Add the loss of MarginRankingLoss for the paddle api2.0 (#26078)

add the api and doc for the margin_ranking_loss and MarginRankingLoss

python/paddle/fluid/tests/unittests/test_nn_margin_rank_loss.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.static import Program, program_guard
+
+
+def calc_margin_rank_loss(x, y, label, margin=0.0, reduction='none'):
+    result = (-1 * label) * (x - y) + margin
+    result = np.maximum(result, 0)
+    if reduction == 'none':
+        return result
+    elif reduction == 'sum':
+        return np.sum(result)
+    elif reduction == 'mean':
+        return np.mean(result)
+
+
+def create_test_case(margin, reduction):
+    class MarginRankingLossCls(unittest.TestCase):
+        def setUp(self):
+            self.x_data = np.random.rand(10, 10).astype("float64")
+            self.y_data = np.random.rand(10, 10).astype("float64")
+            self.label_data = np.random.choice(
+                [-1, 1], size=[10, 10]).astype("float64")
+            self.places = []
+            self.places.append(fluid.CPUPlace())
+            if core.is_compiled_with_cuda():
+                self.places.append(paddle.CUDAPlace(0))
+
+        def run_static_functional_api(self, place):
+            paddle.enable_static()
+            expected = calc_margin_rank_loss(
+                self.x_data,
+                self.y_data,
+                self.label_data,
+                margin=margin,
+                reduction=reduction)
+            with program_guard(Program(), Program()):
+                x = paddle.nn.data(name="x", shape=[10, 10], dtype="float64")
+                y = paddle.nn.data(name="y", shape=[10, 10], dtype="float64")
+                label = paddle.nn.data(
+                    name="label", shape=[10, 10], dtype="float64")
+                result = paddle.nn.functional.margin_ranking_loss(
+                    x, y, label, margin, reduction)
+                exe = paddle.static.Executor(place)
+                result_numpy, = exe.run(feed={
+                    "x": self.x_data,
+                    "y": self.y_data,
+                    "label": self.label_data
+                },
+                                        fetch_list=[result])
+                self.assertTrue(np.allclose(result_numpy, expected))
+
+        def run_static_api(self, place):
+            paddle.enable_static()
+            expected = calc_margin_rank_loss(
+                self.x_data,
+                self.y_data,
+                self.label_data,
+                margin=margin,
+                reduction=reduction)
+            with program_guard(Program(), Program()):
+                x = paddle.nn.data(name="x", shape=[10, 10], dtype="float64")
+                y = paddle.nn.data(name="y", shape=[10, 10], dtype="float64")
+                label = paddle.nn.data(
+                    name="label", shape=[10, 10], dtype="float64")
+                margin_rank_loss = paddle.nn.loss.MarginRankingLoss(
+                    margin=margin, reduction=reduction)
+                result = margin_rank_loss(x, y, label)
+                exe = paddle.static.Executor(place)
+                result_numpy, = exe.run(feed={
+                    "x": self.x_data,
+                    "y": self.y_data,
+                    "label": self.label_data
+                },
+                                        fetch_list=[result])
+                self.assertTrue(np.allclose(result_numpy, expected))
+                self.assertTrue('loss' in result.name)
+
+        def run_dynamic_functional_api(self, place):
+            paddle.disable_static(place)
+            x = paddle.to_variable(self.x_data)
+            y = paddle.to_variable(self.y_data)
+            label = paddle.to_variable(self.label_data)
+
+            result = paddle.nn.functional.margin_ranking_loss(x, y, label,
+                                                              margin, reduction)
+            expected = calc_margin_rank_loss(
+                self.x_data,
+                self.y_data,
+                self.label_data,
+                margin=margin,
+                reduction=reduction)
+            self.assertTrue(np.allclose(result.numpy(), expected))
+
+        def run_dynamic_api(self, place):
+            paddle.disable_static(place)
+            x = paddle.to_variable(self.x_data)
+            y = paddle.to_variable(self.y_data)
+            label = paddle.to_variable(self.label_data)
+            margin_rank_loss = paddle.nn.loss.MarginRankingLoss(
+                margin=margin, reduction=reduction)
+            result = margin_rank_loss(x, y, label)
+            expected = calc_margin_rank_loss(
+                self.x_data,
+                self.y_data,
+                self.label_data,
+                margin=margin,
+                reduction=reduction)
+            self.assertTrue(np.allclose(result.numpy(), expected))
+
+        def run_dynamic_broadcast_api(self, place):
+            paddle.disable_static(place)
+            label_data = np.random.choice([-1, 1], size=[10]).astype("float64")
+            x = paddle.to_variable(self.x_data)
+            y = paddle.to_variable(self.y_data)
+            label = paddle.to_variable(label_data)
+            margin_rank_loss = paddle.nn.loss.MarginRankingLoss(
+                margin=margin, reduction=reduction)
+            result = margin_rank_loss(x, y, label)
+            expected = calc_margin_rank_loss(
+                self.x_data,
+                self.y_data,
+                label_data,
+                margin=margin,
+                reduction=reduction)
+            self.assertTrue(np.allclose(result.numpy(), expected))
+
+        def test_case(self):
+            for place in self.places:
+                self.run_static_api(place)
+                self.run_static_functional_api(place)
+                self.run_dynamic_api(place)
+                self.run_dynamic_functional_api(place)
+                self.run_dynamic_broadcast_api(place)
+
+    cls_name = "TestMarginRankLossCase_{}_{}".format(margin, reduction)
+    MarginRankingLossCls.__name__ = cls_name
+    globals()[cls_name] = MarginRankingLossCls
+
+
+for margin in [0.0, 0.2]:
+    for reduction in ['none', 'mean', 'sum']:
+        create_test_case(margin, reduction)
+
+
+# test case the raise message
+class MarginRakingLossError(unittest.TestCase):
+    paddle.enable_static()
+
+    def test_errors(self):
+        def test_margin_value_error():
+            margin_rank_loss = paddle.nn.loss.MarginRankingLoss(
+                margin=0.1, reduction="reduce_mean")
+
+        self.assertRaises(ValueError, test_margin_value_error)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/nn/__init__.py

