IoU

class hybrid_learning.concepts.train_eval.kpis.batch_kpis.IoU(reduction=BatchReduction.mean, output_thresh=0.5, label_thresh=0.0, smooth=1e-06)[source]

Bases: AbstractIoUMetric

Calc sample-wise intersection over union (IoU) values output batch. The intersection over union for one instance calculates as

\[\frac{intersection}{union} = \frac{TP} {(TP + TN + FP + FN)}\]

with

  • FP / TP: false / true positives, i.e. in- / correctly predicted foreground pixels

  • FN / TN: false / true positives, i.e. in- / correctly predicted background pixels

The following tensor dimensions are allowed:

  • 1D: The tensor is assumed to be 1D without batch dimension.

  • 2D: The tensor is assumed to be 2D without batch dimension.

  • >2D: The tensor is assumed to be 2D with batch dimension 0, width dim. -1, height dim. -2.

Public Data Attributes:

settings

Settings dict for reproduction of instance.
Inherited from : py: class:AbstractIoUMetric

settings

Settings dict for reproduction of instance.
Inherited from : py: class:Module

dump_patches

This allows better BC support for load_state_dict().

T_destination

alias of TypeVar('T_destination', bound=Mapping[str, Tensor])

Public Methods:

forward(outputs, labels)

Sample-wise reduced IoU between binarized in- and output.
Inherited from : py: class:AbstractIoUMetric

forward(outputs, labels)

Sample-wise reduced IoU between binarized in- and output.

smooth_division(dividend, divisor)

Smoothed division using smoothening summand to avoid division by 0.
Inherited from : py: class:AbstractIoULike

forward(outputs, labels)

Sample-wise reduced IoU between binarized in- and output.
Inherited from : py: class:Module

forward(outputs, labels)

Sample-wise reduced IoU between binarized in- and output.

register_buffer(name, tensor[, persistent])

Adds a buffer to the module.

register_parameter(name, param)

Adds a parameter to the module.

add_module(name, module)

Adds a child module to the current module.

get_submodule(target)

Returns the submodule given by target if it exists, otherwise throws an error.

get_parameter(target)

Returns the parameter given by target if it exists, otherwise throws an error.

get_buffer(target)

Returns the buffer given by target if it exists, otherwise throws an error.

apply(fn)

Applies fn recursively to every submodule (as returned by .children()) as well as self.

cuda([device])

Moves all model parameters and buffers to the GPU.

xpu([device])

Moves all model parameters and buffers to the XPU.

cpu()

Moves all model parameters and buffers to the CPU.

type(dst_type)

Casts all parameters and buffers to dst_type.

float()

Casts all floating point parameters and buffers to float datatype.

double()

Casts all floating point parameters and buffers to double datatype.

half()

Casts all floating point parameters and buffers to half datatype.

bfloat16()

Casts all floating point parameters and buffers to bfloat16 datatype.

to_empty(*, device)

Moves the parameters and buffers to the specified device without copying storage.

to(*args, **kwargs)

Moves and/or casts the parameters and buffers.

register_backward_hook(hook)

Registers a backward hook on the module.

register_full_backward_hook(hook)

Registers a backward hook on the module.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the module.

register_forward_hook(hook)

Registers a forward hook on the module.

state_dict([destination, prefix, keep_vars])

Returns a dictionary containing a whole state of the module.

load_state_dict(state_dict[, strict])

Copies parameters and buffers from state_dict into this module and its descendants.

parameters([recurse])

Returns an iterator over module parameters.

named_parameters([prefix, recurse])

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

buffers([recurse])

Returns an iterator over module buffers.

named_buffers([prefix, recurse])

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

children()

Returns an iterator over immediate children modules.

named_children()

Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

modules()

Returns an iterator over all modules in the network.

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.

train([mode])

Sets the module in training mode.

eval()

Sets the module in evaluation mode.

requires_grad_([requires_grad])

Change if autograd should record operations on parameters in this module.

zero_grad([set_to_none])

Sets gradients of all model parameters to zero.

share_memory()

See torch.Tensor.share_memory_()

extra_repr()

Set the extra representation of the module

Special Methods:

__init__([reduction, output_thresh, ...])

Init.
Inherited from : py: class:AbstractIoUMetric

__init__([reduction, output_thresh, ...])

Init.
Inherited from : py: class:AbstractIoULike

__repr__()

Return repr(self).

__str__()

Return str(self).
Inherited from : py: class:Module

__init__([reduction, output_thresh, ...])

Init.

__call__(*input, **kwargs)

Call self as a function.

__setstate__(state)

__getattr__(name)

__setattr__(name, value)

Implement setattr(self, name, value).

__delattr__(name)

Implement delattr(self, name).

__repr__()

Return repr(self).

__dir__()

Default dir() implementation.
Parameters
__init__(reduction=BatchReduction.mean, output_thresh=0.5, label_thresh=0.0, smooth=1e-06)[source]

Init.

Parameters

  • reduction (Union[BatchReduction, Callable[[Tensor], Tensor]]) – reduction method to aggregate the instance-wise results of the batch; must be a callable on a tensor which reduces the 0th dimension; see BatchReduction instances for examples

  • output_thresh (float) – threshold for binarizing the output

  • label_thresh (float) – threshold for binarizing the labels

  • smooth (float) – summand to smooth the IoU value (evade division by 0)

forward(outputs, labels)[source]

Sample-wise reduced IoU between binarized in- and output. Applied reduction is reduction.

Parameters

  • outputs (Tensor) – Output tensors of shape (BATCH x H x W); values must be in [0, 1], and a pixel value > output_thresh means it is foreground

  • labels (Tensor) – Label tensors of shape (BATCH x H x W); values must be in [0, 1], and a pixel value > label_thresh means it is foreground

Returns

tensor containing IoU for each sample along axis 0 reduced by reduction scheme

Return type

Tensor

label_thresh: float

Threshold for binarizing the labels; 1 if > output, 0 else.

output_thresh: float

Threshold for binarizing the output; 1 if > output, 0 else.

reduction: Union[BatchReduction, Callable[[Tensor], Tensor]]

Reduction method to aggregate the instance-wise results of the batch into one value.

property settings

Settings dict for reproduction of instance.

smooth: float

Smoothening summand to avoid division by zero. Division \(\frac{a}{b}\) is changed to \(\frac{a + \text{smooth}}{b + \text{smooth}}\).

training: bool