Fix args from _ to -

configs/recognition/i3d/README.md

@@ -28,7 +28,7 @@ python tools/train.py ${CONFIG_FILE} [optional arguments]
 Example: train I3D model on Kinetics-400 dataset in a deterministic option with periodic validation.
 ```shell
 python tools/train.py configs/recognition/i3d/i3d_r50_32x2x1_100e_kinetics400_rgb.py \
-    --work_dir work_dirs/i3d_r50_32x2x1_100e_kinetics400_rgb \
+    --work-dir work_dirs/i3d_r50_32x2x1_100e_kinetics400_rgb \
     --validate --seed 0 --deterministic
 ```
 

configs/recognition/r2plus1d/README.md

@@ -27,7 +27,7 @@ python tools/train.py ${CONFIG_FILE} [optional arguments]
 Example: train R(2+1)D model on Kinetics-400 dataset in a deterministic option with periodic validation.
 ```shell
 python tools/train.py configs/recognition/r2plus1d/r2plus1d_r34_8x8x1_180e_kinetics400_rgb.py \
-    --work_dir work_dirs/r2plus1d_r34_3d_8x8x1_180e_kinetics400_rgb \
+    --work-dir work_dirs/r2plus1d_r34_3d_8x8x1_180e_kinetics400_rgb \
     --validate --seed 0 --deterministic
 ```
 
@@ -43,7 +43,7 @@ Example: test R(2+1)D model on Kinetics-400 dataset and dump the result to a jso
 ```shell
 python tools/test.py configs/recognition/r2plus1d/r2plus1d_r34_8x8x1_180e_kinetics400_rgb.py \
     checkpoints/SOME_CHECKPOINT.pth --eval top_k_accuracy mean_class_accuracy \
-    --out result.json --average_clips=prob
+    --out result.json --average-clips=prob
 ```
 
 For more details, you can refer to **Test a dataset** part in [getting_started](/docs/getting_started.md#test-a-dataset).

configs/recognition/slowfast/README.md

@@ -27,7 +27,7 @@ python tools/train.py ${CONFIG_FILE} [optional arguments]
 Example: train SlowFast model on Kinetics-400 dataset in a deterministic option with periodic validation.
 ```shell
 python tools/train.py configs/recognition/slowfast/slowfast_r50_4x16x1_256e_kinetics400_rgb.py \
-    --work_dir work_dirs/slowfast_r50_4x16x1_256e_kinetics400_rgb \
+    --work-dir work_dirs/slowfast_r50_4x16x1_256e_kinetics400_rgb \
     --validate --seed 0 --deterministic
 ```
 
@@ -43,7 +43,7 @@ Example: test SlowFast model on Kinetics-400 dataset and dump the result to a js
 ```shell
 python tools/test.py configs/recognition/slowfast/slowfast_r50_4x16x1_256e_kinetics400_rgb.py \
     checkpoints/SOME_CHECKPOINT.pth --eval top_k_accuracy mean_class_accuracy \
-    --out result.json --average_clips=prob
+    --out result.json --average-clips=prob
 ```
 
 For more details, you can refer to **Test a dataset** part in [getting_started](/docs/getting_started.md#test-a-dataset).

configs/recognition/slowonly/README.md

@@ -29,7 +29,7 @@ python tools/train.py ${CONFIG_FILE} [optional arguments]
 Example: train SlowOnly model on Kinetics-400 dataset in a deterministic option with periodic validation.
 ```shell
 python tools/train.py configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb.py \
-    --work_dir work_dirs/slowonly_r50_4x16x1_256e_kinetics400_rgb \
+    --work-dir work_dirs/slowonly_r50_4x16x1_256e_kinetics400_rgb \
     --validate --seed 0 --deterministic
 ```
 
@@ -45,7 +45,7 @@ Example: test SlowOnly model on Kinetics-400 dataset and dump the result to a js
 ```shell
 python tools/test.py configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb.py \
     checkpoints/SOME_CHECKPOINT.pth --eval top_k_accuracy mean_class_accuracy \
-    --out result.json --average_clips=prob
+    --out result.json --average-clips=prob
 ```
 
 For more details, you can refer to **Test a dataset** part in [getting_started](/docs/getting_started.md#test-a-dataset).

