke / reindex / manipulate an index.

Currently, I have to do the following:

Create NEW index

Copy (aka reindex) data from OLD to NEW

Delete OLD index

Make an alias/link from OLD index name to NEW index

Do my stuff

Delete OLD index alias

Create OLD index

Copy (aka reindex) data from NEW to OLD index.

Delete OLD index.

3-4 transition is a race cond. If app is heavily writing to index, you're out of luck. Your index will be constantly created, you won't be able to even make an alias.

6-7 is a potential race condition.

To work around the problem, we need aliases to work with existing indexes.

Also, an index rename functionality should be implemented, so that entire 4-7 would not be needed.

Issue 2:
The packaging tests try to download watcher/latests in the master/2.0 branches and fail to install watcher. If you manually build watcher for 2.0 from source and dump it into the current working directory then they work but this is a pain for some and impossible for others because the watcher repository is private to the elastic organization.
Issue 1:
Symfony version(s) affected: 4.2.x

Description

When using the Validator component with a Comparison constraint, the AbstractComparisonValidator only looks at the ExecutionContext object to compare values, not the root, see these lines: https://github.com/symfony/symfony/blob/master/src/Symfony/Component/Validator/Constraints/AbstractComparisonValidator.php#L51-L53

How to reproduce

Validate an array of data with a comparison constraint:

$validator = Validation::createValidator();
$constraint = new Assert\Collection([
'password' => [
new Constraints\NotBlank(),
],
'repeat-password' => [
new Constraints\IdenticalTo(['propertyPath' => 'password'])
],
]);

$violations = $validator->validate(['password' => 'foo', 'repeat-password' => 'bar', $constraint);

Possible Solution

If the ExecutionContext object is null, use the root of the ExecutionContext to get the compared value.

Issue 2:
Symfony version(s) affected: 4.1.x

Description

When I attach to an object of mine through Yaml or annotation one of the EqualsTo/NotEqualsTo/IdenticalTo/NotIdenticalTo constraints then this line returns the object that the constraint is attached to.

App\Form\Transfer:
properties:
from:
- NotIdenticalTo:
propertyPath: 'to'

So the line above will return the object App\Form\Transfer

If I add the constraint on a form type like this:

$builder
->add('from', ChoiceType::class, [
'constraints' => [
new NotIdenticalTo(['propertyPath' => 'to']),
],
])

The line above will bring back a form object.

This causes the constraint to throw an exception because it reaches on this line and in the Form object the path of the property it does not exist.

How to reproduce

Build a form in the way described above and submit it.
Issue 1:
node:12.0.0

electron: 7.1.1

Silent print settings margins flashback，Flashback occurs if margins are added

margins: {
marginType: 'custom',
left: 0
}

complete

webview.print(
{
silent: true,
printBackground: true,
deviceName: this.printDeviceName,
landscape: true,
margins: {
marginType: 'custom',
left: 0
}
}
)

Issue 2:
Preflight Checklist

I have read the Contributing Guidelines for this project.

I agree to follow the Code of Conduct that this project adheres to.

I have searched the issue tracker for a feature request that matches the one I want to file, without success.

Electron Version

13.1.2

What operating system are you using?

Windows

Operating System Version

Windows 10 Version 10.0.19043.1055

What arch are you using?

x64

Last Known Working Electron version

11.0.0

Expected Behavior

Opening a PDF file in a BrowserWindow should display the PDF file using Chromium's built-in PDF Viewer.

Actual Behavior

Opening a PDF file in a BrowserWindow just shows a broken and seemingly half-loaded PDF Viewer with no PDF content in it whatsoever.

