text:
How to setup with create-react-app

Using npx create-react-app my-app --template typescript I'm having problems to fully make it work. The font seem to not load properly but loading the browser default. Any link with this steps that is not Next but just react?

Hello,
I saw your issue and thought it would be useful to have a create-react-app repo template that others can use. I made a shadcn-ui-cra template repo that you can fork to get a cra app using typescript, tailwind and shadcn-ui.
If you don't want to use a repo and to follow a step by step instruction, I have included it in the README of the same repo.
If an example project section gets added to shadcn/ui I think it this would be a good addition for other peoples reference. I'm also working on a vite and remix example.

So another issue I have with the create-react-app, seems like, is that webpack can't compile the "paths" property.
// tsconfig.json

  "compilerOptions": {
    "baseUrl": "src",
    "allowSyntheticDefaultImports": true,
    "jsx": "react",
    "paths": {
      "@components/*": ["components/*"]
    }
  },
...

// App.tsx
import React from 'react';
import './App.css';
// import Navbar from './components/elements/Navbar'
import Navbar from '@components/elements/Navbar'

function App() {
  return (

        <Navbar />

GitHub Finder

  );
}

export default App;

Module not found: Error: Can't resolve '@components/elements/Navbar' in '/Users/<username>/path/to/project/src'

My guess is that its just a setup in the webpack for create-react-app and nothing can't be done?

+1

The problem you are finding is with absolute paths in cra. create-react-app 3 release gives support for absolute paths but only if its the same as the folder name. So usually you can do src/lib/utils out of the box with cra and webpack. But path alias's like next.js requires some work. Here's how to do it in CRA using a package called craco:

Install the craco dependency:
npm i -D @craco/craco

create 'craco.config.js' and past the following:
/* craco.config.js */
const path = require(`path`);

module.exports = {
  webpack: {
    alias: {
      "@": path.resolve(__dirname, "src/"),
    },
  },
};

Use craco in package.json:
"scripts": {
- "start": "npm start",
- "build": "npm build",
+ "start": "craco start",
+ "build": "craco build",
  "eject": "react-scripts eject"
},

You can now use the @ path alias the same way its used in shadcn/ui:
import { cn } from "@/lib/utils"

I know this is an old issue but thanks for producing this starter.  It's helped me get my CRA setup and working.
Do you happen to know how to get theming working?  I.e light/dark modes?  Nextjs has built in handlers for this unfortunately.

I have updated the shadcn-ui-cra repo with a dark mode implementation. It would be worth taking a look at the theme provider implementation that I got from the vite dark mode guide

I have updated the shadcn-ui-cra repo with a dark mode implementation. It would be worth taking a look at the theme provider implementation that I got from the vite dark mode guide

Does it work for you properly?
I have just set up a new project, added shadcnui which works fine.
Extended with the dark mode following the website, but it doesn't work properly. I see that the html tag receives the class=light property, but it doesn't change when I give a dark as an initial value to to the theme provider.
And if I change it to dark in the browser console it change the button element but not the background.
Setup: basically a vite react typescript fresh install.

This issue has been automatically closed because it received no activity for a while. If you think it was closed by accident, please leave a comment. Thank you.

authors: ahmad1702 ashot hdadr iaingymware redeemefy shadcn

repo: shadcn/ui


text:
Download file from weblink

Is it possible to download a file from a weblink?
See the example below from the downloadthis R package that inspired your quarto shortcode.  https://fmmattioni.github.io/downloadthis/index.html#web-address
## Link in Github repo
download_link(
  link = "https://github.com/fmmattioni/downloadthis/raw/master/inst/example/file_1.pdf",
  button_label = "Download pdf file",
  button_type = "danger",
  has_icon = TRUE,
  icon = "fa fa-save",
  self_contained = FALSE
)

Um, no. I have not implemented download_link in this extension, mostly because this extension is not even needed to create a download button with a web link. I mean you can create the button using the raw html. Consider the following reprex,
---
title: "download_link"
format: html
---

## Quarto

<button class="btn btn-danger">Download Link</button>

Anyway, I will try to implement download_link in this extension for people who want to avoid using raw HTML as soon as possible 😃.

Or the same button could be created by adding css classes. Maybe the extension isn't needed? Maybe just a vignette?
The example below adds the pdf icon using the Quarto FontAwesome extension.
[Download Link {{< fa file-pdf>}}](https://github.com/fmmattioni/downloadthis/raw/master/inst/example/file_1.pdf){.btn .btn-danger}

source: gharchive/issue

authors: DougManuel shafayetShafee

repo: shafayetShafee/downloadthis

license: MIT

license_type: permissive

license_source: github-api


text:
Unable to de-select ranges when using multiple range

<DatePicker
      multiple
      range />

Doesn't allow de-selecting of dates via the calendar.  The DatePanel is basically required in this case to allow removal of date ranges.

Hello
Thank you for your feedback.
I think providing more options to disable dates can be implemented in any project depending on its design. I have provided minimal options in this library. Do you have any suggestions for changing the calendar behavior? Introducing a similar package would be enough.

authors: ctmaloney shahabyazdi

repo: shahabyazdi/react-multi-date-picker


text:
Always clear hydrated stores inside the server-side rendering worker

This change is

Coverage remained the same at ?% when pulling 564bd205780a1d6fa4faaa1ae8a2ca14aafa233e on squadette:fix-clear-hydrated into 4888642197eee2a11db7fa6571660932d1f3886d on shakacode:master.

source: gharchive/pull-request


text:
🛑 Shakedown Social - Uploads is down

In 20d0b17, Shakedown Social - Uploads (https://files.shakedown.social/accounts/avatars/109/357/389/718/265/462/original/dd80d2c95e48cfd2.jpeg) was down:

HTTP code: 403
Response time: 537 ms

Resolved: Shakedown Social - Uploads is back up in 369233c.

repo: shakedown-social/upptime


text:
🛑 Shakedown Social - Uploads is down

In a42f835, Shakedown Social - Uploads (https://files.shakedown.social/accounts/avatars/109/357/389/718/265/462/original/dd80d2c95e48cfd2.jpeg) was down:

HTTP code: 403
Response time: 270 ms

Resolved: Shakedown Social - Uploads is back up in 386a1a2.


text:
🛑 Shakedown Social - Uploads is down

In c9e90ba, Shakedown Social - Uploads (https://files.shakedown.social/accounts/avatars/109/357/389/718/265/462/original/dd80d2c95e48cfd2.jpeg) was down:

HTTP code: 403
Response time: 321 ms

Resolved: Shakedown Social - Uploads is back up in 575d0a8.


text:
ADNI-GFNet-47430004

Hello COMP3710 Teaching Team.
This is my pull request to merge my ADNI-GFNet code into Shakes' topic recognition branch. The task I chose was Number 5, "Classify Alzheimer’s disease (normal and AD) of the ADNI brain data (see Appendix for link) using one of the latest vision transformers such as the GFNet set having a minimum accuracy of 0.8 on the test set."
I achieved an accuracy of 78.4% in the test set, more details are included in the README.md.
The included files are as follows: (this info is in the README.md)
images/ - Directory that contains all the figures and images necessary for visually showing something.
ADNI_GFNet_47430004.yaml - yaml file which can be used to create a conda environment that is able to run the train.py. Details on using it are in Dependencies.
dataset.py - Python file that handles all the dataset/dataloader processes.
LICENSE - License file containing the MIT License of the original repo found in Inspiration.
main.py - Obsolete python file that was created and does not serve a purpose in the repo structure. Left as proof of progress.
predict.py - Python file used to test the accuracy of a given model, which should be located at test/model/GFNet.pth
README.md - This markdown file which describes everything about the repo.
test_dataset.py - Python file used to visually inspect that the dataset.py file is working correctly, especially the normalising transform.
train.py - Python file used to train the GFNet model, and save figures relating to its accuracy over epochs, losses over epochs and the best-case accuracy.
utils.py - Python file containing miscellaneous functions that help the training and predict process work. This file was taken directly from the repo listed in Inspiration, and the source is also listed in the header of the python file.
Thank you.
Donghyug Jeong 47430004

<This is an initial inspection, no action is required at this point.>
File Organizing: Well-organized files.
Problem Solving:

The algorithm solves the problem appropriately.
Accuracy in testing dataset: 0.784. However, this is a validation acc as the student utilized the original train/test set to train the model and observe the validation result. The val loss graph indicates the model training tends to be overfitting.

Model and functions:

It correctly uses PyTorch to construct the GFNet models and functions.
With data augmentation.

Code design: Good. However, some discarded files and commented-off code are expected to be deleted.
Code comment and docstring:

Good code comments
Minimal function docstrings

Difficulty: Hard.
Additional Comments:

Fruitful and reasonable commits
A conclusion is expected in the report to summarize the experiment.

Observational Feedback
Pull Request:
Correctly created the Pull request from Topic Recognition Branch .
Pull request is descriptive about the file structure .
File Organizing: Well-organized files.
Commit Log:
Commit messages are meaningful .
Commits demonstrate a logical development flow.
Commits are regularly made.
Documentation:
The README file provides a clear explanation of the algorithm and the problem it solves.
Good idea  to have a yaml file as included .
Code comments and docstrings should be  included .
Proper GitHub markdown formatting is used, with organized headings, lists, and code blocks.
Can add about Conclusion and  Future Works  of the report

Hi, is this feedback that I should be acting upon?
I'm not exactly sure what to change and how to change it because I can't see any assigned marks anywhere, and some people have received complete comments (with marks) but some people haven't received anything.

They are just initial marks given by some tutors but Shakes will be providing the complete mark for the Report and Code  . Yes this is the feedback you should act upon . Incorporate the mentioned changes in your ReadMe.MD file and then push again .

I've tried to address the comments, but am unsure if I got all of them because I couldn't see any marks lost (usually a good indication of what to change). I hope I'll be able to make more changes later when I get more specific comments.

