add ops.multilabel_soft_margin_loss

docs/api/api_python/mindspore.nn.rst

@@ -249,6 +249,7 @@ Dropout层
     mindspore.nn.MarginRankingLoss
     mindspore.nn.MSELoss
     mindspore.nn.MultiClassDiceLoss
+    mindspore.nn.MultiLabelSoftMarginLoss
     mindspore.nn.NLLLoss
     mindspore.nn.RMSELoss
     mindspore.nn.SampledSoftmaxLoss

docs/api/api_python/mindspore.ops.rst

@@ -45,6 +45,7 @@ mindspore.ops
     mindspore.ops.max_unpool3d
     mindspore.ops.multi_margin_loss
     mindspore.ops.multi_label_margin_loss
+    mindspore.ops.multilabel_soft_margin_loss
     mindspore.ops.kl_div
     mindspore.ops.pad
     mindspore.ops.padding

docs/api/api_python/nn/mindspore.nn.MultiLabelSoftMarginLoss.rst

@@ -0,0 +1,28 @@
+mindspore.nn.MultiLabelSoftMarginLoss
+======================================
+
+.. py:class:: mindspore.nn.MultiLabelSoftMarginLoss(weight=None, reduction='mean')
+
+    基于最大熵计算用于多标签优化的损失。计算公式如下。
+
+    .. math::
+        \mathcal{L}_{D} = - \frac{1}{|D|}\sum_{i = 0}^{|D|}\left(
+        y_{i}\ln\frac{1}{1 + e^{- x_{i}}} + \left( 1 - y_{i}
+        \right)\ln\frac{1}{1 + e^{x_{i}}} \right)
+
+    :math:`\mathcal{L}_{D}` 为损失值，:math:`y_{i}` 为 `target` ,
+    :math:`x_{i}` 为 `x` 。如果 `weight` 不为None，将会和每个分类的loss相乘。
+
+    参数：
+        - **weight** (Union[Tensor, int, float]) - 每个类别的缩放权重。默认值：None。
+        - **reduction** (str) - 指定应用于输出结果的计算方式。取值为"mean"，"sum"，或"none"。默认值："mean"。
+
+    输入：
+        - **x** (Tensor) - shape为(N, C)的Tensor，N为batch size，C为类别个数。
+        - **target** (Tensor) - 目标值，数据类型和shape与 `x` 的相同。
+
+    输出：
+        Tensor，数据类型和 `x` 相同。如果 `reduction` 为"none"，其shape为(N)。否则，其shape为0。
+
+    异常：
+        - **TypeError** - `x` 或 `target` 的维度不等于2。

docs/api/api_python/ops/mindspore.ops.func_multilabel_soft_margin_loss.rst

@@ -0,0 +1,26 @@
+mindspore.ops.multilabel_soft_margin_loss
+=========================================
+
+.. py:function:: mindspore.ops.multilabel_soft_margin_loss(x, target, weight=None, reduction='mean')
+
+    基于最大熵计算用于多标签优化的损失。计算公式如下。
+
+    .. math::
+        \mathcal{L}_{D} = - \frac{1}{|D|}\sum_{i = 0}^{|D|}\left(
+        y_{i}\ln\frac{1}{1 + e^{- x_{i}}} + \left( 1 - y_{i}
+        \right)\ln\frac{1}{1 + e^{x_{i}}} \right)
+
+    :math:`\mathcal{L}_{D}` 为损失值，:math:`y_{i}` 为 `target` ,
+    :math:`x_{i}` 为 `x` 。如果 `weight` 不为None，将会和每个分类的loss相乘。
+
+    参数：
+        - **x** (Tensor) - shape为(N, C)的Tensor，N为batch size，C为类别个数。
+        - **target** (Tensor) - 目标值，数据类型和shape与 `x` 的相同。
+        - **weight** (Union[Tensor, int, float]) - 每个类别的缩放权重。默认值：None。
+        - **reduction** (str) - 指定应用于输出结果的计算方式。取值为"mean"，"sum"，或"none"。默认值："mean"。
+
+    返回：
+        Tensor，数据类型和 `x` 相同。如果 `reduction` 为"none"，其shape为(N)。否则，其shape为0。
+
+    异常：
+        - **TypeError** - `x` 或 `target` 的维度不等于2。

