forked from mindspore-Ecosystem/mindspore
198 lines
6.8 KiB
Python
198 lines
6.8 KiB
Python
# Copyright 2019-2021 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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import mindspore.context as context
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from mindspore import Tensor
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from mindspore.ops import operations as P
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def strided_slice(nptype):
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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x = Tensor(np.arange(0, 2*3*4*5).reshape(2, 3, 4, 5).astype(nptype))
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y = P.StridedSlice()(x, (1, 0, 0, 2), (2, 2, 2, 4), (1, 1, 1, 1))
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expect = np.array([[[[62, 63],
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[67, 68]],
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[[82, 83],
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[87, 88]]]]).astype(nptype)
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assert np.allclose(y.asnumpy(), expect)
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y = P.StridedSlice()(x, (1, 0, 0, 5), (2, 2, 2, 1), (1, 1, 1, -2))
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expect = np.array([[[[64, 62],
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[69, 67]],
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[[84, 82],
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[89, 87]]]]).astype(nptype)
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assert np.allclose(y.asnumpy(), expect)
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y = P.StridedSlice()(x, (1, 0, 0, -1), (2, 2, 2, 1), (1, 1, 1, -1))
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expect = np.array([[[[64, 63, 62],
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[69, 68, 67]],
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[[84, 83, 82],
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[89, 88, 87]]]]).astype(nptype)
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assert np.allclose(y.asnumpy(), expect)
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y = P.StridedSlice()(x, (1, 0, -1, -2), (2, 2, 0, -5), (1, 1, -1, -2))
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expect = np.array([[[[78, 76],
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[73, 71],
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[68, 66]],
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[[98, 96],
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[93, 91],
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[88, 86]]]]).astype(nptype)
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assert np.allclose(y.asnumpy(), expect)
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# ME Infer fault
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# y = P.StridedSlice(begin_mask=0b1000, end_mask=0b0010)(x, (1, 0, 0, 2), (2, 2, 2, 4), (1, 1, 1, 1))
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# expect = np.array([[[[62, 63],
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# [67, 68]],
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# [[82, 83],
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# [87, 88]],
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# [[102, 103],
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# [107, 108]]]]).astype(nptype)
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# assert np.allclose(y.asnumpy(), expect)
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op = P.StridedSlice(begin_mask=0b1000, end_mask=0b0010, ellipsis_mask=0b0100)
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y = op(x, (1, 0, 0, 2), (2, 2, 2, 4), (1, 1, 1, 1))
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expect = np.array([[[[60, 61, 62, 63],
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[65, 66, 67, 68],
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[70, 71, 72, 73],
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[75, 76, 77, 78]],
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[[80, 81, 82, 83],
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[85, 86, 87, 88],
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[90, 91, 92, 93],
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[95, 96, 97, 98]],
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[[100, 101, 102, 103],
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[105, 106, 107, 108],
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[110, 111, 112, 113],
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[115, 116, 117, 118]]]]).astype(nptype)
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assert np.allclose(y.asnumpy(), expect)
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x = Tensor(np.arange(0, 3*4*5).reshape(3, 4, 5).astype(nptype))
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y = P.StridedSlice()(x, (1, 0, 0), (2, -3, 3), (1, 1, 3))
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expect = np.array([[[20]]]).astype(nptype)
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assert np.allclose(y.asnumpy(), expect)
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x_np = np.arange(0, 4*5).reshape(4, 5).astype(nptype)
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y = Tensor(x_np)[:, ::-1]
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expect = x_np[:, ::-1]
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assert np.allclose(y.asnumpy(), expect)
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x = Tensor(np.arange(0, 2 * 3 * 4 * 5 * 4 * 3 * 2).reshape(2, 3, 4, 5, 4, 3, 2).astype(nptype))
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y = P.StridedSlice()(x, (1, 0, 0, 2, 1, 2, 0), (2, 2, 2, 4, 2, 3, 2), (1, 1, 1, 1, 1, 1, 2))
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expect = np.array([[[[[[[1498.]]],
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[[[1522.]]]],
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[[[[1618.]]],
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[[[1642.]]]]],
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[[[[[1978.]]],
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[[[2002.]]]],
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[[[[2098.]]],
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[[[2122.]]]]]]]).astype(nptype)
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assert np.allclose(y.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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def test_strided_slice_float64():
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strided_slice(np.float64)
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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_strided_slice_float32():
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strided_slice(np.float32)
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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_strided_slice_float16():
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strided_slice(np.float16)
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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_strided_slice_int64():
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strided_slice(np.int64)
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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_strided_slice_int32():
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strided_slice(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_strided_slice_int16():
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strided_slice(np.int16)
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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_strided_slice_int8():
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strided_slice(np.int8)
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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_strided_slice_uint64():
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strided_slice(np.uint64)
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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_strided_slice_uint32():
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strided_slice(np.uint32)
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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_strided_slice_uint16():
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strided_slice(np.uint16)
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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_strided_slice_uint8():
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strided_slice(np.uint8)
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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_strided_slice_bool():
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strided_slice(np.bool)
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x = Tensor(np.arange(0, 4*4*4).reshape(4, 4, 4).astype(np.float32))
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y = x[-8:, :8]
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expect = np.array([[[0., 1., 2., 3.],
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[4., 5., 6., 7.],
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[8., 9., 10., 11.],
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[12., 13., 14., 15.]],
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[[16., 17., 18., 19.],
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[20., 21., 22., 23.],
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[24., 25., 26., 27.],
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[28., 29., 30., 31.]],
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[[32., 33., 34., 35.],
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[36., 37., 38., 39.],
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[40., 41., 42., 43.],
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[44., 45., 46., 47.]],
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[[48., 49., 50., 51.],
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[52., 53., 54., 55.],
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[56., 57., 58., 59.],
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[60., 61., 62., 63.]]])
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assert np.allclose(y.asnumpy(), expect)
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