When CustomPainter needs to paint many complex paths in each frame, performance gets degraded when zooming or panning the CustomPaint widget

Please tell us exactly how to reproduce the problem you are running into:

Create a CustomPaint object.
Paint on it a number of complex paths. In my case, I parse an SVG file of a map of Germany and extract the path (d) attribute.
Make the CustomPaint widget zoomable and pannable. I opted to use the MatrixGestureDetector package, which exposes a Matrix4 object to transform the canvas when the user zooms or pans the map. I also faced the same issues when wrapping the CustomPaint with an InteractiveViewer widget though.
Try to move around or zoom the map. I mostly get around 10-20 fps when panning and <10 fps when zooming.

Drawing these paths continuously when the transformation matrix changes seems to take too long in the raster thread. This makes the whole zooming and moving around process really laggy. Issue is more apparent in Android devices (emulator and physical); when I run on iPhone devices refresh is a lot faster.
Alternative routes I tried:

Using InteractiveViewer widget instead of transforming the canvas with a matrix yielded similar results.
Transforming each path individually (path.transform(notifier.value.storage)), instead of transforming the whole canvas (canvas.transform(notifier.value.storage)) worsened the performance.
Using single values (i.e. scale, offsetX, offsetY) to apply the transforms instead of a matrix: similar results.

Optimizations I implemented in the demo:

Using a ValueNotifier instead of setState to update the transformation matrix.
Before drawing a path, check if it is off-screen (out of the viewport). This didn't have any effect as far as I can see; probably the CustomPaint function already takes this into account before drawing.
Optimized the SVG with svgo.
Wrapped CustomPaint with a RepaintBoundary.
Set isComplex: true and willChange: true for the CustomPaint widget.

Wondering if there is any kind of optimization I could apply for this? Or an alternative way to implement fast zooming and panning for a CustomPaint with complex paths like this map?
Demo to reproduce
In the gist I have also attached my pubspec.yaml and the sample SVG file.
Video on master channel/profile/skia tracing enabled
Target Platform/Devices:
Android mostly, iOS devices seem to perform a lot better.
Logs
[√] Flutter (Channel master, 1.25.0-5.0.pre.141, on Microsoft Windows [Version 10.0.18363.1198], locale el-GR)
    • Flutter version 1.25.0-5.0.pre.141 at C:\src\flutter
    • Framework revision 36bed2b4c3 (12 hours ago), 2020-12-09 20:13:04 -0500
    • Engine revision 8518a5bbe6
    • Dart version 2.12.0 (build 2.12.0-135.0.dev)

[√] Android toolchain - develop for Android devices (Android SDK version 30.0.2)
    • Android SDK at C:\Users\whipl\AppData\Local\Android\sdk
    • Platform android-30, build-tools 30.0.2
    • Java binary at: C:/Program Files/Android/Android Studio\jre\bin\java
    • Java version OpenJDK Runtime Environment (build 1.8.0_242-release-1644-b01)
    • All Android licenses accepted.

[√] Chrome - develop for the web
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[√] Android Studio (version 4.1.0)
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    • Java version OpenJDK Runtime Environment (build 1.8.0_242-release-1644-b01)

[√] Android Studio
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[√] Connected device (3 available)
    • STF L09 (mobile) • 7BK0218122001115 • android-arm64  • Android 9 (API 28)
    • Chrome (web)     • chrome           • web-javascript • Google Chrome 87.0.4280.88
    • Edge (web)       • edge             • web-javascript • Microsoft Edge 87.0.664.57

Hi @whiplashoo
Can you please provide a code that does not uses any third-party package?
Thank you

Hi @pedromassangocode
You would need all other packages stripped out?
Here is an updated gist without the matrix_gesture_detector package. It uses a default GestureDetector that updates the transformation matrix when the user zooms or pans (i.e. onScaleUpdate).
There is only one external dependency that I am not able to strip from the demo and it is the path parser from the path_drawing package. It is used in the SVG parser and essentially converts the path data property from the SVG to Dart instructions for making a Path object e.g.:
parseSvgPathData("M 0.092045,0.1 H 160.7516 V 123.98077 H 0.092045 Z")  //The d property of an SVG <path>
converts to:
path = Path();
path.lineTo(0, 0);
path.cubicTo(0, 0, size.width, 0, size.width, 0);
path.cubicTo(size.width, 0, size.width, size.height, size.width, size.height);
path.cubicTo(size.width, size.height, 0, size.height, 0, size.height);
path.cubicTo(0, size.height, 0, 0, 0, 0);

This function is however called only when initializing the app route, to parse the SVG and extract the path instructions to draw it on the canvas; it doesn't affect the performance issue at hand.

flutter doctor -v
[✓] Flutter (Channel master, 1.25.0-9.0.pre.43, on Mac OS X 10.15.7 19H2 darwin-x64, locale en)
    • Flutter version 1.25.0-9.0.pre.43 at /Users/pedromassango/dev/SDKs/flutter_master
    • Framework revision 63062a6443 (17 hours ago), 2020-12-13 23:19:13 +0800
    • Engine revision 4797b06652
    • Dart version 2.12.0 (build 2.12.0-141.0.dev)

[✓] Android toolchain - develop for Android devices (Android SDK version 30.0.2)
    • Android SDK at /Users/pedromassango/Library/Android/sdk
    • Platform android-30, build-tools 30.0.2
    • Java binary at: /Applications/Android Studio.app/Contents/jre/jdk/Contents/Home/bin/java
    • Java version OpenJDK Runtime Environment (build 1.8.0_242-release-1644-b3-6915495)
    • All Android licenses accepted.

