def get_data(name, data_dir, meta_dir, gpu_nums):
isTrain = True if 'train' in name else False
ds = Camvid(data_dir, meta_dir, name, shuffle=True)
if isTrain:
ds = MapData(ds, RandomResize)
if isTrain:
shape_aug = [
RandomCropWithPadding(args.crop_size,IGNORE_LABEL),
Flip(horiz=True),
]
else:
shape_aug = []
ds = AugmentImageComponents(ds, shape_aug, (0, 1), copy=False)
def f(ds):
image, label = ds
m = np.array([104, 116, 122])
const_arr = np.resize(m, (1,1,3)) # NCHW
image = image - const_arr
return image, label
ds = MapData(ds, f)
if isTrain:
ds = BatchData(ds, args.batch_size*gpu_nums)
ds = PrefetchDataZMQ(ds, 1)
else:
ds = BatchData(ds, 1)
return ds
def get_data(name, data_dir, meta_dir, gpu_nums):
isTrain = True if 'train' in name else False
ds = PascalVOC12(data_dir, meta_dir, name, shuffle=True)
if isTrain:
shape_aug = [
RandomResize(xrange=(0.7, 1.5),
yrange=(0.7, 1.5),
aspect_ratio_thres=0.15,
interp=cv2.INTER_NEAREST),
RandomCropWithPadding(args.crop_size, IGNORE_LABEL),
Flip(horiz=True),
]
else:
shape_aug = []
ds = AugmentImageComponents(ds, shape_aug, (0, 1), copy=False)
def f(ds):
image, label = ds
m = np.array([104, 116, 122])
const_arr = np.resize(m, (1, 1, 3)) # NCHW
image = image - const_arr
return image, label
ds = MapData(ds, f)
if isTrain:
ds = BatchData(ds, args.batch_size * gpu_nums)
ds = PrefetchDataZMQ(ds, 1)
else:
ds = BatchData(ds, 1)
return ds