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1# Copyright 2019 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 numpy as np
17import pytest
18
19import mindspore.context as context
20import mindspore.nn as nn
21from mindspore import Tensor
22from mindspore.common.api import ms_function
23from mindspore.ops import operations as P
24from mindspore.ops.operations import _inner_ops as inner
25
26x0 = np.random.rand(2, 3, 4, 4).astype(np.float32)
27axis0 = 3
28keep_dims0 = True
29
30x1 = np.random.rand(2, 3, 4, 4).astype(np.float32)
31axis1 = 3
32keep_dims1 = False
33
34x2 = np.random.rand(2, 3, 1, 4).astype(np.float32)
35axis2 = 2
36keep_dims2 = True
37
38x3 = np.random.rand(2, 3, 1, 4).astype(np.float32)
39axis3 = 2
40keep_dims3 = False
41
42x4 = np.random.rand(2, 3, 4, 1).astype(np.float32)
43axis4 = 3
44keep_dims4 = True
45
46x5 = np.random.rand(2, 3, 4, 1).astype(np.float32)
47axis5 = 3
48keep_dims5 = False
49
50x6 = np.random.rand(2, 3, 4, 4).astype(np.float32)
51axis6 = (1, 2)
52keep_dims6 = False
53
54x7 = np.random.rand(2, 3, 4, 4).astype(np.float32)
55axis7 = (1, 2)
56keep_dims7 = True
57
58x8 = np.random.rand(2, 1, 1, 4).astype(np.float32)
59axis8 = (1, 2)
60keep_dims8 = True
61
62x9 = np.random.rand(2, 1, 1, 4).astype(np.float32)
63axis9 = (1, 2)
64keep_dims9 = False
65
66x10 = np.random.rand(2, 3, 4, 4).astype(np.float32)
67axis10 = (0, 1, 2, 3)
68keep_dims10 = False
69
70x11 = np.random.rand(1, 1, 1, 1).astype(np.float32)
71axis11 = (0, 1, 2, 3)
72keep_dims11 = False
73
74x12 = np.random.rand(2, 3, 4, 4, 5, 6).astype(np.float32)
75axis12 = -2
76keep_dims12 = False
77
78x13 = np.random.rand(2, 3, 4, 4).astype(np.float32)
79axis13 = (-2, -1)
80keep_dims13 = True
81
82x14 = np.random.rand(1, 1, 1, 1).astype(np.float32)
83axis14 = ()
84np_axis14 = None
85keep_dims14 = True
86
87
88class ReduceMean(nn.Cell):
89    def __init__(self):
90        super(ReduceMean, self).__init__()
91
92        self.x0 = Tensor(x0)
93        self.axis0 = axis0
94        self.keep_dims0 = keep_dims0
95
96        self.x1 = Tensor(x1)
97        self.axis1 = axis1
98        self.keep_dims1 = keep_dims1
99
100        self.x2 = Tensor(x2)
101        self.axis2 = axis2
102        self.keep_dims2 = keep_dims2
103
104        self.x3 = Tensor(x3)
105        self.axis3 = axis3
106        self.keep_dims3 = keep_dims3
107
108        self.x4 = Tensor(x4)
109        self.axis4 = axis4
110        self.keep_dims4 = keep_dims4
111
112        self.x5 = Tensor(x5)
113        self.axis5 = axis5
114        self.keep_dims5 = keep_dims5
115
116        self.x6 = Tensor(x6)
117        self.axis6 = axis6
118        self.keep_dims6 = keep_dims6
119
120        self.x7 = Tensor(x7)
121        self.axis7 = axis7
122        self.keep_dims7 = keep_dims7
123
124        self.x8 = Tensor(x8)
125        self.axis8 = axis8
126        self.keep_dims8 = keep_dims8
127
128        self.x9 = Tensor(x9)
129        self.axis9 = axis9
130        self.keep_dims9 = keep_dims9
131
132        self.x10 = Tensor(x10)
133        self.axis10 = axis10
134        self.keep_dims10 = keep_dims10
135
136        self.x11 = Tensor(x11)
137        self.axis11 = axis11
138        self.keep_dims11 = keep_dims11
139
140        self.x12 = Tensor(x12)
141        self.axis12 = axis12
142        self.keep_dims12 = keep_dims12
143
144        self.x13 = Tensor(x13)
145        self.axis13 = axis13
146        self.keep_dims13 = keep_dims13
147
148        self.x14 = Tensor(x14)
149        self.axis14 = axis14
150        self.keep_dims14 = keep_dims14
151
152    @ms_function
