forked from mindspore-Ecosystem/mindspore
171 lines
7.3 KiB
Python
171 lines
7.3 KiB
Python
# Copyright 2020-2021 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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from mindspore import Tensor
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from mindspore.ops import composite as C
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from mindspore.ops.operations import _inner_ops as inner
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import mindspore.nn as nn
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import mindspore.context as context
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def sequence_mask(x, maxlen):
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return C.sequence_mask(Tensor(x.astype(np.int32)), maxlen)
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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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def test_sequence_mask_1d():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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a = np.array([2, 3, 1])
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maxlen = 4
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ms_out = sequence_mask(a, maxlen)
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expected_out = Tensor(np.array([[True, True, False, False],
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[True, True, True, False],
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[True, False, False, False]]))
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np.testing.assert_array_equal(expected_out.asnumpy(), ms_out.asnumpy())
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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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def test_sequence_mask_2d():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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a = np.array([[0, 1, 3, 2], [1, 4, 4, 2]])
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maxlen = 6
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ms_out = sequence_mask(a, maxlen)
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expected_out = Tensor(np.array([[[False, False, False, False, False, False],
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[True, False, False, False, False, False],
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[True, True, True, False, False, False],
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[True, True, False, False, False, False]],
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[[True, False, False, False, False, False],
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[True, True, True, True, False, False],
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[True, True, True, True, False, False],
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[True, True, False, False, False, False]]]))
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np.testing.assert_array_equal(expected_out.asnumpy(), ms_out.asnumpy())
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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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def test_sequence_mask_3d():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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a = np.array([[[2, 2], [1, 1]],
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[[2, 0], [2, 1]],
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[[0, 0], [0, 0]]])
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maxlen = 2
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ms_out = sequence_mask(a, maxlen)
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expected_out = Tensor(np.array([[[[True, True], [True, True]], [[True, False], [True, False]]],
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[[[True, True], [False, False]], [[True, True], [True, False]]],
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[[[False, False], [False, False]], [[False, False], [False, False]]]]))
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np.testing.assert_array_equal(expected_out.asnumpy(), ms_out.asnumpy())
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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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def test_sequence_mask_maxlen_1():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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a = np.array([[[0, 1], [1, 1]],
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[[1, 0], [1, 1]],
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[[0, 1], [0, 1]]])
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maxlen = 1
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ms_out = sequence_mask(a, maxlen)
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expected_out = Tensor(np.array([[[[False], [True]], [[True], [True,]]],
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[[[True], [False]], [[True], [True]]],
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[[[False], [True]], [[False], [True]]]]))
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np.testing.assert_array_equal(expected_out.asnumpy(), ms_out.asnumpy())
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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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def test_sequence_mask_dynamic():
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class SequenceMaskDynamicNet1(nn.Cell):
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def __init__(self, maxlen):
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super(SequenceMaskDynamicNet1, self).__init__()
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self.maxlen = maxlen
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self.convert_to_dynamic_shape = inner.GpuConvertToDynamicShape()
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def construct(self, x):
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converted_to_dynamic_shape = self.convert_to_dynamic_shape(x)
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return C.sequence_mask(converted_to_dynamic_shape, self.maxlen)
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class SequenceMaskDynamicNet2(nn.Cell):
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def __init__(self):
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super(SequenceMaskDynamicNet2, self).__init__()
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self.convert_to_dynamic_shape = inner.GpuConvertToDynamicShape()
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def construct(self, x):
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converted_to_dynamic_shape = self.convert_to_dynamic_shape(x)
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return C.sequence_mask(converted_to_dynamic_shape)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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sequence_mask_net = SequenceMaskDynamicNet1(4)
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a = Tensor(np.array([0, 1, 0, 2, 0, 5]))
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ms_out = sequence_mask_net(a)
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expected_out = Tensor(np.array([[False, False, False, False],
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[True, False, False, False],
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[False, False, False, False],
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[True, True, False, False],
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[False, False, False, False],
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[True, True, True, True]]))
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np.testing.assert_array_equal(expected_out.asnumpy(), ms_out.asnumpy())
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a = Tensor(np.array([[4, 3, 0], [0, 1, 3]]))
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ms_out = sequence_mask_net(a)
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expected_out = Tensor(np.array([[[True, True, True, True],
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[True, True, True, False],
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[False, False, False, False]],
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[[False, False, False, False],
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[True, False, False, False],
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[True, True, True, False]]]))
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np.testing.assert_array_equal(expected_out.asnumpy(), ms_out.asnumpy())
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net_without_maxlen = SequenceMaskDynamicNet2()
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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a = np.array([2, 3, 1])
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ms_out = net_without_maxlen(Tensor(a))
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expected_out = Tensor(np.array([[True, True, False],
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[True, True, True],
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[True, False, False]]))
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np.testing.assert_array_equal(expected_out.asnumpy(), ms_out.asnumpy())
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def sequence_mask_optional(x):
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return C.sequence_mask(Tensor(x.astype(np.int32)))
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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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def test_sequence_mask_optional_maxlen():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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a = np.array([2, 3, 1])
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ms_out = sequence_mask_optional(a)
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expected_out = Tensor(np.array([[True, True, False],
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[True, True, True],
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[True, False, False]]))
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np.testing.assert_array_equal(expected_out.asnumpy(), ms_out.asnumpy())
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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a = np.array([2, 3, 1])
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ms_out = sequence_mask_optional(a)
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expected_out = Tensor(np.array([[True, True, False],
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[True, True, True],
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[True, False, False]]))
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np.testing.assert_array_equal(expected_out.asnumpy(), ms_out.asnumpy())
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