!13941 add vgg19

From: @sanshenghua2
Reviewed-by: @c_34,@oacjiewen
Signed-off-by: @c_34

model_zoo/research/cv/vgg19/README_CN.md

@@ -0,0 +1,298 @@
+# 目录
+
+<!-- TOC -->
+
+- [目录](#目录)
+- [VGG描述](#vgg描述)
+- [模型架构](#模型架构)
+- [数据集](#数据集)
+- [特性](#特性)
+    - [混合精度](#混合精度)
+- [环境要求](#环境要求)
+- [快速入门](#快速入门)
+- [脚本说明](#脚本说明)
+    - [脚本及样例代码](#脚本及样例代码)
+    - [脚本参数](#脚本参数)
+        - [训练](#训练)
+        - [评估](#评估)
+    - [参数配置](#参数配置)
+    - [训练过程](#训练过程)
+        - [训练](#训练-1)
+            - [GPU处理器环境运行VGG19](#gpu处理器环境运行vgg19)
+    - [评估过程](#评估过程)
+        - [评估](#评估-1)
+- [模型描述](#模型描述)
+    - [性能](#性能)
+        - [训练性能](#训练性能)
+        - [评估性能](#评估性能)
+- [随机情况说明](#随机情况说明)
+- [ModelZoo主页](#modelzoo主页)
+
+<!-- /TOC -->
+
+# VGG描述
+
+于2014年提出的VGG是用于大规模图像识别的非常深的卷积网络。它在ImageNet大型视觉识别大赛2014（ILSVRC14）中获得了目标定位第一名和图像分类第二名。
+
+[论文](https://arxiv.org/abs/1409.1556): Simonyan K, zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition[J]. arXiv preprint arXiv:1409.1556, 2014.
+
+# 模型架构
+
+VGG 19网络主要由几个基本模块（包括卷积层和池化层）和三个连续密集层组成。
+这里的基本模块主要包括以下基本操作：  **3×3卷积**和**2×2最大池化**。
+
+# 数据集
+
+## 使用的数据集：[ImageNet2012](http://www.image-net.org/)
+
+- 数据集大小：约146 GB，共1000个类、128万张彩色图像
+    - 训练集：140 GB，1,281,167张图像
+    - 测试集：6.4 GB，50, 000张图像
+- 数据格式：RGB图像。
+    - 注：数据在src/dataset.py中处理。
+
+## 数据集组织方式
+
+  ImageNet2012
+
+  > 将ImageNet2012数据集解压到任意路径，文件夹结构应包含训练数据集和评估数据集，如下所示：
+  >
+  > ```bash
+  > .
+  > └─dataset
+  >   ├─ilsvrc                # 训练数据集
+  >   └─validation_preprocess # 评估数据集
+  > ```
+
+# 特性
+
+## 混合精度
+
+采用[混合精度](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
+
+以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
+
+# 环境要求
+
+- 硬件（Ascend或GPU）
+    - 准备Ascend或GPU处理器搭建硬件环境。如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，审核通过即可获得资源。
+- 框架
+    - [MindSpore](https://www.mindspore.cn/install)
+- 如需查看详情，请参见如下资源：
+    - [MindSpore教程](https://www.mindspore.cn/tutorial/training/zh-CN/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/zh-CN/master/index.html)
+
+# 快速入门
+
+通过官方网站安装MindSpore后，您可以按照如下步骤进行训练和评估：
+
+- Ascend处理器环境运行
+
+```python
+# 训练示例
+python train.py  --data_path=[DATA_PATH] --device_id=[DEVICE_ID] > output.train.log 2>&1 &
+
+# 分布式训练示例
+sh run_distribute_train.sh [RANL_TABLE_JSON] [DATA_PATH]
+
+# 评估示例
+python eval.py --data_path=[DATA_PATH]  --pre_trained=[PRE_TRAINED] > output.eval.log 2>&1 &
+```
+
+分布式训练需要提前创建JSON格式的HCCL配置文件。
+具体操作，参见：
+<https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools>
+
+- GPU处理器环境运行
+
+```python
+# 训练示例
+python train.py --device_target="GPU" --device_id=[DEVICE_ID] --dataset=[DATASET_TYPE] --data_path=[DATA_PATH] > output.train.log 2>&1 &
+
+# 分布式训练示例
+sh run_distribute_train_gpu.sh [DATA_PATH]
+
+# 评估示例
+python eval.py --device_target="GPU" --device_id=[DEVICE_ID] --dataset=[DATASET_TYPE] --data_path=[DATA_PATH]  --pre_trained=[PRE_TRAINED] > output.eval.log 2>&1 &
+```
+
+# 脚本说明
+
+## 脚本及样例代码
+
+```bash
+├── model_zoo
+    ├── README.md                                 // 所有模型相关说明
+    ├── vgg19
+        ├── README.md                             // GoogLeNet相关说明
+        ├── scripts
+        │   ├── run_distribute_train.sh           // Ascend分布式训练shell脚本
+        │   ├── run_distribute_train_gpu.sh       // GPU分布式训练shell脚本
+        ├── src
+        │   ├── utils
+        │   │   ├── logging.py                    // 日志格式设置
+        │   │   ├── sampler.py                    // 为数据集创建采样器
+        │   │   ├── util.py                       // 工具函数
+        │   │   ├── var_init.py                   // 网络参数init方法
+        │   ├── config.py                         // 参数配置
+        │   ├── crossentropy.py                   // 损失计算
+        │   ├── dataset.py                        // 创建数据集
+        │   ├── linear_warmup.py                  // 线性学习率
+        │   ├── warmup_cosine_annealing_lr.py     // 余弦退火学习率
+        │   ├── warmup_step_lr.py                 // 单次或多次迭代学习率
+        │   ├──vgg.py                             // VGG架构
+        ├── train.py                              // 训练脚本
+        ├── eval.py                               // 评估脚本
+```
+
+## 脚本参数
+
+### 训练
+
+```bash
+用法：train.py [--device_target TARGET][--data_path DATA_PATH]
+                [--dataset  DATASET_TYPE][--is_distributed VALUE]
+                [--device_id DEVICE_ID][--pre_trained PRE_TRAINED]
+                [--ckpt_path CHECKPOINT_PATH][--ckpt_interval INTERVAL_STEP]
+
+选项：
+  --device_target       训练后端类型，Ascend或GPU，默认为Ascend。
+  --dataset             数据集类型，cifar10或imagenet2012。
+  --is_distributed      训练方式，是否为分布式训练，值可以是0或1。
+  --data_path           数据集存储路径
+  --device_id           用于训练模型的设备。
+  --pre_trained         预训练检查点文件路径。
+  --ckpt_path           存放检查点的路径。
+  --ckpt_interval       保存检查点的轮次间隔。
+
+```
+
+### 评估
+
+```bash
+用法：eval.py [--device_target TARGET][--data_path DATA_PATH]
+               [--dataset  DATASET_TYPE][--pre_trained PRE_TRAINED]
+               [--device_id DEVICE_ID]
+
+选项：
+  --device_target       评估后端类型，Ascend或GPU，默认为Ascend。
+  --dataset             数据集类型，cifar10或imagenet2012。
+  --data_path           数据集存储路径。
+  --device_id           用于评估模型的设备。
+  --pre_trained         用于评估模型的检查点文件路径。
+```
+
+## 参数配置
+
+在config.py中可以同时配置训练参数和评估参数。
+
+- VGG19配置，ImageNet2012数据集
+
+```bash
+"num_classes": 1000,                 # 数据集类数
+"lr": 0.01,                          # 学习率
+"lr_init": 0.01,                     # 初始学习率
+"lr_max": 0.1,                       # 最大学习率
+"lr_epochs": '30,60,90,120',         # 基于变化lr的轮次
+"lr_scheduler": "cosine_annealing",  # 学习率模式
+"warmup_epochs": 0,                  # 热身轮次数
+"batch_size": 32,                    # 输入张量的批次大小
+"max_epoch": 150,                    # 只对训练有效，推理固定值为1
+"momentum": 0.9,                     # 动量
+"weight_decay": 1e-4,                # 权重衰减
+"loss_scale": 1024,                  # 损失放大
+"label_smooth": 1,                   # 标签平滑
+"label_smooth_factor": 0.1,          # 标签平滑因子
+"buffer_size": 10,                   # 混洗缓冲区大小
+"image_size": '224,224',             # 图像大小
+"pad_mode": 'pad',                   # conv2d的填充方式
+"padding": 1,                        # conv2d的填充值
+"has_bias": True,                    # conv2d是否有偏差
+"batch_norm": False,                 # 在conv2d中是否有batch_norm
+"keep_checkpoint_max": 10,           # 只保留最后一个keep_checkpoint_max检查点
+"initialize_mode": "KaimingNormal",  # conv2d init模式
+"has_dropout": True                  # 是否使用Dropout层
+```
+
+## 训练过程
+
+### 训练
+
+#### GPU处理器环境运行VGG19
+
+- 单设备训练（1p）
+
+```bash
+python train.py  --device_target="GPU" --dataset="imagenet2012" --is_distributed=0 --data_path=$DATA_PATH  > output.train.log 2>&1 &
+```
+
+- 分布式训练
+
+```bash
+# 分布式训练（8p）
+bash scripts/run_distribute_train_gpu.sh /path/ImageNet2012/train"
+```
+
+## 评估过程
+
+### 评估
+
+- 评估过程如下，需要指定数据集类型为“cifar10”或“imagenet2012”。
+
+```bash
+# 使用ImageNet2012数据集
+python eval.py --data_path=your_data_path --dataset="imagenet2012" --device_target="GPU" --pre_trained=./*-150-5004.ckpt > output.eval.log 2>&1 &
+```
+
+- 上述python命令在后台运行，可通过`output.eval.log`文件查看结果。准确率如下：
+
+```bash
+# 使用ImageNet2012数据集
+after allreduce eval: top1_correct=37101, tot=49984,acc=74.23%
+
+after allreduce eval: top5_correct=46007, tot=49984,acc=92.04%
+```
+
+# 模型描述
+
+## 性能
+
+### 训练性能
+
+| 参数           | VGG19(Ascend)                                  |
+| -------------------------- | ---------------------------------------------- |
+| 模型版本                | VGG19                                          |
+| 资源                   | Ascend 910；CPU：2.60GHz，192核；内存：755 GB    |
+| 上传日期              | 2021-03-18                                           |
+| MindSpore版本        | 1.1.1-alpha                                     |
+| 数据集                |ImageNet2012                                     |
+| 训练参数  |epoch=90, steps=2502, batch_size = 64, lr=0.1  |
+| 优化器                  | Momentum                                                        |
+| 损失函数 | SoftmaxCrossEntropy |
+| 输出              | 概率                                                |
+| 损失             |1.5~2.0                                          |
+| 速度 | 8卡：97.4毫秒/步 |
+| 总时长 | 8卡：6.1小时 |
+| 调优检查点 |    1.1 GB（.ckpt 文件）               |
+| 脚本                  |[VGG19](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/vgg19) |                   |
+
+### 评估性能
+
+| 参数  | VGG19(Ascend)                         |
+| ------------------- | --------------------------- |
+| 模型版本      | VGG19                       |
+| 资源        | Ascend 910                  |
+| 上传日期              | 2021-03-18                    |
+| MindSpore版本   | 1.1.1-alpha                 |
+| 数据集 | ImageNet2012，5000张图像 |
+| batch_size          |   64                        |
+| 输出 | 概率 |
+| 准确率 | 8卡：top1_correct 74.23%,top5_correct 92.04%; |
+
+# 随机情况说明
+
+dataset.py中设置了“create_dataset”函数内的种子，同时还使用了train.py中的随机种子。
+
+# ModelZoo主页
+
+ 请浏览官网[主页](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)。  

