svtr_tiny 性能优化 和 修改多卡启动方式为msrun (#810)

* update rec models Readme and fix master_resnet bug

* svtr_tiny GradSampler2D算子优化

Author: iugoood

configs/det/dbnet/README.md

@@ -282,9 +282,15 @@ python tools/train.py -c=configs/det/dbnet/db_r50_icdar15.yaml
 Please set `distribute` in yaml config file to be True.
 
 ```shell
-# n is the number of NPUs
-mpirun --allow-run-as-root -n 2 python tools/train.py --config configs/det/dbnet/db_r50_icdar15.yaml
+# worker_num is the total number of Worker processes participating in the distributed task.
+# local_worker_num is the number of Worker processes pulled up on the current node.
+# The number of processes is equal to the number of NPUs used for training. In the case of single-machine multi-card worker_num and local_worker_num must be the same.
+msrun --worker_num=2 --local_worker_num=2 python tools/train.py --config configs/det/dbnet/db_r50_icdar15.yaml
+
+# Based on verification,binding cores usually results in performance acceleration.Please configure the parameters and run.
+msrun --bind_core=True --worker_num=2 --local_worker_num=2 python tools/train.py --config configs/det/dbnet/db_r50_icdar15.yaml
 ```
+**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/tutorials/experts/en/r2.3.1/parallel/msrun_launcher.html).
 
 The training result (including checkpoints, per-epoch performance and curves) will be saved in the directory parsed by the arg `ckpt_save_dir` in yaml config file. The default directory is `./tmp_det`.
 

configs/det/dbnet/README_CN.md

@@ -263,9 +263,15 @@ python tools/train.py --config configs/det/dbnet/db_r50_icdar15.yaml
 请确保yaml文件中的`distribute`参数为True。
 
 ```shell
-# n is the number of NPUs
-mpirun --allow-run-as-root -n 2 python tools/train.py --config configs/det/dbnet/db_r50_icdar15.yaml
+# worker_num代表分布式总进程数量。
+# local_worker_num代表当前节点进程数量。
+# 进程数量即为训练使用的NPU的数量，单机多卡情况下worker_num和local_worker_num需保持一致。
+msrun --worker_num=2 --local_worker_num=2 python tools/train.py --config configs/det/dbnet/db_r50_icdar15.yaml
+
+# 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
+msrun --bind_core=True --worker_num=2 --local_worker_num=2 python tools/train.py --config configs/det/dbnet/db_r50_icdar15.yaml
 ```
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/tutorials/experts/zh-CN/r2.3.1/parallel/msrun_launcher.html).
 
 训练结果（包括checkpoint、每个epoch的性能和曲线图）将被保存在yaml配置文件的`ckpt_save_dir`参数配置的路径下，默认为`./tmp_det`。
 

configs/det/dbnet/README_CN_PP-OCRv3.md

@@ -330,8 +330,16 @@ model:
 
 ```shell
 # 在多个 Ascend 设备上进行分布式训练
-mpirun --allow-run-as-root -n 4 python tools/train.py --config configs/det/dbnet/db_mobilenetv3_ppocrv3.yaml
+# worker_num代表分布式总进程数量。
+# local_worker_num代表当前节点进程数量。
+# 进程数量即为训练使用的NPU的数量，单机多卡情况下worker_num和local_worker_num需保持一致。
+msrun --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/det/dbnet/db_mobilenetv3_ppocrv3.yaml
+
+# 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
+msrun --bind_core=True --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/det/dbnet/db_mobilenetv3_ppocrv3.yaml
 ```
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/tutorials/experts/zh-CN/r2.3.1/parallel/msrun_launcher.html).
+
 
 * 单卡训练
 

configs/det/east/README.md

@@ -120,9 +120,15 @@ python tools/train.py --config configs/det/east/east_r50_icdar15.yaml
 Please set `distribute` in yaml config file to be True.
 
 ```shell
-# n is the number of NPUs
-mpirun --allow-run-as-root -n 8 python tools/train.py --config configs/det/east/east_r50_icdar15.yaml
+# worker_num is the total number of Worker processes participating in the distributed task.
+# local_worker_num is the number of Worker processes pulled up on the current node.
+# The number of processes is equal to the number of NPUs used for training. In the case of single-machine multi-card worker_num and local_worker_num must be the same.
+msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/det/east/east_r50_icdar15.yaml
+
+# Based on verification,binding cores usually results in performance acceleration.Please configure the parameters and run.
+msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/det/east/east_r50_icdar15.yaml
 ```
+**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/tutorials/experts/en/r2.3.1/parallel/msrun_launcher.html).
 
 The training result (including checkpoints, per-epoch performance and curves) will be saved in the directory parsed by the arg `ckpt_save_dir` in yaml config file. The default directory is `./tmp_det`.
 

configs/det/east/README_CN.md

@@ -124,9 +124,15 @@ python tools/train.py --config configs/det/east/east_r50_icdar15.yaml
 请确保yaml文件中的`distribute`参数为True。
 
 ```shell
-# n is the number of NPUs
-mpirun --allow-run-as-root -n 8 python tools/train.py --config configs/det/east/east_r50_icdar15.yaml
+# worker_num代表分布式总进程数量。
+# local_worker_num代表当前节点进程数量。
+# 进程数量即为训练使用的NPU的数量，单机多卡情况下worker_num和local_worker_num需保持一致。
+msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/det/east/east_r50_icdar15.yaml
+
+# 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
+msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/det/east/east_r50_icdar15.yaml
 ```
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/tutorials/experts/zh-CN/r2.3.1/parallel/msrun_launcher.html).
 
