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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 len(cfg.AUGMENT.MULT_MIN_SIZE_TRAIN) > 0:
min_size = cfg.AUGMENT.MULT_MIN_SIZE_TRAIN
else:
min_size = cfg.INPUT.MIN_SIZE_TRAIN
max_size = cfg.INPUT.MAX_SIZE_TRAIN
flip_horizontal_prob = cfg.AUGMENT.FLIP_PROB_TRAIN
flip_vertical_prob = cfg.AUGMENT.VERTICAL_FLIP_PROB_TRAIN
brightness = cfg.AUGMENT.BRIGHTNESS
contrast = cfg.AUGMENT.CONTRAST
saturation = cfg.AUGMENT.SATURATION
hue = cfg.AUGMENT.HUE
crop_prob = cfg.AUGMENT.CROP_PROB
min_ious = cfg.AUGMENT.CROP_MIN_IOUS
min_crop_size = cfg.AUGMENT.CROP_MIN_SIZE
else:
min_size = cfg.INPUT.MIN_SIZE_TEST
max_size = cfg.INPUT.MAX_SIZE_TEST
flip_horizontal_prob = 0.0
fix_res = cfg.INPUT.FIX_RES
if cfg.INPUT.FORMAT != "":
input_format = cfg.INPUT.FORMAT
elif cfg.INPUT.TO_BGR255:
input_format = "bgr255"
normalize_transform = T.Normalize(mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD, format=input_format)
transform = T.Compose(
[
T.Resize(min_size, max_size, restrict=fix_res),
T.RandomHorizontalFlip(flip_horizontal_prob),
T.ToTensor(),
normalize_transform,
]
)
return transform
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