[Hackathon No.26] (#40487)

* 'triplet_margin_loss'

* 'test_file_corret'

* '2022_03_27'

* 2022_04_05

* 2022-04-17_1

* 2022-04-17

* 2022-04-17_2

* 2022-04-25

* 2022-05-02_V1

* 2022-05-06_V1

* 2022-05-07_V1

* Update loss.py

* Update loss.py

* Update loss.py

* Update loss.py

* Update loss.py

* Update loss.py

* Update loss.py

* Update loss.py

* Update loss.py

* Update test_triplet_margin_loss.py

* Update loss.py

* 2022-06-01_pre-commit

* 2022-06-05

* 2022-06-06

* 2022-06-06

* code_style_check

* code_style_check

* Update loss.py

* 2022-06-07_V2

* Update loss.py

* Update loss.py

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

+# Copyright (c) 2022 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.
+
+import paddle
+import numpy as np
+import unittest
+
+
+def call_TripletMarginLoss_layer(
+    input,
+    positive,
+    negative,
+    p=2,
+    margin=0.3,
+    swap=False,
+    eps=1e-6,
+    reduction='mean',
+):
+    triplet_margin_loss = paddle.nn.TripletMarginLoss(p=p,
+                                                      epsilon=eps,
+                                                      margin=margin,
+                                                      swap=swap,
+                                                      reduction=reduction)
+    res = triplet_margin_loss(
+        input=input,
+        positive=positive,
+        negative=negative,
+    )
+    return res
+
+
+def call_TripletMarginLoss_functional(
+    input,
+    positive,
+    negative,
+    p=2,
+    margin=0.3,
+    swap=False,
+    eps=1e-6,
+    reduction='mean',
+):
+    res = paddle.nn.functional.triplet_margin_loss(input=input,
+                                                   positive=positive,
+                                                   negative=negative,
+                                                   p=p,
+                                                   epsilon=eps,
+                                                   margin=margin,
+                                                   swap=swap,
+                                                   reduction=reduction)
+    return res
+
+
+def test_static(place,
+                input_np,
+                positive_np,
+                negative_np,
+                p=2,
+                margin=0.3,
+                swap=False,
+                eps=1e-6,
+                reduction='mean',
+                functional=False):
+    prog = paddle.static.Program()
+    startup_prog = paddle.static.Program()
+    with paddle.static.program_guard(prog, startup_prog):
+        input = paddle.static.data(name='input',
+                                   shape=input_np.shape,
+                                   dtype='float64')
+        positive = paddle.static.data(name='positive',
+                                      shape=positive_np.shape,
+                                      dtype='float64')
+        negative = paddle.static.data(name='negative',
+                                      shape=negative_np.shape,
+                                      dtype='float64')
+        feed_dict = {
+            "input": input_np,
+            "positive": positive_np,
+            "negative": negative_np
+        }
+
+        if functional:
+            res = call_TripletMarginLoss_functional(input=input,
+                                                    positive=positive,
+                                                    negative=negative,
+                                                    p=p,
+                                                    eps=eps,
+                                                    margin=margin,
+                                                    swap=swap,
+                                                    reduction=reduction)
+        else:
+            res = call_TripletMarginLoss_layer(input=input,
+                                               positive=positive,
+                                               negative=negative,
+                                               p=p,
+                                               eps=eps,
+                                               margin=margin,
+                                               swap=swap,
+                                               reduction=reduction)
+
+        exe = paddle.static.Executor(place)
+        static_result = exe.run(prog, feed=feed_dict, fetch_list=[res])
+    return static_result
+
+
+def test_dygraph(place,
+                 input,
+                 positive,
+                 negative,
+                 p=2,
+                 margin=0.3,
+                 swap=False,
+                 eps=1e-6,
+                 reduction='mean',
+                 functional=False):
+    paddle.disable_static()
+    input = paddle.to_tensor(input)
+    positive = paddle.to_tensor(positive)
+    negative = paddle.to_tensor(negative)
+
+    if functional:
+        dy_res = call_TripletMarginLoss_functional(input=input,
+                                                   positive=positive,
+                                                   negative=negative,
+                                                   p=p,
+                                                   eps=eps,
+                                                   margin=margin,
+                                                   swap=swap,
