Merge branch 'update' into 'master'

Add python path in scripts

See merge request open-mmlab/mmaction-lite!284

docs/getting_started.md

@@ -278,13 +278,13 @@ Here is an example of using 8 GPUs to load TSN checkpoint.
 If you can run MMAction on a cluster managed with [slurm](https://slurm.schedmd.com/), you can use the script `slurm_train.sh`. (This script also supports single machine training.)
 
 ```shell
-./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR} [${GPUS}]
+[GPUS=${GPUS}] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR}
 ```
 
 Here is an example of using 16 GPUs to train TSN on the dev partition in a slurm cluster. (use `GPUS_PER_NODE=8` to specify a single slurm cluster node with 8 GPUs.)
 
 ```shell
-GPUS_PER_NODE=8 ./tools/slurm_train.sh dev tsn_r50_k400 configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py work_dirs/tsn_r50_1x1x3_100e_kinetics400_rgb 16
+GPUS=16 ./tools/slurm_train.sh dev tsn_r50_k400 configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py work_dirs/tsn_r50_1x1x3_100e_kinetics400_rgb
 ```
 
 You can check [slurm_train.sh](../tools/slurm_train.sh) for full arguments and environment variables.
@@ -320,8 +320,8 @@ dist_params = dict(backend='nccl', port=29501)
 Then you can launch two jobs with `config1.py` ang `config2.py`.
 
 ```shell
-CUDA_VISIBLE_DEVICES=0,1,2,3 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR} 4
-CUDA_VISIBLE_DEVICES=4,5,6,7 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR} 4
+CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR}
+CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR}
 ```
 
 ## Useful Tools

docs/install.md

@@ -136,16 +136,10 @@ ln -s $KINETICS400_ROOT data
 
 ### Using multiple MMAction versions
 
-If there are more than one mmaction on your machine, and you want to use them alternatively, the recommended way is to create multiple conda environments and use different environments for different versions.
+The train and test scripts already modify the `PYTHONPATH` to ensure the script use the MMAction in the current directory.
 
-Another way is to insert the following code to the main scripts (`train.py`, `test.py` or any other scripts you run)
-```python
-import os.path as osp
-import sys
-sys.path.insert(0, osp.join(osp.dirname(osp.abspath(__file__)), '../'))
-```
+To use the default MMAction installed in the environment rather than that you are working with, you can remove the following line in those scripts.
 
-Or run the following command in the terminal of corresponding folder to temporally use the current one.
 ```shell
-export PYTHONPATH=`pwd`:$PYTHONPATH
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH
 ```

tools/dist_test.sh

 #!/usr/bin/env bash
 
-PYTHON=${PYTHON:-"python"}
-
 CONFIG=$1
 CHECKPOINT=$2
 GPUS=$3
 PORT=${PORT:-29500}
 
-$PYTHON -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
+python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
     $(dirname "$0")/test.py $CONFIG $CHECKPOINT --launcher pytorch ${@:4}

tools/dist_train.sh

 #!/usr/bin/env bash
 
-PYTHON=${PYTHON:-"python"}
-
 CONFIG=$1
 GPUS=$2
 PORT=${PORT:-29500}
 
-$PYTHON -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
+python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
     $(dirname "$0")/train.py $CONFIG --launcher pytorch ${@:3}

tools/slurm_test.sh

@@ -12,6 +12,7 @@ CPUS_PER_TASK=${CPUS_PER_TASK:-5}
 PY_ARGS=${@:5}
 SRUN_ARGS=${SRUN_ARGS:-""}
 
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
 srun -p ${PARTITION} \
     --job-name=${JOB_NAME} \
     --gres=gpu:${GPUS_PER_NODE} \

tools/slurm_train.sh

@@ -6,12 +6,13 @@ PARTITION=$1
 JOB_NAME=$2
 CONFIG=$3
 WORK_DIR=$4
-GPUS=${5:-8}
+GPUS=${GPUS:-8}
 GPUS_PER_NODE=${GPUS_PER_NODE:-8}
 CPUS_PER_TASK=${CPUS_PER_TASK:-5}
 SRUN_ARGS=${SRUN_ARGS:-""}
-PY_ARGS=${PY_ARGS:-"--validate"}
+PY_ARGS=${@:5}
 
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
 srun -p ${PARTITION} \
     --job-name=${JOB_NAME} \
     --gres=gpu:${GPUS_PER_NODE} \