kornia.losses¶

Reconstruction¶

ssim(img1, img2, window_size, max_val=1.0, eps=1e-12)[source]¶

Function that computes the Structural Similarity (SSIM) index map between two images.

Measures the (SSIM) index between each element in the input x and target y.

The index can be described as:

\[\text{SSIM}(x, y) = \frac{(2\mu_x\mu_y+c_1)(2\sigma_{xy}+c_2)} {(\mu_x^2+\mu_y^2+c_1)(\sigma_x^2+\sigma_y^2+c_2)}\]

where:

\(c_1=(k_1 L)^2\) and \(c_2=(k_2 L)^2\) are two variables to stabilize the division with weak denominator.
\(L\) is the dynamic range of the pixel-values (typically this is \(2^{\#\text{bits per pixel}}-1\)).

Parameters

img1 (Tensor) – the first input image with shape \((B, C, H, W)\).
img2 (Tensor) – the second input image with shape \((B, C, H, W)\).
window_size (int) – the size of the gaussian kernel to smooth the images.
max_val (float, optional) – the dynamic range of the images. Default: 1.0
eps (float, optional) – Small value for numerically stability when dividing. Default: 1e-12

Return type

Tensor

Returns

The ssim index map with shape \((B, C, H, W)\).

Examples

>>> input1 = torch.rand(1, 4, 5, 5)
>>> input2 = torch.rand(1, 4, 5, 5)
>>> ssim_map = ssim(input1, input2, 5)  # 1x4x5x5

ssim_loss(img1, img2, window_size, max_val=1.0, eps=1e-12, reduction='mean')[source]¶

Function that computes a loss based on the SSIM measurement.

The loss, or the Structural dissimilarity (DSSIM) is described as:

\[\text{loss}(x, y) = \frac{1 - \text{SSIM}(x, y)}{2}\]

See ssim() for details about SSIM.

Parameters

img1 (Tensor) – the first input image with shape \((B, C, H, W)\).
img2 (Tensor) – the second input image with shape \((B, C, H, W)\).
window_size (int) – the size of the gaussian kernel to smooth the images.
max_val (float, optional) – the dynamic range of the images. Default: 1.0
eps (float, optional) – Small value for numerically stability when dividing. Default: 1e-12
reduction (str, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Default: 'mean'

Return type

Tensor

Returns

The loss based on the ssim index.

Examples

>>> input1 = torch.rand(1, 4, 5, 5)
>>> input2 = torch.rand(1, 4, 5, 5)
>>> loss = ssim_loss(input1, input2, 5)

psnr(input, target, max_val)[source]¶

Creates a function that calculates the PSNR between 2 images.

PSNR is Peek Signal to Noise Ratio, which is similar to mean squared error. Given an m x n image, the PSNR is:

\[\text{PSNR} = 10 \log_{10} \bigg(\frac{\text{MAX}_I^2}{MSE(I,T)}\bigg)\]

where

\[\text{MSE}(I,T) = \frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1} [I(i,j) - T(i,j)]^2\]

and \(\text{MAX}_I\) is the maximum possible input value (e.g for floating point images \(\text{MAX}_I=1\)).

Parameters

input (Tensor) – the input image with arbitrary shape \((*)\).
labels – the labels image with arbitrary shape \((*)\).
max_val (float) – The maximum value in the input tensor.

Return type

Tensor

Returns

the computed loss as a scalar.

Examples

>>> ones = torch.ones(1)
>>> psnr(ones, 1.2 * ones, 2.) # 10 * log(4/((1.2-1)**2)) / log(10)
tensor(20.0000)

Reference:: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Definition

psnr_loss(input, target, max_val)[source]¶

Function that computes the PSNR loss.

The loss is computed as follows:

\[\text{loss} = -\text{psnr(x, y)}\]

See psnr() for details abut PSNR.

Parameters

input (Tensor) – the input image with shape \((*)\).
labels – the labels image with shape \((*)\).
max_val (float) – The maximum value in the input tensor.

Return type

Tensor

Returns

the computed loss as a scalar.

Examples

>>> ones = torch.ones(1)
>>> psnr_loss(ones, 1.2 * ones, 2.) # 10 * log(4/((1.2-1)**2)) / log(10)
tensor(-20.0000)

total_variation(img)[source]¶

Function that computes Total Variation according to [1].

