forked from mindspore-Ecosystem/mindspore
124 lines
4.9 KiB
Python
124 lines
4.9 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import torch
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import numpy as np
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import pytest
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import mindspore as ms
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
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def test_triplet_margin_loss_float64(mode):
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"""
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Feature: Input type of float64
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Description: Input type of [float64, float64, float64].
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Expectation: success.
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"""
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context.set_context(mode=mode)
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data_type = np.float64
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anchor_array = np.array([[1.3, 20.5, 5.6],
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[3.5, 4.8, 7.2],
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[0.2, 0.01, 1],
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[4, 4.1, 20]]).astype(data_type)
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positive_array = np.array([[2., 10., 1.],
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[6., 7., 10.],
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[13., 4., 1.],
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[0.33, -4, -1.5]]).astype(data_type)
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negative_array = np.array([[2., 21., 6.],
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[68., 9., 10.],
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[131., 25., 16.],
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[0.31, -0.14, -16.]]).astype(data_type)
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margin = np.float32(2.0)
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p = 0
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swap = True
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reduction = "none"
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eps = 1e-5
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anchor = Tensor(anchor_array)
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positive = Tensor(positive_array)
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negative = Tensor(negative_array)
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ms_margin = Tensor(margin)
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triplet_margin_loss = nn.TripletMarginLoss(p=p, eps=eps, swap=swap, reduction=reduction)
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output_ms = triplet_margin_loss(anchor, positive, negative, ms_margin)
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torch_anchor = torch.tensor(anchor_array)
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torch_positive = torch.tensor(positive_array)
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torch_negative = torch.tensor(negative_array)
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expect = torch.nn.functional.triplet_margin_loss(torch_anchor, torch_positive,
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torch_negative, margin=margin,
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p=p, eps=eps, swap=swap,
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reduction=reduction)
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assert np.allclose(output_ms.asnumpy(),
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expect.numpy(),
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rtol=1e-4,
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atol=1e-4,
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equal_nan=False)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
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def test_triplet_margin_loss_float32(mode):
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"""
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Feature: Input type of float32
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Description: Input type of [float32, float32, float32].
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Expectation: success.
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"""
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context.set_context(mode=mode)
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data_type = np.float32
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anchor_array = np.array([[1.3, 20.5, 5.6],
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[3.5, 4.8, 7.2],
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[0.2, 0.01, 1],
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[4, 4.1, 20]]).astype(data_type)
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positive_array = np.array([[2., 10., 1.],
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[6., 7., 10.],
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[13., 4., 1.],
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[0.33, -4, -1.5]]).astype(data_type)
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negative_array = np.array([[2., 21., 6.],
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[68., 9., 10.],
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[131., 25., 16.],
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[0.31, -0.14, -16.]]).astype(data_type)
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margin = np.float32(2.0)
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p = 1
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swap = False
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reduction = "none"
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eps = 1e-6
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anchor = Tensor(anchor_array)
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positive = Tensor(positive_array)
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negative = Tensor(negative_array)
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ms_margin = Tensor(margin)
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triplet_margin_loss = nn.TripletMarginLoss(p=p, eps=eps, swap=swap, reduction=reduction)
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output_ms = triplet_margin_loss(anchor, positive, negative, ms_margin)
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torch_anchor = torch.tensor(anchor_array)
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torch_positive = torch.tensor(positive_array)
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torch_negative = torch.tensor(negative_array)
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expect = torch.nn.functional.triplet_margin_loss(torch_anchor, torch_positive,
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torch_negative, margin=margin,
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p=p, eps=eps, swap=swap,
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reduction=reduction)
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assert np.allclose(output_ms.asnumpy(),
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expect.numpy(),
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rtol=1e-4,
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atol=1e-4,
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equal_nan=False)
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