!11949 add inceptionv3 cpu train script
From: @caojian05 Reviewed-by: @wuxuejian,@oacjiewen Signed-off-by: @wuxuejian
This commit is contained in:
Gitee
commit
c144896aee
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@ -40,6 +40,14 @@ Dataset used can refer to paper.
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- Data format: RGB images.
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- Note: Data will be processed in src/dataset.py
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Dataset used: [CIFAR-10](http://www.cs.toronto.edu/~kriz/cifar.html)
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- Dataset size: 175M, 60,000 32\*32 colorful images in 10 classes
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- Train: 146M, 50,000 images
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- Test: 29M, 10,000 images
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- Data format：binary files
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- Note：Data will be processed in src/dataset.py
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# [Features](#contents)
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## [Mixed Precision(Ascend)](#contents)
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@ -67,8 +75,13 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
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└─Inception-v3
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├─README.md
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├─scripts
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├─run_standalone_train_cpu.sh # launch standalone training with cpu platform
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├─run_standalone_train_gpu.sh # launch standalone training with gpu platform(1p)
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├─run_distribute_train_gpu.sh # launch distributed training with gpu platform(8p)
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├─run_standalone_train.sh # launch standalone training with ascend platform(1p)
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├─run_distribute_train.sh # launch distributed training with ascend platform(8p)
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├─run_eval_cpu.sh # launch evaluation with cpu platform
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├─run_eval_gpu.sh # launch evaluation with gpu platform
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└─run_eval.sh # launch evaluating with ascend platform
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├─src
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├─config.py # parameter configuration
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@ -93,6 +106,8 @@ Major parameters in train.py and config.py are:
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'batch_size' # input batchsize
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'epoch_size' # total epoch numbers
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'num_classes' # dataset class numbers
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'ds_type' # dataset type, such as: imagenet, cifar10
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'ds_sink_mode' # whether enable dataset sink mode
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'smooth_factor' # label smoothing factor
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'aux_factor' # loss factor of aux logit
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'lr_init' # initiate learning rate
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@ -127,6 +142,13 @@ sh scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH
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sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH
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```
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- CPU:
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```shell
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# standalone training
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sh scripts/run_standalone_train_cpu.sh DATA_PATH
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```
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> Notes: RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_ascend.html), and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). For large models like InceptionV3, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
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>
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> This is processor cores binding operation regarding the `device_num` and total processor numbers. If you are not expect to do it, remove the operations `taskset` in `scripts/run_distribute_train.sh`
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@ -137,6 +159,7 @@ sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH
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# training example
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python:
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Ascend: python train.py --dataset_path DATA_PATH --platform Ascend
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CPU: python train.py --dataset_path DATA_PATH --platform CPU
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shell:
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Ascend:
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@ -144,12 +167,17 @@ sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH
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sh scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH
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# standalone training example
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sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH
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CPU:
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sh script/run_standalone_train_cpu.sh DATA_PATH
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```
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### Result
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Training result will be stored in the example path. Checkpoints will be stored at `. /checkpoint` by default, and training log will be redirected to `./log.txt` like followings.
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#### Ascend
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```python
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epoch: 0 step: 1251, loss is 5.7787247
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epoch time: 360760.985 ms, per step time: 288.378 ms
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@ -157,6 +185,18 @@ epoch: 1 step: 1251, loss is 4.392868
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epoch time: 160917.911 ms, per step time: 128.631 ms
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```
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#### CPU
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```bash
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epoch: 1 step: 390, loss is 2.7072601
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epoch time: 6334572.124 ms, per step time: 16242.493 ms
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epoch: 2 step: 390, loss is 2.5908582
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epoch time: 6217897.644 ms, per step time: 15943.327 ms
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epoch: 3 step: 390, loss is 2.5612416
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epoch time: 6358482.104 ms, per step time: 16303.800 ms
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...