Parameters: img (Tensor) – the input image with shape \((N, C, H, W)\) or \((C, H, W)\).
Return type: Tensor
Returns: a scalar with the computer loss.

Examples

>>> total_variation(torch.ones(3, 4, 4))
tensor(0.)

Note

See a working example here.

Reference:: [1] https://en.wikipedia.org/wiki/Total_variation

inverse_depth_smoothness_loss(idepth, image)[source]¶

Criterion that computes image-aware inverse depth smoothness loss.

\[\text{loss} = \left | \partial_x d_{ij} \right | e^{-\left \| \partial_x I_{ij} \right \|} + \left | \partial_y d_{ij} \right | e^{-\left \| \partial_y I_{ij} \right \|}\]

Parameters

idepth (Tensor) – tensor with the inverse depth with shape \((N, 1, H, W)\).
image (Tensor) – tensor with the input image with shape \((N, 3, H, W)\).

Return type

Tensor

Returns

a scalar with the computed loss.

Examples

>>> idepth = torch.rand(1, 1, 4, 5)
>>> image = torch.rand(1, 3, 4, 5)
>>> loss = inverse_depth_smoothness_loss(idepth, image)

Semantic Segmentation¶

binary_focal_loss_with_logits(input, target, alpha=0.25, gamma=2.0, reduction='none', eps=1e-08)[source]¶

Function that computes Binary Focal loss.

\[\text{FL}(p_t) = -\alpha_t (1 - p_t)^{\gamma} \, \text{log}(p_t)\]

where:

\(p_t\) is the model’s estimated probability for each class.

Parameters

input (Tensor) – input data tensor with shape \((N, 1, *)\).
target (Tensor) – the target tensor with shape \((N, 1, *)\).
alpha (float, optional) – Weighting factor for the rare class \(\alpha \in [0, 1]\). Default: 0.25
gamma (float, optional) – Focusing parameter \(\gamma >= 0\). Default: 2.0
reduction (str, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Default: 'none'
eps (float, optional) – for numerically stability when dividing. Default: 1e-08

Return type

Tensor

Returns

the computed loss.

Examples

>>> num_classes = 1
>>> kwargs = {"alpha": 0.25, "gamma": 2.0, "reduction": 'mean'}
>>> logits = torch.tensor([[[[6.325]]],[[[5.26]]],[[[87.49]]]])
>>> labels = torch.tensor([[[1.]],[[1.]],[[0.]]])
>>> binary_focal_loss_with_logits(logits, labels, **kwargs)
tensor(4.6052)

focal_loss(input, target, alpha, gamma=2.0, reduction='none', eps=1e-08)[source]¶

Criterion that computes Focal loss.

According to [LGG+18], the Focal loss is computed as follows:

\[\text{FL}(p_t) = -\alpha_t (1 - p_t)^{\gamma} \, \text{log}(p_t)\]

Where:

\(p_t\) is the model’s estimated probability for each class.

Parameters

input (Tensor) – logits tensor with shape \((N, C, *)\) where C = number of classes.
target (Tensor) – labels tensor with shape \((N, *)\) where each value is \(0 ≤ targets[i] ≤ C−1\).
alpha (float) – Weighting factor \(\alpha \in [0, 1]\).
gamma (float, optional) – Focusing parameter \(\gamma >= 0\). Default: 2.0
reduction (str, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Default: 'none'
eps (float, optional) – Scalar to enforce numerical stabiliy. Default: 1e-08

Return type

Tensor

Returns

the computed loss.

Example

>>> N = 5  # num_classes
>>> input = torch.randn(1, N, 3, 5, requires_grad=True)
>>> target = torch.empty(1, 3, 5, dtype=torch.long).random_(N)
>>> output = focal_loss(input, target, alpha=0.5, gamma=2.0, reduction='mean')
>>> output.backward()

dice_loss(input, target, eps=1e-08)[source]¶

Criterion that computes Sørensen-Dice Coefficient loss.

