Multi-statements are not supported

It seems multiple statements are not supported. These are multiple SQL statements in one string that can be executed in one call.
Here's an example which reports a false error:
import mysql.connection;
import std.stdio;

void main(string[] args)
{
    auto connection = new Connection("localhost", "user", "password", "database");
    auto command    = Command(connection);

    command.sql  = "CREATE DATABASE IF NOT EXISTS foo; USE foo;";

    ulong rowsAffected;

    auto result = command.execSQL(rowsAffected);

    writeln("Got results.");
}

output:
mysql.protocol.packets.MySQLReceivedException@/media/Data/Projects/D/third-party/mysql-native/source/mysql/protocol/commands.d(719): MySQL error: You have an error in your SQL s
yntax; check the manual that corresponds to your MySQL server version for the right syntax to use near 'USE foo' at line 1

There is no error in the SQL. Are there plans to support multi statements?

I'd like to support it as I'd find it useful too. But unless there's some feature to support it in the protocol itself, the library might have to implement an SQL parser (not necessarily out of the question, but might get complicated, especially just to support multiple statements).
In some of my own projects, I have a whole database init script in SQL. For those, I just split the script on ; and foreach to send each to the server. But obviously that's error-prone if there's any statements or comments that happen to have a semicolon in them.
Maybe it would be good enough just detect \; vs ; and to parse out SQL comments? Or are there special cases where that still wouldn't be smart enough?

Yeah there are a lot of cases where delimiting on semi-colons won't work, for example when using stored procedures:
DELIMITER $$
CREATE PROCEDURE country_hos(IN con CHAR(20))
BEGIN
    SELECT Name, HeadOfState FROM Country WHERE Continent = con;
END $$
DELIMITER ;

Notice the delimiter changes.

Sometimes it can be strange being maintainer of a codebase that was originally somebody else's. Every once in a while you find things even you had no idea were there.
Case in point: Turns out there's already a feature for this, built into the MySQL communications protocol, and already exposed by mysql-native:
Connection.enableMultiStatements
SvrCapFlags.MULTI_STATEMENTS
However, I've yet to actually try it out, and it's completely untested in the unittests, so no guarantees. Plus, it seems to be specifically excluded from the defaultClientFlags - maybe there was a reason for that? In any case, it's worth a try.

Thanks for the update.


Updated Makefile (on OSX) to use xcrun (from xcode) versus sw_vers to…

… discover SDK version. This prevents failed builds when using OSX 10.11 with the newest Xcode 10.12.

xcrun doesn't give you the OS_VERSION. It is used to get the SDK version.
You should change SDK_NAME instead of OS_VERSION. Otherwise, if Apple releases a new CRASH_REPORTER your build will break.


Support generic types

ref #2841
tests for annotation itself for now are working, yet there are few more cases when utests are failing for some fixers

nullable types supported


icons rendering performance too slow.

Reproduction link
https://stackblitz.com/edit/ng-zorro-antd-ivy-ddpwnf?file=src/app/app.component.ts
Steps to reproduce
Copy component from stackblitz to local project under angular 15 and ng-zorro 15, and press Reload button.
What is expected?
Reload button freezes page abour 2-3 seconds.
What is actually happening?
Reload button freezes page about 25 seconds.

Environment
Info

ng-zorro-antd
15.0.2

Browser
firefox 109

When i have table with large page size, and i have a column with icons, page rendering goes to slow. On stackblitz all works fine, but locally under latest versions it slow down.

after update stackblitz dependensies to latest, this bug appear on it, so under version 13 it works normal, but under latest 15 it slow down.

The re-enter Angular zone commit was a forward fix for the previous issue where icon re-rendering (where detectChanges() is called) affected other parts of the template because it was called outside of the Angular zone. I was trying to reduce the change detection cycles triggered by a "load + render icon" flow. I could only reduce this number by wrapping changeIcon into runOutsideAngular so any asynchronous stuff happening while the promise is being resolved doesn't force Angular to run tick(). The issue you're talking about has existed before any zone-related changes have been made to the nz-icon directive.
I tested the code locally and it takes 23 seconds for me to render after clicking "Reload". I was able to write the solution where it takes only ~7 s to render:

That's not the best result. But this is x3 boost compared to 23 seconds.
I had to edit the code directly:
// ng-zorro-antd/fesm2020/ng-zorro-antd-icon.mjs

let scheduler = null;
class Scheduler {
  constructor(ngZone) {
    this.ngZone = ngZone;
    this.callbacks = new Set();
    this.scheduled = false;
  }

  schedule(callback) {
    this.callbacks.add(callback);

    if (this.scheduled) {
      return;
    }

    this.scheduled = true;

    requestAnimationFrame(() => {
      if (this.callbacks.size !== 0) {
        this.ngZone.run(() => {
          for (const callback of this.callbacks.values()) {
            callback();
          }
          this.callbacks.clear();
        });
      }

      this.scheduled = false;
    });
  }

  static create(ngZone) {
    return scheduler || (scheduler = new Scheduler(ngZone));
  }
}

class NzIconDirective extends IconDirective {
  constructor(
    ngZone,
    changeDetectorRef,
    elementRef,
    iconService,
    renderer,
    iconPatch
  ) {
    super(iconService, elementRef, renderer);
    this.ngZone = ngZone;
    // ...
    this.scheduler = Scheduler.create(ngZone);
    // ...
  }

  changeIcon2() {
    this.setClassName();
    this.ngZone.runOutsideAngular(() => {
      from(this._changeIcon())
        .pipe(takeUntil(this.destroy$))
        .subscribe({
          next: (svgOrRemove) => {
            this.scheduler.schedule(() => {
              this.changeDetectorRef.detectChanges();
              if (svgOrRemove) {
                this.setSVGData(svgOrRemove);
                this.handleSpin(svgOrRemove);
                this.handleRotate(svgOrRemove);
              }
            });
          },
          error: warn,
        });
    });
  }
}

This is the most basic scheduler implementation. In practice we should be more flexible and don't allow the scheduler to keep more than say 40-50 tasks waiting to be executed. So if callbacks.size becomes greater than 40, we should flush callbacks and start scheduling again.

In my app it's come to the point where I have to replace icons with words or emojis even, because the performance for icons in lists or tables is just too slow.
Is this being worked on? Is there another issue that tracks the overall performance problems with the IconDirective?


