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+# 构建语义分割网络模型应用
+ 
+## 实验介绍
+本实验主要介绍使用MindSpore深度学习框架在PASCAL VOC 2012数据集上训练deeplabv3网络模型。本实验参考MindSpore开源仓库model_zoo中的[deeplabv3 Example](https://gitee.com/mindspore/mindspore/tree/r0.5/model_zoo/deeplabv3) 模型案例。
+
+## deeplabv3简要介绍
+deeplabv1和deeplabv2，即带孔卷积(atrous convolution), 能够明确地调整filters的感受野，并决定DNN计算得到特征的分辨率。
+deeplabv3中提出 Atrous Spatial Pyramid Pooling(ASPP)模块, 挖掘不同尺度的卷积特征，以及编码了全局内容信息的图像层特征，提升分割效果。
+详细介绍参考论文：http://arxiv.org/abs/1706.05587 。
+
+## 实验目的
+* 了解如何使用MindSpore加载常用的PASCAL VOC 2012数据集。
+* 了解MindSpore的model_zoo模块，以及如何使用model_zoo中的模型。
+* 了解deeplabv3这类语义分割模型的基本结构和编程方法。
+
+## 预备知识
+* 熟练使用Python，了解Shell及Linux操作系统基本知识。
+* 具备一定的深度学习理论知识，如Encoder、Decoder、损失函数、优化器，训练策略、Checkpoint等。
+* 了解华为云的基本使用方法，包括[OBS（对象存储）](https://www.huaweicloud.com/product/obs.html) 、[ModelArts（AI开发平台](https://www.huaweicloud.com/product/modelarts.html) 、[训练作业](https://support.huaweicloud.com/engineers-modelarts/modelarts_23_0046.html) 等功能。华为云官网：https://www.huaweicloud.com。
+* 了解并熟悉MindSpore AI计算框架，MindSpore官网：https://www.mindspore.cn/。
+
+## 实验环境
+* MindSpore 0.5.0（MindSpore版本会定期更新，本指导也会定期刷新，与版本配套）。
+* 华为云ModelArts：ModelArts是华为云提供的面向开发者的一站式AI开发平台，集成了昇腾AI处理器资源池，用户可以在该平台下体验MindSpore。ModelArts官网：https://www.huaweicloud.com/product/modelarts.html。
+
+## 实验准备
+### 创建OBS桶
+本实验需要使用华为云OBS存储脚本和数据集，可以参考[快速通过OBS控制台上传下载文件](https://support.huaweicloud.com/qs-obs/obs_qs_0001.html) 了解使用OBS创建桶、上传文件、下载文件的使用方法。当数据集大时，可以使用[OBS Browser+](https://support.huaweicloud.com/browsertg-obs/obs_03_1000.html) 。
+
+> 提示： 华为云新用户使用OBS时通常需要创建和配置“访问密钥”，可以在使用OBS时根据提示完成创建和配置。也可以[参考获取访问密钥并完成ModelArts全局配置](https://support.huaweicloud.com/prepare-modelarts/modelarts_08_0002.html) 获取并配置访问密钥。
+
+创建OBS桶的参考配置如下：
+
+* 区域：华北-北京四
+* 数据冗余存储策略：单AZ存储
+* 桶名称：如ms-course
+* 存储类别：标准存储
+* 桶策略：公共读
+* 归档数据直读：关闭
+* 企业项目、标签等配置：免
+
+## 数据集准备
+[Pascal VOC2012数据集](https://blog.csdn.net/haoji007/article/details/80361587) 主要是针对视觉任务中监督学习提供标签数据，它有二十个类别。主要有四个大类别，分别是人、常见动物、交通车辆、室内家具用品。这里只说与图像分割（segmentation）有关的信息,本用例使用已去除分割标注的颜色，仅保留了分割任务的数据集。VOC2012[官网地址](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html) ，[官方下载地址](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar) 。
+
+本实验指导的数据集可通过如下方式获取：
+* 方式一：针对教学使用的[实验指导](https://gitee.com/mindspore/course)和 [模型案例](https://gitee.com/mindspore/mindspore/tree/r0.5/model_zoo) ，为了节省下载和处理数据集的时间，我们提前准备好了数据集,可直接通过上述的[华为云OBS](https://share-course.obs.cn-north-4.myhuaweicloud.com/dataset/voc2012.zip) （已去除分割标注的颜色，仅保留了分割任务的数据）获取。
+* 方式二：使用moxing接口拷贝数据集，即在ModelArts上使用moxing的拷贝功能直接拷贝共享的数据集到执行容器中：
+    ```
+    import moxing
+    # set moxing/obs auth info, ak:Access Key Id, sk:Secret Access Key, server:endpoint of obs bucket
+    moxing.file.set_auth(ak='VCT2GKI3GJOZBQYJG5WM', sk='t1y8M4Z6bHLSAEGK2bCeRYMjo2S2u0QBqToYbxzB', server="obs.cn-north-4.myhuaweicloud.com")
+    
+    # copy dataset from obs to container/cache
+    moxing.file.copy_parallel(src_url="s3://share-course/dataset/voc2012/", dst_url='/cache/data_path')