configs/recognition/tsm/README.md

@@ -42,7 +42,7 @@ python tools/train.py ${CONFIG_FILE} [optional arguments]
 Example: train TSM model on Kinetics-400 dataset in a deterministic option with periodic validation.
 ```shell
 python tools/train.py configs/recognition/tsm/tsm_r50_1x1x8_50e_kinetics400_rgb.py \
-    --work_dir work_dirs/tsm_r50_1x1x8_100e_kinetics400_rgb \
+    --work-dir work_dirs/tsm_r50_1x1x8_100e_kinetics400_rgb \
     --validate --seed 0 --deterministic
 ```
 

configs/recognition/tsn/README.md

@@ -75,7 +75,7 @@ python tools/train.py ${CONFIG_FILE} [optional arguments]
 Example: train TSN model on Kinetics-400 dataset in a deterministic option with periodic validation.
 ```shell
 python tools/train.py configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py \
-    --work_dir work_dirs/tsn_r50_1x1x3_100e_kinetics400_rgb \
+    --work-dir work_dirs/tsn_r50_1x1x3_100e_kinetics400_rgb \
     --validate --seed 0 --deterministic
 ```
 

docs/data_preparation.md

@@ -46,8 +46,8 @@ You can use the following command to extract frames.
 
 ```shell
 python build_rawframes.py ${SRC_FOLDER} ${OUT_FOLDER} [--task ${TASK}] [--level ${LEVEL}] \
-    [--num_worker ${NUM_WORKER}] [--flow_type ${FLOW_TYPE}] [--out_format ${OUT_FORMAT}] \
-    [--ext ${EXT}] [--new_width ${NEW_WIDTH}] [--new_height ${NEW_HEIGHT}] [--new_short ${NEW_SHORT}]
+    [--num-worker ${NUM_WORKER}] [--flow-type ${FLOW_TYPE}] [--out-format ${OUT_FORMAT}] \
+    [--ext ${EXT}] [--new-width ${NEW_WIDTH}] [--new-height ${NEW_HEIGHT}] [--new-short ${NEW_SHORT}]
     [--resume]
 ```
 
@@ -79,9 +79,9 @@ We provide a convenient script to generate annotation file list. You can use the
 
 ```shell
 cd $MMACTION
-python tools/data/build_file_list.py ${DATASET} ${SRC_FOLDER} [--rgb_prefix ${RGB_PREFIX}] \
-    [--flow_x_prefix ${FLOW_X_PREFIX}] [--flow_y_prefix ${FLOW_Y_PREFIX}] [--num_split ${NUM_SPLIT}] \
-    [--subset ${SUBSET}] [--level ${LEVEL}] [--format ${FORMAT}] [--out_root_path ${OUT_ROOT_PATH}] \
+python tools/data/build_file_list.py ${DATASET} ${SRC_FOLDER} [--rgb-prefix ${RGB_PREFIX}] \
+    [--flow-x-prefix ${FLOW_X_PREFIX}] [--flow-y-prefix ${FLOW_Y_PREFIX}] [--num-split ${NUM_SPLIT}] \
+    [--subset ${SUBSET}] [--level ${LEVEL}] [--format ${FORMAT}] [--out-root-path ${OUT_ROOT_PATH}] \
     [--shuffle]
 ```
 

docs/getting_started.md

@@ -46,12 +46,12 @@ You can use the following commands to test a dataset.
 ```shell
 # single-gpu testing
 python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] \
-    [--proc_per_gpu ${NUM_PROC_PER_GPU}] [--gpu_collect] [--tmpdir ${TMPDIR}] [--average_clips ${AVG_TYPE}] \
+    [--gpu-collect] [--tmpdir ${TMPDIR}] [--average-clips ${AVG_TYPE}] \
     [--launcher ${JOB_LAUNCHER}] [--local_rank ${LOCAL_RANK}]
 
 # multi-gpu testing
 python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] \
-    [--proc_per_gpu ${NUM_PROC_PER_GPU}] [--gpu_collect] [--tmpdir ${TMPDIR}] [--average_clips ${AVG_TYPE}] \
+    [--gpu-collect] [--tmpdir ${TMPDIR}] [--average-clips ${AVG_TYPE}] \
     [--launcher ${JOB_LAUNCHER}] [--local_rank ${LOCAL_RANK}]
 ```
 