model_zoo/research/cv/vgg19/eval.py

@@ -0,0 +1,212 @@
+# Copyright 2021 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.
+# ============================================================================
+"""Eval"""
+import os
+import time
+import argparse
+import datetime
+import glob
+import numpy as np
+import mindspore.nn as nn
+
+from mindspore import Tensor, context
+from mindspore.nn.optim.momentum import Momentum
+from mindspore.train.model import Model
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.ops import operations as P
+from mindspore.ops import functional as F
+from mindspore.common import dtype as mstype
+
+from src.utils.logging import get_logger
+from src.vgg import vgg19
+from src.dataset import vgg_create_dataset
+from src.dataset import classification_dataset
+
+
+class ParameterReduce(nn.Cell):
+    """ParameterReduce"""
+    def __init__(self):
+        super(ParameterReduce, self).__init__()
+        self.cast = P.Cast()
+        self.reduce = P.AllReduce()
+
+    def construct(self, x):
+        one = self.cast(F.scalar_to_array(1.0), mstype.float32)
+        out = x * one
+        ret = self.reduce(out)
+        return ret
+
+
+def parse_args(cloud_args=None):
+    """parse_args"""
+    parser = argparse.ArgumentParser('mindspore classification test')
+    parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
+                        help='device where the code will be implemented. (Default: Ascend)')
+    # dataset related
+    parser.add_argument('--dataset', type=str, choices=["cifar10", "imagenet2012"], default="cifar10")
+    parser.add_argument('--data_path', type=str, default='', help='eval data dir')
+    parser.add_argument('--per_batch_size', default=32, type=int, help='batch size for per npu')
+    # network related
+    parser.add_argument('--graph_ckpt', type=int, default=1, help='graph ckpt or feed ckpt')
+    parser.add_argument('--pre_trained', default='', type=str, help='fully path of pretrained model to load. '
+                        'If it is a direction, it will test all ckpt')
+
+    # logging related
+    parser.add_argument('--log_path', type=str, default='outputs/', help='path to save log')
+    parser.add_argument('--rank', type=int, default=0, help='local rank of distributed')
+    parser.add_argument('--group_size', type=int, default=1, help='world size of distributed')
+
+    args_opt = parser.parse_args()
+    args_opt = merge_args(args_opt, cloud_args)
+
+    if args_opt.dataset == "cifar10":
+        from src.config import cifar_cfg as cfg
+    else:
+        from src.config import imagenet_cfg as cfg
+
+    args_opt.image_size = cfg.image_size
+    args_opt.num_classes = cfg.num_classes
+    args_opt.per_batch_size = cfg.batch_size
+    args_opt.momentum = cfg.momentum
+    args_opt.weight_decay = cfg.weight_decay
+    args_opt.buffer_size = cfg.buffer_size
+    args_opt.pad_mode = cfg.pad_mode
+    args_opt.padding = cfg.padding
+    args_opt.has_bias = cfg.has_bias
+    args_opt.batch_norm = cfg.batch_norm
+    args_opt.initialize_mode = cfg.initialize_mode
+    args_opt.has_dropout = cfg.has_dropout
+
+    args_opt.image_size = list(map(int, args_opt.image_size.split(',')))
+
+    return args_opt
+
+
+def get_top5_acc(top5_arg, gt_class):
+    sub_count = 0
+    for top5, gt in zip(top5_arg, gt_class):
+        if gt in top5:
+            sub_count += 1
+    return sub_count
+
+
+def merge_args(args, cloud_args):
+    """merge_args"""
+    args_dict = vars(args)
+    if isinstance(cloud_args, dict):
+        for key in cloud_args.keys():
+            val = cloud_args[key]
+            if key in args_dict and val:
+                arg_type = type(args_dict[key])
+                if arg_type is not type(None):
+                    val = arg_type(val)
+                args_dict[key] = val
+    return args
+
+
+def test(cloud_args=None):
+    """test"""
+    args = parse_args(cloud_args)
+    context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
+                        device_target=args.device_target, save_graphs=False)
+    if os.getenv('DEVICE_ID', "not_set").isdigit() and args.device_target == "Ascend":
+        context.set_context(device_id=int(os.getenv('DEVICE_ID')))
+
+    args.outputs_dir = os.path.join(args.log_path,
+                                    datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
+
+    args.logger = get_logger(args.outputs_dir, args.rank)
+    args.logger.save_args(args)
+
+    if args.dataset == "cifar10":
+        net = vgg19(num_classes=args.num_classes, args=args)
+        opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, args.momentum,
+                       weight_decay=args.weight_decay)
+        loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+        model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
+
+        param_dict = load_checkpoint(args.pre_trained)
+        load_param_into_net(net, param_dict)
+        net.set_train(False)
+        dataset = vgg_create_dataset(args.data_path, args.image_size, args.per_batch_size, training=False)
+        res = model.eval(dataset)
+        print("result: ", res)
+    else:
+        # network
+        args.logger.important_info('start create network')
+        if os.path.isdir(args.pre_trained):
+            models = list(glob.glob(os.path.join(args.pre_trained, '*.ckpt')))
+            print(models)
+            if args.graph_ckpt:
+                f = lambda x: -1 * int(os.path.splitext(os.path.split(x)[-1])[0].split('-')[-1].split('_')[0])
+            else:
+                f = lambda x: -1 * int(os.path.splitext(os.path.split(x)[-1])[0].split('_')[-1])
+            args.models = sorted(models, key=f)
+        else:
+            args.models = [args.pre_trained,]
+
+        for model in args.models:
+            dataset = classification_dataset(args.data_path, args.image_size, args.per_batch_size, mode='eval')
+            eval_dataloader = dataset.create_tuple_iterator(output_numpy=True, num_epochs=1)
+            network = vgg19(args.num_classes, args, phase="test")
+
+            # pre_trained
+            load_param_into_net(network, load_checkpoint(model))
+            network.add_flags_recursive(fp16=True)
+
+            img_tot = 0
+            top1_correct = 0
+            top5_correct = 0
+
+            network.set_train(False)
+            t_end = time.time()
+            it = 0
+            for data, gt_classes in eval_dataloader:
+                output = network(Tensor(data, mstype.float32))
+                output = output.asnumpy()
+
+                top1_output = np.argmax(output, (-1))
+                top5_output = np.argsort(output)[:, -5:]
+
+                t1_correct = np.equal(top1_output, gt_classes).sum()
+                top1_correct += t1_correct
+                top5_correct += get_top5_acc(top5_output, gt_classes)
+                img_tot += args.per_batch_size
+
+                if args.rank == 0 and it == 0:
+                    t_end = time.time()
+                    it = 1
+            if args.rank == 0:
+                time_used = time.time() - t_end
+                fps = (img_tot - args.per_batch_size) * args.group_size / time_used
+                args.logger.info('Inference Performance: {:.2f} img/sec'.format(fps))
+            results = [[top1_correct], [top5_correct], [img_tot]]
+            args.logger.info('before results={}'.format(results))
+            results = np.array(results)
+
+            args.logger.info('after results={}'.format(results))
+            top1_correct = results[0, 0]
+            top5_correct = results[1, 0]
+            img_tot = results[2, 0]
+            acc1 = 100.0 * top1_correct / img_tot
+            acc5 = 100.0 * top5_correct / img_tot
+            args.logger.info('after allreduce eval: top1_correct={}, tot={},'
+                             'acc={:.2f}%(TOP1)'.format(top1_correct, img_tot, acc1))
+            args.logger.info('after allreduce eval: top5_correct={}, tot={},'
+                             'acc={:.2f}%(TOP5)'.format(top5_correct, img_tot, acc5))
+
+
+if __name__ == "__main__":
+    test()

