forked from mindspore-Ecosystem/mindspore
311 lines
9.5 KiB
Python
311 lines
9.5 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.context as context
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from mindspore import Tensor
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from mindspore.ops.operations.array_ops import SegmentMax, SegmentMin, SegmentMean, SegmentSum, SegmentProd
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from mindspore.nn import Cell
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import mindspore.common.dtype as mstype
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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class SegmentMaxNet(Cell):
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def __init__(self):
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super().__init__()
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self.segmentmax = SegmentMax()
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def construct(self, x, segment_ids):
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return self.segmentmax(x, segment_ids)
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class SegmentMinNet(Cell):
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def __init__(self):
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super().__init__()
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self.segmentmin = SegmentMin()
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def construct(self, x, segment_ids):
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return self.segmentmin(x, segment_ids)
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class SegmentMeanNet(Cell):
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def __init__(self):
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super().__init__()
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self.segmentmean = SegmentMean()
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def construct(self, x, segment_ids):
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return self.segmentmean(x, segment_ids)
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class SegmentSumNet(Cell):
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def __init__(self):
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super().__init__()
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self.segmentsum = SegmentSum()
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def construct(self, x, segment_ids):
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return self.segmentsum(x, segment_ids)
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class SegmentProdNet(Cell):
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def __init__(self):
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super().__init__()
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self.segmentprod = SegmentProd()
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def construct(self, x, segment_ids):
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return self.segmentprod(x, segment_ids)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_segment_max_fp():
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"""
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Feature: SegmentMax operator.
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Description: test cases for SegmentMax operator.
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Expectation: the result match expectation.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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input_x = Tensor([1, 2, 3], mstype.int32)
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segment_ids = Tensor([0, 6, 6], mstype.int32)
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net = SegmentMaxNet()
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expect = np.array([1, 0, 0, 0, 0, 0, 3]).astype(np.int32)
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output_gr = net(input_x, segment_ids).asnumpy()
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np.testing.assert_array_almost_equal(output_gr, expect)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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output_py = net(input_x, segment_ids).asnumpy()
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np.testing.assert_almost_equal(output_py, expect)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_segment_min_fp():
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"""
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Feature: SegmentMin operator.
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Description: test cases for SegmentMin operator.
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Expectation: the result match expectation.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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input_x = Tensor([1, 2, 3, 4], mstype.int32)
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segment_ids = Tensor([0, 0, 1, 5], mstype.int32)
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net = SegmentMinNet()
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expect = np.array([1, 3, 0, 0, 0, 4]).astype(np.int32)
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output_gr = net(input_x, segment_ids).asnumpy()
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np.testing.assert_array_almost_equal(output_gr, expect)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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output_py = net(input_x, segment_ids).asnumpy()
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np.testing.assert_almost_equal(output_py, expect)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_segment_sum_fp():
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"""
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Feature: SegmentSum operator.
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Description: test cases for SegmentSum operator.
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Expectation: the result match expectation.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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input_x = Tensor([1 + 2j, 2 + 2j, 3 + 2j], mstype.float32)
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segment_ids = Tensor([0, 0, 2], mstype.int32)
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net = SegmentSumNet()
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expect = np.array([3 + 4j, 0, 3 + 2j]).astype(np.float32)
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output_gr = net(input_x, segment_ids).asnumpy()
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np.testing.assert_array_almost_equal(output_gr, expect)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_segment_mean_fp():
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"""
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Feature: SegmentMean operator.
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Description: test cases for SegmentMean operator.
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Expectation: the result match expectation.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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input_x = Tensor([2, 2, 3, 4], mstype.float32)
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segment_ids = Tensor([0, 0, 1, 2], mstype.int32)
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net = SegmentMeanNet()
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expect = np.array([2, 3, 4]).astype(np.float32)
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output_gr = net(input_x, segment_ids).asnumpy()
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np.testing.assert_array_almost_equal(output_gr, expect)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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output_py = net(input_x, segment_ids).asnumpy()
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np.testing.assert_almost_equal(output_py, expect)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_segment_prod_fp():
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"""
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Feature: SegmentProd operator.
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Description: test cases for SegmentProd operator.