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    • Xcode at /Applications/Xcode.app/Contents/Developer
    • Xcode 12.1, Build version 12A7403
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Process finished with exit code 0

Hello, I did exactly the same things and my customPainter was constantly rebuilding when zooming and panning so I wrapped it inside a RepaintBoundary and its not rebuilding anymore.
If this work then we may need to implement it directly inside InteractiveViewer but I don't have much experience with it so idk

#72718 is probably related to this.

Similar example of this issue with a video in #78543.

This looks like a duplicate of https://github.com/flutter/flutter/issues/78543. To focus conversation and work to resolve the issue, I am going to close this. Please follow up there. A lot of discussion has already occurred with potential solutions.
If you feel this is a separate issue, let us know and we'll take another look! Thanks!


Grid instead of list

Since I'm having some issues with the wide screen plugin combined with your plugin (screen starts shifting back and forth) but was able to fix it, expanding my other sidebar, I have some suggestions:
Instead of the buttons aligned in a single column list, I'd love to see them arranged in a grid or multi column (maybe as a configurable option!?)
Compatibility for customization with Themify would be awesome!
Nevertheless I love your work and appreciate what you have achieved here! Thanks!

Ok, I'll take a look and try to fix it.

Awesome work realizing this. Thx


Seems to fix Chrome method signature height issue

Or, the extra tall 'blueish' method signatures.
Not sure about where p.signature elements are used.  Used 2.2 simply based on twice
font-size...

Loren,
Used at http://msp-greg.github.io/su_info/index.html.
One test issue - on RubyDoc.info, there are the floated 'permalink' elements.  I can't check html with those easily.

Permalinks come from the yard server, you can test there. I'm trying to understand what the actual issue is here. I see the larger height, but I don't see this reproduced when generating HTML here. Can you list the Ruby version / OS / YARD version, etc., you used to generate the output with?

Sorry, I'm in the middle of some other coding... The issue is only about how some elements render in Chrome vs other browsers.  Images grabbed from RubyDoc.info.  Firefox seems to have a bit larger fonts...
In the two below images, notice the difference in height of the signature elements (light blue backgrounds).  On an iPad, display matches the Firefox styling.
Small item.  Fix makes Chrome render like Firefox / iPad, and doesn't seem to affect Firefox, iPad.
Greg
Chrome screen grab

Firefox screen grab

I guess the confusing part for me is that Chrome renders the same page fine on my machine:

What OS / Chrome version are you running against?

Well, I'm more confused.  I was working on a Vista system (don't ask), but it has the current version of Chrome (47.0.2526.73) and Firefox.  I checked it on a) iPad, b) Android tablet (both Chrome & standard browser), and c) Windows 10, which has the same version of Chrome as the Vista system, and Edge.
The only browser with the tall sections is Chrome on the Vista system.  Hence, never mind...
Greg

I'm not opposed to fixing this, just wanted to understand the root of the issue. I'll check into this and possibly merge if it doesn't cause other issues.

'Fixing this' implies something wrong with YARD.  Now that I've investigated further, I'm not too concerned.  I apologize for assuming that the same version of Chrome would act the same on all desktop platforms...

Closing this, then. Thanks for investigating! :)

Found this on Slashdot, some comments are as recent as fall '14.  Due to Chrome's user agent stylesheet of some block tags.  Chrome does the same thing with H1, H2, & H3, but what's needed to 'override' is unclear and works differently on (at least) H3.
http://stackoverflow.com/questions/5721966/webkit-margin-adds-unwanted-margin-on-text
Happy Holidays


TopN controller with an initial exclusion list

We can create a TopN controller having an initial exclusion list that is obtained from the previous run. This way we don't fragment the same things in this run that have been fragmented in the previous run.

yep, so:

Controller must keep track of mz and RT of things it fragments
From this we then make an exclusion list
Pass this list to controller N+1
Get the things fragmented again
Make new exclusion list by combining both sets of fragmented things....etc etc....


For non-geodesic users, allow command templates or auto search for popular directory formats

Describe the Feature
Currently we can use hard coded values
components:
  abc:
    command: "/bin/terraform-1"

This works if you're using geodesic or nix or similar but it doesn't work if devs want to use native binaries.
The native binaries may be installed via asdf or tfenv or tfswitch.

asdf /Users/user/.asdf/installs/terraform/{version}/bin/terraform
tfenv /home/user/.tfenv/versions/{version}/terraform
tfswitch /home/user/.terraform.versions/terraform_{version}

