urbanlc.model.pipeline_transforms.AppendBUI#

class urbanlc.model.pipeline_transforms.AppendBUI(index_a: int, index_b: int)#

Compute the Built-Up Index (BUI) for PyTorch Tensor.

Attributes

`T_destination`	alias of TypeVar('T_destination', bound=`Dict`[`str`, `Any`])
`dump_patches`
`transform_matrix`

Methods

`add_module`(name, module)	Adds a child module to the current module.
`apply`(fn)	Applies `fn` recursively to every submodule (as returned by `.children()`) as well as self.
`apply_func`(in_tensor, params[, flags])
`apply_non_transform`(input, params, flags[, ...])
`apply_non_transform_box`(input, params, flags)	Process boxes corresponding to the inputs that are no transformation applied.
`apply_non_transform_boxes`(input, params, flags)
`apply_non_transform_class`(input, params, flags)	Process class tags corresponding to the inputs that are no transformation applied.
`apply_non_transform_keypoint`(input, params, ...)	Process keypoints corresponding to the inputs that are no transformation applied.
`apply_non_transform_mask`(input, params, flags)	Process masks corresponding to the inputs that are no transformation applied.
`apply_transform`(input, params, flags[, ...])	Compute the Built-Up Index (BUI) for PyTorch Tensor.
`apply_transform_box`(input, params, flags[, ...])	Process boxes corresponding to the inputs that are transformed.
`apply_transform_boxes`(input, params, flags)
`apply_transform_class`(input, params, flags)	Process class tags corresponding to the inputs that are transformed.
`apply_transform_keypoint`(input, params, flags)	Process keypoints corresponding to the inputs that are transformed.
`apply_transform_mask`(input, params, flags[, ...])	Process masks corresponding to the inputs that are transformed.
`bfloat16`()	Casts all floating point parameters and buffers to `bfloat16` datatype.
`buffers`([recurse])	Returns an iterator over module buffers.
`children`()	Returns an iterator over immediate children modules.
`compute_transformation`(input, params, flags)
`cpu`()	Moves all model parameters and buffers to the CPU.
`cuda`([device])	Moves all model parameters and buffers to the GPU.
`double`()	Casts all floating point parameters and buffers to `double` datatype.
`eval`()	Sets the module in evaluation mode.
`extra_repr`()	Set the extra representation of the module
`float`()	Casts all floating point parameters and buffers to `float` datatype.
`forward`(input[, params])	Perform forward operations.
`forward_parameters`(batch_shape)
`generate_parameters`(batch_shape)
`generate_transformation_matrix`(input, ...)	Generate transformation matrices with the given input and param settings.
`get_buffer`(target)	Returns the buffer given by `target` if it exists, otherwise throws an error.
`get_extra_state`()	Returns any extra state to include in the module's state_dict.
`get_parameter`(target)	Returns the parameter given by `target` if it exists, otherwise throws an error.
`get_submodule`(target)	Returns the submodule given by `target` if it exists, otherwise throws an error.
`half`()	Casts all floating point parameters and buffers to `half` datatype.
`identity_matrix`(input)	Return 3x3 identity matrix.
`inverse_boxes`(input, params, flags[, transform])
`inverse_classes`(input, params, flags[, ...])
`inverse_inputs`(input, params, flags[, transform])
`inverse_keypoints`(input, params, flags[, ...])
`inverse_masks`(input, params, flags[, transform])
`ipu`([device])	Moves all model parameters and buffers to the IPU.
`load_state_dict`(state_dict[, strict])	Copies parameters and buffers from `state_dict` into this module and its descendants.
`modules`()	Returns an iterator over all modules in the network.
`named_buffers`([prefix, recurse])	Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
`named_children`()	Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
`named_modules`([memo, prefix, remove_duplicate])	Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
`named_parameters`([prefix, recurse])	Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
`parameters`([recurse])	Returns an iterator over module parameters.
`register_backward_hook`(hook)	Registers a backward hook on the module.
`register_buffer`(name, tensor[, persistent])	Adds a buffer to the module.
`register_forward_hook`(hook)	Registers a forward hook on the module.
`register_forward_pre_hook`(hook)	Registers a forward pre-hook on the module.
`register_full_backward_hook`(hook)	Registers a backward hook on the module.
`register_load_state_dict_post_hook`(hook)	Registers a post hook to be run after module's `load_state_dict` is called.
`register_module`(name, module)	Alias for `add_module()`.
`register_parameter`(name, param)	Adds a parameter to the module.
`requires_grad_`([requires_grad])	Change if autograd should record operations on parameters in this module.
`set_extra_state`(state)	This function is called from `load_state_dict()` to handle any extra state found within the state_dict.
`set_rng_device_and_dtype`(device, dtype)	Change the random generation device and dtype.
`share_memory`()	See `torch.Tensor.share_memory_()`
`state_dict`(*args[, destination, prefix, ...])	Returns a dictionary containing references to the whole state of the module.
`to`(args, *kwargs)	Moves and/or casts the parameters and buffers.
`to_empty`(*, device)	Moves the parameters and buffers to the specified device without copying storage.
`train`([mode])	Sets the module in training mode.
`transform_boxes`(input, params, flags[, ...])
`transform_classes`(input, params, flags[, ...])
`transform_inputs`(input, params, flags[, ...])
`transform_keypoints`(input, params, flags[, ...])
`transform_masks`(input, params, flags[, ...])
`transform_output_tensor`(output, output_shape)	Standardize output tensors.
`transform_tensor`(input)	Convert any incoming (H, W), (C, H, W) and (B, C, H, W) into (B, C, H, W).
`type`(dst_type)	Casts all parameters and buffers to `dst_type`.
`validate_tensor`(input)	Check if the input tensor is formatted as expected.
`xpu`([device])	Moves all model parameters and buffers to the XPU.
`zero_grad`([set_to_none])	Sets gradients of all model parameters to zero.