Marking

Good/OK/Fair Practice (Design/Commenting, TF/Torch Usage)
Good design and implementation.
Spacing and comments.
Header blocks.
Recognition Problem
OK solution to problem. Model training tends to be overfitting.-1
Driver Script present.
File structure NOT present. Unwanted files present.-1
Good Usage & Demo & Visualisation & Data usage.
Module present.
Commenting missing. Minimal doc strings.-1
No Data leakage found. Uses Data Aug.
Difficulty : Hard.  GFNet (Hard Difficulty)
Commit Log
Some/Adequate Meaningful commit messages. Minimal.-1
Some/Adequate Progressive commits. Lacks flow.-1
Documentation
Readme :Acceptable. -1
Model/technical explanation :Good.
Description and Comments :Good.
Markdown used and PDF submitted. PDF Checked.
Pull Request
Successful Pull Request (Working Algorithm Delivered on Time in Correct Branch).
No Feedback required.
Request Description is good.
TOTAL-6

Marked as per the due date and changes after which aren't necessarily allowed to contribute to grade for fairness.
Subject to approval from Shakes

Hi, I just wanted to ask about what "File structure NOT present. Unwanted files present." Is? Is this for the utils.py file?

authors: DonghyugDavidJeong aniketgupta17 gayanku wangzhaomxy


text:
Combo issue

How to fix it?

wait, it will be fixed in a few minutes

repo: shamhi/HamsterKombatBot


text:
KeyError: '135' when testing gdrnpp on ycbv

Could you please help with this issue?
(gdrnpp) mona@ada:~/gdrnpp_bop2022$ ./core/gdrn_modeling/test_gdrn.sh configs/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv.py 0 output/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv/model_final_wo_optim.pth
++ dirname ./core/gdrn_modeling/test_gdrn.sh
+ this_dir=./core/gdrn_modeling
+ CFG=configs/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv.py
+ CUDA_VISIBLE_DEVICES=0
+ IFS=,
+ read -ra GPUS
+ NGPU=1
+ echo 'use gpu ids: 0 num gpus: 1'
use gpu ids: 0 num gpus: 1
+ CKPT=output/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv/model_final_wo_optim.pth
+ '[' '!' -f output/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv/model_final_wo_optim.pth ']'
+ NCCL_DEBUG=INFO
+ OMP_NUM_THREADS=1
+ MKL_NUM_THREADS=1
+ PYTHONPATH=./core/gdrn_modeling/../..:/home/mona/realsense-ros/install/realsense2_camera_msgs/local/lib/python3.10/dist-packages:/opt/ros/humble/lib/python3.10/site-packages:/opt/ros/humble/local/lib/python3.10/dist-packages
+ CUDA_VISIBLE_DEVICES=0
+ python ./core/gdrn_modeling/main_gdrn.py --config-file configs/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv.py --num-gpus 1 --eval-only --opts MODEL.WEIGHTS=output/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv/model_final_wo_optim.pth
/home/mona/.local/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details.
  warnings.warn(
You requested to import horovod which is missing or not supported for your OS.
/home/mona/.local/lib/python3.10/site-packages/mmcv/device/npu/data_parallel.py:22: UserWarning: Torchaudio's I/O functions now support par-call bakcend dispatch. Importing backend implementation directly is no longer guaranteed to work. Please use `backend` keyword with load/save/info function, instead of calling the udnerlying implementation directly.
  if hasattr(sys.modules[m], '_check_balance'):
/home/mona/gdrnpp_bop2022/core/gdrn_modeling/../../lib/pysixd/misc.py:586: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def get_obj_im_c(K, t):
/home/mona/gdrnpp_bop2022/core/gdrn_modeling/../../lib/pysixd/misc.py:765: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def compute_2d_bbox_xyxy_from_pose(points, pose, K, width=640, height=480, clip=False):
/home/mona/gdrnpp_bop2022/core/gdrn_modeling/../../lib/pysixd/misc.py:793: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def compute_2d_bbox_xyxy_from_pose_v2(points, pose, K, width=640, height=480, clip=False):
/home/mona/gdrnpp_bop2022/core/gdrn_modeling/../../lib/pysixd/misc.py:822: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def compute_2d_bbox_xywh_from_pose(points, pose, K, width=640, height=480, clip=False):
[0103_032055@main_gdrn:216] soft limit:  500000 hard limit:  1048576
[0103_032055@main_gdrn:227] Command Line Args: Namespace(config_file='configs/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv.py', resume=False, eval_only=True, launcher='none', local_rank=0, fp16_allreduce=False, use_adasum=False, num_gpus=1, num_machines=1, machine_rank=0, dist_url='tcp://127.0.0.1:50154', opts={'MODEL.WEIGHTS': 'output/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv/model_final_wo_optim.pth'}, strategy=None)
[0103_032055@main_gdrn:101] optimizer_cfg: {'type': 'Ranger', 'lr': 0.0008, 'weight_decay': 0.01}
[0103_032055@ycbv_d2:594] DBG register dataset: ycbv_train_real
[0103_032055@ycbv_pbr:359] DBG register dataset: ycbv_train_pbr
[0103_032055@ycbv_d2:594] DBG register dataset: ycbv_test
20240102_142055|core.utils.default_args_setup@123: Rank of current process: 0. World size: 1
20240102_142055|core.utils.default_args_setup@124: Environment info:
-------------------------------  -----------------------------------------------------------------------------
sys.platform                     linux
Python                           3.10.4 | packaged by conda-forge | (main, Mar 24 2022, 17:39:04) [GCC 10.3.0]
numpy                            1.26.2
detectron2                       0.6 @/home/mona/anaconda3/envs/gdrnpp/lib/python3.10/site-packages/detectron2
Compiler                         GCC 11.4
CUDA compiler                    CUDA 11.8
detectron2 arch flags            8.9
DETECTRON2_ENV_MODULE            <not set>
PyTorch                          2.1.2+cu118 @/home/mona/.local/lib/python3.10/site-packages/torch
PyTorch debug build              False
torch._C._GLIBCXX_USE_CXX11_ABI  False
GPU available                    Yes
GPU 0                            NVIDIA RTX 6000 Ada Generation (arch=8.9)
Driver version                   535.104.12
CUDA_HOME                        /usr/local/cuda-11.8
Pillow                           10.0.1
torchvision                      0.16.2+cu118 @/home/mona/.local/lib/python3.10/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                           0.1.9
cv2                              4.8.1
-------------------------------  -----------------------------------------------------------------------------
PyTorch built with:
  - GCC 9.3
  - C++ Version: 201703
  - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX512
  - CUDA Runtime 11.8
  - NVCC architecture flags: -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;-gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_90,code=sm_90
  - CuDNN 8.7
  - Magma 2.6.1
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -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_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.2, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

20240102_142055|core.utils.default_args_setup@126: Command line arguments: Namespace(config_file='configs/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv.py', resume=False, eval_only=True, launcher='none', local_rank=0, fp16_allreduce=False, use_adasum=False, num_gpus=1, num_machines=1, machine_rank=0, dist_url='tcp://127.0.0.1:50154', opts={'MODEL.WEIGHTS': 'output/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv/model_final_wo_optim.pth'}, strategy=None)
20240102_142055|core.utils.default_args_setup@128: Contents of args.config_file=configs/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv.py:
# about 3 days
_base_ = ["../../_base_/gdrn_base.py"]

OUTPUT_DIR = "output/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv"
INPUT = dict(
    DZI_PAD_SCALE=1.5,
    TRUNCATE_FG=True,
    CHANGE_BG_PROB=0.5,
    COLOR_AUG_PROB=0.8,
    COLOR_AUG_TYPE="code",
    COLOR_AUG_CODE=(
        "Sequential(["
        # Sometimes(0.5, PerspectiveTransform(0.05)),
        # Sometimes(0.5, CropAndPad(percent=(-0.05, 0.1))),
        # Sometimes(0.5, Affine(scale=(1.0, 1.2))),
        "Sometimes(0.5, CoarseDropout( p=0.2, size_percent=0.05) ),"
        "Sometimes(0.4, GaussianBlur((0., 3.))),"
        "Sometimes(0.3, pillike.EnhanceSharpness(factor=(0., 50.))),"
        "Sometimes(0.3, pillike.EnhanceContrast(factor=(0.2, 50.))),"
        "Sometimes(0.5, pillike.EnhanceBrightness(factor=(0.1, 6.))),"
        "Sometimes(0.3, pillike.EnhanceColor(factor=(0., 20.))),"
        "Sometimes(0.5, Add((-25, 25), per_channel=0.3)),"
        "Sometimes(0.3, Invert(0.2, per_channel=True)),"
        "Sometimes(0.5, Multiply((0.6, 1.4), per_channel=0.5)),"
        "Sometimes(0.5, Multiply((0.6, 1.4))),"
        "Sometimes(0.1, AdditiveGaussianNoise(scale=10, per_channel=True)),"
        "Sometimes(0.5, iaa.contrast.LinearContrast((0.5, 2.2), per_channel=0.3)),"
        "Sometimes(0.5, Grayscale(alpha=(0.0, 1.0))),"  # maybe remove for det
        "], random_order=True)"
        # cosy+aae
    ),
)

SOLVER = dict(
    IMS_PER_BATCH=48,
    TOTAL_EPOCHS=40,  # 10
    LR_SCHEDULER_NAME="flat_and_anneal",
    ANNEAL_METHOD="cosine",  # "cosine"
    ANNEAL_POINT=0.72,
    OPTIMIZER_CFG=dict(_delete_=True, type="Ranger", lr=8e-4, weight_decay=0.01),
    WEIGHT_DECAY=0.0,
    WARMUP_FACTOR=0.001,
    WARMUP_ITERS=1000,
)

DATASETS = dict(
    TRAIN=("ycbv_train_real", "ycbv_train_pbr"),
    TEST=("ycbv_test",),
    DET_FILES_TEST=("datasets/BOP_DATASETS/ycbv/test/test_bboxes/yolox_x_640_ycbv_real_pbr_ycbv_bop_test.json",),
    SYM_OBJS=[
        "024_bowl",
        "036_wood_block",
        "051_large_clamp",
        "052_extra_large_clamp",
        "061_foam_brick",
    ],  # used for custom evalutor
)

DATALOADER = dict(
    # Number of data loading threads
    NUM_WORKERS=8,
    FILTER_VISIB_THR=0.3,
)