+                                                   reduction=reduction)
+    else:
+        dy_res = call_TripletMarginLoss_layer(input=input,
+                                              positive=positive,
+                                              negative=negative,
+                                              p=p,
+                                              eps=eps,
+                                              margin=margin,
+                                              swap=swap,
+                                              reduction=reduction)
+    dy_result = dy_res.numpy()
+    paddle.enable_static()
+    return dy_result
+
+
+def calc_triplet_margin_loss(
+    input,
+    positive,
+    negative,
+    p=2,
+    margin=0.3,
+    swap=False,
+    reduction='mean',
+):
+    positive_dist = np.linalg.norm((input - positive), p, axis=1)
+    negative_dist = np.linalg.norm((input - negative), p, axis=1)
+
+    if swap:
+        swap_dist = np.linalg.norm((positive - negative), p, axis=1)
+        negative_dist = np.minimum(negative_dist, swap_dist)
+    expected = np.maximum(positive_dist - negative_dist + margin, 0)
+
+    if reduction == 'mean':
+        expected = np.mean(expected)
+    elif reduction == 'sum':
+        expected = np.sum(expected)
+    else:
+        expected = expected
+
+    return expected
+
+
+class TestTripletMarginLoss(unittest.TestCase):
+
+    def test_TripletMarginLoss(self):
+        shape = (2, 2)
+        input = np.random.uniform(0.1, 0.8, size=shape).astype(np.float64)
+        positive = np.random.uniform(0, 2, size=shape).astype(np.float64)
+        negative = np.random.uniform(0, 2, size=shape).astype(np.float64)
+
+        places = [paddle.CPUPlace()]
+        if paddle.device.is_compiled_with_cuda():
+            places.append(paddle.CUDAPlace(0))
+        reductions = ['sum', 'mean', 'none']
+        for place in places:
+            for reduction in reductions:
+                expected = calc_triplet_margin_loss(input=input,
+                                                    positive=positive,
+                                                    negative=negative,
+                                                    reduction=reduction)
+
+                dy_result = test_dygraph(
+                    place=place,
+                    input=input,
+                    positive=positive,
+                    negative=negative,
+                    reduction=reduction,
+                )
+
+                static_result = test_static(
+                    place=place,
+                    input_np=input,
+                    positive_np=positive,
+                    negative_np=negative,
+                    reduction=reduction,
+                )
+                self.assertTrue(np.allclose(static_result, expected))
+                self.assertTrue(np.allclose(static_result, dy_result))
+                self.assertTrue(np.allclose(dy_result, expected))
+                static_functional = test_static(place=place,
+                                                input_np=input,
+                                                positive_np=positive,
+                                                negative_np=negative,
+                                                reduction=reduction,
+                                                functional=True)
+                dy_functional = test_dygraph(place=place,
+                                             input=input,
+                                             positive=positive,
+                                             negative=negative,
+                                             reduction=reduction,
+                                             functional=True)
+                self.assertTrue(np.allclose(static_functional, expected))
+                self.assertTrue(np.allclose(static_functional, dy_functional))
+                self.assertTrue(np.allclose(dy_functional, expected))
+
+    def test_TripletMarginLoss_error(self):
+        paddle.disable_static()
+        self.assertRaises(ValueError,
+                          paddle.nn.loss.TripletMarginLoss,
+                          reduction="unsupport reduction")
+        input = paddle.to_tensor([[0.1, 0.3]], dtype='float32')
+        positive = paddle.to_tensor([[0.0, 1.0]], dtype='float32')
+        negative = paddle.to_tensor([[0.2, 0.1]], dtype='float32')
+        self.assertRaises(ValueError,
+                          paddle.nn.functional.triplet_margin_loss,
+                          input=input,
+                          positive=positive,
+                          negative=negative,
+                          reduction="unsupport reduction")
+        paddle.enable_static()
+
+    def test_TripletMarginLoss_dimension(self):
+        paddle.disable_static()
+
+        input = paddle.to_tensor([[0.1, 0.3], [1, 2]], dtype='float32')
+        positive = paddle.to_tensor([[0.0, 1.0]], dtype='float32')
+        negative = paddle.to_tensor([[0.2, 0.1]], dtype='float32')
+        self.assertRaises(
+            ValueError,
+            paddle.nn.functional.triplet_margin_loss,
+            input=input,
+            positive=positive,
+            negative=negative,
+        )
+        TMLoss = paddle.nn.loss.TripletMarginLoss()
+        self.assertRaises(
+            ValueError,