Expected Behavior
An improved templating system
Use Case
Allow using asdf, tfsec, tfenv or any terraform version manager by supplying command templates. This also removes hard coding binaries in the yaml leaving only the input that matters which is the version.
Describe Ideal Solution
It would be nice in atmos.yaml to allow adding formats to check for
terraform:
  command_templates:
    - "/Users/user/.asdf/installs/terraform/{version}/bin/terraform"
    - "/home/user/.tfenv/versions/{version}/terraform"
    - "/home/user/.terraform.versions/terraform_{version}"

then for components, we can use something like this
components:
  abc:
    # should resolve to latest binary installed locally if available
    # e.g. `/Users/user/.asdf/installs/terraform/1.6.2/bin/terraform`
    command_version: "1"
    # should resolve to latest binary installed locally if available
    # e.g. `/Users/user/.asdf/installs/terraform/1.5.7/bin/terraform`
    command_version: "1.5"
    # should resolve to exact binary installed locally if available
    # e.g. `/Users/user/.asdf/installs/terraform/1.6.2/bin/terraform`
    command_version: "1.6.2"

Alternatives Considered
No response
Additional Context
No response

@nitrocode why not just write:
components:
  abc:
    command: "terraform"

Then it should use your search path, and be compatible with tools like asdf

The way asdf works is by auto switching to the tool version using a direnv-like .tool-versions file within a directory.
This file can be added in the components/terraform/abc/.tool-versions by doing the following
asdf plugin add terraform
asdf install terraform 1.2.3
cd components/terraform/abc
asdf local terraform 1.2.3

✗ cat .tool-versions
terraform 1.2.3

But then you have an issue if you have multiple instantiations of abc that use different terraform versions.
components:
  abc:
    command: "terraform"

components:
  abc/legacy:
    command: "terraform0.12"

Today, we can do explicitly set these but then it's only compatible with a single tool asdf or tfenv or tfswitch due to the path structure.
components:
  abc:
    command: /Users/user/.asdf/installs/terraform/1.6.2/bin/terraform

components:
  abc/legacy:
    command: /Users/user/.asdf/installs/terraform/0.12.5/bin/terraform

I don't know there's a more generic way to solve this than what we've done.I think this is solved with asdf though like this:
components:
  abc:
    command: "asdf exec terraform"

components:
  abc/legacy:
    command: "asdf exec terraform"

And specifying the .tool-versions file in each component folder.

If that works for you, let's document using it together with atmos.

I haven't tested this but it sounds like it would work. More docs sound good to me. Thanks for following up


every time i launch minecraft, i have to fix the hi-res filter.

i play at 1080p with resolution set to 2.0x and hi-res filter set to linear. every time i launch the game, its still set to linear but actually uses nearest. i have to set it to nearest and then back to liner to get it to work again.

I just realized this is happening to me too. No wonder it's been looking so bad. I really hope this gets fixed because now I have to remember to toggle the setting every time. I don't understand why linear is not the default anyway, as increasing the render resolution is usually done to improve visual quality and nearest neighbor looks horrible.

After a little bit of testing:
v2.0.1 + Minecraft 1.16.5 -> no issue
v3.0.0 + Minecraft 1.17.1 -> setting not honored, needs toggling every time the game is launched
I can also confirm that in the config file, the setting is being saved correctly as "LINEAR", it's just not being applied. (This can also be confirmed by the fact that when the settings dialog is opened it says it's using linear but it's not.)
I don't know if that helps to narrow it down at all. I can provide any additional debugging steps that are required.
Basic system info:
Windows 7 x64 / MC 1.17.1 stock launcher / Java 16.0.1 (AdoptOpenJDK) / 2GB heap
Mods in use:
advancementinfo-1.17.1-fabric0.36.1-1.2.1.jar
appleskin-fabric-mc1.17-2.1.2.jar
BetterControls-1.17+-v1.2.0.jar
cleardespawn-1.17.1-fabric0.36.1-1.1.1.jar
continuebutton-1.0.4.jar
ctm-fabric-0.5.1+1.17.jar
custom-fog-1.6.0.jar
dynamic-fps-2.0.4.jar
easiercrafting-1.17.1-fabric0.36.1-1.6.7.jar
easiervillagertrading-1.17.1-fabric0.36.1-1.5.4.jar
EditSign-1.17.1-2.2.2.jar
enhancedblockentities-0.4.1+1.17.jar
fabric-api-0.37.1+1.17.jar
FatExperienceOrbs-1.0.1.jar
ferritecore-3.0.1-fabric.jar
horse-stats-vanilla-4.1.8.jar
hydrogen-fabric-mc1.17.1-0.3.jar
indium-1.0.0-dev.d797129+mc1.17.1.jar
lazydfu-0.1.2.jar
litematica-fabric-1.17.1-0.0.0-dev.20210713.103711.jar
lithium-fabric-mc1.17.1-0.7.3.jar
malilib-fabric-1.17.1-0.10.0-dev.24.jar
minihud-fabric-1.17.1-0.19.0-dev.20210707.150016.jar
modmenu-2.0.4.jar
okzoomer-5.0.0-beta.2+1.17.jar
resolution-control-plus-3.0.0.jar
sodium-fabric-mc1.17.1-0.3.0+build.5.jar
starlight-1.0.0-RC3+fabric.1.17.x.jar
tweakeroo-fabric-1.17.1-0.10.0-dev.20210710.155746.jar
winwonders-0.1.2.jar

Nothing obvious in the logfile. I can try creating a new install with just resolution-control-plus-3.0.0.jar to narrow down a mod conflict (possibly something with sodium/indium?)

