torchvf 0.1.1

Vector fields for instance segmentation in PyTorch.

TorchVF

WORK IN PROGRESS.

TorchVF is a unifying Python library for using vector fields for lightweight proposal-free instance segmentation. The TorchVF library provides generalizable functions to automate ground truth vector field computation, interpolation of discretely sampled vector fields, numeric integration solvers, clustering functions, and various other utilities.

This repository also provides all configs, code, and tools necessary to reproduce the results in my article on vector field based methods.

Quick Start

For anyone interested in learning about vector field based methods, see my article. TorchVF can be used to compute the instance segmentation given the semantic segmentation and vector field via the following code:

# Consider we have a vector field `vf` and semantic segmentation `semantic`, 
# we can derive the instance segmentation via the following code: 

from torchvf.numerics import interp_vf, ivp_solver, init_values_semantic
from torchvf.utils import cluster

# Step 1: Convert our discretely sampled vector field into continuous vector
# field through bilinear interpolation. 
vf = interp_vf(vf, mode = "bilinear")

# Step 2. Convert our semantic segmentation `semantic` into a set of
# initial-values to be integrated through our vector field `vf`.
init_values = init_values_semantic(semantic, device = "cuda:0")

# Step 3. Integrate our initial-values `init_values` through our vector field
# `vf` for 25 steps with a step size of 0.1 using Euler's method for numeric 
# integration. 
solutions = ivp_solver(
    vf, 
    init_values, 
    dx = 0.1,
    n_steps = 25,
    solver = "euler"
)[-1] # Get the final solution. 

# Clustering can only be done on the CPU. 
solutions = solutions.cpu()
semantic = semantic.cpu()

# Step 4. Cluster the integrated semantic points `solutions` to obtain the
# instance segmentation. 
instance_segmentation = cluster(
    solutions, 
    semantic[0], 
    eps = 2.25,
    min_samples = 15,
    snap_noise = False
)

Supported Features

Interpolators:

Interpolator	Implemented
Nearest Neighbor	:white_check_mark:
Nearest Neighbor Batched	:white_check_square:
Bilinear	:white_check_mark:
Bilinear Batched	:white_check_mark:

Numeric Integration Solvers:

Interpolator	Implemented
Euler's Method	:white_check_mark:
Midpoint Method	:white_check_mark:
Runge Kutta (4th Order)	:white_check_mark:
Adaptive Dormand Prince	:white_check_square:

Clustering Schemes:

Interpolator	Implemented
DBSCAN (Scikit-learn)	:white_check_mark:
DCSCAN (PyTorch)	:white_check_square:
...?	:white_check_square:

Vector Field Computation:

Interpolator	Implemented
Truncated SDF + Kernel	:white_check_mark:
Affinity Derived	:white_check_mark:
Omnipose	:white_check_square:
Centroid Based	:white_check_square:

Other Utilities:

• Tiler wrapper for models.
• Semantic -> euclidean conversion.
• The IVP vector field loss function.
• Tversky and Dice semantic loss functions.
• Training and evalution scripts.
• Various pretrained models on the BPCIS dataset.
• Modeling for the presented H1 and H2 models.
• mAP IoU, F1, IoU metrics.

Dependencies

The ultimate goal of TorchVF is to be solely dependent on PyTorch. Although at the moment, the signed distance function computation relies on Seung Lab's euclidean distance transform library and the DBSCAN clustering implementation relies on Scikit-learn. Furthermore, NumPy appears in various places (mAP IoU metric, clustering, ...).

Reproducability

This installation guide is for people who want to reproduce the results in my article on vector field based methods. First, clone the repository:

git clone https://github.com/ryanirl/torchvf.git

Installing the Weights

Weights include H1 and H2 for the bacterial phase contrast, bacterial fluorescence, and worm subsets of the BPCIS dataset. And can be found here (157.5 MB zipped | 185.5 MB unzipped).

Once you download the weights:

• Unzip the file.
• Replace the torchvf/weights file with the downloaded and unzipped torchvf_weights file.
• Rename torchvf/torchvf_weights to torchvf/weights.

Installing the BPCIS Dataset

Download the BPCIS dataset here. Then setup the file system this way:

├── torchvf/
├── data/
│   └── bpcis/
│       ├── bact_fluor_train/
│       ├── bact_fluor_test/
│       ├── bact_phase_train/
│       ├── bact_phase_test/
│       ├── worm_train/
│       └── worm_test/
├── weights/
└── ***

If you have cloned the library, downloaded the weights, and downloaded the BPCIS dataset you should be able to do python3 scripts/eval.py --config_dir ./configs/eval/bpcis_bact_fluor.py. This will run evaluation on the bacterial fluorescence subset using the config file from the downloaded weights.

Usage

Work in progress.

Citation

@article{TorchVF,
   author = {Ryan Peters},
   title = {TorchVF: Vector Fields for Instance Segmentation},
   year = 2022
}

License

Distributed under the Apache-2.0 license. See LICENSE for more information.