def random_crop_generator3d(
batch_size: int,
input_size: Tuple[int, int, int],
size: Union[Tuple[int, int, int], torch.Tensor],
resize_to: Optional[Tuple[int, int, int]] = None,
same_on_batch: bool = False,
device: torch.device = torch.device('cpu'),
dtype: torch.dtype = torch.float32
) -> Dict[str, torch.Tensor]:
r"""Get parameters for ```crop``` transformation for crop transform.
Args:
batch_size (int): the tensor batch size.
input_size (tuple): Input image shape, like (d, h, w).
size (tuple): Desired size of the crop operation, like (d, h, w).
If tensor, it must be (B, 3).
resize_to (tuple): Desired output size of the crop, like (d, h, w). If None, no resize will be performed.
same_on_batch (bool): apply the same transformation across the batch. Default: False.
device (torch.device): the device on which the random numbers will be generated. Default: cpu.
dtype (torch.dtype): the data type of the generated random numbers. Default: float32.
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
- src (torch.Tensor): cropping bounding boxes with a shape of (B, 8, 3).
- dst (torch.Tensor): output bounding boxes with a shape (B, 8, 3).
Note:
The generated random numbers are not reproducible across different devices and dtypes.
"""
_device, _dtype = _extract_device_dtype([size if isinstance(size, torch.Tensor) else None])
if not isinstance(size, torch.Tensor):
size = torch.tensor(size, device=device, dtype=dtype).repeat(batch_size, 1)
else:
size = size.to(device=device, dtype=dtype)
assert size.shape == torch.Size([batch_size, 3]), (
"If `size` is a tensor, it must be shaped as (B, 3). "
f"Got {size.shape} while expecting {torch.Size([batch_size, 3])}.")
assert len(input_size) == 3 and isinstance(input_size[0], (int,)) and isinstance(input_size[1], (int,)) \
and isinstance(input_size[2], (int,)) and input_size[0] > 0 and input_size[1] > 0 and input_size[2] > 0, \
f"`input_size` must be a tuple of 3 positive integers. Got {input_size}."
x_diff = input_size[2] - size[:, 2] + 1
y_diff = input_size[1] - size[:, 1] + 1
z_diff = input_size[0] - size[:, 0] + 1
if (x_diff < 0).any() or (y_diff < 0).any() or (z_diff < 0).any():
raise ValueError("input_size %s cannot be smaller than crop size %s in any dimension."
% (str(input_size), str(size)))
if batch_size == 0:
return dict(
src=torch.zeros([0, 8, 3], device=_device, dtype=_dtype),
dst=torch.zeros([0, 8, 3], device=_device, dtype=_dtype),
)
if same_on_batch:
# If same_on_batch, select the first then repeat.
x_start = _adapted_uniform((batch_size,), 0, x_diff[0], same_on_batch)
y_start = _adapted_uniform((batch_size,), 0, y_diff[0], same_on_batch)
z_start = _adapted_uniform((batch_size,), 0, z_diff[0], same_on_batch)
else:
x_start = _adapted_uniform((1,), 0, x_diff, same_on_batch)
y_start = _adapted_uniform((1,), 0, y_diff, same_on_batch)
z_start = _adapted_uniform((1,), 0, z_diff, same_on_batch)
crop_src = bbox_generator3d(
x_start.view(-1),
y_start.view(-1),
z_start.view(-1),
size[:, 2] - 1,
size[:, 1] - 1,
size[:, 0] - 1).long()
if resize_to is None:
crop_dst = bbox_generator3d(
torch.tensor([0] * batch_size, device=device, dtype=dtype),
torch.tensor([0] * batch_size, device=device, dtype=dtype),
torch.tensor([0] * batch_size, device=device, dtype=dtype),
size[:, 2] - 1,
size[:, 1] - 1,
size[:, 0] - 1).long()
else:
assert len(resize_to) == 3 and isinstance(resize_to[0], (int,)) and isinstance(resize_to[1], (int,)) \
and isinstance(resize_to[2], (int,)) and resize_to[0] > 0 and resize_to[1] > 0 and resize_to[2] > 0, \
f"`resize_to` must be a tuple of 3 positive integers. Got {resize_to}."
