mindspore/tests/ut/python/communication/test_data_parallel_lenet.py

# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
@File   : test_data_parallel_lenet.py
@Desc   : test data parallel lenet
"""
import os
import numpy as np

import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor, Model
from mindspore.context import ParallelMode
from mindspore.nn.optim import Momentum
from mindspore.ops import operations as P

_current_dir = os.path.dirname(os.path.realpath(__file__)) + "/../test_data"


class LeNet5(nn.Cell):
    """LeNet5 definition"""

    def __init__(self):
        super(LeNet5, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Dense(16 * 5 * 5, 120)
        self.fc2 = nn.Dense(120, 84)
        self.fc3 = nn.Dense(84, 10)
        self.relu = nn.ReLU()
        self.max_pool2d = nn.MaxPool2d(kernel_size=2)
        self.flatten = P.Flatten()

    def construct(self, x):
        x = self.max_pool2d(self.relu(self.conv1(x)))
        x = self.max_pool2d(self.relu(self.conv2(x)))
        x = self.flatten(x)
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)
        return x


class DatasetLenet():
    """DatasetLenet definition"""

    def __init__(self, predict, label, length=3):
        self.predict = predict
        self.label = label
        self.index = 0
        self.length = length

    def __iter__(self):
        return self

    def __next__(self):
        if self.index >= self.length:
            raise StopIteration
        self.index += 1
        return self.predict, self.label

    def reset(self):
        self.index = 0


def test_lenet5_train_step_training_pynative():
    """test_lenet5_train_step_training_pynative"""
    context.set_context(mode=context.PYNATIVE_MODE)
    context.reset_auto_parallel_context()
    context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL,
                                      device_num=8, gradients_mean=True)
    predict = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
    label = Tensor(np.zeros([1, 10]).astype(np.float32))
    DatasetLenet(predict, label, 2)
    network = LeNet5()
    loss_fn = nn.SoftmaxCrossEntropyWithLogits()
    optimizer = Momentum(network.get_parameters(), learning_rate=0.1, momentum=0.9)
    Model(network=network, loss_fn=loss_fn, optimizer=optimizer)
    context.set_context(mode=context.GRAPH_MODE)
    context.reset_auto_parallel_context()