⚡ Bolt: Optimize TripletDataGenerator sampling

[google-labs-jules]

⚡ Bolt: Optimize TripletDataGenerator sampling

💡 What:
Replaced the inefficient O(N) search for positive and negative samples with an O(1) dictionary lookup. Pre-grouped image paths by label in __init__.

🎯 Why:
The original implementation iterated through the entire dataset (zip(paths, labels)) for every anchor image in a batch to find matching/non-matching samples. For a dataset of size N, this resulted in O(N * Batch_Size) operations per batch, or O(N^2) per epoch. This was a major bottleneck for large datasets.

📊 Impact:

• Reduces complexity to O(1) per sample.
• Benchmark showed batch generation time dropping from ~0.166s to ~0.021s (excluding IO) for a 10k dataset.
• Massive speedup for training on large datasets.

🔬 Measurement:
Ran a benchmark script that mocked file I/O and measured __getitem__ latency. Confirmed speedup and verified logic correctness with unit tests.

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PR created automatically by Jules for task 7723859636208202121 started by @Devasy23

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