Confirmed that it is not a mod conflict, as I can reproduce the problem with a fresh install containing only
fabric-api-0.37.1+1.17.jar
modmenu-2.0.4.jar
resolution-control-plus-3.0.0.jar

Steps to reproduce:

install above mods
set render scale to 2x and linear
quit game, restart
note that distant thin objects have lots of aliasing
open the resolution control+ settings menu
scaling type says linear, click it to toggle to nearest, and note that the screen does not change when it toggles
click it a second time to go back to linear, this time the screen changes and looks much more anti-aliased, not at all like when the game started

can confirm on 1.17.1, with very little mods installed

Same issue here. 1.18.2

Same issue here. 1.18.2

from fork?

Same issue here. 1.18.2

from fork?

I am using the one on the bukkit page.

even happens on 1.19, still

is this mod dead?

This is still an issue

Don’t know if theyre gonna ever fix it, I’ve at least gotten used to exiting and reentering fullscreen to fix it every time I restart my game

I contacted the developer a few weeks ago and they said that they're not planning on maintaining ResolutionControl+ for the time being.
Of course, it's open source and it's github, so if anyone else knows how to fix this or other issues, I reckon they're welcome to do so and either fork the project or make a pull request with fixed code.

Are you referring to UltimateBoomer or fantahund99?
fantahund99 has been maintaining the extension, updating it to 1.18 and 1.19 — they uploaded a 1.19.3 version on Curseforge just a few weeks ago. I guess they were granted CF upload privileges but they must be working out of a different Github repo because this one has not been touched.
The scaling issue still described here still exists in their 1.19.3 port, too, though.

Are you referring to UltimateBoomer or fantahund99?
fantahund99 has been maintaining the extension, updating it to 1.18 and 1.19 — they uploaded a 1.19.3 version on Curseforge just a few weeks ago. I guess they were granted CF upload privileges but they must be working out of a different Github repo because this one has not been touched.
The scaling issue described here still exists in their 1.19.3 port, too, though.

Oh, excuse me, I wasn't aware. I was referring to UltimateBoomer.

Found fantahund's repo: https://github.com/fantahund/Resolution-Control/tree/1.19.3
I was going to suggest that we move this issue there for visibility, but they have Issues disabled so there's no way to do that.
@fantahund Do you have any thoughts about this longstanding bug?

Issues are now active with me. I have to take a look at the problem in the next few days.

Issues are now active with me. I have to take a look at the problem in the next few days.

I copied over this issue to the fork: https://github.com/fantahund/Resolution-Control/issues/1

I finally got tired of this and debugged the issue. Fix is easy:
+++ b/src/main/java/io/github/ultimateboomer/resolutioncontrol/ResolutionControlMod.java
@@ -141,10 +141,12 @@ public class ResolutionControlMod implements ModInitializer {
                Window window = getWindow();
                if (framebuffer == null) {
                        this.shouldScale = true; // so we get the right dimensions
-                       framebuffer = new WindowFramebuffer(
-                                       window.getFramebufferWidth(),
-                                       window.getFramebufferHeight()
-                       );
+                       var w = window.getFramebufferWidth();
+                       var h = window.getFramebufferHeight();
+                       framebuffer = new WindowFramebuffer(w, h);
+
+                       // necessary to trigger initFbo() so that scaling algorithm is set correctly
+                       framebuffer.resize(w, h, MinecraftClient.IS_SYSTEM_MAC);
                        calculateSize();
                }

The issue is the framebuffer is being created without the scaling attributes being applied. A dummy call to resize() causes that to happen via initFbo().

This is wonderful work purple512!
I took the time to fork the project and add your changes to fix this issue for 1.18.x, 1.19.x, 1.19.3, 1.19.4 and 1.20.x.
Compiled them and added to the releases.
I added a link to your comment to the top of the README.md to credit you.

Great stuff, hopefully people can test this and make sure I didn't do a dumb.


Test: Updated documentation in debugging.md

asdfasdfsqa

since this was just a test - We'll be closing this again


Memory overflow error in coco evaluation, but I used 8 3090 graphics cards and did not encounter it when evaluating the lvis dataset

I encountered torch.cuda.OutOfMemoryError: CUDA out of memory error, but I used 8 3090 graphics cards and did not encounter it when evaluating the lvis dataset, I tried to reduce the batch_size to 8 and even replaced it with vit_b/s but still did not get it solved; in which The configuration file configs/open-vocabulary/coco/vits.yaml for vit_s has a file naming error, CLASS_PROTOTYPES: "weights/initial/open-vocabulary/prototypes/coco/class_ prototypes_all.vitl14.pth" should be CLASS_PROTOTYPES: "weights/initial/open-vocabulary/prototypes/coco/class_prototypes_all.vits14.pth".
The second problem is that I am encountering a lot of warning messages (although this may not affect normal operation), but I noticed the warning message "xFormers not available", which I don't really understand exactly what it means, so please can you give an explanation. Although I tried to install other versions of xFormers such as xformers==0.0.18 and xformers==0.0.20, they rely on torch2, which can conflict with the detectron2 framework, as described in the official detectron2 configuration: https://github.com/ facebookresearch/detectron2/releases
This did not go so well as you mentioned.