According to [1], we compute the Sørensen-Dice Coefficient as follows:

\[\text{Dice}(x, class) = \frac{2 |X| \cap |Y|}{|X| + |Y|}\]

Where:

\(X\) expects to be the scores of each class.
\(Y\) expects to be the one-hot tensor with the class labels.

the loss, is finally computed as:

\[\text{loss}(x, class) = 1 - \text{Dice}(x, class)\]

Reference:: [1] https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient

Parameters

input (Tensor) – logits tensor with shape \((N, C, H, W)\) where C = number of classes.
labels – labels tensor with shape \((N, H, W)\) where each value is \(0 ≤ targets[i] ≤ C−1\).
eps (float, optional) – Scalar to enforce numerical stabiliy. Default: 1e-08

Return type

Tensor

Returns

the computed loss.

Example

>>> N = 5  # num_classes
>>> input = torch.randn(1, N, 3, 5, requires_grad=True)
>>> target = torch.empty(1, 3, 5, dtype=torch.long).random_(N)
>>> output = dice_loss(input, target)
>>> output.backward()

tversky_loss(input, target, alpha, beta, eps=1e-08)[source]¶

Criterion that computes Tversky Coefficient loss.

According to [SEG17], we compute the Tversky Coefficient as follows:

\[\text{S}(P, G, \alpha; \beta) = \frac{|PG|}{|PG| + \alpha |P \setminus G| + \beta |G \setminus P|}\]

Where:

\(P\) and \(G\) are the predicted and ground truth binary labels.
\(\alpha\) and \(\beta\) control the magnitude of the penalties for FPs and FNs, respectively.

Note

\(\alpha = \beta = 0.5\) => dice coeff
\(\alpha = \beta = 1\) => tanimoto coeff
\(\alpha + \beta = 1\) => F beta coeff

Parameters

input (Tensor) – logits tensor with shape \((N, C, H, W)\) where C = number of classes.
target (Tensor) – labels tensor with shape \((N, H, W)\) where each value is \(0 ≤ targets[i] ≤ C−1\).
alpha (float) – the first coefficient in the denominator.
beta (float) – the second coefficient in the denominator.
eps (float, optional) – scalar for numerical stability. Default: 1e-08

Return type

Tensor

Returns

the computed loss.

Example

>>> N = 5  # num_classes
>>> input = torch.randn(1, N, 3, 5, requires_grad=True)
>>> target = torch.empty(1, 3, 5, dtype=torch.long).random_(N)
>>> output = tversky_loss(input, target, alpha=0.5, beta=0.5)
>>> output.backward()

Distributions¶

js_div_loss_2d(input, target, reduction='mean')[source]¶

Calculates the Jensen-Shannon divergence loss between heatmaps.

Parameters

input (Tensor) – the input tensor with shape \((B, N, H, W)\).
target (Tensor) – the target tensor with shape \((B, N, H, W)\).
reduction (str, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Default: 'mean'

Examples

>>> input = torch.full((1, 1, 2, 4), 0.125)
>>> loss = js_div_loss_2d(input, input)
>>> loss.item()
0.0

kl_div_loss_2d(input, target, reduction='mean')[source]¶

Calculates the Kullback-Leibler divergence loss between heatmaps.

Parameters

input (Tensor) – the input tensor with shape \((B, N, H, W)\).
target (Tensor) – the target tensor with shape \((B, N, H, W)\).
reduction (str, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Default: 'mean'

Examples

>>> input = torch.full((1, 1, 2, 4), 0.125)
>>> loss = js_div_loss_2d(input, input)
>>> loss.item()
0.0

Module¶

class DiceLoss(eps=1e-08)[source]¶

Criterion that computes Sørensen-Dice Coefficient loss.

According to [1], we compute the Sørensen-Dice Coefficient as follows:

\[\text{Dice}(x, class) = \frac{2 |X| \cap |Y|}{|X| + |Y|}\]

Where:

\(X\) expects to be the scores of each class.
\(Y\) expects to be the one-hot tensor with the class labels.

the loss, is finally computed as:

\[\text{loss}(x, class) = 1 - \text{Dice}(x, class)\]

Reference:: [1] https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient

Parameters: eps (float, optional) – Scalar to enforce numerical stabiliy. Default: 1e-08

Shape:

Input: \((N, C, H, W)\) where C = number of classes.
Target: \((N, H, W)\) where each value is \(0 ≤ targets[i] ≤ C−1\).

