forked from mindspore-Ecosystem/mindspore
!34221 [feat] [assistant] [ops] [I51VSC] Add new operator nn.HuberLoss
Merge pull request !34221 from fujianzhao/huberloss
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@ -202,6 +202,7 @@ Dropout层
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mindspore.nn.CosineEmbeddingLoss
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mindspore.nn.DiceLoss
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mindspore.nn.FocalLoss
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mindspore.nn.HuberLoss
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mindspore.nn.L1Loss
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mindspore.nn.MSELoss
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mindspore.nn.MultiClassDiceLoss
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@ -0,0 +1,50 @@
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mindspore.nn.HuberLoss
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=============================
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.. py:class:: mindspore.nn.HuberLoss(reduction='mean', delta=1.0)
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HuberLoss计算预测值和目标值之间的误差。它兼有L1Loss和MSELoss的优点。
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假设 :math:`x` 和 :math:`y` 为一维Tensor，长度 :math:`N` ，则计算 :math:`x` 和 :math:`y` 的loss而不进行降维操作（即reduction参数设置为"none"）的公式如下：
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.. math::
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\ell(x, y) = L = \{l_1,\dots,l_N\}^\top
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以及
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.. math::
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l_n = \begin{cases}
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0.5 * (x_n - y_n)^2, & \text{if } |x_n - y_n| < delta; \\
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delta * (|x_n - y_n| - 0.5 * delta), & \text{otherwise. }
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\end{cases}
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其中， :math:`N` 为batch size。如果 `reduction` 不是"none"，则：
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.. math::
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\ell(x, y) =
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\begin{cases}
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\operatorname{mean}(L), & \text{if reduction} = \text{'mean';}\\
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\operatorname{sum}(L), & \text{if reduction} = \text{'sum'.}
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\end{cases}
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**参数：**
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**reduction** (str) - 应用于loss的reduction类型。取值为"mean"，"sum"，或"none"。默认值："mean"。如果 `reduction` 为'mean'或'sum'，则输出一个标量Tensor；如果 `reduction` 为'none'，则输出Tensor的shape为广播后的shape。
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**delta** (Union[int, float]) - 两种损失之间变化的阈值。 该值必须为正。 默认值：1.0。
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**输入：**
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- **logits** (Tensor) - 输入预测值，任意维度的Tensor。其数据类型为float16或float32。
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- **label** (Tensor) - 目标值，通常情况下与 `logits` 的shape和dtype相同。但是如果 `logits` 和 `labels` 的shape不同，需要保证他们之间可以互相广播。
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**输出：**
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Tensor或Scalar，如果 `reduction` 为'none'，则为shape和数据类型与输入'logits'相的Tensor。否则，输出为Scalar。
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**异常：**
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- **TypeError** - `logits` 或 `labels` 的数据类型既不是float16也不是float32。
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- **TypeError** - `delta` 不是float或int。
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- **ValueError** - `delta` 的值小于或等于0。
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- **ValueError** - `reduction` 不为"mean"、"sum"或"none"。
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- **ValueError** - `logits` 和 `labels` 有不同的shape，且不能互相广播。
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@ -202,6 +202,7 @@ Loss Function
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mindspore.nn.CosineEmbeddingLoss
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mindspore.nn.DiceLoss
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mindspore.nn.FocalLoss
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mindspore.nn.HuberLoss
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mindspore.nn.L1Loss
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mindspore.nn.MSELoss
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mindspore.nn.MultiClassDiceLoss
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@ -1,4 +1,4 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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# Copyright 2020-2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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@ -22,10 +22,10 @@ It shows how well the model works on a dataset and the optimization target which
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from .loss import LossBase, L1Loss, MSELoss, SmoothL1Loss, SoftMarginLoss, FocalLoss,\
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SoftmaxCrossEntropyWithLogits, BCELoss, CosineEmbeddingLoss, \
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SampledSoftmaxLoss, DiceLoss, BCEWithLogitsLoss, MultiClassDiceLoss,\
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RMSELoss, MAELoss
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RMSELoss, MAELoss, HuberLoss
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__all__ = ['LossBase', 'L1Loss', 'MSELoss', 'SmoothL1Loss', 'SoftMarginLoss', 'FocalLoss',
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'SoftmaxCrossEntropyWithLogits', 'BCELoss', 'BCEWithLogitsLoss',
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'CosineEmbeddingLoss', 'SampledSoftmaxLoss', 'DiceLoss', 'MultiClassDiceLoss',
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'RMSELoss', 'MAELoss']
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'RMSELoss', 'MAELoss', 'HuberLoss']
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@ -1,4 +1,4 @@
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# Copyright 2020-2021 Huawei Technologies Co., Ltd
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# Copyright 2020-2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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@ -1471,3 +1471,119 @@ class FocalLoss(LossBase):
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loss = (-1 * weight * labelss * log_probability).mean(axis=-1)
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return self.get_loss(loss)
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@constexpr
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def _dtype_check(logits_dtype, labels_dtype, prim_name):
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"""Check dtype."""
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if logits_dtype not in [mstype.float32, mstype.float16]:
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raise TypeError("For {}, the logits_dtype must be float32 or float16, but got {}.".format(prim_name,
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logits_dtype))
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if logits_dtype != labels_dtype:
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raise TypeError("For {}, the labels_dtype must equal to logits_dtype {}, but got {}".format(prim_name,
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logits_dtype,
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labels_dtype))
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class HuberLoss(LossBase):
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r"""
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HuberLoss calculate the error between the predicted value and the target value.