docs/api/api_python_en/mindspore.nn.rst

@@ -249,6 +249,7 @@ Loss Function
     mindspore.nn.MarginRankingLoss
     mindspore.nn.MSELoss
     mindspore.nn.MultiClassDiceLoss
+    mindspore.nn.MultiLabelSoftMarginLoss
     mindspore.nn.NLLLoss
     mindspore.nn.RMSELoss
     mindspore.nn.SampledSoftmaxLoss

docs/api/api_python_en/mindspore.ops.rst

@@ -45,6 +45,7 @@ Neural Network
     mindspore.ops.max_unpool3d
     mindspore.ops.multi_margin_loss
     mindspore.ops.multi_label_margin_loss
+    mindspore.ops.multilabel_soft_margin_loss
     mindspore.ops.kl_div
     mindspore.ops.pad
     mindspore.ops.padding

mindspore/python/mindspore/nn/loss/loss.py

@@ -1269,10 +1269,10 @@ class MultiLabelSoftMarginLoss(LossBase):
     :math:`x_{i}` is the `x`. `weight` will multiply to the loss of each class if given.
 
     Args:
-        weight (int, float): The manual rescaling weight given to each class. Default: None.
-        reduction (string): Specifies which reduction to be applied to the output. It must be one of
+        weight (Union[Tensor, int, float]): The manual rescaling weight given to each class. Default: None.
+        reduction (str): Specifies which reduction to be applied to the output. It must be one of
             'none', 'mean', and 'sum', meaning no reduction, reduce mean and sum on output, respectively.
-            Default 'mean'.
+            Default: 'mean'.
 
     Inputs:
         - **x** (Tensor) - A tensor of shape (N, C), where N is batch size and C is number
@@ -1280,8 +1280,7 @@ class MultiLabelSoftMarginLoss(LossBase):
         - **target** (Tensor) - The label target Tensor which has the same shape as `x`.
 
     Outputs:
-        Tensor or Scalar, if `reduction` is 'none', the output is a tensor of shape (N) with the same data type as `x`.
-        Otherwise it is a scalar.
+        Tensor, the data type is the same as x, if the reduction is 'none', its shape is (N), otherwise it is zero.
 
     Raises:
         ValueError: If the rank of `x` or `target`is not 2.
@@ -1302,27 +1301,10 @@ class MultiLabelSoftMarginLoss(LossBase):
         """Initialize MultiLabelSoftMarginLoss."""
         super(MultiLabelSoftMarginLoss, self).__init__(reduction)
         self.weight = weight
-        self.mul = P.Mul()
-        self.exp = P.Exp()
-        self.add = P.Add()
-        self.log = P.Log()
+        self.reduction = reduction
 
     def construct(self, x, target):
-        _check_is_tensor('x', x, self.cls_name)
-        _check_is_tensor('target', target, self.cls_name)
-        if x.ndim != 2 or target.ndim != 2:
-            raise ValueError(
-                "For 'MultiLabelSoftMarginLoss', the inputs must be 2d tensor, but got shapes: "
-                f"x: {x.shape}, target: {target.shape} "
-            )
-        pos = self.log(self.add(self.exp(-x), 1))
-        neg = self.log(self.add(self.exp(x), 1))
-        loss = target * pos + (1 - target) * neg
-        if self.weight is not None:
-            loss = loss * self.weight
-        class_dim = x.ndim - 1
-        loss = loss.sum(axis=class_dim) / x.shape[class_dim]
-        return self.get_loss(loss)
+        return F.multilabel_soft_margin_loss(x, target, self.weight, self.reduction)
 
 
 class MultiMarginLoss(LossBase):

mindspore/python/mindspore/ops/function/__init__.py

@@ -437,6 +437,7 @@ from .nn_func import (
     glu,
     multi_margin_loss,
     multi_label_margin_loss,
+    multilabel_soft_margin_loss,
     elu,
     gelu,
     hinge_embedding_loss,

mindspore/python/mindspore/ops/function/nn_func.py

@@ -4710,6 +4710,68 @@ def multi_label_margin_loss(inputs, target, reduction='mean'):
     return outputs
 