+    ```
+    
+另外，本实验采用fine-tune的训练方式，为了节省训练时间，我们提前准备好了预训练的[checkpoint文件](https://share-course.obs.myhuaweicloud.com/checkpoint/deeplabv3/deeplabv3_train_14-1_1.ckpt) ,方便直接获取使用。
+
+## 脚本准备
+从MindSpore开源仓库model_zoo中下载[deeplabv3模型案例](https://gitee.com/mindspore/mindspore/tree/r0.5/model_zoo/deeplabv3) 。从[课程gitee仓库](https://gitee.com/mindspore/course) 中下载相关执行脚本。
+
+## 上传文件
+将脚本和数据集上传到OBS桶中，可参考如下组织形式：
+```
+deeplabv3_example
+├── voc2012 # 数据集
+├── checkpoint # ckpt文件存放路径
+└── deeplabv3  # 执行脚本存放路径
+    ├── src # 包括数据集处理、网络定义等
+    └── main.py # 执行脚本，包括训练和推理过程
+```
+
+## 实验步骤
+### 代码梳理
+`main.py`：执行脚本，包含训练和推理过程。主要包括创建数据集、网络定义、网络模型fine_tune等函数。
+
+#### 创建数据集:
+```python
+def create_dataset(args, data_url, epoch_num=1, batch_size=1, usage="train", shuffle=True):
+   """
+   Create Dataset for deeplabv3.
+
+   Args:
+       args (dict): Train parameters.
+       data_url (str): Dataset path.
+       epoch_num (int): Epoch of dataset (default=1).
+       batch_size (int): Batch size of dataset (default=1).
+       usage (str): Whether is use to train or eval (default='train').
+
+   Returns:
+       Dataset.
+   """
+   # create iter dataset
+   dataset = HwVocRawDataset(data_url, usage=usage)
+   dataset_len = len(dataset)
+ 
+   # wrapped with GeneratorDataset
+   dataset = de.GeneratorDataset(dataset, ["image", "label"], sampler=None)
+   dataset.set_dataset_size(dataset_len)
+   dataset = dataset.map(input_columns=["image", "label"], operations=DataTransform(args, usage=usage))
+
+   channelswap_op = C.HWC2CHW()
+   dataset = dataset.map(input_columns="image", operations=channelswap_op)
+
+   # 1464 samples / batch_size 8 = 183 batches
+   # epoch_num is num of steps
+   # 3658 steps / 183 = 20 epochs
+   if usage == "train" and shuffle:
+       dataset = dataset.shuffle(1464)
+   dataset = dataset.batch(batch_size, drop_remainder=(usage == "train"))
+   dataset = dataset.repeat(count=epoch_num)
+   dataset.map_model = 4
+
+   return dataset 
+``` 
+
+#### 定义deeplabv3网络模型：
+```python
+def deeplabv3_resnet50(num_classes, feature_shape, image_pyramid,
+                       infer_scale_sizes, atrous_rates=None, decoder_output_stride=None,
+                       output_stride=16, fine_tune_batch_norm=False):
+   """
+   ResNet50 based deeplabv3 network.
+
+   Args:
+       num_classes (int): Class number.
+       feature_shape (list): Input image shape, [N,C,H,W].
+       image_pyramid (list): Input scales for multi-scale feature extraction.
+       atrous_rates (list): Atrous rates for atrous spatial pyramid pooling.
+       infer_scale_sizes (list): 'The scales to resize images for inference.
+       decoder_output_stride (int): 'The ratio of input to output spatial resolution'
+       output_stride (int): 'The ratio of input to output spatial resolution.'
+       fine_tune_batch_norm (bool): 'Fine tune the batch norm parameters or not'
+
+   Returns:
+       Cell, cell instance of ResNet50 based deeplabv3 neural network.
