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1# Copyright 2020 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15
16import pytest
17import numpy as np
18from mindspore import Tensor
19import mindspore.nn as nn
20import mindspore.context as context
21from mindspore.ops import composite as C
22from mindspore.common.initializer import initializer
23
24
25context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
26
27
28class NetDot(nn.Cell):
29    def construct(self, x, y):
30        return C.dot(x, y)
31
32
33@pytest.mark.level0
34@pytest.mark.platform_x86_cpu
35@pytest.mark.env_onecard
36def test_dot_001():
37    x1_tensor = Tensor(np.array([[1., 2.], [4., 5.]]).astype(np.float32))
38    x2_tensor = Tensor(np.array([[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], \
39                                 [[9., 10.], [11., 12.]]]).astype(np.float32))
40
41    network = NetDot()
42    ms_result_np = network(x1_tensor, x2_tensor)
43    expect_result = np.array([[[7., 10.], [19., 22.], [31., 34.]], \
44                              [[19., 28.], [55., 64.], [91., 100.]]]).astype(np.float32)
45    assert (ms_result_np.asnumpy() == expect_result).all()
46
47
48@pytest.mark.level0
49@pytest.mark.platform_x86_cpu
50@pytest.mark.env_onecard
51def test_dot_002():
52    x1_tensor = Tensor(np.array([[1., 2.], [4., 5.]]).astype(np.float32))
53    x2_tensor = Tensor(np.array([[[1., 2., 3.], [4., 5., 6.]], [[7., 8., 9.], [10., 11., 12.]]]).astype(np.float32))
54
55    network = NetDot()
56    ms_result_np = network(x1_tensor, x2_tensor)
57    expect_result = np.array([[[9., 12., 15.], [27., 30., 33.]], [[24., 33., 42.], [78., 87., 96.]]]).astype(np.float32)
58
59    assert (ms_result_np.asnumpy() == expect_result).all()
60
61
62@pytest.mark.level0
63@pytest.mark.platform_x86_cpu
64@pytest.mark.env_onecard
65def test_dot_003():
66    x1_tensor = initializer(Tensor(np.arange(2 * 3 * 4).reshape(2, 3, 4).astype(np.float32)), [2, 3, 4])
67    x2_tensor = initializer(Tensor(np.arange(1 * 5 * 4 * 2).reshape(1, 5, 4, 2).astype(np.float32)), [1, 5, 4, 2])
68
69    network = NetDot()
70    ms_result_np = network(x1_tensor, x2_tensor)
71    expect_result = np.array([[[[[28., 34.],
72                                 [76., 82.],
73                                 [124., 130.],
74                                 [172., 178.],
75                                 [220., 226.]]],
76                               [[[76., 98.],
77                                 [252., 274.],
78                                 [428., 450.],
79                                 [604., 626.],
80                                 [780., 802.]]],
81                               [[[124., 162.],
82                                 [428., 466.],
83                                 [732., 770.],
84                                 [1036., 1074.],
85                                 [1340., 1378.]]]],
86                              [[[[172., 226.],
87                                 [604., 658.],
88                                 [1036., 1090.],
89                                 [1468., 1522.],
90                                 [1900., 1954.]]],
91                               [[[220., 290.],
92                                 [780., 850.],
93                                 [1340., 1410.],
94                                 [1900., 1970.],
95                                 [2460., 2530.]]],
96                               [[[268., 354.],
97                                 [956., 1042.],
98                                 [1644., 1730.],
99                                 [2332., 2418.],
100                                 [3020., 3106.]]]]]).astype(np.float32)
101
102    assert (ms_result_np.asnumpy() == expect_result).all()
103
104
105@pytest.mark.level0
106@pytest.mark.platform_x86_cpu
107@pytest.mark.env_onecard
108def test_dot_004():
109    x1_tensor = initializer(Tensor(np.arange(3 * 4).reshape(3, 4).astype(np.float32)), [3, 4])
110    x2_tensor = initializer(Tensor(np.arange(4 * 5).reshape(4, 5).astype(np.float32)), [4, 5])
111
112    network = NetDot()
113    ms_result_np = network(x1_tensor, x2_tensor)
