[PaddleHackathon No.14] (#41183)

* 2022-04-28

* 2022-05-04

* 2022-05-05_V1

* 2022-05-05_V1

* Update loss.py

* Update loss.py

* 2022-06-01_hook

* 2022-06-05

* 2022-06-07

* 2022-06-07_V2

* 2022-06-07_V2

* 2022-06-17_codestyle

python/paddle/fluid/tests/unittests/test_multi_label_soft_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_MultiLabelSoftMarginLoss_layer(
+    input,
+    label,
+    weight=None,
+    reduction='mean',
+):
+    multilabel_margin_loss = paddle.nn.MultiLabelSoftMarginLoss(
+        weight=weight, reduction=reduction)
+    res = multilabel_margin_loss(
+        input=input,
+        label=label,
+    )
+    return res
+
+
+def call_MultiLabelSoftMarginLoss_functional(
+    input,
+    label,
+    weight=None,
+    reduction='mean',
+):
+    res = paddle.nn.functional.multi_label_soft_margin_loss(
+        input,
+        label,
+        reduction=reduction,
+        weight=weight,
+    )
+    return res
+
+
+def test_static(place,
+                input_np,
+                label_np,
+                weight_np=None,
+                reduction='mean',
+                functional=False):
+    paddle.enable_static()
+    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')
+        label = paddle.static.data(name='label',
+                                   shape=label_np.shape,
+                                   dtype='float64')
+        feed_dict = {
+            "input": input_np,
+            "label": label_np,
+        }
+        weight = None
+        if weight_np is not None:
+            weight = paddle.static.data(name='weight',
+                                        shape=weight_np.shape,
+                                        dtype='float64')
+            feed_dict['weight'] = weight_np
+
+        if functional:
+            res = call_MultiLabelSoftMarginLoss_functional(input=input,
+                                                           label=label,
+                                                           weight=weight,
+                                                           reduction=reduction)
+        else:
+            res = call_MultiLabelSoftMarginLoss_layer(input=input,
+                                                      label=label,
+                                                      weight=weight,
+                                                      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_np,
+                 label_np,
+                 weight=None,
+                 reduction='mean',
+                 functional=False):
+    with paddle.fluid.dygraph.base.guard():
+        input = paddle.to_tensor(input_np)
+        label = paddle.to_tensor(label_np)
+        if weight is not None:
+            weight = paddle.to_tensor(weight)
+
+        if functional:
+            dy_res = call_MultiLabelSoftMarginLoss_functional(
+                input=input, label=label, weight=weight, reduction=reduction)
+        else:
+            dy_res = call_MultiLabelSoftMarginLoss_layer(input=input,
+                                                         label=label,
+                                                         weight=weight,
+                                                         reduction=reduction)
+        dy_result = dy_res.numpy()
+        return dy_result
+
+
+def calc_multilabel_margin_loss(
+    input,
+    label,
+    weight=None,
+    reduction="mean",
+):
+
+    def LogSigmoid(x):
+        return np.log(1 / (1 + np.exp(-x)))
+
+    loss = -(label * LogSigmoid(input) + (1 - label) * LogSigmoid(-input))
+
+    if weight is not None:
+        loss = loss * weight
+
+    loss = loss.mean(axis=-1)  # only return N loss values
+
+    if reduction == "none":
+        return loss
+    elif reduction == "mean":
+        return np.mean(loss)
+    elif reduction == "sum":
+        return np.sum(loss)
+
+
+class TestMultiLabelMarginLoss(unittest.TestCase):
+
+    def test_MultiLabelSoftMarginLoss(self):
+        input = np.random.uniform(0.1, 0.8, size=(5, 5)).astype(np.float64)
+        label = np.random.randint(0, 2, size=(5, 5)).astype(np.float64)
+
+        places = ['cpu']
+        if paddle.device.is_compiled_with_cuda():