[CUBLAS] Remove unnecessary call causing crashes in getNextComputeStream

All host tasks enqueued in the cuBLAS backend already call CUBLAS_ERROR_FUNC_T_SYNC before returning. This macro calls cuStreamSynchronize, which is all the synchronization needed.
The original call to wait_stream was resulting in a new stream being created and thus the wrong stream being waited on.
$ bin/test_main_blas_ct --gtest_filter='*DotUsmTests.RealSinglePrecision/Column_Major*:*AxpyUsmTests.RealSinglePrecision/Column_Major*'
Run this program with --terse_output to change the way it prints its output.
Note: Google Test filter = *DotUsmTests.RealSinglePrecision/Column_Major*:*AxpyUsmTests.RealSinglePrecision/Column_Major*
[==========] Running 2 tests from 2 test suites.
[----------] Global test environment set-up.
[----------] 1 test from DotUsmTestSuite/DotUsmTests
[ RUN      ] DotUsmTestSuite/DotUsmTests.RealSinglePrecision/Column_Major_NVIDIA_GeForce_GTX_1650
[       OK ] DotUsmTestSuite/DotUsmTests.RealSinglePrecision/Column_Major_NVIDIA_GeForce_GTX_1650 (13 ms)
[----------] 1 test from DotUsmTestSuite/DotUsmTests (13 ms total)

[----------] 1 test from AxpyUsmTestSuite/AxpyUsmTests
[ RUN      ] AxpyUsmTestSuite/AxpyUsmTests.RealSinglePrecision/Column_Major_NVIDIA_GeForce_GTX_1650

UR CUDA ERROR:
	Value:           700
	Name:            CUDA_ERROR_ILLEGAL_ADDRESS
	Description:     an illegal memory access was encountered
	Function:        getNextComputeStream
	Source Location: /home/pierre-andre/Projects/oneAPICore/build/dpcpp-cuda-release/_deps/unified-runtime-src/source/adapters/cuda/queue.cpp:45

This was tested on a GTX 1650 using CUDA 12.2 and 12.3, on Ubuntu 22.04.
Description
Please include a summary of the change. Please also include relevant
motivation and context. See
contribution guidelines
for more details. If the change fixes an issue not documented in the project's
Github issue tracker, please document all steps necessary to reproduce it.
Fixes # (GitHub issue)
Checklist
All Submissions

[ ] Do all unit tests pass locally? Attach a log. No, -> test suites such as bin/test_main_blas_ct are aborted due to other (existing) errors.
[x] Have you formatted the code using clang-format?

Bug fixes

[ ] Have you added relevant regression tests? No, this crashes the unit test suite with default options.
[x] Have you included information on how to reproduce the issue (either in a
GitHub issue or in this PR)?

Both tests suite pass cleanly on cuBLAS when both this PR and https://github.com/oneapi-src/oneMKL/pull/406 are used.


WIP: Improve OIDC demo

part of: https://github.com/stackabletech/marispace-x/issues/59

I simplified the keycloak setup in this kuttl test (for reference): https://github.com/stackabletech/opa-operator/pull/529/files

superseded by: https://github.com/stackabletech/demos/pull/22


Avoid failing CI on TruffleRuby

The current truffleruby-head doesn't seem to work well with cool.io. This CI job is supposed to test ./run_benchmarks.rb, not TruffleRuby. Let's avoid failing the CI until TruffleRuby is fixed.

Could we skip the problematic benchmark instead?
I added it in https://github.com/Shopify/yjit-bench/pull/251 so it would at least notice regressions for existing benchmarks.
If we do continue-on-error, nobody will notice any regression.

The error seems https://github.com/Shopify/yjit-bench/actions/runs/7064460115/job/19232535306 on fluentd.
I will take a look. Weird it regressed.

Fix in https://github.com/oracle/truffleruby/pull/3372

Thanks for fixing it. As of this moment, the truffleruby-head on GitHub Actions is still failing, so I added a way to skip only fluentd for truffleruby-head as you suggested https://github.com/Shopify/yjit-bench/pull/262/commits/23b658e417b2a30d8d854b3792a59da328f4618b.

Self notes:

Failure for liquid-c: https://github.com/Shopify/yjit-bench/actions/runs/7453361047/job/20278563105

RBASIC->klass in iquid-c

Failure for lobsters: https://github.com/Shopify/yjit-bench/actions/runs/7453479468/job/20278936097

Incorrect header handling causing struct timespec to not be defined, probably similar to https://github.com/socketry/io-event/issues/88
old stringio using RBASIC->flags


Issue Importing Wallet from seed

There are two issues I am experiencing. Currently on 1.6.3 in Win11.
Background:
New device, wanted to import seed phrase for old device's Sparrow wallet into new device and have everything work without having to copy over data files.
What worked:
Importing seed phrase into new Sparrow installation resulted in a successful sync for transaction history in Deposit, Premix, and Postmix. Have not tested receiving or sending but have no reason to think either would have issues (except possibly sending from Postmix, due to the problems listed below).
What didn't work:

Badbank was not imported, there is no tab for it either.
Since importing, anytime Sparrow is opened (including the initial state after importing), it seems to be in a 'mixing' state (i.e. the button reads 'Stop Mixing' and is clickable.) Clicking 'Stop Mixing' makes the button unclickable, and it remains in that state until the application is closed.

What I expected:
I expected the badbank to be available, and I expected the postmix wallet to be in a non-mixing state.
My thoughts on user solution:
I might have to bite the bullet and copy the data files over and start afresh. I'll have to double check for patch version but the installation on old device was 1.5.x.
Thank you!
EDIT for line from log:
Caused by: java.lang.NullPointerException: Cannot invoke "com.sparrowwallet.sparrow.whirlpool.Whirlpool.isStarted()" because "<local2>" is null

Did you add the Whirlpool accounts using the option in the Add Account dialog (Settings tab)?
Please post the full exception here from the log.

Did you add the Whirlpool accounts using the option in the Add Account dialog (Settings tab)?
Please post the full exception here from the log.

