Seer Model Performance Discrepancy On LIBERO Tasks

by Sebastian Müller 51 views

Hi Junmo,

Thank you for your interest in the Seer model and for sharing your observations. It's great that you're diving deep into evaluating the model's performance on the LIBERO tasks. Your detailed comparison of the success rates is very helpful in pinpointing the discrepancy you've encountered.

Understanding the Seer Model Performance on LIBERO Tasks

You've highlighted a significant performance difference when evaluating the provided Seer model (33.pth) on the LIBERO tasks. Specifically, you've noted that the log file evaluate_33.pth.log on the website reports a 75% success rate for the KITCHEN_SCENE8_put_both_moka_pots_on_the_stove task, while your local evaluation using the same model, eval.sh script, and default hyperparameters yields only 40% success. This discrepancy is indeed puzzling, as you correctly point out that the results should be similar given the same model and hyperparameters. Let's explore the potential reasons behind this and how we can resolve it.

Analyzing the Discrepancy

The fact that you're observing a lower success rate locally compared to the reported results suggests a few possibilities:

  1. Environment Differences: The evaluation environment, including software versions, libraries, and hardware, can influence the model's performance. Subtle differences in these factors might lead to variations in the outcome.
  2. Data Handling: Although you're using the same model and hyperparameters, ensure that the data loading and preprocessing steps are identical. Discrepancies in how the data is handled could affect the results.
  3. Randomness: Some aspects of the evaluation process might involve randomness, such as initialization or sampling. While the use of default hyperparameters should minimize this, it's still a factor to consider.
  4. Implementation Details: There might be subtle differences in the implementation or configuration between the original evaluation setup and your local setup. These differences, even if seemingly minor, could contribute to the performance gap.

Deep Dive into the Evaluation Logs

To further investigate, let's analyze the provided logs in detail. The logs show the success rates for each task, along with the this_result_list, which indicates the success (1) or failure (0) for each of the 20 trials. By comparing the this_result_list from the website's log and your local log, we can identify specific trials where the model performs differently. For example:

  • Website Log (75% success for KITCHEN_SCENE8):
    Success rates for task 8 KITCHEN_SCENE8_put_both_moka_pots_on_the_stove:
75.0%
this_result_list : [(0, 180), (1, 181), (0, 182), (1, 183), (1, 184), (1, 185), (1, 186), (0, 187), (0, 188), (1, 189), (1, 190), (1, 191), (1, 192), (1, 193), (1, 194), (1, 195), (1, 196), (1, 197), (0, 198), (1, 199)]
  • Local Log (40% success for KITCHEN_SCENE8):
    Success rates for task 8 KITCHEN_SCENE8_put_both_moka_pots_on_the_stove:
40.0%
this_result_list : [(0, 180), (1, 181), (0, 182), (1, 183), (1, 184), (1, 185), (1, 186), (1, 187), (1, 188), (1, 189), (1, 190), (0, 191), (1, 192), (1, 193), (1, 194), (1, 195), (0, 196), (1, 197), (1, 198), (0, 199)]

By comparing these lists, we can see that there are differences in the individual trial outcomes. For instance, in the website log, trials 181, 183, 184, 185, 186, 189, 190, 191, 192, 193, 194, 195, 196, 197, and 199 were successful, while trials 180, 182, 187, 188, and 198 failed. In your local log, there are more failed trials, leading to the lower success rate. Identifying which specific trials consistently fail in your local setup can provide clues about the root cause.

Steps to Troubleshoot the Performance Gap

To address the performance discrepancy, let's go through a systematic troubleshooting process:

1. Verify Environment Consistency

Ensuring a consistent environment is crucial for replicating results. Here's what you should check:

  • Software Versions: Confirm that you're using the same versions of Python, PyTorch, and other relevant libraries as the original evaluation setup. This is a common source of inconsistencies.
  • CUDA and cuDNN: If you're using GPUs, verify that the CUDA and cuDNN versions match the recommended versions for the Seer model. Mismatched versions can lead to performance variations.
  • Operating System: While less likely, differences in the operating system could also play a role. If possible, try to match the OS used in the original evaluation.

2. Validate Data Integrity

Data integrity is paramount to reliable evaluation. Make sure that:

  • Data Files: The LIBERO task data files you're using are identical to those used in the original evaluation. Any corruption or modification of the data can significantly impact results.
  • Data Loading: Double-check the data loading and preprocessing steps in your eval.sh script. Ensure that the data is being loaded and processed in the exact same way as intended.
  • File Paths: Verify that all file paths in your script are correct and that the necessary files are accessible.

