race conditions in MQTT_Publish when queue is full

there seems to some kind of race conditions in MQTT_Publish(), happens sometimes when the queue is full.
I have added some debug printing to QUEUE_Puts and QUEUE_Gets:
MQTT_Publish id: 125
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20286/20480)
QUEUE_Puts: failed to add 192 to queue
QUEUE_Puts: queue size(20480/20480)
QUEUE_Gets: removed 192 from queue
QUEUE_Puts: queue size(20176/20480)
QUEUE_Puts: added 192 to queue
QUEUE_Puts: queue size(20371/20480)

supposed fix:
diff --git a/mqtt/queue.c b/mqtt/queue.c
index 7fa50aa..4a7e146 100644
--- a/mqtt/queue.c
+++ b/mqtt/queue.c
@@ -34,6 +34,11 @@
#include "os_type.h"
#include "mem.h"
#include "proto.h"
+
+uint8_t *last_rb_p_r;
+uint8_t *last_rb_p_w;
+uint32_t last_fill_cnt;
+
void ICACHE_FLASH_ATTR QUEUE_Init(QUEUE queue, int bufferSize)
{
queue->buf = (uint8_t)os_zalloc(bufferSize);
@@ -41,7 +46,20 @@ void ICACHE_FLASH_ATTR QUEUE_Init(QUEUE *queue, int bufferSize)
}
int32_t ICACHE_FLASH_ATTR QUEUE_Puts(QUEUE queue, uint8_t buffer, uint16_t len)
{

return PROTO_AddRb(&queue->rb, buffer, len);

uint32_t ret;

last_rb_p_r = queue->rb.p_r;
last_rb_p_w = queue->rb.p_w;
last_fill_cnt = queue->rb.fill_cnt;

ret = PROTO_AddRb(&queue->rb, buffer, len);
if (ret == -1) {
// rolling ring buffer back
queue->rb.p_r = last_rb_p_r;
queue->rb.p_w = last_rb_p_w;
queue->rb.fill_cnt = last_fill_cnt;
}
return ret;
}
int32_t ICACHE_FLASH_ATTR QUEUE_Gets(QUEUE queue, uint8_t buffer, uint16_t* len, uint16_t maxLen)
{

@st0ff3r can you send a pull-request for this
Thanks

On 18 Nov 2016, at 04.46, Tuan PM<EMAIL_ADDRESS>wrote:
@st0ff3r can you send a pull-request for this

done, and thanx for the nice work :)


[Bug] Missing type definition for preserveTransformations

Bug Report
Describe the bug
preserveTransformations isn't defined, tint is defined but shouldnt be at that specific location

Released/Solved


A issus of MAEViTEncoder's code

Hi!Developers of SlefMedMAE!
While I checking the code of MAEViTEncoder, I see self.patch_embed = embed_layer(img_size=patch_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim). I'd like to know why img_size uses patch_size instead of the size of the img?

same concern


Update IDL program

The IDL program uses a bunch of deprecated account types. Updating to the new account types is non-trivial because of how some of the IDL types are stored in /lang and how the new account types require knowing the program_id of the program ahead of time.
Might also take the opportunity to add some requested security features from https://github.com/coral-xyz/anchor/pull/1215 in which only the program upgrade authority can create and change the IDL. Not too sure about this yet, would need to check with users first how much of a hassle this change would be. Usually program upgrade authorities are gated behind complex multisig arrangement, it might be quite annoying to have to go through the process of signing an IDL update every time, although maybe not if you just update the IDL whenever the program is update, let's find out.
Another security thing is passing the wrong IDL account, noted here https://github.com/coral-xyz/anchor/pull/1215#issuecomment-1372533670, we can look into adding some constraints there too.

I ended up not adding too many extra features just because IDL is already such a weird and outdated program that trying to modify it without making a breaking change is real pain and not necessarily worth the effort if it's already doing the job.


Adding some stuff, but better if the original repo just added everything missing. Feel free to ignore PR

I'm about to be away from my computer for a week.  Ping me if some time goes by and I haven't reviewed this yet.


Revert "Update gradle wrapper and build-tools version for flutter create."

Reverts flutter/flutter#8914

@Hixie

LGTM; feel free to check in reverts to fix the build TBR=@Hixie.

Checking in to clear the build failure.


reverting #13319

Due to missing branches on release repository.
Reverts: #13319 @gstavrinos FYI

I think the problem is the myahrs_driver package which is missing from kinetic.

There should be a lot of branches on the release repo like this with 66: https://github.com/gstavrinos/jaguar-release
Where this release repo only has 8: https://github.com/gstavrinos/jaguar_robot-release


Data binding works fine but get error: Invalid value for  attribute x="NaN..."

This problem has been fixed before as I have seen from other issues but it still gives me troubles with version 0.0.8
Have a look in your browser console when running the follwing fiddle:
http://jsfiddle.net/35xqueef/

I think it's not a ng-highcharts problem, instead Highcharts itself or your config object with an element config property inside your graph.
specifically the problem is with your x axis title align.
this is what triggers the error
<text x="NaN" zIndex="7" text-anchor="undefined" transform="translate(0,0)" class=" highcharts-xaxis-title" style="color:#707070;fill:#707070;" visibility="visible" y="644">S</text>

in your ng-higcharts config object you have:
            xAxis: {
                currentMin: 1,
                currentMax: 100,
                title: {
                    text: 'S',
                    align: "right"
                },
                labels: {
                    formatter: function () {
                        return (this.value + " units");
                    }
                }
            },

I removed the alignproperty and the error disappeared
http://jsfiddle.net/v152o77a/

thank you very much @jaimeguaman !
I used the wrong value for align because I used the wrong documentation: http://api.highcharts.com/highcharts#title.align
but the value needs to be 'low', 'middle' or 'high' as described here: http://api.highcharts.com/highcharts#xAxis.title


Feat: add chat-gpt workflow step

Description of your changes

Add chat-gpt workflow step.
Example usage:
apiVersion: core.oam.dev/v1alpha1
kind: WorkflowRun
metadata:
  name: chat-gpt
  namespace: default
spec:
  workflowSpec:
    steps:
    # apply a deployment with invalid image, this step will fail because of timeout
    # the resource will be passed to chat-gpt step to anaylze
    - name: apply
      type: apply-deployment
      timeout: 3s
      outputs:
        - name: resource
          valueFrom: output.value
      properties:
        image: invalid

    # if apply step failed, send the resource to chat-gpt to diagnose
    - name: chat-diagnose
      if: status.apply.failed
      type: chat-gpt
      inputs:
        - from: resource
          parameterKey: prompt.content
      properties:
        token:
          # specify your token
          value: <your token>
        prompt:
          type: diagnose

    # if apply step succeeded, send the resource to chat-gpt to audit
    - name: chat-audit
      if: status.apply.succeeded
      type: chat-gpt
      inputs:
        - from: resource
          parameterKey: prompt.content
      properties:
        token:
          # or read your token from secret
          secretRef:
            name: chat-gpt-token-secret
            key: token
        prompt:
          type: audit
          lang: Chinese

You can use vela workflow logs to see the output of chatgpt.

I have:

[x] Read and followed KubeVela's contribution process.
[x] Related Docs updated properly. In a new feature or configuration option, an update to the documentation is necessary.
[x] Run make reviewable to ensure this PR is ready for review.
[ ] Added backport release-x.y labels to auto-backport this PR if necessary.

How has this code been tested

Special notes for your reviewer

Let's move this to catalog instead.


Create template for package

is this basically done

yeah - I think closing this makes sense, and opening more specific issues for tweaks down the line.


Add support for C# 9 feature 'not'

C# 9 seems to provide not which can be used together with is, such as in if (x is not null)
We should have a rule that checks for such situations, e.g. for the != operator.

Requires VS 16.8 (https://github.com/dotnet/csharplang/blob/master/Language-Version-History.md)


Can we add a hand presence sample?

I was working on this and couldn't figure out how to do hand presence. I figured it would be helpful if you could add a hand presence tutorial.
Thanks in advance.

What do you mean "hand presence" ?
If that means tracking the controllers and represent them in world, you can look at the two examples, minimal and action. Both are attaching an instance of a model on each detected controllers so you can see where in the tracking space they are located.
If you follow the minimal example, you have to test the device class to know which device is actually a controllers, if you follow the action example, the manifest will bind the left and right controllers to the pose you have defined.

No, I mean using the grip, trigger and buttons to show your hands in the
virtual world.

That's possible, but that requires much more work and that heavily depends on the type of your controller : You have to retrieve the analogue position of all the buttons that represent your fingers. For the Vive controller, only the trigger has an analogue sensor, the grip is a simple on/off button, ...
Once you have your input that should represent your fingers, you must have a rigged hand model with morph sliders for each fingers. You need to map the value you received from the controller to modify the slider and morph the hand in word.

I have a rigged hand model but I don't know how to rotate the bones in Panda3D. Could you help?

Adding a hand presence sample in a fork. This issue will be closed once I'm done!

Thanks for taking care of this ! I don't have the time (and the knowledge) to do the sample right now, so it's really helpful

No problem! The only problem is the hand from Valve's samples are a little weird.

By the way, my fork is zurgeg/panda3d-openvr.

Update! Close to a keyboard and mouse controlled PC version. I'll tell you when it's in VR!

Old issue.


[PE-D][Tester A] Dentist with 0 YOE does not display properly

After the dentist with ID1 is edited to have YOE of 0, the experience section becomes empty.

Labels: type.DocumentationBug severity.Low
original: fuyiqiao/ped#5

#162


fix(windows): Handle root paths that cannot be canonicalized

If the root path element of a pattern could not be canonicalized, scope::fs::push_pattern would enter an infinite loop. This is because path.pop() does nothing and returns false when called on a path with no parent, which can happen even when the path element iterator is non-empty. This could occur on Windows, for example, with a pattern of \\?\**.
Fixes #10815

I've added a test case for the various flavors of Windows root paths. I ran into a number of edge cases that are not currently handled well, which I documented in comments in the test case. I can open an issue for those if you would like. A few examples:

Canonicalization differs depending on if the path exists. For example "C:\Exists" canonicalizes to "\?\C:\Exists" while "C:\DoesNotExit" is left as is.
A similar issue affects UNC network share paths. If the network share is present/reachable when a pattern is added you will get different patterns added to the scope than if the share is not present/reachable.
The path "C:" is different from "C:" (the former refers to the current working directory of the C drive, while the second refers to the root directory of the C drive). There's effectively no way to pass "C:" to add_directory because it unconditionally adds a backslash before the wildcards which leads to a pattern like "C:\**" which doesn't match anything.
If you add a verbatim path (one that starts with "\?"), it won't match non-verbatim paths that refer to the same target.

