!3218 Move mass and fasterrcnn to new model zoo directory.

Merge pull request !3218 from linqingke/mass
This commit is contained in:
mindspore-ci-bot 2020-07-22 15:13:18 +08:00 committed by Gitee
commit ca6da6751f
80 changed files with 10 additions and 10 deletions

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@ -139,4 +139,4 @@ Inference result will be stored in the example path, whose folder name is "infer
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.346
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.562
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.631
```
```

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@ -548,7 +548,7 @@ Run the shell script `run.sh` as followed:
```bash
sh run.sh -t t -n 1 -i 1 -c /mass/config/config.json
```
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.
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.
## Fine-tuning
For fine-tuning a model, config the options in `config.json` firstly:
@ -562,7 +562,7 @@ Run the shell script `run.sh` as followed:
```bash
sh run.sh -t t -n 1 -i 1 -c config/config.json
```
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.
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.
## Inference
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
cd $file_path || exit
cd ../ || exit
rm -rf ./run_mass_$DEVICE_ID
mkdir ./run_mass_$DEVICE_ID
rm -rf ./${task}_mass_$DEVICE_ID
mkdir ./${task}_mass_$DEVICE_ID
cp train.py ./run_mass_$DEVICE_ID
cp eval.py ./run_mass_$DEVICE_ID
cp $configurations ./run_mass_$DEVICE_ID
cp train.py ./${task}_mass_$DEVICE_ID
cp eval.py ./${task}_mass_$DEVICE_ID
cp $configurations ./${task}_mass_$DEVICE_ID
if [ $vocab ]
then
cp $vocab ./run_mass_$DEVICE_ID
cp $vocab ./${task}_mass_$DEVICE_ID
fi
cd ./run_mass_$DEVICE_ID || exit
cd ./${task}_mass_$DEVICE_ID || exit
env > log.log
echo $task
if [ "$task" == "train" ]