@@ -59,9 +59,8 @@ Optional arguments:
 - `GPU_NUM`: Number of GPU used to test model. If not specified, it will be set to 1.
 - `RESULT_FILE`: Filename of the output results. If not specified, the results will not be saved to a file.
 - `EVAL_METRICS`: Items to be evaluated on the results. Allowed values depend on the dataset, e.g., `top_k_accuracy`, `mean_class_accuracy` are available for all datasets in recognition, `mean_average_precision` for Multi-Moments in Time, `AR@AN` for ActivityNet, etc.
-- `NUM_PROC_PER_GPU`: Number of processes per GPU. If not specified, only one process will be assigned for a single gpu.
-- `--gpu_collect`: If specified, recognition results will be collected using gpu communication. Otherwise, it will save the results on different gpus to `TMPDIR` and collect them by the rank 0 worker.
-- `TMPDIR`: Temporary directory used for collecting results from multiple workers, available when `--gpu_collect` is not specified.
+- `--gpu-collect`: If specified, recognition results will be collected using gpu communication. Otherwise, it will save the results on different gpus to `TMPDIR` and collect them by the rank 0 worker.
+- `TMPDIR`: Temporary directory used for collecting results from multiple workers, available when `--gpu-collect` is not specified.
 - `AVG_TYPE`: Items to average the test clips. If set to `prob`, it will apply softmax before averaging the clip scores. Otherwise, it will directly average the clip scores.
 - `JOB_LAUNCHER`: Items for distributed job initialization launcher. Allowed choices are `none`, `pytorch`, `slurm`, `mpi`. Especially, if set to none, it will test in a non-distributed mode.
 - `LOCAL_RANK`: ID for local rank. If not specified, it will be set to 0.
@@ -247,7 +246,7 @@ According to the [Linear Scaling Rule](https://arxiv.org/abs/1706.02677), you ne
 python tools/train.py ${CONFIG_FILE} [optional arguments]
 ```
 
-If you want to specify the working directory in the command, you can add an argument `--work_dir ${YOUR_WORK_DIR}`.
+If you want to specify the working directory in the command, you can add an argument `--work-dir ${YOUR_WORK_DIR}`.
 
 ### Train with multiple GPUs
 
@@ -258,10 +257,10 @@ If you want to specify the working directory in the command, you can add an argu
 Optional arguments are:
 
 - `--validate` (**strongly recommended**): Perform evaluation at every k (default value is 5, which can be modified by changing the `interval` value in `evaluation` dict in each config file) epochs during the training.
-- `--work_dir ${WORK_DIR}`: Override the working directory specified in the config file.
-- `--resume_from ${CHECKPOINT_FILE}`: Resume from a previous checkpoint file.
+- `--work-dir ${WORK_DIR}`: Override the working directory specified in the config file.
+- `--resume-from ${CHECKPOINT_FILE}`: Resume from a previous checkpoint file.
 - `--gpus ${GPU_NUM}`: Number of gpus to use, which is only applicable to non-distributed training.
-- `--gpu_ids ${GPU_IDS}`: IDs of gpus to use, which is only applicable to non-distributed training.
+- `--gpu-ids ${GPU_IDS}`: IDs of gpus to use, which is only applicable to non-distributed training.
 - `--seed ${SEED}`: Seed id for random state in python, numpy and pytorch to generate random numbers.
 - `--deterministic`: If specified, it will set deterministic options for CUDNN backend.
 - `JOB_LAUNCHER`: Items for distributed job initialization launcher. Allowed choices are `none`, `pytorch`, `slurm`, `mpi`. Especially, if set to none, it will test in a non-distributed mode.
@@ -274,7 +273,7 @@ Difference between `resume-from` and `load-from`:
 Here is an example of using 8 GPUs to load TSN checkpoint.
 