Ohhh I did not realize this feature existed. Everything works now. After adding the account the badbank loaded and populated with the correct transactions, and mixing works as expected. Thanks so much @craigraw ! Here was the previous exception in case that's still helpful.
java.lang.RuntimeException: java.lang.reflect.InvocationTargetException
        at javafx.fxml/javafx.fxml.FXMLLoader$MethodHandler.invoke(Unknown Source)
        at javafx.fxml/javafx.fxml.FXMLLoader$ControllerMethodEventHandler.handle(Unknown Source)
        at javafx.base/com.sun.javafx.event.CompositeEventHandler.dispatchBubblingEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.EventHandlerManager.dispatchBubblingEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.EventHandlerManager.dispatchBubblingEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.CompositeEventDispatcher.dispatchBubblingEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.BasicEventDispatcher.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.EventDispatchChainImpl.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.BasicEventDispatcher.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.EventDispatchChainImpl.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.BasicEventDispatcher.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.EventDispatchChainImpl.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.BasicEventDispatcher.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.EventDispatchChainImpl.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.BasicEventDispatcher.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.EventDispatchChainImpl.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.BasicEventDispatcher.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.EventDispatchChainImpl.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.EventUtil.fireEventImpl(Unknown Source)
        at javafx.base/com.sun.javafx.event.EventUtil.fireEvent(Unknown Source)
        at javafx.base/javafx.event.Event.fireEvent(Unknown Source)
        at javafx.graphics/javafx.scene.Node.fireEvent(Unknown Source)
        at javafx.controls/javafx.scene.control.Button.fire(Unknown Source)
        at javafx.controls/com.sun.javafx.scene.control.behavior.ButtonBehavior.mouseReleased(Unknown Source)
        at javafx.controls/com.sun.javafx.scene.control.inputmap.InputMap.handle(Unknown Source)
        at javafx.base/com.sun.javafx.event.CompositeEventHandler$NormalEventHandlerRecord.handleBubblingEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.CompositeEventHandler.dispatchBubblingEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.EventHandlerManager.dispatchBubblingEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.EventHandlerManager.dispatchBubblingEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.CompositeEventDispatcher.dispatchBubblingEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.BasicEventDispatcher.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.EventDispatchChainImpl.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.BasicEventDispatcher.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.EventDispatchChainImpl.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.BasicEventDispatcher.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.EventDispatchChainImpl.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.BasicEventDispatcher.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.EventDispatchChainImpl.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.BasicEventDispatcher.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.EventDispatchChainImpl.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.BasicEventDispatcher.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.EventDispatchChainImpl.dispatchEvent(Unknown Source)
        at javafx.base/com.sun.javafx.event.EventUtil.fireEventImpl(Unknown Source)
        at javafx.base/com.sun.javafx.event.EventUtil.fireEvent(Unknown Source)
        at javafx.base/javafx.event.Event.fireEvent(Unknown Source)
        at javafx.graphics/javafx.scene.Scene$MouseHandler.process(Unknown Source)
        at javafx.graphics/javafx.scene.Scene.processMouseEvent(Unknown Source)
        at javafx.graphics/javafx.scene.Scene$ScenePeerListener.mouseEvent(Unknown Source)
        at javafx.graphics/com.sun.javafx.tk.quantum.GlassViewEventHandler$MouseEventNotification.run(Unknown Source)
        at javafx.graphics/com.sun.javafx.tk.quantum.GlassViewEventHandler$MouseEventNotification.run(Unknown Source)
        at java.base/java.security.AccessController.doPrivileged(Unknown Source)
        at javafx.graphics/com.sun.javafx.tk.quantum.GlassViewEventHandler.lambda$handleMouseEvent$2(Unknown Source)
        at javafx.graphics/com.sun.javafx.tk.quantum.QuantumToolkit.runWithoutRenderLock(Unknown Source)
        at javafx.graphics/com.sun.javafx.tk.quantum.GlassViewEventHandler.handleMouseEvent(Unknown Source)
        at javafx.graphics/com.sun.glass.ui.View.handleMouseEvent(Unknown Source)
        at javafx.graphics/com.sun.glass.ui.View.notifyMouse(Unknown Source)
        at javafx.graphics/com.sun.glass.ui.win.WinApplication._runLoop(Native Method)
        at javafx.graphics/com.sun.glass.ui.win.WinApplication.lambda$runLoop$3(Unknown Source)
        at java.base/java.lang.Thread.run(Unknown Source)
Caused by: java.lang.reflect.InvocationTargetException: null
        at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
        at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
        at java.base/java.lang.reflect.Method.invoke(Unknown Source)
        at com.sun.javafx.reflect.Trampoline.invoke(Unknown Source)
        at jdk.internal.reflect.GeneratedMethodAccessor2.invoke(Unknown Source)
        at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
        at java.base/java.lang.reflect.Method.invoke(Unknown Source)
        at javafx.base/com.sun.javafx.reflect.MethodUtil.invoke(Unknown Source)
        at javafx.fxml/com.sun.javafx.fxml.MethodHelper.invoke(Unknown Source)
        ... 59 common frames omitted
Caused by: java.lang.NullPointerException: Cannot invoke "com.sparrowwallet.sparrow.whirlpool.Whirlpool.isStarted()" because "<local2>" is null
        at com.sparrowwallet.sparrow@1.6.3/com.sparrowwallet.sparrow.wallet.UtxosController.stopMixing(Unknown Source)
        ... 69 common frames omitted


RTX3080 RuntimeError: cublas runtime error : the GPU program failed to execute at /opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/THC/THCBlas.cu:331

CUDA_VISIBLE_DEVICES=1,2 ./tools/dist_train.sh configs/ReDet/ReDet_re50_refpn_1x_dota1.py 2

Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.