Sorry about that. My Windows machine doesn't have signing set up.

I have a few changes locally while testing this PR, that simplifies the code a bit and adds more documentation. Since I can't push to your branch, I will open a follow up PR. Thanks for your help and the test cases as well.


add param proxy_buffering

Hi, please add the parameter proxy_buffering to templates/vhost/locations/proxy.erb

Waiting for this as well.


[Bug]: How to train on multi gpu?

Describe the bug
I attempted to train the conformer model from scratch for multi-gpu when runnung
python3 train_asr.py hparams/conformer_m.yaml --data_folder=/dataSSD/data_aishell/ --data_parallel_backend
then, I encountered the following error

Expected behaviour

To Reproduce
No response
Versions
No response
Relevant log output
No response
Additional context
No response

Hi,
Please switch to Distributed Data Parallel. You can have a look on how to use it with SpeechBrain here: https://speechbrain.readthedocs.io/en/latest/multigpu.html

Hey, any news about this issue pls ? thanks

Hey, thanks the suggestion from @Adel-Moumen.
However, if I switched the strategy to DDP, the result is worse than the result I train on single GPU.
Is there anything wromg from DDP?

Hmmmm I think it should be slightly the same results. What are the results you are getting so far ?

I attempted to train a conformer model(from the recipes of LibriSpeech) for the dataset of AISHELL-1. The result trained on single GPU (global batch size : 128) is 6.2%/6.71% (CER). If I train on multi gpu applied the strategy of DDP (global batch size : 256), the result is 6.74%/7.56% (CER). However, the result from the paper is 4.39%/4.89%. I have tried my best to train in same parameter, but there is still a gap between them.
@Adel-Moumen Is there any suggestions?
Thanks a lot

You should definitely use the Transducer Conformer from this PR https://github.com/speechbrain/speechbrain/pull/1782 not merged yet but it works.

Hey @eric-debug666, any news regarding this issue please?

@Adel-Moumen Thanks for your suggestion. I have trained on the conformer transducer model. Due to the insufficient memory capacity, I didn't apply the dynamic batch size. I set batch size of 8, and gradient accumulation factor as 1(the original setting). The result seemed to be something wrong. The result of third epoch is CER of 98.24(%) and WER of 100(%). In my mind, it indicates that the global batch size is not enough(the command suggests that global batch size should be equal or larger than 128).
@Adel-Moumen  Is there any suggestions for the fixed batch size? Does the batch size of 16 and the accumulation factor of 8 (1681=128) work?

Do you know if there is any workaround regarding Conformer and batch size @TParcollet ? Thanks.

Hi, Dynamic batching must be used when you have little amount of VRAM. It's the best way of adjusting to difficult scenarios as you can tailor with respect to the max number of second per batch === very precise. In most of my experiences with Conformer and libri, I try to have around 1000s as a total batch.

Hey @eric-debug666, do you have any news regarding this issue please? Thanks. :)

Hello,
It's been quite some time without any updates or news on this issue. Consequently, I'm taking the step to close it. Feel free to reopen it if you are still facing the issue.
Best,
Adel


chore: cleanup unused files

Signed-off-by: Leonardo Grasso<EMAIL_ADDRESS>The json file cannot be supported anymore since the new releasing mechanism.
/cc @fntlnz
/cc @leodido
/cc @nibalizer

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LGTM label has been added.  Git tree hash: 2189e94b75062ecfe860c75b331060b7d9295e4d

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incrementChildPrio: Improved swap node position

Neat, I didn't know that worked.
Thanks!


Tensorflow 2.4.0 support

What problem are we trying to solve?:
Deeppavlov currently supports tensorflow version 1.15.2 for running various models.
The code which is built using these models shows security vulnerabilities in whitesource (https://www.whitesourcesoftware.com/) scans. These vulnerabilities are due to outdated version of tensorflow.

How can we solve it?:
We can have support for latest tensorflow version 2.4.0 which has patches for all the security vulnerabilities found in older versions.

Are there other issues that block this solution?:
No.

Hi! Idea is great, and it is on our list. However, given the wide number of available models, we plan to re-factor the library based on the model's popularity, and then we'll work on migration to 2.0. :)
If you want to contribute let us know!

All tensorflow-based models will be removed/replaces with pytorch-based ones in 1.0.0. 1.0.0rc0 is already available.


add featuremap to pressure sensor

Problem
What is being fixed?  Examples:

FeatureMap is not included in XML for Pressure Sensor

Change overview
Added featuremap for pressuresensor
Testing
Compared with Thermostat implementation

you need to run this script and commit the output:
scripts/tools/zap_regen_all.py


Add A Brief Introduction to Neural Networks

Please replace this URL with the one suggested by the author: http://www.dkriesel.com/en/science/neural_networks
This way, the link won't break when the author updates the PDF URL.

Ah, my fault for overlooking that. Fixed!

Thank you! One last thing: please remove the PDF mention, now that the link does not point to a PDF file.

Done, thanks!


Fix key_pair class lookup

The issue: it was not able to detect the correct provider specific class for AuthKeyPair
While we would like to avoid delegation, easier to just point to
the parent manager for auth key pairs
fixes issue with https://github.com/ManageIQ/manageiq/pull/19473
related to https://bugzilla.redhat.com/show_bug.cgi?id=1767747
irb(main):002:0> MiqExpression.miq_adv_search_lists('CloudNetwork', :exp_available_finds)
PostgreSQLAdapter#log_after_checkout, connection_pool: size: 30, connections: 1, in use: 1, waiting_in_queue: 0
Traceback (most recent call last):
       12: from lib/miq_expression.rb:903:in `miq_adv_search_lists'
       11: from lib/miq_expression.rb:802:in `model_details'
       10: from lib/miq_expression.rb:891:in `get_relats'
        9: from lib/miq_expression.rb:942:in `build_relats'
        8: from lib/miq_expression.rb:942:in `each'
        7: from lib/miq_expression.rb:973:in `block in build_relats'
        6: from lib/miq_expression.rb:942:in `build_relats'
        5: from lib/miq_expression.rb:942:in `each'
        4: from lib/miq_expression.rb:952:in `block in build_relats'
NameError (uninitialized constant ManageIQ::Providers::NetworkManager::AuthKeyPair)

:+1:

Ivanchuk backport details:
$ git log -1
commit cb6f2fb2c32a53651adf6b2c5020c2d5837a668d
Author: Jason Frey<EMAIL_ADDRESS>Date:   Fri Nov 8 12:21:21 2019 -0500

    Merge pull request #19486 from kbrock/manager_delegate_fixup

    Fix key_pair class lookup

    (cherry picked from commit 4640261fa9b0acc0eee2084785e5d0570578be86)

    https://bugzilla.redhat.com/show_bug.cgi?id=1785374


mllmTeam/PhoneLM-0.5B-Instruct model

Hi, I guess the mllmTeam/PhoneLM-0.5B-Instruct model.safetensor is wrong, cause the file size and model structure is as same as 1.5B model

Thank you for your attention, I have updated: https://huggingface.co/mllmTeam/PhoneLM-0.5B-Instruct


Moved app compat item

Moved app compat item
This category assigned to this file in the Microsoft/dotnet repo was "Web application" rather than the expected "Web applications", which caused it to be placed in a different directory when migrated to the dotnet/docs repo. It also causes it to appear in a separate category.
The change also corrects the incorrect rendering of app compat issue (see the image below).

//cc @mairaw @chlowell

Links to the include file need updates as well. I ran the generator again, you can see the changes on my fork at chlowell/docs-1#32.

Thanks, @chlowell. I updated the links to the include file as well, and it looks as if the local changes from this PR have addressed all of the issues. Do you think that you should open a PR for your migration, or go with what we have here? Basically, the two versions are in sync, since these are the only changes that have been made to the Microsoft/dotnet repo since the 4.7.2 migration.

This one's already here, so if they're equivalent, I'd say there's no value in retargeting the one on my fork.

I thought that I'd responded to you, @chlowell, but I guess not. I agree that there's no need for a new migration since we've encapsulated all of the changes here.


add field rules to error for golang

Hi
Currently, the reason of validation errors is in text format. This blocks the possibility of creating your error translation mechanism, whether it is a key generation or ready-made translations.
This PR adds field 'rules' to validation error struct. This is necessary to be able to generate your errors.
Please see this PR. Perhaps this functionality could be useful, not only for me.

It's not clear to me from the description what this PR adds, and it's not linked to any existing issue. Can you please update the PR description with an example showing how this PR changes PGV's behavior?

@rmichela description updated


Add ops.random.alpha_dropout and layers.AlphaDropout

Related to #18467
This PR should benefit the users who want to apply the self-normalizing property to their models.
The implementation is taken from:
https://github.com/keras-team/keras/blob/v2.14.0/keras/layers/regularization/alpha_dropout.py#L28-L104

Codecov Report
Attention: 38 lines in your changes are missing coverage. Please review.

Comparison is base (e70f28f) 79.51% compared to head (acd03c1) 55.96%.

Files
Patch %
Lines

keras/backend/torch/random.py
0.00%
12 Missing :warning:

keras/backend/jax/random.py
16.66%
10 Missing :warning:

keras/backend/tensorflow/random.py
9.09%
10 Missing :warning:

keras/layers/regularization/alpha_dropout.py
76.00%
5 Missing and 1 partial :warning:

Additional details and impacted files
@@             Coverage Diff             @@
##           master   #18940       +/-   ##
===========================================
- Coverage   79.51%   55.96%   -23.56%
===========================================
  Files         336      337        +1
  Lines       34975    35052       +77
  Branches     6865     6870        +5
===========================================
- Hits        27812    19616     -8196
- Misses       5585    13972     +8387
+ Partials     1578     1464      -114

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Coverage Δ

keras
55.96% <50.64%> (-23.42%)
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keras-jax
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keras-numpy
55.96% <50.64%> (-0.02%)
:arrow_down:

keras-tensorflow
?

keras-torch
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Fixed pandas diagnostics for pandas>=2.0.0

Description
Closes https://github.com/ESMValGroup/ESMValTool/issues/3196.
Tests are running atm.