+            TMLoss,
+            input=input,
+            positive=positive,
+            negative=negative,
+        )
+        paddle.enable_static()
+
+    def test_TripletMarginLoss_swap(self):
+        reduction = 'mean'
+        place = paddle.CPUPlace()
+        shape = (2, 2)
+        input = np.random.uniform(0.1, 0.8, size=shape).astype(np.float64)
+        positive = np.random.uniform(0, 2, size=shape).astype(np.float64)
+        negative = np.random.uniform(0, 2, size=shape).astype(np.float64)
+        expected = calc_triplet_margin_loss(input=input,
+                                            swap=True,
+                                            positive=positive,
+                                            negative=negative,
+                                            reduction=reduction)
+
+        dy_result = test_dygraph(
+            place=place,
+            swap=True,
+            input=input,
+            positive=positive,
+            negative=negative,
+            reduction=reduction,
+        )
+
+        static_result = test_static(
+            place=place,
+            swap=True,
+            input_np=input,
+            positive_np=positive,
+            negative_np=negative,
+            reduction=reduction,
+        )
+        self.assertTrue(np.allclose(static_result, expected))
+        self.assertTrue(np.allclose(static_result, dy_result))
+        self.assertTrue(np.allclose(dy_result, expected))
+        static_functional = test_static(place=place,
+                                        swap=True,
+                                        input_np=input,
+                                        positive_np=positive,
+                                        negative_np=negative,
+                                        reduction=reduction,
+                                        functional=True)
+        dy_functional = test_dygraph(place=place,
+                                     swap=True,
+                                     input=input,
+                                     positive=positive,
+                                     negative=negative,
+                                     reduction=reduction,
+                                     functional=True)
+        self.assertTrue(np.allclose(static_functional, expected))
+        self.assertTrue(np.allclose(static_functional, dy_functional))
+        self.assertTrue(np.allclose(dy_functional, expected))
+
+    def test_TripletMarginLoss_margin(self):
+        paddle.disable_static()
+
+        input = paddle.to_tensor([[0.1, 0.3]], dtype='float32')
+        positive = paddle.to_tensor([[0.0, 1.0]], dtype='float32')
+        negative = paddle.to_tensor([[0.2, 0.1]], dtype='float32')
+        margin = -0.5
+        self.assertRaises(
+            ValueError,
+            paddle.nn.functional.triplet_margin_loss,
+            margin=margin,
+            input=input,
+            positive=positive,
+            negative=negative,
+        )
+        paddle.enable_static()
+
+    def test_TripletMarginLoss_p(self):
+        p = 3
+        shape = (2, 2)
+        reduction = 'mean'
+        place = paddle.CPUPlace()
+        input = np.random.uniform(0.1, 0.8, size=shape).astype(np.float64)
+        positive = np.random.uniform(0, 2, size=shape).astype(np.float64)
+        negative = np.random.uniform(0, 2, size=shape).astype(np.float64)
+        expected = calc_triplet_margin_loss(input=input,
+                                            p=p,
+                                            positive=positive,
+                                            negative=negative,
+                                            reduction=reduction)
+
+        dy_result = test_dygraph(
+            place=place,
+            p=p,
+            input=input,
+            positive=positive,
+            negative=negative,
+            reduction=reduction,
+        )
+
+        static_result = test_static(
+            place=place,
+            p=p,
+            input_np=input,
+            positive_np=positive,
+            negative_np=negative,
+            reduction=reduction,
+        )
+        self.assertTrue(np.allclose(static_result, expected))
+        self.assertTrue(np.allclose(static_result, dy_result))
+        self.assertTrue(np.allclose(dy_result, expected))
+        static_functional = test_static(place=place,
+                                        p=p,
+                                        input_np=input,
+                                        positive_np=positive,
+                                        negative_np=negative,
+                                        reduction=reduction,
+                                        functional=True)
+        dy_functional = test_dygraph(place=place,
+                                     p=p,
+                                     input=input,
+                                     positive=positive,
+                                     negative=negative,
+                                     reduction=reduction,
+                                     functional=True)
+        self.assertTrue(np.allclose(static_functional, expected))
+        self.assertTrue(np.allclose(static_functional, dy_functional))
+        self.assertTrue(np.allclose(dy_functional, expected))
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/nn/__init__.py

