test_npair_loss_op.py

#   Copyright (c) 2019 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
from paddle.fluid import Program, program_guard


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


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 test_npair_loss(self):
        reg_lambda = 0.002
        num_data, feat_dim, num_classes = 18, 6, 3

        place = core.CPUPlace()
        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)
        row_labels = np.random.randint(0, num_classes,
                                       size=(num_data)).astype(np.float32)
        out_loss = npairloss(embeddings_anchor,
                             embeddings_positive,
                             row_labels,
                             l2_reg=reg_lambda)

        anc = fluid.layers.data(dtype='float32',
                                name='anc',
                                shape=embeddings_anchor.shape,
                                append_batch_size=False)
        pos = fluid.layers.data(dtype='float32',
                                name='pos',
                                shape=embeddings_positive.shape,
                                append_batch_size=False)
        lab = fluid.layers.data(dtype='float32',
                                name='lab',
                                shape=row_labels.shape,
                                append_batch_size=False)

        npair_loss_op = fluid.layers.npair_loss(anchor=anc,
                                                positive=pos,
                                                labels=lab,
                                                l2_reg=reg_lambda)
        out_tensor = exe.run(feed={
            'anc': embeddings_anchor,
            'pos': embeddings_positive,
            'lab': row_labels
        },
                             fetch_list=[npair_loss_op.name])

        self.__assert_close(out_tensor,
                            out_loss,
                            "inference output are different at " + str(place) +
                            ", " + str(np.dtype('float32')) +
                            str(np.array(out_tensor)) + str(out_loss),
                            atol=1e-3)


class TestNpairLossOpError(unittest.TestCase):

    def test_errors(self):
        with program_guard(Program(), Program()):
            anchor_np = np.random.random((2, 4)).astype("float32")
            positive_np = np.random.random((2, 4)).astype("float32")
            labels_np = np.random.random((2)).astype("float32")
            anchor_data = fluid.data(name='anchor',
                                     shape=[2, 4],
                                     dtype='float32')
            positive_data = fluid.data(name='positive',
                                       shape=[2, 4],
                                       dtype='float32')
            labels_data = fluid.data(name='labels', shape=[2], dtype='float32')

            def test_anchor_Variable():
                # the anchor type must be Variable
                fluid.layers.npair_loss(anchor=anchor_np,
                                        positive=positive_data,
                                        labels=labels_data)

            def test_positive_Variable():
                # the positive type must be Variable
                fluid.layers.npair_loss(anchor=anchor_data,
                                        positive=positive_np,
                                        labels=labels_data)

            def test_labels_Variable():
                # the labels type must be Variable
                fluid.layers.npair_loss(anchor=anchor_data,
                                        positive=positive_data,
                                        labels=labels_np)

            self.assertRaises(TypeError, test_anchor_Variable)
            self.assertRaises(TypeError, test_positive_Variable)
            self.assertRaises(TypeError, test_labels_Variable)

            def test_anchor_type():
                # dtype must be float32 or float64
                anchor_data1 = fluid.data(name='anchor1',
                                          shape=[2, 4],
                                          dtype='int32')
                fluid.layers.npair_loss(anchor=anchor_data,
                                        positive=positive_data,
                                        labels=labels_np)

            def test_positive_type():
                # dtype must be float32 or float64
                positive_data1 = fluid.data(name='positive1',
                                            shape=[2, 4],
                                            dtype='int32')
                fluid.layers.npair_loss(anchor=anchor_data,
                                        positive=positive_data1,
                                        labels=labels_np)

            def test_labels_type():
                # dtype must be float32 or float64
                labels_data1 = fluid.data(name='labels1',
                                          shape=[2],
                                          dtype='int32')
                fluid.layers.npair_loss(anchor=anchor_data,
                                        positive=positive_data,
                                        labels=labels_data1)

            self.assertRaises(TypeError, test_anchor_type)
            self.assertRaises(TypeError, test_positive_type)
            self.assertRaises(TypeError, test_labels_type)


if __name__ == '__main__':
    unittest.main()