Note: I followed the configuration exactly as given in your tutorial:
git clone https://github.com/mlzxy/devit.git
conda create -n devit  python=3.9
conda activate devit
pip install -r devit/requirements.txt
pip install -e ./devit
but still encountered the problem mentioned in the first question.
Looking forward to your reply, thanks!
command:
vit=b task=ovd dataset=coco bash scripts/eval.sh
The logs are as follows:
task=ovd, vit=b, dataset=coco, shot=10, split=1, num_gpus=8
xFormers not available
Command Line Args: Namespace(config_file='configs/open-vocabulary/coco/vitb.yaml', resume=False, eval_only=True, num_gpus=8, num_machines=1, machine_rank=0, dist_url='auto', opts=['MODEL.WEIGHTS', 'weights/trained/open-vocabulary/coco/vitb_0079999.pth', 'DE.OFFLINE_RPN_CONFIG', 'configs/RPN/mask_rcnn_R_50_C4_1x_ovd_FSD.yaml', 'OUTPUT_DIR', 'output/eval/open-vocabulary/coco/vitb/'])
xFormers not available
xFormers not available
xFormers not available
xFormers not available
xFormers not available
xFormers not available
xFormers not available
xFormers not available
[10/31 14:52:24 detectron2]: Rank of current process: 0. World size: 8
[10/31 14:52:25 detectron2]: Environment info:

sys.platform            linux
Python                  3.9.18 (main, Sep 11 2023, 13:41:44) [GCC 11.2.0]
numpy                   1.22.4
detectron2              RegionCLIP @/xxx/devit/tools/../detectron2
Compiler                GCC 9.4
CUDA compiler           CUDA 11.8
detectron2 arch flags   8.6
DETECTRON2_ENV_MODULE
PyTorch                 1.13.1+cu117 @/xxx/anaconda3/envs/devit/lib/python3.9/site-packages/torch
PyTorch debug build     False
GPU available           True
GPU 0,1,2,3,4,5,6,7     NVIDIA GeForce RTX 3090 (arch=8.6)
CUDA_HOME               /usr/local/cuda-11.8
Pillow                  9.5.0
torchvision             0.14.1+cu117 @/xxx/anaconda3/envs/devit/lib/python3.9/site-packages/torchvision
torchvision arch flags  3.5, 5.0, 6.0, 7.0, 7.5, 8.0, 8.6
fvcore                  0.1.5.post20221221
iopath                  0.1.8
cv2                     4.8.1

PyTorch built with:

GCC 9.3
C++ Version: 201402
Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
OpenMP 201511 (a.k.a. OpenMP 4.5)
LAPACK is enabled (usually provided by MKL)
NNPACK is enabled
CPU capability usage: AVX2
CUDA Runtime 11.7
NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
CuDNN 8.5
Magma 2.6.1
Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

[10/31 14:52:25 detectron2]: Command line arguments: Namespace(config_file='configs/open-vocabulary/coco/vitb.yaml', resume=False, eval_only=True, num_gpus=8, num_machines=1, machine_rank=0, dist_url='auto', opts=['MODEL.WEIGHTS', 'weights/trained/open-vocabulary/coco/vitb_0079999.pth', 'DE.OFFLINE_RPN_CONFIG', 'configs/RPN/mask_rcnn_R_50_C4_1x_ovd_FSD.yaml', 'OUTPUT_DIR', 'output/eval/open-vocabulary/coco/vitb/'])
[10/31 14:52:25 detectron2]: Contents of args.config_file=configs/open-vocabulary/coco/vitb.yaml:
BASE: "../../Base-RCNN-C4.yaml"
DE:
CLASS_PROTOTYPES: "weights/initial/open-vocabulary/prototypes/coco/class_prototypes_base.vitb14.pth,weights/initial/open-vocabulary/prototypes/coco/class_prototypes_novel.vitb14.pth"
BG_PROTOTYPES: "weights/initial/background/background_prototypes.vitb14.pth"
BG_CLS_LOSS_WEIGHT: 0.2
TOPK: 10
MODEL:
META_ARCHITECTURE: "OpenSetDetectorWithExamples"
BACKBONE:
NAME: "build_dino_v2_vit"
TYPE: "base"
WEIGHTS: ""
MASK_ON: False
RPN:
HEAD_NAME: StandardRPNHead
IN_FEATURES: ["res4"]
ROI_HEADS:
SCORE_THRESH_TEST: 0.001
ROI_BOX_HEAD:
NAME: ""
NUM_FC: 0
POOLER_RESOLUTION: 7
CLS_AGNOSTIC_BBOX_REG: True
PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
DATASETS:
TRAIN: ("coco_2017_ovd_b_train",)
TEST: ("coco_2017_ovd_all_test",)
TEST:
EVAL_PERIOD: 5000
SOLVER:
IMS_PER_BATCH: 8
BASE_LR: 0.002
STEPS: (60000, 80000)
MAX_ITER: 90000
WARMUP_ITERS: 5000
CHECKPOINT_PERIOD: 5000
INPUT:
MIN_SIZE_TRAIN_SAMPLING: choice
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
MAX_SIZE_TRAIN: 1333
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1333
FORMAT: "RGB"
[10/31 14:52:25 detectron2]: Full config saved to output/eval/open-vocabulary/coco/vitb/config.yaml
[10/31 14:52:25 d2.utils.env]: Using a generated random seed 26738411
('coco_2017_ovd_all_test',)
[10/31 14:52:33 fvcore.common.checkpoint]: [Checkpointer] Loading from weights/trained/open-vocabulary/coco/vitb_0079999.pth ...
[10/31 14:52:34 d2.data.datasets.coco]: Loaded 4836 images in COCO format from datasets/coco/annotations/ovd_ins_val2017_all.json
[10/31 14:52:34 d2.data.build]: Distribution of instances among all 65 categories:

