396 lines
12 KiB
Python
396 lines
12 KiB
Python
# Copyright 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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""" test_tensor_setitem """
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import numpy as onp
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import pytest
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from mindspore import Tensor, context
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from mindspore.nn import Cell
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from mindspore import dtype as mstype
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def setup_module():
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context.set_context(mode=context.PYNATIVE_MODE)
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def setup_testcase(input_np, case_fn):
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input_ms = Tensor(input_np)
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class TensorSetItem(Cell):
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def construct(self, x):
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return case_fn(x)
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class NumpySetItem():
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def __call__(self, x):
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return case_fn(x)
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out_ms = TensorSetItem()(input_ms)
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out_np = NumpySetItem()(input_np)
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assert onp.all(out_ms.asnumpy() == out_np)
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class TensorSetItemByList(Cell):
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def construct(self, x):
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x[[0, 1], [1, 2], [1, 3]] = [3, 4]
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x[([0, 1], [0, 2], [1, 1])] = [10, 5]
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x[[0, 1], ..., [0, 1]] = 4
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return x
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class NumpySetItemByList():
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def __call__(self, x):
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x[[0, 1], [1, 2], [1, 3]] = [3, 4]
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x[([0, 1], [0, 2], [1, 1])] = [10, 5]
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x[[0, 1], ..., [0, 1]] = 4
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return x
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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.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_setitem_by_list():
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x = onp.ones((2, 3, 4), dtype=onp.float32)
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def cases(x):
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x[[0, 1], [1, 2], [1, 3]] = [3, 4]
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x[([0, 1], [0, 2], [1, 1])] = [10, 5]
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x[[0, 1], ..., [0, 1]] = 4
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return x
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setup_testcase(x, cases)
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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.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_setitem_with_sequence():
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x = onp.ones((2, 3, 4), dtype=onp.float32)
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def cases(x):
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x[...] = [3]
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x[..., 1] = ([1, 2, 3], [4, 5, 6])
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x[0] = ((0, 1, 2, 3), (4, 5, 6, 7), [8, 9, 10, 11])
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x[1:2] = ((0, 1, 2, 3), (4, 5, 6, 7), [8, 9, 10, 11])
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return x
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setup_testcase(x, cases)
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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.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_setitem_dtype():
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x = onp.ones((2, 3, 4), dtype=onp.float32)
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def cases(x):
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x[...] = 3
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x[..., 1] = 3.0
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x[0] = True
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x[1:2] = ((0, False, 2, 3), (4.0, 5, 6, 7), [True, 9, 10, 11])
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return x
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setup_testcase(x, cases)
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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.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_setitem_by_tuple_with_int():
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x = onp.arange(24).reshape(2, 3, 4).astype(onp.float32)
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def cases(x):
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x[..., 2, False, 1] = -1
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x[0, True, 0, None, True] = -2
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x[0, ..., None] = -3
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x[..., 0, None, 1, True, True, None] = -4
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return x
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setup_testcase(x, cases)
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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.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_setitem_by_tuple_with_list():
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x = onp.arange(24).reshape(2, 3, 4).astype(onp.float32)
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def cases(x):
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x[..., 2, False, 1] = [-1]
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x[0, True, 0, None, True] = [-2, -2, -2, -2]
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x[0, ..., None] = [[-3], [-3], [-3], [-3]]
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x[..., 0, None, 1, True, True, None] = [[[-4]], [[-4]]]
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x[None, True, [1, 0], (False, True, True), [2]] = [[2, 3]]
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return x
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setup_testcase(x, cases)
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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.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_setitem_by_nested_unit_list():
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x = onp.arange(24).reshape(2, 3, 4).astype(onp.float32)
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def cases(x):
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x[[[[0]]], True] = -1
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x[[1], ..., [[[[2]]]]] = -2
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x[0, [[[2]]], [1]] = -3
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return x
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setup_testcase(x, cases)