+
+   Examples:
+       >>> deeplabv3_resnet50(100, [1,3,224,224],[1.0],[1.0])
+   """
+   return deeplabv3(num_classes=num_classes,
+                    feature_shape=feature_shape,
+                    backbone=resnet50_dl(fine_tune_batch_norm),
+                    channel=2048,
+                    depth=256,
+                    infer_scale_sizes=infer_scale_sizes,
+                    atrous_rates=atrous_rates,
+                    decoder_output_stride=decoder_output_stride,
+                    output_stride=output_stride,
+                    fine_tune_batch_norm=fine_tune_batch_norm,
+                    image_pyramid=image_pyramid)
+```
+#### 模型训练过程
+定义LossCallBack类，用于监测模型训练过程的loss值：
+```python
+class LossCallBack(Callback):
+    """
+    Monitor the loss in training.
+    Note:
+        if per_print_times is 0 do not print loss.
+    Args:
+        per_print_times (int): Print loss every times. Default: 1.
+    """
+    def __init__(self, per_print_times=1):
+        super(LossCallBack, self).__init__()
+        if not isinstance(per_print_times, int) or per_print_times < 0:
+            raise ValueError("print_step must be int and >= 0")
+        self._per_print_times = per_print_times
+    def step_end(self, run_context):
+        cb_params = run_context.original_args()
+        print("epoch: {}, step: {}, outputs are {}".format(cb_params.cur_epoch_num, cb_params.cur_step_num,
+                                                           str(cb_params.net_outputs)))
+
+```
+
+定义model_fine_tune函数，用于对网络模型进行微调：
+```python
+  def model_fine_tune(flags, train_net, fix_weight_layer):
+      path = flags.checkpoint_url
+      if path is None:
+         return
+      path = checkpoint_path
+      param_dict = load_checkpoint(path)
+      load_param_into_net(train_net, param_dict)
+      for para in train_net.trainable_params():
+          if fix_weight_layer in para.name:
+              para.requires_grad = False
+```
+
+网络模型的完整训练过程：
+```python
+    train_dataset = create_dataset(args_opt, data_path, config.epoch_size, config.batch_size, usage="train")
+    dataset_size = train_dataset.get_dataset_size()
+    time_cb = TimeMonitor(data_size=dataset_size)
+    callback = [time_cb, LossCallBack()]
+    if config.enable_save_ckpt:
+        config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_steps,
+                                     keep_checkpoint_max=config.save_checkpoint_num)
+        ckpoint_cb = ModelCheckpoint(prefix='checkpoint_deeplabv3', config=config_ck)
+        callback.append(ckpoint_cb)
+    net = deeplabv3_resnet50(config.seg_num_classes, [config.batch_size, 3, args_opt.crop_size, args_opt.crop_size],
+                             infer_scale_sizes=config.eval_scales, atrous_rates=config.atrous_rates,
+                             decoder_output_stride=config.decoder_output_stride, output_stride=config.output_stride,
+                             fine_tune_batch_norm=config.fine_tune_batch_norm, image_pyramid=config.image_pyramid)
+    net.set_train()
+    model_fine_tune(args_opt, net, 'layer')
+    loss = OhemLoss(config.seg_num_classes, config.ignore_label)
+    opt = Momentum(filter(lambda x: 'beta' not in x.name and 'gamma' not in x.name and 'depth' not in x.name and 'bias' not in x.name, net.trainable_params()), learning_rate=config.learning_rate, momentum=config.momentum, weight_decay=config.weight_decay)
+    model = Model(net, loss, opt)
+    model.train(config.epoch_size, train_dataset, callback)
+
+```
+>提示：训练过程中，可通过修改上述示例代码路径下的deeplabv3_example/deeplabv3/src/config.py文件的相关参数来提升训练精度，本实验指导采用默认配置。
+
+训练结果示例：
+```
+epoch: 1, step: 732, outputs are 0.64453894
+Epoch time: 91362.341, per step time: 124.812
+epoch: 2, step: 1464, outputs are 0.13636473
+Epoch time: 25760.597, per step time: 35.192
+epoch: 3, step: 2196, outputs are 0.11666249
+Epoch time: 25503.751, per step time: 34.841
+epoch: 4, step: 2928, outputs are 0.33679807
+Epoch time: 25438.145, per step time: 34.752
+epoch: 5, step: 3660, outputs are 0.7013806
+Epoch time: 25304.372, per step time: 34.569
+epoch: 6, step: 4392, outputs are 0.9661154
+Epoch time: 25466.854, per step time: 34.791
+```
+
+#### 推理过程
+定义mIou指标进行推理性能评估：
+```python
+class MiouPrecision(Metric):
+    """Calculate miou precision."""