Testcase Gist URL

No response

Additional Information

This project needs some serious regression testing for crying out loud. How is it possible that things that were already working fine keep breaking all the time and none of the developers care or even notice?
Issue 1:
Problem

Say you have a component template:

<div class="autocomplete">
<input role="combobox" class="autocomplete-trigger"
aria-autocomplete="list"
aria-active-decendant="" <!-- = Whichever option ID is focused -->
aria-owns="autocomplete-menu">
<ul id="autocomplete-menu" tabindex="-1">
<li *foreach="#opt in options" role="option" [id]="opt.id">Alpha</li>
</ul>
</div>

In this example, use of an ID is necessary for the aria-owns attribute. With the current shadow DOM emulation, it will not work to have multiple instances of this component on the page at the same time because the IDs would be duplicated.

Other use cases include <label>, aria-labelledby, aria-controls, aria-described-by, aria-flowto

Possible solutions

There are a few different ways we could handle this.

Put the burden on the user

<input [id]="myGeneratedUniqueInputId">
<label [for]="myGeneratedUniqueInputId">

This is what people would have done in Angular 1. Adopting this should just be a matter of documentation.

Automatically recognize ID attributes and shim ID isolation

If the user writes:

<input id="name">
<label for="name">

Angular will recognize any standard attributes that use an ID reference (or list of ID references) and automatically convert them into document-unique IDs them.

Add some syntax to identify IDs for unique conversion

Specific syntax aside, something like:

<input ##id="name">
<label ##for="name">

This has the benefit of not having to hard-code the list of ID-reference attributes, but still incurs the same performance cost as the previous solution.

Issue 2:
I'm submitting a ...

[ ] bug report => search github for a similar issue or PR before submitting
[x ] feature request
[ ] support request => Please do not submit support request here, instead see

I'm trying to extend a RouterLink directive to append a prefix to each link. <a myLink="/photos"> -> /myregion/photos, where myregion is a state value, shared by service. Right now the only simple way I see is to copy-paste existing code and change it for my needs, because I tried several obvious solutions and they didn't work

It would be great to have an official way to reuse a code.
Issue 1:
🚀 Feature

A programmatic means (property) to disambiguate when a _LRScheduler should be stepped.

Motivation

Currently, there are a variety of _LRSchedulers found in torch.optim.lr_scheduler. While the majority should be stepped per epoch (e.g., LambdaLR, MultiplicativeLR, StepLR, etc), some are designed to be stepped per training batch (CyclicLR, OneCycleLR, etc). Unfortunately, there is no way (programmatically) to identify which category an arbitrary _LRScheduler falls into without either maintaining a hard-coded list (😢) or parsing the docstring (😢 😢). This makes correctly injecting arbitrary _LRSchedulers difficult to do in an extensible fashion.

Pitch

Add two boolean properties to every _LRScheduler:

steps_on_batch

steps_on_epoch

Currently, neither symbol is used in torch.optim.lr_scheduler, so this should be a strictly additive change as far as I'm aware.

Alternatives

Every scheduler that exists in PyTorch today exclusively steps per batch or per epoch. Therefore, either of the two properties alone would currently satisfy the aforementioned motivation. However, should a scheduler be created that is stepped at both stages, this option would fail. Later adding the second property would be a pseudo-backwards incompatible change as significant amounts of code would likely include if not sched.step_epoch: ... to determine if the scheduler should be stepped per batch (or vice versa).

Given the minimal cost of adding both, the flexibility it enables, and the potential to mitigate future challenges, I have opted to propose both properties.

cc @vincentqb

Issue 2:
I'm not very sure that what is the parameter "tgt" means.

The document says it is the sequence of decoder. However, I have a question that since I have the sequence to decoder, thus I have the result of decoder, why do I have to use transformer for inferencing?

For example, I have a seq2seq task that translate "I love you" from English to Franch "Je t'aime", what is the parameter of "tgt"?

Looking forward answering. Thanks!
Issue 1:
System Information

opencv-python==4.7.0.68
Python 3.10.9
Arch Linux

Detailed description

VideoWriter fails silently when the size of the frame passed to VideoWriter().write() differs from the frameSize specified in the constructor.

I would expect an error, but instead, the written video will not play, which is hard to trace back to a frame size issue.

