forked from mindspore-Ecosystem/mindspore
46 lines
1.7 KiB
Python
46 lines
1.7 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
|
|
#
|
|
# 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 numpy as np
|
|
import pytest
|
|
|
|
import mindspore as ms
|
|
import mindspore.nn as nn
|
|
import mindspore.ops as ops
|
|
from mindspore import Tensor
|
|
|
|
|
|
class MarginRankingLoss(nn.Cell):
|
|
def __init__(self, reduction):
|
|
super(MarginRankingLoss, self).__init__()
|
|
self.reduction = reduction
|
|
|
|
def construct(self, x, y, label, margin):
|
|
return ops.margin_ranking_loss(x, y, label, margin, reduction=self.reduction)
|
|
|
|
|
|
@pytest.mark.parametrize('reduction', ["none", "mean", "sum"])
|
|
def test_margin_ranking_loss(reduction):
|
|
"""
|
|
Feature: test MarginRankingLoss op with reduction none.
|
|
Description: Verify the result of MarginRankingLoss.
|
|
Expectation: expect correct forward result.
|
|
"""
|
|
loss = MarginRankingLoss(reduction)
|
|
input1 = Tensor(np.array([0.3864, -2.4093, -1.4076]), ms.float32)
|
|
input2 = Tensor(np.array([-0.6012, -1.6681, 1.2928]), ms.float32)
|
|
target = Tensor(np.array([-1, -1, 1]), ms.float32)
|
|
loss(input1, input2, target, 0.0)
|