 ```shell
-./tools/dist_train.sh configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py 8 --resume_from work_dirs/tsn_r50_1x1x3_100e_kinetics400_rgb/latest.pth
+./tools/dist_train.sh configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py 8 --resume-from work_dirs/tsn_r50_1x1x3_100e_kinetics400_rgb/latest.pth
 ```
 
 ### Train with multiple machines

tools/data/build_file_list.py

@@ -22,19 +22,19 @@ def parse_args():
     parser.add_argument(
         'src_folder', type=str, help='root directory for the frames or videos')
     parser.add_argument(
-        '--rgb_prefix', type=str, default='img_', help='prefix of rgb frames')
+        '--rgb-prefix', type=str, default='img_', help='prefix of rgb frames')
     parser.add_argument(
-        '--flow_x_prefix',
+        '--flow-x-prefix',
         type=str,
         default='flow_x_',
         help='prefix of flow x frames')
     parser.add_argument(
-        '--flow_y_prefix',
+        '--flow-y-prefix',
         type=str,
         default='flow_y_',
         help='prefix of flow y frames')
     parser.add_argument(
-        '--num_split',
+        '--num-split',
         type=int,
         default=3,
         help='number of split to file list')
@@ -57,7 +57,7 @@ def parse_args():
         choices=['rawframes', 'videos'],
         help='data format')
     parser.add_argument(
-        '--out_root_path',
+        '--out-root-path',
         type=str,
         default='data/',
         help='root path for output')

tools/data/build_rawframes.py

@@ -84,18 +84,18 @@ def parse_args():
         default=2,
         help='directory level of data')
     parser.add_argument(
-        '--num_worker',
+        '--num-worker',
         type=int,
         default=8,
         help='number of workers to build rawframes')
     parser.add_argument(
-        '--flow_type',
+        '--flow-type',
         type=str,
         default=None,
         choices=[None, 'tvl1', 'warp_tvl1', 'farn', 'brox'],
         help='flow type to be generated')
     parser.add_argument(
-        '--out_format',
+        '--out-format',
         type=str,
         default='jpg',
         choices=['jpg', 'h5', 'png'],
@@ -107,15 +107,15 @@ def parse_args():
         choices=['avi', 'mp4', 'webm'],
         help='video file extensions')
     parser.add_argument(
-        '--new_width', type=int, default=0, help='resize image width')
+        '--new-width', type=int, default=0, help='resize image width')
     parser.add_argument(
-        '--new_height', type=int, default=0, help='resize image height')
+        '--new-height', type=int, default=0, help='resize image height')
     parser.add_argument(
-        '--new_short',
+        '--new-short',
         type=int,
         default=0,
         help='resize image short side length keeping ratio')
-    parser.add_argument('--num_gpu', type=int, default=8, help='number of GPU')
+    parser.add_argument('--num-gpu', type=int, default=8, help='number of GPU')
     parser.add_argument(
         '--resume',
         action='store_true',

tools/data/kinetics400/extract_frames.sh

 #! /usr/bin/bash env
 
 cd ../
-python build_rawframes.py ../../data/kinetics400/videos_train/ ../../data/kinetics400/rawframes_train/ --level 2 --flow_type tvl1 --ext mp4 --task both  --new_width 340 --new_height 256
+python build_rawframes.py ../../data/kinetics400/videos_train/ ../../data/kinetics400/rawframes_train/ --level 2 --flow-type tvl1 --ext mp4 --task both  --new-width 340 --new-height 256
 echo "Raw frames (RGB and tv-l1) Generated for train set"
 
-python build_rawframes.py ../../data/kinetics400/videos_val/ ../../data/kinetics400/rawframes_val/ --level 2 --flow_type tvl1 --ext mp4 --task both  --new_width 340 --new_height 256
+python build_rawframes.py ../../data/kinetics400/videos_val/ ../../data/kinetics400/rawframes_val/ --level 2 --flow-type tvl1 --ext mp4 --task both  --new-width 340 --new-height 256
 echo "Raw frames (RGB and tv-l1) Generated for val set"
 
 cd kinetics400/

tools/data/kinetics400/extract_rgb_frames.sh

 #! /usr/bin/bash env
 
 cd ../
-python build_rawframes.py ../../data/kinetics400/videos_train/ ../../data/kinetics400/rawframes_train/ --level 2  --ext mp4 --task rgb  --new_width 340 --new_height 256
+python build_rawframes.py ../../data/kinetics400/videos_train/ ../../data/kinetics400/rawframes_train/ --level 2  --ext mp4 --task rgb  --new-width 340 --new-height 256
 echo "Raw frames (RGB only) generated for train set"
 
-python build_rawframes.py ../../data/kinetics400/videos_val/ ../../data/kinetics400/rawframes_val/ --level 2 --ext mp4 --task rgb  --new_width 340 --new_height 256
+python build_rawframes.py ../../data/kinetics400/videos_val/ ../../data/kinetics400/rawframes_val/ --level 2 --ext mp4 --task rgb  --new-width 340 --new-height 256
 echo "Raw frames (RGB only) generated for val set"
 
 cd kinetics400/

tools/data/kinetics400/generate_rawframes_filelist.sh

 #! /usr/bin/bash env
 
 cd ../../../
-PYTHONPATH=. python tools/data/build_file_list.py kinetics400 data/kinetics400/rawframes_train/ --level 2 --format rawframes --num_split 1 --subset train --shuffle
+PYTHONPATH=. python tools/data/build_file_list.py kinetics400 data/kinetics400/rawframes_train/ --level 2 --format rawframes --num-split 1 --subset train --shuffle
 echo "Train filelist for rawframes generated."
 
-PYTHONPATH=. python tools/data/build_file_list.py kinetics400 data/kinetics400/rawframes_val/ --level 2 --format rawframes --num_split 1 --subset val --shuffle
+PYTHONPATH=. python tools/data/build_file_list.py kinetics400 data/kinetics400/rawframes_val/ --level 2 --format rawframes --num-split 1 --subset val --shuffle
 echo "Val filelist for rawframes generated."
 cd tools/data/kinetics400/

tools/data/kinetics400/generate_videos_filelist.sh

 #! /usr/bin/bash env
 
 cd ../../../
-PYTHONPATH=. python tools/data/build_file_list.py kinetics400 data/kinetics400/videos_train/ --level 2 --format videos --num_split 1 --subset train --shuffle
+PYTHONPATH=. python tools/data/build_file_list.py kinetics400 data/kinetics400/videos_train/ --level 2 --format videos --num-split 1 --subset train --shuffle
 echo "Train filelist for video generated."
 
-PYTHONPATH=. python tools/data/build_file_list.py kinetics400 data/kinetics400/videos_val/ --level 2 --format videos --num_split 1 --subset val --shuffle
+PYTHONPATH=. python tools/data/build_file_list.py kinetics400 data/kinetics400/videos_val/ --level 2 --format videos --num-split 1 --subset val --shuffle
 echo "Val filelist for video generated."
 cd tools/data/kinetics400/

tools/data/kinetics400/preparing_kinetics400.md

@@ -64,7 +64,7 @@ bash extract_frames.sh
 ```
 
