MMDetection Bug Report Template: A Comprehensive Guide

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Hey guys! Running into snags with MMDetection? No worries, this guide breaks down how to submit a stellar bug report. A well-written report helps the awesome MMDetection team squash those bugs faster, so let's dive in!

Why a Good Bug Report Matters

Before we get into the nitty-gritty, let's talk about why a detailed bug report is crucial. Think of it this way: you're a detective, and the MMDetection team are the crime scene investigators. The more clues you provide, the easier it is for them to solve the case (aka fix the bug!). A clear, concise, and complete bug report saves everyone time and ensures that the issue is addressed effectively. Plus, it helps prevent the same bug from popping up again down the road. So, let's get those detective hats on and learn how to write a report that Sherlock Holmes would be proud of.

Checklist: Your Bug-Reporting Pre-Flight Check

Before you even think about submitting a bug report, run through this checklist. It'll save you (and the team) a bunch of time.

  1. Did You Search?

    • First things first: have you scoured the existing issues? Chances are, someone else might have already encountered the same bug. Use the search bar like it's your best friend. Keywords are key! Try different combinations related to the error you're seeing. For example, if you're having trouble with a specific layer, search for that layer name along with terms like "error," "bug," or "crash." If you find a similar issue, add your comments and details there instead of creating a new one. This helps keep things organized and prevents duplicate efforts. Think of it as contributing to a shared knowledge base – the more we collaborate, the better!

  2. FAQ Time!

    • Next up, have you delved into the FAQ documentation? This is MMDetection's treasure trove of common questions and solutions. The FAQ covers a wide range of topics, from installation hiccups to configuration conundrums. It's a goldmine of information, so definitely give it a thorough read before proceeding. You might just find the answer you're looking for without having to file a bug report at all! Plus, familiarizing yourself with the FAQ can give you a better understanding of MMDetection's inner workings, which can be super helpful down the line.

  3. Are You Up-to-Date?

    • Last but not least, are you running the latest version of MMDetection? Bugs get fixed all the time, so there's a chance your issue might already be resolved in the newest release. Check the release notes to see if your bug is mentioned. If not, updating to the latest version is still a good idea – it ensures you have all the latest improvements and fixes. If the bug persists even after updating, then it's time to move on to the bug report itself. Think of it as preventative maintenance for your codebase!

Describing the Bug: Be Clear, Be Concise

Okay, you've done your homework and the bug is still there. Time to describe it! Imagine you're explaining the issue to someone who knows nothing about your setup or the code you're running. Clarity is your superpower here.

  • What's the Bug?

    • Start with a clear and concise description of the bug itself. What's happening? What's supposed to be happening? Avoid jargon and be specific. Instead of saying "it doesn't work," say "the model crashes when I try to train it with a batch size of 32." The more precise you are, the easier it will be for the team to understand the problem. Think of it as writing a headline for your bug report – it should grab attention and convey the core issue immediately. This helps the team prioritize and address the bug efficiently.

Reproduction Steps: Show, Don't Just Tell

This is arguably the most important part of your bug report. The team needs to be able to reproduce the bug on their end to fix it. Think of it as recreating the scene of the crime. The more detailed your instructions, the better.

  1. The Command:

    • What exact command or script did you run? Copy and paste it directly into your report. This eliminates any ambiguity about how you're running MMDetection. Include all the flags and arguments you used. For example, instead of saying "I ran the training script," provide the full command like python tools/train.py configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py. This ensures that the team can run the exact same command and see the same behavior. Think of it as providing a recipe – the more precise the ingredients and instructions, the better the outcome.

  2. Modifications?

    • Did you tweak any code or config files? If so, what changes did you make? Be specific! Include the lines you changed and why you changed them. If you're not sure why you made a change, mention that too. The team needs to know if the bug is related to your modifications or if it's a more general issue. For example, if you changed the learning rate in the config file, mention that explicitly. Think of it as documenting your experiments – it helps the team understand the context of the bug and identify potential causes.

