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Source Detection for Neuron-tree Reconstruction

Introduction

Pytorch code for training and testing source detection models for neuron-tree reconstruction.

Overview

  • source_detection/: includes training and validation scripts.
  • lib/: contains data preparation, model definition, and some utility functions.
  • experiments/: contains *.yaml configuration files to run experiments.

Requirements

The code is developed using python 3.7.2 on Ubuntu 18.04.1. NVIDIA GPUs ared needed to train and test. See requirements.txt for other dependencies.

Quick start

Installation

  1. Install pytorch >= v1.0.0 following official instructions.
  2. Clone this repo, and we will call the directory that you cloned as ${ROOT}
  3. Install dependencies. 
    pip install -r requirements.txt
  4. Download pretrained neuron-detection model, and put it under ${ROOT}/models/pytorch/neuron/

Training with simulated data (optional)

  1. Download ImageNet-pretrained ResNet-18 model and put it under ${ROOT}/models/pytorch/imagenet/
  2. To train with simulated data, run:
CUDA_VISIBLE_DEVICES=$GPU_ID python source_detection/train.py --cfg experiments/simulated/128x128_d256x3_adam_lr1e-3.yaml

Model checkpoints and logs will be saved into output folder while tensorboard logs will be saved into log folder.

Testing with simulated data

To test with simulated data after training, run:

CUDA_VISIBLE_DEVICES=$GPU_ID python source_detection/validate.py --cfg experiments/simulated/128x128_d256x3_adam_lr1e-3.yaml

Tensorboard logs will be saved into log folder.

Testing with real data

You need to put the real data (e.g. srep31332-s1.mat) into ./data/real_dataset/ folder first. To test with real data after training, run:

CUDA_VISIBLE_DEVICES=$GPU_ID python source_detection/test.py --cfg experiments/real_data/128x128_d256x3_adam_lr1e-3.yaml

Tensorboard logs will be saved into log folder. Modify TEST_SET entry (for different input file names) or X_MIN, Y_MIN, X_MAX, Y_MAX entries (for different region in the video) in the .yaml file if you need to. To see Tensorboard logs with images from all time-steps, run:

tensorboard --logdir=$YOUR_LOG_DIR --samples_per_plugin images=0