MODEL = dict(
    LOAD_DETS_TEST=True,
    PIXEL_MEAN=[0.0, 0.0, 0.0],
    PIXEL_STD=[255.0, 255.0, 255.0],
    BBOX_TYPE="AMODAL_CLIP",  # VISIB or AMODAL
    POSE_NET=dict(
        NAME="GDRN_double_mask",
        XYZ_ONLINE=True,
        NUM_CLASSES=21,
        BACKBONE=dict(
            FREEZE=False,
            PRETRAINED="timm",
            INIT_CFG=dict(
                type="timm/convnext_base",
                pretrained=True,
                in_chans=3,
                features_only=True,
                out_indices=(3,),
            ),
        ),
        ## geo head: Mask, XYZ, Region
        GEO_HEAD=dict(
            FREEZE=False,
            INIT_CFG=dict(
                type="TopDownDoubleMaskXyzRegionHead",
                in_dim=1024,  # this is num out channels of backbone conv feature
            ),
            NUM_REGIONS=64,
            XYZ_CLASS_AWARE=True,
            MASK_CLASS_AWARE=True,
            REGION_CLASS_AWARE=True,
        ),
        PNP_NET=dict(
            INIT_CFG=dict(norm="GN", act="gelu"),
            REGION_ATTENTION=True,
            WITH_2D_COORD=True,
            ROT_TYPE="allo_rot6d",
            TRANS_TYPE="centroid_z",
        ),
        LOSS_CFG=dict(
            # xyz loss ----------------------------
            XYZ_LOSS_TYPE="L1",  # L1 | CE_coor
            XYZ_LOSS_MASK_GT="visib",  # trunc | visib | obj
            XYZ_LW=1.0,
            # mask loss ---------------------------
            MASK_LOSS_TYPE="L1",  # L1 | BCE | CE
            MASK_LOSS_GT="trunc",  # trunc | visib | gt
            MASK_LW=1.0,
            # full mask loss ---------------------------
            FULL_MASK_LOSS_TYPE="L1",  # L1 | BCE | CE
            FULL_MASK_LW=1.0,
            # region loss -------------------------
            REGION_LOSS_TYPE="CE",  # CE
            REGION_LOSS_MASK_GT="visib",  # trunc | visib | obj
            REGION_LW=1.0,
            # pm loss --------------
            PM_LOSS_SYM=True,  # NOTE: sym loss
            PM_R_ONLY=True,  # only do R loss in PM
            PM_LW=1.0,
            # centroid loss -------
            CENTROID_LOSS_TYPE="L1",
            CENTROID_LW=1.0,
            # z loss -----------
            Z_LOSS_TYPE="L1",
            Z_LW=1.0,
        ),
    ),
)

VAL = dict(
    DATASET_NAME="ycbv",
    SPLIT_TYPE="",
    SCRIPT_PATH="lib/pysixd/scripts/eval_pose_results_more.py",
    TARGETS_FILENAME="test_targets_bop19.json",
    ERROR_TYPES="vsd,mspd,mssd",
    USE_BOP=True,  # whether to use bop toolkit
)

