From 5d48528f080da76d6961c109cf5c670e45075979 Mon Sep 17 00:00:00 2001 From: yangguohao <70266361+yangguohao@users.noreply.github.com> Date: Mon, 13 Jun 2022 10:58:07 +0800 Subject: [PATCH] =?UTF-8?q?=E3=80=90Hachathon=20No.30=E3=80=91=20(#40545)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 'TripletMarginDistanceLoss' * 'test_file' * '2022_03_27' * 2022-03-31 * 2022-04-05 * 2 * 2022-04-17 * 2022-04-17_2 * 2022-04-17_3 * 2022-04-17_4 * 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 loss.py * 2022-06-01_pre-commit * 2022-06-05 * 2022-06-06 * 2022-06-07 * 2022-06-07_V2 --- .../test_triplet_margin_with_distance_loss.py | 417 ++++++++++++++++++ python/paddle/nn/__init__.py | 6 +- python/paddle/nn/functional/__init__.py | 4 +- python/paddle/nn/functional/loss.py | 127 ++++++ python/paddle/nn/layer/__init__.py | 1 + python/paddle/nn/layer/loss.py | 107 +++++ 6 files changed, 659 insertions(+), 3 deletions(-) create mode 100644 python/paddle/fluid/tests/unittests/test_triplet_margin_with_distance_loss.py diff --git a/python/paddle/fluid/tests/unittests/test_triplet_margin_with_distance_loss.py b/python/paddle/fluid/tests/unittests/test_triplet_margin_with_distance_loss.py new file mode 100644 index 00000000000..0fb8ae22c26 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/test_triplet_margin_with_distance_loss.py @@ -0,0 +1,417 @@ +# 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_TripletMarginDistanceLoss_layer( + input, + positive, + negative, + distance_function=None, + margin=0.3, + swap=False, + reduction='mean', +): + triplet_margin_with_distance_loss = paddle.nn.TripletMarginWithDistanceLoss( + distance_function=distance_function, + margin=margin, + swap=swap, + reduction=reduction) + res = triplet_margin_with_distance_loss( + input=input, + positive=positive, + negative=negative, + ) + return res + + +def call_TripletMaginDistanceLoss_functional( + input, + positive, + negative, + distance_function=None, + margin=0.3, + swap=False, + reduction='mean', +): + res = paddle.nn.functional.triplet_margin_with_distance_loss( + input=input, + positive=positive, + negative=negative, + distance_function=distance_function, + margin=margin, + swap=swap, + reduction=reduction) + return res + + +def test_static(place, + input_np, + positive_np, + negative_np, + distance_function=None, + margin=0.3, + swap=False, + 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_TripletMaginDistanceLoss_functional( + input=input, + positive=positive, + negative=negative, + distance_function=distance_function, + margin=margin, + swap=swap, + reduction=reduction) + else: + res = call_TripletMarginDistanceLoss_layer( + input=input, + positive=positive, + negative=negative, + distance_function=distance_function, + 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, + distance_function=None, + margin=0.3, + swap=False, + 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_TripletMaginDistanceLoss_functional( + input=input, + positive=positive, + negative=negative, + distance_function=distance_function, + margin=margin, + swap=swap, + reduction=reduction) + else: + dy_res = call_TripletMarginDistanceLoss_layer( + input=input, + positive=positive, + negative=negative, + distance_function=distance_function, + margin=margin, + swap=swap, + reduction=reduction) + dy_result = dy_res.numpy() + paddle.enable_static() + return dy_result + + +def calc_triplet_margin_distance_loss( + input, + positive, + negative, + distance_function=None, + margin=0.3, + swap=False, + reduction='mean', +): + distance_function = np.linalg.norm + positive_dist = distance_function((input - positive), 2, axis=1) + negative_dist = distance_function((input - negative), 2, axis=1) + + if swap: + swap_dist = np.linalg.norm((positive - negative), 2, 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 TestTripletMarginWithDistanceLoss(unittest.TestCase): + + def test_TripletMarginDistanceLoss(self): + shape = (5, 5) + 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_distance_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_TripletMarginDistanceLoss_error(self): + paddle.disable_static() + self.assertRaises(ValueError, + paddle.nn.TripletMarginWithDistanceLoss, + 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_with_distance_loss, + input=input, + positive=positive, + negative=negative, + reduction="unsupport reduction") + paddle.enable_static() + + def test_TripletMarginDistanceLoss_distance_function(self): + + def distance_function_1(x1, x2): + return 1.0 - paddle.nn.functional.cosine_similarity(x1, x2) + + def distance_function_2(x1, x2): + return paddle.max(paddle.abs(x1 - x2), axis=1) + + distance_function_list = [distance_function_1, distance_function_2] + shape = (5, 5) + 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) + + place = paddle.CPUPlace() + reduction = 'mean' + for distance_function in distance_function_list: + dy_result = test_dygraph( + place=place, + input=input, + positive=positive, + negative=negative, + distance_function=distance_function, + reduction=reduction, + ) + + static_result = test_static( + place=place, + input_np=input, + positive_np=positive, + negative_np=negative, + distance_function=distance_function, + reduction=reduction, + ) + self.assertTrue(np.allclose(static_result, dy_result)) + static_functional = test_static(place=place, + input_np=input, + positive_np=positive, + negative_np=negative, + distance_function=distance_function, + reduction=reduction, + functional=True) + dy_functional = test_dygraph(place=place, + input=input, + positive=positive, + negative=negative, + distance_function=distance_function, + reduction=reduction, + functional=True) + self.assertTrue(np.allclose(static_functional, dy_functional)) + + def test_TripletMarginWithDistanceLoss_distance_funtion_error(self): + paddle.disable_static() + + def distance_function(x1, x2): + return -1.0 - paddle.nn.functional.cosine_similarity(x1, x2) + + func = distance_function + shape = (5, 5) + 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) + + self.assertRaises( + ValueError, + paddle.nn.functional.triplet_margin_with_distance_loss, + input=input, + positive=positive, + negative=negative, + distance_function=func, + ) + paddle.enable_static() + + def test_TripletMarginDistanceLoss_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_with_distance_loss, + input=input, + positive=positive, + negative=negative, + ) + triplet_margin_with_distance_loss = paddle.nn.loss.TripletMarginWithDistanceLoss( + ) + self.assertRaises( + ValueError, + triplet_margin_with_distance_loss, + input=input, + positive=positive, + negative=negative, + ) + paddle.enable_static() + + def test_TripletMarginWithDistanceLoss_swap(self): + reduction = 'mean' + place = paddle.CPUPlace() + shape = (5, 5) + 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_distance_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_TripletMarginWithDistanceLoss_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_with_distance_loss, + margin=margin, + input=input, + positive=positive, + negative=negative, + ) + paddle.enable_static() + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/nn/__init__.py b/python/paddle/nn/__init__.py index 20b176d7c73..a1e02dab470 100644 --- a/python/paddle/nn/__init__.py +++ b/python/paddle/nn/__init__.py @@ -108,6 +108,7 @@ from .layer.loss import CTCLoss # noqa: F401 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.norm import BatchNorm # noqa: F401 from .layer.norm import SyncBatchNorm # noqa: F401 from .layer.norm import GroupNorm # noqa: F401 @@ -154,7 +155,7 @@ from . import functional # noqa: F401 from . import initializer # noqa: F401 from . import quant # noqa: F401 -#TODO: remove 'diag_embed', 'remove_weight_norm', 'weight_norm' months later. +# TODO: remove 'diag_embed', 'remove_weight_norm', 'weight_norm' months later. import paddle.utils.deprecated as deprecated @@ -191,7 +192,7 @@ def weight_norm(*args): return utils.weight_norm(*args) -__all__ = [ #noqa +__all__ = [ # noqa 'BatchNorm', 'CELU', 'GroupNorm', @@ -314,4 +315,5 @@ __all__ = [ #noqa 'Identity', 'CosineEmbeddingLoss', 'RReLU', + 'TripletMarginWithDistanceLoss', ] diff --git a/python/paddle/nn/functional/__init__.py b/python/paddle/nn/functional/__init__.py index 5de8c775ad7..43ce403ab0b 100644 --- a/python/paddle/nn/functional/__init__.py +++ b/python/paddle/nn/functional/__init__.py @@ -91,6 +91,7 @@ from .loss import square_error_cost # noqa: F401 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 .norm import batch_norm # noqa: F401 from .norm import instance_norm # noqa: F401 from .norm import layer_norm # noqa: F401 @@ -125,7 +126,7 @@ from .extension import temporal_shift # noqa: F401 from .sparse_attention import sparse_attention -__all__ = [ #noqa +__all__ = [ # noqa 'celu', 'conv1d', 'conv1d_transpose', @@ -232,4 +233,5 @@ __all__ = [ #noqa 'fold', 'cosine_embedding_loss', 'rrelu', + 'triplet_margin_with_distance_loss', ] diff --git a/python/paddle/nn/functional/loss.py b/python/paddle/nn/functional/loss.py index 58a8bb65383..c882ab08296 100755 --- a/python/paddle/nn/functional/loss.py +++ b/python/paddle/nn/functional/loss.py @@ -2872,3 +2872,130 @@ def cosine_embedding_loss(input1, return paddle.mean(out, name=name) elif reduction == 'sum': return paddle.sum(out, name=name) + + +def triplet_margin_with_distance_loss(input, + positive, + negative, + distance_function=None, + margin=1.0, + 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, D)`. + + 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 the default distance function + + .. math:: + d(x_i, y_i) = \left\lVert {\bf x}_i - {\bf y}_i \right\rVert_p + + or user can defined their own distance functions. `margin` is a nonnegative margin representing the minimum difference + between the positive and negative distances that is required for the loss to be 0. If `swap` is true, it will compare distance of (input, negative) with + distance of (negative, positive) and change it to the smaller one. For more details see http://www.bmva.org/bmvc/2016/papers/paper119/paper119.pdf. + + 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. + + distance_function (callable, optional): Quantifies the distance between two tensors. if not specified, 2 norm functions will be used. + + margin (float, optional):Default: :math:`1`.A nonnegative margin representing the minimum difference + between the positive and negative distances required for the loss to be 0. + + swap (bool, optional):The distance swap changes the negative distance to the swap distance (distance between positive samples + and negative samples) if swap distance smaller than negative distance. 