3. Check Hyperparameters and Configuration

While you mentioned using default hyperparameters, it's worth confirming that all configuration settings are indeed default. Here’s what to review:

  • eval.sh Script: Carefully examine the eval.sh script to ensure that no unintended modifications have been made. Pay close attention to any command-line arguments or environment variables that might affect the evaluation.
  • Configuration Files: If the Seer model uses configuration files, verify that these files are set to their default values. Incorrect settings can lead to unexpected behavior.
  • Random Seeds: If the evaluation involves any random processes, ensure that the random seeds are set consistently. This can help reduce variability in the results.

4. Reproducibility and Randomness

Randomness can sometimes lead to variations in performance, even with the same model and settings. To address this:

  • Set Random Seeds: Explicitly set random seeds for Python, PyTorch, and any other relevant libraries. This can help make the evaluation more deterministic.
  • Multiple Runs: Run the evaluation multiple times and calculate the average success rate. This can help smooth out any fluctuations due to randomness.
  • Variance Analysis: Analyze the variance in the results across multiple runs. If the variance is high, it suggests that randomness might be a significant factor.

5. Code and Implementation Review

A thorough review of the code and implementation details can often reveal subtle issues that are causing discrepancies:

  • eval.sh Script: Step through the eval.sh script line by line to understand exactly what it's doing. Look for any potential issues in the way the evaluation is being performed.
  • Model Loading: Verify that the model is being loaded correctly and that all the necessary weights are being loaded. Incorrect model loading can lead to poor performance.
  • Evaluation Loop: Examine the evaluation loop in your code. Ensure that the model is being evaluated correctly on each task and that the results are being aggregated properly.

6. Hardware Considerations

While less common, hardware differences can also influence performance. Consider the following:

  • GPU: If you're using a GPU, ensure that it's functioning correctly and that it meets the minimum requirements for the Seer model. Insufficient GPU memory or compute capability can lead to performance issues.
  • CPU: While the GPU is typically the bottleneck for deep learning models, the CPU can also play a role. Ensure that your CPU is not being overloaded during the evaluation.
  • Memory: Verify that you have sufficient system memory (RAM) to run the evaluation. Insufficient memory can lead to performance degradation.

Specific Steps for KITCHEN_SCENE8 Task

Given that the KITCHEN_SCENE8_put_both_moka_pots_on_the_stove task shows the most significant discrepancy, let's focus our attention on this specific task. Here are some additional steps you can take:

  1. Debugging Output: Add print statements or logging to your evaluation code to output intermediate results and variable values during the execution of the KITCHEN_SCENE8 task. This can help you identify exactly where the model is failing.
  2. Visual Inspection: If possible, visualize the model's behavior during the KITCHEN_SCENE8 task. This might involve rendering the environment or plotting the model's predictions. Visual inspection can often reveal issues that are not apparent from the logs alone.
  3. Simplify the Task: Try simplifying the KITCHEN_SCENE8 task by breaking it down into smaller steps. For example, you could evaluate the model's ability to pick up the moka pots separately from its ability to place them on the stove. This can help you isolate the specific subtask where the model is struggling.

Seeking Further Assistance

If you've tried these steps and are still encountering the performance discrepancy, it might be helpful to reach out to the Seer model developers or the LIBERO task creators. They might have insights or suggestions that are specific to the model or the task.

When you reach out, be sure to provide detailed information about your setup, including:

  • Software Versions: Python, PyTorch, CUDA, cuDNN, etc.
  • Hardware: CPU, GPU, memory, etc.
  • Exact Steps to Reproduce: The commands you ran, the scripts you used, and any modifications you made.
  • Log Files: Both the website log and your local log.
  • Specific Observations: Any patterns or insights you've noticed.

By providing this information, you'll make it easier for others to assist you in resolving the issue.

Conclusion

Investigating performance discrepancies in machine learning models can be challenging, but it's also a valuable learning experience. By systematically troubleshooting and analyzing the results, you can gain a deeper understanding of the model's behavior and the factors that influence its performance. Remember, the key is to be methodical, patient, and persistent. You've already taken the first step by identifying the issue and providing detailed information. Keep digging, and you'll likely find the root cause of the discrepancy. Good luck, Junmo!

I hope this comprehensive guide helps you in your investigation. Let me know if you have any further questions or if there's anything else I can assist you with.