model_zoo/research/cv/vgg19/export.py

@@ -0,0 +1,65 @@
+# Copyright 2021 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.
+# ============================================================================
+"""export checkpoint file into models"""
+import argparse
+import numpy as np
+
+from mindspore import Tensor, context
+import mindspore.common.dtype as mstype
+from mindspore.train.serialization import load_checkpoint, export
+
+from src.vgg import vgg19
+
+parser = argparse.ArgumentParser(description='VGG19 export')
+parser.add_argument("--device_id", type=int, default=0, help="Device id")
+parser.add_argument('--dataset', type=str, choices=["cifar10", "imagenet2012"], default="cifar10", help='ckpt file')
+parser.add_argument('--ckpt_file', type=str, required=True, help='vgg19 ckpt file.')
+parser.add_argument('--file_name', type=str, default='vgg19', help='vgg19 output file name.')
+parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR', help='file format')
+parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
+                    help="device target")
+args = parser.parse_args()
+
+if args.dataset == "cifar10":
+    from src.config import cifar_cfg as cfg
+else:
+    from src.config import imagenet_cfg as cfg
+
+args.num_classes = cfg.num_classes
+args.pad_mode = cfg.pad_mode
+args.padding = cfg.padding
+args.has_bias = cfg.has_bias
+args.initialize_mode = cfg.initialize_mode
+args.batch_norm = cfg.batch_norm
+args.has_dropout = cfg.has_dropout
+args.image_size = list(map(int, cfg.image_size.split(',')))
+
+context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+if args.device_target == "Ascend":
+    context.set_context(device_id=args.device_id)
+
+if __name__ == '__main__':
+    if args.dataset == "cifar10":
+        net = vgg19(num_classes=args.num_classes, args=args)
+    else:
+        net = vgg19(args.num_classes, args, phase="test")
+        net.add_flags_recursive(fp19=True)
+
+    load_checkpoint(args.ckpt_file, net=net)
+    net.set_train(False)
+
+    input_data = Tensor(np.zeros([cfg.batch_size, 3, args.image_size[0], args.image_size[1]]), mstype.float32)
+
+    export(net, input_data, file_name=args.file_name, file_format=args.file_format)

model_zoo/research/cv/vgg19/mindspore_hub_conf.py

@@ -0,0 +1,26 @@
+# Copyright 2021 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.
+# ============================================================================
+"""hub config."""
+from src.vgg import vgg19 as VGG19
+
+
+def vgg19(*args, **kwargs):
+    return VGG19(*args, **kwargs)
+
+
+def create_network(name, *args, **kwargs):
+    if name == "vgg19":
+        return vgg19(*args, **kwargs)
+    raise NotImplementedError(f"{name} is not implemented in the repo")

model_zoo/research/cv/vgg19/scripts/run_distribute_train.sh

@@ -0,0 +1,76 @@
+#!/bin/bash
+# Copyright 2021 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.
+# ============================================================================
+
+if [ $# != 2 ] && [ $# != 3 ]
+then
+    echo "Usage: sh run_distribute_train.sh [RANK_TABLE_FILE] [DATA_PATH] [cifar10|imagenet2012]"
+exit 1
+fi
+
+if [ ! -f $1 ]
+then
+    echo "error: RANK_TABLE_FILE=$1 is not a file"
+exit 1
+fi
+
+if [ ! -d $2 ]
+then
+    echo "error: DATA_PATH=$2 is not a directory"
+exit 1
+fi
+
+
+dataset_type='cifar10'
+if [ $# == 3 ]
+then
+    if [ $3 != "cifar10" ] && [ $3 != "imagenet2012" ]
+    then
+        echo "error: the selected dataset is neither cifar10 nor imagenet2012"
+    exit 1
+    fi
+    dataset_type=$3
+fi
+
+export DEVICE_NUM=8
+export RANK_SIZE=8
+export RANK_TABLE_FILE=$1
+
+cpus=`cat /proc/cpuinfo| grep "processor"| wc -l`
+avg=`expr $cpus \/ $RANK_SIZE`
+gap=`expr $avg \- 1`
+
+script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+src_dir=$script_dir/..
+
+start_idx=0
+for((i=0;i<RANK_SIZE;i++))
+do
+    start=`expr $i \* $avg`
+    end=`expr $start \+ $gap`
+    cmdopt=$start"-"$end
+
+    export DEVICE_ID=`expr $i \+ $start_idx`
+    export RANK_ID=$i
+    rm -rf ./train_parallel$DEVICE_ID
+    mkdir ./train_parallel$DEVICE_ID
+    cp $src_dir/*.py ./train_parallel$DEVICE_ID
+    cp -r $src_dir/src ./train_parallel$DEVICE_ID
+    cd ./train_parallel$DEVICE_ID || exit
+    echo "start training for rank $RANK_ID, device $DEVICE_ID, $dataset_type"
+    env > env.log
+    taskset -c $cmdopt python train.py --data_path=$2 --device_target="Ascend" --device_id=$DEVICE_ID --is_distributed=1 --dataset=$dataset_type &> log &
+    cd ..
+done

model_zoo/research/cv/vgg19/scripts/run_distribute_train_gpu.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+# Copyright 2021 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.
+# ============================================================================
+
+echo "=============================================================================================================="
+echo "Please run the script as: "
+echo "bash run_distribute_train_gpu.sh DATA_PATH"
+echo "for example: bash run_distribute_train_gpu.sh /path/ImageNet2012/train"
+echo "=============================================================================================================="
+
+DATA_PATH=$1
+
+mpirun -n 8 --output-filename log_output --merge-stderr-to-stdout \
+  python train.py  \
+    --device_target="GPU" \
+    --dataset="imagenet2012" \
+    --is_distributed=1 \
+    --data_path=$DATA_PATH  > output.train.log 2>&1 &

