cellmap-data 2025.12.24.539

Utility for loading CellMap data for machine learning training, utilizing PyTorch, Xarray, TensorStore, and PyDantic.

CellMap-Data

A comprehensive PyTorch-based data loading and preprocessing library for CellMap biological imaging datasets, designed for efficient machine learning training on large-scale 2D/3D volumetric data.

Overview

CellMap-Data is a specialized data loading utility that bridges the gap between large biological imaging datasets and machine learning frameworks. It provides efficient, memory-optimized data loading for training deep learning models on cell microscopy data, with support for multi-class segmentation, spatial transformations, and advanced augmentation techniques.

Key Features

• 🔬 Biological Data Optimized: Native support for multiscale biological imaging formats (OME-NGFF/Zarr)
• ⚡ High-Performance Loading: Efficient data streaming with TensorStore backend and optimized PyTorch integration
• 🎯 Flexible Target Construction: Support for multi-class segmentation with mutually exclusive class relationships
• 🔄 Advanced Augmentations: Comprehensive spatial and value transformations for robust model training
• 📊 Smart Sampling: Weighted sampling strategies and validation set management
• 🚀 Scalable Architecture: Memory-efficient handling of datasets larger than available RAM
• 🔧 Production Ready: Thread-safe, multiprocess-compatible with extensive test coverage

Installation

pip install cellmap-data

Dependencies

CellMap-Data leverages several powerful libraries:

• PyTorch: Neural network training and tensor operations
• TensorStore: High-performance array storage and retrieval
• Xarray: Labeled multi-dimensional arrays with metadata
• PyDantic: Data validation and settings management
• Zarr: Chunked, compressed array storage

Quick Start

Basic Dataset Setup

from cellmap_data import CellMapDataset

# Define input and target array specifications
input_arrays = {
    "raw": {
        "shape": (64, 64, 64),  # Training patch size
        "scale": (8, 8, 8),     # Voxel resolution in nm
    }
}

target_arrays = {
    "segmentation": {
        "shape": (64, 64, 64),
        "scale": (8, 8, 8),
    }
}

# Create dataset
dataset = CellMapDataset(
    raw_path="/path/to/raw/data.zarr",
    target_path="/path/to/labels/data.zarr",
    classes=["mitochondria", "endoplasmic_reticulum", "nucleus"],
    input_arrays=input_arrays,
    target_arrays=target_arrays,
    is_train=True
)

Data Loading with Augmentations

from cellmap_data import CellMapDataLoader
from cellmap_data.transforms import RandomContrast, GaussianNoise, Binarize
import torchvision.transforms.v2 as T

# Define spatial transformations
spatial_transforms = {
    "mirror": {"axes": {"x": 0.5, "y": 0.5, "z": 0.2}},
    "rotate": {"axes": {"z": [-30, 30]}},
    "transpose": {"axes": ["x", "y"]}
}

# Define value transformations
raw_value_transforms = T.Compose([
    T.ToDtype(torch.float, scale=True),           # Normalize to [0,1] and convert to float
    GaussianNoise(std=0.05),          # Add noise for augmentation
    RandomContrast((0.8, 1.2)),       # Vary contrast
])

target_value_transforms = T.Compose([
    Binarize(threshold=0.5),          # Convert to binary masks
    T.ToDtype(torch.float32)          # Ensure correct dtype
])

# Create dataset with transforms
dataset = CellMapDataset(
    raw_path="/path/to/raw/data.zarr",
    target_path="/path/to/labels/data.zarr",
    classes=["mitochondria", "endoplasmic_reticulum", "nucleus"],
    input_arrays=input_arrays,
    target_arrays=target_arrays,
    spatial_transforms=spatial_transforms,
    raw_value_transforms=raw_value_transforms,
    target_value_transforms=target_value_transforms,
    is_train=True
)

# Configure data loader
loader = CellMapDataLoader(
    dataset,
    batch_size=4,
    num_workers=8,
    weighted_sampler=True,  # Balance classes automatically
    is_train=True
)

# Training loop
for batch in loader:
    inputs = batch["raw"]      # Shape: [batch, channels, z, y, x]
    targets = batch["segmentation"]  # Multi-class targets
    
    # Your training code here
    outputs = model(inputs)
    loss = criterion(outputs, targets)
    loss.backward()

Multi-Dataset Training

from cellmap_data import CellMapDataSplit

# Define datasets from CSV or dictionary
datasplit = CellMapDataSplit(
    csv_path="path/to/datasplit.csv",
    classes=["mitochondria", "er", "nucleus"],
    input_arrays=input_arrays,
    target_arrays=target_arrays,
    spatial_transforms={
        "mirror": {"axes": {"x": 0.5, "y": 0.5}},
        "rotate": {"axes": {"z": [-180, 180]}},
        "transpose": {"axes": ["x", "y"]}
    }
)