+            places.append('gpu')
+        reductions = ['sum', 'mean', 'none']
+        for place in places:
+            for reduction in reductions:
+                expected = calc_multilabel_margin_loss(input=input,
+                                                       label=label,
+                                                       reduction=reduction)
+
+                dy_result = test_dygraph(place=place,
+                                         input_np=input,
+                                         label_np=label,
+                                         reduction=reduction)
+
+                static_result = test_static(place=place,
+                                            input_np=input,
+                                            label_np=label,
+                                            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,
+                                                label_np=label,
+                                                reduction=reduction,
+                                                functional=True)
+                dy_functional = test_dygraph(place=place,
+                                             input_np=input,
+                                             label_np=label,
+                                             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_MultiLabelSoftMarginLoss_error(self):
+        paddle.disable_static()
+        self.assertRaises(ValueError,
+                          paddle.nn.MultiLabelSoftMarginLoss,
+                          reduction="unsupport reduction")
+        input = paddle.to_tensor([[0.1, 0.3]], dtype='float32')
+        label = paddle.to_tensor([[0.0, 1.0]], dtype='float32')
+        self.assertRaises(ValueError,
+                          paddle.nn.functional.multi_label_soft_margin_loss,
+                          input=input,
+                          label=label,
+                          reduction="unsupport reduction")
+        paddle.enable_static()
+
+    def test_MultiLabelSoftMarginLoss_weights(self):
+        input = np.random.uniform(0.1, 0.8, size=(5, 5)).astype(np.float64)
+        label = np.random.randint(0, 2, size=(5, 5)).astype(np.float64)
+        weight = np.random.randint(0, 2, size=(5, 5)).astype(np.float64)
+        place = 'cpu'
+        reduction = 'mean'
+        expected = calc_multilabel_margin_loss(input=input,
+                                               label=label,
+                                               weight=weight,
+                                               reduction=reduction)
+
+        dy_result = test_dygraph(place=place,
+                                 input_np=input,
+                                 label_np=label,
+                                 weight=weight,
+                                 reduction=reduction)
+
+        static_result = test_static(place=place,
+                                    input_np=input,
+                                    label_np=label,
+                                    weight_np=weight,
+                                    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,
+                                        label_np=label,
+                                        weight_np=weight,
+                                        reduction=reduction,
+                                        functional=True)
+        dy_functional = test_dygraph(place=place,
+                                     input_np=input,
+                                     label_np=label,
+                                     weight=weight,
+                                     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_MultiLabelSoftMarginLoss_dimension(self):
+        paddle.disable_static()
+
+        input = paddle.to_tensor([[0.1, 0.3], [1, 2]], dtype='float32')
+        label = paddle.to_tensor([[0.2, 0.1]], dtype='float32')
+        self.assertRaises(ValueError,
+                          paddle.nn.functional.multi_label_soft_margin_loss,
+                          input=input,
+                          label=label)
+        paddle.enable_static()
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/nn/__init__.py

