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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from . import transforms as T
def build_transforms(cfg, is_train=True):
if is_train:
if cfg.INPUT.MIN_SIZE_RANGE_TRAIN[0] == -1:
min_size = cfg.INPUT.MIN_SIZE_TRAIN
else:
assert len(cfg.INPUT.MIN_SIZE_RANGE_TRAIN) == 2, \
"MIN_SIZE_RANGE_TRAIN must have two elements (lower bound, upper bound)"
min_size = range(
cfg.INPUT.MIN_SIZE_RANGE_TRAIN[0],
cfg.INPUT.MIN_SIZE_RANGE_TRAIN[1] + 1
)
max_size = cfg.INPUT.MAX_SIZE_TRAIN
# max_size = None
flip_prob = 0.5 # cfg.INPUT.FLIP_PROB_TRAIN
rotate_prob = cfg.INPUT.ROTATE_PROB_TRAIN
rotate_degree = cfg.INPUT.ROTATE_DEGREE
crop_prob = cfg.INPUT.CROP_PROB_TRAIN
else:
min_size = cfg.INPUT.MIN_SIZE_TEST
max_size = cfg.INPUT.MAX_SIZE_TEST
# max_size = None
flip_prob = 0
rotate_prob = 0
rotate_degree = 0
crop_prob = 0
to_bgr255 = cfg.INPUT.TO_BGR255
normalize_transform = T.Normalize(
mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD, to_bgr255=to_bgr255
)
transform = T.Compose(
[
T.RandomCrop(crop_prob),
T.RandomBrightness(crop_prob),
T.RandomContrast(crop_prob),
T.RandomHue(crop_prob),
T.RandomSaturation(crop_prob),
T.RandomGamma(crop_prob),
T.Resize(min_size, max_size),
T.RandomHorizontalFlip(flip_prob),
T.RandomRotation(rotate_prob, rotate_degree),
T.ToTensor(),
normalize_transform,
]
)
return transform
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