def apply_by_key(
self,
input: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
module: nn.Module,
param: Optional[Dict[str, torch.Tensor]] = None,
dcate: Union[str, int, DataKey] = DataKey.INPUT,
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
if DataKey.get(dcate) in [DataKey.INPUT]:
return self.apply_to_input(input, module, param)
if DataKey.get(dcate) in [DataKey.MASK]:
if isinstance(input, (tuple, )):
return (self.apply_to_mask(input[0], module,
param), *input[1:])
return self.apply_to_mask(input, module, param)
if DataKey.get(dcate) in [DataKey.BBOX, DataKey.BBOX_XYXY]:
if isinstance(input, (tuple, )):
return (self.apply_to_bbox(input[0],
module,
param,
mode='xyxy'), *input[1:])
return self.apply_to_bbox(input, module, param, mode='xyxy')
if DataKey.get(dcate) in [DataKey.BBOX_XYHW]:
if isinstance(input, (tuple, )):
return (self.apply_to_bbox(input[0],
module,
param,
mode='xyhw'), *input[1:])
return self.apply_to_bbox(input, module, param, mode='xyhw')
if DataKey.get(dcate) in [DataKey.KEYPOINTS]:
if isinstance(input, (tuple, )):
return (self.apply_to_keypoints(input[0], module,
param), *input[1:])
return self.apply_to_keypoints(input, module, param)
raise NotImplementedError(f"input type of {dcate} is not implemented.")
def _get_func_by_key(
cls, dcate: Union[str, int,
DataKey]) -> Type[ApplyInverseInterface]:
if DataKey.get(dcate) == DataKey.INPUT:
return InputApplyInverse
if DataKey.get(dcate) in [DataKey.MASK]:
return MaskApplyInverse
if DataKey.get(dcate) in [DataKey.BBOX, DataKey.BBOX_XYXY]:
return BBoxXYXYApplyInverse
if DataKey.get(dcate) in [DataKey.BBOX_XYHW]:
return BBoxXYWHApplyInverse
if DataKey.get(dcate) in [DataKey.KEYPOINTS]:
return KeypointsApplyInverse
raise NotImplementedError(f"input type of {dcate} is not implemented.")
def _get_func_by_key(
cls, dcate: Union[str, int,
DataKey]) -> Type[ApplyInverseInterface]:
if DataKey.get(dcate) == DataKey.INPUT:
return InputApplyInverse
if DataKey.get(dcate) == DataKey.MASK:
return MaskApplyInverse
if DataKey.get(dcate) in [
DataKey.BBOX, DataKey.BBOX_XYXY, DataKey.BBOX_XYWH
]:
# We are converting to (B, 4, 2) internally for all formats.
return BBoxApplyInverse
if DataKey.get(dcate) in [DataKey.KEYPOINTS]:
return KeypointsApplyInverse
raise NotImplementedError(f"input type of {dcate} is not implemented.")
def __init__(
self,
*args: Union[_AugmentationBase, ImageSequential],
data_keys: List[Union[str, int, DataKey]] = [DataKey.INPUT],
same_on_batch: Optional[bool] = None,
return_transform: Optional[bool] = None,
keepdim: Optional[bool] = None,
random_apply: Union[int, bool, Tuple[int, int]] = False,
) -> None:
super(AugmentationSequential, self).__init__(
*args,
same_on_batch=same_on_batch,
return_transform=return_transform,
keepdim=keepdim,
random_apply=random_apply,
)
self.data_keys = [DataKey.get(inp) for inp in data_keys]
assert all(in_type in DataKey for in_type in self.data_keys
), f"`data_keys` must be in {DataKey}. Got {data_keys}."
if self.data_keys[0] != DataKey.INPUT:
raise NotImplementedError(
f"The first input must be {DataKey.INPUT}.")
for arg in args:
if isinstance(arg,
PatchSequential) and not arg.is_intensity_only():
warnings.warn(
"Geometric transformation detected in PatchSeqeuntial, which would break bbox, mask."