model_zoo/research/cv/vgg19/scripts/run_eval.sh

@@ -0,0 +1,32 @@
+#!/bin/bash
+# Copyright 2021 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.
+# ============================================================================
+
+echo "=============================================================================================================="
+echo "Please run the script as: "
+echo "bash run_eval.sh DATA_PATH DATASET_TYPE DEVICE_TYPE CHECKPOINT_PATH"
+echo "for example: bash run_eval.sh /path/ImageNet2012/train cifar10 Ascend /path/a.ckpt "
+echo "=============================================================================================================="
+
+DATA_PATH=$1
+DATASET_TYPE=$2
+DEVICE_TYPE=$3
+CHECKPOINT_PATH=$4
+
+python eval.py \
+    --data_path=$DATA_PATH \
+    --dataset=$DATASET_TYPE \
+    --device_target=$DEVICE_TYPE \
+    --pre_trained=$CHECKPOINT_PATH > output.eval.log 2>&1 &

model_zoo/research/cv/vgg19/src/__init__.py

@@ -0,0 +1,14 @@
+# Copyright 2021 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
+#
+# httpwww.apache.orglicensesLICENSE-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.
+# ============================================================================

model_zoo/research/cv/vgg19/src/config.py

@@ -0,0 +1,72 @@
+# Copyright 2021 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.
+# ============================================================================
+"""
+network config setting, will be used in train.py and eval.py
+"""
+from easydict import EasyDict as edict
+
+# config for vgg19, cifar10
+cifar_cfg = edict({
+    "num_classes": 10,
+    "lr": 0.01,
+    "lr_init": 0.01,
+    "lr_max": 0.1,
+    "lr_epochs": '30,60,90,120',
+    "lr_scheduler": "step",
+    "warmup_epochs": 5,
+    "batch_size": 64,
+    "max_epoch": 70,
+    "momentum": 0.9,
+    "weight_decay": 5e-4,
+    "loss_scale": 1.0,
+    "label_smooth": 0,
+    "label_smooth_factor": 0,
+    "buffer_size": 10,
+    "image_size": '224,224',
+    "pad_mode": 'same',
+    "padding": 0,
+    "has_bias": False,
+    "batch_norm": True,
+    "keep_checkpoint_max": 10,
+    "initialize_mode": "XavierUniform",
+    "has_dropout": False
+})
+
+# config for vgg19, imagenet2012
+imagenet_cfg = edict({
+    "num_classes": 1000,
+    "lr": 0.04,
+    "lr_init": 0.01,
+    "lr_max": 0.1,
+    "lr_epochs": '30,60,90,120',
+    "lr_scheduler": 'cosine_annealing',
+    "warmup_epochs": 0,
+    "batch_size": 64,
+    "max_epoch": 90,
+    "momentum": 0.9,
+    "weight_decay": 1e-4,
+    "loss_scale": 1024,
+    "label_smooth": 1,
+    "label_smooth_factor": 0.1,
+    "buffer_size": 10,
+    "image_size": '224,224',
+    "pad_mode": 'pad',
+    "padding": 1,
+    "has_bias": False,
+    "batch_norm": False,
+    "keep_checkpoint_max": 10,
+    "initialize_mode": "KaimingNormal",
+    "has_dropout": True
+})

model_zoo/research/cv/vgg19/src/crossentropy.py

@@ -0,0 +1,39 @@
+# Copyright 2021 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.
+# ============================================================================
+"""define loss function for network"""
+from mindspore.nn.loss.loss import _Loss
+from mindspore.ops import operations as P
+from mindspore.ops import functional as F
+from mindspore import Tensor
+from mindspore.common import dtype as mstype
+import mindspore.nn as nn
+
+
+class CrossEntropy(_Loss):
+    """the redefined loss function with SoftmaxCrossEntropyWithLogits"""
+
+    def __init__(self, smooth_factor=0., num_classes=1001):
+        super(CrossEntropy, self).__init__()
+        self.onehot = P.OneHot()
+        self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
+        self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32)
+        self.ce = nn.SoftmaxCrossEntropyWithLogits()
+        self.mean = P.ReduceMean(False)
+
+    def construct(self, logit, label):
+        one_hot_label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
+        loss = self.ce(logit, one_hot_label)
+        loss = self.mean(loss, 0)
+        return loss

model_zoo/research/cv/vgg19/src/dataset.py

@@ -0,0 +1,195 @@
+# Copyright 2021 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.
+# ============================================================================
+"""
+dataset processing.
+"""
+import os
+from mindspore.common import dtype as mstype
+import mindspore.dataset as de
+import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.vision.c_transforms as vision
+from PIL import Image, ImageFile
+from src.utils.sampler import DistributedSampler
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+
+def vgg_create_dataset(data_home, image_size, batch_size, rank_id=0, rank_size=1, repeat_num=1, training=True):
+    """Data operations."""
+    data_dir = os.path.join(data_home, "cifar-10-batches-bin")
+    if not training:
+        data_dir = os.path.join(data_home, "cifar-10-verify-bin")
+
+    data_set = de.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id)
+
+    rescale = 1.0 / 255.0
+    shift = 0.0
+
+    # define map operations
+    random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4))  # padding_mode default CONSTANT
+    random_horizontal_op = vision.RandomHorizontalFlip()
+    resize_op = vision.Resize(image_size)  # interpolation default BILINEAR
+    rescale_op = vision.Rescale(rescale, shift)
+    normalize_op = vision.Normalize((0.4465, 0.4822, 0.4914), (0.2010, 0.1994, 0.2023))
+    changeswap_op = vision.HWC2CHW()
+    type_cast_op = C.TypeCast(mstype.int32)
+
+    c_trans = []
+    if training:
+        c_trans = [random_crop_op, random_horizontal_op]
+    c_trans += [resize_op, rescale_op, normalize_op,
+                changeswap_op]
+
+    # apply map operations on images
+    data_set = data_set.map(operations=type_cast_op, input_columns="label")
+    data_set = data_set.map(operations=c_trans, input_columns="image")
+
+    # apply repeat operations
+    data_set = data_set.repeat(repeat_num)
+
+    # apply shuffle operations
+    data_set = data_set.shuffle(buffer_size=10)
+
+    # apply batch operations
+    data_set = data_set.batch(batch_size=batch_size, drop_remainder=True)
+
+    return data_set
+
+
+def classification_dataset(data_dir, image_size, per_batch_size, rank=0, group_size=1,
+                           mode='train',
+                           input_mode='folder',
+                           root='',
+                           num_parallel_workers=None,
+                           shuffle=None,
+                           sampler=None,
+                           repeat_num=1,
+                           class_indexing=None,
+                           drop_remainder=True,
+                           transform=None,
+                           target_transform=None):
+    """
+    A function that returns a dataset for classification. The mode of input dataset could be "folder" or "txt".
+    If it is "folder", all images within one folder have the same label. If it is "txt", all paths of images
+    are written into a textfile.
+
+    Args:
+        data_dir (str): Path to the root directory that contains the dataset for "input_mode="folder"".
+            Or path of the textfile that contains every image's path of the dataset.
+        image_size (Union(int, sequence)): Size of the input images.
+        per_batch_size (int): the batch size of evey step during training.
+        rank (int): The shard ID within num_shards (default=None).
+        group_size (int): Number of shards that the dataset should be divided
+            into (default=None).
+        mode (str): "train" or others. Default: " train".
+        input_mode (str): The form of the input dataset. "folder" or "txt". Default: "folder".
+        root (str): the images path for "input_mode="txt"". Default: " ".
+        num_parallel_workers (int): Number of workers to read the data. Default: None.
+        shuffle (bool): Whether or not to perform shuffle on the dataset
+            (default=None, performs shuffle).
+        sampler (Sampler): Object used to choose samples from the dataset. Default: None.
+        repeat_num (int): the num of repeat dataset.
+        class_indexing (dict): A str-to-int mapping from folder name to index
+            (default=None, the folder names will be sorted
+            alphabetically and each class will be given a
+            unique index starting from 0).
+
+    Examples:
+        >>> from src.dataset import classification_dataset
+        >>> # path to imagefolder directory. This directory needs to contain sub-directories which contain the images
+        >>> data_dir = "/path/to/imagefolder_directory"
+        >>> de_dataset = classification_dataset(data_dir, image_size=[224, 244],
+        >>>                               per_batch_size=64, rank=0, group_size=4)
+        >>> # Path of the textfile that contains every image's path of the dataset.
+        >>> data_dir = "/path/to/dataset/images/train.txt"
+        >>> images_dir = "/path/to/dataset/images"
+        >>> de_dataset = classification_dataset(data_dir, image_size=[224, 244],
+        >>>                               per_batch_size=64, rank=0, group_size=4,
+        >>>                               input_mode="txt", root=images_dir)
+    """
+
+    mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
+    std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
+
+    if transform is None:
+        if mode == 'train':
+            transform_img = [
+                vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0)),
+                vision.RandomHorizontalFlip(prob=0.5),
+                vision.Normalize(mean=mean, std=std),
+                vision.HWC2CHW()
+            ]
+        else:
+            transform_img = [
+                vision.Decode(),
+                vision.Resize((256, 256)),
+                vision.CenterCrop(image_size),
+                vision.Normalize(mean=mean, std=std),
+                vision.HWC2CHW()
+            ]
+    else:
+        transform_img = transform
+
+    if target_transform is None:
+        transform_label = [C.TypeCast(mstype.int32)]
+    else:
+        transform_label = target_transform
+
+    if input_mode == 'folder':
+        de_dataset = de.ImageFolderDataset(data_dir, num_parallel_workers=num_parallel_workers,
+                                           shuffle=shuffle, sampler=sampler, class_indexing=class_indexing,
+                                           num_shards=group_size, shard_id=rank)
+    else:
+        dataset = TxtDataset(root, data_dir)
+        sampler = DistributedSampler(dataset, rank, group_size, shuffle=shuffle)
+        de_dataset = de.GeneratorDataset(dataset, ["image", "label"], sampler=sampler)
+
+    de_dataset = de_dataset.map(operations=transform_img, input_columns="image", num_parallel_workers=8)
+    de_dataset = de_dataset.map(operations=transform_label, input_columns="label", num_parallel_workers=8)
+
+    columns_to_project = ["image", "label"]
+    de_dataset = de_dataset.project(columns=columns_to_project)
+
+    de_dataset = de_dataset.batch(per_batch_size, drop_remainder=drop_remainder)
+    de_dataset = de_dataset.repeat(repeat_num)
+
+    return de_dataset
+
+
+class TxtDataset:
+    """
+    create txt dataset.
+
+    Args:
+    Returns:
+        de_dataset.
+    """
+    def __init__(self, root, txt_name):
+        super(TxtDataset, self).__init__()
+        self.imgs = []
+        self.labels = []
+        fin = open(txt_name, "r")
+        for line in fin:
+            img_name, label = line.strip().split(' ')
+            self.imgs.append(os.path.join(root, img_name))
+            self.labels.append(int(label))
+        fin.close()
+
+    def __getitem__(self, index):
+        img = Image.open(self.imgs[index]).convert('RGB')
+        return img, self.labels[index]
+
+    def __len__(self):
+        return len(self.imgs)

