smartmicroscopy

Deep Learning Examples

A small PyTorch segmentation workflow, CZANN export, and ONNX Runtime inference. These are teaching examples, not validated production models or a guide to rigorous model training.

Setup

From the workshop root, the folder containing pixi.toml:

pixi install --locked

Open the notebooks in VS Code, select the workshop’s default Pixi kernel, and execute cells in order. The environment includes PyTorch, torchvision, czmodel, scikit-image, and ONNX Runtime; no separate notebook pip install is normally needed. GPU access is not required.

The setup uses workshop_utils.py to locate pixi.toml, add the example directory to imports, and report paths. For terminal commands use pixi run --locked ...: a VS Code interpreter selection does not activate an existing Conda/base terminal.

Choose a Notebook

Notebook	Workflow	Required inputs
unpack_czann.ipynb	Extract a CZANN’s metadata and model into a temporary directory.	data/DL_models/cyto2022_nuc2.czann
run_prediction_from_czann.ipynb	Load a CZANN’s ONNX model, predict one image at a time, and compare with labels.	The same CZANN plus nucleus images/labels.
SingleClassSemanticSegmentation_PyTorch.ipynb	Load grayscale PNG pairs, train a simple two-class nuclei model, and export CZANN from an in-memory model or JSON specification.	Nucleus images/labels; JSON specification for the optional disk-conversion section.

Start with unpacking to inspect an existing model, then inference. Training/export is independent: the inference notebook defaults to cyto2022_nuc2.czann, not the model produced by the training notebook. Changing models also requires checking input/output layout and preprocessing.

Inputs and Outputs

• Training and inference read paired files from data/nucleus_data/images/ and data/nucleus_data/labels/. They are paired by sorted filename order; keep the two lists aligned.
• Shared model inputs live in data/DL_models/. Optional JSON conversion reads model_conversion_spec_pytorch.json; its ModelPath must identify the checkpoint saved by the notebook.
• Training saves models/saved_torch_segmentation_model.pt and the direct CZANN export relative to the kernel’s working directory. To use this folder’s models/, run with this folder as the working directory. JSON conversion writes its CZANN to data/DL_models.
• Unpacking uses a temporary directory: extracted files are removed when its context exits. The created inference session can still be used afterward.

Important Caveats

• Training uses 512-pixel crops; inference preprocessing/layout must match the chosen model. Do not assume every CZANN has the same channel order, patch size, intensity range, or output classes.
• The JSON-export example temporarily allowlists the notebook’s own SampleModel while retaining PyTorch’s weights_only=True loading. Only load trusted checkpoints; do not disable safe loading to accept arbitrary files.
• If imports terminate the kernel with an OpenMP conflict, use the matching manifest/lockfile, run pixi install --locked, and restart the kernel. The workshop selects OpenBLAS for NumPy/SciPy. Do not enable KMP_DUPLICATE_LIB_OK.
• Review save paths before rerunning training/export; checkpoints and generated models are outputs, not replacements for the original inputs.

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