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_with_distance_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_with_distance_loss(input, positive, negative, margin=1.0, reduction='none') + print(loss) + # Tensor([0. , 0.57496738, 0. ]) + + + loss = F.triplet_margin_with_distance_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_with_distance_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_with_distance_loss') + check_variable_and_dtype(positive, 'positive', ['float32', 'float64'], + 'triplet_margin_with_distance_loss') + check_variable_and_dtype(negative, 'negative', ['float32', 'float64'], + 'triplet_margin_with_distance_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 = distance_function if distance_function is not None \ + else paddle.nn.PairwiseDistance(2) + + 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) + + if not paddle.all(positive_dist > 0) or not paddle.all(negative_dist > 0): + raise ValueError( + "The positive distance or negative distance should be greater than 0, " + "The distance functions should be checked.") + + 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 diff --git a/python/paddle/nn/layer/__init__.py b/python/paddle/nn/layer/__init__.py index cca8c37645d..a8e3d8ec1d4 100644 --- a/python/paddle/nn/layer/__init__.py +++ b/python/paddle/nn/layer/__init__.py @@ -79,6 +79,7 @@ from .loss import MarginRankingLoss # noqa: F401 from .loss import CTCLoss # noqa: F401 from .loss import SmoothL1Loss # noqa: F401 from .loss import HingeEmbeddingLoss # noqa: F401 +from .loss import TripletMarginWithDistanceLoss from .norm import BatchNorm1D # noqa: F401 from .norm import BatchNorm2D # noqa: F401 from .norm import BatchNorm3D # noqa: F401 diff --git a/python/paddle/nn/layer/loss.py b/python/paddle/nn/layer/loss.py index 0ec60ef4738..9b796d6965c 100644 --- a/python/paddle/nn/layer/loss.py +++ b/python/paddle/nn/layer/loss.py @@ -1400,3 +1400,110 @@ class CosineEmbeddingLoss(Layer): margin=self.margin, reduction=self.reduction, name=self.name) + + +class TripletMarginWithDistanceLoss(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, D)`. + + 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 the default `distance_function` + + .. math:: + d(x_i, y_i) = \left\lVert {\bf x}_i - {\bf y}_i \right\rVert_2 + + or user can define their own distance function. `margin` is a nonnegative margin representing the minimum difference + between the positive and negative distances that is required for the loss to be 0. If `swap` is true, it will compare distance of (input, negative) with + distance of (negative, positive) and change it to the smaller one. For more details see http://www.bmva.org/bmvc/2016/papers/paper119/paper119.pdf. + + Parameters: + distance_function (Callable, Optional): Quantifies the distance between two tensors. if not specified, 2 norm functions will be used. + + margin (float, Optional):Default: :math:`1`.A nonnegative margin representing the minimum difference + between the positive and negative distances required for the loss to be 0. Larger + margins penalize cases where the negative examples are not distant enough from the + anchors, relative to the positives. + + swap (bool, Optional):The distance swap changes the negative distance to the swap distance (distance between positive samples + and negative samples) if swap distance smaller than negative distance. 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`. + + Shapes: + 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. + + output(Tensor): The tensor variable storing the triplet_margin_with_distance_loss of input and positive and negative. + + Return： + A callable object of TripletMarginWithDistanceLoss + + Examples: + .. code-block:: python + + import paddle + from paddle.nn import TripletMarginWithDistanceLoss + + 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_with_distance_loss = TripletMarginWithDistanceLoss(reduction='none') + loss = triplet_margin_with_distance_loss(input, positive, negative,) + print(loss) + # Tensor([0. , 0.57496738, 0. ]) + + triplet_margin_with_distance_loss = TripletMarginWithDistanceLoss(reduction='mean') + loss = triplet_margin_with_distance_loss(input, positive, negative,) + print(loss) + # Tensor([0.19165580]) + + """ + + def __init__(self, + distance_function=None, + margin=1.0, + swap=False, + reduction: str = 'mean', + name=None): + super(TripletMarginWithDistanceLoss, self).__init__() + if reduction not in ['sum', 'mean', 'none']: + raise ValueError( + "The value of 'reduction' in TripletMarginWithDistanceLoss " + "should be 'sum', 'mean' or 'none', but " + "received %s, which is not allowed." % reduction) + self.margin = margin + self.swap = swap + self.reduction = reduction + self.distance_function = distance_function + self.name = name + + def forward(self, input, positive, negative): + return F.triplet_margin_with_distance_loss(input, + positive, + negative, + margin=self.margin, + swap=self.swap, + reduction=self.reduction, + name=self.name) -- GitLab