forked from mindspore-Ecosystem/mindspore
!3218 Move mass and fasterrcnn to new model zoo directory.
Merge pull request !3218 from linqingke/mass
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ca6da6751f
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@ -139,4 +139,4 @@ Inference result will be stored in the example path, whose folder name is "infer
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.346
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.562
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.631
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```
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```
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@ -548,7 +548,7 @@ Run the shell script `run.sh` as followed:
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```bash
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sh run.sh -t t -n 1 -i 1 -c /mass/config/config.json
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```
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Get the log and output files under the path `./run_mass_*/`, and the model file under the path assigned in the `config/config.json` file.
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Get the log and output files under the path `./train_mass_*/`, and the model file under the path assigned in the `config/config.json` file.
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## Fine-tuning
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For fine-tuning a model, config the options in `config.json` firstly:
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@ -562,7 +562,7 @@ Run the shell script `run.sh` as followed:
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```bash
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sh run.sh -t t -n 1 -i 1 -c config/config.json
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```
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Get the log and output files under the path `./run_mass_*/`, and the model file under the path assigned in the `config/config.json` file.
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Get the log and output files under the path `./train_mass_*/`, and the model file under the path assigned in the `config/config.json` file.
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## Inference
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If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/network_migration.html).
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@ -149,19 +149,19 @@ do
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cd $file_path || exit
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cd ../ || exit
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rm -rf ./run_mass_$DEVICE_ID
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mkdir ./run_mass_$DEVICE_ID
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rm -rf ./${task}_mass_$DEVICE_ID
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mkdir ./${task}_mass_$DEVICE_ID
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cp train.py ./run_mass_$DEVICE_ID
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cp eval.py ./run_mass_$DEVICE_ID
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cp $configurations ./run_mass_$DEVICE_ID
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cp train.py ./${task}_mass_$DEVICE_ID
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cp eval.py ./${task}_mass_$DEVICE_ID
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cp $configurations ./${task}_mass_$DEVICE_ID
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if [ $vocab ]
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then
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cp $vocab ./run_mass_$DEVICE_ID
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cp $vocab ./${task}_mass_$DEVICE_ID
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fi
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cd ./run_mass_$DEVICE_ID || exit
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cd ./${task}_mass_$DEVICE_ID || exit
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env > log.log
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echo $task
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if [ "$task" == "train" ]
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