forked from mindspore-Ecosystem/mindspore
110 lines
3.9 KiB
Python
110 lines
3.9 KiB
Python
# Copyright 2020 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 numpy as np
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import pytest
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import mindspore
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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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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class Net(nn.Cell):
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def __init__(self, reduction="none"):
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super(Net, self).__init__()
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self.kl_div_loss = P.KLDivLoss(reduction)
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def construct(self, x, y):
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return self.kl_div_loss(x, y)
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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("dtype", [np.float16, np.float32, np.float64])
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def test_mode_none_and_dtype_with_static_input(dtype):
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"""
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Feature: test none mode with different input dtype.
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Description: input with negative elements.
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Expectation: success.
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"""
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np.random.seed(42)
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prediction = mindspore.Tensor(np.log(np.array([[0.3, 0.7], [0.5, 0.5]])).astype(dtype))
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target = mindspore.Tensor(np.array([[-1, 1], [1, -1]]).astype(dtype))
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net = Net("none")
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loss = net(Tensor(prediction), Tensor(target))
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expect = np.array([[0, 0.35667494], [0.69314718, 0]]).astype(dtype)
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assert np.allclose(loss.asnumpy(), expect)
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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("dtype", [np.float16, np.float32, np.float64])
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def test_mode_mean_and_dtype_with_static_input(dtype):
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"""
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Feature: test mean mode with different input dtype.
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Description: input with negative elements.
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Expectation: success.
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"""
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np.random.seed(42)
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prediction = mindspore.Tensor(np.log(np.array([[0.3, 0.7], [0.5, 0.5]])).astype(dtype))
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target = mindspore.Tensor(np.array([[-1, 1], [1, -1]]).astype(dtype))
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net = Net("mean")
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loss = net(Tensor(prediction), Tensor(target))
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expect = np.array([0.26245553]).astype(dtype)
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assert np.allclose(loss.asnumpy(), expect)
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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("dtype", [np.float16, np.float32, np.float64])
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def test_mode_sum_and_dtype_with_static_input(dtype):
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"""
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Feature: test sum mode with different input dtype.
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Description: input with negative elements.
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Expectation: success.
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"""
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np.random.seed(42)
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prediction = mindspore.Tensor(np.log(np.array([[0.3, 0.7], [0.5, 0.5]])).astype(dtype))
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target = mindspore.Tensor(np.array([[-1, 1], [1, -1]]).astype(dtype))
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net = Net("sum")
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loss = net(Tensor(prediction), Tensor(target))
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expect = np.array([1.04982212]).astype(dtype)
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assert np.allclose(loss.asnumpy(), expect)
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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("dtype", [np.float16, np.float32, np.float64])
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def test_mode_batchmean_and_dtype_with_static_input(dtype):
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"""
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Feature: test batchmean mode with different input dtype.
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Description: input with negative elements.
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Expectation: success.
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"""
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np.random.seed(42)
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prediction = mindspore.Tensor(np.log(np.array([[0.3, 0.7], [0.5, 0.5]])).astype(dtype))
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target = mindspore.Tensor(np.array([[-1, 1], [1, -1]]).astype(dtype))
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net = Net("batchmean")
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loss = net(Tensor(prediction), Tensor(target))
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expect = np.array([0.52491106]).astype(dtype)
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assert np.allclose(loss.asnumpy(), expect)
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