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[ ] 🛠 All checks below this pull request were successful

New or updated recipe/diagnostic

[ ] 🧪 Recipe runs successfully
[x] 🧪 Recipe is well documented
[ ] 🧪 Figure(s) and data look as expected from literature
[x] 🛠 Provenance information has been added

To help with the number of pull requests:

🙏 We kindly ask you to review two other open pull requests in this repository

quick - make it RfR, otherwise I will incur the eternal wrath of @zklaus :rofl:

Will do once the recipes ran successfully :sweat_smile:

docs build fails on RTD - and it looks like a transitory hiccup so not a worry

OK RTD build issue was indeed a fluke :partying_face:

All affected recipes run successfully now (with https://github.com/SciTools/iris/pull/5340) :rocket:

@zklaus - it looks to me that your previous comments were addressed by @schlunma. Would you have further comments on this PR? If not, could you please approve and merge? It would great to have this in soon in order to ease the recipe testing a little


pprof with goji

Hello,
From Profiling Go Programs, I had added import "net/http/pprof". And the following
m.Get("/debug/pprof/", http.HandlerFunc(pprof.Index))
m.Get("/debug/pprof/cmdline", http.HandlerFunc(pprof.Cmdline))
m.Get("/debug/pprof/profile", http.HandlerFunc(pprof.Profile))
m.Get("/debug/pprof/symbol", http.HandlerFunc(pprof.Symbol))

However, when I run go tool pprof http://<IP_ADDRESS>:8000/debug/pprof/profile I still don’t get any profile data. How to exactly use pprof with goji

Have tried adding profile package, this also does not capture the profile. I added it in the main function, where I call the graceful.ListenAndServe. What am I missing here...

I just do the first bit (m.Get("/debug/pprof/", ...) etc.) in my Goji applications and it works for me. Is there something else that could be in play here?
(as an unrelated aside, the http.HandlerFunc is unnecessary)


faster_rcnn_r50_caffe_c4_1x_coco

1、WARNING - The model and loaded state dict do not match exactly
  unexpected key in source state_dict: conv1.weight, conv1.bias, bn1.bias, bn1.weight, bn1.running_mean, bn1.running_var,    layer1.0.downsample.0.weight, layer1.0.downsample.1.bias, layer1.0.downsample.1.weight, layer1.0.downsample.1.running_mean, layer1.0.downsample.1.running_var。。。

2、occupy big memory than cascade-rcnn？
img_scale=(1696, 1440)
samples_per_gpu=2,
workers_per_gpu=2
CUDA_VISIBLE_DEVICES=0,1,2,3  ./tools/dist_train.sh configs/faster_rcnn/my_faster-rcnn-caffe.py 4

Hi @Soulempty
The warning is due to res_layer in share_heads. It is fine.

@xvjiarui,thanks for you answer,but my warning is much more than the res_layer,such as the following image display,is it truly as you response just now?
`2020-07-10 09:27:17,244 - mmdet - INFO - load model from: open-mmlab://detectron2/resnet50_caffe
2020-07-10 09:27:27,342 - mmdet - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: layer4.0.downsample.0.weight, layer4.0.downsample.1.bias, layer4.0.downsample.1.weight, layer4.0.downsample.1.running_mean, layer4.0.downsample.1.running_var, layer4.0.conv1.weight, layer4.0.bn1.bias, layer4.0.bn1.weight, layer4.0.bn1.running_mean, layer4.0.bn1.running_var, layer4.0.conv2.weight, layer4.0.bn2.bias, layer4.0.bn2.weight, layer4.0.bn2.running_mean, layer4.0.bn2.running_var, layer4.0.conv3.weight, layer4.0.bn3.bias, layer4.0.bn3.weight, layer4.0.bn3.running_mean, layer4.0.bn3.running_var, layer4.1.conv1.weight, layer4.1.bn1.bias, layer4.1.bn1.weight, layer4.1.bn1.running_mean, layer4.1.bn1.running_var, layer4.1.conv2.weight, layer4.1.bn2.bias, layer4.1.bn2.weight, layer4.1.bn2.running_mean, layer4.1.bn2.running_var, layer4.1.conv3.weight, layer4.1.bn3.bias, layer4.1.bn3.weight, layer4.1.bn3.running_mean, layer4.1.bn3.running_var, layer4.2.conv1.weight, layer4.2.bn1.bias, layer4.2.bn1.weight, layer4.2.bn1.running_mean, layer4.2.bn1.running_var, layer4.2.conv2.weight, layer4.2.bn2.bias, layer4.2.bn2.weight, layer4.2.bn2.running_mean, layer4.2.bn2.running_var, layer4.2.conv3.weight, layer4.2.bn3.bias, layer4.2.bn3.weight, layer4.2.bn3.running_mean, layer4.2.bn3.running_var, conv1.bias
2020-07-10 09:27:27,510 - mmdet - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: conv1.weight, conv1.bias, bn1.bias, bn1.weight, bn1.running_mean, bn1.running_var, layer1.0.downsample.0.weight, layer1.0.downsample.1.bias, layer1.0.downsample.1.weight, layer1.0.downsample.1.running_mean, layer1.0.downsample.1.running_var, layer1.0.conv1.weight, layer1.0.bn1.bias, layer1.0.bn1.weight, layer1.0.bn1.running_mean, layer1.0.bn1.running_var, layer1.0.conv2.weight, layer1.0.bn2.bias, layer1.0.bn2.weight, layer1.0.bn2.running_mean, layer1.0.bn2.running_var, layer1.0.conv3.weight, layer1.0.bn3.bias, layer1.0.bn3.weight, layer1.0.bn3.running_mean, layer1.0.bn3.running_var, layer1.1.conv1.weight, layer1.1.bn1.bias, layer1.1.bn1.weight, layer1.1.bn1.running_mean, layer1.1.bn1.running_var, layer1.1.conv2.weight, layer1.1.bn2.bias, layer1.1.bn2.weight, layer1.1.bn2.running_mean, layer1.1.bn2.running_var, layer1.1.conv3.weight, layer1.1.bn3.bias, layer1.1.bn3.weight, layer1.1.bn3.running_mean, layer1.1.bn3.running_var, layer1.2.conv1.weight, layer1.2.bn1.bias, layer1.2.bn1.weight, layer1.2.bn1.running_mean, layer1.2.bn1.running_var, layer1.2.conv2.weight, layer1.2.bn2.bias, layer1.2.bn2.weight, layer1.2.bn2.running_mean, layer1.2.bn2.running_var, layer1.2.conv3.weight, layer1.2.bn3.bias, layer1.2.bn3.weight, layer1.2.bn3.running_mean, layer1.2.bn3.running_var, layer2.0.downsample.0.weight, layer2.0.downsample.1.bias, layer2.0.downsample.1.weight, layer2.0.downsample.1.running_mean, layer2.0.downsample.1.running_var, layer2.0.conv1.weight, layer2.0.bn1.bias, layer2.0.bn1.weight, layer2.0.bn1.running_mean, layer2.0.bn1.running_var, layer2.0.conv2.weight, layer2.0.bn2.bias, layer2.0.bn2.weight, layer2.0.bn2.running_mean, layer2.0.bn2.running_var, layer2.0.conv3.weight, layer2.0.bn3.bias, layer2.0.bn3.weight, layer2.0.bn3.running_mean, layer2.0.bn3.running_var, layer2.1.conv1.weight, layer2.1.bn1.bias, layer2.1.bn1.weight, layer2.1.bn1.running_mean, layer2.1.bn1.running_var, layer2.1.conv2.weight, layer2.1.bn2.bias, layer2.1.bn2.weight, layer2.1.bn2.running_mean, layer2.1.bn2.running_var, layer2.1.conv3.weight, layer2.1.bn3.bias, layer2.1.bn3.weight, layer2.1.bn3.running_mean, layer2.1.bn3.running_var, layer2.2.conv1.weight, layer2.2.bn1.bias, layer2.2.bn1.weight, layer2.2.bn1.running_mean, layer2.2.bn1.running_var, layer2.2.conv2.weight, layer2.2.bn2.bias, layer2.2.bn2.weight, layer2.2.bn2.running_mean, layer2.2.bn2.running_var, layer2.2.conv3.weight, layer2.2.bn3.bias, layer2.2.bn3.weight, layer2.2.bn3.running_mean, layer2.2.bn3.running_var, layer2.3.conv1.weight, layer2.3.bn1.bias, layer2.3.bn1.weight, layer2.3.bn1.running_mean, layer2.3.bn1.running_var, layer2.3.conv2.weight, layer2.3.bn2.bias, layer2.3.bn2.weight, layer2.3.bn2.running_mean, layer2.3.bn2.running_var, layer2.3.conv3.weight, layer2.3.bn3.bias, layer2.3.bn3.weight, layer2.3.bn3.running_mean, layer2.3.bn3.running_var, layer3.0.downsample.0.weight, layer3.0.downsample.1.bias, layer3.0.downsample.1.weight, layer3.0.downsample.1.running_mean, layer3.0.downsample.1.running_var, layer3.0.conv1.weight, layer3.0.bn1.bias, layer3.0.bn1.weight, layer3.0.bn1.running_mean, layer3.0.bn1.running_var, layer3.0.conv2.weight, layer3.0.bn2.bias, layer3.0.bn2.weight, layer3.0.bn2.running_mean, layer3.0.bn2.running_var, layer3.0.conv3.weight, layer3.0.bn3.bias, layer3.0.bn3.weight, layer3.0.bn3.running_mean, layer3.0.bn3.running_var, layer3.1.conv1.weight, layer3.1.bn1.bias, layer3.1.bn1.weight, layer3.1.bn1.running_mean, layer3.1.bn1.running_var, layer3.1.conv2.weight, layer3.1.bn2.bias, layer3.1.bn2.weight, layer3.1.bn2.running_mean, layer3.1.bn2.running_var, layer3.1.conv3.weight, layer3.1.bn3.bias, layer3.1.bn3.weight, layer3.1.bn3.running_mean, layer3.1.bn3.running_var, layer3.2.conv1.weight, layer3.2.bn1.bias, layer3.2.bn1.weight, layer3.2.bn1.running_mean, layer3.2.bn1.running_var, layer3.2.conv2.weight, layer3.2.bn2.bias, layer3.2.bn2.weight, layer3.2.bn2.running_mean, layer3.2.bn2.running_var, layer3.2.conv3.weight, layer3.2.bn3.bias, layer3.2.bn3.weight, layer3.2.bn3.running_mean, layer3.2.bn3.running_var, layer3.3.conv1.weight, layer3.3.bn1.bias, layer3.3.bn1.weight, layer3.3.bn1.running_mean, layer3.3.bn1.running_var, layer3.3.conv2.weight, layer3.3.bn2.bias, layer3.3.bn2.weight, layer3.3.bn2.running_mean, layer3.3.bn2.running_var, layer3.3.conv3.weight, layer3.3.bn3.bias, layer3.3.bn3.weight, layer3.3.bn3.running_mean, layer3.3.bn3.running_var, layer3.4.conv1.weight, layer3.4.bn1.bias, layer3.4.bn1.weight, layer3.4.bn1.running_mean, layer3.4.bn1.running_var, layer3.4.conv2.weight, layer3.4.bn2.bias, layer3.4.bn2.weight, layer3.4.bn2.running_mean, layer3.4.bn2.running_var, layer3.4.conv3.weight, layer3.4.bn3.bias, layer3.4.bn3.weight, layer3.4.bn3.running_mean, layer3.4.bn3.running_var, layer3.5.conv1.weight, layer3.5.bn1.bias, layer3.5.bn1.weight, layer3.5.bn1.running_mean, layer3.5.bn1.running_var, layer3.5.conv2.weight, layer3.5.bn2.bias, layer3.5.bn2.weight, layer3.5.bn2.running_mean, layer3.5.bn2.running_var, layer3.5.conv3.weight, layer3.5.bn3.bias, layer3.5.bn3.weight, layer3.5.bn3.running_mean, layer3.5.bn3.running_var
`