@@ -85,6 +85,7 @@ from .layer.loss import MSELoss  #DEFINE_ALIAS
 from .layer.loss import L1Loss  #DEFINE_ALIAS
 from .layer.loss import NLLLoss  #DEFINE_ALIAS
 from .layer.loss import BCELoss  #DEFINE_ALIAS
+from .layer.loss import MarginRankingLoss  #DEFINE_ALIAS
 from .layer.norm import BatchNorm  #DEFINE_ALIAS
 from .layer.norm import GroupNorm  #DEFINE_ALIAS
 from .layer.norm import LayerNorm  #DEFINE_ALIAS

python/paddle/nn/functional/__init__.py

@@ -129,7 +129,7 @@ from .loss import iou_similarity  #DEFINE_ALIAS
 from .loss import kldiv_loss  #DEFINE_ALIAS
 from .loss import l1_loss  #DEFINE_ALIAS
 from .loss import log_loss  #DEFINE_ALIAS
-from .loss import margin_rank_loss  #DEFINE_ALIAS
+from .loss import margin_ranking_loss  #DEFINE_ALIAS
 from .loss import mse_loss  #DEFINE_ALIAS
 from .loss import nll_loss  #DEFINE_ALIAS
 # from .loss import nce        #DEFINE_ALIAS

python/paddle/nn/functional/loss.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 # TODO: define loss functions of neural network  
+import numpy as np
 import paddle
 import paddle.fluid as fluid
 from ...fluid.framework import core, in_dygraph_mode
@@ -38,10 +39,8 @@ from ...fluid.layers import teacher_student_sigmoid_loss  #DEFINE_ALIAS
 
 from ...fluid.layers import edit_distance  #DEFINE_ALIAS
 from ...fluid.layers import huber_loss  #DEFINE_ALIAS
-from ...fluid.layers import margin_rank_loss  #DEFINE_ALIAS
 from ...fluid.layers import sampled_softmax_with_cross_entropy  #DEFINE_ALIAS
 from ...fluid.layer_helper import LayerHelper
-from ...fluid.framework import in_dygraph_mode
 from ...fluid.framework import Variable
 
 __all__ = [
@@ -55,8 +54,8 @@ __all__ = [
     'kldiv_loss',
     'l1_loss',
     'log_loss',
-    'margin_rank_loss',
     'mse_loss',
+    'margin_ranking_loss',
     #       'nce',
     'nll_loss',
     'npair_loss',
@@ -72,6 +71,110 @@ __all__ = [
 ]
 