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```
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## [Eval process](#contents)
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### Usage
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@ -169,15 +209,23 @@ You can start training using python or shell scripts. The usage of shell scripts
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sh scripts/run_eval.sh DEVICE_ID DATA_PATH PATH_CHECKPOINT
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```
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- CPU:
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```python
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sh scripts/run_eval_cpu.sh DATA_PATH PATH_CHECKPOINT
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```
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### Launch
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```python
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# eval example
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python:
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Ascend: python eval.py --dataset_path DATA_PATH --checkpoint PATH_CHECKPOINT --platform Ascend
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CPU: python eval.py --dataset_path DATA_PATH --checkpoint PATH_CHECKPOINT --platform CPU
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shell:
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Ascend: sh scripts/run_eval.sh DEVICE_ID DATA_PATH PATH_CHECKPOINT
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CPU: sh scripts/run_eval_cpu.sh DATA_PATH PATH_CHECKPOINT
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```
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> checkpoint can be produced in training process.
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@ -236,4 +284,4 @@ In dataset.py, we set the seed inside “create_dataset" function. We also use r
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# [ModelZoo Homepage](#contents)
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Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
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Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
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@ -51,6 +51,14 @@ InceptionV3的总体网络架构如下：
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- 数据格式：RGB
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- 注：数据将在src/dataset.py中处理。
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使用的数据集：[CIFAR-10](<http://www.cs.toronto.edu/~kriz/cifar.html>)
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- 数据集大小：175M，共10个类、6万张32*32彩色图像
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- 训练集：146M，共5万张图像
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- 测试集：29M，共1万张图像
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- 数据格式：二进制文件
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- 注：数据将在src/dataset.py中处理。
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# 特性
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## 混合精度（Ascend）
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@ -78,9 +86,14 @@ InceptionV3的总体网络架构如下：
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└─Inception-v3
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├─README.md
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├─scripts
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├─run_standalone_train_cpu.sh # 启动CPU训练
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├─run_standalone_train_gpu.sh # 启动GPU单机训练（单卡）
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├─run_distribute_train_gpu.sh # 启动GPU分布式训练（8卡）
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├─run_standalone_train.sh # 启动Ascend单机训练（单卡）
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├─run_distribute_train.sh # 启动Ascend分布式训练（8卡）
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├─run_eval.sh # 启动Ascend评估
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├─run_eval_cpu.sh # 启动CPU评估
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├─run_eval_gpu.sh # 启动GPU评估
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└─run_eval.sh # 启动Ascend评估
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├─src
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├─config.py # 参数配置
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├─dataset.py # 数据预处理
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@ -106,6 +119,8 @@ train.py和config.py中主要参数如下：
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'batch_size' # 输入张量的批次大小
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'epoch_size' # 总轮次数
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'num_classes' # 数据集类数
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'ds_type' # 数据集类型，如：imagenet, cifar10
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'ds_sink_mode' # 使能数据下沉
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'smooth_factor' # 标签平滑因子
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'aux_factor' # aux logit的损耗因子
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'lr_init' # 初始学习率
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@ -149,6 +164,7 @@ train.py和config.py中主要参数如下：
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# 训练示例
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python:
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Ascend: python train.py --dataset_path /dataset/train --platform Ascend
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CPU: python train.py --dataset_path DATA_PATH --platform CPU
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shell:
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Ascend:
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@ -156,12 +172,17 @@ train.py和config.py中主要参数如下：
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sh scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH
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# 单机训练
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sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH
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CPU:
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sh script/run_standalone_train_cpu.sh DATA_PATH
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```
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### 结果
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训练结果保存在示例路径。检查点默认保存在`checkpoint`，训练日志会重定向到`./log.txt`，如下：
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#### Ascend
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```log
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epoch:0 step:1251, loss is 5.7787247
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Epoch time:360760.985, per step time:288.378
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@ -169,6 +190,18 @@ epoch:1 step:1251, loss is 4.392868
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Epoch time:160917.911, per step time:128.631
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```
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#### CPU
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```bash
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epoch: 1 step: 390, loss is 2.7072601
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epoch time: 6334572.124 ms, per step time: 16242.493 ms
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epoch: 2 step: 390, loss is 2.5908582
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epoch time: 6217897.644 ms, per step time: 15943.327 ms
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epoch: 3 step: 390, loss is 2.5612416
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epoch time: 6358482.104 ms, per step time: 16303.800 ms
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...