@@ -104,6 +104,7 @@ from .layer.loss import NLLLoss  # noqa: F401
 from .layer.loss import BCELoss  # noqa: F401
 from .layer.loss import KLDivLoss  # noqa: F401
 from .layer.loss import MarginRankingLoss  # noqa: F401
+from .layer.loss import MultiLabelSoftMarginLoss
 from .layer.loss import CTCLoss  # noqa: F401
 from .layer.loss import SmoothL1Loss  # noqa: F401
 from .layer.loss import HingeEmbeddingLoss  # noqa: F401
@@ -312,6 +313,7 @@ __all__ = [  # noqa
     'MaxUnPool1D',
     'MaxUnPool2D',
     'MaxUnPool3D',
+    'MultiLabelSoftMarginLoss',
     'HingeEmbeddingLoss',
     'Identity',
     'CosineEmbeddingLoss',

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 multi_label_soft_margin_loss
 from .loss import triplet_margin_with_distance_loss
 from .loss import triplet_margin_loss
 from .norm import batch_norm  # noqa: F401
@@ -206,6 +207,7 @@ __all__ = [  # noqa
     'log_loss',
     'mse_loss',
     'margin_ranking_loss',
+    'multi_label_soft_margin_loss',
     'nll_loss',
     'npair_loss',
     'sigmoid_focal_loss',

python/paddle/nn/functional/loss.py

@@ -2668,6 +2668,86 @@ def sigmoid_focal_loss(logit,
     return loss
 
 
+def multi_label_soft_margin_loss(input,
+                                 label,
+                                 weight=None,
+                                 reduction="mean",
+                                 name=None):
+    r"""
+
+        Parameters:
+            input (Tensor): Input tensor, the data type is float32 or float64. Shape is (N, C), where C is number of classes, and if shape is more than 2D, this is (N, C, D1, D2,..., Dk), k >= 1.
+            label (Tensor): Label tensor, the data type is float32 or float64. The shape of label is the same as the shape of input.
+            weight (Tensor,optional): a manual rescaling weight given to each class.
+                    If given, has to be a Tensor of size C and the data type is float32, float64.
+                    Default is ``'None'`` .
+            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`.
+
+	Shape:
+            input: N-D Tensor, the shape is [N, \*], N is batch size and `\*` means number of classes, available dtype is float32, float64. The sum operationoperates over all the elements.
+            label: N-D Tensor, same shape as the input.
+            weight:N-D Tensor, the shape is [N,1]
+            output: scalar. If :attr:`reduction` is ``'none'``, then same shape as the input.
+
+	Returns:
+            Tensor, The tensor variable storing the multi_label_soft_margin_loss of input and label.
+
+	Examples:
+            .. code-block:: python
+
+                import paddle
+                import paddle.nn.functional as F
+                input = paddle.to_tensor([[1, -2, 3], [0, -1, 2], [1, 0, 1]], dtype=paddle.float32)
+                # label elements in {1., -1.}
+                label = paddle.to_tensor([[-1, 1, -1], [1, 1, 1], [1, -1, 1]], dtype=paddle.float32)
+                loss = F.multi_label_soft_margin_loss(input, label, reduction='none')
+                print(loss)
+                # Tensor([3.49625897, 0.71111226, 0.43989015])
+                loss = F.multi_label_soft_margin_loss(input, label, reduction='mean')
+                print(loss)
+                # Tensor([1.54908717])
+    """
+    if reduction not in ['sum', 'mean', 'none']:
+        raise ValueError(
+            "'reduction' in 'multi_label_soft_margin_loss' should be 'sum', 'mean' or 'none', "
+            "but received {}.".format(reduction))
+
+    if not (input.shape == label.shape):
+        raise ValueError("The input and label should have same dimension,"
+                         "but received {}!={}".format(input.shape, label.shape))
+
+    if not _non_static_mode():
+        check_variable_and_dtype(input, 'input', ['float32', 'float64'],
+                                 'multilabel_soft_margin_loss')
+        check_variable_and_dtype(label, 'label', ['float32', 'float64'],
+                                 'multilabel_soft_margin_loss')
+
+    loss = -(label * paddle.nn.functional.log_sigmoid(input) +
+             (1 - label) * paddle.nn.functional.log_sigmoid(-input))
+
+    if weight is not None:
+        if not _non_static_mode():
+            check_variable_and_dtype(weight, 'weight', ['float32', 'float64'],
+                                     'multilabel_soft_margin_loss')
+        loss = loss * weight
+
+    loss = loss.mean(axis=-1)  # only return N loss values
+
+    if reduction == "none":
+        return loss
+    elif reduction == "mean":
+        return paddle.mean(loss)
+    elif reduction == "sum":
+        return paddle.sum(loss)
+
+
 def hinge_embedding_loss(input, label, margin=1.0, reduction='mean', name=None):
     r"""
     This operator calculates hinge_embedding_loss. Measures the loss given an input tensor :math:`x` and a labels tensor :math:`y`(containing 1 or -1).