 
+def margin_ranking_loss(input,
+                        other,
+                        target,
+                        margin=0.0,
+                        reduction='mean',
+                        name=None):
+    """
+
+    This op the calcluate the the margin rank loss between the input x, y and target, use the math function as follows. 
+
+    .. math:: 
+        margin\_rank\_loss = max(0, -target * (input - other) + margin)
+
+    If :attr:`reduction` set to ``'mean'``, the reduced mean loss is:
+
+    .. math::
+        Out = MEAN(margin\_rank\_loss)
+
+    If :attr:`reduction` set to ``'sum'``, the reduced sum loss is:
+
+    .. math::
+        Out = SUM(margin\_rank\_loss)
+
+    If :attr:`reduction` set to ``'none'``, just return the origin ``margin_rank_loss``.
+
+    Parameters:
+        input(Tensor): the first input tensor, it's data type should be float32, float64.
+        other(Tensor): the second input tensor, it's data type should be float32, float64.
+        target(Tensor): the target value corresponding to input, it's data type should be float32, float64. 
+        margin (float, optional): The margin value to add, default value is 0;
+        reduction (str, optional): Indicate the reduction to apply to the loss, the candicates are ``'none'``, ``'mean'``, ``'sum'``.If :attr:`reduction` is ``'none'``, the unreduced loss is returned; If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned. If :attr:`reduction` is ``'sum'``, the reduced sum loss is returned. Default is ``'mean'``.
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns: Tensor, if :attr:`reduction` is ``'mean'`` or ``'sum'``, the out shape is :math:`[1]`, otherwise the shape is the same as `input` .The same dtype as input tensor.
+
+    Examples:
+
+        .. code-block:: python
+
+            import numpy as np 
+            import paddle 
+            
+            paddle.disable_static()
+             
+            x = paddle.to_variable(np.array([[1, 2], [3, 4]]).astype('float32'))
+            y = paddle.to_variable(np.array([[2, 1], [2, 4]]).astype('float32'))
+            target = paddle.to_variable(np.array([[1, -1], [-1, -1]]).astype('float32'))
+            loss = paddle.nn.functional.margin_ranking_loss(x, y, target) 
+            print(loss.numpy()) # [0.75]
+    """
+    if fluid.framework.in_dygraph_mode():
+        out = core.ops.elementwise_sub(other, input)
+        out = core.ops.elementwise_mul(out, target)
+        if margin != 0.0:
+            margin = fluid.dygraph.base.to_variable([margin], dtype=out.dtype)
+            out = core.ops.elementwise_add(out, margin)
+        out = core.ops.relu(out)
+        if reduction == 'sum':
+            return core.ops.reduce_sum(out, 'reduce_all', True)
+        elif reduction == 'mean':
+            return core.ops.mean(out)
+        return out
+
+    helper = LayerHelper("margin_ranking_loss", **locals())
+    fluid.data_feeder.check_variable_and_dtype(
+        input, 'input', ['float32', 'float64'], 'margin_rank_loss')
+    fluid.data_feeder.check_variable_and_dtype(
+        other, 'other', ['float32', 'float64'], 'margin_rank_loss')
+    fluid.data_feeder.check_variable_and_dtype(
+        target, 'target', ['float32', 'float64'], 'margin_rank_loss')
+
+    out = paddle.elementwise_sub(other, input)
+    out = paddle.multiply(out, target)
+
+    if margin != 0.0:
+        margin_var = out.block.create_var(dtype=out.dtype)
+        paddle.fill_constant([1], out.dtype, margin, out=margin_var)
+        out = paddle.add(out, margin_var)
+
+    result_out = helper.create_variable_for_type_inference(input.dtype)
+
+    if reduction == 'none':
+        helper.append_op(
+            type="relu", inputs={"X": out}, outputs={"Out": result_out})
+        return result_out
+    elif reduction == 'sum':
+        out = paddle.nn.functional.relu(out)
+        attrs = {"dim": [0], "keep_dim": False, "reduce_all": True}
+        helper.append_op(
+            type="reduce_sum",
+            inputs={"X": out},
+            outputs={"Out": result_out},
+            attrs=attrs)
+        return result_out
+    elif reduction == 'mean':
+        out = paddle.nn.functional.relu(out)
+        helper.append_op(
+            type="mean",
+            inputs={"X": out},
+            outputs={"Out": result_out},
+            attrs={})
+        return result_out
+
+
 def l1_loss(x, label, reduction='mean', name=None):
     """
     This operator computes the L1 Loss of Tensor ``x`` and ``label`` as follows.

