!16885 modify fcn8s readme
From: @Maigee Reviewed-by: @c_34,@wuxuejian Signed-off-by: @c_34
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@ -55,10 +55,10 @@ Dataset used:
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- running on Ascend with default parameters
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```python
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# run training example
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# Ascend单卡训练示例
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python train.py --device_id device_id
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# run evaluation example with default parameters
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# Ascend评估示例
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python eval.py --device_id device_id
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```
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@ -102,7 +102,7 @@ Dataset used:
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- config for FCN8s
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```python
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```default_config.yaml
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# dataset
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'data_file': '/data/workspace/mindspore_dataset/FCN/FCN/dataset/MINDRECORED_NAME.mindrecord', # path and name of one mindrecord file
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'train_batch_size': 32,
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@ -157,12 +157,14 @@ Dataset used:
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- running on Ascend with default parameters
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```python 单卡训练
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sh scripts/run_standalone_train.sh DEVICE_ID
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```
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```python
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# Ascend单卡训练示例
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python train.py --device_id device_id
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or
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sh scripts/run_standalone_train.sh [DEVICE_ID]
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```python 分布式训练
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sh scripts/run_train.sh DEVICE_NUM RANK_TABLE_FILES
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#Ascend八卡并行训练
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sh scripts/run_train.sh [DEVICE_NUM] rank_table.json
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```
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训练时，训练过程中的epch和step以及此时的loss和精确度会呈现log.txt中:
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@ -174,6 +176,79 @@ Dataset used:
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此模型的checkpoint会在默认路径下存储
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- 如果要在modelarts上进行模型的训练，可以参考modelarts的[官方指导文档](https://support.huaweicloud.com/modelarts/) 开始进行模型的训练和推理，具体操作如下：
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```ModelArts
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# 在ModelArts上使用分布式训练示例:
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# 数据集存放方式
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# ├── VOC2012 # dir
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# ├── VOCdevkit # VOCdevkit dir
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# ├── Please refer to VOCdevkit structure
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# ├── benchmark_RELEASE # benchmark_RELEASE dir
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# ├── Please refer to benchmark_RELEASE structure
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# ├── backbone # backbone dir
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# ├── vgg_predtrained.ckpt
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# ├── predtrained # predtrained dir
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# ├── FCN8s_1-133_300.ckpt
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# ├── checkpoint # checkpoint dir
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# ├── FCN8s_1-133_300.ckpt
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# ├── vocaug_mindrecords # train dataset dir
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# ├── voctrain.mindrecords0
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# ├── voctrain.mindrecords0.db
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# ├── voctrain.mindrecords1
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# ├── voctrain.mindrecords1.db
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# ├── voctrain.mindrecords2
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# ├── voctrain.mindrecords2.db
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# ├── voctrain.mindrecords3
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# ├── voctrain.mindrecords3.db
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# ├── voctrain.mindrecords4
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# ├── voctrain.mindrecords4.db
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# ├── voctrain.mindrecords5
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# ├── voctrain.mindrecords5.db
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# ├── voctrain.mindrecords6
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# ├── voctrain.mindrecords6.db
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# ├── voctrain.mindrecords7
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# ├── voctrain.mindrecords7.db
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# (1) 选择a(修改yaml文件参数)或者b(ModelArts创建训练作业修改参数)其中一种方式
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# a. 设置 "enable_modelarts=True"
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# 设置 "ckpt_dir=/cache/train/outputs_FCN8s/"
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# 设置 "ckpt_vgg16=/cache/data/backbone/vgg_predtrain file" 如果没有预训练 ckpt_vgg16=""
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# 设置 "ckpt_pre_trained=/cache/data/predtrained/pred file" 如果无需继续训练 ckpt_pre_trained=""
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# 设置 "data_file=/cache/data/vocaug_mindrecords/voctrain.mindrecords0"
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# b. 增加 "enable_modelarts=True" 参数在modearts的界面上
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# 在modelarts的界面上设置方法a所需要的参数
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# 注意：路径参数不需要加引号
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# (2)设置网络配置文件的路径 "_config_path=/The path of config in default_config.yaml/"
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# (3) 在modelarts的界面上设置代码的路径 "/path/FCN8s"
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# (4) 在modelarts的界面上设置模型的启动文件 "train.py"
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# (5) 在modelarts的界面上设置模型的数据路径 ".../VOC2012"(选择VOC2012文件夹路径)
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# 模型的输出路径"Output file path" 和模型的日志路径 "Job log path"
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# (6) 开始模型的训练
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# 在modelarts上使用模型推理的示例
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# (1) 把训练好的模型地方到桶的对应位置
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# (2) 选择a或者b其中一种方式
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# a. 设置 "enable_modelarts=True"
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# 设置 "data_root=/cache/data/VOCdevkit/VOC2012/"
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# 设置 "data_lst=./ImageSets/Segmentation/val.txt"
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# 设置 "ckpt_file=/cache/data/checkpoint/ckpt file name"
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# b. 增加 "enable_modelarts=True" 参数在modearts的界面上
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# 在modelarts的界面上设置方法a所需要的参数
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# 注意：路径参数不需要加引号
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# (3) 设置网络配置文件的路径 "_config_path=/The path of config in default_config.yaml/"
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# (4) 在modelarts的界面上设置代码的路径 "/path/FCN8s"
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# (5) 在modelarts的界面上设置模型的启动文件 "eval.py"
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# (6) 在modelarts的界面上设置模型的数据路径 ".../VOC2012"(选择VOC2012文件夹路径) ,
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# 模型的输出路径"Output file path" 和模型的日志路径 "Job log path"
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# (7) 开始模型的推理
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```
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## [评估步骤](#contents)
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### 评估
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@ -186,7 +261,7 @@ Dataset used:
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python eval.py
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```
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```python shell脚本验证
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```shell 评估
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sh scripts/run_eval.sh DATA_ROOT DATA_LST CKPT_PATH
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```
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