)
def inverse_by_key(
self,
input: torch.Tensor,
module: nn.Module,
param: Optional[Dict[str, torch.Tensor]] = None,
dcate: Union[str, int, DataKey] = DataKey.INPUT,
) -> torch.Tensor:
if DataKey.get(dcate) in [DataKey.INPUT, DataKey.MASK]:
return self.inverse_input(input, module, param)
if DataKey.get(dcate) in [DataKey.BBOX, DataKey.BBOX_XYXY]:
return self.inverse_bbox(input, module, param, mode='xyxy')
if DataKey.get(dcate) in [DataKey.BBOX_XYHW]:
return self.inverse_bbox(input, module, param, mode='xyhw')
if DataKey.get(dcate) in [DataKey.KEYPOINTS]:
return self.inverse_keypoints(input, module, param)
raise NotImplementedError(f"input type of {dcate} is not implemented.")
def apply_by_key(
self,
input: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
module_name: str,
module: Optional[nn.Module] = None,
param: Optional[ParamItem] = None,
dcate: Union[str, int, DataKey] = DataKey.INPUT,
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
if param is not None:
assert module_name == param.name
if module is None:
# TODO (jian): double check why typing is crashing
module = self.get_submodule(module_name) # type: ignore
if DataKey.get(dcate) in [DataKey.INPUT]:
return self.apply_to_input(input, module_name, module, param)
if DataKey.get(dcate) in [DataKey.MASK]:
if isinstance(input, (tuple, )):
return (self.apply_to_mask(input[0], module,
param), *input[1:])
return self.apply_to_mask(input, module, param)
if DataKey.get(dcate) in [DataKey.BBOX, DataKey.BBOX_XYXY]:
if isinstance(input, (tuple, )):
return (self.apply_to_bbox(input[0],
module,
param,
mode='xyxy'), *input[1:])
return self.apply_to_bbox(input, module, param, mode='xyxy')
if DataKey.get(dcate) in [DataKey.BBOX_XYHW]:
if isinstance(input, (tuple, )):
return (self.apply_to_bbox(input[0],
module,
param,
mode='xyhw'), *input[1:])
return self.apply_to_bbox(input, module, param, mode='xyhw')
if DataKey.get(dcate) in [DataKey.KEYPOINTS]:
if isinstance(input, (tuple, )):
return (self.apply_to_keypoints(input[0], module,
param), *input[1:])
return self.apply_to_keypoints(input, module, param)
raise NotImplementedError(f"input type of {dcate} is not implemented.")
def apply_by_key(
self,
input: TensorWithTransformMat,
label: Optional[torch.Tensor],
module: Optional[nn.Module],
param: ParamItem,
dcate: Union[str, int, DataKey] = DataKey.INPUT,
) -> Tuple[TensorWithTransformMat, Optional[torch.Tensor]]:
if module is None:
module = self.get_submodule(param.name)
if DataKey.get(dcate) in [DataKey.INPUT]:
return self.apply_to_input(input,
label,
module=module,
param=param)
if DataKey.get(dcate) in [DataKey.MASK]:
if isinstance(input, (tuple, )):
return (self.apply_to_mask(input[0], module,
param), *input[1:]), None
return self.apply_to_mask(input, module, param), None
if DataKey.get(dcate) in [DataKey.BBOX, DataKey.BBOX_XYXY]:
if isinstance(input, (tuple, )):
return (self.apply_to_bbox(input[0],
module,
param,
mode='xyxy'), *input[1:]), None
return self.apply_to_bbox(input, module, param, mode='xyxy'), None
if DataKey.get(dcate) in [DataKey.BBOX_XYHW]:
if isinstance(input, (tuple, )):
return (self.apply_to_bbox(input[0],
module,
param,
mode='xyhw'), *input[1:]), None
return self.apply_to_bbox(input, module, param, mode='xyhw'), None
if DataKey.get(dcate) in [DataKey.KEYPOINTS]:
if isinstance(input, (tuple, )):
return (self.apply_to_keypoints(input[0], module,
param), *input[1:]), None
return self.apply_to_keypoints(input, module, param), None
raise NotImplementedError(f"input type of {dcate} is not implemented.")
def _arguments_preproc(self, *args: Tensor, data_keys: List[DataKey]):
inp: List[Any] = []
for arg, dcate in zip(args, data_keys):
if DataKey.get(dcate) in [
DataKey.INPUT, DataKey.MASK, DataKey.KEYPOINTS
]:
inp.append(arg)
elif DataKey.get(dcate) in [
DataKey.BBOX, DataKey.BBOX_XYXY, DataKey.BBOX_XYWH
]:
if DataKey.get(dcate) in [DataKey.BBOX]:
mode = "vertices_plus"
elif DataKey.get(dcate) in [DataKey.BBOX_XYXY]:
mode = "xyxy"
elif DataKey.get(dcate) in [DataKey.BBOX_XYWH]:
mode = "xywh"
else:
raise ValueError(
f"Unsupported mode `{DataKey.get(dcate).name}`.")
