def get_data(train_or_test, batch_size):
is_train = train_or_test == 'train'
filename_list = cfg.train_list if is_train else cfg.test_list
ds = Data(filename_list,
shuffle=is_train,
flip=is_train,
random_crop=is_train,
test_set=not is_train)
sample_num = ds.size()
if is_train:
augmentors = [
imgaug.RandomOrderAug([
imgaug.BrightnessScale((0.6, 1.4), clip=False),
imgaug.Contrast((0.6, 1.4), clip=False),
imgaug.Saturation(0.4, rgb=False),
imgaug.Lighting(
0.1,
eigval=np.asarray([0.2175, 0.0188, 0.0045][::-1]) * 255.0,
eigvec=np.array([[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]],
dtype='float32')[::-1, ::-1])
]),
]
else:
augmentors = []
# ds = AugmentImageComponent(ds, augmentors)
ds = BatchData(ds, batch_size, remainder=not is_train)
if is_train:
ds = PrefetchDataZMQ(ds, min(6, multiprocessing.cpu_count()))
return ds, sample_num
def get_data(train_or_test, batch_size):
is_train = train_or_test == 'train'
ds = Data(train_or_test)
sample_num = ds.size()
if is_train:
augmentors = [
imgaug.RandomOrderAug([
imgaug.Brightness(30, clip=False),
imgaug.Contrast((0.8, 1.2), clip=False),
imgaug.Saturation(0.4),
imgaug.Lighting(0.1,
eigval=[0.2175, 0.0188, 0.0045][::-1],
eigvec=np.array([[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]],
dtype='float32')[::-1, ::-1])
]),
# imgaug.Clip(),
imgaug.ToUint8()
]
else:
augmentors = [imgaug.ToUint8()]
ds = AugmentImageComponent(ds, augmentors)
if is_train:
ds = PrefetchDataZMQ(ds, min(8, multiprocessing.cpu_count()))
ds = BatchData(ds, batch_size, remainder=not is_train)
return ds, sample_num
def get_data(train_or_test, batch_size):
is_train = train_or_test == 'train'
filename_list = cfg.train_list if is_train else cfg.test_list
ds = Data(filename_list, rotate=False, flip_ver=is_train, flip_horiz=is_train, shuffle=is_train)
sample_num = ds.size()
augmentors = [
# random rotate and flip should be applied to both input and label, thus cannot be added here
imgaug.SaltPepperNoise(white_prob=0.01, black_prob=0.01),
imgaug.ToUint8()
]
ds = AugmentImageComponent(ds, augmentors)
if is_train:
ds = PrefetchDataZMQ(ds, min(8, multiprocessing.cpu_count()))
ds = BatchData(ds, batch_size, remainder = not is_train)
return ds, sample_num