 训练结果（包括checkpoint、每个epoch的性能和曲线图）将被保存在yaml配置文件的`ckpt_save_dir`参数配置的路径下，默认为`./tmp_det`。
 

configs/det/psenet/README.md

@@ -147,9 +147,17 @@ python tools/train.py --config configs/det/psenet/pse_r152_icdar15.yaml
 Please set `distribute` in yaml config file to be True.
 
 ```shell
-# n is the number of NPUs
-mpirun --allow-run-as-root -n 8 python tools/train.py --config configs/det/psenet/pse_r152_icdar15.yaml
+# worker_num is the total number of Worker processes participating in the distributed task.
+# local_worker_num is the number of Worker processes pulled up on the current node.
+# The number of processes is equal to the number of NPUs used for training. In the case of single-machine multi-card worker_num and local_worker_num must be the same.
+msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/det/psenet/pse_r152_icdar15.yaml
+
+# Based on verification,binding cores usually results in performance acceleration.Please configure the parameters and run.
+msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/det/psenet/pse_r152_icdar15.yaml
+
 ```
+**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/tutorials/experts/en/r2.3.1/parallel/msrun_launcher.html).
+
 
 The training result (including checkpoints, per-epoch performance and curves) will be saved in the directory parsed by the arg `ckpt_save_dir` in yaml config file. The default directory is `./tmp_det`.
 

configs/det/psenet/README_CN.md

@@ -147,9 +147,15 @@ python tools/train.py --config configs/det/psenet/pse_r152_icdar15.yaml
 请确保yaml文件中的`distribute`参数为True。
 
 ```shell
-# n is the number of NPUs
-mpirun --allow-run-as-root -n 8 python tools/train.py --config configs/det/psenet/pse_r152_icdar15.yaml
+# worker_num代表分布式总进程数量。
+# local_worker_num代表当前节点进程数量。
+# 进程数量即为训练使用的NPU的数量，单机多卡情况下worker_num和local_worker_num需保持一致。
+msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/det/psenet/pse_r152_icdar15.yaml
+
+# 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
+msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/det/psenet/pse_r152_icdar15.yaml
 ```
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/tutorials/experts/zh-CN/r2.3.1/parallel/msrun_launcher.html).
 
 训练结果（包括checkpoint、每个epoch的性能和曲线图）将被保存在yaml配置文件的`ckpt_save_dir`参数配置的路径下，默认为`./tmp_det`。
 

configs/layout/yolov8/README.md

@@ -99,8 +99,15 @@ It is easy to reproduce the reported results with the pre-defined training recip
 
 ```shell
 # distributed training on multiple Ascend devices
-mpirun --allow-run-as-root -n 4 python tools/train.py --config configs/layout/yolov8/yolov8n.yaml
+# worker_num is the total number of Worker processes participating in the distributed task.
+# local_worker_num is the number of Worker processes pulled up on the current node.
+# The number of processes is equal to the number of NPUs used for training. In the case of single-machine multi-card worker_num and local_worker_num must be the same.
+msrun --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/layout/yolov8/yolov8n.yaml
+
+# Based on verification,binding cores usually results in performance acceleration.Please configure the parameters and run.
+msrun --bind_core=True --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/layout/yolov8/yolov8n.yaml
 ```
+**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/tutorials/experts/en/r2.3.1/parallel/msrun_launcher.html).
 
 
 * Standalone Training

configs/layout/yolov8/README_CN.md

@@ -113,8 +113,15 @@ eval:
 
 ```shell
 # 在多个 Ascend 设备上进行分布式训练
-mpirun --allow-run-as-root -n 4 python tools/train.py --config configs/layout/yolov8/yolov8n.yaml
+# worker_num代表分布式总进程数量。
+# local_worker_num代表当前节点进程数量。
+# 进程数量即为训练使用的NPU的数量，单机多卡情况下worker_num和local_worker_num需保持一致。
+msrun --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/layout/yolov8/yolov8n.yaml
+
+# 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
+msrun --bind_core=True --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/layout/yolov8/yolov8n.yaml
 ```
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/tutorials/experts/zh-CN/r2.3.1/parallel/msrun_launcher.html).
 
 
 * 单卡训练

configs/rec/abinet/README.md

@@ -221,8 +221,16 @@ It is easy to reproduce the reported results with the pre-defined training recip
 
 ```shell
 # distributed training on multiple Ascend devices
-mpirun --allow-run-as-root -n 8 python tools/train.py --config configs/rec/abinet/abinet_resnet45_en.yaml
+# worker_num is the total number of Worker processes participating in the distributed task.
+# local_worker_num is the number of Worker processes pulled up on the current node.
+# The number of processes is equal to the number of NPUs used for training. In the case of single-machine multi-card worker_num and local_worker_num must be the same.
+msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/rec/abinet/abinet_resnet45_en.yaml
+
+# Based on verification,binding cores usually results in performance acceleration.Please configure the parameters and run.
+msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/rec/abinet/abinet_resnet45_en.yaml
 ```
+**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/tutorials/experts/en/r2.3.1/parallel/msrun_launcher.html).
+
 The pre-trained model needs to be loaded during ABINet model training, and the weight of the pre-trained model is
 from [abinet_pretrain_en.ckpt](https://download.mindspore.cn/toolkits/mindocr/abinet/abinet_pretrain_en-821ca20b.ckpt). It is needed to add the path of the pretrained weight to the model pretrained in "configs/rec/abinet/abinet_resnet45_en.yaml".