inp.append(Boxes.from_tensor(arg, mode=mode)) # type: ignore
else:
raise NotImplementedError(
f"input type of {dcate} is not implemented.")
return inp
def __init__(
self,
*args: Union[_AugmentationBase, ImageSequential],
data_keys: List[Union[str, int, DataKey]] = [DataKey.INPUT],
same_on_batch: Optional[bool] = None,
return_transform: Optional[bool] = None,
keepdim: Optional[bool] = None,
random_apply: Union[int, bool, Tuple[int, int]] = False,
random_apply_weights: Optional[List[float]] = None,
) -> None:
super().__init__(
*args,
same_on_batch=same_on_batch,
return_transform=return_transform,
keepdim=keepdim,
random_apply=random_apply,
random_apply_weights=random_apply_weights,
)
self.data_keys = [DataKey.get(inp) for inp in data_keys]
if not all(in_type in DataKey for in_type in self.data_keys):
raise AssertionError(
f"`data_keys` must be in {DataKey}. Got {data_keys}.")
if self.data_keys[0] != DataKey.INPUT:
raise NotImplementedError(
f"The first input must be {DataKey.INPUT}.")
self.contains_video_sequential: bool = False
self.contains_3d_augmentation: bool = False
for arg in args:
if isinstance(arg,
PatchSequential) and not arg.is_intensity_only():
warnings.warn(
"Geometric transformation detected in PatchSeqeuntial, which would break bbox, mask."
)
if isinstance(arg, VideoSequential):
self.contains_video_sequential = True
# NOTE: only for images are supported for 3D.
if isinstance(arg, AugmentationBase3D):
self.contains_3d_augmentation = True
self._transform_matrix: Optional[Tensor] = None
def __init__(
self,
*args: _AugmentationBase,
data_keys: List[Union[str, int, DataKey]] = [DataKey.INPUT],
same_on_batch: Optional[bool] = None,
return_transform: Optional[bool] = None,
keepdim: Optional[bool] = None,
) -> None:
super(AugmentationSequential,
self).__init__(*args,
same_on_batch=same_on_batch,
return_transform=return_transform,
keepdim=keepdim)
self.data_keys = [DataKey.get(inp) for inp in data_keys]
assert all(in_type in DataKey for in_type in self.data_keys
), f"`data_keys` must be in {DataKey}. Got {data_keys}."
if self.data_keys[0] != DataKey.INPUT:
raise NotImplementedError(
f"The first input must be {DataKey.INPUT}.")
def forward( # type: ignore
self,
*args: TensorWithTransformMat,
label: Optional[torch.Tensor] = None,
params: Optional[List[ParamItem]] = None,
data_keys: Optional[List[Union[str, int, DataKey]]] = None,
) -> Union[TensorWithTransformMat, Tuple[TensorWithTransformMat,
Optional[torch.Tensor]],
List[TensorWithTransformMat], Tuple[
List[TensorWithTransformMat], Optional[torch.Tensor]], ]:
"""Compute multiple tensors simultaneously according to ``self.data_keys``."""
if data_keys is None:
data_keys = cast(List[Union[str, int, DataKey]], self.data_keys)
else:
data_keys = [DataKey.get(inp) for inp in data_keys]
if len(args) != len(data_keys):
raise AssertionError(
f"The number of inputs must align with the number of data_keys. Got {len(args)} and {len(data_keys)}."
)
if params is None:
if DataKey.INPUT in data_keys:
_input = args[data_keys.index(DataKey.INPUT)]
if isinstance(_input, (tuple, list)):
inp = _input[0]
else:
inp = _input
if self.contains_video_sequential:
_, out_shape = self.autofill_dim(inp, dim_range=(3, 5))
else:
_, out_shape = self.autofill_dim(inp, dim_range=(2, 4))
params = self.forward_parameters(out_shape)
else:
raise ValueError(
"`params` must be provided whilst INPUT is not in data_keys."