153    def construct(self):
154        return (P.ReduceMean(self.keep_dims0)(self.x0, self.axis0),
155                P.ReduceMean(self.keep_dims1)(self.x1, self.axis1),
156                P.ReduceMean(self.keep_dims2)(self.x2, self.axis2),
157                P.ReduceMean(self.keep_dims3)(self.x3, self.axis3),
158                P.ReduceMean(self.keep_dims4)(self.x4, self.axis4),
159                P.ReduceMean(self.keep_dims5)(self.x5, self.axis5),
160                P.ReduceMean(self.keep_dims6)(self.x6, self.axis6),
161                P.ReduceMean(self.keep_dims7)(self.x7, self.axis7),
162                P.ReduceMean(self.keep_dims8)(self.x8, self.axis8),
163                P.ReduceMean(self.keep_dims9)(self.x9, self.axis9),
164                P.ReduceMean(self.keep_dims10)(self.x10, self.axis10),
165                P.ReduceMean(self.keep_dims11)(self.x11, self.axis11),
166                P.ReduceMean(self.keep_dims12)(self.x12, self.axis12),
167                P.ReduceMean(self.keep_dims13)(self.x13, self.axis13),
168                P.ReduceMean(self.keep_dims14)(self.x14, self.axis14))
169
170
171@pytest.mark.level0
172@pytest.mark.platform_x86_gpu_training
173@pytest.mark.env_onecard
174def test_ReduceMean():
175    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
176    reduce_mean = ReduceMean()
177    output = reduce_mean()
178
179    expect0 = np.mean(x0, axis=axis0, keepdims=keep_dims0)
180    diff0 = abs(output[0].asnumpy() - expect0)
181    error0 = np.ones(shape=expect0.shape) * 1.0e-5
182    assert np.all(diff0 < error0)
183    assert output[0].shape == expect0.shape
184
185    expect1 = np.mean(x1, axis=axis1, keepdims=keep_dims1)
186    diff1 = abs(output[1].asnumpy() - expect1)
187    error1 = np.ones(shape=expect1.shape) * 1.0e-5
188    assert np.all(diff1 < error1)
189    assert output[1].shape == expect1.shape
190
191    expect2 = np.mean(x2, axis=axis2, keepdims=keep_dims2)
192    diff2 = abs(output[2].asnumpy() - expect2)
193    error2 = np.ones(shape=expect2.shape) * 1.0e-5
194    assert np.all(diff2 < error2)
195    assert output[2].shape == expect2.shape
196
197    expect3 = np.mean(x3, axis=axis3, keepdims=keep_dims3)
198    diff3 = abs(output[3].asnumpy() - expect3)
199    error3 = np.ones(shape=expect3.shape) * 1.0e-5
200    assert np.all(diff3 < error3)
201    assert output[3].shape == expect3.shape
202
203    expect4 = np.mean(x4, axis=axis4, keepdims=keep_dims4)
204    diff4 = abs(output[4].asnumpy() - expect4)
205    error4 = np.ones(shape=expect4.shape) * 1.0e-5
206    assert np.all(diff4 < error4)
207    assert output[4].shape == expect4.shape
208
209    expect5 = np.mean(x5, axis=axis5, keepdims=keep_dims5)
210    diff5 = abs(output[5].asnumpy() - expect5)
211    error5 = np.ones(shape=expect5.shape) * 1.0e-5
212    assert np.all(diff5 < error5)
213    assert output[5].shape == expect5.shape
214
215    expect6 = np.mean(x6, axis=axis6, keepdims=keep_dims6)
216    diff6 = abs(output[6].asnumpy() - expect6)
217    error6 = np.ones(shape=expect6.shape) * 1.0e-5
218    assert np.all(diff6 < error6)
219    assert output[6].shape == expect6.shape
220
221    expect7 = np.mean(x7, axis=axis7, keepdims=keep_dims7)
222    diff7 = abs(output[7].asnumpy() - expect7)
223    error7 = np.ones(shape=expect7.shape) * 1.0e-5
224    assert np.all(diff7 < error7)
225    assert output[7].shape == expect7.shape
226
227    expect8 = np.mean(x8, axis=axis8, keepdims=keep_dims8)
228    diff8 = abs(output[8].asnumpy() - expect8)
229    error8 = np.ones(shape=expect8.shape) * 1.0e-5
230    assert np.all(diff8 < error8)
