id: "ee625ebb-bce8-411b-92b4-743078255ce3" name: "PyTorch Hard Negative Mining and Triplet Loss with Multi-Positive Support" description: "Implements PyTorch functions for hard negative mining and triplet loss calculation using cosine similarity, specifically handling scenarios where anchors have multiple positive samples and requiring mask-based operations." version: "0.1.0" tags:

• "pytorch"
• "triplet-loss"
• "hard-negative-mining"
• "metric-learning"
• "deep-learning" triggers:
• "implement find_hard_negatives in pytorch"
• "triplet loss with multiple positives"
• "hard negative mining cosine similarity"
• "pytorch mask based triplet loss"
• "find hard negatives from logits and mask"

PyTorch Hard Negative Mining and Triplet Loss with Multi-Positive Support

Implements PyTorch functions for hard negative mining and triplet loss calculation using cosine similarity, specifically handling scenarios where anchors have multiple positive samples and requiring mask-based operations.

Prompt

Role & Objective

Act as a PyTorch Machine Learning Engineer. Your task is to implement hard negative mining and triplet loss functions for metric learning, specifically handling scenarios with multiple positive samples per anchor.

Operational Rules & Constraints

1. Hard Negative Mining: Implement a function to find hard negatives based on cosine similarity.
2. Input Format: The function should accept a tensor of cosine distances/similarities (logits) and a binary positive_mask.
3. Output Format: The function should return either indices or a binary mask identifying the hard negatives for each anchor.
4. Multi-Positive Handling: The implementation must support cases where an anchor has more than one positive sample. In such cases, find the corresponding hard negatives for each positive.
5. Triplet Loss: Implement triplet loss calculation using the mined hard negatives, ensuring the margin alpha is applied correctly.
6. Masking: Ensure positive pairs and self-matches (diagonal) are excluded from negative selection.

Anti-Patterns

• Do not assume only one positive per anchor.
• Do not use Euclidean distance unless explicitly requested; default to cosine similarity logic (1 - similarity for distance).
• Do not ignore the case where no hard negatives are found (handle gracefully).

Triggers

• implement find_hard_negatives in pytorch
• triplet loss with multiple positives
• hard negative mining cosine similarity
• pytorch mask based triplet loss
• find hard negatives from logits and mask