python/paddle/nn/layer/__init__.py

@@ -76,6 +76,7 @@ from .loss import NLLLoss  # noqa: F401
 from .loss import BCELoss  # noqa: F401
 from .loss import KLDivLoss  # noqa: F401
 from .loss import MarginRankingLoss  # noqa: F401
+from .loss import MultiLabelSoftMarginLoss
 from .loss import CTCLoss  # noqa: F401
 from .loss import SmoothL1Loss  # noqa: F401
 from .loss import HingeEmbeddingLoss  # noqa: F401

python/paddle/nn/layer/loss.py

@@ -1217,6 +1217,84 @@ class SmoothL1Loss(Layer):
                                 name=self.name)
 
 
+class MultiLabelSoftMarginLoss(Layer):
+    r"""Creates a criterion that optimizes a multi-class multi-classification
+        hinge loss (margin-based loss) between input :math:`x` (a 2D mini-batch `Tensor`)
+        and output :math:`y` (which is a 2D `Tensor` of target class indices).
+        For each sample in the mini-batch:
+
+        .. math::
+            \text{loss}(x, y) = \sum_{ij}\frac{\max(0, 1 - (x[y[j]] - x[i]))}{\text{x.size}(0)}
+
+        where :math:`x \in \left\{0, \; \cdots , \; \text{x.size}(0) - 1\right\}`, \
+        :math:`y \in \left\{0, \; \cdots , \; \text{y.size}(0) - 1\right\}`, \
+        :math:`0 \leq y[j] \leq \text{x.size}(0)-1`, \
+        and :math:`i \neq y[j]` for all :math:`i` and :math:`j`.
+        :math:`y` and :math:`x` must have the same size.
+
+        Parameters:
+	        weight (Tensor,optional): a manual rescaling weight given to each class.
+                    If given, has to be a Tensor of size C and the data type is float32, float64.
+                    Default is ``'None'`` .
+            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. Shape is (N, C), where C is number of classes, and if shape is more than 2D, this is (N, C, D1, D2,..., Dk), k >= 1.
+            label (Tensor): Label tensor containing 1 or -1, the data type is float32 or float64. The shape of label is the same as the shape of input.
+
+        Shape:
+            input: N-D Tensor, the shape is [N, \*], N is batch size and `\*` means number of classes, available dtype is float32, float64. The sum operationoperates over all the elements.
+            label: N-D Tensor, same shape as the input.
+            output: scalar. If :attr:`reduction` is ``'none'``, then same shape as the input.
+
+        Returns:
+            A callable object of MultiLabelSoftMarginLoss.
+
+        Examples:
+            .. code-block:: python
+
+                import paddle
+                import paddle.nn as nn
+
+                input = paddle.to_tensor([[1, -2, 3], [0, -1, 2], [1, 0, 1]], dtype=paddle.float32)
+                label = paddle.to_tensor([[-1, 1, -1], [1, 1, 1], [1, -1, 1]], dtype=paddle.float32)
+
+                multi_label_soft_margin_loss = nn.MultiLabelSoftMarginLoss(reduction='none')
+                loss = multi_label_soft_margin_loss(input, label)
+                print(loss)
+                # Tensor([3.49625897, 0.71111226, 0.43989015])
+
+                multi_label_soft_margin_loss = nn.MultiLabelSoftMarginLoss(reduction='mean')
+                loss = multi_label_soft_margin_loss(input, label)
+                print(loss)
+                # Tensor([1.54908717])
+        """
+
+    def __init__(self, weight=None, reduction="mean", name=None):
+        super(MultiLabelSoftMarginLoss, self).__init__()
+        if reduction not in ['sum', 'mean', 'none']:
+            raise ValueError(
+                "'reduction' in 'MultiLabelSoftMarginloss' should be 'sum', 'mean' or 'none', "
+                "but received {}.".format(reduction))
+        self.weight = weight
+        self.reduction = reduction
+        self.name = name
+
+    def forward(self, input, label):
+        return F.multi_label_soft_margin_loss(input,
+                                              label,
+                                              weight=self.weight,
+                                              reduction=self.reduction,
+                                              name=self.name)
+
+
 class HingeEmbeddingLoss(Layer):
     r"""
     This operator calculates hinge_embedding_loss. Measures the loss given an input tensor :math:`x` and a labels tensor :math:`y`(containing 1 or -1).