mindspore.ops.smooth_l1_loss

mindspore.ops.smooth_l1_loss(input, target, beta=1.0, reduction='none')

Computes smooth L1 loss, a robust L1 loss.

SmoothL1Loss is a Loss similar to MSELoss but less sensitive to outliers as described in the Fast R-CNN by Ross Girshick.

Given two input $x,y$ of length $N$, the unreduced SmoothL1Loss can be described as follows:

$$\begin{array}{r}{L}_i=\{\begin{array}{ll}\frac{0.5(x_i-y_i{)}^2}{\beta },& \text{if }|x_i-y_i|<\beta \\ |x_i-y_i|-0.5\ast \beta ,& \text{otherwise. }\end{array}\end{array}$$

If reduction is not none, then:

$$\begin{array}{r}L=\{\begin{array}{ll}\mathrm{mean}(L_i),& \text{if reduction}=\text{'mean';}\\ \mathrm{sum}(L_i),& \text{if reduction}=\text{'sum'.}\end{array}\end{array}$$

Here $\text{beta}$ controls the point where the loss function changes from quadratic to linear. $\text{beta}>0$ , its default value is 1.0. $N$ is the batch size.

Parameters

• input (Tensor) – Tensor of shape $(N,\ast )$ where $\ast$ means, any number of additional dimensions.

• target (Tensor) – Ground truth data, tensor of shape $(N,\ast )$, same shape and dtype as the input.

• beta (float) – A parameter used to control the point where the function will change between L1 to L2 loss. The value should be greater than zero. Default: 1.0.

• reduction (str) – Apply specific reduction method to the output: ‘none’, ‘mean’ or ‘sum’. Default: ‘none’.

Returns

Tensor, if reduction is ‘none’, then output is a tensor with the same shape as input. Otherwise, the shape of output tensor is (1,).

Raises

• TypeError – If beta is not a float.

• ValueError – If reduction is not one of ‘none’, ‘mean’, ‘sum’.

• TypeError – If dtype of input or target is not one of float16, float32, float64.

• ValueError – If beta is less than or equal to 0.

• ValueError – If shape of input is not the same as target.

Supported Platforms:

Ascend GPU CPU

Examples

>>> logits = Tensor(np.array([1, 2, 3]), mindspore.float32)
>>> labels = Tensor(np.array([1, 2, 2]), mindspore.float32)
>>> output = ops.smooth_l1_loss(logits, labels)
>>> print(output)
[0.  0.  0.5]