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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# ============================================================================
15import numpy as np
16import pytest
17
18import mindspore
19import mindspore.context as context
20import mindspore.nn as nn
21import mindspore.ops as ops
22from mindspore import Tensor
23
24context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
25
26
27class TestTimeDistributed(nn.Cell):
28    def __init__(self, cell, time_axis, reshape_with_axis=None):
29        super(TestTimeDistributed, self).__init__()
30        self.time_distributed = nn.TimeDistributed(cell, time_axis, reshape_with_axis)
31
32    def construct(self, inputs):
33        return self.time_distributed(inputs)
34
35
36@pytest.mark.level0
37@pytest.mark.platform_x86_gpu_training
38@pytest.mark.env_onecard
39def test_time_distributed_conv2d():
40    inputs = np.random.randint(0, 10, [32, 12, 10, 10])
41    conv2d = nn.Conv2d(12, 24, 4, has_bias=False, weight_init='normal')
42    output_expect = conv2d(Tensor(inputs, mindspore.float32)).asnumpy()
43    inputs = inputs.reshape([32, 1, 12, 10, 10]).repeat(6, axis=1)
44    time_distributed = TestTimeDistributed(conv2d, time_axis=1, reshape_with_axis=0)
45    output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy()
46    for i in range(output.shape[1]):
47        assert np.all(np.abs(output[:, i, :] - output_expect) < 1e-5)
48    print("Conv2D layer wrapped successful")
49
50
51@pytest.mark.level0
52@pytest.mark.platform_x86_gpu_training
53@pytest.mark.env_onecard
54def test_time_distributed_maxpool2d():
55    inputs = np.random.randint(0, 10, [32, 12, 10, 10])
56    pool = nn.MaxPool2d(kernel_size=3, stride=1)
57    output_expect = pool(Tensor(inputs, mindspore.float32)).asnumpy()
58    inputs = inputs.reshape([32, 1, 12, 10, 10]).repeat(6, axis=1)
59    time_distributed = TestTimeDistributed(pool, time_axis=1, reshape_with_axis=0)
60    output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy()
61    for i in range(output.shape[1]):
62        assert np.all(output[:, i, :] == output_expect)
63    print("MaxPooling2D layer wrapped successful")
64
65
66@pytest.mark.level0
67@pytest.mark.platform_x86_gpu_training
68@pytest.mark.env_onecard
69def test_time_distributed_dense():
70    inputs = np.random.randint(0, 10, [32, 10])
71    dense = nn.Dense(10, 6)
72    output_expect = dense(Tensor(inputs, mindspore.float32)).asnumpy()
73    inputs = inputs.reshape([32, 1, 10]).repeat(6, axis=1)
74    time_distributed = TestTimeDistributed(dense, time_axis=1, reshape_with_axis=0)
75    output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy()
76    for i in range(output.shape[1]):
77        assert np.all(output[:, i, :] == output_expect)
78    print("Dense layer wrapped successful")
79
80
81@pytest.mark.level0
82@pytest.mark.platform_x86_gpu_training
83@pytest.mark.env_onecard
84def test_time_distributed_dense_with_reshape_axis_not_first():
85    inputs = np.random.randint(0, 10, [32, 10])
86    dense = nn.Dense(10, 6)
87    output_expect = dense(Tensor(inputs, mindspore.float32)).asnumpy()
88    inputs = inputs.reshape([1, 32, 10]).repeat(6, axis=0)
89    time_distributed = TestTimeDistributed(dense, time_axis=0, reshape_with_axis=1)
90    output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy()
91    for i in range(output.shape[0]):
92        assert np.all(output[i, :] == output_expect)
93    print("Dense layer wrapped successful")
94
95
96@pytest.mark.level0
97@pytest.mark.platform_x86_gpu_training
98@pytest.mark.env_onecard
99def test_time_distributed_argmax():
100    inputs = np.random.randint(0, 10, [3, 4])
101    argmax = ops.Argmax(output_type=mindspore.int32, axis=1)
102    output_expect = argmax(Tensor(inputs, mindspore.float32)).asnumpy()
103    inputs = inputs.reshape([3, 1, 4]).repeat(6, axis=1)
104    time_distributed = TestTimeDistributed(argmax, time_axis=1, reshape_with_axis=0)
105    output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy()
106    for i in range(output.shape[1]):
107        assert np.all(output[:, i] == output_expect)
108    print("Argmax op wrapped successful")
109
