---

name: pytorch-metric-learning description: "Routes PyTorch Metric Learning tasks across metric-learning components, training workflows, embedding evaluation, dataset loading, and sampling." disable-model-invocation: true metadata: disco-role: operating license: MIT

PyTorch Metric Learning

Use this skill for the pytorch-metric-learning package when the task is about metric-learning losses, miners, trainers, testers, inference, datasets, or samplers.

Install

For most workflows, install the package with the CPU-friendly hooks extra so the evaluation utilities and logging helpers are available:

pip install pytorch-metric-learning[with-hooks-cpu]

If you are working from a local checkout, editable install works well too:

pip install -e .[with-hooks-cpu]

Install a compatible torch / torchvision pair first if your environment does not already have them. The repository's docs and tests assume those packages are available for the trainer, tester, and example workflows.

Use the GPU hooks extra only when you explicitly need the faiss GPU path. Read references/repo-provenance.md when you need to compare this skill with the source checkout or decide whether it should be refreshed.

Quick smoke check

Run the bundled import check when you want to confirm that the package and its main public submodules are available from the current environment:

python scripts/check_import.py

Route map

components

Use for choosing, combining, or customizing losses, miners, distances, reducers, regularizers, CrossBatchMemory, SelfSupervisedLoss, or custom component classes.

training

Use for MetricLossOnly, TrainWithClassifier, CascadedEmbeddings, DeepAdversarialMetricLearning, TwoStreamMetricLoss, hook wiring, checkpointing, and logging.

evaluation

Use for AccuracyCalculator, the tester classes, nearest-neighbor inference, and faiss-backed search or clustering.

data

Use for packaged datasets, dataset extension, class-balanced sampling, hierarchical sampling, and fixed-triplet or offline-mining samplers.

How to route

• Start with the most specific sub-skill when the user already knows the workflow family.
• If the user only names a loss, miner, or tuple-shape error, start with components.
• If the user only names a trainer, hook, or logging problem, start with training.
• If the user only names precision@1, mAP, a tester, or an index/search question, start with evaluation.
• If the user only names a dataset, split, or sampler, start with data.

When to read more

• references/troubleshooting.md for install/import and package-wide failure patterns.
• references/repo-provenance.md when you need to decide whether this skill matches the current repository state.
• The relevant sub-skill SKILL.md and its bundled references/ / scripts/ for workflow details.

Public package surface

The package exports separate modules for losses, miners, reducers, regularizers, samplers, trainers, testers, datasets, and utilities. The most common entry points are:

• pytorch_metric_learning.losses
• pytorch_metric_learning.miners
• pytorch_metric_learning.reducers
• pytorch_metric_learning.regularizers
• pytorch_metric_learning.samplers
• pytorch_metric_learning.trainers
• pytorch_metric_learning.testers
• pytorch_metric_learning.datasets
• pytorch_metric_learning.utils.accuracy_calculator
• pytorch_metric_learning.utils.inference

Use the appropriate sub-skill reference for the deeper API map instead of trying to memorize every class here.