category
#instances
category
#instances
category
#instances

person
10777
bicycle
314
car
1918

motorcycle
367
airplane
143
bus
283

train
190
truck
414
boat
424

bench
411
bird
427
cat
202

dog
218
horse
272
sheep
354

cow
372
elephant
252
bear
71

zebra
266
giraffe
232
backpack
371

umbrella
407
handbag
540
tie
252

suitcase
299
frisbee
115
skis
241

snowboard
69
kite
327
skateboard
179

surfboard
267
bottle
1013
cup
895

fork
215
knife
325
spoon
253

bowl
623
banana
370
apple
236

sandwich
177
orange
285
broccoli
312

carrot
365
pizza
284
donut
328

cake
310
chair
1771
couch
261

bed
163
toilet
179
tv
288

laptop
231
mouse
106
remote
283

keyboard
153
microwave
55
oven
143

toaster
9
sink
225
refrigerator
126

book
1129
clock
267
vase
274

scissors
36
toothbrush
57

total
32721

[10/31 14:52:34 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]

[10/31 14:52:34 d2.data.common]: Serializing 4836 elements to byte tensors and concatenating them all ...

[10/31 14:52:34 d2.data.common]: Serialized dataset takes 17.62 MiB

('coco_2017_ovd_all_test',)

('coco_2017_ovd_all_test',)

('coco_2017_ovd_all_test',)

[10/31 14:52:35 d2.evaluation.evaluator]: Start inference on 605 batches

('coco_2017_ovd_all_test',)

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/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/devit/tools/../detectron2/structures/boxes.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)

tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)

/xxx/anaconda3/envs/devit/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)

return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]

/xxx/anaconda3/envs/devit/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)

return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]

/xxx/anaconda3/envs/devit/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)

return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]

/xxx/anaconda3/envs/devit/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)

return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]

/xxx/anaconda3/envs/devit/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)

return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]

/xxx/anaconda3/envs/devit/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)

return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]

/xxx/anaconda3/envs/devit/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)

return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]

/xxx/anaconda3/envs/devit/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)

return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]

Traceback (most recent call last):

File "/xxx/devit/tools/train_net.py", line 202, in

launch(

File "/xxx/devit/tools/../detectron2/engine/launch.py", line 67, in launch
mp.spawn(
File "/xxx/anaconda3/envs/devit/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 240, in spawn
return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
File "/xxx/anaconda3/envs/devit/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 198, in start_processes
while not context.join():
File "/xxx/anaconda3/envs/devit/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 160, in join
raise ProcessRaisedException(msg, error_index, failed_process.pid)
torch.multiprocessing.spawn.ProcessRaisedException:
-- Process 6 terminated with the following error:
Traceback (most recent call last):
File "/xxx/anaconda3/envs/devit/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 69, in _wrap
fn(i, *args)
File "/xxx/devit/tools/../detectron2/engine/launch.py", line 125, in _distributed_worker
main_func(*args)
File "/xxx/devit/tools/train_net.py", line 178, in main
res = Trainer.test(cfg, model)
File "/xxx/devit/tools/../detectron2/engine/defaults.py", line 618, in test
results_i = inference_on_dataset(model, data_loader, evaluator)
File "/xxx/devit/tools/../detectron2/evaluation/evaluator.py", line 159, in inference_on_dataset
outputs = model(inputs)
File "/xxx/anaconda3/envs/devit/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/xxx/devit/tools/../detectron2/modeling/meta_arch/devit.py", line 1309, in forward
embedding = rp(embedding)
File "/xxx/anaconda3/envs/devit/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/xxx/anaconda3/envs/devit/lib/python3.9/site-packages/torch/nn/modules/container.py", line 204, in forward
input = module(input)
File "/xxx/anaconda3/envs/devit/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/xxx/anaconda3/envs/devit/lib/python3.9/site-packages/torch/nn/modules/batchnorm.py", line 171, in forward
return F.batch_norm(
File "/xxx/anaconda3/envs/devit/lib/python3.9/site-packages/torch/nn/functional.py", line 2450, in batch_norm
return torch.batch_norm(
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.82 GiB (GPU 6; 23.69 GiB total capacity; 19.57 GiB already allocated; 2.44 GiB free; 19.84 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
/xxx/anaconda3/envs/devit/lib/python3.9/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 140 leaked semaphore objects to clean up at shutdown
warnings.warn('resource_tracker: There appear to be %d '

Hi, @study-hard-forever for the first problem, I would suggest reduce K from 10 to 5 or 3. It shouldn't hurt accuracy too much according to the ablation study but will significantly reduce memory requirements. The fact is that because of turning C-classification into C binary classification, the space complexity (on a single GPU) of RCNN branch becomes roughly $O(K^2)$.
For the second problem, I also had that a lot before. The solution could be the steps followed:

Use torch 1.x (e.g. 1.13) instead of torch2
Install xformers 0.0.18 from source (have to because it is not available from pypi).  Then the memory consumption of ViT will be reduced a lot, and the warnings will be mostly gone.