 These two commands above can generate images with size 340x256, if you want to generate images with short edge 320 (320p),
-you can change the args `--new_width 340 --new_height 256` to `--new_short 320`.
+you can change the args `--new-width 340 --new-height 256` to `--new-short 320`.
 More details can be found in [data_preparation](/docs/data_preparation.md)
 
 ## Step 4. Generate File List

tools/data/mit/extract_frames.sh

 #! /usr/bin/bash env
 
 cd ../
-python build_rawframes.py ../../data/mit/videos/training ../../data/mit/rawframes/training/ --level 2 --flow_type tvl1 --ext mp4 --task both
+python build_rawframes.py ../../data/mit/videos/training ../../data/mit/rawframes/training/ --level 2 --flow-type tvl1 --ext mp4 --task both
 echo "Raw frames (RGB and tv-l1) Generated for train set"
 
-python build_rawframes.py ../../data/mit/vides/validation/ ../../data/mit/rawframes/validation/ --level 2 --flow_type tvl1 --ext mp4 --task both
+python build_rawframes.py ../../data/mit/vides/validation/ ../../data/mit/rawframes/validation/ --level 2 --flow-type tvl1 --ext mp4 --task both
 echo "Raw frames (RGB and tv-l1) Generated for val set"
 
 cd mit/

tools/data/mit/generate_rawframes_filelist.sh

 #! /usr/bin/bash env
 
 cd ../../../
-PYTHONPATH=. python tools/data/build_file_list.py mit data/mit/rawframes/training/ --level 2 --format rawframes --num_split 1 --subset train --shuffle
+PYTHONPATH=. python tools/data/build_file_list.py mit data/mit/rawframes/training/ --level 2 --format rawframes --num-split 1 --subset train --shuffle
 echo "Train filelist for rawframes generated."
 