TEST = dict(EVAL_PERIOD=0, VIS=False, TEST_BBOX_TYPE="est")  # gt | est

20240102_142055|core.utils.default_args_setup@135: Running with full config:
Config (path: configs/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv.py): {'OUTPUT_ROOT': 'output', 'OUTPUT_DIR': 'output/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv', 'EXP_NAME': '', 'DEBUG': False, 'SEED': -1, 'CUDNN_BENCHMARK': True, 'IM_BACKEND': 'cv2', 'VIS_PERIOD': 0, 'INPUT': {'FORMAT': 'BGR', 'MIN_SIZE_TRAIN': 480, 'MAX_SIZE_TRAIN': 640, 'MIN_SIZE_TRAIN_SAMPLING': 'choice', 'MIN_SIZE_TEST': 480, 'MAX_SIZE_TEST': 640, 'WITH_DEPTH': False, 'BP_DEPTH': False, 'AUG_DEPTH': False, 'NORM_DEPTH': False, 'DROP_DEPTH_RATIO': 0.2, 'DROP_DEPTH_PROB': 0.5, 'ADD_NOISE_DEPTH_LEVEL': 0.01, 'ADD_NOISE_DEPTH_PROB': 0.9, 'COLOR_AUG_PROB': 0.8, 'COLOR_AUG_TYPE': 'code', 'COLOR_AUG_CODE': 'Sequential([Sometimes(0.5, CoarseDropout( p=0.2, size_percent=0.05) ),Sometimes(0.4, GaussianBlur((0., 3.))),Sometimes(0.3, pillike.EnhanceSharpness(factor=(0., 50.))),Sometimes(0.3, pillike.EnhanceContrast(factor=(0.2, 50.))),Sometimes(0.5, pillike.EnhanceBrightness(factor=(0.1, 6.))),Sometimes(0.3, pillike.EnhanceColor(factor=(0., 20.))),Sometimes(0.5, Add((-25, 25), per_channel=0.3)),Sometimes(0.3, Invert(0.2, per_channel=True)),Sometimes(0.5, Multiply((0.6, 1.4), per_channel=0.5)),Sometimes(0.5, Multiply((0.6, 1.4))),Sometimes(0.1, AdditiveGaussianNoise(scale=10, per_channel=True)),Sometimes(0.5, iaa.contrast.LinearContrast((0.5, 2.2), per_channel=0.3)),Sometimes(0.5, Grayscale(alpha=(0.0, 1.0))),], random_order=True)', 'COLOR_AUG_SYN_ONLY': False, 'RANDOM_FLIP': 'none', 'WITH_BG_DEPTH': False, 'BG_DEPTH_FACTOR': 10000.0, 'BG_TYPE': 'VOC_table', 'BG_IMGS_ROOT': 'datasets/VOCdevkit/VOC2012/', 'NUM_BG_IMGS': 10000, 'CHANGE_BG_PROB': 0.5, 'TRUNCATE_FG': True, 'BG_KEEP_ASPECT_RATIO': True, 'DZI_TYPE': 'uniform', 'DZI_PAD_SCALE': 1.5, 'DZI_SCALE_RATIO': 0.25, 'DZI_SHIFT_RATIO': 0.25, 'SMOOTH_XYZ': False}, 'DATASETS': {'TRAIN': ('ycbv_train_real', 'ycbv_train_pbr'), 'TRAIN2': (), 'TRAIN2_RATIO': 0.0, 'DATA_LEN_WITH_TRAIN2': True, 'PROPOSAL_FILES_TRAIN': (), 'PRECOMPUTED_PROPOSAL_TOPK_TRAIN': 2000, 'TEST': ('ycbv_test',), 'PROPOSAL_FILES_TEST': (), 'PRECOMPUTED_PROPOSAL_TOPK_TEST': 1000, 'DET_FILES_TRAIN': (), 'DET_TOPK_PER_OBJ_TRAIN': 1, 'DET_TOPK_PER_IM_TRAIN': 30, 'DET_THR_TRAIN': 0.0, 'DET_FILES_TEST': ('datasets/BOP_DATASETS/ycbv/test/test_bboxes/yolox_x_640_ycbv_real_pbr_ycbv_bop_test.json',), 'DET_TOPK_PER_OBJ': 1, 'DET_TOPK_PER_IM': 30, 'DET_THR': 0.0, 'INIT_POSE_FILES_TEST': (), 'INIT_POSE_TOPK_PER_OBJ': 1, 'INIT_POSE_TOPK_PER_IM': 30, 'INIT_POSE_THR': 0.0, 'SYM_OBJS': ['024_bowl', '036_wood_block', '051_large_clamp', '052_extra_large_clamp', '061_foam_brick'], 'EVAL_SCENE_IDS': None}, 'DATALOADER': {'NUM_WORKERS': 8, 'PERSISTENT_WORKERS': False, 'MAX_OBJS_TRAIN': 120, 'ASPECT_RATIO_GROUPING': False, 'SAMPLER_TRAIN': 'TrainingSampler', 'REPEAT_THRESHOLD': 0.0, 'FILTER_EMPTY_ANNOTATIONS': True, 'FILTER_EMPTY_DETS': True, 'FILTER_VISIB_THR': 0.3, 'REMOVE_ANNO_KEYS': []}, 'SOLVER': {'IMS_PER_BATCH': 48, 'REFERENCE_BS': 48, 'TOTAL_EPOCHS': 40, 'OPTIMIZER_CFG': {'type': 'Ranger', 'lr': 0.0008, 'weight_decay': 0.01}, 'GAMMA': 0.1, 'BIAS_LR_FACTOR': 1.0, 'LR_SCHEDULER_NAME': 'flat_and_anneal', 'WARMUP_METHOD': 'linear', 'WARMUP_FACTOR': 0.001, 'WARMUP_ITERS': 1000, 'ANNEAL_METHOD': 'cosine', 'ANNEAL_POINT': 0.72, 'POLY_POWER': 0.9, 'REL_STEPS': (0.5, 0.75), 'CHECKPOINT_PERIOD': 5, 'CHECKPOINT_BY_EPOCH': True, 'MAX_TO_KEEP': 5, 'CLIP_GRADIENTS': {'ENABLED': False, 'CLIP_TYPE': 'value', 'CLIP_VALUE': 1.0, 'NORM_TYPE': 2.0}, 'SET_NAN_GRAD_TO_ZERO': False, 'AMP': {'ENABLED': False}, 'WEIGHT_DECAY': 0.01, 'OPTIMIZER_NAME': 'Ranger', 'BASE_LR': 0.0008, 'MOMENTUM': 0.9}, 'TRAIN': {'PRINT_FREQ': 100, 'VERBOSE': False, 'VIS': False, 'VIS_IMG': False}, 'VAL': {'DATASET_NAME': 'ycbv', 'SCRIPT_PATH': 'lib/pysixd/scripts/eval_pose_results_more.py', 'RESULTS_PATH': '', 'TARGETS_FILENAME': 'test_targets_bop19.json', 'ERROR_TYPES': 'vsd,mspd,mssd', 'RENDERER_TYPE': 'cpp', 'SPLIT': 'test', 'SPLIT_TYPE': '', 'N_TOP': 1, 'EVAL_CACHED': False, 'SCORE_ONLY': False, 'EVAL_PRINT_ONLY': False, 'EVAL_PRECISION': False, 'USE_BOP': True, 'SAVE_BOP_CSV_ONLY': False}, 'TEST': {'EVAL_PERIOD': 0, 'VIS': False, 'TEST_BBOX_TYPE': 'est', 'PRECISE_BN': {'ENABLED': False, 'NUM_ITER': 200}, 'AMP_TEST': False, 'COLOR_AUG': False, 'USE_PNP': False, 'SAVE_RESULTS_ONLY': False, 'PNP_TYPE': 'ransac_pnp', 'USE_DEPTH_REFINE': False, 'DEPTH_REFINE_ITER': 2, 'DEPTH_REFINE_THRESHOLD': 0.8, 'USE_COOR_Z_REFINE': False}, 'DIST_PARAMS': {'backend': 'nccl'}, 'MODEL': {'DEVICE': 'cuda', 'WEIGHTS': 'output/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv/model_final_wo_optim.pth', 'PIXEL_MEAN': [0.0, 0.0, 0.0], 'PIXEL_STD': [255.0, 255.0, 255.0], 'LOAD_DETS_TEST': True, 'BBOX_CROP_REAL': False, 'BBOX_CROP_SYN': False, 'BBOX_TYPE': 'AMODAL_CLIP', 'EMA': {'ENABLED': False, 'INIT_CFG': {'decay': 0.9999, 'updates': 0}}, 'POSE_NET': {'NAME': 'GDRN_double_mask', 'XYZ_ONLINE': True, 'XYZ_BP': True, 'NUM_CLASSES': 21, 'USE_MTL': False, 'INPUT_RES': 256, 'OUTPUT_RES': 64, 'BACKBONE': {'FREEZE': False, 'PRETRAINED': 'timm', 'INIT_CFG': {'type': 'timm/convnext_base', 'in_chans': 3, 'features_only': True, 'pretrained': True, 'out_indices': (3,)}}, 'DEPTH_BACKBONE': {'ENABLED': False, 'FREEZE': False, 'PRETRAINED': 'timm', 'INIT_CFG': {'type': 'timm/resnet18', 'in_chans': 1, 'features_only': True, 'pretrained': True, 'out_indices': (4,)}}, 'FUSE_RGBD_TYPE': 'cat', 'NECK': {'ENABLED': False, 'FREEZE': False, 'LR_MULT': 1.0, 'INIT_CFG': {'type': 'FPN', 'in_channels': [256, 512, 1024, 2048], 'out_channels': 256, 'num_outs': 4}}, 'GEO_HEAD': {'FREEZE': False, 'LR_MULT': 1.0, 'INIT_CFG': {'type': 'TopDownDoubleMaskXyzRegionHead', 'in_dim': 1024, 'up_types': ('deconv', 'bilinear', 'bilinear'), 'deconv_kernel_size': 3, 'num_conv_per_block': 2, 'feat_dim': 256, 'feat_kernel_size': 3, 'norm': 'GN', 'num_gn_groups': 32, 'act': 'GELU', 'out_kernel_size': 1, 'out_layer_shared': True}, 'XYZ_BIN': 64, 'XYZ_CLASS_AWARE': True, 'MASK_CLASS_AWARE': True, 'REGION_CLASS_AWARE': True, 'MASK_THR_TEST': 0.5, 'NUM_REGIONS': 64}, 'PNP_NET': {'FREEZE': False, 'LR_MULT': 1.0, 'INIT_CFG': {'type': 'ConvPnPNet', 'norm': 'GN', 'act': 'gelu', 'num_gn_groups': 32, 'drop_prob': 0.0, 'denormalize_by_extent': True}, 'WITH_2D_COORD': True, 'COORD_2D_TYPE': 'abs', 'REGION_ATTENTION': True, 'MASK_ATTENTION': 'none', 'ROT_TYPE': 'allo_rot6d', 'TRANS_TYPE': 'centroid_z', 'Z_TYPE': 'REL'}, 'LOSS_CFG': {'XYZ_LOSS_TYPE': 'L1', 'XYZ_LOSS_MASK_GT': 'visib', 'XYZ_LW': 1.0, 'FULL_MASK_LOSS_TYPE': 'L1', 'FULL_MASK_LW': 1.0, 'MASK_LOSS_TYPE': 'L1', 'MASK_LOSS_GT': 'trunc', 'MASK_LW': 1.0, 'REGION_LOSS_TYPE': 'CE', 'REGION_LOSS_MASK_GT': 'visib', 'REGION_LW': 1.0, 'NUM_PM_POINTS': 3000, 'PM_LOSS_TYPE': 'L1', 'PM_SMOOTH_L1_BETA': 1.0, 'PM_LOSS_SYM': True, 'PM_NORM_BY_EXTENT': False, 'PM_R_ONLY': True, 'PM_DISENTANGLE_T': False, 'PM_DISENTANGLE_Z': False, 'PM_T_USE_POINTS': True, 'PM_LW': 1.0, 'ROT_LOSS_TYPE': 'angular', 'ROT_LW': 0.0, 'CENTROID_LOSS_TYPE': 'L1', 'CENTROID_LW': 1.0, 'Z_LOSS_TYPE': 'L1', 'Z_LW': 1.0, 'TRANS_LOSS_TYPE': 'L1', 'TRANS_LOSS_DISENTANGLE': True, 'TRANS_LW': 0.0, 'BIND_LOSS_TYPE': 'L1', 'BIND_LW': 0.0}}, 'KEYPOINT_ON': False, 'LOAD_PROPOSALS': False}, 'EXP_ID': 'convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv_test', 'RESUME': False}
Global seed set to 55675198
20240102_142055|core.utils.default_args_setup@144: Full config saved to output/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv.py
20240102_142055|d2.utils.env@41: Using a generated random seed 55675198
20240102_142055|core.utils.default_args_setup@162: Used mmcv backend: cv2
20240102_142055|__main__@157: Used GDRN module name: GDRN_double_mask
20240102_142056|timm.models.helpers@244: Loading pretrained weights from url (https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth)
20240102_142057|__main__@159: Model:
GDRN_DoubleMask(
  (backbone): FeatureListNet(
    (stem_0): Conv2d(3, 128, kernel_size=(4, 4), stride=(4, 4))
    (stem_1): LayerNorm2d((128,), eps=1e-06, elementwise_affine=True)
    (stages_0): ConvNeXtStage(
      (downsample): Identity()
      (blocks): Sequential(
        (0): ConvNeXtBlock(
          (conv_dw): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=128)
          (norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=128, out_features=512, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=512, out_features=128, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (1): ConvNeXtBlock(
          (conv_dw): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=128)
          (norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=128, out_features=512, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=512, out_features=128, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (2): ConvNeXtBlock(
          (conv_dw): Conv2d(128, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=128)
          (norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=128, out_features=512, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=512, out_features=128, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
      )
    )
    (stages_1): ConvNeXtStage(
      (downsample): Sequential(
        (0): LayerNorm2d((128,), eps=1e-06, elementwise_affine=True)
        (1): Conv2d(128, 256, kernel_size=(2, 2), stride=(2, 2))
      )
      (blocks): Sequential(
        (0): ConvNeXtBlock(
          (conv_dw): Conv2d(256, 256, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=256)
          (norm): LayerNorm((256,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=256, out_features=1024, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=1024, out_features=256, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (1): ConvNeXtBlock(
          (conv_dw): Conv2d(256, 256, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=256)
          (norm): LayerNorm((256,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=256, out_features=1024, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=1024, out_features=256, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (2): ConvNeXtBlock(
          (conv_dw): Conv2d(256, 256, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=256)
          (norm): LayerNorm((256,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=256, out_features=1024, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=1024, out_features=256, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
      )
    )
    (stages_2): ConvNeXtStage(
      (downsample): Sequential(
        (0): LayerNorm2d((256,), eps=1e-06, elementwise_affine=True)
        (1): Conv2d(256, 512, kernel_size=(2, 2), stride=(2, 2))
      )
      (blocks): Sequential(
        (0): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (1): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (2): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (3): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (4): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (5): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (6): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (7): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (8): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (9): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (10): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (11): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (12): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (13): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (14): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (15): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (16): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (17): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (18): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (19): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (20): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (21): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (22): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (23): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (24): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (25): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (26): ConvNeXtBlock(
          (conv_dw): Conv2d(512, 512, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=512)
          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=512, out_features=2048, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=2048, out_features=512, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
      )
    )
    (stages_3): ConvNeXtStage(
      (downsample): Sequential(
        (0): LayerNorm2d((512,), eps=1e-06, elementwise_affine=True)
        (1): Conv2d(512, 1024, kernel_size=(2, 2), stride=(2, 2))
      )
      (blocks): Sequential(
        (0): ConvNeXtBlock(
          (conv_dw): Conv2d(1024, 1024, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=1024)
          (norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=1024, out_features=4096, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=4096, out_features=1024, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (1): ConvNeXtBlock(
          (conv_dw): Conv2d(1024, 1024, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=1024)
          (norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=1024, out_features=4096, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=4096, out_features=1024, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
        (2): ConvNeXtBlock(
          (conv_dw): Conv2d(1024, 1024, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=1024)
          (norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=1024, out_features=4096, bias=True)
            (act): GELU(approximate='none')
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=4096, out_features=1024, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (drop_path): Identity()
        )
      )
    )
  )
  (geo_head_net): TopDownDoubleMaskXyzRegionHead(
    (features): ModuleList(
      (0): ConvTranspose2d(1024, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1), bias=False)
      (1): GroupNorm(32, 256, eps=1e-05, affine=True)
      (2): GELU(approximate='none')
      (3-4): 2 x ConvModule(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (norm): GroupNorm(32, 256, eps=1e-05, affine=True)
        (gn): GroupNorm(32, 256, eps=1e-05, affine=True)
        (activate): GELU(approximate='none')
      )
      (5): UpsamplingBilinear2d(scale_factor=2.0, mode='bilinear')
      (6-7): 2 x ConvModule(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (norm): GroupNorm(32, 256, eps=1e-05, affine=True)
        (gn): GroupNorm(32, 256, eps=1e-05, affine=True)
        (activate): GELU(approximate='none')
      )
      (8): UpsamplingBilinear2d(scale_factor=2.0, mode='bilinear')
      (9-10): 2 x ConvModule(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (norm): GroupNorm(32, 256, eps=1e-05, affine=True)
        (gn): GroupNorm(32, 256, eps=1e-05, affine=True)
        (activate): GELU(approximate='none')
      )
    )
    (out_layer): Conv2d(256, 1470, kernel_size=(1, 1), stride=(1, 1))
  )
  (pnp_net): ConvPnPNet(
    (act): GELU(approximate='none')
    (dropblock): LinearScheduler(
      (dropblock): DropBlock2D()
    )
    (features): ModuleList(
      (0): Conv2d(69, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (1): GroupNorm(32, 128, eps=1e-05, affine=True)
      (2): GELU(approximate='none')
      (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (4): GroupNorm(32, 128, eps=1e-05, affine=True)
      (5): GELU(approximate='none')
      (6): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (7): GroupNorm(32, 128, eps=1e-05, affine=True)
      (8): GELU(approximate='none')
    )
    (fc1): Linear(in_features=8192, out_features=1024, bias=True)
    (fc2): Linear(in_features=1024, out_features=256, bias=True)
    (fc_r): Linear(in_features=256, out_features=6, bias=True)
    (fc_t): Linear(in_features=256, out_features=3, bias=True)
  )
)
20240102_142057|__main__@171: 102.873543M params
20240102_142057|d2.checkpoint.detection_checkpoint@38: [DetectionCheckpointer] Loading from output/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv/model_final_wo_optim.pth ...
20240102_142057|fvcore.common.checkpoint@150: [Checkpointer] Loading from output/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv/model_final_wo_optim.pth ...
20240102_142057|core.gdrn_modeling.datasets.ycbv_d2@310: loading dataset dicts: ycbv_test
  0%|          | 5/2949 [00:00<00:29, 99.03it/s]
20240102_142058|ERR|__main__@233: An error has been caught in function '<module>', process 'MainProcess' (37572), thread 'MainThread' (140224339037056):
Traceback (most recent call last):

> File "/home/mona/gdrnpp_bop2022/./core/gdrn_modeling/main_gdrn.py", line 233, in <module>
    main(args)
    │    └ Namespace(config_file='configs/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv.py', resume...
    └ <function main at 0x7f8746b1e5f0>

  File "/home/mona/gdrnpp_bop2022/./core/gdrn_modeling/main_gdrn.py", line 205, in main
    ).run(args, cfg)
          │     └ Config (path: configs/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv.py): {'OUTPUT_ROOT':...
          └ Namespace(config_file='configs/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv.py', resume...