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```
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## 评估过程
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### 用法
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@ -181,15 +214,23 @@ Epoch time:160917.911, per step time:128.631
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sh scripts/run_eval.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
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```
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- CPU:
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```python
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sh scripts/run_eval_cpu.sh DATA_PATH PATH_CHECKPOINT
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```
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### 启动
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``` launch
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# 评估示例
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python:
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Ascend: python eval.py --dataset_path DATA_DIR --checkpoint PATH_CHECKPOINT --platform Ascend
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CPU: python eval.py --dataset_path DATA_PATH --checkpoint PATH_CHECKPOINT --platform CPU
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shell:
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Ascend: sh scripts/run_eval.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
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CPU: sh scripts/run_eval_cpu.sh DATA_PATH PATH_CHECKPOINT
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```
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> 训练过程中可以生成检查点。
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@ -21,33 +21,48 @@ from mindspore import context
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from mindspore.train.model import Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from src.config import config_gpu as cfg
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from src.dataset import create_dataset
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from src.config import config_gpu, config_ascend, config_cpu
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from src.dataset import create_dataset_imagenet, create_dataset_cifar10
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from src.inception_v3 import InceptionV3
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from src.loss import CrossEntropy_Val
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CFG_DICT = {
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"Ascend": config_ascend,
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"GPU": config_gpu,
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"CPU": config_cpu,
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}
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DS_DICT = {
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"imagenet": create_dataset_imagenet,
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"cifar10": create_dataset_cifar10,
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}
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='image classification evaluation')
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parser.add_argument('--checkpoint', type=str, default='', help='checkpoint of inception-v3 (Default: None)')
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parser.add_argument('--dataset_path', type=str, default='', help='Dataset path')
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parser.add_argument('--platform', type=str, default='GPU', choices=('Ascend', 'GPU'), help='run platform')
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parser.add_argument('--platform', type=str, default='GPU', choices=('Ascend', 'GPU', 'CPU'), help='run platform')
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args_opt = parser.parse_args()
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if args_opt.platform == 'Ascend':
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(device_id=device_id)
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cfg = CFG_DICT[args_opt.platform]
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create_dataset = DS_DICT[cfg.ds_type]
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.platform)
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net = InceptionV3(num_classes=cfg.num_classes, is_training=False)
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ckpt = load_checkpoint(args_opt.checkpoint)
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load_param_into_net(net, ckpt)
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net.set_train(False)
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dataset = create_dataset(args_opt.dataset_path, False, 0, 1)
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cfg.rank = 0
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cfg.group_size = 1
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dataset = create_dataset(args_opt.dataset_path, False, cfg)
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loss = CrossEntropy_Val(smooth_factor=0.1, num_classes=cfg.num_classes)
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eval_metrics = {'Loss': nn.Loss(),
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'Top1-Acc': nn.Top1CategoricalAccuracy(),
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'Top5-Acc': nn.Top5CategoricalAccuracy()}
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model = Model(net, loss, optimizer=None, metrics=eval_metrics)
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metrics = model.eval(dataset)
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metrics = model.eval(dataset, dataset_sink_mode=cfg.ds_sink_mode)
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print("metric: ", metrics)
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@ -0,0 +1,18 @@
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#!/bin/bash
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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DATA_DIR=$1
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PATH_CHECKPOINT=$2
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python ./eval.py --platform 'CPU' --dataset_path $DATA_DIR --checkpoint $PATH_CHECKPOINT > eval.log 2>&1 &
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@ -0,0 +1,18 @@
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#!/bin/bash
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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DATA_DIR=$1
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python ./train.py --platform 'CPU' --dataset_path $DATA_DIR > train.log 2>&1 &
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@ -26,6 +26,8 @@ config_gpu = edict({
