text: # 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.
  # ============================================================================
  
  import os
  import argparse
  
  import mindspore.context as context
  import mindspore.dataset as ds
  import mindspore.dataset.transforms.c_transforms as C
  import mindspore.dataset.transforms.vision.c_transforms as CV
  import mindspore.nn as nn
  from mindspore.common import dtype as mstype
  from mindspore.dataset.transforms.vision import Inter
  from mindspore.nn.metrics import Accuracy
  from mindspore.train import Model
  from mindspore.train.callback import LossMonitor
  from mindspore.common.initializer import TruncatedNormal
  
  parser = argparse.ArgumentParser(description='test_ps_lenet')
  parser.add_argument("--device_target", type=str, default="Ascend")
  parser.add_argument("--dataset_path", type=str, default="/home/workspace/mindspore_dataset/mnist")
  args, _ = parser.parse_known_args()
  device_target = args.device_target
  dataset_path = args.dataset_path
  context.set_context(mode=context.GRAPH_MODE, device_target=device_target)
  context.set_ps_context(enable_ps=True)
  
  def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
      """weight initial for conv layer"""
      weight = weight_variable()
      return nn.Conv2d(in_channels, out_channels,
                       kernel_size=kernel_size, stride=stride, padding=padding,
                       weight_init=weight, has_bias=False, pad_mode="valid")
  
  
  def fc_with_initialize(input_channels, out_channels):
      """weight initial for fc layer"""
      weight = weight_variable()
      bias = weight_variable()
      return nn.Dense(input_channels, out_channels, weight, bias)
  
  
  def weight_variable():
      """weight initial"""
      return TruncatedNormal(0.02)
  
  
  class LeNet5(nn.Cell):
      def __init__(self, num_class=10, channel=1):
          super(LeNet5, self).__init__()
          self.num_class = num_class
          self.conv1 = conv(channel, 6, 5)
          self.conv2 = conv(6, 16, 5)
          self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
          self.fc2 = fc_with_initialize(120, 84)
          self.fc3 = fc_with_initialize(84, self.num_class)
          self.relu = nn.ReLU()
          self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
          self.flatten = nn.Flatten()
  
      def construct(self, x):
          x = self.conv1(x)
          x = self.relu(x)
          x = self.max_pool2d(x)
          x = self.conv2(x)
          x = self.relu(x)
          x = self.max_pool2d(x)
          x = self.flatten(x)
          x = self.fc1(x)
          x = self.relu(x)
          x = self.fc2(x)
          x = self.relu(x)
          x = self.fc3(x)
          return x
  
  def create_dataset(data_path, batch_size=32, repeat_size=1,
                     num_parallel_workers=1):
      """
      create dataset for train or test
      """
      # define dataset
      mnist_ds = ds.MnistDataset(data_path)
  
      resize_height, resize_width = 32, 32
      rescale = 1.0 / 255.0
      shift = 0.0
      rescale_nml = 1 / 0.3081
      shift_nml = -1 * 0.1307 / 0.3081
  
      # define map operations
      resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)  # Bilinear mode
      rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
      rescale_op = CV.Rescale(rescale, shift)
      hwc2chw_op = CV.HWC2CHW()
      type_cast_op = C.TypeCast(mstype.int32)
  
      # apply map operations on images
      mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers)
      mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)
      mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)
      mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)
      mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)
  
      # apply DatasetOps
      buffer_size = 10000
      mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)  # 10000 as in LeNet train script
      mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
      mnist_ds = mnist_ds.repeat(repeat_size)
  
      return mnist_ds
  
  if __name__ == "__main__":
      network = LeNet5(10)
      network.set_param_ps()
      net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
      net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9)
      model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
  
      ds_train = create_dataset(os.path.join(dataset_path, "train"), 32, 1)
      model.train(1, ds_train, callbacks=[LossMonitor()], dataset_sink_mode=False)
  
      ds_eval = create_dataset(os.path.join(dataset_path, "test"), 32, 1)
      acc = model.eval(ds_eval, dataset_sink_mode=False)
  
      print("Accuracy:", acc['Accuracy'])
      assert acc['Accuracy'] > 0.93
  