 
+def multilabel_soft_margin_loss(x, target, weight=None, reduction='mean'):
+    r"""
+    Calculates the MultiLabelSoftMarginLoss.
+    Create a criterion for optimizing multi-label one-to-total loss based on maximum entropy.
+
+    .. math::
+        \mathcal{L}_{D} = - \frac{1}{|D|}\sum_{i = 0}^{|D|}\left(
+        y_{i}\ln\frac{1}{1 + e^{- x_{i}}} + \left( 1 - y_{i}
+        \right)\ln\frac{1}{1 + e^{x_{i}}} \right)
+
+    where :math:`\mathcal{L}_{D}` is the loss, :math:`y_{i}` is the `target`,
+    :math:`x_{i}` is the `x`. `weight` will multiply to the loss of each class if given.
+
+    Args:
+        x (Tensor): A tensor of shape (N, C), where N is batch size and C is number of classes.
+        target (Tensor): The label target Tensor which has the same shape as `x`.
+        weight (Union[Tensor, int, float]): The manual rescaling weight given to each class. Default: None.
+        reduction (str): Specifies which reduction to be applied to the output. It must be one of
+            'none', 'mean', and 'sum', meaning no reduction, reduce mean and sum on output, respectively.
+            Default: 'mean'.
+
+    Returns:
+        Tensor, the data type is the same as x, if the reduction is 'none', its shape is (N), otherwise it is zero.
+
+    Raises:
+        ValueError: If the rank of `x` or `target`is not 2.
+
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
+    Examples:
+        >>> x = Tensor([[0.3, 0.6, 0.6], [0.9, 0.4, 0.2]])
+        >>> target = Tensor([[0.0, 0.0, 1.0], [0.0, 0.0, 1.0]])
+        >>> loss = ops.multilabel_soft_margin_loss(x, target, reduction='mean')
+        >>> out = loss(x, target)
+        >>> print(out.asnumpy())
+        0.84693956
+    """
+    cls_name = "multilabel_soft_margin_loss"
+    _check_is_tensor('x', x, cls_name)
+    _check_is_tensor('target', target, cls_name)
+    if x.ndim != 2 or target.ndim != 2:
+        raise ValueError(
+            "For 'MultiLabelSoftMarginLoss', the inputs must be 2d tensor, but got shapes: "
+            f"x: {x.shape}, target: {target.shape} "
+        )
+
+    mul_op = _get_cache_prim(P.Mul)()
+    exp_op = _get_cache_prim(P.Exp)()
+    add_op = _get_cache_prim(P.Add)()
+    log_op = _get_cache_prim(P.Log)()
+
+    pos = log_op(add_op(exp_op(-x), 1))
+    neg = log_op(add_op(exp_op(x), 1))
+    loss = mul_op(target, pos) + mul_op(1 - target, neg)
+    if weight is not None:
+        loss = mul_op(loss, weight)
+    class_dim = x.ndim - 1
+    loss = loss.sum(axis=class_dim) / x.shape[class_dim]
+    return _get_loss(loss, reduction, cls_name)
+
+
 def elu(input_x, alpha=1.0):
     r"""
     Exponential Linear Unit activation function.
@@ -5177,6 +5239,7 @@ __all__ = [
     'margin_ranking_loss',
     'multi_margin_loss',
     'multi_label_margin_loss',
+    'multilabel_soft_margin_loss',
     'elu',
     'gelu',
     'hinge_embedding_loss',