Hi @Soulempty
The warning is due to res_layer in share_heads. It is fine.

it may caused by  model and loaded state dict do not match exactly.How to solve this problem?

@xvjiarui,thanks for you answer,but my warning is much more than the res_layer,such as the following image display,is it truly as you response just now?
`2020-07-10 09:27:17,244 - mmdet - INFO - load model from: open-mmlab://detectron2/resnet50_caffe
2020-07-10 09:27:27,342 - mmdet - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: layer4.0.downsample.0.weight, layer4.0.downsample.1.bias, layer4.0.downsample.1.weight, layer4.0.downsample.1.running_mean, layer4.0.downsample.1.running_var, layer4.0.conv1.weight, layer4.0.bn1.bias, layer4.0.bn1.weight, layer4.0.bn1.running_mean, layer4.0.bn1.running_var, layer4.0.conv2.weight, layer4.0.bn2.bias, layer4.0.bn2.weight, layer4.0.bn2.running_mean, layer4.0.bn2.running_var, layer4.0.conv3.weight, layer4.0.bn3.bias, layer4.0.bn3.weight, layer4.0.bn3.running_mean, layer4.0.bn3.running_var, layer4.1.conv1.weight, layer4.1.bn1.bias, layer4.1.bn1.weight, layer4.1.bn1.running_mean, layer4.1.bn1.running_var, layer4.1.conv2.weight, layer4.1.bn2.bias, layer4.1.bn2.weight, layer4.1.bn2.running_mean, layer4.1.bn2.running_var, layer4.1.conv3.weight, layer4.1.bn3.bias, layer4.1.bn3.weight, layer4.1.bn3.running_mean, layer4.1.bn3.running_var, layer4.2.conv1.weight, layer4.2.bn1.bias, layer4.2.bn1.weight, layer4.2.bn1.running_mean, layer4.2.bn1.running_var, layer4.2.conv2.weight, layer4.2.bn2.bias, layer4.2.bn2.weight, layer4.2.bn2.running_mean, layer4.2.bn2.running_var, layer4.2.conv3.weight, layer4.2.bn3.bias, layer4.2.bn3.weight, layer4.2.bn3.running_mean, layer4.2.bn3.running_var, conv1.bias
2020-07-10 09:27:27,510 - mmdet - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: conv1.weight, conv1.bias, bn1.bias, bn1.weight, bn1.running_mean, bn1.running_var, layer1.0.downsample.0.weight, layer1.0.downsample.1.bias, layer1.0.downsample.1.weight, layer1.0.downsample.1.running_mean, layer1.0.downsample.1.running_var, layer1.0.conv1.weight, layer1.0.bn1.bias, layer1.0.bn1.weight, layer1.0.bn1.running_mean, layer1.0.bn1.running_var, layer1.0.conv2.weight, layer1.0.bn2.bias, layer1.0.bn2.weight, layer1.0.bn2.running_mean, layer1.0.bn2.running_var, layer1.0.conv3.weight, layer1.0.bn3.bias, layer1.0.bn3.weight, layer1.0.bn3.running_mean, layer1.0.bn3.running_var, layer1.1.conv1.weight, layer1.1.bn1.bias, layer1.1.bn1.weight, layer1.1.bn1.running_mean, layer1.1.bn1.running_var, layer1.1.conv2.weight, layer1.1.bn2.bias, layer1.1.bn2.weight, layer1.1.bn2.running_mean, layer1.1.bn2.running_var, layer1.1.conv3.weight, layer1.1.bn3.bias, layer1.1.bn3.weight, layer1.1.bn3.running_mean, layer1.1.bn3.running_var, layer1.2.conv1.weight, layer1.2.bn1.bias, layer1.2.bn1.weight, layer1.2.bn1.running_mean, layer1.2.bn1.running_var, layer1.2.conv2.weight, layer1.2.bn2.bias, layer1.2.bn2.weight, layer1.2.bn2.running_mean, layer1.2.bn2.running_var, layer1.2.conv3.weight, layer1.2.bn3.bias, layer1.2.bn3.weight, layer1.2.bn3.running_mean, layer1.2.bn3.running_var, layer2.0.downsample.0.weight, layer2.0.downsample.1.bias, layer2.0.downsample.1.weight, layer2.0.downsample.1.running_mean, layer2.0.downsample.1.running_var, layer2.0.conv1.weight, layer2.0.bn1.bias, layer2.0.bn1.weight, layer2.0.bn1.running_mean, layer2.0.bn1.running_var, layer2.0.conv2.weight, layer2.0.bn2.bias, layer2.0.bn2.weight, layer2.0.bn2.running_mean, layer2.0.bn2.running_var, layer2.0.conv3.weight, layer2.0.bn3.bias, layer2.0.bn3.weight, layer2.0.bn3.running_mean, layer2.0.bn3.running_var, layer2.1.conv1.weight, layer2.1.bn1.bias, layer2.1.bn1.weight, layer2.1.bn1.running_mean, layer2.1.bn1.running_var, layer2.1.conv2.weight, layer2.1.bn2.bias, layer2.1.bn2.weight, layer2.1.bn2.running_mean, layer2.1.bn2.running_var, layer2.1.conv3.weight, layer2.1.bn3.bias, layer2.1.bn3.weight, layer2.1.bn3.running_mean, layer2.1.bn3.running_var, layer2.2.conv1.weight, layer2.2.bn1.bias, layer2.2.bn1.weight, layer2.2.bn1.running_mean, layer2.2.bn1.running_var, layer2.2.conv2.weight, layer2.2.bn2.bias, layer2.2.bn2.weight, layer2.2.bn2.running_mean, layer2.2.bn2.running_var, layer2.2.conv3.weight, layer2.2.bn3.bias, layer2.2.bn3.weight, layer2.2.bn3.running_mean, layer2.2.bn3.running_var, layer2.3.conv1.weight, layer2.3.bn1.bias, layer2.3.bn1.weight, layer2.3.bn1.running_mean, layer2.3.bn1.running_var, layer2.3.conv2.weight, layer2.3.bn2.bias, layer2.3.bn2.weight, layer2.3.bn2.running_mean, layer2.3.bn2.running_var, layer2.3.conv3.weight, layer2.3.bn3.bias, layer2.3.bn3.weight, layer2.3.bn3.running_mean, layer2.3.bn3.running_var, layer3.0.downsample.0.weight, layer3.0.downsample.1.bias, layer3.0.downsample.1.weight, layer3.0.downsample.1.running_mean, layer3.0.downsample.1.running_var, layer3.0.conv1.weight, layer3.0.bn1.bias, layer3.0.bn1.weight, layer3.0.bn1.running_mean, layer3.0.bn1.running_var, layer3.0.conv2.weight, layer3.0.bn2.bias, layer3.0.bn2.weight, layer3.0.bn2.running_mean, layer3.0.bn2.running_var, layer3.0.conv3.weight, layer3.0.bn3.bias, layer3.0.bn3.weight, layer3.0.bn3.running_mean, layer3.0.bn3.running_var, layer3.1.conv1.weight, layer3.1.bn1.bias, layer3.1.bn1.weight, layer3.1.bn1.running_mean, layer3.1.bn1.running_var, layer3.1.conv2.weight, layer3.1.bn2.bias, layer3.1.bn2.weight, layer3.1.bn2.running_mean, layer3.1.bn2.running_var, layer3.1.conv3.weight, layer3.1.bn3.bias, layer3.1.bn3.weight, layer3.1.bn3.running_mean, layer3.1.bn3.running_var, layer3.2.conv1.weight, layer3.2.bn1.bias, layer3.2.bn1.weight, layer3.2.bn1.running_mean, layer3.2.bn1.running_var, layer3.2.conv2.weight, layer3.2.bn2.bias, layer3.2.bn2.weight, layer3.2.bn2.running_mean, layer3.2.bn2.running_var, layer3.2.conv3.weight, layer3.2.bn3.bias, layer3.2.bn3.weight, layer3.2.bn3.running_mean, layer3.2.bn3.running_var, layer3.3.conv1.weight, layer3.3.bn1.bias, layer3.3.bn1.weight, layer3.3.bn1.running_mean, layer3.3.bn1.running_var, layer3.3.conv2.weight, layer3.3.bn2.bias, layer3.3.bn2.weight, layer3.3.bn2.running_mean, layer3.3.bn2.running_var, layer3.3.conv3.weight, layer3.3.bn3.bias, layer3.3.bn3.weight, layer3.3.bn3.running_mean, layer3.3.bn3.running_var, layer3.4.conv1.weight, layer3.4.bn1.bias, layer3.4.bn1.weight, layer3.4.bn1.running_mean, layer3.4.bn1.running_var, layer3.4.conv2.weight, layer3.4.bn2.bias, layer3.4.bn2.weight, layer3.4.bn2.running_mean, layer3.4.bn2.running_var, layer3.4.conv3.weight, layer3.4.bn3.bias, layer3.4.bn3.weight, layer3.4.bn3.running_mean, layer3.4.bn3.running_var, layer3.5.conv1.weight, layer3.5.bn1.bias, layer3.5.bn1.weight, layer3.5.bn1.running_mean, layer3.5.bn1.running_var, layer3.5.conv2.weight, layer3.5.bn2.bias, layer3.5.bn2.weight, layer3.5.bn2.running_mean, layer3.5.bn2.running_var, layer3.5.conv3.weight, layer3.5.bn3.bias, layer3.5.bn3.weight, layer3.5.bn3.running_mean, layer3.5.bn3.running_var
`

i met the same problem...have you solved it?


service-node-port-range: 79-32767 is not a sane default

ClusterConfiguration.yml#8 defines service-node-port-range: 79-32767 but this is not a good practice because can cause conflicts with other service ports.