@@ -109,6 +109,7 @@ from .layer.loss import SmoothL1Loss  # noqa: F401
 from .layer.loss import HingeEmbeddingLoss  # noqa: F401
 from .layer.loss import CosineEmbeddingLoss  # noqa: F401
 from .layer.loss import TripletMarginWithDistanceLoss
+from .layer.loss import TripletMarginLoss
 from .layer.norm import BatchNorm  # noqa: F401
 from .layer.norm import SyncBatchNorm  # noqa: F401
 from .layer.norm import GroupNorm  # noqa: F401
@@ -316,4 +317,5 @@ __all__ = [  # noqa
     'CosineEmbeddingLoss',
     'RReLU',
     'TripletMarginWithDistanceLoss',
+    'TripletMarginLoss',
 ]

python/paddle/nn/functional/__init__.py

@@ -92,6 +92,7 @@ from .loss import ctc_loss  # noqa: F401
 from .loss import hinge_embedding_loss  # noqa: F401
 from .loss import cosine_embedding_loss  # noqa: F401
 from .loss import triplet_margin_with_distance_loss
+from .loss import triplet_margin_loss
 from .norm import batch_norm  # noqa: F401
 from .norm import instance_norm  # noqa: F401
 from .norm import layer_norm  # noqa: F401
@@ -234,4 +235,5 @@ __all__ = [  # noqa
     'cosine_embedding_loss',
     'rrelu',
     'triplet_margin_with_distance_loss',
+    'triplet_margin_loss',
 ]

python/paddle/nn/functional/loss.py

@@ -28,7 +28,7 @@ from ...static import Variable
 from paddle.utils import deprecated
 from paddle import _C_ops
 from paddle import in_dynamic_mode
-from paddle.framework import core
+from paddle.framework import core, _non_static_mode
 from ...fluid.framework import _in_legacy_dygraph, in_dygraph_mode, _non_static_mode, _current_expected_place
 