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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.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_setitem_with_broadcast():
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x = onp.arange(2*3*4*5*6).reshape(2, 3, 4, 5, 6).astype(onp.float32)
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v1 = onp.full((1, 4, 5), -1).tolist()
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v2 = onp.full((4, 1, 6), -2).tolist()
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def cases(x):
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x[..., 4] = v1
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x[0, 2] = v2
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x[1, 0, ..., 3] = [[-3], [-3], [-3], [-3]]
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x[0, ..., 1, 3, 5] = -4
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return x
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setup_testcase(x, cases)
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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.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_setitem_mul_by_scalar():
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x = onp.ones((4, 5), dtype=onp.float32)
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def cases(x):
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x[1, :] = x[1, :]*2
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x[:, 2] = x[:, 3]*3.0
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return x
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setup_testcase(x, cases)
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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.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_setitem_by_slice():
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x = onp.ones((3, 4, 5), dtype=onp.float32)
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def cases(x):
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x[1:2] = 2
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x[-3:1] = 3
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x[-10:3:2] = 4
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x[5:0:3] = 5
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x[5:5:5] = 6
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x[-1:2] = 7
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x[1:0:-1] = 8
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return x
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setup_testcase(x, cases)
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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.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_setitem_by_tuple_of_slices():
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x = onp.ones((3, 4, 5), dtype=onp.float32)
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def cases(x):
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x[1:2, 2] = 2
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x[0, -4:1] = 3
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x[1, -10:3:2] = 4
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x[5:0:3, 3] = 5
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x[1:1, 2:2] = 6
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return x
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setup_testcase(x, cases)
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class TensorItemSetWithNumber(Cell):
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def construct(self, tensor, number_value):
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ret = tensor.itemset(number_value)
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return ret
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@pytest.mark.level1
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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.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_itemset_with_number():
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net = TensorItemSetWithNumber()
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input_1d_np = onp.ndarray([1]).astype(onp.float32)
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input_1d_ms = Tensor(input_1d_np, mstype.float32)
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input_3d_np = onp.arange(60).reshape(3, 4, 5).astype(onp.int32)
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input_3d_ms = Tensor(input_3d_np, mstype.float32)
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value_np_1, value_np_2 = 1, 2.0
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output_1d_ms_1 = net(input_1d_ms, value_np_1)
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output_1d_ms_2 = net(input_1d_ms, value_np_2)
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input_1d_np.itemset(value_np_1)
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assert onp.all(output_1d_ms_1.asnumpy() == input_1d_np)
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input_1d_np.itemset(value_np_2)
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assert onp.all(output_1d_ms_2.asnumpy() == input_1d_np)
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with pytest.raises(IndexError):
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net(input_3d_ms, value_np_1)
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with pytest.raises(IndexError):
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net(input_3d_ms, value_np_2)
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class TensorItemSetByItemWithNumber(Cell):
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def construct(self, tensor, index, number_value):
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ret = tensor.itemset(index, number_value)
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return ret
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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.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_setitem_dim_expand():
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x = onp.ones((2, 3, 4), dtype=onp.float32)
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def cases(x):
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x[None, True, [1, 0], (False, True, True), [2]] = 2
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x[([[0]]), ..., [[1]]] = [[[3, 3, 3]]]
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x[0:1] = [[2, 3, 4, 5]]
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x[..., (0, 1, 2), None, :, True, None] = [[[3], [3], [3], [3]]]
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return x
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setup_testcase(x, cases)
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@pytest.mark.level1
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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.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_itemset_by_number_with_number():
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net = TensorItemSetByItemWithNumber()
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input_1d_np = onp.ndarray([1]).astype(onp.float32)
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input_1d_ms = Tensor(input_1d_np, mstype.float32)
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input_3d_np = onp.arange(60).reshape(3, 4, 5).astype(onp.int32)
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input_3d_ms = Tensor(input_3d_np, mstype.float32)
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index_np_1, index_np_2, index_np_3, index_np_4 = 0, 30, 60, 2.0
