test_triplet_margin_with_distance_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_TripletMarginDistanceLoss_layer(
        input,
        positive,
        negative,
        distance_function=None,
        margin=0.3,
        swap=False,
        reduction='mean', ):
    triplet_margin_with_distance_loss = paddle.nn.TripletMarginWithDistanceLoss(
        distance_function=distance_function,
        margin=margin,
        swap=swap,
        reduction=reduction)
    res = triplet_margin_with_distance_loss(
        input=input,
        positive=positive,
        negative=negative, )
    return res


def call_TripletMaginDistanceLoss_functional(
        input,
        positive,
        negative,
        distance_function=None,
        margin=0.3,
        swap=False,
        reduction='mean', ):
    res = paddle.nn.functional.triplet_margin_with_distance_loss(
        input=input,
        positive=positive,
        negative=negative,
        distance_function=distance_function,
        margin=margin,
        swap=swap,
        reduction=reduction)
    return res


def test_static(place,
                input_np,
                positive_np,
                negative_np,
                distance_function=None,
                margin=0.3,
                swap=False,
                reduction='mean',
                functional=False):
    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')
        positive = paddle.static.data(
            name='positive', shape=positive_np.shape, dtype='float64')
        negative = paddle.static.data(
            name='negative', shape=negative_np.shape, dtype='float64')
        feed_dict = {
            "input": input_np,
            "positive": positive_np,
            "negative": negative_np
        }

        if functional:
            res = call_TripletMaginDistanceLoss_functional(
                input=input,
                positive=positive,
                negative=negative,
                distance_function=distance_function,
                margin=margin,
                swap=swap,
                reduction=reduction)
        else:
            res = call_TripletMarginDistanceLoss_layer(
                input=input,
                positive=positive,
                negative=negative,
                distance_function=distance_function,
                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,
                 distance_function=None,
                 margin=0.3,
                 swap=False,
                 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_TripletMaginDistanceLoss_functional(
            input=input,
            positive=positive,
            negative=negative,
            distance_function=distance_function,
            margin=margin,
            swap=swap,
            reduction=reduction)
    else:
        dy_res = call_TripletMarginDistanceLoss_layer(
            input=input,
            positive=positive,
            negative=negative,
            distance_function=distance_function,
            margin=margin,
            swap=swap,
            reduction=reduction)
    dy_result = dy_res.numpy()
    paddle.enable_static()
    return dy_result


def calc_triplet_margin_distance_loss(
        input,
        positive,
        negative,
        distance_function=None,
        margin=0.3,
        swap=False,
        reduction='mean', ):
    distance_function = np.linalg.norm
    positive_dist = distance_function((input - positive), 2, axis=1)
    negative_dist = distance_function((input - negative), 2, axis=1)

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

        places = [paddle.CPUPlace()]
        if paddle.device.is_compiled_with_cuda():
            places.append(paddle.CUDAPlace(0))
        reductions = ['sum', 'mean', 'none']
        for place in places:
            for reduction in reductions:
                expected = calc_triplet_margin_distance_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_TripletMarginDistanceLoss_error(self):
        paddle.disable_static()
        self.assertRaises(
            ValueError,
            paddle.nn.TripletMarginWithDistanceLoss,
            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_with_distance_loss,
            input=input,
            positive=positive,
            negative=negative,
            reduction="unsupport reduction")
        paddle.enable_static()

    def test_TripletMarginDistanceLoss_distance_function(self):
        def distance_function_1(x1, x2):
            return 1.0 - paddle.nn.functional.cosine_similarity(x1, x2)

        def distance_function_2(x1, x2):
            return paddle.max(paddle.abs(x1 - x2), axis=1)

        distance_function_list = [distance_function_1, distance_function_2]
        input = np.random.uniform(0.1, 0.8, size=(20, 30)).astype(np.float64)
        positive = np.random.uniform(0, 2, size=(20, 30)).astype(np.float64)
        negative = np.random.uniform(0, 2, size=(20, 30)).astype(np.float64)

        place = paddle.CPUPlace()
        reduction = 'mean'
        for distance_function in distance_function_list:
            dy_result = test_dygraph(
                place=place,
                input=input,
                positive=positive,
                negative=negative,
                distance_function=distance_function,
                reduction=reduction, )

            static_result = test_static(
                place=place,
                input_np=input,
                positive_np=positive,
                negative_np=negative,
                distance_function=distance_function,
                reduction=reduction, )
            self.assertTrue(np.allclose(static_result, dy_result))
            static_functional = test_static(
                place=place,
                input_np=input,
                positive_np=positive,
                negative_np=negative,
                distance_function=distance_function,
                reduction=reduction,
                functional=True)
            dy_functional = test_dygraph(
                place=place,
                input=input,
                positive=positive,
                negative=negative,
                distance_function=distance_function,
                reduction=reduction,
                functional=True)
            self.assertTrue(np.allclose(static_functional, dy_functional))

    def test_TripletMarginWithDistanceLoss_distance_funtion_error(self):
        paddle.disable_static()

        def distance_function(x1, x2):
            return -1.0 - paddle.nn.functional.cosine_similarity(x1, x2)

        func = distance_function
        input = np.random.uniform(0.1, 0.8, size=(20, 30)).astype(np.float64)
        positive = np.random.uniform(0, 2, size=(20, 30)).astype(np.float64)
        negative = np.random.uniform(0, 2, size=(20, 30)).astype(np.float64)

        self.assertRaises(
            ValueError,
            paddle.nn.functional.triplet_margin_with_distance_loss,
            input=input,
            positive=positive,
            negative=negative,
            distance_function=func, )
        paddle.enable_static()

    def test_TripletMarginDistanceLoss_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_with_distance_loss,
            input=input,
            positive=positive,
            negative=negative, )
        triplet_margin_with_distance_loss = paddle.nn.loss.TripletMarginWithDistanceLoss(
        )
        self.assertRaises(
            ValueError,
            triplet_margin_with_distance_loss,
            input=input,
            positive=positive,
            negative=negative, )
        paddle.enable_static()

    def test_TripletMarginWithDistanceLoss_swap(self):
        reduction = 'mean'
        place = paddle.CPUPlace()
        input = np.random.uniform(0.1, 0.8, size=(20, 30)).astype(np.float64)
        positive = np.random.uniform(0, 2, size=(20, 30)).astype(np.float64)
        negative = np.random.uniform(0, 2, size=(20, 30)).astype(np.float64)
        expected = calc_triplet_margin_distance_loss(
            input=input,
            swap=True,
            positive=positive,
            negative=negative,
            reduction=reduction)

        dy_result = test_dygraph(
            place=place,
            swap=True,
            input=input,
            positive=positive,
            negative=negative,
            reduction=reduction, )

        static_result = test_static(
            place=place,
            swap=True,
            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,
            swap=True,
            input_np=input,
            positive_np=positive,
            negative_np=negative,
            reduction=reduction,
            functional=True)
        dy_functional = test_dygraph(
            place=place,
            swap=True,
            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_TripletMarginWithDistanceLoss_margin(self):
        paddle.disable_static()

        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')
        margin = -0.5
        self.assertRaises(
            ValueError,
            paddle.nn.functional.triplet_margin_with_distance_loss,
            margin=margin,
            input=input,
            positive=positive,
            negative=negative, )
        paddle.enable_static()


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