# Access combined datasets
train_loader = CellMapDataLoader(
    datasplit.train_datasets_combined,
    batch_size=8,
    weighted_sampler=True
)

val_loader = CellMapDataLoader(
    datasplit.validation_datasets_combined,
    batch_size=16,
    is_train=False
)

Core Components

CellMapDataset

The foundational dataset class that handles individual image volumes:

dataset = CellMapDataset(
    raw_path="path/to/raw.zarr",
    target_path="path/to/gt.zarr", 
    classes=["class1", "class2"],
    input_arrays=input_arrays,
    target_arrays=target_arrays,
    is_train=True,
    pad=True,  # Pad arrays to requested size if needed
    device="cuda"
)

Key Features:

• Automatic 2D/3D handling and slicing
• Multiscale data support
• Memory-efficient random cropping
• Class balancing and weighting
• Spatial transformation pipeline

CellMapMultiDataset

Combines multiple datasets for training across different samples:

from cellmap_data import CellMapMultiDataset

multi_dataset = CellMapMultiDataset(
    classes=classes,
    input_arrays=input_arrays,
    target_arrays=target_arrays,
    datasets=[dataset1, dataset2, dataset3]
)

# Weighted sampling across datasets
sampler = multi_dataset.get_weighted_sampler(batch_size=4)

CellMapDataLoader

High-performance data loader built on PyTorch's optimized DataLoader:

loader = CellMapDataLoader(
    dataset,
    batch_size=32,
    num_workers=12,
    weighted_sampler=True,
    device="cuda",
    prefetch_factor=4,        # Preload batches for better GPU utilization
    persistent_workers=True,  # Keep workers alive between epochs
    pin_memory=True,          # Fast CPU-to-GPU transfer
    iterations_per_epoch=1000  # For large datasets
)

# Optimized GPU memory transfer
loader.to("cuda", non_blocking=True)

Optimizations (powered by PyTorch DataLoader):

• Prefetch Factor: Background data loading to maximize GPU utilization
• Pin Memory: Fast CPU-to-GPU transfers via pinned memory (auto-enabled on CUDA)
• Persistent Workers: Reduced overhead by keeping workers alive between epochs
• PyTorch's Optimized Multiprocessing: Battle-tested parallel data loading
• Smart Defaults: Automatic optimization based on hardware configuration

See DataLoader Optimization Guide for performance tuning tips.

CellMapDataSplit

Manages train/validation splits with configuration:

datasplit = CellMapDataSplit(
    dataset_dict={
        "train": [
            {"raw": "path1/raw.zarr", "gt": "path1/gt.zarr"},
            {"raw": "path2/raw.zarr", "gt": "path2/gt.zarr"}
        ],
        "validate": [
            {"raw": "path3/raw.zarr", "gt": "path3/gt.zarr"}
        ]
    },
    classes=classes,
    input_arrays=input_arrays,
    target_arrays=target_arrays
)

Advanced Features

Spatial Transformations

Comprehensive augmentation pipeline for robust training:

spatial_transforms = {
    "mirror": {
        "axes": {"x": 0.5, "y": 0.5, "z": 0.1}  # Probability per axis
    },
    "rotate": {
        "axes": {"z": [-45, 45], "y": [-15, 15]}  # Angle ranges
    },
    "transpose": {
        "axes": ["x", "y"]  # Axes to randomly reorder
    }
}

Value Transformations

Built-in preprocessing and augmentation transforms:

from cellmap_data.transforms import (
    GaussianNoise, RandomContrast, 
    RandomGamma, Binarize, NaNtoNum, GaussianBlur
)

# Input preprocessing
raw_transforms = T.Compose([
    T.ToDtype(torch.float, scale=True),      # Normalize to [0,1]
    GaussianNoise(std=0.1),      # Add noise
    RandomContrast((0.8, 1.2)),  # Vary contrast
    NaNtoNum({"nan": 0})         # Handle NaN values
])

# Target preprocessing
target_transforms = T.Compose([
    Binarize(threshold=0.5),     # Convert to binary
    T.ToDtype(torch.float32)     # Ensure float32
])

Class Relationship Handling

Support for mutually exclusive classes and true negative inference:

# Define class relationships
class_relation_dict = {
    "mitochondria": ["cytoplasm", "nucleus"],     # Mutually exclusive
    "endoplasmic_reticulum": ["mitochondria"],    # Cannot overlap
}

dataset = CellMapDataset(
    # ... other parameters ...
    classes=["mitochondria", "er", "nucleus", "cytoplasm"],
    class_relation_dict=class_relation_dict,
    # True negatives automatically inferred from relationships
)

Memory-Efficient Large Dataset Handling

For datasets larger than available memory:

# Use subset sampling for large datasets
loader = CellMapDataLoader(
    large_dataset,
    batch_size=8,
    iterations_per_epoch=5000,  # Subsample each epoch
    weighted_sampler=True
)

# Refresh sampler between epochs
for epoch in range(num_epochs):
    loader.refresh()  # New random subset
    for batch in loader:
        # Training code
        ...