110
111@pytest.mark.level0
112@pytest.mark.platform_x86_gpu_training
113@pytest.mark.env_onecard
114def test_time_distributed_flatten():
115    inputs = np.random.randint(0, 10, [3, 4, 5])
116    flatten = nn.Flatten()
117    output_expect = flatten(Tensor(inputs, mindspore.float32)).asnumpy()
118    inputs = inputs.reshape([3, 1, 4, 5]).repeat(6, axis=1)
119    time_distributed = TestTimeDistributed(flatten, time_axis=1, reshape_with_axis=0)
120    output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy()
121    for i in range(output.shape[1]):
122        assert np.all(output[:, i, :] == output_expect)
123    print("Flatten op wrapped successful")
124
125
126@pytest.mark.level0
127@pytest.mark.platform_x86_gpu_training
128@pytest.mark.env_onecard
129def test_time_distributed_conv2d_no_reshape_axis():
130    inputs = np.random.randint(0, 10, [32, 12, 10, 10])
131    conv2d = nn.Conv2d(12, 24, 4, has_bias=False, weight_init='normal')
132    output_expect = conv2d(Tensor(inputs, mindspore.float32)).asnumpy()
133    inputs = inputs.reshape([32, 1, 12, 10, 10]).repeat(6, axis=1)
134    time_distributed = TestTimeDistributed(conv2d, time_axis=1)
135    output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy()
136    for i in range(output.shape[1]):
137        assert np.all(output[:, i, :] == output_expect)
138    print("Conv2D layer with no reshape axis wrapped successful")
139
140
141@pytest.mark.level0
142@pytest.mark.platform_x86_gpu_training
143@pytest.mark.env_onecard
144def test_time_distributed_maxpool2d_no_reshape_axis():
145    inputs = np.random.randint(0, 10, [32, 12, 10, 10])
146    pool = nn.MaxPool2d(kernel_size=3, stride=1)
147    output_expect = pool(Tensor(inputs, mindspore.float32)).asnumpy()
148    inputs = inputs.reshape([32, 1, 12, 10, 10]).repeat(6, axis=1)
149    time_distributed = TestTimeDistributed(pool, time_axis=1)
150    output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy()
151    for i in range(output.shape[1]):
152        assert np.all(output[:, i, :] == output_expect)
153    print("MaxPooling2D layer with no reshape axis wrapped successful")
154
155
156@pytest.mark.level0
157@pytest.mark.platform_x86_gpu_training
158@pytest.mark.env_onecard
159def test_time_distributed_dense_no_reshape_axis():
160    inputs = np.random.randint(0, 10, [32, 10])
161    dense = nn.Dense(10, 6)
162    output_expect = dense(Tensor(inputs, mindspore.float32)).asnumpy()
163    inputs = inputs.reshape([32, 1, 10]).repeat(6, axis=1)
164    time_distributed = TestTimeDistributed(dense, time_axis=1)
165    output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy()
166    for i in range(output.shape[1]):
167        assert np.all(output[:, i, :] == output_expect)
168    print("Dense layer with no reshape axis wrapped successful")
169
170
171@pytest.mark.level0
172@pytest.mark.platform_x86_gpu_training
173@pytest.mark.env_onecard
174def test_time_distributed_argmax_no_reshape_axis():
175    inputs = np.random.randint(0, 10, [3, 4])
176    argmax = ops.Argmax(output_type=mindspore.int32, axis=1)
177    output_expect = argmax(Tensor(inputs, mindspore.float32)).asnumpy()
178    inputs = inputs.reshape([3, 1, 4]).repeat(6, axis=1)
179    time_distributed = TestTimeDistributed(argmax, time_axis=1)
180    output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy()
181    for i in range(output.shape[1]):
182        assert np.all(output[:, i] == output_expect)
183    print("Argmax op with no reshape axis wrapped successful")
184
185
186@pytest.mark.level0
187@pytest.mark.platform_x86_gpu_training
188@pytest.mark.env_onecard
189def test_time_distributed_flatten_no_reshape_axis():
190    inputs = np.random.randint(0, 10, [3, 4, 5])
191    flatten = nn.Flatten()
192    output_expect = flatten(Tensor(inputs, mindspore.float32)).asnumpy()
193    inputs = inputs.reshape([3, 1, 4, 5]).repeat(6, axis=1)
194    time_distributed = TestTimeDistributed(flatten, time_axis=1)
195    output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy()
196    for i in range(output.shape[1]):
197        assert np.all(output[:, i, :] == output_expect)
198    print("Flatten op with no reshape axis wrapped successful")
199