 __all__ = []
@@ -2999,3 +2999,124 @@ def triplet_margin_with_distance_loss(input,
         return paddle.sum(loss, name=name)
     elif reduction == 'none':
         return loss
+
+
+def triplet_margin_loss(input,
+                        positive,
+                        negative,
+                        margin=1.0,
+                        p=2,
+                        epsilon=1e-6,
+                        swap=False,
+                        reduction='mean',
+                        name=None):
+    r"""
+        Measures the triplet loss given an input
+        tensors :math:`x1`, :math:`x2`, :math:`x3` and a margin with a value greater than :math:`0`.
+        This is used for measuring a relative similarity between samples. A triplet
+        is composed by `input`, `positive` and `negative` (i.e., `input`, `positive examples` and `negative
+        examples` respectively). The shapes of all input tensors should be
+        :math:`(N, *)`.
+
+        The loss function for each sample in the mini-batch is:
+
+        .. math::
+            L(input, pos, neg) = \max \{d(input_i, pos_i) - d(input_i, neg_i) + {\rm margin}, 0\}
+
+
+        where
+
+        .. math::
+            d(x_i, y_i) = \left\lVert {\bf x}_i - {\bf y}_i \right\rVert_p
+
+    Parameters:
+        input (Tensor): Input tensor, the data type is float32 or float64.
+            the shape is [N, \*], N is batch size and `\*` means any number of additional dimensions, available dtype is float32, float64.
+
+        positive (Tensor): Positive tensor, the data type is float32 or float64.
+            The shape of label is the same as the shape of input.
+
+        negative (Tensor): Negative tensor, the data type is float32 or float64.
+            The shape of label is the same as the shape of input.
+
+        margin (float, Optional): Default: :math:`1`.
+
+        p (int, Optional): The norm degree for pairwise distance. Default: :math:`2`.
+
+        epsilon (float, Optional): Add small value to avoid division by zero,
+            default value is 1e-6.
+
+        swap (bool,Optional): The distance swap change the negative distance to the distance between
+            positive sample and negative sample. For more details, see `Learning shallow convolutional feature descriptors with triplet losses`.
+            Default: ``False``.
+
+
+        reduction (str, Optional):Indicate how to average the loss by batch_size.
+            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 summed loss is returned.
+            Default: ``'mean'``
+
+        name (str, Optional): Name for the operation (optional, default is None).
+            For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        Output: Tensor. The tensor variable storing the triplet_margin_loss of input and positive and negative.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import paddle.nn.functional as F
+
+            input = paddle.to_tensor([[1, 5, 3], [0, 3, 2], [1, 4, 1]], dtype=paddle.float32)
+            positive= paddle.to_tensor([[5, 1, 2], [3, 2, 1], [3, -1, 1]], dtype=paddle.float32)
+            negative = paddle.to_tensor([[2, 1, -3], [1, 1, -1], [4, -2, 1]], dtype=paddle.float32)
+            loss = F.triplet_margin_loss(input, positive, negative, margin=1.0, reduction='none')
+            print(loss)
+            # Tensor([0.        , 0.57496738, 0.        ])
+
+
+            loss = F.triplet_margin_loss(input, positive, negative, margin=1.0, reduction='mean')
+            print(loss)
+            # Tensor([0.19165580])
+
+    """
+    if reduction not in ['sum', 'mean', 'none']:
+        raise ValueError(
+            "'reduction' in 'triplet_margin_loss' should be 'sum', 'mean' or 'none', "
+            "but received {}.".format(reduction))
+    if margin < 0:
+        raise ValueError(
+            "The margin between positive samples and negative samples should be greater than 0."
+        )
+    if not _non_static_mode():
+        check_variable_and_dtype(input, 'input', ['float32', 'float64'],
+                                 'triplet_margin_loss')
+        check_variable_and_dtype(positive, 'positive', ['float32', 'float64'],
+                                 'triplet_margin_loss')
+        check_variable_and_dtype(negative, 'negative', ['float32', 'float64'],
+                                 'triplet_margin_loss')
+
+    if not (input.shape == positive.shape == negative.shape):
+        raise ValueError("input's shape must equal to "
+                         "positive's shape and  "
+                         "negative's shape")
+
+    distance_function = paddle.nn.PairwiseDistance(p, epsilon=epsilon)
+    positive_dist = distance_function(input, positive)
+    negative_dist = distance_function(input, negative)
+
+    if swap:
+        swap_dist = distance_function(positive, negative)
+        negative_dist = paddle.minimum(negative_dist, swap_dist)
+
+    loss = paddle.clip(positive_dist - negative_dist + margin, min=0.0)
+
+    if reduction == 'mean':
+        return paddle.mean(loss, name=name)
+    elif reduction == 'sum':
+        return paddle.sum(loss, name=name)
+    elif reduction == 'none':
+        return loss

python/paddle/nn/layer/__init__.py

@@ -80,6 +80,7 @@ from .loss import CTCLoss  # noqa: F401
 from .loss import SmoothL1Loss  # noqa: F401
 from .loss import HingeEmbeddingLoss  # noqa: F401
 from .loss import TripletMarginWithDistanceLoss
+from .loss import TripletMarginLoss
 from .norm import BatchNorm1D  # noqa: F401
 from .norm import BatchNorm2D  # noqa: F401
 from .norm import BatchNorm3D  # noqa: F401

