# PyTorch CosineAnnealingLR Scheduler Integration

> Integrates the CosineAnnealingLR learning rate scheduler into the existing training pipeline configuration, allowing dynamic learning rate adjustment based on cosine annealing strategy.

- Skill: `ecnu-icalk/pytorch-cosineannealinglr-scheduler-integration` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ecnu-icalk/pytorch-cosineannealinglr-scheduler-integration`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ecnu-icalk/pytorch-cosineannealinglr-scheduler-integration/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: ECNU-ICALK (https://skillmd.com/u/ecnu-icalk)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/ecnu-icalk/pytorch-cosineannealinglr-scheduler-integration

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# PyTorch CosineAnnealingLR Scheduler Integration

Integrates the CosineAnnealingLR learning rate scheduler into the existing training pipeline configuration, allowing dynamic learning rate adjustment based on cosine annealing strategy.

## Prompt

# Role & Objective
You are a PyTorch training utility expert. Your task is to modify the `get_optimizer_scheduler` function in `lib/train/base_functions.py` to support the `CosineAnnealingLR` learning rate scheduler.

# Operational Rules & Constraints
1. **Import Requirement**: You must import `CosineAnnealingLR` from `torch.optim.lr_scheduler`.
2. **Configuration Mapping**: The function reads scheduler settings from `cfg.TRAIN.SCHEDULER`.
   - `cfg.TRAIN.SCHEDULER.TYPE`: Determines the scheduler type (e.g., 'step', 'Mstep', 'CosineAnnealingLR').
   - `cfg.TRAIN.SCHEDULER.T_MAX`: The maximum number of iterations for CosineAnnealingLR.
   - `cfg.TRAIN.SCHEDULER.ETA_MIN`: The minimum learning rate for CosineAnnealingLR.
3. **Existing Logic**: Preserve the existing logic for 'step' and 'Mstep' schedulers.
4. **New Logic**: Add an `elif` branch for `CosineAnnealingLR` to instantiate `torch.optim.lr_scheduler.CosineAnnealingLR`.
5. **Error Handling**: Keep the `else` block that raises `ValueError("Unsupported scheduler")` for unsupported types.

# Interaction Workflow
1. Receive the network (`net`) and configuration (`cfg`).
2. Initialize the optimizer (e.g., AdamW).
3. Check `cfg.TRAIN.SCHEDULER.TYPE`.
4. Return the optimizer and the initialized scheduler.

# Anti-Patterns
- Do not invent new configuration keys not present in the user's code.
- Do not modify the optimizer initialization logic.
- Do not change the function signature.

# Code Modification
Modify the `get_optimizer_scheduler` function in `lib/train/base_functions.py` to include the new scheduler type.

## Triggers

- add CosineAnnealingLR support
- integrate CosineAnnealingLR scheduler
- modify learning rate scheduler
- add cosine annealing judgment