torch-staintools 1.0.7

GPU-accelerated stain normalization and augmentation

Torch-StainTools

A fast, GPU-friendly PyTorch toolkit for stain normalization and augmentation of histopathological images.

Torch-StainTools implements GPU-accelerated stain augmentation and normalization algorithms (Reinhard, Macenko, Vahadane) with batch processing and caching for on-the-fly large-scale computational pathology pipelines.

What's New (~ 1.0.7)

• **Version **: full vectorization support and dynamic shape tracking from Dynamo.

• Alternative linear concentration solvers: 'qr' (QR Decomposition) and 'pinv' (Moore-Penrose inverse)

• Color/Texture-based Hash as cache key if no unique identifiers (e.g., filenames) are available.

What It Does

• GPU acceleration and vectorized execution for batch inputs .

• Optional TorchDynamo graph compilation (torch.compile) for high-throughput execution

• On-the-fly stain normalization and augmentation.

• Stain matrix caching to avoid redundant computation across tiles.

• Encapsulation as nn.Module . Easy to plug into existing neural network pipelines.

• Tissue masking support. Optional and customizable.

Citation

If this toolkit helps you in your publication, please feel free to cite with the following bibtex entry:

@software{zhou_2024_10496083,
  author       = {Zhou, Yufei},
  title        = {CielAl/torch-staintools: V1.0.4 Release},
  month        = jan,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {v1.0.4},
  doi          = {10.5281/zenodo.10496083},
  url          = {https://doi.org/10.5281/zenodo.10496083}
}

Normalization Showcase

Torch-Staintools

Comparison: StainTools

Augmentation

Torch-Staintools

Comparison: StainTools

Performance Benchmark

Single Large ROI (2500 $\times$ 2500 $\times$ 3; No Caching)

• Representative preprocessing scenario for large tissue ROIs.
• GPU execution with TorchDynamo (torch.compile) enabled.

Transformation

Method	CPU [s]	GPU [s]	StainTool [s]
Vahadane	119.00	4.60	20.90
Macenko	5.57	0.48	20.70
Reinhard	0.84	0.02	0.41

Fitting (Click to Expand)

Fitting (one-time cost)

Method	CPU [s]	GPU [s]	StainTool [s]
Vahadane	132.00	5.20	19.10
Macenko	6.99	0.06	20.00
Reinhard	0.42	0.01	0.08

Batched Small Tiles (81 tiles, 256$\times$256$\times$3)

• Splitting 2500 $\times$ 2500 $\times$ 3 ROI into a batch of 81 smaller patches (256$\times$256$\times$3).

• Representative on-the-fly processing scenario for training and inference.

• TorchDynamo (torch.compile) enabled.

Batch Transformation

Method	No Cache [s]	Stain Matrix Cached [s]	Speedup
Vahadane	6.62	0.019	348x Faster
Macenko	0.023	0.020	1.15x Faster

Batchified Concentration Computation

• Split the sample images under ./test_images (size 2500x2500x3) into 81 non-overlapping 256x256x3 tiles as a batch.
• For the StainTools baseline, a for-loop is implemented to get the individual concentration of each of the numpy array of the 81 tiles.
• torch.compile enabled.

Method	CPU[s]	GPU[s]
FISTA (concentration_solver='fista')	1.47	0.24
ISTA (concentration_solver='ista')	3.12	0.31
CD (concentration_solver='cd')	29.30s	4.87
LS (concentration_solver='ls')	0.22	0.097
StainTools (SPAMS)	16.60	N/A

Installation

• From Repository:

pip install git+https://github.com/CielAl/torch-staintools.git

• From PyPI:

pip install torch-staintools

Documentation

Detail documentation regarding the code base can be found in the GitPages.

Minimal Usage and Tips

• For details, follow the example in demo.py
• Normalizers are implemented as torch.nn.Module and can be integrated like a standalone network component.
• qr and pinv concentration solvers are on par with ls for batch concentration computation. But ls (i.e., torch.linalg.lstsq) may fail on GPU for a single larger input image (width and height). This happens with the default cusolver backend. Try using magma instead:

import torch 
torch.backends.cuda.preferred_linalg_library('magma')

Example

# We enable the torch.compile (note this is True by default)
from torch_staintools.normalizer import NormalizerBuilder
# ######### Vahadane
target_tensor = ... # any batch float inputs in B x C x H x W, value range in [0., 1.] 
norm_tensor  = ... # any batch float inputs in B x C x H x W, value range in [0., 1.] 
target_tensor = target_tensor.cuda()
norm_tensor = norm_tensor.cuda()
normalizer_vahadane = NormalizerBuilder.build('vahadane',
                                              concentration_solver='qr',
                                              use_cache=True
                                              )
normalizer_vahadane = normalizer_vahadane.cuda()
normalizer_vahadane.fit(target_tensor)
norm_out = normalizer_vahadane(norm_tensor)

# ###### Augmentation
# augment by: alpha * concentration + beta, while alpha is uniformly randomly sampled from (1 - sigma_alpha, 1 + sigma_alpha),
# and beta is uniformly randomly sampled from (-sigma_beta, sigma_beta).
from torch_staintools.augmentor import AugmentorBuilder
augmentor = AugmentorBuilder.build('vahadane',
                                   use_cache=True,
                                   )
# move augmentor to the corresponding device
augmentor = augmentor.cuda()

num_augment = 5
# multiple copies of different random augmentation of the same tile may be generated
for _ in range(num_augment):
  aug_out = augmentor(norm_tensor)

# dump the cache of stain matrices for future usage
augmentor.dump_cache('./cache.pickle')

Stain Matrix Caching

Stain matrix estimation can dominate runtime (especially for Vahadane). To reduce overhead, Normalizer and Augmentor support an in-memory, device-specific cache for stain matrices (typically 2×3 for H&E/RGB).

Why it matters: cached stain matrices can be reused across images, yielding substantial speedups in batch and on-the-fly pipelines.

How it works

• Cache contents can be saved and exported for reuse in future.
• Enable with use_cache=True when constructing a Normalizer or Augmentor
• Cached entries are keyed per image (e.g., filename or slide identifier)
• For batched inputs (B×C×H×W), provide one key per image in the batch

Fallback behavior

• If caching is enabled but no cache_key is provided, a texture- and color-based hash is computed automatically.
• Visually similar images are likely to reuse stain matrices, while collisions across dissimilar images are minimized.

Enable Cache / Loading

# set `use_cache` to True
# specify `load_path` to read from existing cache data
NormalizerBuilder.build('vahadane',
                        concentration_solver='qr',
                        use_cache=True,
                        load_path='path_to_cache'
                        )
# Alternatively, read cache manually
normalizer.load_cache('path_to_cach')

If unique identifiers (UID) of images are available

# explicitly set cache_keys in normalization passes.
normalizer(input_batch, cache_keys=list_of_uid)
augmentor(input_batch, cache_keys=list_of_uid)

If cache_keys are not available

# color/texture-based hash keys are internally computed.
normalizer_vahadane(input_batch)
augmentor(input_batch)
# 

Export cache

# dump to path
normalizer.dump_cache("/folder/cache.tch")

Load existing cache

Acknowledgments

• Some codes are inspired from torchvahadane, torchstain, and StainTools
• Sample images in the demo and ReadMe.md are selected from The Cancer Genome Atlas Program(TCGA) dataset.