ReResNet Orientation: 8 Fix Params: False
ReResNet Orientation: 8 Fix Params: False
2021-05-19 21:40:15,266 - INFO - Distributed training: True
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
2021-05-19 21:40:42,380 - INFO - load model from: /home/neo/desktop/ReDet/tools/work_dirs/ReResNet_pretrain/re_resnet50_c8_batch256-12933bc2.pth
loading annotations into memory...
2021-05-19 21:40:42,542 - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: backbone.conv1.weights, backbone.conv1.basisexpansion.block_expansion('irrep_0', 'regular').sampled_basis, backbone.bn1.indices_8, backbone.bn1.batch_norm_[8].weight, backbone.bn1.batch_norm_[8].bias, backbone.bn1.batch_norm_[8].running_mean, backbone.bn1.batch_norm_[8].running_var, backbone.bn1.batch_norm_[8].num_batches_tracked, backbone.layer1.0.conv1.weights, backbone.layer1.0.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.0.bn1.indices_8, backbone.layer1.0.bn1.batch_norm_[8].weight, backbone.layer1.0.bn1.batch_norm_[8].bias, backbone.layer1.0.bn1.batch_norm_[8].running_mean, backbone.layer1.0.bn1.batch_norm_[8].running_var, backbone.layer1.0.bn1.batch_norm_[8].num_batches_tracked, backbone.layer1.0.conv2.weights, backbone.layer1.0.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.0.bn2.indices_8, backbone.layer1.0.bn2.batch_norm_[8].weight, backbone.layer1.0.bn2.batch_norm_[8].bias, backbone.layer1.0.bn2.batch_norm_[8].running_mean, backbone.layer1.0.bn2.batch_norm_[8].running_var, backbone.layer1.0.bn2.batch_norm_[8].num_batches_tracked, backbone.layer1.0.conv3.weights, backbone.layer1.0.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.0.bn3.indices_8, backbone.layer1.0.bn3.batch_norm_[8].weight, backbone.layer1.0.bn3.batch_norm_[8].bias, backbone.layer1.0.bn3.batch_norm_[8].running_mean, backbone.layer1.0.bn3.batch_norm_[8].running_var, backbone.layer1.0.bn3.batch_norm_[8].num_batches_tracked, backbone.layer1.0.downsample.0.weights, backbone.layer1.0.downsample.0.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.0.downsample.1.indices_8, backbone.layer1.0.downsample.1.batch_norm_[8].weight, backbone.layer1.0.downsample.1.batch_norm_[8].bias, backbone.layer1.0.downsample.1.batch_norm_[8].running_mean, backbone.layer1.0.downsample.1.batch_norm_[8].running_var, backbone.layer1.0.downsample.1.batch_norm_[8].num_batches_tracked, backbone.layer1.1.conv1.weights, backbone.layer1.1.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.1.bn1.indices_8, backbone.layer1.1.bn1.batch_norm_[8].weight, backbone.layer1.1.bn1.batch_norm_[8].bias, backbone.layer1.1.bn1.batch_norm_[8].running_mean, backbone.layer1.1.bn1.batch_norm_[8].running_var, backbone.layer1.1.bn1.batch_norm_[8].num_batches_tracked, backbone.layer1.1.conv2.weights, backbone.layer1.1.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.1.bn2.indices_8, backbone.layer1.1.bn2.batch_norm_[8].weight, backbone.layer1.1.bn2.batch_norm_[8].bias, backbone.layer1.1.bn2.batch_norm_[8].running_mean, backbone.layer1.1.bn2.batch_norm_[8].running_var, backbone.layer1.1.bn2.batch_norm_[8].num_batches_tracked, backbone.layer1.1.conv3.weights, backbone.layer1.1.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.1.bn3.indices_8, backbone.layer1.1.bn3.batch_norm_[8].weight, backbone.layer1.1.bn3.batch_norm_[8].bias, backbone.layer1.1.bn3.batch_norm_[8].running_mean, backbone.layer1.1.bn3.batch_norm_[8].running_var, backbone.layer1.1.bn3.batch_norm_[8].num_batches_tracked, backbone.layer1.2.conv1.weights, backbone.layer1.2.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.2.bn1.indices_8, backbone.layer1.2.bn1.batch_norm_[8].weight, backbone.layer1.2.bn1.batch_norm_[8].bias, backbone.layer1.2.bn1.batch_norm_[8].running_mean, backbone.layer1.2.bn1.batch_norm_[8].running_var, backbone.layer1.2.bn1.batch_norm_[8].num_batches_tracked, backbone.layer1.2.conv2.weights, backbone.layer1.2.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.2.bn2.indices_8, backbone.layer1.2.bn2.batch_norm_[8].weight, 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Done (t=1.70s)
creating index...
loading annotations into memory...
index created!
Done (t=1.77s)
creating index...
index created!
2021-05-19 21:48:10,559 - INFO - Start running, host: neo@neo, work_dir: /home/neo/desktop/ReDet/work_dirs/ReDet_re50_refpn_1x_dota1
2021-05-19 21:48:10,559 - INFO - workflow: [('train', 1)], max: 12 epochs
Traceback (most recent call last):
File "./tools/train.py", line 95, in
main()
File "./tools/train.py", line 91, in main
logger=logger)
File "/home/neo/desktop/ReDet/mmdet/apis/train.py", line 59, in train_detector
_dist_train(model, dataset, cfg, validate=validate)
File "/home/neo/desktop/ReDet/mmdet/apis/train.py", line 171, in _dist_train
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/mmcv/runner/runner.py", line 358, in run
epoch_runner(data_loaders[i], **kwargs)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/mmcv/runner/runner.py", line 255, in train
self.model.train()
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1064, in train
module.train(mode)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1064, in train
module.train(mode)
File "/home/neo/desktop/ReDet/mmdet/models/backbones/re_resnet.py", line 727, in train
self._freeze_stages()
File "/home/neo/desktop/ReDet/mmdet/models/backbones/re_resnet.py", line 693, in _freeze_stages
m.eval()
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1080, in eval
return self.train(False)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1064, in train
module.train(mode)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1064, in train
module.train(mode)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/e2cnn-0.1.7-py3.7.egg/e2cnn/nn/modules/r2_conv/r2convolution.py", line 386, in train
_filter, _bias = self.expand_parameters()
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/e2cnn-0.1.7-py3.7.egg/e2cnn/nn/modules/r2_conv/r2convolution.py", line 303, in expand_parameters
_filter = self.basisexpansion(self.weights)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/e2cnn-0.1.7-py3.7.egg/e2cnn/nn/modules/r2_conv/basisexpansion_blocks.py", line 334, in forward
_filter = self._expand_block(weights, io_pair).reshape(out_indices[2], in_indices[2], self.S)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/e2cnn-0.1.7-py3.7.egg/e2cnn/nn/modules/r2_conv/basisexpansion_blocks.py", line 301, in _expand_block
_filter = block_expansion(coefficients)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/e2cnn-0.1.7-py3.7.egg/e2cnn/nn/modules/r2_conv/basisexpansion_singleblock.py", line 99, in forward
return torch.einsum('boi...,kb->koi...', self.sampled_basis, weights) #.transpose(1, 2).contiguous()
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/functional.py", line 201, in einsum
return torch._C._VariableFunctions.einsum(equation, operands)
RuntimeError: cublas runtime error : the GPU program failed to execute at /opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/THC/THCBlas.cu:331
Traceback (most recent call last):
File "./tools/train.py", line 95, in
main()
File "./tools/train.py", line 91, in main
logger=logger)
File "/home/neo/desktop/ReDet/mmdet/apis/train.py", line 59, in train_detector
_dist_train(model, dataset, cfg, validate=validate)
File "/home/neo/desktop/ReDet/mmdet/apis/train.py", line 171, in _dist_train
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/mmcv/runner/runner.py", line 358, in run
epoch_runner(data_loaders[i], **kwargs)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/mmcv/runner/runner.py", line 255, in train
self.model.train()
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1064, in train
module.train(mode)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1064, in train
module.train(mode)
File "/home/neo/desktop/ReDet/mmdet/models/backbones/re_resnet.py", line 727, in train
self._freeze_stages()
File "/home/neo/desktop/ReDet/mmdet/models/backbones/re_resnet.py", line 693, in _freeze_stages
m.eval()
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1080, in eval
return self.train(False)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1064, in train
module.train(mode)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1064, in train
module.train(mode)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/e2cnn-0.1.7-py3.7.egg/e2cnn/nn/modules/r2_conv/r2convolution.py", line 386, in train
_filter, _bias = self.expand_parameters()
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/e2cnn-0.1.7-py3.7.egg/e2cnn/nn/modules/r2_conv/r2convolution.py", line 303, in expand_parameters
_filter = self.basisexpansion(self.weights)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/e2cnn-0.1.7-py3.7.egg/e2cnn/nn/modules/r2_conv/basisexpansion_blocks.py", line 334, in forward