-PYTHONPATH=. python tools/data/build_file_list.py mit data/mit/rawframes/validation/ --level 2 --format rawframes --num_split 1 --subset val --shuffle
+PYTHONPATH=. python tools/data/build_file_list.py mit data/mit/rawframes/validation/ --level 2 --format rawframes --num-split 1 --subset val --shuffle
 echo "Val filelist for rawframes generated."
 cd tools/data/mit/

tools/data/mit/generate_videos_filelist.sh

 #! /usr/bin/bash env
 
 cd ../../../
-PYTHONPATH=. python tools/data/build_file_list.py mit data/mit/videos/training/ --level 2 --format videos --num_split 1 --subset train --shuffle
+PYTHONPATH=. python tools/data/build_file_list.py mit data/mit/videos/training/ --level 2 --format videos --num-split 1 --subset train --shuffle
 echo "Train filelist for videos generated."
 
-PYTHONPATH=. python tools/data/build_file_list.py mit data/mit/videos/validation/ --level 2 --format videos --num_split 1 --subset val --shuffle
+PYTHONPATH=. python tools/data/build_file_list.py mit data/mit/videos/validation/ --level 2 --format videos --num-split 1 --subset val --shuffle
 echo "Val filelist for videos generated."
 cd tools/data/mit/

tools/data/mmit/extract_frames.sh

 #! /usr/bin/bash env
 
 cd ../
-python build_rawframes.py ../../data/mmit/videos/ ../../../data/mmit/rawframes/ --task both --level 2 --flow_type tvl1 --ext mp4
+python build_rawframes.py ../../data/mmit/videos/ ../../../data/mmit/rawframes/ --task both --level 2 --flow-type tvl1 --ext mp4
 echo "Raw frames (RGB and Flow) Generated"
 cd mmit/

tools/data/mmit/generate_rawframes_filelist.sh

 #! /usr/bin/bash env
 
 cd ../../../
-PYTHONPATH=. python tools/data/build_file_list.py mmit data/mmit/rawframes/ --level 2 --format rawframes --num_split 1 --subset train --shuffle
+PYTHONPATH=. python tools/data/build_file_list.py mmit data/mmit/rawframes/ --level 2 --format rawframes --num-split 1 --subset train --shuffle
 echo "Train filelist for rawframes generated."
 
-PYTHONPATH=. python tools/data/build_file_list.py mmit data/mmit/rawframes/ --level 2 --format rawframes --num_split 1 --subset val --shuffle
+PYTHONPATH=. python tools/data/build_file_list.py mmit data/mmit/rawframes/ --level 2 --format rawframes --num-split 1 --subset val --shuffle
 echo "Val filelist for rawframes generated."
 cd tools/data/mmit/

tools/data/mmit/generate_videos_filelist.sh

 #! /usr/bin/bash env
 
 cd ../../../
-PYTHONPATH=. python tools/data/build_file_list.py mmit data/mmit/videos/ --level 2 --format videos --num_split 1 --subset train --shuffle
+PYTHONPATH=. python tools/data/build_file_list.py mmit data/mmit/videos/ --level 2 --format videos --num-split 1 --subset train --shuffle
 echo "Train filelist for videos generated."
 
-PYTHONPATH=. python tools/data/build_file_list.py mmit data/mmit/videos/ --level 2 --format videos --num_split 1 --subset val --shuffle
+PYTHONPATH=. python tools/data/build_file_list.py mmit data/mmit/videos/ --level 2 --format videos --num-split 1 --subset val --shuffle
 echo "Val filelist for videos generated."
 cd tools/data/mmit/

tools/data/sthv1/extract_frames.sh

 #! /usr/bin/bash env
 
 cd ../
-python build_rawframes.py ../../data/sthv1/videos/ ../../data/sthv1/rawframes/ --task both --level 1 --flow_type tvl1 --ext webm
+python build_rawframes.py ../../data/sthv1/videos/ ../../data/sthv1/rawframes/ --task both --level 1 --flow-type tvl1 --ext webm
 echo "Raw frames (RGB and tv-l1) Generated"
 cd sthv1/

