test_triplet_margin_loss.py

# Copyright (c) 2020 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 paddle.fluid as fluid
import numpy as np
import unittest



def call_TripletMarginLoss_layer(input,
                    positive,
                    negative,
                    p = 2,
                    margin=0.3,
                    swap=False,
                    eps = 1e-6,
                    reduction='mean',):
    triplet_margin_loss = paddle.nn.TripletMarginLoss(p=p,eps=eps,
        margin=margin,swap=swap,reduction=reduction)
    res = triplet_margin_loss(input=input,positive=positive,negative=negative,)
    return res


def call_TripletMarginLoss_functional(input,
                        positive,
                        negative,
                        p = 2,
                        margin=0.3,
                        swap=False,
                        eps = 1e-6,
                        reduction='mean',):
    res = paddle.nn.functional.triplet_margin_loss(
        input=input,positive=positive,negative=negative,p=p,eps=eps,
        margin=margin,swap=swap,reduction=reduction)
    return res


def test_static(place,
                input_np,
                positive_np,
                negative_np,
                p=2,
                margin=0.3,
                swap=False,
                eps=1e-6,
                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.fluid.data(
            name='input', shape=input_np.shape, dtype='float64')
        positive = paddle.fluid.data(
            name='positive', shape=positive_np.shape, dtype='float64')
        negative = paddle.fluid.data(
            name='negative', shape=negative_np.shape, dtype='float64')
        feed_dict = {"input": input_np, "positive": positive_np, "negative":negative_np}


        if functional:
            res = call_TripletMarginLoss_functional(input=input,positive=positive,negative=negative,p=p,eps=eps,
        margin=margin,swap=swap,reduction=reduction)
        else:
            res = call_TripletMarginLoss_layer(input=input,positive=positive,negative=negative,p=p,eps=eps,
        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,
                 p=2,
                 margin=0.3,
                 swap=False,
                 eps=1e-6,
                 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_TripletMarginLoss_functional(input=input,positive=positive,negative=negative,p=p,eps=eps,
        margin=margin,swap=swap,reduction=reduction)
    else:
        dy_res = call_TripletMarginLoss_layer(input=input,positive=positive,negative=negative,p=p,eps=eps,
        margin=margin,swap=swap,reduction=reduction)
    dy_result = dy_res.numpy()
    paddle.enable_static()
    return dy_result


def calc_triplet_margin_loss(input,
                 positive,
                 negative,
                 p=2,
                 margin=0.3,
                 swap=False,
                 reduction='mean',):
    positive_dist = np.linalg.norm((input - positive), p, axis=1)
    negative_dist = np.linalg.norm((input - negative), p, axis=1)

    if swap:
        swap_dist = np.linalg.norm((positive - negative), p, 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 TestTripletMarginLoss(unittest.TestCase):
    def test_TripletMarginLoss(self):
        input = np.random.uniform(0.1, 0.8, size=(20, 30)).astype(np.float64)
        positive = np.random.randint(0, 2, size=(20, 30)).astype(np.float64)
        negative = np.random.randint(0, 2, size=(20, 30)).astype(np.float64)

        places = [fluid.CPUPlace()]
        if fluid.core.is_compiled_with_cuda():
            places.append(fluid.CUDAPlace(0))
        reductions = ['sum', 'mean', 'none']
        for place in places:
            for reduction in reductions:
                expected = calc_triplet_margin_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_TripletMarginLoss_error(self):
        paddle.disable_static()
        self.assertRaises(
            ValueError,
            paddle.nn.TripletMarginLoss,
            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_loss,
            input=input,
            positive=positive,
            negative=negative,
            reduction="unsupport reduction")
        paddle.enable_static()

    def test_TripletMarginLoss_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_loss,
            input=input,
            positive=positive,
            negative=negative,)
        TMLoss = paddle.nn.TripletMarginLoss()
        self.assertRaises(
            ValueError,
            TMLoss,
            input=input,
            positive=positive,
            negative=negative,)
        paddle.enable_static()

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