def __init__(self, args, addnoise=True):
super(RandomAffineFlow, self).__init__()
self._args = args
self._interp2 = Interp2(clamp=False)
self._flow_interp2 = Interp2(clamp=False)
self._meshgrid = Meshgrid()
self._identity = _IdentityParams()
self._random_mirror = RandomMirror()
self._addnoise = addnoise
self.register_buffer("_noise1", torch.FloatTensor())
self.register_buffer("_noise2", torch.FloatTensor())
self.register_buffer("_xbounds", torch.FloatTensor([-1, -1, 1, 1]))
self.register_buffer("_ybounds", torch.FloatTensor([-1, 1, -1, 1]))
def __init__(self, args, addnoise=True, crop=None):
super(RandomAffineFlowOccKITTI, self).__init__()
self._args = args
self._interp2 = Interp2(clamp=False)
self._flow_interp2 = Interp2MaskBinary(clamp=False)
self._meshgrid = Meshgrid()
self._identity = _IdentityParams()
self._random_mirror = RandomMirror(vertical=False)
self._addnoise = addnoise
self._crop = crop
self.register_buffer("_noise1", torch.FloatTensor())
self.register_buffer("_noise2", torch.FloatTensor())
self.register_buffer("_xbounds", torch.FloatTensor([-1, -1, 1, 1]))
self.register_buffer("_ybounds", torch.FloatTensor([-1, 1, -1, 1]))
self.register_buffer("_x", torch.IntTensor(1))
self.register_buffer("_y", torch.IntTensor(1))
def __init__(self,
args,
photometric=True,
trans=0.07,
scale=[0.93, 1.0],
resize=[256, 832]):
super(Augmentation_ScaleCrop, self).__init__()
# init
self._args = args
self._photometric = photometric
self._photo_augmentation = PhotometricAugmentation()
self._batch = None
self._device = None
self._identity = _IdentityParams()
self._meshgrid = Meshgrid()
# Augmentation Parameters
self._min_scale = scale[0]
self._max_scale = scale[1]
self._max_trans = trans
self._resize = resize