Thanks for your reply. I remember I also had some problems yesterday when installing the 0.0.18 version from source, I'll try it again later, I'll also turn the K down a bit to try, thank you.

Note that when I use certain versions of xformers on some GPU, e.g., 2080 Ti I remember, it sometimes produces inaccurate inference results. Don't know why but I would recommend you install this version of xformers from source.
commit da278628761928581f52b81647d82236a88c3afb (HEAD, tag: v0.0.18)

Install xformers 0.0.18 from this source works  on V 100, GOOD!

Note that when I use certain versions of xformers on some GPU, e.g., 2080 Ti I remember, it sometimes produces inaccurate inference results. Don't know why but I would recommend you install this version of xformers from source.
commit da278628761928581f52b81647d82236a88c3afb (HEAD, tag: v0.0.18)

Install xformers 0.0.18 from this source works on V 100, GOOD!

Ok, thank you for your reply. If there is a chance in the future, I will try it on V100.

Hi, @study-hard-forever for the first problem, I would suggest reduce K from 10 to 5 or 3. It shouldn't hurt accuracy too much according to the ablation study but will significantly reduce memory requirements. The fact is that because of turning C-classification into C binary classification, the space complexity (on a single GPU) of RCNN branch becomes roughly O(K2).
For the second problem, I also had that a lot before. The solution could be the steps followed:

Use torch 1.x (e.g. 1.13) instead of torch2
Install xformers 0.0.18 from source (have to because it is not available from pypi).  Then the memory consumption of ViT will be reduced a lot, and the warnings will be mostly gone.

Just reduce TOPK from 10 to 3, but I don't know if this will reduce the accuracy of the model.


Swipe on left side when fullscreen

I find this a little hard to describe, but when performing a swipe motion on the left side of the screen only when on fullscreen, the swipe jumps to the other side of the screen, this does not happen on any of the other 3 sides and also doesn't happen while on windowed mode
Here's a short video that might describe it better
https://giant.gfycat.com/AlarmingKindheartedGenet.webm

Thank you for your report.
Could you try again with the current master version, please? This issue might be solved by https://github.com/Genymobile/scrcpy/commit/a7fe9ad779057f7a2698fc1769dce3c3786993ee.

arf, my fix ignores all events outside the device screen, which is not good: a "drop" of a drag&drop outside the screen is not dispatched.

I provided a better fix on the dev branch: https://github.com/Genymobile/scrcpy/commit/fefb9816a99e6f8fa59befcfb70ba87112f90a8d.


config type is any

Currently the main config value needed to connect is typed as any.
export class Near {
    readonly config: any;
    readonly connection: Connection;
    readonly accountCreator: AccountCreator;

    constructor(config: any) {

I assume this was for expediency, but do we now have an accepted shape of the config?

@willemneal whole library originally was JS. Probably good idea to add these when adding more documentation.

similar to #278 as related to "improving TypeScript compatibility"

This has been addressed :).


Bump Django to v1.9

Bump Django version to v1.9

Today I'll review this, cut a last tag for 1.8.x, then accept it in.

:+1:


Type conversion between numeric types

In the README it mentions that conversion between int/floating cannot be done implicitly. Is this really not possible?
If a user wants to set a starting position vector as the origin, having
[start]
position = [0,0,0]

fail to be read by toml::get<std::vector<double>>(start_table.at("position")) is quite frustrating. I appreciate that doing implicit lossy conversions (1.2 as an int for example) might be an implementation choice, but the inability to get 0 as a double seems to be a bit arbitrary. If it is technically possible but not something you feel the need to implement, then I might have a look.
Thanks for the great library!

I basically agree with your point, it would be more useful if it can convert int64_t into float and vice versa. But there is a reason why it is not supported.
When we specify a type that does not require any type conversion, it returns a reference (like int64_t&) that enables us to modify the content through it.
int64_t& x = toml::find<int64_t>(input, "x");
x = 42; // overwrite "x" in `input`

But, it is not possible to bind int64_t as a reference to other type, like double&. double is one of the types that is contained in the internal storage of toml::value and does not require type conversion (if the toml::value contains Float value). So if double is specified as a template argument of toml::get, it should returns double&, for the sake of consistency. Here, if the value actually contains int64_t, we cannot bind it to double& and the return type must be double. The problem is that we cannot know the actual contained type before parsing a file. If we want to support conversion, the return type of toml::get (whether it is a reference or a value) should be determined after checking what value is contained, that means that the type should be changed at runtime, and it is impossible.
So the available options are

returning a value by copy. conversion via toml::get becomes handy, but modification is impossible. (breaking change)
returning a value by reference. conversion via toml::get has restriction, but modification is possible. (current implementation)
returning a wrapper type that automatically casts a value in assignment operator, and making the code complicated. (breaking change)
add a totally new function, that might be named as toml::as<T>, that always returns a value instead of reference. (minor update)

I think the most feasible option is the last one.