crop_dst = torch.tensor([[
[0, 0, 0],
[resize_to[-1] - 1, 0, 0],
[resize_to[-1] - 1, resize_to[-2] - 1, 0],
[0, resize_to[-2] - 1, 0],
[0, 0, resize_to[-3] - 1],
[resize_to[-1] - 1, 0, resize_to[-3] - 1],
[resize_to[-1] - 1, resize_to[-2] - 1, resize_to[-3] - 1],
[0, resize_to[-2] - 1, resize_to[-3] - 1],
]], device=device, dtype=torch.long).repeat(batch_size, 1, 1)
return dict(src=crop_src.to(device=_device),
dst=crop_dst.to(device=_device))
def random_crop_generator3d(
batch_size: int,
input_size: Tuple[int, int, int],
size: Union[Tuple[int, int, int], torch.Tensor],
resize_to: Optional[Tuple[int, int, int]] = None,
same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
r"""Get parameters for ```crop``` transformation for crop transform.
Args:
batch_size (int): the tensor batch size.
input_size (tuple): Input image shape, like (d, h, w).
size (tuple): Desired size of the crop operation, like (d, h, w).
If tensor, it must be (B, 3).
resize_to (tuple): Desired output size of the crop, like (d, h, w). If None, no resize will be performed.
same_on_batch (bool): apply the same transformation across the batch. Default: False.
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
"""
if not isinstance(size, torch.Tensor):
size = torch.tensor(size).repeat(batch_size, 1)
assert size.shape == torch.Size([batch_size, 3]), \
f"If `size` is a tensor, it must be shaped as (B, 3). Got {size.shape}."
x_diff = input_size[2] - size[:, 2] + 1
y_diff = input_size[1] - size[:, 1] + 1
z_diff = input_size[0] - size[:, 0] + 1
if (x_diff < 0).any() or (y_diff < 0).any() or (z_diff < 0).any():
raise ValueError(
"input_size %s cannot be smaller than crop size %s in any dimension."
% (str(input_size), str(size)))
if same_on_batch:
# If same_on_batch, select the first then repeat.
x_start = _adapted_uniform((batch_size, ), 0, x_diff[0],
same_on_batch).long()
y_start = _adapted_uniform((batch_size, ), 0, y_diff[0],
same_on_batch).long()
z_start = _adapted_uniform((batch_size, ), 0, z_diff[0],
same_on_batch).long()
else:
x_start = _adapted_uniform((1, ), 0, x_diff, same_on_batch).long()
y_start = _adapted_uniform((1, ), 0, y_diff, same_on_batch).long()
z_start = _adapted_uniform((1, ), 0, z_diff, same_on_batch).long()
crop_src = bbox_generator3d(x_start.view(-1), y_start.view(-1),
z_start.view(-1), size[:, 2] - 1,
size[:, 1] - 1, size[:, 0] - 1)
if resize_to is None:
crop_dst = bbox_generator3d(torch.tensor([0] * batch_size),
torch.tensor([0] * batch_size),
torch.tensor([0] * batch_size),
size[:, 2] - 1, size[:, 1] - 1,
size[:, 0] - 1)
else:
crop_dst = torch.tensor([[
[0, 0, 0],
[resize_to[-1] - 1, 0, 0],
[resize_to[-1] - 1, resize_to[-2] - 1, 0],
[0, resize_to[-2] - 1, 0],
[0, 0, resize_to[-3] - 1],
[resize_to[-1] - 1, 0, resize_to[-3] - 1],
[resize_to[-1] - 1, resize_to[-2] - 1, resize_to[-3] - 1],
[0, resize_to[-2] - 1, resize_to[-3] - 1],
]]).repeat(batch_size, 1, 1)
return dict(src=crop_src, dst=crop_dst)