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cfg.py
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import numpy as np
WEIGHT_DECAY = 0.00005
SAVE_IMAGE_HEIGHT = 256
SAVE_IMAGE_WIDTH = 256
# SAVE_IMAGE_HEIGHT = 286
# SAVE_IMAGE_WIDTH = 286
RESNET_FEAT_DIM = 2048
SS_SPLIT_PATH = './data/standard_split/'
PS_SPLIT_PATH = './data/proposed_split/'
PREPROCESSED_DATA_PATH = './data/preprocessed/'
# CUB DATASET INFO
CUB_PATH = './data/CUB_200_2011/CUB_200_2011'
CUB_ATT_DIM = 312
CUB_CLASS_NUM = 200
CUB_TRAIN_CLASS_NUM = 150
CUB_IMAGE_NUM = 11788
CUB_TEST_UNSEEN_NUM = 2967
CUB_TEST_SEEN_NUM = 1764
CUB_WORD2VEC_DIM = 400
CUB_GLOVE_DIM = 400
CUB_WORDNET_DIM = 319
# SUN DATASET INFO
SUN_PATH = './data/SUN'
SUN_ATT_DIM = 102
SUN_CLASS_NUM = 717
SUN_TRAIN_CLASS_NUM = 645
SUN_IMAGE_NUM = 14340
SUN_TEST_UNSEEN_NUM = 1440
SUN_TEST_SEEN_NUM = 2580
# AWA2 DATASET INFO
AWA2_PATH = './data/Animals_with_Attributes2'
AWA2_ATT_DIM = 85
AWA2_CLASS_NUM = 50
AWA2_TRAIN_CLASS_NUM = 40
AWA2_IMAGE_NUM = 37322
AWA2_TEST_UNSEEN_NUM = 7913
AWA2_TEST_SEEN_NUM = 5882
# APY DATASET INFO
APY_PATH = './data/aPY'
APY_ATT_DIM = 64
APY_CLASS_NUM = 32
APY_TRAIN_CLASS_NUM = 20
APY_IMAGE_NUM = 15339
APY_TEST_UNSEEN_NUM = 7924
APY_TEST_SEEN_NUM = 1483
# PRETRAIN MODEL PATH
PRETRAIN_MODEL = './model/pretrained/imagenet/'
GENERATOR_MODEL = './model/pretrained/generator/'
CLASSIFICATION_MODEL = './model/pretrained/classifier/'