+    def __init__(self, num_class=21):
+        super(MiouPrecision, self).__init__()
+        if not isinstance(num_class, int):
+            raise TypeError('num_class should be integer type, but got {}'.format(type(num_class)))
+        if num_class < 1:
+            raise ValueError('num_class must be at least 1, but got {}'.format(num_class))
+        self._num_class = num_class
+        self._mIoU = []
+        self.clear()
+
+    def clear(self):
+        self._hist = np.zeros((self._num_class, self._num_class))
+        self._mIoU = []
+
+    def update(self, *inputs):
+        if len(inputs) != 2:
+            raise ValueError('Need 2 inputs (y_pred, y), but got {}'.format(len(inputs)))
+        predict_in = self._convert_data(inputs[0])
+        label_in = self._convert_data(inputs[1])
+        if predict_in.shape[1] != self._num_class:
+            raise ValueError('Class number not match, last input data contain {} classes, but current data contain {} '
+                             'classes'.format(self._num_class, predict_in.shape[1]))
+        pred = np.argmax(predict_in, axis=1)
+        label = label_in
+        if len(label.flatten()) != len(pred.flatten()):
+            print('Skipping: len(gt) = {:d}, len(pred) = {:d}'.format(len(label.flatten()), len(pred.flatten())))
+            raise ValueError('Class number not match, last input data contain {} classes, but current data contain {} '
+                             'classes'.format(self._num_class, predict_in.shape[1]))
+        self._hist = confuse_matrix(label.flatten(), pred.flatten(), self._num_class)
+        mIoUs = iou(self._hist)
+        self._mIoU.append(mIoUs)
+
+    def eval(self):
+        """
+        Computes the mIoU categorical accuracy.
+        """
+        mIoU = np.nanmean(self._mIoU)
+        print('mIoU = {}'.format(mIoU))
+        return mIoU
+
+```
+
+模型完整推理过程：
+```python
+eval_dataset = create_dataset(args_opt, data_path, config.epoch_size, config.batch_size, usage="eval")
+    net = deeplabv3_resnet50(config.seg_num_classes, [config.batch_size, 3, args_opt.crop_size, args_opt.crop_size],
+                             infer_scale_sizes=config.eval_scales, atrous_rates=config.atrous_rates,
+                             decoder_output_stride=config.decoder_output_stride, output_stride=config.output_stride,
+                             fine_tune_batch_norm=config.fine_tune_batch_norm, image_pyramid=config.image_pyramid)
+
+    param_dict = load_checkpoint(eval_checkpoint_path)
+    load_param_into_net(net, param_dict)
+    mIou = MiouPrecision(config.seg_num_classes)
+    metrics = {'mIou': mIou}
+    loss = OhemLoss(config.seg_num_classes, config.ignore_label)
+    model = Model(net, loss, metrics=metrics)
+    model.eval(eval_dataset)
+```
+>提示：将上述训练完的checkpoint文件进行加载推理，本实验采用训练完的最后一个checkpoint文件，即checkpoint_deeplabv3-6_732.ckpt。
+
+推理结果示例：
+```
+mIoU = 0.6148479926928656
+```
+
+由于ModelArts创建训练作业时，运行参数会通过脚本传参的方式输入给脚本代码，脚本必须解析传参才能在代码中使用相应参数。如data_url和train_url，分别对应数据存储路径(OBS路径)和训练输出路径(OBS路径)。脚本需对传参进行解析后赋值到args_opt变量里，在后续代码里可以使用。
+```python
+parser = argparse.ArgumentParser(description="deeplabv3 training")
+parser.add_argument("--distribute", type=str, default="false", help="Run distribute, default is false.")