  File "/home/mona/.local/lib/python3.10/site-packages/pytorch_lightning/lite/lite.py", line 408, in _run_impl
    return run_method(*args, **kwargs)
           │           │       └ {}
           │           └ (Namespace(config_file='configs/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv.py', resum...
           └ functools.partial(<bound method LightningLite._run_with_strategy_setup of <__main__.Lite object at 0x7f87467adab0>>, <bound m...
  File "/home/mona/.local/lib/python3.10/site-packages/pytorch_lightning/lite/lite.py", line 413, in _run_with_strategy_setup
    return run_method(*args, **kwargs)
           │           │       └ {}
           │           └ (Namespace(config_file='configs/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv.py', resum...
           └ <bound method Lite.run of <__main__.Lite object at 0x7f87467adab0>>

  File "/home/mona/gdrnpp_bop2022/./core/gdrn_modeling/main_gdrn.py", line 183, in run
    return self.do_test(cfg, model)
           │    │       │    └ _LiteModule(
           │    │       │        (_module): GDRN_DoubleMask(
           │    │       │          (backbone): FeatureListNet(
           │    │       │            (stem_0): Conv2d(3, 128, kernel_size=(4, 4),...
           │    │       └ Config (path: configs/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv.py): {'OUTPUT_ROOT':...
           │    └ <function GDRN_Lite.do_test at 0x7f874dd80d30>
           └ <__main__.Lite object at 0x7f87467adab0>

  File "/home/mona/gdrnpp_bop2022/core/gdrn_modeling/../../core/gdrn_modeling/engine/engine.py", line 157, in do_test
    data_loader = build_gdrn_test_loader(cfg, dataset_name, train_objs=evaluator.train_objs)
                  │                      │    │                        │         └ ['002_master_chef_can', '003_cracker_box', '004_sugar_box', '005_tomato_soup_can', '006_mustard_bottle', '007_tuna_fish_can',...
                  │                      │    │                        └ <core.gdrn_modeling.engine.gdrn_evaluator.GDRN_Evaluator object at 0x7f8746777820>
                  │                      │    └ 'ycbv_test'
                  │                      └ Config (path: configs/gdrn/ycbv/convnext_a6_AugCosyAAEGray_BG05_mlL1_DMask_amodalClipBox_classAware_ycbv.py): {'OUTPUT_ROOT':...
                  └ <function build_gdrn_test_loader at 0x7f874dd36290>

  File "/home/mona/gdrnpp_bop2022/core/gdrn_modeling/../../core/gdrn_modeling/datasets/data_loader.py", line 914, in build_gdrn_test_loader
    dataset_dicts = get_detection_dataset_dicts(
                    └ <function get_detection_dataset_dicts at 0x7f87830d4af0>

  File "/home/mona/anaconda3/envs/gdrnpp/lib/python3.10/site-packages/detectron2/data/build.py", line 253, in get_detection_dataset_dicts
    dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in names]
                     │              │                                     └ ['ycbv_test']
                     │              └ <function _DatasetCatalog.get at 0x7f87830b8e50>
                     └ DatasetCatalog(registered datasets: coco_2014_train, coco_2014_val, coco_2014_minival, coco_2014_valminusminival, coco_2017_t...
  File "/home/mona/anaconda3/envs/gdrnpp/lib/python3.10/site-packages/detectron2/data/build.py", line 253, in <listcomp>
    dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in names]
                     │              │   │                 └ 'ycbv_test'
                     │              │   └ 'ycbv_test'
                     │              └ <function _DatasetCatalog.get at 0x7f87830b8e50>
                     └ DatasetCatalog(registered datasets: coco_2014_train, coco_2014_val, coco_2014_minival, coco_2014_valminusminival, coco_2017_t...
  File "/home/mona/anaconda3/envs/gdrnpp/lib/python3.10/site-packages/detectron2/data/catalog.py", line 58, in get
    return f()
           └ <core.gdrn_modeling.datasets.ycbv_d2.YCBV_Dataset object at 0x7f87467adf90>

  File "/home/mona/gdrnpp_bop2022/core/gdrn_modeling/../../core/gdrn_modeling/datasets/ycbv_d2.py", line 316, in __call__
    dataset_dicts.extend(self._load_from_idx_file(ann_file, image_root))
    │             │      │    │                   │         └ '/home/mona/gdrnpp_bop2022/datasets/BOP_DATASETS/ycbv/test'
    │             │      │    │                   └ '/home/mona/gdrnpp_bop2022/datasets/BOP_DATASETS/ycbv/image_sets/keyframe.txt'
    │             │      │    └ <function YCBV_Dataset._load_from_idx_file at 0x7f874f6872e0>
    │             │      └ <core.gdrn_modeling.datasets.ycbv_d2.YCBV_Dataset object at 0x7f87467adf90>
    │             └ <method 'extend' of 'list' objects>
    └ []

  File "/home/mona/gdrnpp_bop2022/core/gdrn_modeling/../../core/gdrn_modeling/datasets/ycbv_d2.py", line 135, in _load_from_idx_file
    cam_anno = np.array(scene_cam_dicts[scene_id][str_im_id]["cam_K"], dtype=np.float32).reshape(3, 3)
               │  │     │               │         │                          │  └ <class 'numpy.float32'>
               │  │     │               │         │                          └ <module 'numpy' from '/home/mona/.local/lib/python3.10/site-packages/numpy/__init__.py'>
               │  │     │               │         └ '135'
               │  │     │               └ 48
               │  │     └ {48: {'1': {'cam_K': [1066.778, 0.0, 312.9869, 0.0, 1067.487, 241.3109, 0.0, 0.0, 1.0], 'cam_R_w2c': [0.775038, 0.630563, -0....
               │  └ <built-in function array>
               └ <module 'numpy' from '/home/mona/.local/lib/python3.10/site-packages/numpy/__init__.py'>

KeyError: '135'

20240103_142209|core.gdrn_modeling.datasets.ycbv_d2@313: loading dataset dicts: ycbv_test
  0%|          | 0/2949 [00:00<?, ?it/s]scene_id:  48
str_im_id:  1
scene_id:  48
str_im_id:  36
scene_id:  48
str_im_id:  47
scene_id:  48
str_im_id:  83
scene_id:  48
str_im_id:  112
scene_id:  48
str_im_id:  135
  0%|          | 5/2949 [00:00<00:29, 100.26it/s]
20240103_142210|ERR|__main__@233: An error has been caught in function '<module>', process 'MainProcess' (257602), thread 'MainThread' (140686709906304):
Traceback (most recent call last):

trained from scratch and got inference and didn't have this issue so closing it.


text:
Update sbt-scalafix to 0.9.29

Updates ch.epfl.scala:sbt-scalafix from 0.9.11 to 0.9.29.
GitHub Release Notes - Version Diff
I'll automatically update this PR to resolve conflicts as long as you don't change it yourself.
If you'd like to skip this version, you can just close this PR. If you have any feedback, just mention me in the comments below.
Configure Scala Steward for your repository with a .scala-steward.conf file.
Have a fantastic day writing Scala!

Ignore future updates
Add this to your .scala-steward.conf file to ignore future updates of this dependency:
updates.ignore = [ { groupId = "ch.epfl.scala", artifactId = "sbt-scalafix" } ]

labels: sbt-plugin-update, semver-patch

Superseded by #122.

repo: shankarshastri/scala-proj-template


text:
fix: send change back to from address when outputs contain OP_RETURN_DATA

Description

Pull Request Type

[ ] :bug: Bug fix (Non-breaking Change: Fixes an issue)
[ ] :hammer_and_wrench: Chore (Non-breaking Change: Doc updates, pkg upgrades, typos, etc..)
[ ] :nail_care: New Feature (Breaking/Non-breaking Change)

Issue (if applicable)

closes #
Risk

Testing

Engineering

Operations

Screenshots (if applicable)

Current dependencies on/for this PR:

develop

PR #5937   👈

This stack of pull requests is managed by Graphite.

repo: shapeshift/web


text:
Use Chakra UI for design system

I needed a modal component to extract the login flow into its own components and opted to pick a design system library to figure that out.
Not attached the choice or anything, but I ended up using https://github.com/chakra-ui/chakra-ui just because it had Modals, a React API with accessibility and responsive design, default colors, and support for dark mode. We can definitely change this - I don't want to commit to this decision without your input!
Here's how it looks so far:

Feel free to merge this if you think it's fine and want to built on top of it!

Sounds good to me! Chakra seems cool.

Sounds good to me as well.
On Sat, Aug 21, 2021, 10:33 PM Tanishq Kancharla @.***>
wrote:

Merged #2
https://github.com/sharkstewardsdeveloper/shark-reporting/pull/2 into
main.
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authors: CodeJonesW moonrise-tk ronaldsmartin

repo: sharkstewardsdeveloper/shark-reporting


text:
Get a officeHome not set and could not be auto-detected error

Exception in thread "main" java.lang.IllegalStateException: officeHome not set and could not be auto-detected
How to fix it?

set office home like:
OfficeManager officeManager = new DefaultOfficeManagerConfiguration().setOfficeHome("C:\Program Files (x86)\OpenOffice 4").buildOfficeManager();

    OfficeManager officeManager = LocalOfficeManager.builder().officeHome("/opt/libreoffice6.1").install().build();

authors: 2720851545 chareice softprg

license: mit

license_source: bigquery


text:
TextLayout.Metrics throws ObjectDisposedException

After upgrading to 4.1.0 from 4.0.1, I get that TextLayout.Metrics throws an ObjectDisposedException. My current flow is that I create a new TextLayout and then call TextLayout.Metrics on it.
Since upgrading, this throws an ObjectDisposedException. When calling TextLayout.Metrics in the immediate window, the metrics start showing up. Not sure why this stopped working, or what has changed in SharpDX 4.1.0 that changed the behaviour.
Downgrading to 4.0.1 resolves this issue. Any ideas what might go wrong?

