Attention-Gated Networks

Use this repo skill when a task involves the Attention-Gated-Networks PyTorch repository or its medical imaging workflows: ultrasound scan-plane classification, Sononet Grid Attention, Attention U-Net style segmentation, NIfTI/HDF5 data layouts, CUDA setup, and attention or feature-map visualization.

Start here

• Read package-overview.md for package purpose, installation guidance, config families, generated helpers, and the CUDA policy.
• Read troubleshooting.md for cross-cutting install/import, CUDA, Visdom, dependency, and config portability failures.
• Read repo-provenance.md before deciding whether this skill is current for a checkout or before running a refresh.
• Use repo-routing-metadata.json only for router/import metadata.
• Run check_env.py after installation to verify imports, CUDA, and tiny model smokes.

Route map

User request	Read
Train, test, debug, or configure ultrasound scan-plane classification	classification
Use Sononet, Sononet2, Sononet Grid Attention, aggregated classifier, or attention overlays	classification
Arrange ultrasound HDF5 splits, labels, class weights, or samplers	classification data layout
Train, validate, or configure 2D/3D U-Net segmentation models	segmentation
Use CT deep supervision, multi-attention U-Net, non-local blocks, or NIfTI outputs	segmentation
Export 3D attention maps, feature maps, NIfTI predictions, or validation metrics	segmentation workflows
Diagnose shared install, CUDA, dependency, or config issues	troubleshooting

Minimal install check

After installing the repository and dependencies, run:

python scripts/check_env.py --repo-root /path/to/Attention-Gated-Networks --mode all

If the package is installed and importable without a checkout on sys.path, the helper can still run from any working directory; --repo-root is only needed to point at a local checkout during development or editable installs.

Successful output should include import success, CUDA availability, a tiny classification output, an attention-classifier output, a tiny segmentation output, and check-env-ok.

Runtime and external inputs

The helpers require the repository checkout on --repo-root (or an installed AttentionGatedNetworks package) plus the legacy runtime dependencies listed in package-overview.md. Datasets and trained weights/checkpoints are external inputs; this skill does not fabricate or bundle them. Stock configs contain private historical /vol/... paths and must be copied with data_path.* and output/checkpoint paths overridden.

For a config supplied as a relative path, always pass --repo-root; the helper resolves the config from that root and any relative data path from the config's parent, never from the process cwd. Validate before running:

python scripts/check_env.py --repo-root /path/to/Attention-Gated-Networks \
  --config configs/config_sononet_grid_att_8.json --mode imports

This intentionally fails fast on private or missing data paths. The original train_classifaction.py also contains a 10-hour final-epoch sleep; it is left untouched as source evidence, while the bundled runner omits that hold.

Important defaults

• Distribution: AttentionGatedNetworks version 1.0.
• Import packages: models, dataio, utils.
• Required backend for the selected workflows: CUDA. The unmodified wrappers call .cuda() for models and tensors.
• Core dependencies: PyTorch, torchvision, torchsample, NumPy/SciPy, matplotlib, scikit-image, h5py, pandas, tqdm, Visdom, nibabel, scikit-learn, OpenCV, dominate, and SimpleITK for validation exports.
• Configs contain historical machine-specific dataset paths; copy the field structure but replace paths and output directories before use.
• Use the bundled scripts under this generated skill rather than hard-coded source visualization/validation scripts.

Bundled helpers

• scripts/check_env.py: import, CUDA, classification, attention-classifier, and segmentation smoke checks.
• sub-skills/classification/scripts/run_classifier.py: training/testing replacement for classification workflows.
• sub-skills/classification/scripts/export_attention_overlay.py: safe attention overlay export for grid-attention classifiers.
• sub-skills/segmentation/scripts/run_segmentation.py: training replacement for segmentation workflows.
• sub-skills/segmentation/scripts/validate_and_export_maps.py: validation, NIfTI export, and feature/attention-map helper.

Avoid when

Do not use this skill for unrelated modern MONAI/nnU-Net/TorchIO workflows unless the task explicitly names Attention-Gated Networks or needs to port ideas from this repository. Do not use it as proof that CPU-only execution works; the selected repo workflows were verified as CUDA-required.