231    assert output[8].shape == expect8.shape
232
233    expect9 = np.mean(x9, axis=axis9, keepdims=keep_dims9)
234    diff9 = abs(output[9].asnumpy() - expect9)
235    error9 = np.ones(shape=expect9.shape) * 1.0e-5
236    assert np.all(diff9 < error9)
237    assert output[9].shape == expect9.shape
238
239    expect10 = np.mean(x10, axis=axis10, keepdims=keep_dims10)
240    diff10 = abs(output[10].asnumpy() - expect10)
241    error10 = np.ones(shape=expect10.shape) * 1.0e-5
242    assert np.all(diff10 < error10)
243    assert output[10].shape == expect10.shape
244
245    expect11 = np.mean(x11, axis=axis11, keepdims=keep_dims11)
246    diff11 = abs(output[11].asnumpy() - expect11)
247    error11 = np.ones(shape=expect11.shape) * 1.0e-5
248    assert np.all(diff11 < error11)
249    assert output[11].shape == expect11.shape
250
251    expect12 = np.mean(x12, axis=axis12, keepdims=keep_dims12)
252    diff12 = abs(output[12].asnumpy() - expect12)
253    error12 = np.ones(shape=expect12.shape) * 1.0e-5
254    assert np.all(diff12 < error12)
255    assert output[12].shape == expect12.shape
256
257    expect13 = np.mean(x13, axis=axis13, keepdims=keep_dims13)
258    diff13 = abs(output[13].asnumpy() - expect13)
259    error13 = np.ones(shape=expect13.shape) * 1.0e-5
260    assert np.all(diff13 < error13)
261    assert output[13].shape == expect13.shape
262
263    expect14 = np.mean(x14, axis=np_axis14, keepdims=keep_dims14)
264    diff14 = abs(output[14].asnumpy() - expect14)
265    error14 = np.ones(shape=expect14.shape) * 1.0e-5
266    assert np.all(diff14 < error14)
267    assert output[14].shape == expect14.shape
268
269class ReduceMeanDynamic(nn.Cell):
270    def __init__(self, x, axis, keepdims=False):
271        super(ReduceMeanDynamic, self).__init__()
272        self.test_dynamic = inner.GpuConvertToDynamicShape()
273        self.reducemean = P.ReduceMean(keep_dims=keepdims)
274        self.x = x
275        self.axis = axis
276
277    def construct(self):
278        dynamic_x = self.test_dynamic(self.x)
279        output = self.reducemean(dynamic_x, self.axis)
280        return output
281
282@pytest.mark.level0
283@pytest.mark.platform_x86_gpu_training
284@pytest.mark.env_onecard
285def test_dynamic_reduce_mean_keepdims_true():
286    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
287    net1 = ReduceMeanDynamic(Tensor(x14), axis14, keepdims=True)
288    net2 = ReduceMeanDynamic(Tensor(x0), axis0, keepdims=True)
289    output1 = net1()
290    output2 = net2()
291
292    expect_1 = np.mean(x14, axis=np_axis14, keepdims=True)
293    diff_1 = abs(output1.asnumpy() - expect_1)
294    error_1 = np.ones(shape=expect_1.shape) * 1.0e-5
295    assert np.all(diff_1 < error_1)
296    assert output1.shape == expect_1.shape
297
298    expect_2 = np.mean(x0, axis=axis0, keepdims=True)
299    diff_2 = abs(output2.asnumpy() - expect_2)
300    error_2 = np.ones(shape=expect_2.shape) * 1.0e-5
301    assert np.all(diff_2 < error_2)
302    assert output2.shape == expect_2.shape
303
304@pytest.mark.level0
305@pytest.mark.platform_x86_gpu_training
306@pytest.mark.env_onecard
307def test_dynamic_reduce_mean_keepdims_false():
308    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
309    net = ReduceMeanDynamic(Tensor(x12), axis12, keepdims=False)
310    output = net()
311
312    expect = np.mean(x12, axis=axis12, keepdims=False)
313    diff = abs(output.asnumpy() - expect)
314    error = np.ones(shape=expect.shape) * 1.0e-5
315    assert np.all(diff < error)
316    assert output.shape == expect.shape
317