I have the same issue and thats my current workaround:
var metrics = new DWrite.TextMetrics();

            try
            {
                metrics = TextLayout.Metrics;
            }
            catch (ObjectDisposedException)
            {
                metrics = TextLayout.Metrics;
            }

What's the stack trace of the exception?

I try to get the Stacktrace later this day when I have time.

The exception is:
'Layout.Metrics' threw an exception of type 'System.ObjectDisposedException'
    Data: {System.Collections.ListDictionaryInternal}
    HResult: -2146232798
    HelpLink: null
    InnerException: null
    Message: "Cannot access a disposed object.\r\nObject name: 'Cannot add a reference to a nonreferenced item'."
    ObjectName: "Cannot add a reference to a nonreferenced item"
    Source: "SharpDX"
    StackTrace: "   at SharpDX.CallbackBase.AddReference()\r\n   at SharpDX.DirectWrite.TextLayout.GetMetrics(TextMetrics& textMetrics)\r\n   at SharpDX.DirectWrite.TextLayout.get_Metrics()"
    TargetSite: {Int32 AddReference()}

calling textLayout.Metrics again (from Visual Studio's immediate window for example) results in a correct result.

Any chance you can get the type of the this object in the AddReference call?

I've tried, but haven't been able to pinpoint what "this" is in this case. Unfortunately I was unable to build a debug build of the SharpDX code to see if I could dive deeper into the exception. Perhaps @Gillibald has had more success?

I had no luck too. That part is unmanaged and couldn't figure out to make it possible to debug. Probably have to build everything from source.

Update: I've seem to have narrowed it down to calls to Release() on the custom font loaders that I use. These loaders are copied from the CustomFont example: https://github.com/sharpdx/SharpDX-Samples/tree/f4fb0fe0f12e3edc4eda3f6307d135a66fa172cd/Desktop/DirectWrite/CustomFont
Now to find out why Release() is called on these fontloaders in 4.1/4.2 where they apparently weren't called in 4.0.1.

OK, I seem to have fixed it (for my case) by rewriting the example ResourceFontFileStream to not extend CallbackBase, but provide a (dummy) implementation.
This resulted in the following code, which seems to work, although I am a bit worried that I am setting comObject in QueryInterface to zero.
/// <summary>
/// This FontFileStream implem is reading data from a <see cref="DataStream"/>.
/// </summary>
public class ResourceFontFileStream : FontFileStream
{
    private readonly DataStream _stream;
    private bool disposedValue = false; // To detect redundant calls to Dispose()

    public IDisposable Shadow { get; set; }

    /// <summary>
    /// Initializes a new instance of the <see cref="ResourceFontFileStream"/> class.
    /// </summary>
    /// <param name="stream">The stream.</param>
    public ResourceFontFileStream(DataStream stream) => _stream = stream;

    /// <summary>
    /// Reads a fragment from a font file.
    /// </summary>
    /// <param name="fragmentStart">When this method returns, contains an address of a  reference to the start of the font file fragment.  This parameter is passed uninitialized.</param>
    /// <param name="fileOffset">The offset of the fragment, in bytes, from the beginning of the font file.</param>
    /// <param name="fragmentSize">The size of the file fragment, in bytes.</param>
    /// <param name="fragmentContext">When this method returns, contains the address of</param>
    /// <remarks>
    /// Note that ReadFileFragment implementations must check whether the requested font file fragment is within the file bounds. Otherwise, an error should be returned from ReadFileFragment.   {{DirectWrite}} may invoke <see cref="SharpDX.DirectWrite.FontFileStream"/> methods on the same object from multiple threads simultaneously. Therefore, ReadFileFragment implementations that rely on internal mutable state must serialize access to such state across multiple threads. For example, an implementation that uses separate Seek and Read operations to read a file fragment must place the code block containing Seek and Read calls under a lock or a critical section.
    /// </remarks>
    /// <unmanaged>HRESULT IDWriteFontFileStream::ReadFileFragment([Out, Buffer] const void** fragmentStart,[None] __int64 fileOffset,[None] __int64 fragmentSize,[Out] void** fragmentContext)</unmanaged>
    public void ReadFileFragment(out IntPtr fragmentStart, long fileOffset, long fragmentSize, out IntPtr fragmentContext)
    {
        lock (this)
        {
            fragmentContext = IntPtr.Zero;
            _stream.Position = fileOffset;
            fragmentStart = _stream.PositionPointer;
        }
    }

    /// <summary>
    /// Releases a fragment from a file.
    /// </summary>
    /// <param name="fragmentContext">A reference to the client-defined context of a font fragment returned from {{ReadFileFragment}}.</param>
    /// <unmanaged>void IDWriteFontFileStream::ReleaseFileFragment([None] void* fragmentContext)</unmanaged>
    public void ReleaseFileFragment(IntPtr fragmentContext)
    {
        // Nothing to release. No context are used
    }

    /// <summary>
    /// Obtains the total size of a file.
    /// </summary>
    /// <returns>the total size of the file.</returns>
    /// <remarks>
    /// Implementing GetFileSize() for asynchronously loaded font files may require downloading the complete file contents. Therefore, this method should be used only for operations that either require a complete font file to be loaded (for example, copying a font file) or that need to make decisions based on the value of the file size (for example, validation against a persisted file size).
    /// </remarks>
    /// <unmanaged>HRESULT IDWriteFontFileStream::GetFileSize([Out] __int64* fileSize)</unmanaged>
    public long GetFileSize() => _stream.Length;

    /// <summary>
    /// Obtains the last modified time of the file.
    /// </summary>
    /// <returns>
    /// the last modified time of the file in the format that represents the number of 100-nanosecond intervals since January 1, 1601 (UTC).
    /// </returns>
    /// <remarks>
    /// The "last modified time" is used by DirectWrite font selection algorithms to determine whether one font resource is more up to date than another one.
    /// </remarks>
    /// <unmanaged>HRESULT IDWriteFontFileStream::GetLastWriteTime([Out] __int64* lastWriteTime)</unmanaged>
    public long GetLastWriteTime() => 0;

    public Result QueryInterface(ref Guid guid, out IntPtr comObject)
    {
        comObject = IntPtr.Zero;
        return Result.Ok;
    }

    public int AddReference() => 0;

    public int Release() => 0;

    protected virtual void Dispose(bool disposing)
    {
        if (!disposedValue)
        {
            if (disposing)
            {
                _stream.Dispose();
            }

            disposedValue = true;
        }
    }

    // This code added to correctly implement the disposable pattern.
    public void Dispose()
    {
        Dispose(true);
        GC.SuppressFinalize(this);
    }
}

Burned a couple hours today looking at this issue.
@MagicMau Your code "works", but it will leak memory because the underlying COM shadow objects will never get disposed.  You can see how the comObject is set in https://github.com/sharpdx/SharpDX/blob/1f89381bc08f2d8cf8d294c6c5fce8dc85a849a1/Source/SharpDX/CallbackBase.cs#L87 , but unfortunately ShadowContainer is private so we can't get access to it in our classes.
One of the problems is that the SharpDX Sample for custom fonts isn't done correctly, at least in the way the SharpDX library works.  CreateStreamFromKey needs to actually create a create a new ResourceFontFileStream(stream) instead of returning the one that was created in the constructor.  So modify this https://github.com/sharpdx/SharpDX-Samples/blob/f4fb0fe0f12e3edc4eda3f6307d135a66fa172cd/Desktop/DirectWrite/CustomFont/ResourceFontLoader.cs#L111 to do so.   The reason is that when the font stream is first read,  it gets AddReference and Release called multiple times and will actually dispose the underlying Shadow object (see here: https://github.com/sharpdx/SharpDX/blob/1f89381bc08f2d8cf8d294c6c5fce8dc85a849a1/Source/SharpDX/CallbackBase.cs#L75 )
Then, when we finally call textLayout.Metrics it calls AddReference and tries to get the metrics, but it fails because the underlying FontFileStreamShadow COM object is disposed.
What I have working is to override the Dispose method from CallbackBase with this to always release the shadow object
 protected override void Dispose(bool disposing)         {             // Dispose native resources             var callback = ((ICallbackable)this);             if (callback.Shadow != null)             {                 callback.Shadow.Dispose();                 callback.Shadow = null;             }         }
To summarize

Keep CallbackBase in ResourceFontFileStream
Add the Dispose override to ResourceFontFileStream and ResourceFontLoader
Have the CreateStreamFromKey method actually construct a new ResourceFontFileStream

@Gillibald This fix would be better than the try/catch that is in Avalonia.

@jgimness Thanks for the fix will try it out. Would have never been able to fix that myself 👍

authors: Gillibald MagicMau jgimness jkoritzinsky

repo: sharpdx/SharpDX


text:
Time-dependent test

https://github.com/sharplispers/scheduler/blob/bafc33cd99adc12b922870df3c51f9cbc927baff/scheduler-tests.lisp#L82
This check fails because by now compute-next-occurance doesn't find 1 Jan 2023. Instead it finds 2034 for me.
Presumably you can just change it to 2034, and hope that the world comes to an end bfore that.....
BTW: it is "occurrence".

haha, good one! :) thanks for the report. I'll fix it right away.

authors: Yehouda dkochmanski

repo: sharplispers/scheduler


text:
Not a know element - Angular 6

Trying to use with Angular 6. Installed, added to app.module, added config to app.component. However, getting an error when adding the <easy-ngx-google-analytics [config]="easyAnalyticsConfig"> to app.component.html. Any ideas how to fix?

Hi Mike,
Are you sure you added the EasyNgxGoogleAnalyticsModule in app.module.ts
in the import section like
imports: [
      BrowserModule,
      EasyNgxGoogleAnalyticsModule,
      RouterModule.forRoot(appRoutes)
]

If yes, Can you try out the steps mention https://stackoverflow.com/a/44430230/8199107.

