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227 lines
8.1 KiB
227 lines
8.1 KiB
# Copyright 2020 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 pytest
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import numpy as np
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from mindspore import Tensor
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import mindspore.nn as nn
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import mindspore.context as context
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from mindspore.ops import composite as C
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from mindspore.common.initializer import initializer
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class NetDot(nn.Cell):
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def construct(self, x, y):
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return C.dot(x, y)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_dot_001():
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x1_tensor = Tensor(np.array([[1., 2.], [4., 5.]]).astype(np.float32))
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x2_tensor = Tensor(np.array([[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], \
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[[9., 10.], [11., 12.]]]).astype(np.float32))
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network = NetDot()
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ms_result_np = network(x1_tensor, x2_tensor)
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expect_result = np.array([[[7., 10.], [19., 22.], [31., 34.]], \
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[[19., 28.], [55., 64.], [91., 100.]]]).astype(np.float32)
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assert (ms_result_np.asnumpy() == expect_result).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_dot_002():
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x1_tensor = Tensor(np.array([[1., 2.], [4., 5.]]).astype(np.float32))
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x2_tensor = Tensor(np.array([[[1., 2., 3.], [4., 5., 6.]], [[7., 8., 9.], [10., 11., 12.]]]).astype(np.float32))
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network = NetDot()
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ms_result_np = network(x1_tensor, x2_tensor)
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expect_result = np.array([[[9., 12., 15.], [27., 30., 33.]], [[24., 33., 42.], [78., 87., 96.]]]).astype(np.float32)
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assert (ms_result_np.asnumpy() == expect_result).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_dot_003():
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x1_tensor = initializer(Tensor(np.arange(2 * 3 * 4).reshape(2, 3, 4).astype(np.float32)), [2, 3, 4])
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x2_tensor = initializer(Tensor(np.arange(1 * 5 * 4 * 2).reshape(1, 5, 4, 2).astype(np.float32)), [1, 5, 4, 2])
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network = NetDot()
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ms_result_np = network(x1_tensor, x2_tensor)
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expect_result = np.array([[[[[28., 34.],
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[76., 82.],
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[124., 130.],
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[172., 178.],
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[220., 226.]]],
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[[[76., 98.],
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[252., 274.],
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[428., 450.],
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[604., 626.],
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[780., 802.]]],
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[[[124., 162.],
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[428., 466.],
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[732., 770.],
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[1036., 1074.],
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[1340., 1378.]]]],
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[[[[172., 226.],
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[604., 658.],
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[1036., 1090.],
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[1468., 1522.],
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[1900., 1954.]]],
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[[[220., 290.],
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[780., 850.],
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[1340., 1410.],
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[1900., 1970.],
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[2460., 2530.]]],
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[[[268., 354.],
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[956., 1042.],
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[1644., 1730.],
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[2332., 2418.],
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[3020., 3106.]]]]]).astype(np.float32)
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assert (ms_result_np.asnumpy() == expect_result).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_dot_004():
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x1_tensor = initializer(Tensor(np.arange(3 * 4).reshape(3, 4).astype(np.float32)), [3, 4])
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x2_tensor = initializer(Tensor(np.arange(4 * 5).reshape(4, 5).astype(np.float32)), [4, 5])
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network = NetDot()
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ms_result_np = network(x1_tensor, x2_tensor)
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expect_result = np.array([[70., 76., 82., 88., 94.],
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[190., 212., 234., 256., 278.],
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[310., 348., 386., 424., 462.]]).astype(np.float32)
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assert (ms_result_np.asnumpy() == expect_result).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_dot_005():
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x1_tensor = initializer(Tensor(np.arange(2 * 3 * 4).reshape(2, 3, 4).astype(np.float32)), [2, 3, 4])
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x2_tensor = initializer(Tensor(np.arange(4 * 5).reshape(4, 5).astype(np.float32)), [4, 5])
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network = NetDot()
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ms_result_np = network(x1_tensor, x2_tensor)
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expect_result = np.array([[[70., 76., 82., 88., 94.],
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[190., 212., 234., 256., 278.],
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[310., 348., 386., 424., 462.]],
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[[430., 484., 538., 592., 646.],
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[550., 620., 690., 760., 830.],
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[670., 756., 842., 928., 1014.]]]).astype(np.float32)
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assert (ms_result_np.asnumpy() == expect_result).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_dot_006():
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x1_tensor = initializer(Tensor(np.arange(4).reshape(4).astype(np.float32)), [4])
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x2_tensor = initializer(Tensor(np.arange(2 * 4 * 5).reshape(2, 4, 5).astype(np.float32)), [2, 4, 5])
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network = NetDot()
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try:
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network(x1_tensor, x2_tensor)
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except ValueError as e:
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assert ValueError == type(e)
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def test_dot_007():
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x1_tensor = initializer(Tensor(np.arange(4).reshape(4).astype(np.float32)), [4])
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x2_tensor = initializer(Tensor(np.arange(4 * 4).reshape(4, 4).astype(np.float32)), [4, 4])
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network = NetDot()
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try:
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network(x2_tensor, x1_tensor)
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except ValueError as e:
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assert ValueError == type(e)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_dot_008():
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x1_tensor = Tensor(np.array([]).astype(np.float32))
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x2_tensor = Tensor(np.array([[[1., 2.], [3., 4.]],
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[[5., 6.], [7., 8.]],
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[[9., 10.], [11., 12.]]]).astype(np.float32))
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network = NetDot()
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try:
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network(x2_tensor, x1_tensor)
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except ValueError as e:
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assert ValueError == type(e)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_dot_009():
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# for document
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input_x1 = Tensor(np.array(np.ones(shape=[2, 3])).astype(np.float32))
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input_x2 = Tensor(np.array(np.ones(shape=[1, 2, 3])).astype(np.float32))
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network = NetDot()
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try:
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network(input_x1, input_x2)
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except ValueError as e:
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assert ValueError == type(e)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_dot_010():
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# for document
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input_x1 = Tensor(np.array(np.ones(shape=[2, 3])).astype(np.float32))
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input_x2 = Tensor(np.array(np.ones(shape=[1, 3, 2])).astype(np.float32))
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network = NetDot()
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ms_result_np = network(input_x1, input_x2)
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expect_result = np.array([[[3., 3.]],
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[[3., 3.]]]).astype(np.float32)
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assert (ms_result_np.asnumpy() == expect_result).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_dot_011():
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# for document
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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input_x1 = Tensor(np.array(np.ones(shape=[2, 3])).astype(np.float32))
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input_x2 = Tensor(np.array(np.ones(shape=[1, 3, 2])).astype(np.float32))
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network = NetDot()
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ms_result_np = network(input_x1, input_x2)
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expect_result = np.array([[[3., 3.]],
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[[3., 3.]]]).astype(np.float32)
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assert (ms_result_np.asnumpy() == expect_result).all()
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