Road Segmentation Project - Computational Intelligence Lab (2022)

• Road Segmentation Project - Computational Intelligence Lab (2022)
  • Set up the environnement
  • Download data
  • Run the code
    • Run the baselines
      • Support Vector Classifier baseline (baseline-svc)
      • Patch-CNN baseline (baseline-patch-cnn)
      • Vanilla-UNet baseline (baseline-unet)
    • Run our models
      • Fine-tuning UNet (unet)
      • USwinBaseNet (swin-unet)
  • Create an ensemble submission

Set up the environnement

Install conda, then:

conda create -n cil python=3.8
conda activate cil
pip install -r requirements.txt
pre-commit install

Download data

From the root of this repository:

kaggle competitions download -c cil-road-segmentation-2022
unzip cil-road-segmentation-2022.zip
mkdir data
mv training data
mv test data

Then with python:

from glob import sample
import os

VAL_SIZE = 10

for img in sample(glob("data/training/images/*.png"), VAL_SIZE):
	os.rename(img, img.replace('training', 'validation'))
	mask = img.replace('images', 'groundtruth')
	os.rename(mask, mask.replace('training', 'validation'))

Run the code

The code/run.py script runs with the following arguments, and needs to be executed from root directoy of this repo:

python code/run.py 	[-h] [--model-type {small,base}]
			[--loss {bce,dice,mixed,focal,twersky,f1,patch-f1}]
			--train-dir TRAIN_DIR [--model-save-dir MODEL_SAVE_DIR] [--no-augment]
			--val-dir VAL_DIR --test-dir TEST_DIR [--n_epochs N_EPOCHS]
			[--batch_size BATCH_SIZE] [--checkpoint_path CHECKPOINT_PATH]
			{baseline-svc,baseline-unet,baseline-patch-cnn,unet,swin-unet}

Argument	Description	Choices	Default value
model (positional)	Model to use for training	baseline-svc, baseline-unet, baseline-patch-cnn, unet, swin-unet	-
--train-dir	Path to the training directory	-	None, required
--val-dir	Path to the validation directory	-	None, required
--test-dir	Path to the test directory	-	None, required
--model-save-dir	Path where the model will be saved	-	.
--no-augment	If added to the command, the dataset will not be augmented (i.e. the initial dataset will be used instead)	-	.
--n_epochs	Number of epochs to train on	-	100
--batch_size	Path of a model checkpoint to load to resume training	-	128
--model-type	For model swin-unet (will be ignored otherwise), to select which pre-trained model we would like to use	small, base	base
--checkpoint_path	Path of a model checkpoint to load to resume training	-	None
-l, --loss	Loss to train with	bce, dice, mixed, focal, twersky, f1, patch-f1	bce

Run the baselines

From the root of this folder:

Support Vector Classifier baseline (baseline-svc)

python code/run.py baseline-svc \
  --train-dir "data/training" \
  --test-dir "data/test" \
  --val-dir "data/validation" \

Patch-CNN baseline (baseline-patch-cnn)

python code/run.py baseline-patch-cnn \
  --train-dir "data/training" \
  --test-dir "data/test" \
  --val-dir "data/validation" \
  --n_epochs 20 \
  --batch_size 128

Vanilla-UNet baseline (baseline-unet)

SAVE_DIR="<where you want the best weights to be stored>"

python code/run.py baseline-unet \
  --train-dir "data/training" \
  --test-dir "data/test" \
  --val-dir "data/validation" \
  --n_epochs 35 \
  --batch_size 4 \
  --model-save-dir $SAVE_DIR

Run our models

Fine-tuning UNet (unet)

Basic usage:

SAVE_DIR="<where you want the best weights to be stored>"
N_EPOCHS=200
BATCH_SIZE=4
LOSS="<choose your loss>"

python code/run.py unet \
  --train-dir "data/training" \
  --test-dir "data/test" \
  --val-dir "data/validation" \
  --loss $LOSS \
  --n_epochs $N_EPOCHS \
  --batch_size $BATCH_SIZE \
  --model-save-dir $SAVE_DIR

USwinBaseNet (swin-unet)

Link to the paper

Basic usage:

SAVE_DIR="<where you want the best weights to be stored>"
N_EPOCHS=200
BATCH_SIZE=2
LOSS="<choose your loss>"

python code/run.py swin-unet \
  --train-dir "data/training" \
  --test-dir "data/test" \
  --val-dir "data/validation" \
  --loss $LOSS \
  --n_epochs $N_EPOCHS \
  --batch_size $BATCH_SIZE \
  --model-save-dir $SAVE_DIR

Create an ensemble submission

You can make create an ensemble submission with this Python script, with which you can add as many .csv files as you want.

The final result is determined by majority vote.

python tools/create_ensemble_submission.py file1.csv file2.csv ... -o "submission/ensemble.csv"