Yes.
imports: [
BrowserModule,
BrowserAnimationsModule,
EasyNgxGoogleAnalyticsModule,
FormsModule,
...
Even without adding the code into the html, compiling gives this error.
ERROR in Error during template compile of 'EasyNgxGoogleAnalyticsModule'
Function calls are not supported in decorators in 'NgModule'
'NgModule' calls a function at ../@angular/core/core.ts(194,50).
I have done the steps per your suggestion (except restarting the cli. Not sure how that is done, but did quit and restart the entire development environment.

Hi Mike,
I tried creating a new angular 6 app. And imported the EasyNgxGoogleAnalyticsModule. I had no issue in doing so. I have uploaded it into https://drive.google.com/file/d/19OYslQak7lkmSJ2-DnWR9bWjVNeQHoHe/view?usp=sharing
You can refer the code if you want.
I believe there might be a bug in the way you are importing modules. If you have multiple levels of angular modules. There might be a conflict as well.

Thanks, I'll give it a try later and let you know. Thanks for your efforts.
Best wishes,
Mike
On Fri, Aug 3, 2018 at 11:12 AM, sharukahmed notifications@github.com
wrote:

Hi Mike,
I tried creating a new angular 6 app. And imported the
EasyNgxGoogleAnalyticsModule. I had no issue in doing so. I have uploaded
it into https://drive.google.com/file/d/19OYslQak7lkmSJ2-
DnWR9bWjVNeQHoHe/view?usp=sharing
You can refer the code if you want.
I believe there might be a bug in the way you are importing modules. If
you have multiple levels of angular modules. There might be a conflict as
well.
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
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Well, what I found in adding EasyNgxGoogleAnalyticsModule to the version you provided was the results were different than my original project. However, there were still warnings.
development:ang6 mrobinson$ ng build

Date: 2018-08-03T22:06:57.701Z
Hash: bc06fbc0a4d96233d3f9
Time: 10270ms
chunk {main} main.js, main.js.map (main) 13.4 kB [initial] [rendered]
chunk {polyfills} polyfills.js, polyfills.js.map (polyfills) 227 kB [initial] [rendered]
chunk {runtime} runtime.js, runtime.js.map (runtime) 5.22 kB [entry] [rendered]
chunk {styles} styles.js, styles.js.map (styles) 15.6 kB [initial] [rendered]
chunk {vendor} vendor.js, vendor.js.map (vendor) 4.31 MB [initial] [rendered]

WARNING in ./node_modules/easy-ngx-google-analytics/node_modules/@angular/core/@angular/core.es5.js
5659:15-36 Critical dependency: the request of a dependency is an expression

WARNING in ./node_modules/easy-ngx-google-analytics/node_modules/@angular/core/@angular/core.es5.js
5675:15-102 Critical dependency: the request of a dependency is an expression

I am using TS 2.7.2 to be within bounds of the @amgular/cli compiler. I don't know if that is the difference or now. I am not understanding the warning messages or whether that would effect the actual functioning either.
I then updated the packages in my app (which were behind a few minor versions). Once I did that, I no longer got EasyNgxGoogleAnalyticsModule errors on compiling, but got the same warning messages as above.
However, I still do get the error message I reported initially when adding <easy-ngx-google-analytics [config]="easyAnalyticsConfig"> to the html of my app as well as the sample app you sent.

authors: mike-robinson sharukahmed

repo: sharukworld/easy-ngx-google-analytics


text:
Kanban Review Issue

Details
We are a team of 2 people which includes @shayan1234554321 and @gdumani. So we removed the team 3 cards in kanban and also assigned ourselves to the relevant cards
URL
Url to the project: Kanban Project

Hi Team,
Your project is complete!  There is nothing else to say other than... it's time to merge it :shipit:
Congratulations! 🎉
Highlights 💯

Descriptive info✔
Tasks distributed equally ✔

Optional suggestions
Cheers and Happy coding!👏👏👏
Feel free to leave any questions or comments in the PR thread if something is not 100% clear.
Please, remember to tag me in your question so I can receive the notification.

As described in the Code reviews limits policy  you have a limited number of reviews per project (check the exact number in your Dashboard). If you think that the code review was not fair, you can request a second opinion using this form.


text:
Cleans up window/renderer leaks

SDL_Quit won't do this for you :)
As an aside, DestroyRenderer will clean up any associated textures, but it's nice to display that explicitly as an example.

Thanks @jblakeLincoln - really helpful. Clearly I missed this in updating. Cheers


text:
Update instructions regarding VS2015

AF 3.5 needs to be compiled with VS2015 or newer. The prebuild dependencies have already been updated.
Closes shehzan10/arrayfire-windows-scripts#9

ok, done.


text:
Upgrade Angular 8

Hey Folks,
Is there any tracking to update to Angular 8? I saw the support to Angular 7 is already in place but structure changes like ViewChild() is blocking developments on Angular 8.
Is there any hack to use this using Angualr 8?

I use it in Angular 8, still works (95% fine i'd say)

I use it in Angular 8, still works (95% fine i'd say)
But an update for Angular 8 would be very much appreciated !

Ivy not working now with ngu-carousel :(

Now we have a pull request for this. Take a look and let's make it work
https://github.com/sheikalthaf/ngu-carousel/pull/164

@fasfelipe I try to use your fork directly from Github.
ERROR in Unable to write a reference to NguCarousel in D:/Go/inframe/ui/node_modules/@ngu/carousel/projects/carousel/src/lib/ngu-carousel/ngu-carousel.component.ts from D:/Go/inframe/ui/node_modules/@ngu/carousel/projects/carousel/src/lib/ngu-carousel.module.ts
** Angular Live Development Server is listening on localhost:4200, open your browser on http://localhost:4200/ **
i ｢wdm｣: Failed to compile.

authors: AntoineJeanDev DanVLopes fasfelipe kolkov

repo: sheikalthaf/ngu-carousel


text:
No progress in learning

I am trying to build my network with 21M text file, but whatever I do, it gets stuck at train_loss ~1.6 and does not progress any more. I tried changing:

network size (from 64 to 1024)
number of layers (2 and 3)
batch_size (from 10 to 100)
sequence length (from 10 to 1000)

But nothing helps and I always get my network to stop learning and stuck at about 1.6-1.7 train_loss.
How can I diagnose the problem? Can someone advise?

I've been playing with this repo for half a day and had good luck training datasets from 500KiB (Donald Trump Tweets) to 11MiB (KJV Bible).  With default settings the train_loss drops to around 1.2 quite quickly (10 minutes w/ 1x GPU).  Going below that takes longer time with mixed subjective performance gains.
I haven't calculated the theoretical minimum, perhaps 1? Need to read up more and check into the stuff about perplexity as well.
But directly to your question, how does the sample output look after training for an hour or so?  Is it beginning to work at least, or just noise?
A few thoughts:

Try training on just 1MB subsection of the training data with num_layers 1 or 2 to start.
Bigger values for rnn_size, num_layers, seq_length take longer to convergence on low train_loss values in my limited experience.
If values are too low, you'll converge quickly on a non-optimal solution.

Good luck! Hope you've had success!

I hope you've had better luck, we also just added a number of PRs this weekend. Keep an eye out for a better README w/ descriptions of the parameters.
If you do have some luck feel free to post back here with your settings, data sets, and results!
Thanks

authors: prontiol ubergarm

repo: sherjilozair/char-rnn-tensorflow


text:
Default value format options

Set  'Simple item value' option to be the default selected value on the value format property within the dropdown component
Set 'Simple Id' option to be the default selected value on the value format property within the entity picker component

NB: This should also be applied on the dataTable component setting form
Related Item
#1039

Hi @AlexStepantsov, you forgot to apply the change on dataTable: https://function-adminportal-test.shesha.dev/dynamic/TestModule/valueformat-table

I will retest the item after the changes for this https://github.com/shesha-io/shesha-framework/issues/1045 has been implemented

repo: shesha-io/shesha-framework


text:
Added another greedy algorithm

Added Prim's Algorithm in Greedy Algorithm folder

@apoorva1823000 will take time to merge. I am not available right now.


text:
Support for ip_restrictions on function apps

This adds support for setting ip_restriction, so users can restrict access to the UI.
variables.tf
variable "allowed_ip_addresses" {
  type        = list(string)
  description = "A list of allowed ip addresses that can access the ACMEBot UI."
  default     = []
}

main.tf
site_config {
    ftps_state      = "Disabled"
    min_tls_version = "1.2"
    dynamic "ip_restriction" {
      for_each = var.allowed_ip_addresses
      content {
        ip_address = ip_restriction.value
      }
    }
  }

When calling the module:
module "keyvault_acmebot" {
  source  = "shibayan/keyvault-acmebot/azurerm"
  version = "~> 1.0"

  function_app_name     = "func-acmebot-module"
  app_service_plan_name = "plan-acmebot-module"
  storage_account_name  = "stacmebotmodule"
  app_insights_name     = "appi-acmebot-module"
  resource_group_name   = azurerm_resource_group.default.name
  location              = azurerm_resource_group.default.location
  mail_address          = "YOUR-EMAIL-ADDRESS"
  vault_uri             = azurerm_key_vault.default.vault_uri
  allowed_ip_addresses = var.allowed_ip_addresses

  azure_dns = {
    subscription_id = data.azurerm_client_config.current.subscription_id
  }
}

Side note--I notice the CI pipeline isn't running terraform validate, which would be helpful in catching issues besides just running the standard lint check with terraform fmt. Is this something you'd be interested in adding @shibayan ?

@shibayan Also under site_config there needs to be possibility for default action as default is Allow :) ip_restriction_default_action

authors: marekvesely-direct rudolphjacksonm


text:
freeform template transformation with '-none' does not honor newlines

21 kubectl exec "$SENTINEL" -n aegis-system -- aegis
22 -w "aegis-workload-demo"
23 -n "default"
24 -f "none"
25 -s '{"username": "admin", "password": "cashcow!", "value": "AegisRocks!"}'
26 -t "\n\n\nUSERNAM ---- sadfa as s {{.username}} --- -asd as--  \t\t {{.password}} asfasdfa {{.value}}}" \

removed none; so this issue solved itself :) .

repo: shieldworks/aegis


text:
Mermaid diagrams

Hi
Any plans on mermaid support?
https://mermaidjs.github.io/usage.html

Mermaid support would be awesome!

