# e.g. 2 with group size 4 means # - two unique images # - four image versions for each (flips, crops, etc.) # - total batch size 8 __C.TRAIN.NUM_GROUPS = 4 __C.TRAIN.GROUP_SIZE = 2 # ignore certain class indicies when reporting val IoU # (e.g. Synthia doesnt have all Cityscapes categories) __C.VAL = AttrDict() __C.VAL.IGNORE_CLASS = [] # ---------------------------------------------------------------------------- # # Dataset options (+ augmentation options) # ---------------------------------------------------------------------------- # __C.DATASET = AttrDict() __C.DATASET.CROP_SIZE = [512, 512] __C.DATASET.VAL_CROP = True # use center crop for validation (rescale otherwise) __C.DATASET.RND_CROP = True __C.DATASET.RND_BLUR = True __C.DATASET.RND_GREYSCALE = 0.0 __C.DATASET.RND_HFLIP = True __C.DATASET.RND_JITTER = 0.0 # scale range for target consistency __C.DATASET.RND_ZOOM = [0.5, 1.2] # horisontal flip with consistency __C.DATASET.GUIDED_HFLIP = False # source-specific augmentations __C.DATASET.SRC_RND_BLUR = False
__C.METRICS = AttrDict()
# For IN5k, we train with the IN5k training set, but we still eval on IN1k val.
# We assume in the 5k-category list, the IN1k categories are the first 1k ones.
# So we use the following options to evaluate the first N-way on val.
__C.METRICS.EVAL_FIRST_N = False # deprecated
__C.METRICS.FIRST_N = 1000
__C.DATADIR_TRAIN_TIME = \
b'kinetics/alllist'
__C.DATADIR_TEST_TIME = \
b'data/kinetics/kinetics_lmdb_gfsai'
__C.FILENAME_GT = \
b'data/kinetics/val_all_list.txt'
__C.DATADIR = b''
__C.DATASET = b''
__C.ROOT_GPU_ID = 0
__C.CUDNN_WORKSPACE_LIMIT = 256
__C.RNG_SEED = 2
__C.NUM_GPUS = 8
__C.VIDEO_DECODER_THREADS = 4
""" This dir is to cache shared indexing of the datasets.
"""
__C.OUTPUT_DIR = b'gen'
__C.LOG_PERIOD = 10
__C.PROF_DAG = False
__C.RPN.POST_NMS_TOP_N_TEST = 1000
__C.RPN.NMS_THRESH = 0.7
__C.RPN.FG_IOU_THRESH = 0.7
__C.RPN.BG_IOU_THRESH = 0.3
__C.RPN.BATCH_SIZE_PER_IMAGE = 256
__C.RPN.POSITIVE_FRACTION = 0.5
#
# Test Paramaters
#
__C.TEST = AttrDict()
__C.TEST.THRESHOLD = 0.5
#
# Dataset, Word Vectors Directory
#
__C.DATASET = "VRD"
__C.DATASET_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'data', __C.DATASET)
__C.WORD_VECTORS_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'data', 'wordvectors',
'GoogleNews-vectors-negative300.bin')