_filter = self._expand_block(weights, io_pair).reshape(out_indices[2], in_indices[2], self.S)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/e2cnn-0.1.7-py3.7.egg/e2cnn/nn/modules/r2_conv/basisexpansion_blocks.py", line 301, in _expand_block
_filter = block_expansion(coefficients)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/e2cnn-0.1.7-py3.7.egg/e2cnn/nn/modules/r2_conv/basisexpansion_singleblock.py", line 99, in forward
return torch.einsum('boi...,kb->koi...', self.sampled_basis, weights) #.transpose(1, 2).contiguous()
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/functional.py", line 201, in einsum
return torch._C._VariableFunctions.einsum(equation, operands)
RuntimeError: cublas runtime error : the GPU program failed to execute at /opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/THC/THCBlas.cu:331
Traceback (most recent call last):
File "/home/neo/anaconda3/envs/redet/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/distributed/launch.py", line 253, in
main()
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/distributed/launch.py", line 249, in main
cmd=cmd)
subprocess.CalledProcessError: Command '['/home/neo/anaconda3/envs/redet/bin/python', '-u', './tools/train.py', '--local_rank=1', 'configs/ReDet/ReDet_re50_refpn_1x_dota1.py', '--launcher', 'pytorch']' returned non-zero exit status 1.
`
Hi, csuhan! I run this algorithm with RTX3080*2,the env is as follows：
_libgcc_mutex             0.1                 conda_forge    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
_openmp_mutex             4.5                       1_gnu    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
addict                    2.4.0                    pypi_0    pypi
blas                      1.0                         mkl    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
bzip2                     1.0.8                h7f98852_4    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
ca-certificates           2020.12.5            ha878542_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
certifi                   2020.12.5        py37h89c1867_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
chardet                   4.0.0                    pypi_0    pypi
cudatoolkit               11.1.1               h6406543_8    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
cycler                    0.10.0                   pypi_0    pypi
cython                    0.29.23          py37hcd2ae1e_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
e2cnn                     0.1.7                    pypi_0    pypi
ffmpeg                    4.3                  hf484d3e_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch
freetype                  2.10.4               h0708190_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
gmp                       6.2.1                h58526e2_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
gnutls                    3.6.13               h85f3911_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
idna                      2.10                     pypi_0    pypi
intel-openmp              2021.2.0           h06a4308_610    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
jpeg                      9b                   h024ee3a_2    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
kiwisolver                1.3.1                    pypi_0    pypi
lame                      3.100             h7f98852_1001    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
lcms2                     2.12                 h3be6417_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
ld_impl_linux-64          2.35.1               hea4e1c9_2    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libffi                    3.3                  h58526e2_2    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libgcc-ng                 9.3.0               h2828fa1_19    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libgomp                   9.3.0               h2828fa1_19    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libiconv                  1.16                 h516909a_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libpng                    1.6.37               h21135ba_2    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libstdcxx-ng              9.3.0               h6de172a_19    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libtiff                   4.1.0                h2733197_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
libuv                     1.41.0               h7f98852_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
lz4-c                     1.9.3                h9c3ff4c_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
matplotlib                3.4.2                    pypi_0    pypi
mkl                       2021.2.0           h06a4308_296    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
mkl-service               2.3.0            py37h27cfd23_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
mkl_fft                   1.3.0            py37h42c9631_2    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
mkl_random                1.2.1            py37ha9443f7_2    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
mmcv                      0.2.13                   pypi_0    pypi
mmdet                     0.6.0+unknown             dev_0
ncurses                   6.2                  h58526e2_4    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
nettle                    3.6                  he412f7d_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
ninja                     1.10.2               h4bd325d_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
numpy                     1.20.1           py37h93e21f0_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
numpy-base                1.20.1           py37h7d8b39e_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
olefile                   0.46               pyh9f0ad1d_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
opencv-python             <IP_ADDRESS>                 pypi_0    pypi
openh264                  2.1.1                h780b84a_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
openssl                   1.1.1k               h7f98852_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pillow                    6.2.2                    pypi_0    pypi
pip                       21.1.1             pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pycocotools               2.0.2                    pypi_0    pypi
pyparsing                 2.4.7                    pypi_0    pypi
python                    3.7.10          hffdb5ce_100_cpython    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
python-dateutil           2.8.1                    pypi_0    pypi
python_abi                3.7                     1_cp37m    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pytorch                   1.8.0           py3.7_cuda11.1_cudnn8.0.5_0
pyyaml                    5.4.1                    pypi_0    pypi
readline                  8.1                  h46c0cb4_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
requests                  2.25.1                   pypi_0    pypi
scipy                     1.6.3                    pypi_0    pypi
setuptools                49.6.0           py37h89c1867_3    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
shapely                   1.7.1                    pypi_0    pypi
six                       1.16.0             pyh6c4a22f_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
sqlite                    3.35.5               h74cdb3f_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
terminaltables            3.1.0                    pypi_0    pypi
tk                        8.6.10               h21135ba_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
torchvision               0.9.0                py37_cu111
tqdm                      4.60.0                   pypi_0    pypi
typing_extensions         <IP_ADDRESS>                    py_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
urllib3                   1.26.4                   pypi_0    pypi
wheel                     0.36.2             pyhd3deb0d_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
xz                        5.2.5                h516909a_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
zlib                      1.2.11            h516909a_1010    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
zstd                      1.4.9                ha95c52a_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge

