forked from mindspore-Ecosystem/mindspore
147 lines
5.4 KiB
Python
147 lines
5.4 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 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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import mindspore.ops as ops
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from mindspore import Tensor, Parameter
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# all cases tested against dchip
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class TestScatterDivNet(nn.Cell):
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def __init__(self, inputx):
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super(TestScatterDivNet, self).__init__()
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self.scattre_div = ops.ScatterDiv()
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self.inputx = Parameter(inputx, name="inputx")
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def construct(self, indices, updates):
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out = self.scattre_div(self.inputx, indices, updates)
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return out
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def scattre_div_forward(nptype, expected):
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inputx = Tensor(np.arange(0, 9).reshape((3, 3)).astype(nptype))
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indices = Tensor(np.array([[[1, 0, 2], [2, 2, 0]], [[1, 0, 1], [2, 1, 2]]]).astype(np.int32))
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updates = Tensor(np.arange(34, 70).reshape((2, 2, 3, 3)).astype(nptype))
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net = TestScatterDivNet(inputx)
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output = net(indices, updates)
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np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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def scattre_div_dynamic_updates():
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inputx = Tensor(np.ones((4, 2)).astype(np.float16))
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indices = Tensor(np.array([[0, 2], [3, 1]]).astype(np.int32))
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updates = Tensor(np.arange(4, 12).reshape((2, 2, 2)).astype(np.float16))
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updates_dy = Tensor(shape=(2, None, 2), dtype=mindspore.float16)
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net = TestScatterDivNet(inputx)
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net.set_inputs(indices, updates_dy)
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output = net(indices, updates)
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expected = np.array(
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[[0.25, 0.2], [0.1, 0.0909], [0.1666, 0.1428], [0.125, 0.1111]]
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).astype(np.float16)
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np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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def scattre_div_dynamic_indices():
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inputx = Tensor(np.ones((2, 3)).astype(np.float32))
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indices = Tensor(np.array([[0, 2], [3, 1]]).astype(np.int32))
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indices_dy = Tensor(shape=(2, None), dtype=mindspore.int32)
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updates = Tensor(np.arange(1, 13).reshape((2, 2, 3)).astype(np.float32))
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net = TestScatterDivNet(inputx)
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net.set_inputs(indices_dy, updates)
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output = net(indices, updates)
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expected = np.array(
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[[1., 0.5, 0.33333334], [0.1, 0.09090909, 0.08333334]]
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).astype(np.float32)
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np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_scattre_div_forward_float16():
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"""
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Feature: test scattre_div forward.
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Description: test float16 inputs.
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Expectation: the result match with numpy result
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"""
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expected = np.array([[0.000e+00, 9.418e-06, 1.764e-05],
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[4.768e-07, 5.364e-07, 6.557e-07],
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[0.000e+00, 0.000e+00, 0.000e+00]]).astype(np.float16)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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scattre_div_forward(np.float16, expected)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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scattre_div_forward(np.float16, expected)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_scattre_div_forward_float32():
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"""
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Feature: test scattre_div forward.
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Description: test float32 inputs.
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Expectation: the result match with numpy result
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"""
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expected = np.array([[0.0000000e+00, 9.3984954e-06, 1.7640885e-05],
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[4.5712085e-07, 5.6227748e-07, 6.4949910e-07],
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[1.8554973e-08, 1.9582270e-08, 2.0286041e-08]]).astype(np.float32)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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scattre_div_forward(np.float32, expected)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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scattre_div_forward(np.float32, expected)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_scattre_div_dynamic_indices():
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"""
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Feature: test scattre_div dynamic shape.
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Description: indices is dynamic shape.
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Expectation: the result match with numpy result
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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scattre_div_dynamic_indices()
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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scattre_div_dynamic_indices()
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_scattre_div_dynamic_updates():
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"""
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Feature: test scattre_div dynamic shape.
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Description: updates is dynamic shape.
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Expectation: the result match with numpy result
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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scattre_div_dynamic_updates()
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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scattre_div_dynamic_updates()
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