Example

>>> N = 5  # num_classes
>>> criterion = DiceLoss()
>>> input = torch.randn(1, N, 3, 5, requires_grad=True)
>>> target = torch.empty(1, 3, 5, dtype=torch.long).random_(N)
>>> output = criterion(input, target)
>>> output.backward()

class TverskyLoss(alpha, beta, eps=1e-08)[source]¶

Criterion that computes Tversky Coefficient loss.

According to [SEG17], we compute the Tversky Coefficient as follows:

\[\text{S}(P, G, \alpha; \beta) = \frac{|PG|}{|PG| + \alpha |P \setminus G| + \beta |G \setminus P|}\]

Where:

\(P\) and \(G\) are the predicted and ground truth binary labels.
\(\alpha\) and \(\beta\) control the magnitude of the penalties for FPs and FNs, respectively.

Note

\(\alpha = \beta = 0.5\) => dice coeff
\(\alpha = \beta = 1\) => tanimoto coeff
\(\alpha + \beta = 1\) => F beta coeff

Parameters

alpha (float) – the first coefficient in the denominator.
beta (float) – the second coefficient in the denominator.
eps (float, optional) – scalar for numerical stability. Default: 1e-08

Shape:

Input: \((N, C, H, W)\) where C = number of classes.
Target: \((N, H, W)\) where each value is \(0 ≤ targets[i] ≤ C−1\).

Examples

>>> N = 5  # num_classes
>>> criterion = TverskyLoss(alpha=0.5, beta=0.5)
>>> input = torch.randn(1, N, 3, 5, requires_grad=True)
>>> target = torch.empty(1, 3, 5, dtype=torch.long).random_(N)
>>> output = criterion(input, target)
>>> output.backward()

class FocalLoss(alpha, gamma=2.0, reduction='none', eps=1e-08)[source]¶

Criterion that computes Focal loss.

According to [LGG+18], the Focal loss is computed as follows:

\[\text{FL}(p_t) = -\alpha_t (1 - p_t)^{\gamma} \, \text{log}(p_t)\]

Where:

\(p_t\) is the model’s estimated probability for each class.

Parameters

alpha (float) – Weighting factor \(\alpha \in [0, 1]\).
gamma (float, optional) – Focusing parameter \(\gamma >= 0\). Default: 2.0
reduction (str, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Default: 'none'
eps (float, optional) – Scalar to enforce numerical stabiliy. Default: 1e-08

Shape:

Input: \((N, C, *)\) where C = number of classes.
Target: \((N, *)\) where each value is \(0 ≤ targets[i] ≤ C−1\).

Example

>>> N = 5  # num_classes
>>> kwargs = {"alpha": 0.5, "gamma": 2.0, "reduction": 'mean'}
>>> criterion = FocalLoss(**kwargs)
>>> input = torch.randn(1, N, 3, 5, requires_grad=True)
>>> target = torch.empty(1, 3, 5, dtype=torch.long).random_(N)
>>> output = criterion(input, target)
>>> output.backward()

class SSIM(window_size, max_val=1.0, eps=1e-12)[source]¶

Creates a module that computes the Structural Similarity (SSIM) index between two images.

Measures the (SSIM) index between each element in the input x and target y.

The index can be described as:

\[\text{SSIM}(x, y) = \frac{(2\mu_x\mu_y+c_1)(2\sigma_{xy}+c_2)} {(\mu_x^2+\mu_y^2+c_1)(\sigma_x^2+\sigma_y^2+c_2)}\]

where:

\(c_1=(k_1 L)^2\) and \(c_2=(k_2 L)^2\) are two variables to stabilize the division with weak denominator.
\(L\) is the dynamic range of the pixel-values (typically this is \(2^{\#\text{bits per pixel}}-1\)).

Parameters

window_size (int) – the size of the gaussian kernel to smooth the images.
max_val (float, optional) – the dynamic range of the images. Default: 1.0
eps (float, optional) – Small value for numerically stability when dividing. Default: 1e-12

Shape:

Input: \((B, C, H, W)\).
Target \((B, C, H, W)\).
Output: \((B, C, H, W)\).

