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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"""
16@File   : test_data_parallel_lenet.py
17@Desc   : test data parallel lenet
18"""
19import os
20import numpy as np
21
22import mindspore.context as context
23import mindspore.nn as nn
24from mindspore import Tensor, Model
25from mindspore.context import ParallelMode
26from mindspore.nn.optim import Momentum
27from mindspore.ops import operations as P
28
29_current_dir = os.path.dirname(os.path.realpath(__file__)) + "/../test_data"
30
31
32class LeNet5(nn.Cell):
33    """LeNet5 definition"""
34
35    def __init__(self):
36        super(LeNet5, self).__init__()
37        self.conv1 = nn.Conv2d(1, 6, 5)
38        self.conv2 = nn.Conv2d(6, 16, 5)
39        self.fc1 = nn.Dense(16 * 5 * 5, 120)
40        self.fc2 = nn.Dense(120, 84)
41        self.fc3 = nn.Dense(84, 10)
42        self.relu = nn.ReLU()
43        self.max_pool2d = nn.MaxPool2d(kernel_size=2)
44        self.flatten = P.Flatten()
45
46    def construct(self, x):
47        x = self.max_pool2d(self.relu(self.conv1(x)))
48        x = self.max_pool2d(self.relu(self.conv2(x)))
49        x = self.flatten(x)
50        x = self.relu(self.fc1(x))
51        x = self.relu(self.fc2(x))
52        x = self.fc3(x)
53        return x
54
55
56class DatasetLenet():
57    """DatasetLenet definition"""
58
59    def __init__(self, predict, label, length=3):
60        self.predict = predict
61        self.label = label
62        self.index = 0
63        self.length = length
64
65    def __iter__(self):
66        return self
67
68    def __next__(self):
69        if self.index >= self.length:
70            raise StopIteration
71        self.index += 1
72        return self.predict, self.label
73
74    def reset(self):
75        self.index = 0
76
77
78def test_lenet5_train_step_training_pynative():
79    """test_lenet5_train_step_training_pynative"""
80    context.set_context(mode=context.PYNATIVE_MODE)
81    context.reset_auto_parallel_context()
82    context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL,
83                                      device_num=8, gradients_mean=True)
84    predict = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
85    label = Tensor(np.zeros([1, 10]).astype(np.float32))
86    DatasetLenet(predict, label, 2)
87    network = LeNet5()
88    loss_fn = nn.SoftmaxCrossEntropyWithLogits()
89    optimizer = Momentum(network.get_parameters(), learning_rate=0.1, momentum=0.9)
90    Model(network=network, loss_fn=loss_fn, optimizer=optimizer)
91    context.set_context(mode=context.GRAPH_MODE)
92    context.reset_auto_parallel_context()
93