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'batch_size': 128,
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'epoch_size': 250,
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'num_classes': 1000,
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'ds_type': 'imagenet',
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'ds_sink_mode': True,
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'smooth_factor': 0.1,
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'aux_factor': 0.2,
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'lr_init': 0.00004,
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@ -51,6 +53,8 @@ config_ascend = edict({
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'batch_size': 128,
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'epoch_size': 250,
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'num_classes': 1000,
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'ds_type': 'imagenet',
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'ds_sink_mode': True,
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'smooth_factor': 0.1,
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'aux_factor': 0.2,
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'lr_init': 0.00004,
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@ -67,3 +71,30 @@ config_ascend = edict({
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'has_bias': False,
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'amp_level': 'O3'
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})
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config_cpu = edict({
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'random_seed': 1,
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'work_nums': 8,
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'decay_method': 'cosine',
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"loss_scale": 1024,
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'batch_size': 128,
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'epoch_size': 120,
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'num_classes': 10,
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'ds_type': 'cifar10',
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'ds_sink_mode': False,
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'smooth_factor': 0.1,
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'aux_factor': 0.2,
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'lr_init': 0.00004,
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'lr_max': 0.1,
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'lr_end': 0.000004,
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'warmup_epochs': 1,
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'weight_decay': 0.00004,
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'momentum': 0.9,
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'opt_eps': 1.0,
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'keep_checkpoint_max': 10,
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'ckpt_path': './',
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'is_save_on_master': 0,
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'dropout_keep_prob': 0.8,
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'has_bias': False,
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'amp_level': 'O0',
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})
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@ -15,32 +15,32 @@
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"""
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Data operations, will be used in train.py and eval.py
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"""
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import os
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import mindspore.common.dtype as mstype
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.c_transforms as C2
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import mindspore.dataset.vision.c_transforms as C
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from src.config import config_gpu as cfg
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def create_dataset(dataset_path, do_train, rank, group_size, repeat_num=1):
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def create_dataset_imagenet(dataset_path, do_train, cfg, repeat_num=1):
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"""
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create a train or eval dataset
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Args:
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dataset_path(string): the path of dataset.
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do_train(bool): whether dataset is used for train or eval.
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rank (int): The shard ID within num_shards (default=None).
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group_size (int): Number of shards that the dataset should be divided into (default=None).
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cfg (dict): the config for creating dataset.
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repeat_num(int): the repeat times of dataset. Default: 1.
|
||||
|
||||
Returns:
|
||||
dataset
|
||||
"""
|
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if group_size == 1:
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if cfg.group_size == 1:
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data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=cfg.work_nums, shuffle=True)
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else:
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data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=cfg.work_nums, shuffle=True,
|
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num_shards=group_size, shard_id=rank)
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num_shards=cfg.group_size, shard_id=cfg.rank)
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# define map operations
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if do_train:
|
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trans = [
|
||||
|
|
@ -67,3 +67,44 @@ def create_dataset(dataset_path, do_train, rank, group_size, repeat_num=1):
|
|||
# apply dataset repeat operation
|
||||
data_set = data_set.repeat(repeat_num)
|
||||
return data_set
|
||||
|
||||
|
||||
def create_dataset_cifar10(dataset_path, do_train, cfg, repeat_num=1):
|
||||
"""
|
||||
create a train or eval dataset
|
||||
|
||||
Args:
|
||||
dataset_path(string): the path of dataset.
|
||||
do_train(bool): whether dataset is used for train or eval.
|
||||
cfg (dict): the config for creating dataset.
|
||||
repeat_num(int): the repeat times of dataset. Default: 1.