Well, the idea is that nginx-controller has to be on 80 and 443 especially for that reason: have k8s the one to hold the web servers.
Unfortunately I am not aware of another solution to have few ranges.

should anyone modify this range, the nginx controller helm chart port should be also updated in addons.yaml
I am closing, as I don't see other better and intuitive solutin


add a value inspector in Jupyter

I was talking with Laura this morning to make here realize that she can use JupyterLab for R as well. One of the key functionality tht is not there yet (preventing here from moving from Rstudio) is the absence of a variable inspector.
On the jupyterlab repository there is a long thread (https://github.com/jupyterlab/jupyterlab/issues/443) started in 2016. The conclusion is hat they'll leave it to community plugin. Lucky for us this plugin already exist: https://github.com/lckr/jupyterlab-variableInspector
I tried to install it manually from my side for testing but I when I run jupyter labextension install @lckr/jupyterlab_variableinspector I get a access error:
An error occurred.
PermissionError: [Errno 13] Permission denied: '/usr/local/share/jupyter/lab/extensions/lckr-jupyterlab_variableinspector-3.0.9.tgz'
See the log file for details:  /tmp/jupyterlab-debug-339w11lo.log

Could we install it on test to check the functionality and if satisfying we install it for everyone ?
demo:

jenkins was not happy with the deployment let's investigate what goes wrong


Flutter App Apk does not works well after releasing it on PlayStore, its showing Gray Screen but it works well on emulator and Release Apk

While working with the release apk it works well but after releasing it on the play store the specific screens are not working where the screen contains the Raizerpay payment features. It also works well on an emulator.  What are the issues?
I used this one command for generating the release apk file
flutter build apk --debug flutter build apk --profile flutter build apk --release
The logs am getting after opening that screen:
--
`
PS D:\rex\temp\wholesome_apk> flutter logs
Showing 2109119DI logs:
I/flutter (29933): 1
I/flutter (29933): https://wholesomeeten.gives/we/diet/dinner/5/6365e0d9411901d50e38fe25
I/flutter (29933): 200
I/flutter (29933): Razorpay key is {"key":"rzp_live_LBkX46VLBD2Fsx"}
I/flutter (29933): rzp_live_LBkX46VLBD2Fsx
I/flutter (29933): https://wholesomeeten.gives/we/payments/get-razorpay-key
I/flutter (29933): https://wholesomeeten.gives/we/payments/planExpiry/6365e0d9411901d50e38fe25
I/flutter (29933): 2022-11-05 09:38:18.253940
I/flutter (29933): {details: {Meal: {name: Oats Rava Dosa, img: https://recipesimages.blob.core.windows.net/recipescontainer/481_Oats_Rava_Dosa.jpg, quantity: 6 Dosa, cal: 420, cooktime: 20, preptime: 5}, Meal Condiment: [{name: Thotakura Pappu, img: https://recipesimages.blob.core.windows.net/recipescontainer/263_Amaranth_Dal.jpg, quantity: 2 cup, cal: 120, cooktime: 20, preptime: 360}, {name: Chinta Chiguru Pappu, img: https://recipesimages.blob.core.windows.net/recipescontainer/262_Spinach_Dal.jpg, quantity: 1 cup, cal: 60, cooktime: 20, preptime: 360}]}}
I/flutter (29933): Hello
I/flutter (29933): Oats Rava Dosa
I/flutter (29933): Connection reset by peer
I/flutter (29933): Error occured in MealDetailModel Snacks
I/flutter (29933): type 'Null' is not a subtype of type 'FutureOr'
E/flutter (29933): [ERROR:flutter/runtime/dart_vm_initializer.cc(41)] Unhandled Exception: type 'Null' is not a subtype of type 'MealDetailModel'
E/flutter (29933): #0      _HomeScreenState.getDataForMeal (package:onboarding_ui/screens/home.dart:113:5)
E/flutter (29933):
E/flutter (29933):
I/flutter (29933): 200
I/flutter (29933): Razorpay key is {"key":"rzp_live_LBkX46VLBD2Fsx"}
I/flutter (29933): rzp_live_LBkX46VLBD2Fsx
I/flutter (29933): [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]
I/flutter (29933): object
I/flutter (29933): 1
I/flutter (29933): BreakFast
I/flutter (29933): https://wholesomeeten.gives/we/diet/breakfast/5/6365e0d9411901d50e38fe25
I/flutter (29933): {details: {Meal: {name: Aloo Paratha, img: https://recipesimages.blob.core.windows.net/recipescontainer/483_Aloo_Paratha.jpg, quantity: 6 Paratha, cal: 420, cooktime: 15, preptime: 30}, Meal Condiment: [{name: Mutton Gravy, img: https://recipesimages.blob.core.windows.net/recipescontainer/265_Mutton_Curry.jpg, quantity: 3 Cup, cal: 120, cooktime: 40, preptime: 10}, {name: Tindora Chutney, img: https://recipesimages.blob.core.windows.net/recipescontainer/186_Ridge_Gourd_Chutney.jpg, quantity: 2 cup, cal: 60, cooktime: 5, preptime: 5}]}}
I/flutter (29933): Hello
I/flutter (29933): Aloo Paratha
I/flutter (29933): 200
I/flutter (29933): 200
I/flutter (29933): {details: {Meal: {name: Poha, img: https://recipesimages.blob.core.windows.net/recipescontainer/474_Poha.jpg, quantity: 1 Cup, cal: 312, cooktime: 10, preptime: 5}, Meal Condiment: [{name: Veg Kurma, img: https://recipesimages.blob.core.windows.net/recipescontainer/466_veg_Kurma.jpg, quantity: 5 Cup, cal: 96, cooktime: 25, preptime: 15}, {name: Tomato Pachadi, img: https://recipesimages.blob.core.windows.net/recipescontainer/471_Tomato_Chutney.jpg, quantity: 2 Cup, cal: 72, cooktime: 5, preptime: 5}]}}
I/flutter (29933): Hello
I/flutter (29933): Poha
I/flutter (29933): https://wholesomeeten.gives/we/payments/get-razorpay-key
I/flutter (29933): https://wholesomeeten.gives/we/payments/planExpiry/6365e0d9411901d50e38fe25
I/flutter (29933): 2022-11-05 09:38:18.253940
I/flutter (29933): {details: {Meal: {name: Aloo Paratha, img: https://recipesimages.blob.core.windows.net/recipescontainer/483_Aloo_Paratha.jpg, quantity: 6 Paratha, cal: 420, cooktime: 15, preptime: 30}, Meal Condiment: [{name: Mutton Gravy, img: https://recipesimages.blob.core.windows.net/recipescontainer/265_Mutton_Curry.jpg, quantity: 3 Cup, cal: 120, cooktime: 40, preptime: 10}, {name: Tindora Chutney, img: https://recipesimages.blob.core.windows.net/recipescontainer/186_Ridge_Gourd_Chutney.jpg, quantity: 2 cup, cal: 60, cooktime: 5, preptime: 5}]}}
I/flutter (29933): Hello
I/flutter (29933): Aloo Paratha
I/flutter (29933): 200
I/flutter (29933): Razorpay key is {"key":"rzp_live_LBkX46VLBD2Fsx"}
I/flutter (29933): rzp_live_LBkX46VLBD2Fsx
`

ERROR:flutter/runtime/dart_vm_initializer.cc(41)] Unhandled Exception: type 'Null' is not a subtype of type 'MealDetailModel'

This may happen due to improper way of initializing or fetching data by the model.
A Tip:
Use try catch when fetching data to avoid grey screen. Hope that works.

I/flutter (29933): type 'Null' is not a subtype of type 'FutureOr'
E/flutter (29933): [ERROR:flutter/runtime/dart_vm_initializer.cc(41)] Unhandled Exception: type 'Null' is not a subtype of type 'MealDetailModel'

@ravimandal06
this looks like a runtime error, not a bug with flutter

Based on the error log and it most probably seems to be a code implementation issue rather than a bug in flutter. You'll need to revisit your code.
Closing from here.


NOT ENFORCED clause is not recognized by the ADS SQL editor

Azure Data Studio Version: 1.33.0-insider (user setup)
Commit: ed280cb0034a8408e91e52f0d4409553bd9a10e5
OS Version: Windows_NT x64 10.0.19043

Steps to Reproduce:

Connect to Azure SQL DW (aka Synapse, aka dedicated SQL pool)
Right-click the connection in the server explorer and select "New Query"
Paste the below query into the editor:

CREATE TABLE [TEST]
(
    [ID] INT NOT NULL,
    CONSTRAINT [TEST_C_ID_PK]
        PRIMARY KEY NONCLUSTERED ([ID]) NOT ENFORCED
)

Actual behavior:
NOT keywords gets a red squiggle underline indicating a syntax error:

Expected behavior:
There is a SQL DW specific NOT ENFORCED clause that is required for primary keys/unique constraints. We should understand it and do not raise a parse error.
This problem also appears when using SQL Database Projects extension and editing SQL scripts, even when target platform is set to SQL DW for the project. This does not appear to prevent the actual build/publish, but does result in an error being displayed on the "problems" pane, making actual errors harder to identify.

The language service error detection is coming from an upstream component, SQL Parser.  We will pickup this fix once it's addressed in that component.  For reference this is the same behavior in SSMS, which also consumes this dependency.