+parser.add_argument('--data_url', required=True, default=None, help='Train data url')
+parser.add_argument('--train_url', required=True, default=None, help='Train data output url')
+parser.add_argument('--checkpoint_url', default=None, help='Checkpoint path')
+args_opt = parser.parse_args()
+
+```
+
+MindSpore暂时没有提供直接访问OBS数据的接口，需要通过MoXing提供的API与OBS交互。将OBS中存储的数据拷贝至执行容器,可参考本实验：
+```python
+import moxing as mox
+mox.file.copy_parallel(src_url=args_opt.data_url, dst_url='voc2012/')
+mox.file.copy_parallel(src_url=args_opt.checkpoint_url, dst_url='checkpoint/')
+```
+模型训练使用的是拷贝至当前执行容器路径下的相应文件：
+```python
+data_path = "./voc2012"
+train_checkpoint_path = "./checkpoint/deeplabv3_train_14-1_1.ckpt" #预训练的ckpt
+```
+
+>提示：如若需将训练输出（如模型Checkpoint文件）从执行容器拷贝至OBS，请参考：
+>```python
+>import moxing
+># dst_url形如's3://OBS/PATH'，将ckpt目录拷贝至OBS后，可在OBS的`args_opt.train_url`目录下看到ckpt目录
+>moxing.file.copy_parallel(src_url='ckpt', dst_url=os.path.join(args_opt.train_url, 'ckpt'))
+>```
+
+## 创建训练作业
+可以参考[使用常用框架训练模型](https://support.huaweicloud.com/engineers-modelarts/modelarts_23_0238.html)来创建并启动训练作业。
+
+创建训练作业的参考配置：
+
+* 算法来源：常用框架->Ascend-Powered-Engine->MindSpore
+* 代码目录：如选择上述新建的OBS桶中的deeplabv3_example/deeplabv3/
+* 启动文件：如选择上述新建的OBS桶中的deeplabv3_example/deeplabv3/下的main.py
+* 数据来源：数据存储位置->选择上述新建的OBS桶中的deeplabv3_example/的voc2012目录
+* 训练输出位置：选择上述新建的OBS桶中的deeplabv3_example/目录，并在其中创建output目录
+* 运行参数：点击增加运行参数，分别输入checkpoint_url参数和对应具体路径值的参数，如本实验输入为s3://ms-course(桶名称)/deeplabv3_example/checkpoint/。
+* 作业日志路径：选择上述新建的OBS桶中的deeplabv3_example/目录，并在其中创建log目录
+* 规格：Ascend:1*Ascend 910
+* 其他均为默认
+
+点击提交以开始训练，查看训练过程：
+1. 在训练作业列表里可以看到刚创建的训练作业，在训练作业页面可以看到版本管理。
+2. 点击运行中的训练作业，在展开的窗口中可以查看作业配置信息，以及训练过程中的日志，日志会不断刷新，等训练作业完成后也可以下载日志到本地进行查看。
+
+> 提示：ModelArts提供了[PyCharm ToolKit工具](https://support.huaweicloud.com/tg-modelarts/modelarts_15_0003.html) ，方便基于MindSpore框架的脚本开发和调试；
+> 在使用PyCharm ToolKit工具进行传参训练时，注意参数key-value的书写格式，如本实验设置：checkpoint_url=s3://ms-course(桶名称)/deeplabv3_example/checkpoint/ 。
+> 或者可用ModelArts下的开发环境[Notebook](https://support.huaweicloud.com/engineers-modelarts/modelarts_23_0034.html) 进行基于MindSpore框架的脚本开发和调试。
+
+## 实验结论
+本实验主要介绍使用MindSpore在voc2012数据集上训练和推理deeplabv3网络模型，了解以下知识点：
+* 加载VOC2012数据集并进行相关数据增强等预处理操作；
+* 了解deeplabv3网络模型结构及其在MindSpore框架下的实现；
+* 使用fine-tune功能对模型进行微调；
+* 使用自定义Callback实现性能监测；
+* 使用自定义的Miou指标进行模型推理性能评估。
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diff --git a/deeplabv3/main.py b/deeplabv3/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..47353d95f98929463bb93b81e074ff817264fa90
--- /dev/null
+++ b/deeplabv3/main.py
@@ -0,0 +1,115 @@
+# # 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.
+# # ============================================================================
+"""train."""
+import argparse
+from mindspore import context
+from mindspore.communication.management import init
+from mindspore.nn.optim.momentum import Momentum
+from mindspore import Model, ParallelMode
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.train.callback import Callback, CheckpointConfig, ModelCheckpoint, TimeMonitor
+from src.md_dataset import create_dataset
+from src.losses import OhemLoss
+from src.deeplabv3 import deeplabv3_resnet50
+from src.config import config
+from src.miou_precision import MiouPrecision
+
+parser = argparse.ArgumentParser(description="Deeplabv3 training")
+parser.add_argument("--distribute", type=str, default="false", help="Run distribute, default is false.")
