mindspore.ops.SmoothL1Loss

class mindspore.ops.SmoothL1Loss(*args, **kwargs)[source]

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.

The updating formulas of SmoothL1Loss algorithm are as follows,

\[\text{SmoothL1Loss} = \begin{cases} \frac{0.5 x^{2}}{\text{beta}}, &if \left |x \right | < \text{beta} \cr \left |x \right|-0.5 \text{beta}, &\text{otherwise}\end{cases}\]

where \(X\) represents prediction. \(Y\) represents target. \(loss\) represents output.

Parameters

beta (float) – A parameter used to control the point where the function will change from quadratic to linear. Default: 1.0.

Inputs:

  • prediction (Tensor) - Predict data. Data type must be float16 or float32.

  • target (Tensor) - Ground truth data, with the same type and shape as prediction.

Outputs:

Tensor, with the same type and shape as prediction.

Raises

  • TypeError – If beta is not a float.

  • TypeError – If prediction or target is not a Tensor.

  • TypeError – If dtype of prediction or target is neither float16 nor float32.

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

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

Supported Platforms:

Ascend GPU CPU

Examples

>>> loss = ops.SmoothL1Loss()
>>> input_data = Tensor(np.array([1, 2, 3]), mindspore.float32)
>>> target_data = Tensor(np.array([1, 2, 2]), mindspore.float32)
>>> output = loss(input_data, target_data)
>>> print(output)
[0.  0.  0.5]