Understood, I hadn't appreciated the reference problem. To forge ahead, I worked around it in my code with a simple toml::find wrapper template that if the return type template std::is_floating_point::value is true, then it uses a specialization that checks at runtime if the parsed type is_integer() with the toml::value methods and if it is does the conversion.
Your proposed solution sounds perfect though, thank you for taking the time to read and think about my question!

Could it give a warning if a key exists but with wrong type?


callUUID should be present as an argument on all events

For example, mute doesn't explicitly list which call was muted. We should audit all the events and be sure we pass back the callUUID on each one.

Reported by @danjenkins


HADOOP-18691. Add a CallerContext getter on the Schedulable interface

Description of PR
This patch will help us solve a FairCallQueue impersonation issue we have on the Apache Ozone project.
 The issue:  On Ozone the FairCallQueue doesn't work when used with the Ozone S3G. The OzoneManager and the S3G are using a single permanent connection for communication, which is hiding all S3G client users under  a special S3G user. For every request that comes through the S3G, getUserGroupInformation() from the Schedulable returns the special s3g user. Therefore, there is no impersonation and the FairCallQueue is ineffective.
We would like to expose the CallerContext field from the Call class and use that to carry the information needed by the IdentityProvider. On Ozone, we will set the correct information on the CallerContext before issuing the request and then provide an IdentityProvider implementation which will access the new CallerContext getter and use it to return the username to makeIdentity().
Here is a draft patch on Ozone side that utilizes these changes: https://github.com/apache/ozone/pull/4116
hadoop jira: https://issues.apache.org/jira/browse/HADOOP-18691
How was this patch tested?
No new tests were added for this patch as it doesn't make any functional changes but only exposing existing data. It was tested on Ozone side.
Here is a gist with steps and information on how the FairCallQueue was tested using this patch and an OzoneIdentityProvider impl: https://gist.github.com/xBis7/b8247986e718417a4b48320eab6efeda
For code changes:

[x] Does the title or this PR starts with the corresponding JIRA issue id (e.g. 'HADOOP-17799. Your PR title ...')?
[ ] Object storage: have the integration tests been executed and the endpoint declared according to the connector-specific documentation?
[ ] If adding new dependencies to the code, are these dependencies licensed in a way that is compatible for inclusion under ASF 2.0?
[ ] If applicable, have you updated the LICENSE, LICENSE-binary, NOTICE-binary files?

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Thanks @xBis7 . lgtm apart from one comment above.
I'd like to invite @steveloughran to vet the interface method addition here as well.

@smengcl Thanks for looking into this PR. I've addressed your comment.

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Thanks @xBis7 . Latest changes lgtm. Looks like all comments from @steveloughran are addressed.
I will merge after a few days or when Steve approves this.

Thanks @xBis7 for the PR.


Obtaining fieldKeys through R interface

To obtain an overview of available field keys that can be used in a query I was trying to use the following query
tables <- client$query('import "influxdata/influxdb/schema" schema.fieldKeys(bucket: "bucket_0001")')

The following error might be obvious as this does not return time serie information
#datatype string long string
The following `from` values were not present in `x`: nanotime
Error in FUN(X[[i]], ...) :
  cannot coerce '_time' to 'time': column does not exist

Is it possible to use this package for obtaining non time related information?

So sorry for the delayed response. To handle non-time related info, pass POSIXctCol parameter with NULL value to query, eg.
> tables <- client$query('import "influxdata/influxdb/schema" schema.fieldKeys(bucket: "telegraf")', POSIXctCol = NULL)
#datatype string long string
The following `from` values were not present in `x`: nanotime
> tables
[[1]]
             _value
1       usage_guest
2  usage_guest_nice
3        usage_idle
4      usage_iowait
5         usage_irq
6        usage_nice
7     usage_softirq
8       usage_steal
9      usage_system
10       usage_user


QTCreator-less build instructions

I didn't want to install with a gui so I got cross-compilation working with just the toolchain and some basic dependencies. It should help with automating the build. Unfortunately deploying is not automated yet, but this seems pretty trivial to do (and this may fit better into https://github.com/dixonary/draft-reMarkable/pull/9)
Adds a table of contents too.

these build instructions also work for other apps like https://github.com/dixonary/fingerterm-reMarkable

This may(?) also help out what https://github.com/dixonary/draft-reMarkable/issues/3 was asking for, and if the deploy step is automated as well, this would address the CI stuff brought up in https://github.com/dixonary/draft-reMarkable/issues/6

This looks great, thanks! I was wondering on a good process for compiling without qtcreator.
Is it ready to merge or do you want to make any other changes?

@dixonary  I think it's as good as Im going to get it 😅

Thank you! Hopefully this will help some people :)


Make index.pidx mandatory in init command

Issue extracted from https://github.com/Open-CMSIS-Pack/cpackget/pull/50#issuecomment-1092498508

a) let's make index-url mandatory - where index-url may point to a local index.pidx file being e.g. generated using vidx2pidx or a a schema compliant file without entries

@chaws I believe this is a valid use case, even though we are not supporting it today in MDK and ArmDS as they use the fixed download location. However I think that people could use the vidx2pidx utility to create their own set of "public" packs or even extend a public index with a company internal pack index.


merge from template-coveralls

package.json

chore(deps): add ^11.10.6 remove ^11.10.5 (devDependencies.npm-pkgbuild)

:tada: This PR is included in version 6.4.5 :tada:
The release is available on:

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