python/paddle/nn/layer/__init__.py

@@ -62,6 +62,7 @@ from .loss import MSELoss  #DEFINE_ALIAS
 from .loss import L1Loss  #DEFINE_ALIAS
 from .loss import NLLLoss  #DEFINE_ALIAS
 from .loss import BCELoss  #DEFINE_ALIAS
+from .loss import MarginRankingLoss  #DEFINE_ALIAS
 from .norm import BatchNorm  #DEFINE_ALIAS
 from .norm import GroupNorm  #DEFINE_ALIAS
 from .norm import LayerNorm  #DEFINE_ALIAS

python/paddle/nn/layer/loss.py

@@ -13,7 +13,9 @@
 # limitations under the License.
 
 # TODO: define loss functions of neural network  
+import numpy as np
 import paddle.fluid as fluid
+import paddle.fluid.core as core
 import paddle
 from .. import functional as F
 
@@ -23,7 +25,8 @@ __all__ = [
     'MSELoss',
     'L1Loss',
     'NLLLoss',
-    'BCELoss'
+    'BCELoss',
+    'MarginRankingLoss'
 ]
 
 
@@ -569,3 +572,72 @@ class NLLLoss(fluid.dygraph.Layer):
             ignore_index=self._ignore_index,
             reduction=self._reduction,
             name=self._name)
+
+
+class MarginRankingLoss(fluid.dygraph.Layer):
+    """
+
+    This interface is used to construct a callable object of the ``MarginRankingLoss`` class.
+    The MarginRankingLoss layer calculates the margin rank loss between the input, other and target 
+    , use the math function as follows.
+
+    .. math:: 
+        margin\_rank\_loss = max(0, -target * (input - other) + margin)
+
+    If :attr:`reduction` set to ``'mean'``, the reduced mean loss is:
+
+    .. math::
+        Out = MEAN(margin\_rank\_loss)
+
+    If :attr:`reduction` set to ``'sum'``, the reduced sum loss is:
+
+    .. math::
+        Out = SUM(margin\_rank\_loss)
+
+    If :attr:`reduction` set to ``'none'``, just return the origin ``margin_rank_loss``.
+
+    Parameters:
+        margin (float, optional): The margin value to add, default value is 0;
+        reduction (str, optional): Indicate the reduction to apply to the loss, the candicates are ``'none'``, ``'mean'``, ``'sum'``.If :attr:`reduction` is ``'none'``, the unreduced loss is returned; If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned. If :attr:`reduction` is ``'sum'``, the reduced sum loss is returned. Default is ``'mean'``.
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+    Shape: 
+        input: N-D Tensor, the shape is [N, *], N is batch size and `*` means any number of additional dimensions., available dtype is float32, float64.
+        other: N-D Tensor, `other` have the same shape and dtype as `input`.
+        target: N-D Tensor, target have the same shape and dtype as `input`.
+        out: If :attr:`reduction` is ``'mean'`` or ``'sum'`` , the out shape is :math:`[1]`, otherwise the shape is the same as `input` .The same dtype as input tensor.
+
+    Returns:
+        A callable object of MarginRankingLoss.
+
+    Examples:
+
+        .. code-block:: python
+
+            import numpy as np 
+            import paddle 
+            
+            paddle.disable_static()
+             
+            input = paddle.to_variable(np.array([[1, 2], [3, 4]]).astype("float32"))
+            other = paddle.to_variable(np.array([[2, 1], [2, 4]]).astype("float32"))
+            target = paddle.to_variable(np.array([[1, -1], [-1, -1]]).astype("float32"))
+            margin_rank_loss = paddle.nn.MarginRankingLoss()
+            loss = margin_rank_loss(input, other, target) 
+            print(loss.numpy()) # [0.75]
+    """
+
+    def __init__(self, margin=0.0, reduction='mean', name=None):
+        if reduction not in ['sum', 'mean', 'none']:
+            raise ValueError(
+                "The value of 'reduction' in L1Loss should be 'sum', 'mean' or 'none', but "
+                "received %s, which is not allowed." % reduction)
+        super(MarginRankingLoss, self).__init__()
+        self.margin = margin
+        self.reduction = reduction
+        self.name = name
+
+    def forward(self, input, other, target):
+        out = paddle.nn.functional.margin_ranking_loss(
+            input, other, target, self.margin, self.reduction, self.name)
+        return out