model_zoo/research/cv/vgg19/src/linear_warmup.py

@@ -0,0 +1,23 @@
+# Copyright 2021 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.
+# ============================================================================
+"""
+linear warm up learning rate.
+"""
+
+
+def linear_warmup_lr(current_step, warmup_steps, base_lr, init_lr):
+    lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
+    lr = float(init_lr) + lr_inc * current_step
+    return lr

model_zoo/research/cv/vgg19/src/utils/logging.py

@@ -0,0 +1,82 @@
+# Copyright 2021 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.
+# ============================================================================
+"""
+get logger.
+"""
+import logging
+import os
+import sys
+from datetime import datetime
+
+class LOGGER(logging.Logger):
+    """
+    set up logging file.
+
+    Args:
+        logger_name (string): logger name.
+        log_dir (string): path of logger.
+
+    Returns:
+        string, logger path
+    """
+    def __init__(self, logger_name, rank=0):
+        super(LOGGER, self).__init__(logger_name)
+        if rank % 8 == 0:
+            console = logging.StreamHandler(sys.stdout)
+            console.setLevel(logging.INFO)
+            formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
+            console.setFormatter(formatter)
+            self.addHandler(console)
+
+    def setup_logging_file(self, log_dir, rank=0):
+        """set up log file"""
+        self.rank = rank
+        if not os.path.exists(log_dir):
+            os.makedirs(log_dir, exist_ok=True)
+        log_name = datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S') + '_rank_{}.log'.format(rank)
+        self.log_fn = os.path.join(log_dir, log_name)
+        fh = logging.FileHandler(self.log_fn)
+        fh.setLevel(logging.INFO)
+        formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
+        fh.setFormatter(formatter)
+        self.addHandler(fh)
+
+    def info(self, msg, *args, **kwargs):
+        if self.isEnabledFor(logging.INFO):
+            self._log(logging.INFO, msg, args, **kwargs)
+
+    def save_args(self, args):
+        self.info('Args:')
+        args_dict = vars(args)
+        for key in args_dict.keys():
+            self.info('--> %s: %s', key, args_dict[key])
+        self.info('')
+
+    def important_info(self, msg, *args, **kwargs):
+        if self.isEnabledFor(logging.INFO) and self.rank == 0:
+            line_width = 2
+            important_msg = '\n'
+            important_msg += ('*'*70 + '\n')*line_width
+            important_msg += ('*'*line_width + '\n')*2
+            important_msg += '*'*line_width + ' '*8 + msg + '\n'
+            important_msg += ('*'*line_width + '\n')*2
+            important_msg += ('*'*70 + '\n')*line_width
+            self.info(important_msg, *args, **kwargs)
+
+
+def get_logger(path, rank):
+    logger = LOGGER("mindversion", rank)
+    logger.setup_logging_file(path, rank)
+    return logger

model_zoo/research/cv/vgg19/src/utils/sampler.py

@@ -0,0 +1,53 @@
+# Copyright 2021 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.
+# ============================================================================
+"""
+choose samples from the dataset
+"""
+import math
+import numpy as np
+
+class DistributedSampler():
+    """
+    sampling the dataset.
+
+    Args:
+    Returns:
+        num_samples, number of samples.
+    """
+    def __init__(self, dataset, rank, group_size, shuffle=True, seed=0):
+        self.dataset = dataset
+        self.rank = rank
+        self.group_size = group_size
+        self.dataset_length = len(self.dataset)
+        self.num_samples = int(math.ceil(self.dataset_length * 1.0 / self.group_size))
+        self.total_size = self.num_samples * self.group_size
+        self.shuffle = shuffle
+        self.seed = seed
+
+    def __iter__(self):
+        if self.shuffle:
+            self.seed = (self.seed + 1) & 0xffffffff
+            np.random.seed(self.seed)
+            indices = np.random.permutation(self.dataset_length).tolist()
+        else:
+            indices = list(range(len(self.dataset_length)))
+
+        indices += indices[:(self.total_size - len(indices))]
+        indices = indices[self.rank::self.group_size]
+        return iter(indices)
+
+    def __len__(self):
+        return self.num_samples
+ 

model_zoo/research/cv/vgg19/src/utils/util.py

@@ -0,0 +1,36 @@
+# Copyright 2021 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.
+# ============================================================================
+"""Util class or function."""
+
+
+def get_param_groups(network):
+    """Param groups for optimizer."""
+    decay_params = []
+    no_decay_params = []
+    for x in network.trainable_params():
+        parameter_name = x.name
+        if parameter_name.endswith('.bias'):
+            # all bias not using weight decay
+            no_decay_params.append(x)
+        elif parameter_name.endswith('.gamma'):
+            # bn weight bias not using weight decay, be carefully for now x not include BN
+            no_decay_params.append(x)
+        elif parameter_name.endswith('.beta'):
+            # bn weight bias not using weight decay, be carefully for now x not include BN
+            no_decay_params.append(x)
+        else:
+            decay_params.append(x)
+
+    return [{'params': no_decay_params, 'weight_decay': 0.0}, {'params': decay_params}]