[ Client ] Support Protobuf  AutoSchema

Fixes #12969
Master Issue: #12969
Motivation

Support protobuf  AutoSchema

Modifications

Support protobuf  AutoSchema

Verifying this change

[x] Make sure that the change passes the CI checks.

Does this pull request potentially affect one of the following parts:

The schema: (yes)

Documentation
Need to update docs?

[x] doc-required

@freeznet  PTAL :)

/pulsarbot rerun-failure-checks

/pulsarbot rerun-failure-checks

/pulsarbot rerun-failure-checks

@codelipenghui  @congbobo184  @eolivelli   PTAL :)

I think PROTOBUF schema is also baesd on avro. Why not GenericProtobufSchema inheritage  GenericSchemaImpl just like GenericAvroSchema and GenericJsonSchema. I think they all AvroBaseStructSchema.

I think PROTOBUF schema is also baesd on avro. Why not GenericProtobufSchema inheritage  GenericSchemaImpl just like GenericAvroSchema and GenericJsonSchema. I think they all AvroBaseStructSchema.

I think PROTOBUF schema is also baesd on avro. Why not GenericProtobufSchema inheritage GenericSchemaImpl just like GenericAvroSchema and GenericJsonSchema. I think they all AvroBaseStructSchema.

I once wanted to use Avro, but I got some problems when deserializing, I think something might be missing.

@freeznet PTAL :)

I once wanted to use Avro, but I got some problems when deserializing, I think something might be missing.

Maybe we should follow this way. That looks like more clear, do not you think?

@freeznet PTAL :)

I once wanted to use Avro, but I got some problems when deserializing, I think something might be missing.

Maybe we should follow this way. That looks like more clear, do not you think?

I have tried to use GenericProtobufSchema  inheritage  GenericSchemaImpl, but an exception occurred during parsing.  I think it should be a format problem during serialization.
maybe i can spend more times to use another way to resolve this problem. but I think we can support the feature first, and then optimize it. What do you think?

/pulsarbot rerun-failure-checks

/pulsarbot rerun-failure-checks

/pulsarbot rerun-failure-checks

@freeznet PTAL :)

I once wanted to use Avro, but I got some problems when deserializing, I think something might be missing.

Maybe we should follow this way. That looks like more clear, do not you think?

@frankxieke I have tried to use GenericProtobufSchema inheritage GenericSchemaImpl, but an exception occurred during parsing. I think it should be a format problem during serialization. maybe i can spend more times to use another way to resolve this problem. but I think we can support the feature first, and then optimize it. What do you think? :)

I am not very sure about this problem. Maybe we should wait for other reviewer's option.   thanks for you time.

@freeznet PTAL :)

I once wanted to use Avro, but I got some problems when deserializing, I think something might be missing.

Maybe we should follow this way. That looks like more clear, do not you think?

@frankxieke I have tried to use GenericProtobufSchema inheritage GenericSchemaImpl, but an exception occurred during parsing. I think it should be a format problem during serialization. maybe i can spend more times to use another way to resolve this problem. but I think we can support the feature first, and then optimize it. What do you think? :)

I am not very sure about this problem. Maybe we should wait for other reviewer's option. thanks for you time.

Very thanks for your suggestion, During the review period of other reviewers, I will continue try to use GenericAvroSchema .

/pulsarbot rerun-failure-checks

Hi @congbobo184 @mattisonchao
For the doc side:

Need to add Protobuf to the AUTO_CONSUME section as below?

Need to add any other explanations?
FYI: we already have documented these info:

@Anonymitaet
Hi , Anonymitaet
i think we just need to add Protobuf to the AUTO_CONSUME section as below.

@congbobo184
I have refactored Protobuf and Protobuf Native.
PTAL, when you have time : )

@congbobo184
I feel I did something wrong, I need to resubmit a PR to support this feature.

@congbobo184
I think i got somethings wrong.
When the consumer use AUTO_CONSUME, an error will be generated if the consumer does not have the same Protobuf class.
Now I have tried some methods:

Use Avro to support this feature. (Although GenericRecord can be built, you will get an error when read the value, because we use Protobuf serialization, but use Avro to deserialize).
Use Protobuf Descriptor. ( Since only FieldDescriptor is stored during Serialization, Protobuf Descriptor cannot be generate by this data. )

Could you give me more advice for this PR :)

@congbobo184
I think i got somethings wrong.
When the consumer use AUTO_CONSUME, an error will be generated if the consumer does not have the same Protobuf class.
Now I have tried some methods:

Use Avro to support this feature. (Although GenericRecord can be built, you will get an error when read the value, because we use Protobuf serialization, but use Avro to deserialize).
Use Protobuf Descriptor. ( Since only FieldDescriptor is stored during Serialization, Protobuf Descriptor cannot be generate by this data. )

Could you give me more advice for this PR :)

@eolivelli
Would you have time to help me with this problem ?

Avro-based protobuf will lose some properties during serialization, and it is difficult to complete the task according to the current situation.
This is why Pulsar created Protobuf-Native Schema.
I think I can try another method to use the current properties to generate the protobuf dynamic descriptor.
If it cannot be completed, I will close this PR.


supress 'page not found in cache' message?

Maybe with a -q, --quiet flag?
https://github.com/dbrgn/tealdeer/blob/master/src/main.rs#L246

Yeah, why not? A pull request would be welcome.
Maybe the -q flag could also silence the "cache old" warning: https://github.com/dbrgn/tealdeer/blob/master/src/main.rs#L144-L150

mind assigning me and I'll take a crack at it this week?


SEO Optimization for Vite

Hi guys,
I've been using Vite for past couple months and indeed is quite fast and useful.
At the moment we're going to production with the website and the app/platform, so it is time to focus on SEO optimizations.
I was checking Vite documentation, event searching for keywords like "SEO", " seo optimization", "search engine optimization" but did not find useful information.
Here are some important informations about the website, which is the one we need to optimize (no need for the app/platform)... we use Vite (^2.8.0) + Vue 3, but javascript on the website instead of typescript. The website is SPA
i've already tried plugins as VueUse/Head (https://github.com/vueuse/head), but did not work.
So my question here is how to do SEO Optimizations like add meta tags like "title", "description" and  "og's"?
Originally posted by @zmotinstitute in https://github.com/vitejs/vite/discussions/9789

To add more context in this thread i've added all the steps to reproduce this error

You're not correctly registering the vueuse/head plugin. Instead of passing head as an additional argument to the Vue3Sanitize registration:
app.use(router).use(Vue3Sanitize, overridenOptions, head).mount('#app')

...register it as its own plugin with a separate call to .use():
app.use(router).use(Vue3Sanitize, overridenOptions).use(head).mount('#app')

Thanks @jonaskuske ... indeed it worked and the small details matter the most.
I just have another issue which maybe you could have an idea how to fix.
I'd like to implement JSON+LD structured data in order to improve SEO even more.
At the moment i'd like to add Logo structured data and Frequently Asked Question (FAQ).
I just did the implementation with a script array into the useHead () object as you can see below

The script property does render on header, but the structured data into the "json" one not... it returns [object Object] for both structured data (logo and faq) as you can see below.

Any suggestions about this implementation?

Yep! You need to set the JSON on the "children" key, not "json", as you want to render the JSON inside the tags and not as an attribute. Also, you need to turn the JSON into a string using JSON.stringify:
script: [
  {
    type: 'application/ld+json',
    children: JSON.stringify(
      [
        {
          '@type': 'Organisation',
          // ...
        },
        {
          '@type': 'Question',
          // ...
        }
      ]
    )
  }
]


Duplicating product with options gives error

Duplicating a product with option (Small, Medium, Large) results in the following error:
A new entity was found through the relationship 'Concrete\Package\CommunityStore\Src\CommunityStore\Product\ProductVariation\ProductVariation#options' that was not configured to cascade persist operations for entity: Concrete\Package\CommunityStore\Src\CommunityStore\Product\ProductVariation\ProductVariationOptionItem@0000000000a41e160000000007a45974. To solve this issue: Either explicitly call EntityManager#persist() on this unknown entity or configure cascade persist this association in the mapping for example @ManyToOne(..,cascade={"persist"}). If you cannot find out which entity causes the problem implement 'Concrete\Package\CommunityStore\Src\CommunityStore\Product\ProductVariation\ProductVariationOptionItem#__toString()' to get a clue.

What version are you running?  I've got a feeling this was fixed recently. Try clearing your cache too.
I just happened to have a tab open with a product with options - hitting the duplicate button resulted in a successful copy:

I've got v.<IP_ADDRESS>

There's been some work on product variation/option saving since then;
https://github.com/concrete5-community-store/community_store/commit/a350b509e4faf756e731554b96e66e682e80b6a5
Try updating to the latest version

Updated and issue is resolved. Many thanks


Adding option for calculating variance and standard deviation

We can add option for calculating variance and standard deviation in mean mode median calculator.
#gssoc21

Feel free to ask your doubts, if any
Deadline 25 March,2021 (11:59 PM IST)


Yandex Browser

I installed Yandex browser and could not connect to internet at all until I disabled DNSCrypt altogether!

Maybe because you are behind a firewall or a proxy that blocks connections to port 443. Or you have an antivirus that prevents local resolvers from working (apparently, this is the case with some Kaspersky products).


Investigate about form

Investigate about creating a form or maybe a google form.
https://docs.google.com/document/d/1NjaGujf9XZnsCqHsKOFZYxwZWZh7lhhDi_wjI2qVuiA/edit?usp=sharing

Consider to be added add into the document:

Using Google forms but from a custom interface. This would require extra work but in exchange we would have it integrated in our own page, instead of a redirection, and we would have full control of the style.
It was proposed to add a contact form, therefore we should consider how to use the chosen solution for it.

Pending update with the latest requirements discussed with the client.


internationalization

Support for other languages.
I'm only good at English, but I'd love to see pull requests with translations for your language. Requests for internationalization-type things that aren't supported right now are welcome too. (example: I hear some languages swap commas and decimal points, like "1.234,56" instead of the English "1,234.56"? Also, not everyone groups digits in threes, but I don't know any other patterns.)