|
||||
|
||||
Returns:
|
||||
dataset
|
||||
"""
|
||||
dataset_path = os.path.join(dataset_path, "cifar-10-batches-bin" if do_train else "cifar-10-verify-bin")
|
||||
if cfg.group_size == 1:
|
||||
data_set = ds.Cifar10Dataset(dataset_path, num_parallel_workers=cfg.work_nums, shuffle=True)
|
||||
else:
|
||||
data_set = ds.Cifar10Dataset(dataset_path, num_parallel_workers=cfg.work_nums, shuffle=True,
|
||||
num_shards=cfg.group_size, shard_id=cfg.rank)
|
||||
|
||||
# define map operations
|
||||
trans = []
|
||||
if do_train:
|
||||
trans.append(C.RandomCrop((32, 32), (4, 4, 4, 4)))
|
||||
trans.append(C.RandomHorizontalFlip(prob=0.5))
|
||||
|
||||
trans.append(C.Resize((299, 299)))
|
||||
trans.append(C.Rescale(1.0 / 255.0, 0.0))
|
||||
trans.append(C.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010]))
|
||||
trans.append(C.HWC2CHW())
|
||||
|
||||
type_cast_op = C2.TypeCast(mstype.int32)
|
||||
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=cfg.work_nums)
|
||||
data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=cfg.work_nums)
|
||||
# apply batch operations
|
||||
data_set = data_set.batch(cfg.batch_size, drop_remainder=do_train)
|
||||
# apply dataset repeat operation
|
||||
data_set = data_set.repeat(repeat_num)
|
||||
return data_set
|
||||
|
|
|
|||
|
|
@ -29,14 +29,24 @@ from mindspore.train.loss_scale_manager import FixedLossScaleManager
|
|||
from mindspore.common.initializer import XavierUniform, initializer
|
||||
from mindspore.common import set_seed
|
||||
|
||||
from src.config import config_gpu, config_ascend
|
||||
from src.dataset import create_dataset
|
||||
from src.config import config_gpu, config_ascend, config_cpu
|
||||
from src.dataset import create_dataset_imagenet, create_dataset_cifar10
|
||||
from src.inception_v3 import InceptionV3
|
||||
from src.lr_generator import get_lr
|
||||
from src.loss import CrossEntropy
|
||||
|
||||
set_seed(1)
|
||||
|
||||
CFG_DICT = {
|
||||
"Ascend": config_ascend,
|
||||
"GPU": config_gpu,
|
||||
"CPU": config_cpu,
|
||||
}
|
||||
|
||||
DS_DICT = {
|
||||
"imagenet": create_dataset_imagenet,
|
||||
"cifar10": create_dataset_cifar10,
|
||||
}
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='image classification training')
|
||||
|
|
@ -44,13 +54,16 @@ if __name__ == '__main__':
|
|||
parser.add_argument('--resume', type=str, default='', help='resume training with existed checkpoint')
|
||||
parser.add_argument('--is_distributed', action='store_true', default=False,
|
||||
help='distributed training')
|
||||
parser.add_argument('--platform', type=str, default='GPU', choices=('Ascend', 'GPU'), help='run platform')
|
||||
parser.add_argument('--platform', type=str, default='GPU', choices=('Ascend', 'GPU', 'CPU'), help='run platform')
|
||||
args_opt = parser.parse_args()
|
||||
|
||||
cfg = CFG_DICT[args_opt.platform]
|
||||
create_dataset = DS_DICT[cfg.ds_type]
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.platform, save_graphs=False)
|
||||
if os.getenv('DEVICE_ID', "not_set").isdigit():
|
||||
context.set_context(device_id=int(os.getenv('DEVICE_ID')))
|
||||
cfg = config_ascend if args_opt.platform == 'Ascend' else config_gpu
|
||||
|
||||
# init distributed
|
||||
if args_opt.is_distributed:
|
||||
init()
|
||||
|
|
@ -64,7 +77,7 @@ if __name__ == '__main__':
|
|||
cfg.group_size = 1
|
||||
|
||||
# dataloader
|
||||
dataset = create_dataset(args_opt.dataset_path, True, cfg.rank, cfg.group_size)
|
||||
dataset = create_dataset(args_opt.dataset_path, True, cfg)
|
||||
batches_per_epoch = dataset.get_dataset_size()
|
||||
|
||||
# network
|
||||
|
|
@ -120,8 +133,8 @@ if __name__ == '__main__':
|
|||
if args_opt.is_distributed & cfg.is_save_on_master:
|
||||
if cfg.rank == 0:
|
||||
callbacks.append(ckpoint_cb)
|
||||
model.train(cfg.epoch_size, dataset, callbacks=callbacks, dataset_sink_mode=True)
|
||||
model.train(cfg.epoch_size, dataset, callbacks=callbacks, dataset_sink_mode=cfg.ds_sink_mode)
|
||||
else:
|
||||
callbacks.append(ckpoint_cb)
|
||||
model.train(cfg.epoch_size, dataset, callbacks=callbacks, dataset_sink_mode=True)
|
||||
model.train(cfg.epoch_size, dataset, callbacks=callbacks, dataset_sink_mode=cfg.ds_sink_mode)
|
||||
print("train success")
|
||||
|
|
|
|||
Loading…
Reference in New Issue