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It has the advantages of both L1Loss and MSELoss.
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Assuming that the :math:`x` and :math:`y` are 1-D Tensor, length :math:`N`, then calculate the loss of :math:`x` and
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:math:`y` without dimensionality reduction (the reduction parameter is set to "none"). The formula is as follows:
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.. math::
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\ell(x, y) = L = \{l_1,\dots,l_N\}^\top
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with
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.. math::
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l_n = \begin{cases}
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0.5 * (x_n - y_n)^2, & \text{if } |x_n - y_n| < delta; \\
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delta * (|x_n - y_n| - 0.5 * delta), & \text{otherwise. }
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\end{cases}
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where :math:`N` is the batch size. If `reduction` is not 'none', then:
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.. math::
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\ell(x, y) =
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\begin{cases}
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\operatorname{mean}(L), & \text{if reduction} = \text{'mean';}\\
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\operatorname{sum}(L), & \text{if reduction} = \text{'sum'.}
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\end{cases}
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Args:
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reduction (str): Type of reduction to be applied to loss. The optional values are "mean", "sum", and "none".
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Default: "mean". If `reduction` is "mean" or "sum", then output a scalar Tensor, if `reduction` is "none",
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the shape of the output Tensor is the broadcasted shape.
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delta (Union[int, float]): The threshold to change between two type of loss.
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The value must be positive. Default: 1.0.
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Inputs:
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- **logits** (Tensor) - Input logits with shape :math:`(N, *)` where :math:`*` means, any number
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of additional dimensions. The data type must be float16 or float32.
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- **labels** (Tensor) - Ground truth label with shape :math:`(N, *)`, same dtype as `logits`.
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It supports the shape of `logits` is different from the shape of `labels` and they should be
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broadcasted to each other.
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Outputs:
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Tensor or Scalar, if `reduction` is "none", its shape is the same as `logits`.
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Otherwise, a scalar value will be returned.
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Raises:
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TypeError: If data type of `logits` or `labels` is neither float16 nor float32.
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TypeError: If dtype of `delta` is neither float nor int.
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ValueError: If `delta` is less than or equal to 0.
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ValueError: If `reduction` is not one of 'none', 'mean', 'sum'.
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ValueError: If `logits` and `labels` have different shapes and cannot be broadcasted to each other.
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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Examples:
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>>> # Case 1: logits.shape = labels.shape = (3,)
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>>> loss = nn.HuberLoss()
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>>> logits = Tensor(np.array([1, 2, 3]), mindspore.float32)
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>>> labels = Tensor(np.array([1, 2, 2]), mindspore.float32)
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>>> output = loss(logits, labels)
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>>> print(output)
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0.16666667
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>>> # Case 2: logits.shape = (3,), labels.shape = (2, 3)
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>>> loss = nn.HuberLoss(reduction='none')
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>>> logits = Tensor(np.array([1, 2, 3]), mindspore.float32)
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>>> labels = Tensor(np.array([[1, 1, 1], [1, 2, 2]]), mindspore.float32)
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>>> output = loss(logits, labels)
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>>> print(output)
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[[0. 0.5 1.5]
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[0. 0. 0.5]]
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"""
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def __init__(self, reduction='mean', delta=1.0):
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"""Initialize HuberLoss."""
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super(HuberLoss, self).__init__(reduction=reduction)
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validator.check_value_type('delta', delta, [float, int], self.cls_name)
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validator.check_number("delta", delta, 0.0, Rel.GT, self.cls_name)
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self.sub = P.Sub()
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self.mul = P.Mul()
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self.abs = P.Abs()
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self.less = P.Less()
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self.square = P.Square()
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self.select = P.Select()
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self.dtype = P.DType()
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self.delta = delta
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self.delta_half = 0.5 * self.delta
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def construct(self, logits, labels):
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_check_is_tensor('logits', logits, self.cls_name)
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_check_is_tensor('labels', labels, self.cls_name)
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logits_dtype = self.dtype(logits)
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labels_dtype = self.dtype(labels)
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_dtype_check(logits_dtype, labels_dtype, self.cls_name)
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z = self.abs(self.sub(logits, labels))
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condition = self.less(z, self.delta)
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l1 = self.mul(0.5, self.square(z))
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l2 = self.mul(self.delta, self.sub(z, self.delta_half))
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loss = self.select(condition, l1, l2)
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return self.get_loss(loss)
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@ -1,4 +1,4 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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# Copyright 2020-2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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@ -218,3 +218,15 @@ def test_mae_loss():
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input_data = Tensor(np.array([[1, 2, 3], [2, 3, 2]]).astype(np.float32))
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target_data = Tensor(np.array([[0, 0, 5], [1, 2, 3]]).astype(np.float32))
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loss(input_data, target_data)
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def test_huber_loss():
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"""
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Feature: Test HuberLoss.
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Description: Test HuberLoss functional.
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Expectation: Success.
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"""
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loss = nn.HuberLoss()
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input_data = Tensor(np.array([[1, 2, 3], [2, 3, 4]]).astype(np.float32))
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target_data = Tensor(np.array([[0, 2, 5], [3, 1, 1]]).astype(np.float32))
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loss(input_data, target_data)
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