)
outputs: List[TensorWithTransformMat] = [None] * len(
data_keys) # type: ignore
if DataKey.INPUT in data_keys:
idx = data_keys.index(DataKey.INPUT)
out = super().forward(args[idx], label, params=params)
if self.return_label:
input, label = cast(
Tuple[TensorWithTransformMat, torch.Tensor], out)
else:
input = cast(TensorWithTransformMat, out)
outputs[idx] = input
self.return_label = label is not None or self.contains_label_operations(
params)
for idx, (input, dcate, out) in enumerate(zip(args, data_keys,
outputs)):
if out is not None:
continue
for param in params:
module = self.get_submodule(param.name)
if dcate == DataKey.INPUT:
input, label = self.apply_to_input(input,
label,
module=module,
param=param)
elif isinstance(
module,
IntensityAugmentationBase2D) and dcate in DataKey:
pass # Do nothing
elif isinstance(module,
ImageSequential) and module.is_intensity_only(
) and dcate in DataKey:
pass # Do nothing
elif isinstance(module, VideoSequential) and dcate not in [
DataKey.INPUT, DataKey.MASK
]:
batch_size: int = input.size(0)
input = input.view(-1, *input.shape[2:])
input, label = ApplyInverse.apply_by_key(
input, label, module, param, dcate)
input = input.view(batch_size, -1, *input.shape[1:])
elif isinstance(module, PatchSequential):
raise NotImplementedError(
"Geometric involved PatchSequential is not supported.")
elif isinstance(module, (
GeometricAugmentationBase2D,
ImageSequential,
)) and dcate in DataKey:
input, label = ApplyInverse.apply_by_key(
input, label, module, param, dcate)
elif isinstance(module, (SequentialBase, )):
raise ValueError(f"Unsupported Sequential {module}.")
else:
raise NotImplementedError(
f"data_key {dcate} is not implemented for {module}.")
outputs[idx] = input
return self.__packup_output__(outputs, label)
def forward( # type: ignore
self,
*args: Tensor,
label: Optional[Tensor] = None,
params: Optional[List[ParamItem]] = None,
data_keys: Optional[List[Union[str, int, DataKey]]] = None,
) -> Union[Tensor, Tuple[Tensor, Optional[Tensor]], List[Tensor], Tuple[
List[Tensor], Optional[Tensor]], ]:
"""Compute multiple tensors simultaneously according to ``self.data_keys``."""
_data_keys: List[DataKey]
if data_keys is None:
_data_keys = self.data_keys
else:
_data_keys = [DataKey.get(inp) for inp in data_keys]
self.data_keys = _data_keys
self._validate_args_datakeys(*args, data_keys=_data_keys)
args = self._arguments_preproc(*args, data_keys=_data_keys)
if params is None:
# image data must exist if params is not provided.
if DataKey.INPUT in _data_keys:
_input = args[_data_keys.index(DataKey.INPUT)]
inp = _input
if isinstance(inp, (tuple, list)):
raise ValueError(
f"`INPUT` should be a tensor but `{type(inp)}` received."
)
# A video input shall be BCDHW while an image input shall be BCHW
if self.contains_video_sequential or self.contains_3d_augmentation:
_, out_shape = self.autofill_dim(inp, dim_range=(3, 5))
else:
_, out_shape = self.autofill_dim(inp, dim_range=(2, 4))
params = self.forward_parameters(out_shape)
else:
raise ValueError(
"`params` must be provided whilst INPUT is not in data_keys."
)
outputs: List[Tensor] = [None] * len(_data_keys) # type: ignore
# Forward the first image data to freeze the parameters.
if DataKey.INPUT in _data_keys:
idx = _data_keys.index(DataKey.INPUT)
_inp = args[idx]
_out = super().forward(_inp, label, params=params) # type: ignore
self._transform_matrix = self.get_transformation_matrix(
_inp, params=params)
if self.return_label:
_input, label = cast(Tuple[Tensor, Tensor], _out)
else:
_input = cast(Tensor, _out)
outputs[idx] = _input
self.return_label = self.return_label or label is not None or self.contains_label_operations(
params)
for idx, (arg, dcate, out) in enumerate(zip(args, _data_keys,
outputs)):
if out is not None:
continue
# Using tensors straight-away
if isinstance(arg, (Boxes, )):
input = arg.data # all boxes are in (B, N, 4, 2) format now.
