Segment Anything 2 (SA-2) Problems

[SaneRelapse]

Version Incompatibility:
Problem: Segment Anything 2 (SA-2) relies on outdated versions of PyTorch (specifically torch>=2.3.1), which may not be compatible with the latest versions or dependencies. This mismatch can lead to runtime errors or failure to load the model.
Impact: Users may face significant obstacles when attempting to use the latest features or improvements in PyTorch, making the integration of SA-2 less seamless and more prone to issues.
Missing or Corrupt Files:
Problem: Essential files like fbgemm.dll might be missing or CORRUPTED in the Torch installation. This problem, possibly by design or deliberate, persists even when torch>=2.3.1 and torchvision>=0.18.1 are installed.
Impact: Results in errors or crashes during model execution, further complicating the use of SA-2 and potentially halting development progress.
Dependency Conflicts:
Problem: Conflicts between different versions of Torch or other required libraries can arise.
Impact: This can cause unexpected behavior or integration issues, making it challenging to implement and utilize SA-2 effectively.
Environment Configuration Issues:
Problem: Incorrect environment setup, such as missing paths or incorrect environment variables, can prevent SA-2 from loading or executing properly.
Impact: Users may encounter difficulties in configuring their environments correctly, contributing to a Kafkaesque experience in setting up and using SA-2.
Installation Problems:
Problem: Issues during the installation process of Torch or SA-2 can occur, leading to incomplete or faulty installations.
Impact: This results in additional obstacles for users, making the use of SA-2 cumbersome and increasing the complexity of deployment.
Kafkaesque Experience
Deploying SA-2 in PyCharm ultimately resulted in failure, despite following extensive troubleshooting steps and ensuring all components were correctly installed and configured.

Steps Taken by User to Remedy the Problem:

Create a Clean Virtual Environment

Upgrade pip, setuptools, and wheel

Uninstall and Reinstall PyTorch and Torchvision with CUDA Support

Check Drivers:
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2024 NVIDIA Corporation
Built on Thu_Jun__6_03:03:05_Pacific_Daylight_Time_2024
Cuda compilation tools, release 12.5, V12.5.82
Build cuda_12.5.r12.5/compiler.34385749_0

Verify PyTorch Installation:
import torch
print(torch.version.cuda) # Should print '12.1' or later
print(torch.cuda.is_available()) # Should print 'True' if CUDA is correctly configured

Check TensorFlow Installation:
import tensorflow as tf
print(tf.test.is_built_with_cuda()) # Should print 'True'
print(tf.config.list_physical_devices('GPU')) # Should list your GPU if CUDA is correctly configured

Ensure Latest Visual C++ Redistributable Installed

Outcome:

Running pip install torch>=2.3.1 torchvision>=0.18.1 did not result in a successful Segment Anything 2 build, even after proper installation.
Running pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124 also did not result in a successful Segment Anything 2 build.
The use and implementation of Meta's Segment Anything 2 are notably Kafkaesque due to these challenges, involving outdated dependencies, complex setup processes, and various installation issues that contribute to a less-than-seamless experience.

Possible solutions
To address version compatibility issues in software like Segment Anything 2 (SA-2) using AI, consider the following theoretical approach:

AI-Driven Version Compatibility Management
Concept:

Utilize an AI-based system to dynamically manage and resolve version compatibility issues between libraries and dependencies. This system would leverage machine learning algorithms to predict and adapt to changes in software environments, ensuring seamless integration and operation of complex systems like SA-2.

Mechanism:

AI Model for Dependency Prediction:

Training: Train an AI model on historical data about version compatibility issues, including different combinations of software versions, configurations, and associated problems.
Prediction: The model predicts potential compatibility issues based on the current environment and proposed updates or installations.
Automated Resolution Engine:

Dynamic Adaptation: Use AI to dynamically adjust versions of dependencies based on real-time feedback and prediction outcomes. This engine could automatically select compatible versions of libraries and adjust configurations to fit the needs of SA-2.
Patch Generation: AI could also generate custom patches or updates to resolve specific issues, based on known compatibility problems and fixes.
Version Compatibility Database:

Centralized Knowledge Base: Maintain a comprehensive database of known compatibility issues and solutions. AI algorithms can continuously update this database with new findings and community inputs.
Recommendations: Provide recommendations for specific version combinations and configurations based on the latest data from the database.
Continuous Monitoring and Feedback:

Real-time Monitoring: Implement real-time monitoring of the software environment to detect any emerging compatibility issues as they occur.
Feedback Loop: Use feedback from users and automated systems to refine the AI models and improve accuracy over time.
User Interaction Interface:

Smart Interface: Develop a user-friendly interface where users can input their environment details and receive tailored recommendations or automated adjustments from the AI system.
Guided Troubleshooting: Offer guided troubleshooting steps based on AI-driven analysis of compatibility issues.
Impact:

Seamless Integration: Reduces the complexity of integrating SA-2 with various versions of dependencies by automating compatibility management.
Reduced Downtime: Minimizes downtime and errors associated with manual version management.
Enhanced User Experience: Provides a smoother and more efficient setup process for users, reducing the Kafkaesque experience of dealing with version conflicts.
By leveraging AI in this way, the process of managing and resolving version compatibility issues can become more streamlined and adaptive, addressing one of the core challenges in using and implementing sophisticated software systems like Segment Anything 2.

Meta working alongside and collaborating with the maintainers & Designers of Pytorch, PyCharm, TensorFlow is a imperative to build the necessary tools the public needs.

Meta should consider Building a Python IDE, smart interpreter and or ML/Machine vision framework for the public built around their tools that's completely open source Apache-BSD style or MIT.

As a member of the community i would like to thank Meta and their contributors for all their hard work, Turning dreams and ideas into reality.

[ronghanghu]

Hi, this is likely due to a failure in SAM 2 CUDA extension building. We have recently made the CUDA extension step optional (in #155) as a workaround to this problem.

You can pull the latest code and reinstall via

# run the line below inside the SAM 2 repo
git pull;
pip uninstall -y SAM-2;
rm -f sam2/*.so;
pip install -e ".[demo]"

which allows using SAM 2 without CUDA extension (the results should stay the same in most cases, see INSTALL.md for details).

[ChaosSamKo]

Hi, this is likely due to a failure in SAM 2 CUDA extension building. We have recently made the CUDA extension step optional (in #155) as a workaround to this problem.

You can pull the latest code and reinstall via

# run the line below inside the SAM 2 repo
git pull;
pip uninstall -y SAM-2;
rm -f sam2/*.so;
pip install -e ".[demo]"

which allows using SAM 2 without CUDA extension (the results should stay the same in most cases, see INSTALL.md for details).

This still results in

OSError: [WinError 126] The specified module could not be found. Error loading "C:\Users\XXX\AppData\Local\Temp\pip-build-env-wtnw5dlp\overlay\Lib\site-packages\torch\lib\fbgemm.dll" or one of its dependencies.

when running pip install -e ".[demo]"

even though I confirmed that:

import torch
print(torch.version.cuda) #='12.1' 
print(torch.cuda.is_available()) #=True

[ronghanghu]

Hi @ChaosSamKo, if you are running on Windows, it's strongly recommended to use Windows Subsystem for Linux (WSL) with Ubuntu. Building CUDA extension on Windows is usually much more challenging.

Specifically regarding this issue, you may be able to resolve it via installing Visual C++ as mentioned in https://discuss.pytorch.org/t/failed-to-import-pytorch-fbgemm-dll-or-one-of-its-dependencies-is-missing/201969