DEVICE : cuda # device used for training and evaluation (cpu, cuda, cuda0, cuda1, ...)

SAVE_DIR : './outputs_test/' # output folder name used for saving the model, logs and inference results

ADDENDUM : 'will-be-appended-at-the-end-of-logfoldername'

MODEL:

NAME : UperNetForSemanticSegmentation # name of the model you are using

BACKBONE : ConvNeXt-T_CVST # model variant

PRETRAINED : 'Location of pretrained-backbone model' # backbone model's weight

DATASET:

NAME : pascalaug # dataset name to be trained with (camvid, cityscapes, ade20k)

ROOT : data-dir-path # dataset root path

IGNORE_LABEL : -1

N_CLS : 21

SEED : 0

TRAIN:

BASE_SIZE : 512

IMAGE_SIZE : [473, 473] # training image size in (h, w)

BATCH_SIZE : 32 # batch size used to train

EPOCHS : 50 # number of epochs to train

EVAL_INTERVAL : 32 # evaluation interval during training

ADVERSARIAL : true

ATTACK : pgd

LOSS_FN : pgd

EPS : 4

N_ITERS : 5

FREEZE : false

AMP : false # use AMP in training

DDP : true # use DDP training

LOSS:

NAME : CrossEntropy # loss function name (ohemce, ce, dice)

CLS_WEIGHTS : false # use class weights in loss calculation

OPTIMIZER:

NAME : AdamW # optimizer name

LR : 0.001 # initial learning rate used in optimizer

WEIGHT_DECAY : 0.01 # decay rate used in optimizer

SCHEDULER:

NAME : warmuppolylr # scheduler name

POWER : 1.0 # scheduler power

WARMUP : 10 # warmup epochs used in scheduler

WARMUP_RATIO : 0.00001 # warmup ratio

EVAL:

NAME : pascalvoc

BACKBONE : ConvNeXt-T_CVST

N_CLS : 21

MODEL_PATH : 'path to checkpoint of evaluation model'

BASE_SIZE : 512

IMAGE_SIZE : [473, 473]

BATCH_SIZE : 24 # evaluation image size in (h, w)