cellmap_flow.finetune.build_corrections
=======================================

.. py:module:: cellmap_flow.finetune.build_corrections

.. autoapi-nested-parse::

   Build a finetuning corrections directory from a crops manifest, headlessly.

   The dashboard builds the same thing interactively: it creates an
   ``annotation_volume.zarr`` covering the raw dataset at the model's output
   voxel size, imports each crop of a YAML manifest into it at the crop's
   physical offset, and writes the ``_virtual_sources.json`` manifest the
   trainer reads. That path needs a running Flask session, a MinIO server and
   the model's geometry from a live inference server. None of that is needed to
   train from ground-truth crops on the cluster, so this module does the same
   three steps with the same functions and nothing else::

       python -m cellmap_flow.finetune.build_corrections \
           --raw /nrs/cellmap/data/<ds>/<ds>.zarr/recon-1/em/fibsem-uint8 \
           --repo cellmap/mito-aff-unet-setup-16 \
           --crops finetune_yamls/<ds>/mito_group.yaml \
           --output /nrs/.../training/<session>/corrections \
           --patches-per-epoch 360

   Geometry comes from the HuggingFace ``metadata.json`` (input/output shape
   and voxel size), so no weights are downloaded and no GPU is touched. Pass
   ``--input-shape/--output-shape/--input-voxel-size/--output-voxel-size`` to
   override or to describe a model that has no metadata.

   Every build leaves ``build_record.json`` next to the manifest: the crops
   YAML verbatim, the resolved geometry, per-crop voxel counts and offsets,
   and the git commit of this checkout. That is the provenance a run registry
   can point at.



Attributes
----------

.. autoapisummary::

   cellmap_flow.finetune.build_corrections.logger
   cellmap_flow.finetune.build_corrections.DEFAULT_INPUT_NORM


Functions
---------

.. autoapisummary::

   cellmap_flow.finetune.build_corrections.geometry_from_hf_metadata
   cellmap_flow.finetune.build_corrections.build_corrections
   cellmap_flow.finetune.build_corrections.main


Module Contents
---------------

.. py:data:: logger

.. py:data:: DEFAULT_INPUT_NORM

.. py:function:: geometry_from_hf_metadata(repo, revision=None)

   (input_shape, output_shape, input_voxel_size, output_voxel_size,
   out_channels, channels_names, model_name) from a repo's metadata.json.


.. py:function:: build_corrections(*, raw_dataset_path, crops_yaml, output_dir, repo=None, revision=None, input_shape=None, output_shape=None, input_voxel_size=None, output_voxel_size=None, model_name=None, patches_per_epoch=None, jitter_voxels=None, seed=None, dense_to_sparse_ratio=None, input_norm=None, postprocess=None, resample=False, extra_record=None)

   Create ``output_dir`` with the annotation volume, imported crops and
   trainer manifest. Returns the build record (also written to disk).

   ``output_dir`` must not already contain a volume: this builds from
   scratch so the record describes everything in it.

   ``patches_per_epoch``, ``jitter_voxels``, ``seed`` and
   ``dense_to_sparse_ratio`` are the CLI's flags: each one given (not None)
   wins over the crops YAML's own setting, which applies otherwise.
   ``resample`` is the dashboard's Resample box: the volume at the model's
   own voxel sizes, read resampled, instead of at the nearest raw level.


.. py:function:: main(argv=None)