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name: paddlevit description: "Guide PaddleViT computer-vision workflows for transformer and MLP image classification, object detection, semantic segmentation, DINO self-supervision, GANs, configuration, distributed/AMP execution, and Paddle deployment with explicit data, backend, and checkpoint boundaries." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

PaddleViT

Use this skill when a task names PaddleViT/PPViT, its standalone model folders, PaddlePaddle vision transformers, or a workflow that matches one of its classification, detection, segmentation, self-supervised, GAN, or export surfaces. PaddleViT is a collection of source-rooted projects, not one installable Python package: each model folder commonly has its own config.py, model module, dataset code, and entry script.

Start with a bounded plan

Before writing a command, record:

1. task family and exact model directory;
2. operation: inspect, configure, train, evaluate, predict, export, or port;
3. YAML config, recursive BASE files, data root/split, checkpoint prefix, and output directory;
4. expected device (cpu, one CUDA device, or distributed CUDA), budget, and whether downloads or long training are permitted;
5. preprocessing (channel order, resize/crop, normalization) and expected output shape/metric.

Run the read-only environment probe before attributing failures to a model:

python scripts/check_paddlevit_environment.py --help
python scripts/check_paddlevit_environment.py --json

Install PaddlePaddle using the official wheel that matches the host and CUDA runtime. The repository documents PaddlePaddle 2.1-era APIs; current releases may need a per-model smoke or a small compatibility patch. Add yacs and pyyaml for the shared configuration pattern. Segmentation additionally uses OpenCV, SciPy, and cityscapesScripts; COCO detection uses pycocotools; GAN LSUN workflows use lmdb. Do not install every optional dependency or download weights/data merely to inspect a config.

Route by user intent

• Classification: ViT, DeiT, Swin, VOLO, CSwin, PVTv2, BEiT, mobile/MLP/Conv/Rep families, MAE fine-tuning, ImageNet-style data, and facial-expression Swin.
• Detection: DETR, Swin/PVTv2 detection, COCO layout, transforms, matching, RPN/RoI/FPN, train/eval and post-process.
• Segmentation: SETR, UperNet, DPT, Segmenter, Trans2Seg, SegFormer, TopFormer, dataset layouts, demo and mIoU validation.
• Self-supervised: DINO multi-crop teacher/student training, checkpoint/resume, AMP and multi-GPU boundaries.
• Generative: TransGAN, Styleformer, CIFAR/STL10/CelebA/LSUN-LMDB data and FID/PSNR/SSIM evaluation.
• Deployment and operations: config precedence, environment diagnosis, AMP/distributed launch, static export, Paddle Inference, prediction, quantization and optional weight port.

Read model-catalog.md when a request does not name a model family. Read configuration.md when YAML, CLI overrides, source-root imports, or checkpoint paths are involved. Read troubleshooting.md before retrying an import, backend, data, export, or checkpoint failure.

Source-root discipline

Run one family in one fresh process. Put only the selected model directory (and its documented sibling utilities) on PYTHONPATH; many folders contain bare modules named config, datasets, utils, or models. Never combine several model directories in one interpreter and assume imports are unambiguous. Generated helper scripts are source-independent and must not be used as proof that a source model's current API is compatible.

Evidence and safety gates

A config parse, dependency import, or tiny CPU forward does not prove CUDA, AMP, distributed, dataset, checkpoint, or benchmark behavior. For GPU claims, run a tiny operation on the requested CUDA device. For a real metric, require a real compatible split and checkpoint. Treat training, multi-scale/slide segmentation, COCO evaluation, DINO ImageNet pretraining, GAN FID, multi-GPU launch, export, quantization, and cross-framework porting as explicit operations with an approved output path and stop condition.

Do not overwrite checkpoints, delete demo output trees, mutate a dataset, or fetch external weights/data without explicit authorization. Prefer a new output prefix and read-only preflight. Record the source commit and effective config in any downstream experiment handoff; consult repo-provenance.md before deciding whether a checkout is stale.