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value_np_1, value_np_2 = 1, 2.0
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output_1d_ms_1 = net(input_1d_ms, index_np_1, value_np_1)
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output_1d_ms_2 = net(input_1d_ms, index_np_1, value_np_2)
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output_3d_ms_1 = net(input_3d_ms, index_np_1, value_np_1)
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output_3d_ms_2 = net(output_3d_ms_1, index_np_1, value_np_2)
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output_3d_ms_3 = net(output_3d_ms_2, index_np_2, value_np_1)
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output_3d_ms_4 = net(output_3d_ms_3, index_np_2, value_np_2)
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input_1d_np.itemset(index_np_1, value_np_1)
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assert onp.all(output_1d_ms_1.asnumpy() == input_1d_np)
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input_1d_np.itemset(index_np_1, value_np_2)
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assert onp.all(output_1d_ms_2.asnumpy() == input_1d_np)
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input_3d_np.itemset(index_np_1, value_np_1)
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assert onp.all(output_3d_ms_1.asnumpy() == input_3d_np)
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input_3d_np.itemset(index_np_1, value_np_2)
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assert onp.all(output_3d_ms_2.asnumpy() == input_3d_np)
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input_3d_np.itemset(index_np_2, value_np_1)
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assert onp.all(output_3d_ms_3.asnumpy() == input_3d_np)
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input_3d_np.itemset(index_np_2, value_np_2)
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assert onp.all(output_3d_ms_4.asnumpy() == input_3d_np)
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with pytest.raises(IndexError):
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net(input_1d_ms, index_np_2, value_np_1)
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with pytest.raises(IndexError):
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net(input_1d_ms, index_np_2, value_np_2)
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with pytest.raises(TypeError):
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net(input_1d_ms, index_np_4, value_np_1)
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with pytest.raises(TypeError):
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net(input_1d_ms, index_np_4, value_np_2)
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with pytest.raises(IndexError):
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net(input_3d_ms, index_np_3, value_np_1)
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with pytest.raises(IndexError):
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net(input_3d_ms, index_np_3, value_np_2)
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with pytest.raises(TypeError):
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net(input_3d_ms, index_np_4, value_np_1)
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with pytest.raises(TypeError):
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net(input_3d_ms, index_np_4, value_np_2)
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@pytest.mark.level1
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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.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_itemset_by_tuple_with_number():
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net = TensorItemSetByItemWithNumber()
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input_1d_np = onp.ndarray([1]).astype(onp.float32)
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input_1d_ms = Tensor(input_1d_np, mstype.float32)
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input_3d_np = onp.arange(60).reshape(3, 4, 5).astype(onp.int32)
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input_3d_ms = Tensor(input_3d_np, mstype.float32)
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index_np_1, index_np_2, index_np_3, index_np_4, index_np_5 = (0,), (1, 2), (1, 1, 0), (3, 4, 5), (1, 2, 3, 4)
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value_np_1, value_np_2 = 1, 2.0
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output_1d_ms_1 = net(input_1d_ms, index_np_1, value_np_1)
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input_1d_np.itemset(index_np_1, value_np_1)
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assert onp.all(output_1d_ms_1.asnumpy() == input_1d_np)
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output_1d_ms_2 = net(input_1d_ms, index_np_1, value_np_2)
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input_1d_np.itemset(index_np_1, value_np_2)
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assert onp.all(output_1d_ms_2.asnumpy() == input_1d_np)
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output_3d_ms_1 = net(input_3d_ms, index_np_3, value_np_1)
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input_3d_np.itemset(index_np_3, value_np_1)
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assert onp.all(output_3d_ms_1.asnumpy() == input_3d_np)
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output_3d_ms_2 = net(input_3d_ms, index_np_3, value_np_2)
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input_3d_np.itemset(index_np_3, value_np_2)
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assert onp.all(output_3d_ms_2.asnumpy() == input_3d_np)
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with pytest.raises(ValueError):
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net(input_1d_ms, index_np_2, value_np_1)
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with pytest.raises(ValueError):
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net(input_1d_ms, index_np_2, value_np_2)
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with pytest.raises(ValueError):
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net(input_3d_ms, index_np_1, value_np_1)
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with pytest.raises(ValueError):
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net(input_3d_ms, index_np_1, value_np_2)
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with pytest.raises(ValueError):
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net(input_3d_ms, index_np_2, value_np_1)
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with pytest.raises(ValueError):
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net(input_3d_ms, index_np_2, value_np_2)
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with pytest.raises(IndexError):
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net(input_3d_ms, index_np_4, value_np_1)
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with pytest.raises(IndexError):
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net(input_3d_ms, index_np_4, value_np_2)
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with pytest.raises(ValueError):
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net(input_3d_ms, index_np_5, value_np_1)
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with pytest.raises(ValueError):
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net(input_3d_ms, index_np_5, value_np_2)
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