# 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')