mindspore.ops.smooth_l1_loss
- mindspore.ops.smooth_l1_loss(input, target, beta=1.0, reduction='none')[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.
Given two input \(x,\ y\) of length \(N\), the unreduced SmoothL1Loss can be described as follows:
\[\begin{split}L_{i} = \begin{cases} \frac{0.5 (x_i - y_i)^{2}}{\beta}, & \text{if } |x_i - y_i| < \beta \\ |x_i - y_i| - 0.5 * \beta, & \text{otherwise. } \end{cases}\end{split}\]If reduction is not none, then:
\[\begin{split}L = \begin{cases} \operatorname{mean}(L_{i}), & \text{if reduction} = \text{'mean';}\\ \operatorname{sum}(L_{i}), & \text{if reduction} = \text{'sum'.} \end{cases}\end{split}\]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 \((N, *)\) where \(*\) means, any number of additional dimensions. Data type is float16, float32 or float64.
target (Tensor) – Ground truth data, tensor of shape \((N, *)\), 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, optional) –
Apply specific reduction method to the output:
'none'
,'mean'
,'sum'
. Default:'none'
.'none'
: no reduction will be applied.'mean'
: compute and return the mean of elements in the output.'sum'
: the output elements will be summed.
- 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
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> 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]