python/paddle/nn/layer/loss.py

@@ -1507,3 +1507,109 @@ class TripletMarginWithDistanceLoss(Layer):
                                                    swap=self.swap,
                                                    reduction=self.reduction,
                                                    name=self.name)
+
+
+class TripletMarginLoss(Layer):
+    r"""
+    Creates a criterion that measures the triplet loss given an input
+    tensors :math:`x1`, :math:`x2`, :math:`x3` and a margin with a value greater than :math:`0`.
+    This is used for measuring a relative similarity between samples. A triplet
+    is composed by `input`, `positive` and `negative` (i.e., `input`, `positive examples` and `negative
+    examples` respectively). The shapes of all input tensors should be
+    :math:`(N, *)`.
+
+    The loss function for each sample in the mini-batch is:
+
+    .. math::
+        L(input, pos, neg) = \max \{d(input_i, pos_i) - d(input_i, neg_i) + {\rm margin}, 0\}
+
+
+    where
+
+    .. math::
+        d(x_i, y_i) = \left\lVert {\bf x}_i - {\bf y}_i \right\rVert_p
+
+    Parameters:
+        margin (float, Optional):Default: :math:`1`.
+
+        p (int, Optional):The norm degree for pairwise distance. Default: :math:`2`.
+
+        epsilon (float, Optional):Add small value to avoid division by zero,
+            default value is 1e-6.
+
+        swap (bool, Optional):The distance swap change the negative distance to the distance between
+            positive sample and negative sample. For more details, see `Learning shallow convolutional feature descriptors with triplet losses`.
+            Default: ``False``.
+
+        reduction (str, Optional):Indicate how to average the loss by batch_size.
+                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 summed loss is returned.
+                Default: ``'mean'``
+
+        name (str,Optional): Name for the operation (optional, default is None).
+                For more information, please refer to :ref:`api_guide_Name`.
+
+    Call Parameters:
+        input (Tensor):Input tensor, the data type is float32 or float64.
+        the shape is [N, \*], N is batch size and `\*` means any number of additional dimensions, available dtype is float32, float64.
+
+        positive (Tensor):Positive tensor, the data type is float32 or float64.
+        The shape of label is the same as the shape of input.
+
+        negative (Tensor):Negative tensor, the data type is float32 or float64.
+        The shape of label is the same as the shape of input.
+
+    Returns:
+        Tensor. The tensor variable storing the triplet_margin_loss of input and positive and negative.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+
+            input = paddle.to_tensor([[1, 5, 3], [0, 3, 2], [1, 4, 1]], dtype=paddle.float32)
+            positive= paddle.to_tensor([[5, 1, 2], [3, 2, 1], [3, -1, 1]], dtype=paddle.float32)
+            negative = paddle.to_tensor([[2, 1, -3], [1, 1, -1], [4, -2, 1]], dtype=paddle.float32)
+            triplet_margin_loss = paddle.nn.TripletMarginLoss(reduction='none')
+            loss = triplet_margin_loss(input, positive, negative)
+            print(loss)
+            # Tensor([0.        , 0.57496738, 0.        ])
+	    
+            triplet_margin_loss = paddle.nn.TripletMarginLoss(margin=1.0, swap=True, reduction='mean', )
+            loss = triplet_margin_loss(input, positive, negative,)
+            print(loss)
+            # Tensor([0.19165580])
+
+    """
+
+    def __init__(self,
+                 margin=1.0,
+                 p=2.,
+                 epsilon=1e-6,
+                 swap=False,
+                 reduction='mean',
+                 name=None):
+        super(TripletMarginLoss, self).__init__()
+        if reduction not in ['sum', 'mean', 'none']:
+            raise ValueError(
+                "The value of 'reduction' in TripletMarginLoss should be 'sum', 'mean' or 'none', but "
+                "received %s, which is not allowed." % reduction)
+        self.margin = margin
+        self.p = p
+        self.epsilon = epsilon
+        self.swap = swap
+        self.reduction = reduction
+        self.name = name
+
+    def forward(self, input, positive, negative):
+        return F.triplet_margin_loss(input,
+                                     positive,
+                                     negative,
+                                     margin=self.margin,
+                                     p=self.p,
+                                     epsilon=self.epsilon,
+                                     swap=self.swap,
+                                     reduction=self.reduction,
+                                     name=self.name)