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name: swin-transformer description: "Use this repo skill for Microsoft Swin-Transformer image-classification model, config, data, checkpoint, SimMIM, Swin-MoE, and optional CUDA acceleration workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Swin-Transformer Repo Skill

Use this skill when a task involves the official Microsoft Swin-Transformer image-classification codebase: Swin V1/V2, Swin-MLP, SimMIM masked image modeling, ImageNet data layouts, pretrained checkpoint handling, supervised training/evaluation commands, Swin-MoE, or the optional fused CUDA window-process extension.

This is a self-contained operating guide. It does not require the original source checkout used during skill creation. When a workflow needs code execution, use the user's current checkout or installed copy of Swin-Transformer and the bundled helper scripts in this skill.

Quick routing

User intent	Read next
Build or inspect SwinTransformer, SwinTransformerV2, SwinMLP, build_model, tensor shapes, FLOPs, or CPU smoke models	sub-skills/core-models/SKILL.md
Validate ImageNet folder/zip/22K layouts, understand --zip, map files, SimMIM data, checkpoint resume/pretrained behavior, or 22K-to-1K head remapping	sub-skills/data-and-checkpoints/SKILL.md
Construct or debug supervised main.py commands for training, fine-tuning, evaluation, throughput, AMP, DDP launch, or --opts overrides	sub-skills/training-eval-cli/SKILL.md
Run or adapt SimMIM pretraining/fine-tuning/evaluation workflows and mask/loss checks	sub-skills/simmim-workflows/SKILL.md
Work with Swin-MoE, Tutel, Apex fused optimizers/layernorm, or the fused CUDA window-process extension	sub-skills/moe-and-acceleration/SKILL.md
Need a config/model family map before choosing a route	references/model-zoo-and-configs.md
Need shared YACS config and flag behavior	references/configuration.md
Need cross-cutting troubleshooting	references/troubleshooting.md
Need to verify that a checkout/environment is usable	scripts/check_env.py

Baseline prerequisites

Swin-Transformer is a research-code checkout rather than an ordinary packaged distribution. For code execution, future agents should work against a current checkout or source tree on PYTHONPATH and install the runtime dependencies relevant to the selected workflow:

• Required for baseline inspection and CPU smoke checks: Python, PyTorch, torchvision, timm==0.4.12, yacs, PyYAML, numpy, scipy, and termcolor.
• Required for full supervised or SimMIM training/evaluation: CUDA-capable PyTorch, GPUs, ImageNet-style data, checkpoints when evaluating/fine-tuning, and distributed launcher environment.
• Optional: Apex for fused layernorm/fused optimizers, Tutel for Swin-MoE, and a compiled swin_window_process CUDA extension for --fused_window_process.

Minimal import smoke for a checkout:

python - <<'PY'
from config import get_config
from models import build_model
from data import build_loader
print('Swin-Transformer modules import')
PY

If that fails, read references/troubleshooting.md before running full training.

Safe helper scripts

• Run scripts/check_env.py --repo-root <checkout> to check required imports and optional CUDA/Tutel/Apex/fused-extension availability without training or downloading.
• Run scripts/inspect_swin_config.py --repo-root <checkout> --cfg <config.yaml> to summarize a YAML config and flag risky settings.
• Run scripts/swin_cli_command_builder.py --help to assemble copyable command templates for supervised, SimMIM, and MoE workflows.

These helpers are safe by default: they do not download models or data, do not run distributed training, and do not build CUDA extensions.

Important boundaries

• This skill covers image classification and the code in this repository. Object detection, semantic segmentation, video action recognition, feature distillation, and other downstream projects linked by the public README are separate repositories and are not covered here.
• Full accuracy reproduction and throughput benchmarking are intentionally not used as skill-verification gates because they require large datasets, checkpoints, GPUs, and long distributed runs.
• CPU smoke checks validate config/model/data plumbing only. They are not evidence that CUDA training, Swin-MoE, Apex, or the fused window kernel works.

Provenance and staleness

Read references/repo-provenance.md before deciding whether this skill matches a particular checkout. If source APIs, config layout, CLI flags, or dependency behavior differ, refresh this repo skill before relying on it.