Migrate npairs_loss

tensorflow_addons/losses/BUILD

@@ -10,6 +10,7 @@ py_library(
         "focal_loss.py",
         "lifted.py",
         "metric_learning.py",
+        "npairs.py",
         "sparsemax_loss.py",
         "triplet.py",
     ],
@@ -46,6 +47,19 @@ py_test(
     ],
 )
 
+py_test(
+    name = "npairs_test",
+    size = "small",
+    srcs = [
+        "npairs_test.py",
+    ],
+    main = "npairs_test.py",
+    srcs_version = "PY2AND3",
+    deps = [
+        ":losses",
+    ],
+)
+
 py_test(
     name = "sparsemax_loss_test",
     size = "small",

tensorflow_addons/losses/README.md

@@ -6,6 +6,7 @@
 | contrastive |  @WindQAQ | [email protected] |
 | focal_loss |   |  |
 | lifted |  |  |
+| npairs | @WindQAQ | [email protected] |
 | sparsemax_loss | @AndreasMadsen | [email protected] |
 | triplet |   |  |
 
@@ -15,6 +16,7 @@
 | contrastive | ContrastiveLoss | http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf |
 | focal_loss | SigmoidFocalCrossEntropy | https://arxiv.org/abs/1708.02002  |
 | lifted | LiftedStructLoss | https://arxiv.org/abs/1511.06452       |
+| npairs | NpairsLoss | http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf |
 | sparsemax_loss | SparsemaxLoss |  https://arxiv.org/abs/1602.02068 |
 | triplet | TripletSemiHardLoss | https://arxiv.org/abs/1503.03832       |
 

tensorflow_addons/losses/__init__.py

@@ -21,5 +21,6 @@
 from tensorflow_addons.losses.contrastive import contrastive_loss, ContrastiveLoss
 from tensorflow_addons.losses.focal_loss import sigmoid_focal_crossentropy, SigmoidFocalCrossEntropy
 from tensorflow_addons.losses.lifted import lifted_struct_loss, LiftedStructLoss
+from tensorflow_addons.losses.npairs import npairs_loss, NpairsLoss
 from tensorflow_addons.losses.sparsemax_loss import sparsemax_loss, SparsemaxLoss
 from tensorflow_addons.losses.triplet import triplet_semihard_loss, TripletSemiHardLoss

tensorflow_addons/losses/npairs.py

@@ -0,0 +1,95 @@
+# Copyright 2019 The TensorFlow 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.
+# ==============================================================================
+"""Implements npairs loss."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+from tensorflow_addons.utils import keras_utils
+
+
+@keras_utils.register_keras_custom_object
+@tf.function
+def npairs_loss(y_true, y_pred):
+    """Computes the npairs loss between `y_true` and `y_pred`.
+
+    Npairs loss expects paired data where a pair is composed of samples from
+    the same labels and each pairs in the minibatch have different labels.
+    The loss takes each row of the pair-wise similarity matrix, `y_pred`,
+    as logits and the remapped multi-class labels, `y_true`, as labels.
+
+    The similarity matrix `y_pred` between two embedding matrices `a` and `b`
+    with shape `[batch_size, hidden_size]` can be computed as follows:
+
+    ```python
+    # y_pred = a * b^T
+    y_pred = tf.matmul(a, b, transpose_a=False, transpose_b=True)
+    ```
+
+    See: http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf
+
+    Args:
+      y_true: 1-D integer `Tensor` with shape `[batch_size]` of
+        multi-class labels.
+      y_pred: 2-D float `Tensor` with shape `[batch_size, batch_size]` of
+        similarity matrix between embedding matrices.
+
+    Returns:
+      npairs_loss: float scalar.
+    """
+    y_pred = tf.convert_to_tensor(y_pred)
+    y_true = tf.cast(y_true, y_pred.dtype)
+
+    # Expand to [batch_size, 1]
+    y_true = tf.expand_dims(y_true, -1)
+    y_true = tf.cast(tf.equal(y_true, tf.transpose(y_true)), y_pred.dtype)
+    y_true /= tf.math.reduce_sum(y_true, 1, keepdims=True)
+
+    loss = tf.nn.softmax_cross_entropy_with_logits(
+        logits=y_pred, labels=y_true)
+
+    return tf.math.reduce_mean(loss)
+
+
+@keras_utils.register_keras_custom_object
+class NpairsLoss(tf.keras.losses.Loss):
+    """Computes the npairs loss between `y_true` and `y_pred`.
+
+    Npairs loss expects paired data where a pair is composed of samples from
+    the same labels and each pairs in the minibatch have different labels.
+    The loss takes each row of the pair-wise similarity matrix, `y_pred`,
+    as logits and the remapped multi-class labels, `y_true`, as labels.
+
+    The similarity matrix `y_pred` between two embedding matrices `a` and `b`
+    with shape `[batch_size, hidden_size]` can be computed as follows:
+
+    ```python
+    # y_pred = a * b^T
+    y_pred = tf.matmul(a, b, transpose_a=False, transpose_b=True)
+    ```
+
+    See: http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf
+
+    Args:
+      name: (Optional) name for the loss.
+    """
+
+    def __init__(self, name="npairs_loss"):
+        super(NpairsLoss, self).__init__(
+            reduction=tf.keras.losses.Reduction.NONE, name=name)
+
+    def call(self, y_true, y_pred):
+        return npairs_loss(y_true, y_pred)

tensorflow_addons/losses/npairs_test.py

@@ -0,0 +1,58 @@
+# Copyright 2019 The TensorFlow 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.
+# ==============================================================================
+"""Tests for npairs loss."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+from tensorflow_addons.losses import npairs
+from tensorflow_addons.utils import test_utils
+
+
+@test_utils.run_all_in_graph_and_eager_modes
+class NpairsLossTest(tf.test.TestCase):
+    def test_config(self):
+        nl_obj = npairs.NpairsLoss(name="nl")
+        self.assertEqual(nl_obj.name, "nl")
+        self.assertEqual(nl_obj.reduction, tf.keras.losses.Reduction.NONE)
+
+    def test_unweighted(self):
+        nl_obj = npairs.NpairsLoss()
+        # batch size = 4, hidden size = 2
+        y_true = tf.constant([0, 1, 2, 3], dtype=tf.int64)
+        # features of anchors
+        f = tf.constant([[1., 1.], [1., -1.], [-1., 1.], [-1., -1.]],
+                        dtype=tf.float32)
+        # features of positive samples
+        fp = tf.constant([[1., 1.], [1., -1.], [-1., 1.], [-1., -1.]],
+                         dtype=tf.float32)
+        # similarity matrix
+        y_pred = tf.matmul(f, fp, transpose_a=False, transpose_b=True)
+        loss = nl_obj(y_true, y_pred)
+
+        # Loss = 1/4 * \sum_i log(1 + \sum_{j != i} exp(f_i*fp_j^T-f_i*f_i^T))
+        # Compute loss for i = 0, 1, 2, 3 without multiplier 1/4
+        # i = 0 => log(1 + sum([exp(-2), exp(-2), exp(-4)])) = 0.253846
+        # i = 1 => log(1 + sum([exp(-2), exp(-4), exp(-2)])) = 0.253846
+        # i = 2 => log(1 + sum([exp(-2), exp(-4), exp(-2)])) = 0.253846
+        # i = 3 => log(1 + sum([exp(-4), exp(-2), exp(-2)])) = 0.253846
+        # Loss = (0.253856 + 0.253856 + 0.253856 + 0.253856) / 4 = 0.253856
+
+        self.assertAllClose(loss, 0.253856)
+
+
+if __name__ == "__main__":
+    tf.test.main()