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Walkthrough
The changes involve refactoring how video files and configurations are managed across various modules. Key modifications include transitioning from list-based to dictionary-based video reader management, enhancing error handling for dataset retrieval, and shifting to batch processing for model evaluations. Additionally, input data normalization and improved logging practices were introduced to enhance clarity and robustness in handling datasets and model outputs.
Changes
| Files | Change Summary |
|---|---|
dreem/datasets/sleap_dataset.py |
Refactored video handling from a list to a dictionary for video readers, improved error handling in get_instances, and updated destructor for proper resource management. |
dreem/inference/eval.py |
Modified configuration handling in run function to support batch processing with CSV input, improved user prompts for pod index, and retained checkpoint loading conditionally. |
dreem/inference/track.py |
Updated run function to replace checkpoint loading with batch configuration handling, enhanced error prompts, and refined logging for hyperparameters. |
dreem/io/config.py |
Enhanced get_dataset and get_dataloader methods to return None for empty datasets, added logging for warnings, and implemented consistent file sorting for labels and videos. |
dreem/models/embedding.py |
Added normalization for the times tensor in the _learned_temp_embedding method, ensuring consistent input scaling. |
dreem/models/gtr_runner.py |
Simplified video name extraction and updated ground truth track ID handling by using list comprehension instead of tensor methods, improving code clarity. |
Sequence Diagram(s)
sequenceDiagram
participant User
participant Config
participant Model
participant Dataset
User->>Config: Request batch configuration
Config->>Model: Load hyperparameters from CSV
Model-->>User: Confirm hyperparameters loaded
User->>Dataset: Request dataset
Dataset-->>User: Return dataset or None
User->>Model: Start evaluation with dataset
Model-->>User: Return evaluation results🐰 In the meadow where I hop and play,
New changes come to brighten the day!
With videos neat and datasets bright,
Batch processing makes everything right!
So let’s dance and twirl in delight,
For clearer paths and data in sight! 🌼✨
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Here we switch to using sleap_io as the video backend for animal data
Summary by CodeRabbit
New Features
Bug Fixes
Refactor
Style