tools/data/sthv1/generate_rawframes_filelist.sh

 #! /usr/bin/bash env
 
 cd ../../../
-PYTHONPATH=. python tools/data/build_file_list.py sthv1 data/sthv1/rawframes/ --num_split 1 --level 1 --subset train --format rawframe --shuffle
-PYTHONPATH=. python tools/data/build_file_list.py sthv1 data/sthv1/rawframes/ --num_split 1 --level 1 --subset val --format rawframe --shuffle
+PYTHONPATH=. python tools/data/build_file_list.py sthv1 data/sthv1/rawframes/ --num-split 1 --level 1 --subset train --format rawframe --shuffle
+PYTHONPATH=. python tools/data/build_file_list.py sthv1 data/sthv1/rawframes/ --num-split 1 --level 1 --subset val --format rawframe --shuffle
 echo "Filelist for rawframes generated."
 
 cd tools/data/sthv1/

tools/data/sthv1/generate_videos_filelist.sh

 #! /usr/bin/bash env
 
 cd ../../../
-PYTHONPATH=. python tools/data/build_file_list.py sthv1 data/sthv1/videos/ --num_split 1 --level 1 --subset train --format videos --shuffle
-PYTHONPATH=. python tools/data/build_file_list.py sthv1 data/sthv1/videos/ --num_split 1 --level 1 --subset val --format videos --shuffle
+PYTHONPATH=. python tools/data/build_file_list.py sthv1 data/sthv1/videos/ --num-split 1 --level 1 --subset train --format videos --shuffle
+PYTHONPATH=. python tools/data/build_file_list.py sthv1 data/sthv1/videos/ --num-split 1 --level 1 --subset val --format videos --shuffle
 echo "Filelist for videos generated."
 
 cd tools/data/sthv1/

tools/data/sthv2/extract_frames.sh

 #! /usr/bin/bash env
 
 cd ../
-python build_rawframes.py ../../data/sthv2/videos/ ../../data/sthv2/rawframes/ --task both --level 1 --flow_type tvl1 --ext webm
+python build_rawframes.py ../../data/sthv2/videos/ ../../data/sthv2/rawframes/ --task both --level 1 --flow-type tvl1 --ext webm
 echo "Raw frames (RGB and tv-l1) Generated"
 cd sthv2/

tools/data/sthv2/generate_rawframes_filelist.sh

 #! /usr/bin/bash env
 
 cd ../../../
-PYTHONPATH=. python tools/data/build_file_list.py sthv2 data/sthv2/rawframes/ --num_split 1 --level 1 --subset train --format rawframe --shuffle
-PYTHONPATH=. python tools/data/build_file_list.py sthv2 data/sthv2/rawframes/ --num_split 1 --level 1 --subset val --format rawframe --shuffle
+PYTHONPATH=. python tools/data/build_file_list.py sthv2 data/sthv2/rawframes/ --num-split 1 --level 1 --subset train --format rawframe --shuffle
+PYTHONPATH=. python tools/data/build_file_list.py sthv2 data/sthv2/rawframes/ --num-split 1 --level 1 --subset val --format rawframe --shuffle
 echo "Filelist for rawframes generated."
 
 cd tools/data/sthv2/

tools/data/sthv2/generate_videos_filelist.sh

 #! /usr/bin/bash env
 
 cd ../../../
-PYTHONPATH=. python tools/data/build_file_list.py sthv2 data/sthv2/videos/ --num_split 1 --level 1 --subset train --format videos --shuffle
-PYTHONPATH=. python tools/data/build_file_list.py sthv2 data/sthv2/videos/ --num_split 1 --level 1 --subset val --format videos --shuffle
+PYTHONPATH=. python tools/data/build_file_list.py sthv2 data/sthv2/videos/ --num-split 1 --level 1 --subset train --format videos --shuffle
+PYTHONPATH=. python tools/data/build_file_list.py sthv2 data/sthv2/videos/ --num-split 1 --level 1 --subset val --format videos --shuffle
 echo "Filelist for videos generated."
 