In french (fr-FR) , numbers are formatted this way: 124 452,53

In Bulgarian(bg-BG), numbers are formatted exactly like in French.

for reference: https://en.wikipedia.org/wiki/Decimal_mark


Add a button to toggle the search pane

I think that it would be nice to have a button on the right side of the "Search" header to toggle the pane.
When the pane is closed, the button could be a "+", ex:

When the pane is open, the button would become a "-", ex:

This would only toggle the pane visibility, not clear the form.

Fixed by https://github.com/bnjbvr/kresus/commit/29f9fd59d2ed564bf5a3fbb0580270fe50ce24ea


Having two files with the same name breaks the menu highlighting

I have two forms.html files in my materials, one in a "components" folder, the other in "structures". When I’m on the second one, its the menu item corresponding to the first one that is highlighted.
As far as I can see the code doesn’t pay attention to the current group or category. Do you have any recommendation on how you’d fix this? I could try patching it and do a PR then.

Hmm this is a tricky one. You're correct in that the setActiveItem() function in fabricator.js doesn't account for anything other than the hash. It assumes a 1:1 between hash:id.
A solution might be to modify the parsedItems and setActive functions to include the location.pathname.


Native compiling error with cuda 11.4 or 11.6, VPI 1.1.11 or 1.2,in any scenario

Hi, I receive this error with any configuration a try of Cuda,VPI,dnn,cuDNN,OpenCV, it happened me in Jetson NX a while ago too, always in 20.04
colcon build  && source install/setup.bash
[0.283s] WARNING:colcon.colcon_core.verb:Some selected packages are already built in one or more underlay workspaces:
'image_geometry' is in: /opt/ros/foxy
'image_transport' is in: /opt/ros/foxy
'rcpputils' is in: /opt/ros/foxy
'camera_calibration_parsers' is in: /opt/ros/foxy
'cv_bridge' is in: /opt/ros/foxy
'camera_info_manager' is in: /opt/ros/foxy
If a package in a merged underlay workspace is overridden and it installs headers, then all packages in the overlay must sort their include directories by workspace order. Failure to do so may result in build failures or undefined behavior at run time.
If the overridden package is used by another package in any underlay, then the overriding package in the overlay must be API and ABI compatible or undefined behavior at run time may occur.
If you understand the risks and want to override a package anyways, add the following to the command line:
--allow-overriding camera_calibration_parsers camera_info_manager cv_bridge image_geometry image_transport rcpputils
This may be promoted to an error in a future release of colcon-core.

Thanks, I will try out


[Bug]

Summary
A clear and concise description of what the bug is, and what you expected to happen.
How to reproduce

Go to '...'
Click on '...'
Scroll down to '...'
See error

Please include screenshots.
Additional context
Add any other context about the problem here.

Got it

Thank you @jbird1repo! First time here. Just checking the system. ;)

I can't login certain browsers using my admin account<EMAIL_ADDRESS>I put my information in the text box.
I put my password into the text box.
I expected to be allowed to login.
I was not allowed to login.

git init
git remote add origin git@github.com:So-Hip-Pix/refactored-enigma.git


In the mono image, how to transform depth to point cloud ?

In the monocular image, how to get the point cloud from depth image?

I use the pre-trained DORN model. You can download it from https://github.com/hufu6371/DORN .

DORN just make the first step depth estimation from RGB image to RGB-depth, but not provide generate point cloud.

You can use my code to convert disparity to point clouds. https://github.com/mileyan/pseudo_lidar#convert-the-disparities-to-point-clouds

Update: Please add --is_depth in the command.

Hi, do we need to do any processing before using the depth generated by DORN to generate pointcloud?
I use the depth generated by DORN with https://github.com/hufu6371/DORN/blob/master/demo_kitti.py .
Juding from the code, the depth is saved to .png, and the result looks well.
depth = depth_prediction(args.filename) depth = depth*256.0 depth = depth.astype(np.uint16) img_id = args.filename.split('/') img_id = img_id[len(img_id)-1] img_id = img_id[0:len(img_id)-4] if not os.path.exists(args.outputroot):     os.makedirs(args.outputroot) cv2.imwrite(str(args.outputroot + '/' + img_id + '_pred.png'), depth)

However,  the point cloud generated with the provided code is obviously wrong. Do I need to do some preprocessing with the depth (for example divided by 256) to use it?

I solved the problem described above and successfully generated valid pointcloud with depth generated by DORN.
Some tips:

You should use the caffe provided in the DORN repository instead of any latest versions.
If you choose to generate depth by modify the kitti demo code (which I think should be the most convenient way), you need to adjust the data type of the depth as indicated in devkit of KITTI Depth by simply adding: depth = disp_map.astype(np.float) / 256 at here before project depth to point cloud.

Thanks so much. I have update the code.

Hi, DeriZsy. Just using the depth image to generate the point clouds or need to predict the disparities first? I have already using the DORN caffe version demo code generate the depth image.

Hi, DeriZsy. Just using the depth image to generate the point clouds or need to predict the disparities first? I have already using the DORN caffe version demo code generate the depth image.

Use the depth directly. Notice the 'is-depth' flag here in the code for lidar generation in this repo.

First move the depth image to predict_disparity folder?

First move the depth image to predict_disparity folder?

plz read the code yourself... then you got all the answers...  brian is a good thing

when the mono depth image generate the point clouds, each need the camera calibration file. If using common other image out of KITTI there is no camera calibration file, so cannot generate to the point clouds.

when the mono depth image generate the point clouds, each need the camera calibration file. If using common other image out of KITTI there is no camera calibration file, so cannot generate to the point clouds.

Yes, you need calibration parameters when you generate the point cloud.


postfix integration should not require sudo to root

Reading the source code to integrations-core/postfix/check.py I note that it does a sudo to root to run the find command.
This is noted in the docs / comments :

WARNING: the user that dd-agent runs as must have sudo access for the 'find' command
| sudo access is not required when running dd-agent as root (not recommended)
  |
  | example /etc/sudoers entry:
  | dd-agent ALL=(ALL) NOPASSWD:/usr/bin/find /var/spool/postfix* -type f

root should not be required here - postfix user should be sufficient. That would be combined with a '-u postfix' on line 64's sudo command to allow this to work.
This is a concern because find has a -exec parameter and your command list has a wildcard in it - this could be used to run arbitrary commands as root if the dd-agent user is compromised.

More to the point, what was wrong with just running mailq?

Reopening since there's an ongoing fix here: #665

Fix was merged, closing


Design: Citizenship tab

Designs
https://www.figma.com/design/cvV48t0f7O2znT6QBxK0Zj/Tonomy-ID?node-id=9297-6525&t=nLX4KxCQVnwjaRng-1

looks good. graphics need work


Gut on Windows can't get any input

Describe the bug
On Windows, gut can't be used because you can't pass the telemetry consent.
It keeps asking "please enter either y or n".
Expected behavior
Entering "y" should be accounted by gut. Now, it seems like gut receives another input than the one sent
Desktop (please complete the following information):

OS: Windows 11

Possible reason
This is the function that reads an input for gut.
func InputLine(message string) (string, error) {
	fmt.Printf("%s ", message)
	reader := bufio.NewReader(os.Stdin)
	input, err := reader.ReadString('\n')
	if err != nil {
		return "", err
	}
	// Remove the delimiter
	return strings.TrimSuffix(input, "\n"), nil
}

At the end, it returns the input with \n being omitted because it's added when the user enters to submit.
However, windows doesn't end lines with \n but with \r\n.
It means gut sees y\r rather than y .
In the next PR, I will add platform specific code to handle this case


ScoreAnalysis.summary() for debugging purproses

During POC work it's very handy to be able to drop a score analysis in System.out.print().
ScoreExplanation has this, with the getSummary() method. It shows all constraints and the top 3 constraint matches per constraint.
Why only top 3 matches? In practice during debugging you need to know the first match often, to be able to prove the constraint is there with one match. But the other 297 matches are verbosity that kills the usefulness of getSummary() for debugging purposes. So top 3 is ideal.
If matches weren't fetched, no matches are shown in summary().

It should probably also get a toString() method.
Seperate issue for @Christopher-Chianelli : enable this in python land too :)
print(score_analysis)
<timefold.solver.score._score_analysis.ScoreAnalysis object at 0x3614cba10>


update documentation to reflect that it is old api

This doc is for the old python SDK for Service Bus API that got moved into the control_client namespace. Reflect this change in documentation but leave for reference.

Can one of the admins verify this patch?

Can one of the admins verify this patch?

Codecov Report

Merging #4844 into master will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##           master    #4844   +/-   ##
=======================================
  Coverage   49.78%   49.78%
=======================================
  Files        6196     6196
  Lines      174081   174081
=======================================
  Hits        86669    86669
  Misses      87412    87412

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How to pass "api_definition_string"

Hi Team,
Can you help me with a sample on how to pass the "api_definition_string" parameter in the api-violations call?
Thanks!

Well, the API spec is here which hopefully makes some sense if you're into swagger/openapi 😄
But the crux of it is that you need to POST a JSON structure to /api-violations, encoded as a string. So for example the following spec:
openapi: "3.0.0"
info:
  version: 1.0.0

Would be encoded in a request as:
{ "api_definition_string":"openapi: \"3.0.0\"\ninfo:\n  version: 1.0.0" }

Hope that helps, otherwise please give some examples of what you've tried.

Thanks @roxspring !
Let me give it a try and will let you know.

Many thanks @roxspring !!! It worked.


chore print exception along with error sql when run sql file

Proposed changes
Issue Number: close #xxx
Previously when we run one sql file which would throw exception, the regression framework would not print the whole sql statement which cause this exception. It would be annoying when the sql file consists of numerous sql statement, this pr mainly helps debug.
Problem summary
Describe your changes.
Checklist(Required)

[ ] Does it affect the original behavior
[ ] Has unit tests been added
[ ] Has document been added or modified
[ ] Does it need to update dependencies
[ ] Is this PR support rollback (If NO, please explain WHY)

Further comments
If this is a relatively large or complex change, kick off the discussion at<EMAIL_ADDRESS>by explaining why you chose the solution you did and what alternatives you considered, etc...

run buildall

run buildall

run buildall

run buildall

run buildall

run buildall

run buildall


Custom flatten character(s)

I'm experimenting with paiges to implement a Scala prettyprinter and it's been a pure joy so far, progress is going great! Thank your for making an amazing library.
One feature I've found missing is the ability to customize the flatten character. Currently lines can either be flattened into space or empty. The choice of the space character seems a bit ad-hoc, would it be possible to provide a custom Char or even Seq[Char]? For example, in some cases I've wanted try and flatten line to a semicolon followed by a space.

