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configs.py
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configs.py
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import numpy as np
# DATASET PARAMETERS
TRAIN_DIR = 'D:/VOCdata/VOC2012AUG/'
VAL_DIR = TRAIN_DIR
stages = 3
TRAIN_LIST = ['D:/VOCdata/VOC2012AUG/train_aug.txt'] * stages
VAL_LIST = ['D:/VOCdata/VOC2012AUG/val.txt'] * stages
SHORTER_SIDE = [350] * stages
CROP_SIZE = [500] * stages
NORMALISE_PARAMS = [1. / 255, # SCALE
np.array([0.485, 0.456, 0.406]).reshape((1, 1, 3)), # MEAN
np.array([0.229, 0.224, 0.225]).reshape((1, 1, 3))] # STD
BATCH_SIZE = [4] * stages
NUM_WORKERS = 16
NUM_CLASSES = [21] * stages
LOW_SCALE = [0.5] * stages
HIGH_SCALE = [2.0] * stages
IGNORE_LABEL = 255
# ENCODER PARAMETERS
ENC = '50'
ENC_PRETRAINED = False # pre-trained on ImageNet or randomly initialised
# GENERAL
EVALUATE = False
FREEZE_BN = [False] * stages
NUM_SEGM_EPOCHS = [100] * stages
PRINT_EVERY = 10
RANDOM_SEED = 42
SNAPSHOT_DIR = './ckpt/'
CKPT_PATH = './ckpt/checkpoint.pth.tar'
MODEL_PATH = './models/my_model.pth'
VAL_EVERY = [5] * stages # how often to record validation scores
# OPTIMISERS' PARAMETERS
LR_ENC = [5e-4, 2.5e-4, 1e-4] # TO FREEZE, PUT 0
LR_DEC = [5e-3, 2.5e-3, 1e-3]
MOM_ENC = [0.9] * stages # TO FREEZE, PUT 0
MOM_DEC = [0.9] * stages
WD_ENC = [1e-5] * stages # TO FREEZE, PUT 0
WD_DEC = [1e-5] * stages
OPTIM_DEC = 'sgd'
palette = [(0, 0, 0),
(128, 0, 0),
(0, 128, 0),
(128, 128, 0),
(0, 0, 128),
(128, 0, 128),
(0, 128, 128),
(128, 128, 128),
(64, 0, 0),
(192, 0, 0),
(64, 128, 0),
(192, 128, 0),
(64, 0, 128),
(192, 0, 128),
(64, 128, 128),
(192, 128, 128),
(0, 64, 0),
(128, 64, 0),
(0, 192, 0),
(128, 192, 0),
(0, 64, 128)]
classes = {'aeroplane' : 1, 'bicycle' : 2, 'bird' : 3, 'boat' : 4,
'bottle' : 5, 'bus' : 6, 'car' : 7, 'cat' : 8,
'chair' : 9, 'cow' : 10, 'diningtable' : 11, 'dog' : 12,
'horse' : 13, 'motorbike' : 14, 'person' : 15, 'potted-plant' : 16,
'sheep' : 17, 'sofa' : 18, 'train' : 19, 'tv/monitor' : 20}