**### The questions are:
1.I used the pretrain pth,but got
"unexpected key in source state_dict: backbone.conv1.weights, backbone.conv1.basisexpansion.block_expansion('irrep_0', 'regular').sampled_basis, backbone.bn1.indices_8, ",
how can I fix this?
2.When I input"python tools/train.py /home/neo/desktop/ReDet-master/configs/ReDet/ReDet_re50_refpn_1x_dota1.py", all worked correctly!
But when I input"CUDA_VISIBLE_DEVICES=1,2 ./tools/dist_train.sh configs/ReDet/ReDet_re50_refpn_1x_dota1.py 2", I got "RuntimeError: cublas runtime error : the GPU program failed".
Any suggestions would be appreciative.**

I guess the pretrained model was not correctly loaded. See our guide and check your config please.
May be the mismatch between cuda and torch?

I guess the pretrained model was not correctly loaded. See our guide and check your config please.
May be the mismatch between cuda and torch?

Thanks for your replies!
1.I've checked the config, and have no clue at all   T_T
If it is possible that the problem is:your pth model is build on torch1.3, my env is torch1.8?
How to convert the pretrain model...
2."I input"python tools/train.py /home/neo/desktop/ReDet-master/configs/ReDet/ReDet_re50_refpn_1x_dota1.py", all worked correctly!"
"RuntimeError: cublas runtime error : the GPU program failed".  only happend when I train data with multiple GPUs  T_T

Our codes do not directly support pytorch1.8. Try lower pytorch, or modify some codes (ref to #1 )

Our codes do not directly support pytorch1.8. Try lower pytorch, or modify some codes (ref to #1 )

感谢回复！ 一开始就是照着您发的这个改了才能compile的。
可能确实还是版本的问题。请问把您代码中用到的模块复制到一个高版本的mmdet中有可行性吗？

Our codes do not directly support pytorch1.8. Try lower pytorch, or modify some codes (ref to #1 )

感谢回复！ 一开始就是照着您发的这个改了才能compile的。
可能确实还是版本的问题。请问把您代码中用到的模块复制到一个高版本的mmdet中有可行性吗？

按照 #1 修改后的cuda扩展是可以使用的


fix(core): generic in getMockedNgDefOf #985

closes #985

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v13.1.1 has been released and contains a fix for the issue. Feel free to reopen the issue or to submit a new one if you meet any problems.


Handle additions to signatures in Py3.12 dataclasses

Fixes #5346.
I've written a potentially slightly inefficient helper function to inspect the signature of dataclasses.field and
dataclasses._DataclassParams and drop any arguments that are unsupported (i.e. ones that we're passing because they apply to later Python versions).

Thanks


Notice - Breaking Change in 1.5 To Document

#944 replaces the existing collector with one that's built using ocb (this is a Best Practice). However, this will break any downstream consumers who rely on the existing collector-contrib image, so we need to make sure that it's communicated to consumers

We are rolling back this change so it will no longer be breaking


applyMiddleware + __resolveReference

Good evening everybody! I have a federated schema the looks like this:
const resolveItemReference = async ({ id }: Pick<Item, 'id'>) => this.itemRepo.findByPk(id);

const rawSchema = await buildFederatedSchema(
    {
      resolvers: [__dirname + '/../resolvers/**/*.ts'],
      orphanedTypes: [Item],
    },
    {
      Item: { __resolveReference: resolveItemReference },
    });

If I pass rawSchema to the ApolloServer initiation, I can hit this reference resolver from another service via gateway, and it works fine. But when I need to bundle this schema with permissions from graphql-shield wrapping them with applyMiddleware call, I can no longer access this reference resolver:
const schema = applyMiddleware(rawSchema, permissions);  - schema is then passed to the new ApolloServer call
The orphanedType Item is still available to the gateway, but when I try to query any of the Item fields via reference resolver, I am getting null for each of them. Also, the reference resolver function is not getting invoked at all.
Please share your thoughts on how this can be fixed. Thanks for your help.

I am having the same issue. @aoleksiiuk-karsun Have you found any workaround?

@shayzun yes, I've managed to make it work - you need to remove reference resolvers from the buildFederatedSchema call, and then add them with addResolversToSchema call. The working setup of the example above looks as follows:
import { addResolversToSchema } from 'apollo-graphql';

const resolveItemReference = async ({ id }: Pick<Item, 'id'>) => this.itemRepo.findByPk(id);

// 1. build federated schema
const rawSchema = await buildFederatedSchema({
      resolvers: [__dirname + '/../resolvers/**/*.ts'],
      orphanedTypes: [Item],
    });

// 2. apply shield permissions
 schema = applyMiddleware(schema, permissions);

// 3. create an object with reference resolvers
 const referenceResolvers = {
    Item: { __resolveReference: resolveItemReference }
  };

// 4. add them to the schema
addResolversToSchema(schema, referenceResolvers);

With this shield'ed queries, mutations and types are still protected, and you have your reference resolvers as part of the schema. Hope this helps. Cheers!

Has anyone gotten this working recently?  buildSubgraphSchema doesn't take an orphanedTypes param.  Anyone have a solution for the latest libs?  I'm on federation 0.33 and apollo-server 3.5

Having same issue @dborstelmann. Did you ever find a fix for this using the newer version of apollo-server and federation?


How to start taplo lsp now

I font that taplo lsp stdio does not work anymore

It works the same way, it's not a default feature anymore though. If you're installing it via cargo you'll have to pass the --feature lsp flag.

It works the same way, it's not a default feature anymore though. If you're installing it via cargo you'll have to pass the --feature lsp flag.

Thanks

I added a section about this on the website, the cli will also throw an appropriate error about it.

@tamasfe Thanks! I cannot start lsp then find this issue.


Bind fail when running pio

While trying tutorial, I stucked in some error.
Kwangins-MacBook-Pro:incubator-predictionio kwangin$ pio status
[INFO] [Console$] Inspecting PredictionIO...
[INFO] [Console$] PredictionIO 0.9.6 is installed at /Users/kwangin/workspace/github_source/incubator-predictionio/PredictionIO-0.9.6
[INFO] [Console$] Inspecting Apache Spark...
[INFO] [Console$] Apache Spark is installed at /Users/kwangin/workspace/github_source/incubator-predictionio/PredictionIO-0.9.6/vendors/spark-1.5.1-bin-hadoop2.6
[INFO] [Console$] Apache Spark 1.5.1 detected (meets minimum requirement of 1.3.0)
[INFO] [Console$] Inspecting storage backend connections...
[INFO] [Storage$] Verifying Meta Data Backend (Source: PGSQL)...
[INFO] [Storage$] Verifying Model Data Backend (Source: PGSQL)...
[INFO] [Storage$] Verifying Event Data Backend (Source: PGSQL)...
[INFO] [Storage$] Test writing to Event Store (App Id 0)...
[INFO] [Console$] (sleeping 5 seconds for all messages to show up...)
[INFO] [Console$] Your system is all ready to go.
Kwangins-MacBook-Pro:incubator-predictionio kwangin$ pio eventserver &
[1] 12303
Kwangins-MacBook-Pro:incubator-predictionio kwangin$ [INFO] [Console$] Creating Event Server at <IP_ADDRESS>:7070
[WARN] [HttpListener] Bind to /<IP_ADDRESS>:7070 failed
[ERROR] [EventServerActor] Command failed.

