test=develop

paddle/fluid/API.spec

@@ -220,6 +220,7 @@ paddle.fluid.layers.psroi_pool ArgSpec(args=['input', 'rois', 'output_channels',
 paddle.fluid.layers.teacher_student_sigmoid_loss ArgSpec(args=['input', 'label', 'soft_max_up_bound', 'soft_max_lower_bound'], varargs=None, keywords=None, defaults=(15.0, -15.0))
 paddle.fluid.layers.huber_loss ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.tree_conv ArgSpec(args=['nodes_vector', 'edge_set', 'output_size', 'num_filters', 'max_depth', 'act', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(1, 2, 'tanh', None, None, None))
+paddle.fluid.layers.npair_loss ArgSpec(args=['anchor', 'positive', 'labels', 'l2_reg'], varargs=None, keywords=None, defaults=(0.002,))
 paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
 paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
 paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)

python/paddle/fluid/layers/nn.py

@@ -186,6 +186,7 @@ __all__ = [
     'teacher_student_sigmoid_loss',
     'huber_loss',
     'tree_conv',
+    'npair_loss',
 ]
 
 kIgnoreIndex = -100
@@ -10560,3 +10561,52 @@ def tree_conv(nodes_vector,
     else:
         pre_activation = out
     return helper.append_activation(pre_activation)
+
+
+def npair_loss(anchor, positive, labels, l2_reg=0.002):
+    '''
+  **Npair Loss Layer**
+  
+  see http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf
+
+  Npair loss requires paired data. Npair loss has two parts, the first part is L2 
+  regularizer on the embedding vector, the second part is cross entropy loss which
+  takes the similarity matrix of anchor and positive as logits.
+
+  Args:
+    anchor(Variable): embedding vector for the anchor image. shape=[batch_size, embedding_dims]
+    positive(Variable): embedding vector for the positive image. shape=[batch_size, embedding_dims]
+    labels(Varieble): 1-D tensor. shape=[batch_size]
+    l2_res(float32): L2 regularization term on embedding vector, default: 0.02
+
+  Returns:
+    npair loss(Variable): return npair loss, shape=[1]
+
+  Examples:
+    .. code-block:: python
+
+       npair_loss = fluid.layers.npair_loss(anchor, positive, labels, l2_reg)
+  '''
+    Beta = 0.25
+    batch_size = labels.shape[0]
+
+    labels = reshape(labels, shape=[batch_size, 1], inplace=True)
+    labels = expand(labels, expand_times=[1, batch_size])
+
+    from .control_flow import equal
+    from .ops import square
+
+    labels = equal(labels, transpose(labels, perm=[1, 0])).astype('float32')
+    labels = labels / reduce_sum(labels, dim=1, keep_dim=True)
+
+    l2loss = reduce_mean(reduce_sum(square(anchor), 1)) \
+             + reduce_mean(reduce_sum(square(positive), 1))
+    l2loss = l2loss * Beta * l2_reg
+
+    similarity_matrix = matmul(
+        anchor, positive, transpose_x=False, transpose_y=True)
+    softmax_value = softmax(similarity_matrix)
+    cross_entropy = -1 * reduce_sum(labels * log(softmax_value), 0)
+    celoss = reduce_mean(cross_entropy)
+
+    return l2loss + celoss

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

+#   Copyright (c) 2018 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 paddle.fluid as fluid
+import paddle.fluid.core as core
+import numpy as np
+
+
+def npairloss(anchor, positive, labels, l2_reg=0.002):
+    def softmax_cross_entropy_with_logits(logits, labels):
+        logits = np.exp(logits)
+        logits = logits / np.sum(logits, axis=1).reshape(-1, 1)
+
+        return np.mean(
+            -np.sum(labels * np.log(logits), axis=1), dtype=np.float32)
+
+    batch_size = labels.shape[0]
+
+    labels = np.reshape(labels, (batch_size, 1))
+    labels = np.equal(labels, labels.transpose()).astype(float)
+    labels = labels / np.sum(labels, axis=1, keepdims=True)
+
+    l2loss = np.mean(np.sum(np.power(anchor, 2), 1)) + np.mean(
+        np.sum(np.power(positive, 2), 1))
+    l2loss = (l2loss * 0.25 * l2_reg).astype(np.float32)
+
+    similarity_matrix = np.matmul(anchor, positive.transpose())
+    celoss = np.mean(
+        softmax_cross_entropy_with_logits(similarity_matrix, labels))
+
+    return l2loss + celoss
+
+
+def create_or_get_tensor(scope, var_name, var, place):
+    tensor = scope.var(var_name).get_tensor()
+    if var is not None:
+        assert isinstance(var, np.ndarray)
+        tensor.set_recursive_sequence_lengths([])
+        tensor.set(var, place)
+    return tensor
+
+
+class TestNpairLossOp(unittest.TestCase):
+    def setUp(self):
+        self.dtype = np.float32
+
+    def __assert_close(self, tensor, np_array, msg, atol=1e-4):
+        self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg)
+
+    def check_with_place(self, place, dtype, shape):
+        reg_lambda = 0.002
+        num_data, feat_dim, num_classes = shape[0], shape[1], shape[2]
+
+        exe = fluid.Executor(place)
+        exe.run(fluid.default_startup_program())
+        embeddings_anchor = np.random.rand(num_data,
+                                           feat_dim).astype(np.float32)
+        embeddings_positive = np.random.rand(num_data,
+                                             feat_dim).astype(np.float32)
+        labels = np.random.randint(
+            0, num_classes, size=(num_data)).astype(np.float32)
+        out_loss = npairloss(
+            embeddings_anchor, embeddings_positive, labels, l2_reg=reg_lambda)
+
+        anchor_tensor = fluid.layers.data(
+            name='anchor',
+            shape=[num_data, feat_dim],
+            dtype=self.dtype,
+            append_batch_size=False)
+        positive_tensor = fluid.layers.data(
+            name='positive',
+            shape=[num_data, feat_dim],
+            dtype=self.dtype,
+            append_batch_size=False)
+        labels_tensor = fluid.layers.data(
+            name='labels',
+            shape=[num_data],
+            dtype=self.dtype,
+            append_batch_size=False)
+
+        npair_loss_op = fluid.layers.npair_loss(
+            anchor=anchor_tensor,
+            positive=positive_tensor,
+            labels=labels_tensor,
+            l2_reg=reg_lambda)
+        out_tensor = exe.run(feed={
+            'anchor': embeddings_anchor,
+            'positive': embeddings_positive,
+            'labels': labels
+        },
+                             fetch_list=[npair_loss_op.name])
+
+        self.__assert_close(
+            out_tensor,
+            out_loss,
+            "inference output are different at " + str(place) + ", " +
+            str(np.dtype(dtype)) + str(np.array(out_tensor)) + str(out_loss),
+            atol=1e-3)
+
+    def test_check_output(self):
+        places = [core.CPUPlace()]
+        if core.is_compiled_with_cuda() and core.ops_support_gpu("npair_loss"):
+            places.append(core.CUDAPlace(0))
+
+        for place in places:
+            self.check_with_place(place, self.dtype, [18, 6, 3])
+
+
+if __name__ == '__main__':
+    unittest.main()