Have you guys found any similar plugin with mermaid support? @alem0lars @RidaAyed

@youssoftaha https://github.com/previm/previm

@RidaAyed Any idea how to add mathjax support to it?

authors: RidaAyed alem0lars youssoftaha

repo: shime/livedown


text:
request: UnoCSSについて書いてほしい

概要
UnoCSSについて書いてほしい
詳細
UnoCSSの記事が確かに日本市場に出回ってない。勿体無い
備考欄
きっかけはこちらのツイートです。
https://x.com/ryoppippi/status/1815656280297476527

@shinGangan
某所でゆる募見てから気になって、勉強目的で記事を書き始めました
https://github.com/kosuke222naito/zenn-contents/blob/main/articles/what_is_unocss.md
現状公式ドキュメントの翻訳のような内容になっています。
使用経験が今のところないのですが、こちらアサインいただけないでしょうか？
よろしくお願いします！

@kosuke222naito
是非執筆お願いしたいです！
アサインしておきます～🙆♀

ご要望あればレビューしますのでお気軽にメンションください！

@kosuke222naito
ZennのPublicationに招待しました！
Zenn登録のメールアドレスに連絡来ていると思いますのでお手隙の際に確認お願いします〜

執筆ひと段落しました🙇♂
https://zenn.dev/comm_vue_nuxt/articles/what_is_unocss
だいぶ長くなってしまったので不安です
公開してしまって問題ないでしょうか？
確認お願いします！

@kosuke222naito
レビューはマストじゃないのでご自身の判断で公開してもらって大丈夫ですよー🙆♀
不安ということなので、私の方でさくっと見ておきますね！

ありがとうございます、、、！

@kosuke222naito
全体的によく記載されており充実した内容だったなと思いました〜！
以下はレビューコメントです！

[!NOTE]
Qから始まるやつは疑問です。不要だったらスルーしてください。
Sから始まるやつは提案です。採用については一任です。
prefix無しのものはレビュー指摘です

全体通して
Q. 本記事の対象読者はどんな人で、読者にどんな情報を持って帰ってもらうのがゴールになりそうですか？
全体的によくまとまっており充実感がある文章だと思いました。
ただ、対象読者、想定読者が異なった場合、非常に文章が長いなとも感じました。
例えば、読者想定が
・UnoCSSをあまり知らない、少し気になっているから知ってみたい
・普段は他言語や他FWを触っており、流行ってそうだから読んでみるかで記事を読んだ人
などは最後まで読めずに離脱する恐れが高いです。もし私が他言語界隈の人だったら途中で離脱してると思います。
逆に、「UnoCSSについてさらに詳しく知りたいんだ」という読者からすると最後まで読みたいグッドなモノです。
また、「読者にどんな情報を持って帰ってもらうのがゴールになりそうですか？」という質問も想定読者と同様に重要となってくるものです。
例えば「UnoCSSをとにかく知ってほしい」というゴールであれば紹介編と詳細解説編の2本に分けた方がユーザーライク(この場合はユーザビリティかな？)かなと思います。
私が執筆者の場合は記事全体がfatになるのを避けるため、「連載します」と明示した上で記事を分割して読みやすさを向上させるかなと思います(所感)
そのため、「本記事の対象読者はどんな人で、読者にどんな情報を持って帰ってもらうのがゴールになりそうですか？」というのをご教示頂けると幸いです。
せっかく素晴らしいまとめ、素晴らしい記事が多くのユーザーから読まれないということを避けられたらいいなと思ってる次第です。
ドキュメント 項

UnoCSS 作者である Anthony Fu 氏のブログを読むことでより UnoCSS についてのより深い理解や、UnoCSS の背景知識を得ることできます。

この下のリンクは実際にUnoCSSについてのブログリンクの方が良いかと思います。
(私はリンク飛んでからUnoCSS記事をめっちゃ探してしまいました、、、)
S. ドキュメントの下に3行まとめなどのまとめ項を置くのはどうでしょうか？
記事がfatになる場合、ユーザーが途中で離脱する恐れが高まります。
そのため、先に簡単なまとめ (3行まとめみたいなやつ)を用意するのはいかがでしょうか。
途中離脱するユーザーにとって少しでもUnoCSSが有益であると早い段階で明示できると良いですね
S. 別の記事になりますがUnoCSS、Tailwind CSS、Panda CSSの比較記事などいかがでしょうか？
Tailwind CSSユーザーからすると「UnoCSSはTailwind CSSとどういうところが同じで、どういう点が異なるのか」が気になっていると思います。
そのため、実装的な詳細比較記事を切り出して1本執筆しても良いなと感じました。
合わせて、今後Tailwind CSSと並び台頭する恐れのあるPanda CSSも比較対象に入れると対象読者が広がって良いのではないかと考えています。
( 余談：Panda CSSは以前実装してたので実装方法などについてはお気軽に相談ください〜🙆)

本当にありがとうございます！！

Q. 本記事の対象読者はどんな人で、読者にどんな情報を持って帰ってもらうのがゴールになりそうですか？

今回ちゃんとした目的は決めれていなかったのですが、公式ドキュメント(英語)を日本語に直す過程で勉強したいと個人的に思っていました。
私自身フロント側に明るくなく、想定読者が見えていませんでした。
一度想定読者について考えてみて、記事の分割をする方向でいきたいなと思っています。

ドキュメント 項

UnoCSS 作者である Anthony Fu 氏のブログを読むことでより UnoCSS についてのより深い理解や、UnoCSS の背景知識を得ることできます。

この下のリンクは実際にUnoCSSについてのブログリンクの方が良いかと思います。
(私はリンク飛んでからUnoCSS記事をめっちゃ探してしまいました、、、)

指摘ありがとうございます🙇♂
UnoCSS の記事のリンクにしてしまうように直したいと思います。

S. ドキュメントの下に3行まとめなどのまとめ項を置くのはどうでしょうか？

ありがとうございます、まとめ項とか何も用意できてなかったです、、、
全体を見直してみて、長くなってしまった場合はまとめを記述したいと思います！！

S. 別の記事になりますがUnoCSS、Tailwind CSS、Panda CSSの比較記事などいかがでしょうか？

こちらもゆくゆくは書いてみたいです！
今回 Tailwind CSS と Windi CSS の比較を記述できたのは、公式ドキュメントで記載があり、それを翻訳 & 整理したためです。
Panda CSS について言及するためにはどちらもより理解する必要がありそうです。
今回は難しそうなので、別 issue などにしていただけるとありがたいです🙇♂
改めて、レビューありがとうございます！！
雰囲気で記事を書きすぎていたのがわかりました、少し時間かかってしまうかもしれませんがよろしくお願いいたします。

@kosuke222naito
読者の話

私自身フロント側に明るくなく、想定読者が見えていませんでした。
一度想定読者について考えてみて、記事の分割をする方向でいきたいなと思っています。

ご自身がフロントに明るくないという点は非常に読者視点に立ちやすい利点かなと思ってます！
「フロントを普段実施していない自分がもし読者だった場合、この記事は読みたいのか？どんな点が読み応えあり、どんな点が読みにくいのか？」という客観的視点に立てて、対象読者を想像しやすいです🙆
執筆者は書き手であると同時に読み手なので、読み手である自分が客観的に読みやすいか、興味を引くかどうかというのも1つの執筆指針になりやすいです(参考までに)
他CSSとの比較について

S. 別の記事になりますがUnoCSS、Tailwind CSS、Panda CSSの比較記事などいかがでしょうか？

こちらもゆくゆくは書いてみたいです！
今回 Tailwind CSS と Windi CSS の比較を記述できたのは、公式ドキュメントで記載があり、それを翻訳 & 整理したためです。
Panda CSS について言及するためにはどちらもより理解する必要がありそうです。
今回は難しそうなので、別 issue などにしていただけるとありがたいです🙇♂

本件は優先度低めなのでもし執筆モチベと執筆余裕が高まった際にご自身で起票頂けると助かります〜！
その際に、私をmention頂ければ引き続きレビューやります〜
先行優位性の観点から早めに出したほうが評価を全部拾いやすい vs 中途半端なものを出して埋もれることを避けたいだと思いますので、柔軟にいきましょう💪
ohter

改めて、レビューありがとうございます！！
雰囲気で記事を書きすぎていたのがわかりました、少し時間かかってしまうかもしれませんがよろしくお願いいたします。

全然問題ないですよ〜🙆
最初の方は執筆のハードルあると思いますので、納得いくものをどんどん執筆して積み上げていきましょう💪
お力添えできる範囲で全然お手伝いしますので！

@shinGangan
時間空いてしまいました、、、
https://zenn.dev/comm_vue_nuxt/articles/what_is_unocss
指摘いただいた事項をもとに、書き直しました！
何かご意見あればいただきたいです🙇♂
よろしくお願いします

@kosuke222naito
前回よりも読みやすかったです！各章のまとめを適宜挟んでいる点がグッドでした🙆
1点だけ質問です。
Q1. 上記スレッドでは記事を分割して連載するという話が出ていましたが、本記事は1本で完結する方針って感じで合ってますか？
本文構成を見る限りだと分割しないのかなーと感じたので、念のため質問しています！

@shinGangan
ありがとうございます😭
分割について、
紹介編と詳細解説編
の2つのことを考えていました🙇♂

@kosuke222naito

分割について、
紹介編と詳細解説編
の2つのことを考えていました🙇♂

こちら承知ですー🙆
もう一本書き上がったら引き続きメンションください〜
メンション来たらレビューします。
イシューも分けてもらって大丈夫ですので、柔軟にやりやすいようにイシュー分割してください⭕
(1記事1イシューくらいでいいと思ってる派です)

@shinGangan
わかりました！
公開も2記事揃えた方がいいですよね？👀

公開も2記事揃えた方がいいですよね？👀

まとめてでもいいですし、2週連続で投稿して連載感を出すのもいいかなと思ってます。
こちらは一任しますー！お好みでお決めくださいませ

記事公開済み
https://zenn.dev/comm_vue_nuxt/articles/what_is_unocss

@kosuke222naito
LGTM🎉


text:
Add an option to disable rebasing of relativeTo

Implement #12

appveyor failed because it couldn't run the tests...

https://github.com/shinnn/broccoli-clean-css/pull/1#issuecomment-56367481
@kanongil says:

Anyway, I didn't really add options but rather fix the handling of the existing ones.

That pull request did solve the fact that there was no way to import from the input tree, but it made it so that it's impossible to import relative to the current working directory.
I don't see how we can keep both behaviours without adding an option

@shinnn Any update on this? Would like to hear more thoughts from you.

See #12

authors: XrXr shinnn

repo: shinnn/broccoli-clean-css


text:
long awaited -> long-awaited

https://www.merriam-webster.com/dictionary/awaited

too many false positives

repo: shinotype/shino-qa-tool