Related:

https://stackoverflow.com/questions/75000164/cant-play-video-created-with-opencv-videowriter

Steps to reproduce

import cv2

FPS = 30
KEY_ESC = 27
OUTPUT_FILE = "vid.mp4"

cam = cv2.VideoCapture(0)

codec = cv2.VideoWriter.fourcc(*"mp4v") # MPEG-4 http://mp4ra.org/#/codecs
frame_size = cam.read()[1].shape[:2] # wrong (see SO link)
video_writer = cv2.VideoWriter(OUTPUT_FILE, codec, FPS, frame_size)

# record until user exits with ESC
while True:
success, image = cam.read()
cv2.imshow("window", image)

video_writer.write(image)

if cv2.waitKey(5) == KEY_ESC:
break

Issue submission checklist

I report the issue, it's not a question

I checked the problem with documentation, FAQ, open issues, forum.opencv.org, Stack Overflow, etc and have not found any solution

I updated to the latest OpenCV version and the issue is still there

There is reproducer code and related data files (videos, images, onnx, etc)

Issue 2:
System information (version)

OpenCV => ❔

Operating System / Platform => ❔

Compiler => ❔

Detailed description

Steps to reproduce
Issue 1:
Please go to Stack Overflow for help and support:

https://stackoverflow.com/questions/tagged/tensorflow

If you open a GitHub issue, here is our policy:

It must be a bug or a feature request.

The form below must be filled out.

It shouldn't be a TensorBoard issue. Those go here.

Here's why we have that policy: TensorFlow developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow):

OS Platform and Distribution (e.g., Linux Ubuntu 16.04):

TensorFlow installed from (source or binary):

TensorFlow version (use command below):

Python version:

Bazel version (if compiling from source):

GCC/Compiler version (if compiling from source):

CUDA/cuDNN version:

GPU model and memory:

Exact command to reproduce:

You can collect some of this information using our environment capture script:

https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh

You can obtain the TensorFlow version with

python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"

Describe the problem

Describe the problem clearly here. Be sure to convey here why it's a bug in TensorFlow or a feature request.

Source code / logs

Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached. Try to provide a reproducible test case that is the bare minimum necessary to generate the problem.

Issue 2:
I encountered an issue with tf.image.encode_jpeg. I got a lot of images to preprocess. The code that caused the problem is like this:

for imagefile in image_list:

im = cv2.imread(imagefile)

image_data = tf.image.encode_jpeg(im, format='rgb')

The memory usage kept increasing as image.encode_jpeg was called. Eventually the 256G mem server ran out of memory.
Issue 1:
Loads of operations in the embedding example are not supported on GPU. Some documentation detailing this would be good.

I tried to get around the problem by using 'tf.ConfigProto(allow_soft_placement=True)'. However now I get 'Executor failed to create kernel. Invalid argument: AttrValue must not have reference type value of float_ref for attr 'tensor_type'

Thanks

Abhishek

Issue 2:
Please go to Stack Overflow for help and support:

https://stackoverflow.com/questions/tagged/tensorflow

If you open a GitHub issue, here is our policy:

It must be a bug or a feature request.

The form below must be filled out.

It shouldn't be a TensorBoard issue. Those go here.

Here's why we have that policy: TensorFlow developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Custom code! You can find it here: https://github.com/NERSC/CosmoFlow/tree/master/SegFault

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): SUSE Linux 12.2

TensorFlow installed from (source or binary): Source

TensorFlow version (use command below): ('v1.3.0-rc1-3112-g65b6a75', '1.4.0-rc0') Note this is NOT compiled with the Intel MKL options.

Python version: 2.7.13

Bazel version (if compiling from source): 0.6.0

GCC/Compiler version (if compiling from source): 4.8.5

CUDA/cuDNN version: N/A

GPU model and memory: N/A, Running on x86_64 Intel Haswell node

Exact command to reproduce: See README in https://github.com/NERSC/CosmoFlow/tree/master/SegFault

Describe the problem

A seg fault when training a tf.nn.conv3d with minibatch size more than 2 on a single Intel Haswell. The seg fault occurs at line 187.