Examples

>>> input1 = torch.rand(1, 4, 5, 5)
>>> input2 = torch.rand(1, 4, 5, 5)
>>> ssim = SSIM(5)
>>> ssim_map = ssim(input1, input2)  # 1x4x5x5

class SSIMLoss(window_size, max_val=1.0, eps=1e-12, reduction='mean')[source]¶

Creates a criterion that computes a loss based on the SSIM measurement.

The loss, or the Structural dissimilarity (DSSIM) is described as:

\[\text{loss}(x, y) = \frac{1 - \text{SSIM}(x, y)}{2}\]

See ssim_loss() for details about SSIM.

Parameters

window_size (int) – the size of the gaussian kernel to smooth the images.
max_val (float, optional) – the dynamic range of the images. Default: 1.0
eps (float, optional) – Small value for numerically stability when dividing. Default: 1e-12
reduction (str, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Default: 'mean'

Returns

The loss based on the ssim index.

Examples

>>> input1 = torch.rand(1, 4, 5, 5)
>>> input2 = torch.rand(1, 4, 5, 5)
>>> criterion = SSIMLoss(5)
>>> loss = criterion(input1, input2)

class InverseDepthSmoothnessLoss[source]¶

Criterion that computes image-aware inverse depth smoothness loss.

\[\text{loss} = \left | \partial_x d_{ij} \right | e^{-\left \| \partial_x I_{ij} \right \|} + \left | \partial_y d_{ij} \right | e^{-\left \| \partial_y I_{ij} \right \|}\]

Shape:

Inverse Depth: \((N, 1, H, W)\)
Image: \((N, 3, H, W)\)
Output: scalar

Examples

>>> idepth = torch.rand(1, 1, 4, 5)
>>> image = torch.rand(1, 3, 4, 5)
>>> smooth = InverseDepthSmoothnessLoss()
>>> loss = smooth(idepth, image)

class TotalVariation[source]¶

Computes the Total Variation according to [1].

Shape:

Input: \((N, C, H, W)\) or \((C, H, W)\).
Output: \((N,)\) or scalar.

Examples

>>> tv = TotalVariation()
>>> output = tv(torch.ones((2, 3, 4, 4), requires_grad=True))
>>> output.data
tensor([0., 0.])
>>> output.sum().backward()  # grad can be implicitly created only for scalar outputs

Reference:: [1] https://en.wikipedia.org/wiki/Total_variation

class PSNRLoss(max_val)[source]¶

Creates a criterion that calculates the PSNR loss.

The loss is computed as follows:

\[\text{loss} = -\text{psnr(x, y)}\]

See psnr() for details abut PSNR.

Parameters: max_val (float) – The maximum value in the input tensor.

Shape:

Input: arbitrary dimensional tensor \((*)\).
Target: arbitrary dimensional tensor \((*)\) same shape as input.
Output: a scalar.

Examples

>>> ones = torch.ones(1)
>>> criterion = PSNRLoss(2.)
>>> criterion(ones, 1.2 * ones) # 10 * log(4/((1.2-1)**2)) / log(10)
tensor(-20.0000)

class BinaryFocalLossWithLogits(alpha, gamma=2.0, reduction='none')[source]¶

Criterion that computes Focal loss.

According to [LGG+18], the Focal loss is computed as follows:

\[\text{FL}(p_t) = -\alpha_t (1 - p_t)^{\gamma} \, \text{log}(p_t)\]

where:

\(p_t\) is the model’s estimated probability for each class.

Parameters

alpha) – Weighting factor for the rare class \(\alpha \in [0, 1]\).
gamma (float, optional) – Focusing parameter \(\gamma >= 0\). Default: 2.0
reduction (str, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Default: 'none'

Shape:

Input: \((N, 1, *)\).
Target: \((N, 1, *)\).

Examples

>>> N = 1  # num_classes
>>> kwargs = {"alpha": 0.25, "gamma": 2.0, "reduction": 'mean'}
>>> loss = BinaryFocalLossWithLogits(**kwargs)
>>> input = torch.randn(1, N, 3, 5, requires_grad=True)
>>> target = torch.empty(1, 3, 5, dtype=torch.long).random_(N)
>>> output = loss(input, target)
>>> output.backward()