Setting seems no problem. Does someone know about this?

Hi, please redirect usage questions to<EMAIL_ADDRESS>for more exposure to the rest of the community. Subscription instructions can be found at http://predictionio.incubator.apache.org/support/.
Do you already have something using port 7070? You may also take a look at pio.log in the working directory (i.e. where you ran pio eventserver) for more details.


Ann. IR Calculation

https://github.com/quantopian/alphalens/blob/738b1a9f538b8cc575c9d431e44890540c52a145/alphalens/plotting.py#L173-L174
Not sure what this calculation is trying to do. I am assuming that IR stands for "Information Ratio". The Information ratio is the ratio of (excess return over a benchmark) / (active risk). According to "The Fundamental Law of Active Management" (FLAM) by Richard Grinold (Journal of Portfolio Management, vol 15, no. 3 1989), the information ratio is a function of the Information Coefficient (IC) and Breadth (N)(breadth being defined as the number of independent forecasts made per year):

Source:  Extending the Fundamental Law of Investment Management - JP Morgan US Equity Quantitative Research
Using this version of the FLAM, it seems that this piece of code is using the number of trading days as breadth (i.e. the number of independent forecasts in a year), which I think is reasonable, but I'm not understanding why the code divides the IC mean by the IC standard deviation.
I can make a change, but would like some input to make sure I am not missing something.

Funny, if ic_data was the returns that computation would be the Information Ratio (or Sharpe Ratio) assuming the benchmark returns (o risk free returns for SR) being zero.
@twiecki you know anything about that?

Just to add some more information, this is the relevant commit

@twiecki In that case, I'm thinking the calculation to annualize the IC IR/Sharpe ratio may be a bit off. It looks like the code is using the standard formula to annualize a sharpe ratio of excess returns. However, since the information coefficient is independent of the time period calculated it can't be annualized the same way as a return (although the standard deviation in the denominator can be annualized as usual). See my calculations below (which ignore compounding).  T is the number of periods in a year (i.e. T = 252 trading days).

The yellow box represents the current methodology. I would propose the small change represented by the green box and coded below (which is essentially just moving the parentheses):
ic_summary_table["Ann. IR"] = ic_data.mean() / (ic_data.std() * np.sqrt(252) )
Please let me know your thoughts and if I am off base here.
Regarding the name, maybe "Ann. IC IR" or "Ann. IC Sharpe", but I would differ to you and your colleagues on that. Either way, I think it is fine. I think I got confused because of the way it was attempting to annualize the ratio. I thought maybe you were using the IR Ratio proposed in the Fundamental Law of Active Management that I referenced in my earlier comment.

@MichaelJMath your proposed change makes sense to me.
@twiecki what do you think?

@MichaelJMath would you like to submit a PR?


Add ubuntu support

Add ubuntu support

Sorry, this repo is unmaintained. I either suggest puppetlabs/apt or voxpupuli/puppet-unattended_upgrades. Alternatively, I'll gladly make you a collaborator so you can test and merge :smile:


Per command ionice or nice

Would it be possible to add configuration for using ionice and nice per symfony command? Right now it needs to be set globally, for example in the cron, for all commands/tasks.

Hi @FluffyDiscord, if I understand ionice and nice correctly, the only task type that would benefit would be the ProcessTask. I'm not sure how we could utilize for the other task types.

These could be also used for CommandTasks. You might have some rather heavy bulk operations using commands and ensuring that command runs "at low priority" might just prevent the other server's services from fighting with resources

That's the thing. The bundle would need to spawn new process for each command that wants to use one/both of them.


Marching cubes meshing is done incorrectly(?)

First up - big kudos. Really great work, by far the best I've seen.
I just wanted to point out that the generation of the mesh from the field is being done incorrectly; there's probably an 'off-by-0.5' error somewhere in the generation of the mesh via marching cubes (I haven't had a chance to look myself yet - but will when I have some free time). I suspect the underlying model is just fine.
You can see the result of the sample generation here:

If you look at the cushion, you will see a terracing effect - this is a sign that the sampling method used in the cubes is duplicating reads of the edges of a 'cell', instead of reading into the neighbourhood correctly (oft caused by chunking); it can also be caused by rounding of coordinates.
This same issue also seems to be affecting the generation of the normals.

you may be on to something

reading https://github.com/VAST-AI-Research/TripoSR/blob/main/tsr/system.py
line 163 and after will see if the marching cubes not working correctly or simply an issue with the resolution 256
it could be the  threshold as well , will post any updates , i just need to install it .

I've taken a brief look last night and think it might be an issue upstream in the torchmcubes library.
I'm planning on taking a look this weekend and will see if I can resolve. One thing that could be helpful is exporting a 3D array to disk -- that way I can test the output in another mesher.

kind of  better results

system.py line 171
change threshold from 25 to 5
i think this works for solid objects like an apple if it's a tree then it might
resolutions wont fix it 512 takes about 21gb vrm and 1024 about 44GB
when generating a complex object like a tree  best not to go lower then threshold  10 or it will be a big blob
the torchmcubes or isosurface_helper have something wrong , or it could be that's how it works .
still can get my head around bunch of things scene_codes is what type , what happens in isosurface_helper  ...
will see what i can do to replace it with a better reconstruction algo  like directly in trimesh or a pointcloud to mesh method
i just need to find the 3d points
cheers

simply change model.extract_mesh(scene_codes) to model.extract_mesh(scene_codes, resolution=some_integer).

Any chance this could be exposed in the Gradio UI?

I've taken a brief look last night and think it might be an issue upstream in the torchmcubes library.
I'm planning on taking a look this weekend and will see if I can resolve. One thing that could be helpful is exporting a 3D array to disk -- that way I can test the output in another mesher.

Any options to export an internal representation would be useful. Meshes are the lowest common denominator and it's interesting to start thinking of ways to get output between different apps and platforms without baking out a mesh.

simply change model.extract_mesh(scene_codes) to model.extract_mesh(scene_codes, resolution=some_integer).