Source code / logs

GDB log: https://github.com/NERSC/CosmoFlow/blob/master/SegFault/gdbTrace.log

It looks like some kind of cyclic dependency in Eigen::TensorEvaluator.
Issue 1:
Material-UI

v1.0.0-beta.25

document -> 'component demos'

https://material-ui.com/demos/tables/

'Sorting & Selecting' section ,condsandbox stop working(HTTP Error 414)

Issue 2:
Hello, i have created a simple Create React App + Material-UI application with some of the examples from the docs, like checkboxes, grid spacing, checkboxes, modals, etc, packaged it using Cordova and ran it on my IPAD.

I am experiencing (especialy when clicking a checkbox or a toggle) the 300 ms delay.

With modals instead i am seeing a double click problem, the first it fires on touch tap, opens the modal then after the delay it fires a click that triggers the closing of the modal.

I was curious if on the official website it would behave in the same manner, so, using the browser of the IPAD i went to https://material-ui.com/demos/selection-controls/ and everything works just fine.

By looking at the code on github i wasnt able to find no special configuration that you do for making this work, so i dont know if its a problem of Cordova , React, Webpack,

or ME :)

Thank you.

I have searched the issues of this repository and believe that this is not a duplicate.

Tech
Version

Material-UI
1.0.0-beta.29

React
16.2.0

browser

etc
Issue 1:
Push all hold logic up to the pyplot layer.

Assume that if client code wants to clear the axes, they will call it them selves.

Tagged an v2.0 as I see no way to do this in a reverse compatible way.

Issue 2:
Problem

One serious problem is how to deal with text having both latex and html-like tags. MEP29

Proposed Solution

This MEP proposes to add lightweight markup to the text artist.

Additional context and prior art

Using different size/color/family in a text annotation is difficult because the text method accepts argument for size/color/family/weight/etc. that are used for the whole text. But, if one wants, for example, to have different colors, one has to look at the gallery where one such example is provided: http://matplotlib.org/examples/text_labels_and_annotations/rainbow_text.html

This example takes a list of strings as well as a list of colors which makes it cumbersome to use. An alternative would be to use a restricted set of pango-like markup and to interpret this markup.
Issue 1:
Challenge Bonfire: Friendly Date Ranges

Strange test cases. I think they is expired:

friendly(["2015-07-01", "2015-07-04"]) should return ["July 1st","4th"]

friendly(["2016-03-01", "2016-05-05"]) should return ["March 1st","May 5th"]

"July 1st","4th"

"March 1st","May 5th"

User can't understand that it is 2015 and 2016 years.

Issue 2:
Challenge http://freecodecamp.com/challenges/bonfire-friendly-date-ranges has an issue. Please describe how to reproduce it, and include links to screenshots if possible.

I know this has been debated before, and I understand that if the dates are within 11 months it can be inferred that it is the following year, but test 4 (below) should require a year given that this is a requirement: "Secondly, if the starting year is the current year, and the ending year can be inferred by the reader, the year should be omitted."

As the 4th test is 2016/2016, it can't be inferred the year is 2016 unless there is assumed all dates are in the future, it could have been in 2015.

Could either the test condition, or the task description be changed to reflect this?
Issue 1:
Previously if you were at the terminal and typed "atom" it opened the current directory in atom. As of a recent release, it now opens an empty window. To achieve the same result as before one must now type "atom .". I understand making changes and that relearning will be required, but it does seem like consistency across github CLI's should be important. GitHub for Mac opens the current directory if you simply type "github". This means one github product requires "." for the current directory and another does not. Now I'm left in a state of never getting it right for either of them. :)

Issue 2:
Recently atom has changed to require atom . instead of just atom to open the working directory. This has been a really frustrating change. How do I make atom open the working directory without the . argument?