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Expectation: the result match expectation.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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input_x = Tensor([1, 2, 3, 4], mstype.float32)
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segment_ids = Tensor([0, 0, 1, 2], mstype.int32)
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net = SegmentProdNet()
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expect = np.array([2, 3, 4]).astype(np.float32)
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output_gr = net(input_x, segment_ids).asnumpy()
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np.testing.assert_array_almost_equal(output_gr, expect)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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output_py = net(input_x, segment_ids).asnumpy()
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np.testing.assert_almost_equal(output_py, expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu
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@pytest.mark.env_onecard
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def test_segment_max_dyn():
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"""
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Feature: test SegmentMax op in gpu.
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Description: test the op in dynamic shape.
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Expectation: expect correct shape result.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = SegmentMaxNet()
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x_dyn = Tensor(shape=[None, 3], dtype=mstype.float64)
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segment_ids_dyn = Tensor(shape=[None], dtype=mstype.int64)
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net.set_inputs(x_dyn, segment_ids_dyn)
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x = Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], mstype.float64)
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segment_ids = Tensor([0, 0, 2], mstype.int64)
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output = net(x, segment_ids)
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expect_shape = (3, 3)
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assert expect_shape == output.asnumpy().shape
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu
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@pytest.mark.env_onecard
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def test_segment_min_dyn():
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"""
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Feature: test SegmentMin op in gpu.
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Description: test the op in dynamic shape.
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Expectation: expect correct shape result.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = SegmentMinNet()
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x_dyn = Tensor(shape=[None, 3], dtype=mstype.float64)
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segment_ids_dyn = Tensor(shape=[None], dtype=mstype.int64)
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net.set_inputs(x_dyn, segment_ids_dyn)
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x = Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], mstype.float64)
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segment_ids = Tensor([0, 0, 2], mstype.int64)
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output = net(x, segment_ids)
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expect_shape = (3, 3)
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assert expect_shape == output.asnumpy().shape
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu
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@pytest.mark.env_onecard
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def test_segment_sum_dyn():
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"""
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Feature: test SegmentSum op in gpu.
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Description: test the op in dynamic shape.
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Expectation: expect correct shape result.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = SegmentSumNet()
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x_dyn = Tensor(shape=[None, 3], dtype=mstype.float64)
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segment_ids_dyn = Tensor(shape=[None], dtype=mstype.int64)
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net.set_inputs(x_dyn, segment_ids_dyn)
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x = Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], mstype.float64)
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segment_ids = Tensor([0, 0, 2], mstype.int64)
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output = net(x, segment_ids)
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expect_shape = (3, 3)
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assert expect_shape == output.asnumpy().shape
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu
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@pytest.mark.env_onecard
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def test_segment_mean_dyn():
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"""
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Feature: test SegmentMean op in gpu.
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Description: test the op in dynamic shape.
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Expectation: expect correct shape result.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = SegmentMeanNet()
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x_dyn = Tensor(shape=[None, 3], dtype=mstype.float64)
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segment_ids_dyn = Tensor(shape=[None], dtype=mstype.int64)
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net.set_inputs(x_dyn, segment_ids_dyn)
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x = Tensor([[1, 2, 3], [1, 2, 3], [7, 8, 9]], mstype.float64)
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segment_ids = Tensor([0, 0, 2], mstype.int64)
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output = net(x, segment_ids)
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expect_shape = (3, 3)
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assert expect_shape == output.asnumpy().shape
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu
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@pytest.mark.env_onecard
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def test_segment_prod_dyn():
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"""
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Feature: test SegmentProd op in gpu.
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Description: test the op in dynamic shape.
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Expectation: expect correct shape result.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = SegmentProdNet()
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x_dyn = Tensor(shape=[None, 3], dtype=mstype.float64)
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segment_ids_dyn = Tensor(shape=[None], dtype=mstype.int64)
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net.set_inputs(x_dyn, segment_ids_dyn)
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x = Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], mstype.float64)
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segment_ids = Tensor([0, 0, 2], mstype.int64)
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output = net(x, segment_ids)
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expect_shape = (3, 3)
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assert expect_shape == output.asnumpy().shape
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