 cd tools/data/sthv2/

tools/data/thumos14/extract_frames.sh

 #! /usr/bin/bash env
 
 cd ../
-python build_rawframes.py ../../data/thumos14/videos/validation/ ../../data/thumos14/rawframes/validation/ --level 1 --flow_type tvl1 --ext mp4 --task both
+python build_rawframes.py ../../data/thumos14/videos/validation/ ../../data/thumos14/rawframes/validation/ --level 1 --flow-type tvl1 --ext mp4 --task both
 echo "Raw frames (RGB and tv-l1) Generated for val set"
 
-python build_rawframes.py ../../data/thumos14/videos/test/ ../../data/thumos14/rawframes/test/ --level 1 --flow_type tvl1 --ext mp4 --task both
+python build_rawframes.py ../../data/thumos14/videos/test/ ../../data/thumos14/rawframes/test/ --level 1 --flow-type tvl1 --ext mp4 --task both
 echo "Raw frames (RGB and tv-l1) Generated for test set"
 
 cd thumos14/

tools/data/ucf101/extract_frames.sh

@@ -3,6 +3,6 @@
 FLOW_TYPE=$1
 
 cd ../
-python build_rawframes.py ../../data/ucf101/videos/ ../../data/ucf101/rawframes/ --task both --level 2 --flow_type ${FLOW_TYPE}
+python build_rawframes.py ../../data/ucf101/videos/ ../../data/ucf101/rawframes/ --task both --level 2 --flow-type ${FLOW_TYPE}
 echo "Raw frames (RGB and Flow) Generated"
 cd ucf101/

tools/gen_flow.py

@@ -80,8 +80,8 @@ def extract_dense_flow(path,
                         and 'farneback'. Default: 'tvl1'.
     """
 
+    frames = []
     if osp.exists(path):
-        frames = []
         vid = cv2.VideoCapture(path)
         flag, f = vid.read()
         while flag:

tools/slurm_train.sh

@@ -21,4 +21,4 @@ srun -p ${PARTITION} \
     --cpus-per-task=${CPUS_PER_TASK} \
     --kill-on-bad-exit=1 \
     ${SRUN_ARGS} \
-    python -u tools/train.py ${CONFIG} --work_dir=${WORK_DIR} --launcher="slurm" ${PY_ARGS}
+    python -u tools/train.py ${CONFIG} --work-dir=${WORK_DIR} --launcher="slurm" ${PY_ARGS}

tools/test.py

@@ -28,21 +28,16 @@ def parse_args():
         ' "top_k_accuracy", "mean_class_accuracy" for video dataset')
     parser.add_argument('--show', action='store_true', help='show results')
     parser.add_argument(
-        '--proc_per_gpu',
-        default=1,
-        type=int,
-        help='Number of processes per GPU')
-    parser.add_argument(
-        '--gpu_collect',
+        '--gpu-collect',
         action='store_true',
         help='whether to use gpu to collect results')
     parser.add_argument(
         '--tmpdir',
         help='tmp directory used for collecting results from multiple '
-        'workers, available when gpu_collect is not specified')
+        'workers, available when gpu-collect is not specified')
     parser.add_argument('--options', nargs='+', help='custom options')
     parser.add_argument(
-        '--average_clips',
+        '--average-clips',
         choices=['score', 'prob'],
         default='score',
         help='average type when averaging test clips')

tools/train.py

@@ -19,9 +19,9 @@ from mmaction.utils import collect_env, get_root_logger
 def parse_args():
     parser = argparse.ArgumentParser(description='Train a recognizer')
     parser.add_argument('config', help='train config file path')
-    parser.add_argument('--work_dir', help='the dir to save logs and models')
+    parser.add_argument('--work-dir', help='the dir to save logs and models')
     parser.add_argument(
-        '--resume_from', help='the checkpoint file to resume from')
+        '--resume-from', help='the checkpoint file to resume from')
     parser.add_argument(
         '--validate',
         action='store_true',
@@ -33,7 +33,7 @@ def parse_args():
         help='number of gpus to use '
         '(only applicable to non-distributed training)')
     group_gpus.add_argument(
-        '--gpu_ids',
+        '--gpu-ids',
         type=int,
         nargs='+',
         help='ids of gpus to use '