114    expect_result = np.array([[70., 76., 82., 88., 94.],
115                              [190., 212., 234., 256., 278.],
116                              [310., 348., 386., 424., 462.]]).astype(np.float32)
117
118    assert (ms_result_np.asnumpy() == expect_result).all()
119
120
121@pytest.mark.level0
122@pytest.mark.platform_x86_cpu
123@pytest.mark.env_onecard
124def test_dot_005():
125    x1_tensor = initializer(Tensor(np.arange(2 * 3 * 4).reshape(2, 3, 4).astype(np.float32)), [2, 3, 4])
126    x2_tensor = initializer(Tensor(np.arange(4 * 5).reshape(4, 5).astype(np.float32)), [4, 5])
127
128    network = NetDot()
129    ms_result_np = network(x1_tensor, x2_tensor)
130    expect_result = np.array([[[70., 76., 82., 88., 94.],
131                               [190., 212., 234., 256., 278.],
132                               [310., 348., 386., 424., 462.]],
133                              [[430., 484., 538., 592., 646.],
134                               [550., 620., 690., 760., 830.],
135                               [670., 756., 842., 928., 1014.]]]).astype(np.float32)
136
137    assert (ms_result_np.asnumpy() == expect_result).all()
138
139
140@pytest.mark.level0
141@pytest.mark.platform_x86_cpu
142@pytest.mark.env_onecard
143def test_dot_006():
144    x1_tensor = initializer(Tensor(np.arange(4).reshape(4).astype(np.float32)), [4])
145    x2_tensor = initializer(Tensor(np.arange(2 * 4 * 5).reshape(2, 4, 5).astype(np.float32)), [2, 4, 5])
146
147    network = NetDot()
148    try:
149        network(x1_tensor, x2_tensor)
150    except ValueError as e:
151        assert ValueError == type(e)
152
153
154def test_dot_007():
155    x1_tensor = initializer(Tensor(np.arange(4).reshape(4).astype(np.float32)), [4])
156    x2_tensor = initializer(Tensor(np.arange(4 * 4).reshape(4, 4).astype(np.float32)), [4, 4])
157
158    network = NetDot()
159    try:
160        network(x2_tensor, x1_tensor)
161    except ValueError as e:
162        assert ValueError == type(e)
163
164
165@pytest.mark.level0
166@pytest.mark.platform_x86_cpu
167@pytest.mark.env_onecard
168def test_dot_008():
169    x1_tensor = Tensor(np.array([]).astype(np.float32))
170    x2_tensor = Tensor(np.array([[[1., 2.], [3., 4.]],
171                                 [[5., 6.], [7., 8.]],
172                                 [[9., 10.], [11., 12.]]]).astype(np.float32))
173
174    network = NetDot()
175    try:
176        network(x2_tensor, x1_tensor)
177    except ValueError as e:
178        assert ValueError == type(e)
179
180
181@pytest.mark.level0
182@pytest.mark.platform_x86_cpu
183@pytest.mark.env_onecard
184def test_dot_009():
185    # for document
186    input_x1 = Tensor(np.array(np.ones(shape=[2, 3])).astype(np.float32))
187    input_x2 = Tensor(np.array(np.ones(shape=[1, 2, 3])).astype(np.float32))
188
189    network = NetDot()
190    try:
191        network(input_x1, input_x2)
192    except ValueError as e:
193        assert ValueError == type(e)
194
195
196@pytest.mark.level0
197@pytest.mark.platform_x86_cpu
198@pytest.mark.env_onecard
199def test_dot_010():
200    # for document
201    input_x1 = Tensor(np.array(np.ones(shape=[2, 3])).astype(np.float32))
202    input_x2 = Tensor(np.array(np.ones(shape=[1, 3, 2])).astype(np.float32))
203
204    network = NetDot()
205    ms_result_np = network(input_x1, input_x2)
206    expect_result = np.array([[[3., 3.]],
207                              [[3., 3.]]]).astype(np.float32)
208
209    assert (ms_result_np.asnumpy() == expect_result).all()
210
211
212@pytest.mark.level0
213@pytest.mark.platform_x86_cpu
214@pytest.mark.env_onecard
215def test_dot_011():
216    # for document
217    context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
218    input_x1 = Tensor(np.array(np.ones(shape=[2, 3])).astype(np.float32))
219    input_x2 = Tensor(np.array(np.ones(shape=[1, 3, 2])).astype(np.float32))
220
221    network = NetDot()
222    ms_result_np = network(input_x1, input_x2)
223    expect_result = np.array([[[3., 3.]],
224                              [[3., 3.]]]).astype(np.float32)
225
226    assert (ms_result_np.asnumpy() == expect_result).all()
227