def get_downsampled_imagenet_augmented_data(subset, options,
do_multiprocess=True, do_validation=False, shuffle=None):
isTrain = subset == 'train' and do_multiprocess
shuffle = shuffle if shuffle is not None else isTrain
reret = re.search(r'^imagenet([0-9]*)$', options.ds_name)
input_size = int(reret.group(1))
ds = DownsampledImageNet(_data_batch_dir(options.data_dir, input_size),\
subset, shuffle, input_size, do_validation=do_validation)
pp_mean = ds.mean_img
paste_size = ds.input_size * 5 // 4
crop_size = ds.input_size
if isTrain:
augmentors = [
imgaug.CenterPaste((paste_size, paste_size)),
imgaug.RandomCrop((crop_size, crop_size)),
imgaug.Flip(horiz=True),
imgaug.MapImage(lambda x: (x - pp_mean)/128.0),
]
else:
augmentors = [
imgaug.MapImage(lambda x: (x - pp_mean)/128.0)
]
ds = AugmentImageComponent(ds, augmentors)
ds = BatchData(ds, options.batch_size // options.nr_gpu, remainder=not isTrain)
if do_multiprocess:
ds = PrefetchData(ds, 4, 2)
return ds
def get_data(train_or_test):
isTrain = train_or_test == 'train'
pp_mean = dataset.SVHNDigit.get_per_pixel_mean()
if isTrain:
d1 = dataset.SVHNDigit('train')
d2 = dataset.SVHNDigit('extra')
ds = RandomMixData([d1, d2])
else:
ds = dataset.SVHNDigit('test')
if isTrain:
augmentors = [
imgaug.CenterPaste((40, 40)),
imgaug.RandomCrop((32, 32)),
#imgaug.Flip(horiz=True),
imgaug.BrightnessAdd(10),
imgaug.Contrast((0.8, 1.2)),
imgaug.GaussianDeform( # this is slow
[(0.2, 0.2), (0.2, 0.8), (0.8, 0.8), (0.8, 0.2)], (32, 32),
0.2, 3),
imgaug.MapImage(lambda x: x - pp_mean),
]
else:
augmentors = [imgaug.MapImage(lambda x: x - pp_mean)]
ds = AugmentImageComponent(ds, augmentors)
ds = BatchData(ds, 128, remainder=not isTrain)
if isTrain:
ds = PrefetchData(ds, 5, 5)
return ds
def get_cifar_augmented_data(subset,
options,
do_multiprocess=True,
do_validation=False,
shuffle=None):
isTrain = subset == 'train' and do_multiprocess
shuffle = shuffle if shuffle is not None else isTrain
if options.num_classes == 10 and options.ds_name == 'cifar10':
ds = dataset.Cifar10(subset,
shuffle=shuffle,
do_validation=do_validation)
cutout_length = 16
n_holes = 1
elif options.num_classes == 100 and options.ds_name == 'cifar100':
ds = dataset.Cifar100(subset,
shuffle=shuffle,
do_validation=do_validation)
cutout_length = 8
n_holes = 1
else:
raise ValueError(
'Number of classes must be set to 10(default) or 100 for CIFAR')
logger.info('{} set has n_samples: {}'.format(subset, len(ds.data)))
pp_mean = ds.get_per_pixel_mean()
if isTrain:
logger.info('Will do cut-out with length={} n_holes={}'.format(
cutout_length, n_holes))
augmentors = [
imgaug.CenterPaste((40, 40)),
imgaug.RandomCrop((32, 32)),
imgaug.Flip(horiz=True),
imgaug.MapImage(lambda x: (x - pp_mean) / 128.0),
Cutout(length=cutout_length, n_holes=n_holes),
]
else:
augmentors = [imgaug.MapImage(lambda x: (x - pp_mean) / 128.0)]
ds = AugmentImageComponent(ds, augmentors)
ds = BatchData(ds,
options.batch_size // options.nr_gpu,
remainder=not isTrain)
if do_multiprocess:
ds = PrefetchData(ds, 3, 2)
return ds
def get_data(name, batch):
isTrain = name == 'train'
if isTrain:
augmentors = [
imgaug.ResizeShortestEdge(256, cv2.INTER_CUBIC),
imgaug.RandomCrop(224),
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]),
imgaug.Flip(horiz=True)]
else:
augmentors = [
imgaug.ResizeShortestEdge(256, cv2.INTER_CUBIC),
imgaug.CenterCrop((224, 224))]
return get_imagenet_dataflow(args.data, name, batch, augmentors)
def get_data(self, train_or_test):
isTrain = train_or_test == 'train'
ds = dataset.Cifar10(train_or_test, dir='.')
pp_mean = ds.get_per_pixel_mean()
if isTrain:
augmentors = [
imgaug.CenterPaste((40, 40)),
imgaug.RandomCrop((32, 32)),
imgaug.Flip(horiz=True),
# imgaug.Brightness(20),
# imgaug.Contrast((0.6,1.4)),
imgaug.MapImage(lambda x: x - pp_mean),
]
else:
augmentors = [imgaug.MapImage(lambda x: x - pp_mean)]
ds = AugmentImageComponent(ds, augmentors)
ds = BatchData(ds, self.batch_size, remainder=not isTrain)
if isTrain:
ds = PrefetchData(ds, 3, 2)
return ds