model_zoo/research/cv/vgg19/src/utils/var_init.py

@@ -0,0 +1,210 @@
+# Copyright 2021 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.
+# ============================================================================
+"""
+Initialize.
+"""
+import math
+from functools import reduce
+import numpy as np
+import mindspore.nn as nn
+from mindspore.common import initializer as init
+
+def _calculate_gain(nonlinearity, param=None):
+    r"""
+    Return the recommended gain value for the given nonlinearity function.
+
+    The values are as follows:
+    ================= ====================================================
+    nonlinearity      gain
+    ================= ====================================================
+    Linear / Identity :math:`1`
+    Conv{1,2,3}D      :math:`1`
+    Sigmoid           :math:`1`
+    Tanh              :math:`\frac{5}{3}`
+    ReLU              :math:`\sqrt{2}`
+    Leaky Relu        :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}`
+    ================= ====================================================
+
+    Args:
+        nonlinearity: the non-linear function
+        param: optional parameter for the non-linear function
+
+    Examples:
+        >>> gain = calculate_gain('leaky_relu', 0.2)  # leaky_relu with negative_slope=0.2
+    """
+    linear_fns = ['linear', 'conv1d', 'conv2d', 'conv3d', 'conv_transpose1d', 'conv_transpose2d', 'conv_transpose3d']
+    if nonlinearity in linear_fns or nonlinearity == 'sigmoid':
+        return 1
+    if nonlinearity == 'tanh':
+        return 5.0 / 3
+    if nonlinearity == 'relu':
+        return math.sqrt(2.0)
+    if nonlinearity == 'leaky_relu':
+        if param is None:
+            negative_slope = 0.01
+        elif not isinstance(param, bool) and isinstance(param, int) or isinstance(param, float):
+            negative_slope = param
+        else:
+            raise ValueError("negative_slope {} not a valid number".format(param))
+        return math.sqrt(2.0 / (1 + negative_slope ** 2))
+
+    raise ValueError("Unsupported nonlinearity {}".format(nonlinearity))
+
+def _assignment(arr, num):
+    """Assign the value of `num` to `arr`."""
+    if arr.shape == ():
+        arr = arr.reshape((1))
+        arr[:] = num
+        arr = arr.reshape(())
+    else:
+        if isinstance(num, np.ndarray):
+            arr[:] = num[:]
+        else:
+            arr[:] = num
+    return arr
+
+def _calculate_in_and_out(arr):
+    """
+    Calculate n_in and n_out.
+
+    Args:
+        arr (Array): Input array.
+
+    Returns:
+        Tuple, a tuple with two elements, the first element is `n_in` and the second element is `n_out`.
+    """
+    dim = len(arr.shape)
+    if dim < 2:
+        raise ValueError("If initialize data with xavier uniform, the dimension of data must greater than 1.")
+
+    n_in = arr.shape[1]
+    n_out = arr.shape[0]
+
+    if dim > 2:
+        counter = reduce(lambda x, y: x * y, arr.shape[2:])
+        n_in *= counter
+        n_out *= counter
+    return n_in, n_out
+
+def _select_fan(array, mode):
+    mode = mode.lower()
+    valid_modes = ['fan_in', 'fan_out']
+    if mode not in valid_modes:
+        raise ValueError("Mode {} not supported, please use one of {}".format(mode, valid_modes))
+
+    fan_in, fan_out = _calculate_in_and_out(array)
+    return fan_in if mode == 'fan_in' else fan_out
+
+class KaimingInit(init.Initializer):
+    r"""
+    Base Class. Initialize the array with He kaiming algorithm.
+
+    Args:
+        a: the negative slope of the rectifier used after this layer (only
+            used with ``'leaky_relu'``)
+        mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
+            preserves the magnitude of the variance of the weights in the
+            forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
+            backwards pass.
+        nonlinearity: the non-linear function, recommended to use only with
+            ``'relu'`` or ``'leaky_relu'`` (default).
+    """
+    def __init__(self, a=0, mode='fan_in', nonlinearity='leaky_relu'):
+        super(KaimingInit, self).__init__()
+        self.mode = mode
+        self.gain = _calculate_gain(nonlinearity, a)
+    def _initialize(self, arr):
+        pass
+
+
+class KaimingUniform(KaimingInit):
+    r"""
+    Initialize the array with He kaiming uniform algorithm. The resulting tensor will
+    have values sampled from :math:`\mathcal{U}(-\text{bound}, \text{bound})` where
+
+    .. math::
+        \text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}}
+
+    Input:
+        arr (Array): The array to be assigned.
+
+    Returns:
+        Array, assigned array.
+
+    Examples:
+        >>> w = np.empty(3, 5)
+        >>> KaimingUniform(w, mode='fan_in', nonlinearity='relu')
+    """
+
+    def _initialize(self, arr):
+        fan = _select_fan(arr, self.mode)
+        bound = math.sqrt(3.0) * self.gain / math.sqrt(fan)
+        data = np.random.uniform(-bound, bound, arr.shape)
+
+        _assignment(arr, data)
+
+
+class KaimingNormal(KaimingInit):
+    r"""
+    Initialize the array with He kaiming normal algorithm. The resulting tensor will
+    have values sampled from :math:`\mathcal{N}(0, \text{std}^2)` where
+
+    .. math::
+        \text{std} = \frac{\text{gain}}{\sqrt{\text{fan\_mode}}}
+
+    Input:
+        arr (Array): The array to be assigned.
+
+    Returns:
+        Array, assigned array.
+
+    Examples:
+        >>> w = np.empty(3, 5)
+        >>> KaimingNormal(w, mode='fan_out', nonlinearity='relu')
+    """
+
+    def _initialize(self, arr):
+        fan = _select_fan(arr, self.mode)
+        std = self.gain / math.sqrt(fan)
+        data = np.random.normal(0, std, arr.shape)
+
+        _assignment(arr, data)
+
+
+def default_recurisive_init(custom_cell):
+    """default_recurisive_init"""
+    for _, cell in custom_cell.cells_and_names():
+        if isinstance(cell, nn.Conv2d):
+            cell.weight.set_data(init.initializer(KaimingUniform(a=math.sqrt(5)),
+                                                  cell.weight.shape,
+                                                  cell.weight.dtype))
+            if cell.bias is not None:
+                fan_in, _ = _calculate_in_and_out(cell.weight)
+                bound = 1 / math.sqrt(fan_in)
+                cell.bias.set_data(init.initializer(init.Uniform(bound),
+                                                    cell.bias.shape,
+                                                    cell.bias.dtype))
+        elif isinstance(cell, nn.Dense):
+            cell.weight.set_data(init.initializer(KaimingUniform(a=math.sqrt(5)),
+                                                  cell.weight.shape,
+                                                  cell.weight.dtype))
+            if cell.bias is not None:
+                fan_in, _ = _calculate_in_and_out(cell.weight)
+                bound = 1 / math.sqrt(fan_in)
+                cell.bias.set_data(init.initializer(init.Uniform(bound),
+                                                    cell.bias.shape,
+                                                    cell.bias.dtype))
+        elif isinstance(cell, (nn.BatchNorm2d, nn.BatchNorm1d)):
+            pass