Any chance this could be exposed in the Gradio UI?

here is how to fully expose it in the UI
add those lines after line 107 in gradio app .py
                resolution= gr.Slider(
                    label="resolution of mesh",
                    minimum=128,
                    maximum=512,
                    value=256,
                    step=64,
                )
                threshold = gr.Slider(
                    label="threshold  of merging the marching cubes cells or what ever",
                    minimum=1,
                    maximum=100,
                    value=25,
                    step=1,
                )

then in line  142: inputs=[input_image, do_remove_background, foreground_ratio],
change to : inputs=[input_image, do_remove_background, foreground_ratio,resolution,threshold ],
then in line  60  mesh = model.extract_mesh(scene_codes)[0]
change to : mesh = model.extract_mesh(scene_codes,resolution,threshold )[0]
still wont change the quality of the mesh just how dense it is , will give it a try again perhaps threshold need to be 25 or resolution/2.
will test that when get back to the desk

I think it could be due to marching cubes with resolution 256. Could you please try with higher resolutions and see if things get better? To use different resolutions, simply change model.extract_mesh(scene_codes) to model.extract_mesh(scene_codes, resolution=some_integer).

resolutions  have no effects . it the cell merging as AdamFrisby said

Threshold should actually be more like 0. It's probably intended as a basic de-noising mechanism; but it's going to cause weird cutoffs.
It might be worth testing plugging in an alternative de-noiser, like this one: https://github.com/hkuadithya/CUDA-NLML-MRI-Denoising

Threshold should actually be more like 0. It's probably intended as a basic de-noising mechanism; but it's going to cause weird cutoffs.
It might be worth testing plugging in an alternative de-noiser, like this one: https://github.com/hkuadithya/CUDA-NLML-MRI-Denoising

at 0.1 is just a big blob

it mess up the color vertex as well
0 is just an error  cannot reshape tensor
Threshold  is  simply giving a set of points , how far/bad those points are  before we completely disregard them from the final mesh

kind of better results  system.py line 171 change threshold from 25 to 5 i think this works for solid objects like an apple resolutions wont fix it 512 takes about 21gb vrm and 1024 about 44GB when generating a complex object like a tree best not to go lower then threshold 10 or it will be a big blob the torchmcubes or isosurface_helper have something wrong , or it could be that's how it works . still can get my head around bunch of things scene_codes is what type , what happens in isosurface_helper ... will see what i can do to replace it with a better reconstruction algo like directly in trimesh or a pointcloud to mesh method i just need to find the 3d points cheers

1024 at 44GB ?
Peek.2024-03-06.14-35.mp4
Almost 😉
Morbid Interest:
I don't think the front is as noticeably different other than around the eyes and mouth:
The model files for reference: 256+1024.zip
The source input image as reference:  Reference: https://civitai.com/images/6932312
Basic threshold comparison:

thank you for verifying the resolutions , as for the  threshold  if the subject has multiple sub mesh islands like the hair on that cat or branches on a tree it will be bad plus no correct vertex colors , 10 is the best value
and use lower values for simple objects like a box apple or a chair , it's a hit or miss thing
tldr , the 3d points generated from this model is solid gold the mesh generation has an issue .


Is there any request rate limiting functionality?

Hi, I'm sorry if I'm being inattentive, but I did not see any request rate limiting functionality, when Warframe Market's ToS caps it below 3 requests per second.
I'm not sure if it's an intentional design choise, but I would be pleased if the library did rate limiting for me.
I used requests-ratelimiter in my script which replaces requests library with basically zero effort required.
So am I missing something here?

Interesting. I always suppose that users should take the responsibility for the limit of ToS since the APIs should be stateless.
Though it is relatively easy to apply rate-limiters to the existing APIs, there would be invisible stateful counting to control the request rate. And I think it is a bad design.
Anyway, I would consider adding a process-level rate-limiters in the near future update. And anyone who needs this can add an optional argument in every API function call. Thank you for suggestions.


Algolia search for Spanish translation

@maxiloc We'd like to go live soon with a Spanish translation that will be at es.vuejs.org. Would it be possible to set up an Algolia key for that translation?

Hi @chrisvfritz ,
Congratulations, your search is now ready!
I've successfully configured the underlying crawler and it will now run every 24h.
You're now a few steps away from having it working on your website:

Copy the following CSS/JS snippets and add them to your page

<!-- at the end of the HEAD -->
<link rel="stylesheet"<EMAIL_ADDRESS>/>

<!-- at the end of the BODY -->
<script type="text/javascript"<EMAIL_ADDRESS><script type="text/javascript"> docsearch({
apiKey: 'fd19a0f4b0ab402d2b6f9f95e9003e3b',
indexName: 'vuejs-es',
inputSelector: '### REPLACE ME ####',
debug: false // Set debug to true if you want to inspect the dropdown
});
</script>

Add a search input in your page if you don't have any yet. Then update the inputSelector value in JS snippet to a CSS selector that targets your search input field.
Optionally customize the look and feel by following the DocSearch documentation (https://community.algolia.com/docsearch/styling.html)
You can also check your configuration in our GitHub repo (https://github.com/algolia/docsearch-configs/blob/master/configs/vuejs-es.json).
<EMAIL_ADDRESS>has already an Algolia account. Analytics available from the Algolia dashboard by selecting the application DOCSEARCH (access granted)
Please open a pull request if want to leverage your configuration!

Feel free to get back to us if you have any issues or questions regarding the integration.
We'd also be happy to get your feedback and thoughts about DocSearch - so we can continue to improve it.
Have a nice day :)

Thanks! 😄

@sdras @chrisvfritz is that the algolia key you are requesting?


Quick tweaks to README.md

Updated wording for Why use Gradle-Fury as when originally reading, I did not realize the Wiki statement related.
Removed temporary warning. Maybe this should be moved to the version compatibility section?
Fixed broken markdown for the Maven Site Plugin URL.

Additionally, I would recommend changing the GitHub Description for https://github.com/chrisdoyle/gradle-fury with "We have moved to https://github.com/gradle-fury/gradle-fury/" as some users may not scroll all the way down to the README, and this will catch their attention sooner. (Especially since this project is not seen as a fork.)
Lastly, next time you migrate to a new repository, I would consider using GitHub's Transfer function.

Looks good--thanks! I re-targeted the PR to the develop branch since we are following the Gitflow model. Also, great advice about repo migration, I had no idea about the GitHub Transfer function. I have updated the repo under my account as suggested.


logging/alerting - create namespaces/deploy stuff on demand

Logging/Alert related namespaces and services should be created in k8s cluster only when the feature is turned on (enabled on app)

Besides the namespace/deployment resources, other resources in management server like goroutine/handler/lifecycle should be created on demand.


updates animation curves

utilises swift easing curves

Coverage remained the same at 100.0% when pulling 7e5e8fafbc5c7884c38ee744c0ca28257bacc156 on animation-patch into 4c28882a9587f37082b6ab37dc44abd618e7e5ae on master.