tests/st/nn/test_multilabelsoftmarginloss.py

@@ -0,0 +1,72 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import pytest
+
+import mindspore.common.dtype as mstype
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore import context
+
+
+class MultiLabelSoftMarginLossNet(nn.Cell):
+    def __init__(self, weight=None, reduction='mean'):
+        super(MultiLabelSoftMarginLossNet, self).__init__()
+        self.multilabel_soft_margin_loss = nn.MultiLabelSoftMarginLoss(weight=weight, reduction=reduction)
+
+    def construct(self, x, target):
+        return self.multilabel_soft_margin_loss(x, target)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_arm_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
+@pytest.mark.parametrize('weight', [None, Tensor([1.0, 1.5, 0.8], mstype.float32)])
+@pytest.mark.parametrize('reduction', ['mean', 'none', 'sum'])
+def test_multilabel_soft_margin_loss(mode, weight, reduction):
+    """
+    Feature: MultiLabelSoftMarginLoss with weight=[None, Tensor([1.0, 1.5, 0.8], mstype.float32)],
+    reduction=['mean', 'none', 'sum']
+    Description: Verify the result of MultiLabelSoftMarginLoss
+    Expectation: success
+    """
+    context.set_context(mode=mode)
+    net = MultiLabelSoftMarginLossNet(weight=weight, reduction=reduction)
+    arr1 = np.array([[0.3, 0.6, 0.6], [0.9, 0.4, 0.2]], np.float32)
+    arr2 = np.array([[0.0, 0.0, 1.0], [0.0, 0.0, 1.0]], np.float32)
+    x = Tensor(arr1, mstype.float32)
+    label = Tensor(arr2, mstype.float32)
+    output = net(x, label)
+    if weight is None:
+        if reduction == 'mean':
+            expected = np.array(0.846940, np.float32)
+        elif reduction == 'sum':
+            expected = np.array(1.693880, np.float32)
+        else:
+            expected = np.array([0.776444, 0.917436], np.float32)
+    else:
+        if reduction == 'mean':
+            expected = np.array(0.974961, np.float32)
+        elif reduction == 'sum':
+            expected = np.array(1.949922, np.float32)
+        else:
+            expected = np.array([0.920193, 1.029729], np.float32)
+    assert np.allclose(output.asnumpy(), expected)

tests/st/ops/test_multilabel_soft_margin_loss.py

@@ -0,0 +1,74 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import pytest
+
+import mindspore.common.dtype as mstype
+import mindspore.nn as nn
+import mindspore.ops as ops
+from mindspore import Tensor
+from mindspore import context
+
+
+class MultiLabelSoftMarginLossNet(nn.Cell):
+    def __init__(self, weight=None, reduction='mean'):
+        super(MultiLabelSoftMarginLossNet, self).__init__()
+        self.weight = weight
+        self.reduction = reduction
+
+    def construct(self, x, target):
+        return ops.multilabel_soft_margin_loss(x, target, self.weight, self.reduction)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_arm_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
+@pytest.mark.parametrize('weight', [None, Tensor([1.0, 1.5, 0.8], mstype.float32)])
+@pytest.mark.parametrize('reduction', ['mean', 'none', 'sum'])
+def test_multilabel_soft_margin_loss(mode, weight, reduction):
+    """
+    Feature: MultiLabelSoftMarginLoss with weight=[None, Tensor([1.0, 1.5, 0.8], mstype.float32)],
+    reduction=['mean', 'none', 'sum']
+    Description: Verify the result of MultiLabelSoftMarginLoss
+    Expectation: success
+    """
+    context.set_context(mode=mode)
+    net = MultiLabelSoftMarginLossNet(weight=weight, reduction=reduction)
+    arr1 = np.array([[0.3, 0.6, 0.6], [0.9, 0.4, 0.2]], np.float32)
+    arr2 = np.array([[0.0, 0.0, 1.0], [0.0, 0.0, 1.0]], np.float32)
+    x = Tensor(arr1, mstype.float32)
+    label = Tensor(arr2, mstype.float32)
+    output = net(x, label)
+    if weight is None:
+        if reduction == 'mean':
+            expected = np.array(0.846940, np.float32)
+        elif reduction == 'sum':
+            expected = np.array(1.693880, np.float32)
+        else:
+            expected = np.array([0.776444, 0.917436], np.float32)
+    else:
+        if reduction == 'mean':
+            expected = np.array(0.974961, np.float32)
+        elif reduction == 'sum':
+            expected = np.array(1.949922, np.float32)
+        else:
+            expected = np.array([0.920193, 1.029729], np.float32)
+    assert np.allclose(output.asnumpy(), expected)