model_zoo/research/cv/vgg19/src/vgg.py

@@ -0,0 +1,150 @@
+# Copyright 2021 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.
+# ============================================================================
+"""
+Image classifiation.
+"""
+import math
+import mindspore.nn as nn
+import mindspore.common.dtype as mstype
+from mindspore.common import initializer as init
+from mindspore.common.initializer import initializer
+from .utils.var_init import default_recurisive_init, KaimingNormal
+
+
+def _make_layer(base, args, batch_norm):
+    """Make stage network of VGG."""
+    layers = []
+    in_channels = 3
+    for v in base:
+        if v == 'M':
+            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
+        else:
+            weight = 'ones'
+            if args.initialize_mode == "XavierUniform":
+                weight_shape = (v, in_channels, 3, 3)
+                weight = initializer('XavierUniform', shape=weight_shape, dtype=mstype.float32)
+
+            conv2d = nn.Conv2d(in_channels=in_channels,
+                               out_channels=v,
+                               kernel_size=3,
+                               padding=args.padding,
+                               pad_mode=args.pad_mode,
+                               has_bias=args.has_bias,
+                               weight_init=weight)
+            if batch_norm:
+                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU()]
+            else:
+                layers += [conv2d, nn.ReLU()]
+            in_channels = v
+    return nn.SequentialCell(layers)
+
+
+class Vgg(nn.Cell):
+    """
+    VGG network definition.
+
+    Args:
+        base (list): Configuration for different layers, mainly the channel number of Conv layer.
+        num_classes (int): Class numbers. Default: 1000.
+        batch_norm (bool): Whether to do the batchnorm. Default: False.
+        batch_size (int): Batch size. Default: 1.
+        include_top(bool): Whether to include the 3 fully-connected layers at the top of the network. Default: True.
+
+    Returns:
+        Tensor, infer output tensor.
+
+    Examples:
+        >>> Vgg([64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
+        >>>     num_classes=1000, batch_norm=False, batch_size=1)
+    """
+
+    def __init__(self, base, num_classes=1000, batch_norm=False, batch_size=1, args=None, phase="train",
+                 include_top=True):
+        super(Vgg, self).__init__()
+        _ = batch_size
+        self.layers = _make_layer(base, args, batch_norm=batch_norm)
+        self.include_top = include_top
+        self.flatten = nn.Flatten()
+        dropout_ratio = 0.5
+        if not args.has_dropout or phase == "test":
+            dropout_ratio = 1.0
+        self.classifier = nn.SequentialCell([
+            nn.Dense(512 * 7 * 7, 4096),
+            nn.ReLU(),
+            nn.Dropout(dropout_ratio),
+            nn.Dense(4096, 4096),
+            nn.ReLU(),
+            nn.Dropout(dropout_ratio),
+            nn.Dense(4096, num_classes)])
+        if args.initialize_mode == "KaimingNormal":
+            default_recurisive_init(self)
+            self.custom_init_weight()
+
+    def construct(self, x):
+        x = self.layers(x)
+        if self.include_top:
+            x = self.flatten(x)
+            x = self.classifier(x)
+        return x
+
+    def custom_init_weight(self):
+        """
+        Init the weight of Conv2d and Dense in the net.
+        """
+        for _, cell in self.cells_and_names():
+            if isinstance(cell, nn.Conv2d):
+                cell.weight.set_data(init.initializer(
+                    KaimingNormal(a=math.sqrt(5), mode='fan_out', nonlinearity='relu'),
+                    cell.weight.shape, cell.weight.dtype))
+                if cell.bias is not None:
+                    cell.bias.set_data(init.initializer(
+                        'zeros', cell.bias.shape, cell.bias.dtype))
+            elif isinstance(cell, nn.Dense):
+                cell.weight.set_data(init.initializer(
+                    init.Normal(0.01), cell.weight.shape, cell.weight.dtype))
+                if cell.bias is not None:
+                    cell.bias.set_data(init.initializer(
+                        'zeros', cell.bias.shape, cell.bias.dtype))
+
+
+cfg = {
+    '11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
+    '13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
+    '16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
+    '19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
+}
+
+
+def vgg19(num_classes=1000, args=None, phase="train"):
+    """
+    Get Vgg19 neural network with batch normalization.
+
+    Args:
+        num_classes (int): Class numbers. Default: 1000.
+        args(namespace): param for net init.
+        phase(str): train or test mode.
+
+    Returns:
+        Cell, cell instance of Vgg19 neural network with batch normalization.
+
+    Examples:
+        >>> vgg19(num_classes=1000, args=args)
+    """
+
+    if args is None:
+        from .config import cifar_cfg
+        args = cifar_cfg
+    net = Vgg(cfg['19'], num_classes=num_classes, args=args, batch_norm=args.batch_norm, phase=phase)
+    return net

model_zoo/research/cv/vgg19/src/warmup_cosine_annealing_lr.py

@@ -0,0 +1,40 @@
+# Copyright 2021 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.
+# ============================================================================
+"""
+warm up cosine annealing learning rate.
+"""
+import math
+import numpy as np
+
+from .linear_warmup import linear_warmup_lr
+
+
+def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch, T_max, eta_min=0):
+    """warm up cosine annealing learning rate."""
+    base_lr = lr
+    warmup_init_lr = 0
+    total_steps = int(max_epoch * steps_per_epoch)
+    warmup_steps = int(warmup_epochs * steps_per_epoch)
+
+    lr_each_step = []
+    for i in range(total_steps):
+        last_epoch = i // steps_per_epoch
+        if i < warmup_steps:
+            lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
+        else:
+            lr = eta_min + (base_lr - eta_min) * (1. + math.cos(math.pi*last_epoch / T_max)) / 2
+        lr_each_step.append(lr)
+
+    return np.array(lr_each_step).astype(np.float32)

model_zoo/research/cv/vgg19/src/warmup_step_lr.py

@@ -0,0 +1,84 @@
+# Copyright 2021 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.
+# ============================================================================
+"""
+warm up step learning rate.
+"""
+from collections import Counter
+import numpy as np
+
+from .linear_warmup import linear_warmup_lr
+
+
+def lr_steps(global_step, lr_init, lr_max, warmup_epochs, total_epochs, steps_per_epoch):
+    """Set learning rate."""
+    lr_each_step = []
+    total_steps = steps_per_epoch * total_epochs
+    warmup_steps = steps_per_epoch * warmup_epochs
+    if warmup_steps != 0:
+        inc_each_step = (float(lr_max) - float(lr_init)) / float(warmup_steps)
+    else:
+        inc_each_step = 0
+    for i in range(total_steps):
+        if i < warmup_steps:
+            lr_value = float(lr_init) + inc_each_step * float(i)
+        else:
+            base = (1.0 - (float(i) - float(warmup_steps)) / (float(total_steps) - float(warmup_steps)))
+            lr_value = float(lr_max) * base * base
+            if lr_value < 0.0:
+                lr_value = 0.0
+        lr_each_step.append(lr_value)
+
+    current_step = global_step
+    lr_each_step = np.array(lr_each_step).astype(np.float32)
+    learning_rate = lr_each_step[current_step:]
+
+    return learning_rate
+
+
+def warmup_step_lr(lr, lr_epochs, steps_per_epoch, warmup_epochs, max_epoch, gamma=0.1):
+    """warmup_step_lr"""
+    base_lr = lr
+    warmup_init_lr = 0
+    total_steps = int(max_epoch * steps_per_epoch)
+    warmup_steps = int(warmup_epochs * steps_per_epoch)
+    milestones = lr_epochs
+    milestones_steps = []
+    for milestone in milestones:
+        milestones_step = milestone * steps_per_epoch
+        milestones_steps.append(milestones_step)
+
+    lr_each_step = []
+    lr = base_lr
+    milestones_steps_counter = Counter(milestones_steps)
+    for i in range(total_steps):
+        if i < warmup_steps:
+            lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
+        else:
+            lr = lr * gamma**milestones_steps_counter[i]
+        lr_each_step.append(lr)
+
+    return np.array(lr_each_step).astype(np.float32)
+
+
+def multi_step_lr(lr, milestones, steps_per_epoch, max_epoch, gamma=0.1):
+    return warmup_step_lr(lr, milestones, steps_per_epoch, 0, max_epoch, gamma=gamma)
+
+
+def step_lr(lr, epoch_size, steps_per_epoch, max_epoch, gamma=0.1):
+    lr_epochs = []
+    for i in range(1, max_epoch):
+        if i % epoch_size == 0:
+            lr_epochs.append(i)
+    return multi_step_lr(lr, lr_epochs, steps_per_epoch, max_epoch, gamma=gamma)