Writing Predictions

Generate predictions and write to disk efficiently:

from cellmap_data import CellMapDatasetWriter

writer = CellMapDatasetWriter(
    raw_path="input.zarr",
    target_path="predictions.zarr", 
    classes=["class1", "class2"],
    input_arrays=input_arrays,
    target_arrays=target_arrays,
    target_bounds={"array": {"x": [0, 1000], "y": [0, 1000], "z": [0, 100]}}
)

# Write predictions tile by tile
for idx in range(len(writer)):
    inputs = writer[idx]
    predictions = model(inputs)
    writer[idx] = {"segmentation": predictions}

Data Format Support

Input Formats

• OME-NGFF/Zarr: Primary format with multiscale support and full read/write capabilities
• Local/S3/GCS: Various storage backends via TensorStore

Multiscale Support

Automatic handling of multiscale datasets:

# Automatically selects appropriate scale level
dataset = CellMapDataset(
    raw_path="data.zarr",  # Contains s0, s1, s2, ... scale levels
    target_path="labels.zarr",
    # ... other parameters ...
)

# Multiscale input arrays can be specified
input_arrays = {
    "raw_4nm": {
        "shape": (128, 128, 128),
        "scale": (4, 4, 4),
    },
    "raw_8nm": {
        "shape": (64, 64, 64),
        "scale": (8, 8, 8),
    }
}

Performance Optimization

Memory Management

• Efficient tensor operations with minimal copying
• Automatic GPU memory management
• Streaming data loading for large volumes

Parallel Processing

• Multi-threaded data loading
• CUDA streams for GPU optimization
• Process-safe dataset pickling

Caching Strategy

• Persistent ThreadPoolExecutor for reduced overhead
• Optimized coordinate transformations
• Minimal redundant computations

Use Cases

1. Cell Segmentation Training

# Multi-class cell segmentation
classes = ["cell_boundary", "mitochondria", "nucleus", "er"]
spatial_transforms = {
    "mirror": {"axes": {"x": 0.5, "y": 0.5}},
    "rotate": {"axes": {"z": [-180, 180]}}
}

dataset = CellMapDataset(
    raw_path="em_data.zarr",
    target_path="segmentation_labels.zarr",
    classes=classes,
    input_arrays={"em": {"shape": (128, 128, 128), "scale": (4, 4, 4)}},
    target_arrays={"labels": {"shape": (128, 128, 128), "scale": (4, 4, 4)}},
    spatial_transforms=spatial_transforms,
    is_train=True
)

2. Large-Scale Multi-Dataset Training

# Training across multiple biological samples
datasplit = CellMapDataSplit(
    csv_path="multi_sample_split.csv",
    classes=organelle_classes,
    input_arrays=input_config,
    target_arrays=target_config,
    spatial_transforms=augmentation_config
)

# Balanced sampling across datasets
train_loader = CellMapDataLoader(
    datasplit.train_datasets_combined,
    batch_size=16,
    weighted_sampler=True,
    num_workers=16
)

3. Inference and Prediction Writing

# Generate predictions on new data
writer = CellMapDatasetWriter(
    raw_path="new_sample.zarr",
    target_path="predictions.zarr",
    classes=trained_classes,
    input_arrays=inference_config,
    target_arrays=output_config,
    target_bounds=volume_bounds
)

# Process in tiles
for idx in writer.writer_indices:  # Non-overlapping tiles
    batch = writer[idx]
    with torch.no_grad():
        predictions = model(batch["input"])
    writer[idx] = {"segmentation": predictions}

Best Practices

Dataset Configuration

• Choose patch sizes that fit comfortably in GPU memory
• Enable padding for datasets smaller than patch size

Training Optimization

• Use weighted sampling for imbalanced datasets
• Configure appropriate number of workers (typically 2x CPU cores)
• Enable CUDA streams for multi-GPU setups

Memory Optimization

• Monitor memory usage with large datasets
• Use iterations_per_epoch for very large datasets
• Refresh samplers between epochs for dataset variety

Debugging

• Start with small patch sizes and single workers
• Use force_has_data=True for testing with empty datasets
• Check dataset.verify() before training

API Reference

For complete API documentation, visit: https://janelia-cellmap.github.io/cellmap-data/

Contributing

We welcome contributions! Please see our contributing guidelines for details on:

• Code style and standards
• Testing requirements
• Documentation expectations
• Pull request process

Citation

If you use CellMap-Data in your research, please cite:

@software{cellmap_data,
  title={CellMap-Data: PyTorch Data Loading for Biological Imaging},
  author={Rhoades, Jeff and the CellMap Team},
  url={https://github.com/janelia-cellmap/cellmap-data},
  year={2024}
}

License

This project is licensed under the BSD 3-Clause License - see the LICENSE file for details.

Support

• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discussions
• 📧 Contact: [e-mail]