  3. Dataset Details:

    • What dataset did you use? Is it a standard dataset (like COCO or Pascal VOC) or a custom one? If it's a custom dataset, provide details about its format, size, and any preprocessing steps you performed. The dataset can often be the source of bugs, so it's crucial to provide this information. For example, if you're using a custom dataset, describe how you structured the annotations and images. Think of it as providing the evidence – the more details you give, the easier it is to trace the bug back to its origin.

Environment: The Bug's Habitat

The environment you're running MMDetection in can play a huge role in bugs. Providing this information upfront saves the team a lot of back-and-forth.

  1. collect_env.py is Your Friend:

    • Run python mmdet/utils/collect_env.py and paste the output into your report. This script gathers all the essential environment details, like your operating system, Python version, PyTorch version, CUDA version, and more. It's a one-stop shop for environment information! Think of it as a quick system check – it provides a snapshot of your setup that helps the team identify potential compatibility issues.

  2. Extra Details:

    • Add anything else that might be relevant. How did you install PyTorch (pip, conda, source)? Are there any environment variables that might be affecting things (like $PATH, $LD_LIBRARY_PATH, $PYTHONPATH)? The more information you provide, the better. For example, if you installed PyTorch from source with specific CUDA flags, mention that. Think of it as leaving no stone unturned – every detail can help the team narrow down the cause of the bug.

Error Traceback: The Bug's Footprint

If you're seeing an error message or traceback, paste it in! This is like finding the bug's footprints at the scene of the crime. It gives the team a direct look at what went wrong and where. Use code blocks (```) to format it nicely so it's easy to read. A traceback provides a wealth of information, including the file, line number, and function where the error occurred. This allows the team to pinpoint the exact location of the bug in the code. Think of it as providing a map – it guides the team directly to the source of the problem.

Bug Fix (If You Have One!)

Hey, if you've already figured out the fix, that's amazing! Share your insights! Explain the reason for the bug and how you fixed it. Even better, if you're willing to create a pull request (PR) with the fix, let the team know! This makes the whole process even smoother and faster. Think of it as providing the solution – it not only fixes the bug but also helps the community learn and grow.

Example of a Great Bug Report

To give you a super clear picture, here's a fictional example of a top-notch bug report:

Title: Training crashes with OOM error when using Faster R-CNN and batch size 32

Description:

Training crashes with an out-of-memory (OOM) error after a few iterations when using Faster R-CNN with a ResNet-50 backbone and a batch size of 32 on a single GPU.

Reproduction:

  1. Run the following command:

    python tools/train.py configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py --batch-size 32

  2. No modifications were made to the code or config files.

  3. Using the COCO 2017 dataset.

Environment:

Sys platform: Linux-5.11.0-41-generic-x86_64-with-glibc2.29
Python: 3.8.10 (default, Sep  3 2021, 21:25:54) [GCC 7.5.0]
CUDA available: True
GPU 0: NVIDIA GeForce RTX 3090
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.2, V11.2.152
GCC: gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
PyTorch: 1.9.0+cu111
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library - DFTI and FFTW functions version ...

MMCV: 1.3.8
MMCV compiling details: 

MMPCV: 0.6.2

MMPTHW: None

MMEngine: None

TorchVision: 0.10.0+cu111
OpenCV: 4.5.3

Error Traceback:

... (long traceback here) ...
OutOfMemoryError: CUDA out of memory.

Bug Fix:

Suspect the OOM error is due to the large batch size and the ResNet-50 backbone. Reducing the batch size to 16 resolves the issue. I plan to create a PR to add a warning message when a large batch size is used with a ResNet-50 backbone on a single GPU.

Wrapping Up

So, there you have it! Writing a stellar bug report is a crucial skill for any MMDetection user. By following these guidelines, you'll not only help the team squash bugs faster but also contribute to a stronger and more robust library for everyone. Happy bug hunting, guys!