---

name: fastmri description: "Use fastMRI for MRI reconstruction data loading, MRI operators, reconstruction models, Lightning training, evaluation, and submission preparation." disable-model-invocation: true metadata: disco-role: operating license: MIT

fastMRI

Use this repo skill when a task involves the fastmri Python package, fastMRI HDF5 k-space data, MRI reconstruction baselines, U-Net/VarNet models, PyTorch Lightning training, or fastMRI metric/submission-style outputs.

This skill targets the packaged fastmri library and selected self-contained workflows distilled from the repository examples. It assumes the user provides local fastMRI-style HDF5 data, checkpoints, and any optional external tools; it does not download datasets, model weights, or BART automatically.

Start Here

1. Confirm the active Python can import fastmri; for data/model workflows also confirm torch, h5py, pytorch_lightning, torchmetrics, and requests if fastmri.data is imported.
2. Identify the task family from the route table below, then open the matching sub-skill before writing code or commands.
3. Keep original fastMRI data/checkpoints outside the skill; use bundled scripts only as helpers and generate user outputs in user-specified locations.
4. Prefer CPU-safe smoke checks and tiny fixtures before long training, GPU inference, external downloads, or leaderboard packaging.

Sub-Skill Routes

User intent	Read
Inspect .h5 keys, choose singlecoil vs multicoil, build SliceDataset/CombinedSliceDataset, configure masks/transforms, or debug cache/sample-rate issues.	data-loading
Use complex tensor conventions, centered FFT/IFFT, rss, crop/normalization helpers, SSIMLoss, or array metric shape checks.	mri-operators
Instantiate/debug U-Net, VarNet, NormUnet, AdaptiveVarNet, LOUPE/policy modules, tiny model shape checks, or offline pretrained inference.	model-architectures
Build PyTorch Lightning train/test workflows with FastMriDataModule, UnetModule, VarNetModule, checkpoints, CPU fast-dev, DDP, or combine_train_val.	lightning-training
Save reconstructions, validate prediction HDF5 files, compute MSE/NMSE/PSNR/SSIM, build zero-filled baselines, convert v2 filenames, or prepare submission outputs.	evaluation-submission

Shared References and Scripts

• Package overview summarizes package scope, installed dependency facts, selected examples, and advanced/example-only branches.
• Troubleshooting covers cross-cutting install/import, optional dependency, data, backend, and workflow failure modes.
• Repo provenance records the source commit, package version, dirty-state baseline, and evidence paths used to create this skill.
• Routing metadata provides structured scenario placement for managed repo-skills-router import.
• Environment check script prints a JSON import/backend/dependency summary for the active Python environment.

Common Task Patterns

• Data-to-training: read data-loading, then lightning-training, then evaluation-submission for output validation.
• Tensor/operator debugging: read mri-operators first, then route to model-architectures if the error happens inside U-Net/VarNet.
• Pretrained inference: read model-architectures and require a user-supplied local checkpoint unless the user explicitly authorizes network downloads.
• Submission preparation: read evaluation-submission, validate prediction files before metrics, and treat public leaderboard upload as unavailable unless the user confirms a current destination.

Important Caveats

• The package metadata declares core dependencies but this checkout's fastmri.data also imports requests; install requests if ModuleNotFoundError: requests appears.
• The README notes a historical h5py/HDF5 memory leak concern for tensor conversion; if users see memory growth during large HDF5 reads, prefer a conda h5py build with an older compatible HDF5 as described in the package docs.
• The repository examples often assume GPU/DDP defaults, repo-local fastmri_dirs.yaml, public checkpoint downloads, or separate pinned environments. The skill distills those workflows into explicit-path, offline-safe guidance instead of depending on original example files.
• banding_removal/, BART compressed sensing, adaptive VarNet, feature VarNet, RadiologyJohnson2022, notebooks, annotation visualization, external prostate data/code, and raw data manifests are reference or advanced evidence unless a user explicitly asks for those areas and accepts their extra dependencies.