+parser.add_argument('--data_url', required=True, default=None, help='Train data url')
+parser.add_argument('--train_url', required=True, default=None, help='Train data output url')
+parser.add_argument('--checkpoint_url', default=None, help='Checkpoint path')
+args_opt = parser.parse_args()
+print(args_opt)
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") #无需指定DEVICE_ID
+
+data_path = "./voc2012"
+train_checkpoint_path = "./checkpoint/deeplabv3_train_14-1_1.ckpt" #预训练的ckpt
+eval_checkpoint_path = "./checkpoint_deeplabv3-%s_732.ckpt" % config.epoch_size #训练结束存的ckpt
+
+class LossCallBack(Callback):
+    """
+    Monitor the loss in training.
+    Note:
+        if per_print_times is 0 do not print loss.
+    Args:
+        per_print_times (int): Print loss every times. Default: 1.
+    """
+    def __init__(self, per_print_times=1):
+        super(LossCallBack, self).__init__()
+        if not isinstance(per_print_times, int) or per_print_times < 0:
+            raise ValueError("print_step must be int and >= 0")
+        self._per_print_times = per_print_times
+    def step_end(self, run_context):
+        cb_params = run_context.original_args()
+        print("epoch: {}, step: {}, outputs are {}".format(cb_params.cur_epoch_num, cb_params.cur_step_num,
+                                                           str(cb_params.net_outputs)))
+def model_fine_tune(flags, train_net, fix_weight_layer):
+    path = flags.checkpoint_url
+    if path is None:
+        return
+    path = train_checkpoint_path
+    param_dict = load_checkpoint(path)
+    load_param_into_net(train_net, param_dict)
+    for para in train_net.trainable_params():
+        if fix_weight_layer in para.name:
+            para.requires_grad = False
+
+if __name__ == "__main__":
+    if args_opt.distribute == "true":
+        context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True)
+        init()
+    args_opt.base_size = config.crop_size
+    args_opt.crop_size = config.crop_size
+
+    import moxing as mox
+    mox.file.copy_parallel(src_url=args_opt.data_url, dst_url='voc2012/')
+    mox.file.copy_parallel(src_url=args_opt.checkpoint_url, dst_url='checkpoint/')
+
+    # train
+    train_dataset = create_dataset(args_opt, data_path, config.epoch_size, config.batch_size, usage="train")
+    dataset_size = train_dataset.get_dataset_size()
+    time_cb = TimeMonitor(data_size=dataset_size)
+    callback = [time_cb, LossCallBack()]
+    if config.enable_save_ckpt:
+        config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_steps,
+                                     keep_checkpoint_max=config.save_checkpoint_num)
+        ckpoint_cb = ModelCheckpoint(prefix='checkpoint_deeplabv3', config=config_ck)
+        callback.append(ckpoint_cb)
+    net = deeplabv3_resnet50(config.seg_num_classes, [config.batch_size, 3, args_opt.crop_size, args_opt.crop_size],
+                             infer_scale_sizes=config.eval_scales, atrous_rates=config.atrous_rates,
+                             decoder_output_stride=config.decoder_output_stride, output_stride=config.output_stride,
+                             fine_tune_batch_norm=config.fine_tune_batch_norm, image_pyramid=config.image_pyramid)
+    net.set_train()
+    model_fine_tune(args_opt, net, 'layer')
+    loss = OhemLoss(config.seg_num_classes, config.ignore_label)
+    opt = Momentum(filter(lambda x: 'beta' not in x.name and 'gamma' not in x.name and 'depth' not in x.name and 'bias' not in x.name, net.trainable_params()), learning_rate=config.learning_rate, momentum=config.momentum, weight_decay=config.weight_decay)
+    model = Model(net, loss, opt)
+    model.train(config.epoch_size, train_dataset, callback)
+
+    # eval
+    eval_dataset = create_dataset(args_opt, data_path, config.epoch_size, config.batch_size, usage="eval")
+    net = deeplabv3_resnet50(config.seg_num_classes, [config.batch_size, 3, args_opt.crop_size, args_opt.crop_size],
+                             infer_scale_sizes=config.eval_scales, atrous_rates=config.atrous_rates,
+                             decoder_output_stride=config.decoder_output_stride, output_stride=config.output_stride,
+                             fine_tune_batch_norm=config.fine_tune_batch_norm, image_pyramid=config.image_pyramid)
+
+    param_dict = load_checkpoint(eval_checkpoint_path)
+    load_param_into_net(net, param_dict)
+    mIou = MiouPrecision(config.seg_num_classes)
+    metrics = {'mIou': mIou}
+    loss = OhemLoss(config.seg_num_classes, config.ignore_label)
+    model = Model(net, loss, metrics=metrics)
+    model.eval(eval_dataset)
\ No newline at end of file