box_embeddings.parameterizations.tf_box_tensor

Base class for creating a wrapper around the torch.Tensor to represent boxes

A BoxTensor contains single tensor which represents single or multiple boxes.

note:

Have to use composition instead of inheritance because currently it is not safe to interit from torch.Tensor because creating an instance of such a class will always make it a leaf node. This works for torch.nn.Parameter but won’t work for a general BoxTensor. This most likely will change in the future as pytorch starts offical support for inheriting from a Tensor. Give this point some thought when this happens.

Module Contents

logger
TFTBoxTensor
class TFBoxTensor(data: Union[tensorflow.Tensor, Tuple[tensorflow.Tensor, tensorflow.Tensor]], *args: Any, **kwargs: Any)

Bases: object

Base class defining the interface for BoxTensor.

w2z_ratio :int = 2
reinit(self, data: Union[tensorflow.Tensor, Tuple[tensorflow.Tensor, tensorflow.Tensor]]) → None

Constructor

Parameters

data – Tensor of shape (…, zZ, num_dims). Here, zZ=2, where the 0th dim is for bottom left corner and 1st dim is for top right corner of the box

Returns: None

Raises

ValueError – If new shape is different than old shape

property kwargs(self) → Dict

Configuration attribute values

Returns

Dict

property args(self) → Tuple
property z(self) → tensorflow.Tensor

Lower left coordinate as Tensor

Returns

lower left corner

Return type

Tensor

property Z(self) → tensorflow.Tensor

Top right coordinate as Tensor

Returns

top right corner

Return type

Tensor

property centre(self) → tensorflow.Tensor

Centre coordinate as Tensor

Returns

Center

Return type

Tensor

classmethod check_if_valid_zZ(cls: Type[TFTBoxTensor], z: tensorflow.Tensor, Z: tensorflow.Tensor) → None

Check of (z,Z) form a valid box.

If your child class parameterization bounds the boxes to some universe box then this is the right place to check that.

Parameters

  • z – Lower left coordinate of shape (…, hidden_dims)

  • Z – Top right coordinate of shape (…, hidden_dims)

Raises

  • ValueError – If z and Z do not have the same shape

  • ValueError – If Z < z

classmethod W(cls: Type[TFTBoxTensor], z: tensorflow.Tensor, Z: tensorflow.Tensor, *args: Any, **kwargs: Any) → tensorflow.Tensor

Given (z,Z), it returns one set of valid box weights W, such that Box(W) = (z,Z).

For the base BoxTensor class, we just return z and Z stacked together. If you implement any new parameterization for boxes. You most likely need to override this method.

Parameters

  • z – Lower left coordinate of shape (…, hidden_dims)

  • Z – Top right coordinate of shape (…, hidden_dims)

  • *args – TODO

  • **kwargs – TODO

Returns

Parameters of the box. In base class implementation, this

will have shape (…, 2, hidden_dims).

Return type

Tensor

property box_shape(self) → Tuple

Shape of z, Z and center.

Returns

Shape of z, Z and center.

Note

This is not the shape of the data attribute.

broadcast(self, target_shape: Tuple) → None

Broadcasts the internal data member in-place such that z and Z return tensors that can be automatically broadcasted to perform arithmetic operations with shape target_shape.

Ex:

target_shape = (4,5,10)

  1. self.box_shape = (10,) => (1,1,10)

  2. self.box_shape = (3,) => ValueError

  3. self.box_shape = (4,10) => (4,1,10)

  4. self.box_shape = (4,2,10) => ValueError

  5. self.box_shape = (5,10) => (1,5,10)

Note

This operation will not result in self.z, self.Z and self.center returning tensor of shape target_shape but it will result in return a tensor which is arithmetic compatible with target_shape.

Parameters

target_shape – Shape of the broadcast target. Usually will be the shape of the tensor you wish to use z, Z with. For instance, if you wish to add self box’s center [shape=(batch, hidden_dim)] with other box whose center’s shape is (batch, extra_dim, hidden_dim), then this function will reshape the data such that the resulting center has shape (batch, 1, hidden_dim).

Raises

ValueError – If bad target

..todo::

Add an extra argument repeat which tell the function to repeat values till target is satisfied. This is needed for gumbel_intersection, where the broadcasted tensors need to be stacked.

classmethod zZ_to_embedding(cls, z: tensorflow.Tensor, Z: tensorflow.Tensor, *args: Any, **kwargs: Any) → tensorflow.Tensor
classmethod from_zZ(cls: Type[TFTBoxTensor], z: tensorflow.Tensor, Z: tensorflow.Tensor, *args: Any, **kwargs: Any) → TFTBoxTensor

Creates a box for the given min-max coordinates (z,Z).

In the this base implementation we do this by stacking z and Z along -2 dim to form W.

Parameters

  • z – lower left

  • Z – top right

  • *args – extra arguments for child class

  • **kwargs – extra arguments for child class

Returns

A BoxTensor

like_this_from_zZ(self, z: tensorflow.Tensor, Z: tensorflow.Tensor) → TFBoxTensor

Creates a box for the given min-max coordinates (z,Z). This is similar to the class method :method:`from_zZ`, but uses the attributes on self and not external args, kwargs.

For the base class, since we do not have extra attributes, we simply call from_zZ.

Parameters

  • z – lower left

  • Z – top right

Returns

A BoxTensor

classmethod from_vector(cls, vector: tensorflow.Tensor, *args: Any, **kwargs: Any) → TFTBoxTensor

Creates a box for a vector. In this base implementation the vector is split into two pieces and these are used as z,Z.

Parameters

  • vector – tensor

  • *args – extra arguments for child class

  • **kwargs – extra arguments for child class

Returns

A BoxTensor

Raises

ValueError – if last dimension is not even

box_reshape(self, target_shape: Tuple) → TFBoxTensor

Reshape the z,Z and center.

Ex:

  1. self.box_shape = (5,10), target_shape = (-1,10), creates box_shape (5,10)

2. self.box_shape = (5,4,10), target_shape = (-1,10), creates box_shape (20,10) 4. self.box_shape = (20,10), target_shape = (10,2,10), creates box_shape (10,2,10) 3. self.box_shape = (5,), target_shape = (-1,10), raises RuntimeError 5. self.box_shape = (5,10), target_shape = (2,10), raises RuntimeError

Parameters

target_shape – TODO

Returns

TBoxTensor

Raises

  • RuntimeError – If space dimensions, ie. the last dimensions do not match.

  • RuntimeError – If cannot reshape the extra dimensions and torch.reshape raises.

class TFBoxFactory(name: str, kwargs_dict: Dict = None)

Bases: box_embeddings.common.registrable.Registrable

A factory class which will be subclassed(one for each box type).

box_registry :Dict[str, Tuple[Type[TFBoxTensor], Optional[str]]]
classmethod register_box_class(cls, name: str, constructor: str = None, exist_ok: bool = False) → Callable[[Type[TFBoxTensor]], Type[TFBoxTensor]]

This is different from allennlp registrable because what this class registers is not subclasses but subclasses of BoxTensor

Parameters

  • name – TODO

  • constructor – TODO

  • exist_ok – TODO

Returns

()