else:
input = arg
for param in params:
module = self.get_submodule(param.name)
if dcate == DataKey.INPUT:
input, label = self.apply_to_input(input,
label,
module=module,
param=param)
elif isinstance(module, IntensityAugmentationBase2D) and dcate in DataKey \
and not isinstance(module, RandomErasing):
pass # Do nothing
elif isinstance(module,
ImageSequential) and module.is_intensity_only(
) and dcate in DataKey:
pass # Do nothing
elif isinstance(module, VideoSequential) and dcate not in [
DataKey.INPUT, DataKey.MASK
]:
batch_size: int = input.size(0)
input = input.view(-1, *input.shape[2:])
input, label = ApplyInverse.apply_by_key(
input, label, module, param, dcate)
input = input.view(batch_size, -1, *input.shape[1:])
elif isinstance(module, PatchSequential):
raise NotImplementedError(
"Geometric involved PatchSequential is not supported.")
elif isinstance(module, (GeometricAugmentationBase2D, ImageSequential, RandomErasing)) \
and dcate in DataKey:
input, label = ApplyInverse.apply_by_key(
input, label, module, param, dcate)
elif isinstance(module, (SequentialBase, )):
raise ValueError(f"Unsupported Sequential {module}.")
else:
raise NotImplementedError(
f"data_key {dcate} is not implemented for {module}.")
if isinstance(arg, (Boxes, )):
arg._data = input
outputs[idx] = arg.to_tensor()
else:
outputs[idx] = input
return self.__packup_output__(outputs, label)
def inverse( # type: ignore
self,
*args: Tensor,
params: Optional[List[ParamItem]] = None,
data_keys: Optional[List[Union[str, int, DataKey]]] = None,
) -> Union[Tensor, List[Tensor]]:
"""Reverse the transformation applied.
Number of input tensors must align with the number of``data_keys``. If ``data_keys`` is not set, use
``self.data_keys`` by default.
"""
if data_keys is None:
data_keys = cast(List[Union[str, int, DataKey]], self.data_keys)
_data_keys: List[DataKey] = [DataKey.get(inp) for inp in data_keys]
if len(args) != len(data_keys):
raise AssertionError(
"The number of inputs must align with the number of data_keys, "
f"Got {len(args)} and {len(data_keys)}.")
args = self._arguments_preproc(*args, data_keys=_data_keys)
if params is None:
if self._params is None:
raise ValueError(
"No parameters available for inversing, please run a forward pass first "
"or passing valid params into this function.")
params = self._params
outputs: List[Tensor] = [None] * len(data_keys) # type: ignore
for idx, (arg, dcate) in enumerate(zip(args, data_keys)):
if dcate == DataKey.INPUT and isinstance(arg, (tuple, list)):
input, _ = arg # ignore the transformation matrix whilst inverse
# Using tensors straight-away
elif isinstance(arg, (Boxes, )):
input = arg.data # all boxes are in (B, N, 4, 2) format now.
else:
input = arg
for (name, module), param in zip_longest(
list(self.get_forward_sequence(params))[::-1],
params[::-1]):
if isinstance(module, (_AugmentationBase, ImageSequential)):
param = params[name] if name in params else param
else:
param = None
if isinstance(module, IntensityAugmentationBase2D) and dcate in DataKey \
and not isinstance(module, RandomErasing):
pass # Do nothing
elif isinstance(module,
ImageSequential) and module.is_intensity_only(
) and dcate in DataKey:
pass # Do nothing
elif isinstance(module, VideoSequential) and dcate not in [
DataKey.INPUT, DataKey.MASK
]:
batch_size: int = input.size(0)
input = input.view(-1, *input.shape[2:])
input = ApplyInverse.inverse_by_key(
input, module, param, dcate)
input = input.view(batch_size, -1, *input.shape[1:])
elif isinstance(module, PatchSequential):
raise NotImplementedError(
"Geometric involved PatchSequential is not supported.")
elif isinstance(module, (GeometricAugmentationBase2D, ImageSequential, RandomErasing)) \
and dcate in DataKey:
input = ApplyInverse.inverse_by_key(
input, module, param, dcate)
elif isinstance(module, (SequentialBase, )):
raise ValueError(f"Unsupported Sequential {module}.")
else:
raise NotImplementedError(
f"data_key {dcate} is not implemented for {module}.")
if isinstance(arg, (Boxes, )):
arg._data = input
outputs[idx] = arg.to_tensor()
else:
outputs[idx] = input
if len(outputs) == 1 and isinstance(outputs, (tuple, list)):
return outputs[0]
return outputs