model_zoo/research/cv/vgg19/train.py

@@ -0,0 +1,235 @@
+# Copyright 2021 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 vgg19 example on cifar10########################
+"""
+import argparse
+import datetime
+import os
+
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore import context
+from mindspore.communication.management import init, get_rank, get_group_size
+from mindspore.nn.optim.momentum import Momentum
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
+from mindspore.train.model import Model
+from mindspore.context import ParallelMode
+from mindspore.train.serialization import load_param_into_net, load_checkpoint
+from mindspore.train.loss_scale_manager import FixedLossScaleManager
+from mindspore.common import set_seed
+from src.dataset import vgg_create_dataset
+from src.dataset import classification_dataset
+
+from src.crossentropy import CrossEntropy
+from src.warmup_step_lr import warmup_step_lr
+from src.warmup_cosine_annealing_lr import warmup_cosine_annealing_lr
+from src.warmup_step_lr import lr_steps
+from src.utils.logging import get_logger
+from src.utils.util import get_param_groups
+from src.vgg import vgg19
+
+
+set_seed(1)
+
+
+def parse_args(cloud_args=None):
+    """parameters"""
+    parser = argparse.ArgumentParser('mindspore classification training')
+    parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
+                        help='device where the code will be implemented. (Default: Ascend)')
+    parser.add_argument('--device_id', type=int, default=1, help='device id of GPU or Ascend. (Default: None)')
+
+    # dataset related
+    parser.add_argument('--dataset', type=str, choices=["cifar10", "imagenet2012"], default="cifar10")
+    parser.add_argument('--data_path', type=str, default='', help='train data dir')
+
+    # network related
+    parser.add_argument('--pre_trained', default='', type=str, help='model_path, local pretrained model to load')
+    parser.add_argument('--lr_gamma', type=float, default=0.1,
+                        help='decrease lr by a factor of exponential lr_scheduler')
+    parser.add_argument('--eta_min', type=float, default=0., help='eta_min in cosine_annealing scheduler')
+    parser.add_argument('--T_max', type=int, default=90, help='T-max in cosine_annealing scheduler')
+
+    # logging and checkpoint related
+    parser.add_argument('--log_interval', type=int, default=1, help='logging interval')
+    parser.add_argument('--ckpt_path', type=str, default='outputs/', help='checkpoint save location')
+    parser.add_argument('--ckpt_interval', type=int, default=1, help='ckpt_interval')
+    parser.add_argument('--is_save_on_master', type=int, default=1, help='save ckpt on master or all rank')
+
+    # distributed related
+    parser.add_argument('--is_distributed', type=int, default=0, help='if multi device')
+    parser.add_argument('--rank', type=int, default=0, help='local rank of distributed')
+    parser.add_argument('--group_size', type=int, default=1, help='world size of distributed')
+    args_opt = parser.parse_args()
+    args_opt = merge_args(args_opt, cloud_args)
+
+    if args_opt.dataset == "cifar10":
+        from src.config import cifar_cfg as cfg
+    else:
+        from src.config import imagenet_cfg as cfg
+
+    args_opt.label_smooth = cfg.label_smooth
+    args_opt.label_smooth_factor = cfg.label_smooth_factor
+    args_opt.lr_scheduler = cfg.lr_scheduler
+    args_opt.loss_scale = cfg.loss_scale
+    args_opt.max_epoch = cfg.max_epoch
+    args_opt.warmup_epochs = cfg.warmup_epochs
+    args_opt.lr = cfg.lr
+    args_opt.lr_init = cfg.lr_init
+    args_opt.lr_max = cfg.lr_max
+    args_opt.momentum = cfg.momentum
+    args_opt.weight_decay = cfg.weight_decay
+    args_opt.per_batch_size = cfg.batch_size
+    args_opt.num_classes = cfg.num_classes
+    args_opt.buffer_size = cfg.buffer_size
+    args_opt.ckpt_save_max = cfg.keep_checkpoint_max
+    args_opt.pad_mode = cfg.pad_mode
+    args_opt.padding = cfg.padding
+    args_opt.has_bias = cfg.has_bias
+    args_opt.batch_norm = cfg.batch_norm
+    args_opt.initialize_mode = cfg.initialize_mode
+    args_opt.has_dropout = cfg.has_dropout
+
+    args_opt.lr_epochs = list(map(int, cfg.lr_epochs.split(',')))
+    args_opt.image_size = list(map(int, cfg.image_size.split(',')))
+
+    return args_opt
+
+
+def merge_args(args_opt, cloud_args):
+    """dictionary"""
+    args_dict = vars(args_opt)
+    if isinstance(cloud_args, dict):
+        for key_arg in cloud_args.keys():
+            val = cloud_args[key_arg]
+            if key_arg in args_dict and val:
+                arg_type = type(args_dict[key_arg])
+                if arg_type is not None:
+                    val = arg_type(val)
+                args_dict[key_arg] = val
+    return args_opt
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+    device_num = int(os.environ.get("DEVICE_NUM", 1))
+    if args.is_distributed:
+        if args.device_target == "Ascend":
+            init()
+            context.set_context(device_id=args.device_id)
+        elif args.device_target == "GPU":
+            init()
+
+        args.rank = get_rank()
+        args.group_size = get_group_size()
+        device_num = args.group_size
+        context.reset_auto_parallel_context()
+        context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
+                                          gradients_mean=True, all_reduce_fusion_config=[2, 18])
+    else:
+        if args.device_target == "Ascend":
+            context.set_context(device_id=args.device_id)
+
+    # select for master rank save ckpt or all rank save, compatible for model parallel
+    args.rank_save_ckpt_flag = 0
+    if args.is_save_on_master:
+        if args.rank == 0:
+            args.rank_save_ckpt_flag = 1
+    else:
+        args.rank_save_ckpt_flag = 1
+
+    # logger
+    args.outputs_dir = os.path.join(args.ckpt_path,
+                                    datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
+    args.logger = get_logger(args.outputs_dir, args.rank)
+
+    if args.dataset == "cifar10":
+        dataset = vgg_create_dataset(args.data_path, args.image_size, args.per_batch_size, args.rank, args.group_size)
+    else:
+        dataset = classification_dataset(args.data_path, args.image_size, args.per_batch_size,
+                                         args.rank, args.group_size)
+
+    batch_num = dataset.get_dataset_size()
+    args.steps_per_epoch = dataset.get_dataset_size()
+    args.logger.save_args(args)
+
+    # network
+    args.logger.important_info('start create network')
+
+    # get network and init
+    network = vgg19(args.num_classes, args)
+
+    # pre_trained
+    if args.pre_trained:
+        load_param_into_net(network, load_checkpoint(args.pre_trained))
+
+    # lr scheduler
+    if args.lr_scheduler == 'exponential':
+        lr = warmup_step_lr(args.lr,
+                            args.lr_epochs,
+                            args.steps_per_epoch,
+                            args.warmup_epochs,
+                            args.max_epoch,
+                            gamma=args.lr_gamma,
+                            )
+    elif args.lr_scheduler == 'cosine_annealing':
+        lr = warmup_cosine_annealing_lr(args.lr,
+                                        args.steps_per_epoch,
+                                        args.warmup_epochs,
+                                        args.max_epoch,
+                                        args.T_max,
+                                        args.eta_min)
+    elif args.lr_scheduler == 'step':
+        lr = lr_steps(0, lr_init=args.lr_init, lr_max=args.lr_max, warmup_epochs=args.warmup_epochs,
+                      total_epochs=args.max_epoch, steps_per_epoch=batch_num)
+    else:
+        raise NotImplementedError(args.lr_scheduler)
+
+    # optimizer
+    opt = Momentum(params=get_param_groups(network),
+                   learning_rate=Tensor(lr),
+                   momentum=args.momentum,
+                   weight_decay=args.weight_decay,
+                   loss_scale=args.loss_scale)
+
+    if args.dataset == "cifar10":
+        loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+        model = Model(network, loss_fn=loss, optimizer=opt, metrics={'acc'},
+                      amp_level="O2", keep_batchnorm_fp32=False, loss_scale_manager=None)
+    else:
+        if not args.label_smooth:
+            args.label_smooth_factor = 0.0
+        loss = CrossEntropy(smooth_factor=args.label_smooth_factor, num_classes=args.num_classes)
+
+        loss_scale_manager = FixedLossScaleManager(args.loss_scale, drop_overflow_update=False)
+        model = Model(network, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale_manager, amp_level="O2")
+
+    # define callbacks
+    time_cb = TimeMonitor(data_size=batch_num)
+    loss_cb = LossMonitor(per_print_times=batch_num)
+    callbacks = [time_cb, loss_cb]
+    if args.rank_save_ckpt_flag:
+        ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval * args.steps_per_epoch,
+                                       keep_checkpoint_max=args.ckpt_save_max)
+        save_ckpt_path = os.path.join(args.outputs_dir, 'ckpt_' + str(args.rank) + '/')
+        ckpt_cb = ModelCheckpoint(config=ckpt_config,
+                                  directory=save_ckpt_path,
+                                  prefix='{}'.format(args.rank))
+        callbacks.append(ckpt_cb)
+
+    model.train(args.max_epoch, dataset, callbacks=callbacks)