I have not had the need for this feature and I won't work on it anytime soon so I will close this ticket.


cmd/compile: PPC64 folds (MOVWZreg (RLWINM x)) incorrectly in some cases

Go version
master
Output of go env in your module/workspace:
GOARCH=ppc64le

What did you do?
Adding the following codegen test to bitRotateAndMask:
        // ppc64x: "MOVWZ", "RLWNM\t[$]1, R[0-9]*, [$]28, [$]3, R"
        io64[3] = uint64(bits.RotateLeft32(io32[0],1) & 0xF000000F)

What did you see happen?
The MOVWZ is missing.
What did you expect to see?
The MOVWZ opcode should be present for certain usages of RLWINM to ensure the upper 32 bits are cleared when the value is expected to be zero-extended.

Do we need this fix backported?

No, I added this rule in 1.23.


Apply fixes option

Overview
This PR is for https://github.com/roocs/34e-mngmt/issues/68
Changes:

Add apply fixes option

Related Issue / Discussion
Following on from the call on Friday - I'm still not really sure what to do about the daops version. I've added the apply_fixes daops branch into install_requires but let me know what is best.

@ellesmith88 Looks good to me :-)

@ellesmith88

Following on from the call on Friday - I'm still not really sure what to do about the daops version. I've added the apply_fixes daops branch into install_requires but let me know what is best.

I would also have done it like this :)
For local development I would install the dependent library (daops) manually from source into my conda environment.

@cehbrecht This is failing because it's not using the most up to date roocs.ini from roocs-utils but I can't see where to fix it

@ellesmith88 the roocs.ini for testing is generated using a template:
https://github.com/roocs/rook/blob/993cf2cc701ddf74caebcf0f7e9fef4c39c2df66/tests/common.py#L19

@ellesmith88 the roocs.ini for testing is generated using a template:
https://github.com/roocs/rook/blob/993cf2cc701ddf74caebcf0f7e9fef4c39c2df66/tests/common.py#L19

Doesn't this only override the base directories for the tests? Some tests are failing because it can't find theelasticsearchsection in the config, which is something I added yesterday and is only on the master branch of roocs-utils, not in a version.

@ellesmith88 the roocs.ini for testing is generated using a template:
https://github.com/roocs/rook/blob/993cf2cc701ddf74caebcf0f7e9fef4c39c2df66/tests/common.py#L19

Doesn't this only override the base directories for the tests? Some tests are failing because it can't find theelasticsearchsection in the config, which is something I added yesterday and is only on the master branch of roocs-utils, not in a version.

well ... it generates the complete roocs.ini used for testing. We need this to dynamically set the the root path of the test data. You can add the missing parts of the config to the template.

well ... it generates the complete roocs.ini used for testing. We need this to dynamically set the the root path of the test data. You can add the missing parts of the config to the template.

This will solve the problem but I have done the same thing in daops without needing to edit the template. I think its because when clisops is installed it installs roocs-utils 0.1.5 as a dependency and then the git version in setup.py doesn't override this, so the wrong roocs-utils is being used.

well ... it generates the complete roocs.ini used for testing. We need this to dynamically set the the root path of the test data. You can add the missing parts of the config to the template.

This will solve the problem but I have done the same thing in daops without needing to edit the template. I think its because when clisops is installed it installs roocs-utils 0.1.5 as a dependency and then the git version in setup.py doesn't override this, so the wrong roocs-utils is being used.

ok ... so to get the right versions, clisops also needs to be installed from git master ...

@ellesmith88 looks like it is working now :) Ok to merge?


minor svcstats & sanity fix

service stats fix
ssh slowness fix for ubuntu

apart from some minor comments, LGTM

@jojimt @abhinandanpb : Do we still need this ? If yes then we need to submit new PR for this.


snoreToast is having GPL license , it's affecting audit of our release even-though it is devDependencies

Any updates? We're not getting to use it in our company because of it as well. The worst thing is "react-scripts" depends on it as well.

Hi. snoreToast uses a LGPL License, not straight up GPL. Also, this to me looks like a problem with the auditing more than snoreToasts license?
As this is a devDependency as far as I know, there isn't anything in the license preventing you to use a tool of GPL to make software. This would be like if you weren't allowed to use Gimp to make icons.

@mikaelbr yeah, but auditing processes tend to be automated, and explaining exceptions to security teams never straightforward. Also, it is true for devDependency, but once a library has been "permitted", anyone on a company can potentially use it and distribute, breaking the license.
Best way to get your package into companies is to use MIT.
Any chance we can see a license change?

Hi again. Just for the record, snoreToast is a dependency of this project so (unfortunately perhaps) I don't have any control over the license. Honestly I doubt it will change.
Also I think you'll find a lot of different opinions on permissive licenses and how this is managed. I don't have any wish to actively recruit companies to use my package, or to pursue a scenario there everything is permissive license so companies can use it without thinking about licenses other than "just getting things for free while they make money". It seems like you say Best way to get your package into companies is to use MIT as if it is some sort of reward.
Please take a time to consider what you are actually asking here. You are asking someone using their time to make something and giving it away for free to do a major change just so that companies don't have to actively think about licenses while making money on the works of others. Also, it is actually free to use, and pretty permissive, but it's "kind of a hassle" because of some internal corporate processes. I'm sorry if I seem harsh here, but I'm not interested in fixing your internal politics.


STM32F4 and IAR init

Is there any strategy to reconcile the IAR init process (data zero and copy init) with the current code for SystemInit?  The functions called through SystemInit reference variables that are later initialized in __iar_program_start (through __iar_data_init3), overwriting whatever initialization was performed.
Examples:
SystemCoreClock in system_stm32f4xx.c
TimMasterHandle in hal_tick.c
It seems a bit backwards to set variables, then they get "initialized" later, and then you have to either set them again or have the "initialized" value be what you wanted it to be in the first place.

Can we close this one?
//Gustav


Save as incorrect file extension

VSCode Version: 1.13.0
OS Version: Win 10

Steps to Reproduce:

Open a not support file type but still a text file (e.g. a.xyz)
Save as a b.xyz by enter b in the save file field. (File type show as Plain Text)
If the destination does not have a .txt file, it will save as b.xyz. But if the destination have a .txt file, it will save as b.txt

VSCode Version: 1.17.1
OS Version: Mac OS Sierra

it happened on VSCode for Mac too

I meet same question when I tried to save *.in.
How can I fix it?

On a related note, I get kinda lost when saving a new empty file because I can't see which other code files are in the directory, unless I manually change the type to 'All files *.*'

I have same issue, and I think my issue with ti would be solved if the default was "all files" instead of plain text. Is it possible to change this setting as a user?

I cannot believe this is still an issue after two years.  Just took a moment to figure out why all my .py files were .py.txt.

Apparently this is disabled by design inside Electron, to safeguard it from adding *.* to the file name, as can be seen in shell/browser/ui/file_dialog_win#L206.
// By default, *.* will be added to the file name if file type is "*.*". In
// Electron, we disable it to make a better experience.
// ...
for (size_t i = 0; i < filterspec.size(); ++i) {
	if (std::wstring(filterspec[i].pszSpec) != L"*.*") {
		// SetFileTypeIndex is regarded as one-based index.
		dialog->SetFileTypeIndex(i + 1);
		dialog->SetDefaultExtension(filterspec[i].pszSpec);
		break;
	}
}

There has to be a way though, because many other programs can do it. I'll try and test if this issue actually happens later, but I'll have to build my own Electron as well I think.

This issue seems to originate from an upstream component and is not something VSCode can fix by making a change to the code. As such, I will go ahead and close it. If you can identify the component that is causing the issue, please report it upstream:

for UI, OS integration: https://github.com/electron/electron
for Rendering: https://bugs.chromium.org/p/chromium/issues/list
for file IO: https://github.com/nodejs/node

Thanks for understanding.


No Issue Hooks question

Hi Guys,
I I want to use BEC to restart my sever, but can't get this to work,
"hooks": [
       "type": "beforeStart",
           "program": "bec.exe",
           "params": ["/i", "server-manager-0\0\BEC\StartBEC.bat"]
],

Did I miss something?
Thanks

If you want to run a batch file, your "program" needs to be the batchfile itself or "cmd.exe"
Notes:
"" is a escape character in JSON, so you to replace it with double \ ("\").
You were missing some brackets which denote objects
The path has to be relative to where the servermanager.exe is located
Probably "server-manager-0" is the folder where the  servermanager.exe is located, so it needs to be removed
If you use the batch as program, the backslashes can be replaced by normal slashes
This one should work
"hooks": [
  {
       "type": "beforeStart",
       "program": "./0/BEC/StartBEC.bat"
  }
],

I must still be missing something this error keeps coming up.
ERROR     | Hooks        | beforeStart Hook (./0/BEC/StartBEC.bat ) failed - [{"status":1,"stdout":"","stderr":"'.' is not recognized as an internal or external command,\r\noperable program or batch file.\r\n"}]

Works great thanks

Does  the afterStart work? I am using
"hooks": [
    {
        "type": "afterStart",
        "program": "./chernarusplus/bec/Startbec.bat"
	}
],


Updating Toml to sure PathPrefix instead of Path

This works out of the box with zero changes.
As per: http://www.gorillatoolkit.org/pkg/mux
Once can use PathPrefix instead of Path, which provides much more predictable usage.
I.e. when i push /web here and / over there, I expect sub directories to work as well.
Tested with Latest & KV

LGTM

Thanks @Russell-IO :)
Once rebased, LGTM
/cc @vdemeester

LGTM 🐸 but needs a rebase :sweat_smile:

@samber, could you rebase your PR please?

@emilevauge euuuh...
it's not mine ;-)
=> @Russell-IO

Sorry @samber :smile:


type identifier should not trigger an parsing error when part of an assertion

Will fixes #58248
I'm not sure if there are any other cases I've missed. I'll take a closer look this week, but feel free to give feedbacks :pray:
This should be ok:
const type = 'x';

type satisfies string;
type as string;

// EDIT: the following example is obsolete
// const interface = 'x';
// interface satisfies string;
// interface as string;

Also, like @whzx5byb mentioned, you should add an interface satisfies/interface as test to ensure there's no regressions

Is there any interest for this PR?