[Fix] Fix feature extraction script

tools/data/activitynet/README.md

@@ -109,28 +109,28 @@ After finetuning TSN on ActivityNet, you can use it to extract both RGB and Flow
 
 ```shell
 python ../../misc/clip_feature_extraction.py tsn_extract_rgb_feat_config.py \
-  /path/to/rgb_checkpoint.pth ../../../data/ActivityNet/rgb_tarin_feat.pkl \
+  /path/to/rgb_checkpoint.pth ../../../data/ActivityNet/rgb_tarin_feat \
   --video-list ../../../data/ActivityNet/anet_train_video.txt \
   --video-root ../../../data/ActivityNet/rawframes \
-  --dump-score
+  --dump-score --long-video-mode
 
 python ../../misc/clip_feature_extraction.py tsn_extract_rgb_feat_config.py \
-  path/to/rgb_checkpoint.pth ../../../data/ActivityNet/rgb_val_feat.pkl \
+  path/to/rgb_checkpoint.pth ../../../data/ActivityNet/rgb_val_feat \
   --video-list ../../../data/ActivityNet/anet_val_video.txt \
   --video-root ../../../data/ActivityNet/rawframes \
-  --dump-score
+  --dump-score --long-video-mode
 
 python ../../misc/clip_feature_extraction.py tsn_extract_flow_feat_config.py \
-  /path/to/flow_checkpoint.pth ../../../data/ActivityNet/flow_tarin_feat.pkl \
+  /path/to/flow_checkpoint.pth ../../../data/ActivityNet/flow_tarin_feat \
   --video-list ../../../data/ActivityNet/anet_train_video.txt \
   --video-root ../../../data/ActivityNet/rawframes \
-  --dump-score
+  --dump-score --long-video-mode
 
 python ../../misc/clip_feature_extraction.py tsn_extract_flow_feat_config.py \
-  /path/to/flow_checkpoint.pth ../../../data/ActivityNet/flow_val_feat.pkl \
+  /path/to/flow_checkpoint.pth ../../../data/ActivityNet/flow_val_feat \
   --video-list ../../../data/ActivityNet/anet_val_video.txt \
   --video-root ../../../data/ActivityNet/rawframes \
-  --dump-score
+  --dump-score --long-video-mode
 ```
 
 After feature extraction, you can use our post processing scripts to concat RGB and Flow feature, generate the `100-t X 400-d` feature for Action Detection.

tools/data/activitynet/README_zh-CN.md

@@ -108,28 +108,28 @@ python generate_rawframes_filelist.py
 
 ```shell
 python ../../misc/clip_feature_extraction.py tsn_extract_rgb_feat_config.py \
-  /path/to/rgb_checkpoint.pth ../../../data/ActivityNet/rgb_tarin_feat.pkl \
+  /path/to/rgb_checkpoint.pth ../../../data/ActivityNet/rgb_tarin_feat \
   --video-list ../../../data/ActivityNet/anet_train_video.txt \
   --video-root ../../../data/ActivityNet/rawframes \
-  --dump-score
+  --dump-score --long-video-mode
 
 python ../../misc/clip_feature_extraction.py tsn_extract_rgb_feat_config.py \
-  path/to/rgb_checkpoint.pth ../../../data/ActivityNet/rgb_val_feat.pkl \
+  path/to/rgb_checkpoint.pth ../../../data/ActivityNet/rgb_val_feat \
   --video-list ../../../data/ActivityNet/anet_val_video.txt \
   --video-root ../../../data/ActivityNet/rawframes \
-  --dump-score
+  --dump-score --long-video-mode
 
 python ../../misc/clip_feature_extraction.py tsn_extract_flow_feat_config.py \
-  /path/to/flow_checkpoint.pth ../../../data/ActivityNet/flow_tarin_feat.pkl \
+  /path/to/flow_checkpoint.pth ../../../data/ActivityNet/flow_tarin_feat \
   --video-list ../../../data/ActivityNet/anet_train_video.txt \
   --video-root ../../../data/ActivityNet/rawframes \
-  --dump-score
+  --dump-score --long-video-mode
 
 python ../../misc/clip_feature_extraction.py tsn_extract_flow_feat_config.py \
-  /path/to/flow_checkpoint.pth ../../../data/ActivityNet/flow_val_feat.pkl \
+  /path/to/flow_checkpoint.pth ../../../data/ActivityNet/flow_val_feat \
   --video-list ../../../data/ActivityNet/anet_val_video.txt \
   --video-root ../../../data/ActivityNet/rawframes \
-  --dump-score
+  --dump-score --long-video-mode
 ```
 
 在提取完特征后，用户可以使用后处理脚本整合 RGB 特征和光流特征，生成 `100-t X 400-d` 维度的特征用于时序动作检测。

tools/data/activitynet/tsn_extract_flow_feat_config.py

@@ -10,8 +10,8 @@
 
 # dataset settings
 dataset_type = 'RawframeDataset'
-data_root_val = 'data/kinetics400/rawframes_val'
-ann_file_val = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
+data_root_val = 'data/activitynet/rawframes_val'
+ann_file_val = 'data/activitynet/activitynet_val_list_rawframes.txt'
 
 file_client_args = dict(io_backend='disk')
 

tools/data/activitynet/tsn_extract_rgb_feat_config.py

@@ -5,8 +5,8 @@
 
 # dataset settings
 dataset_type = 'RawframeDataset'
-data_root_val = 'data/kinetics400/rawframes_val'
-ann_file_val = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
+data_root_val = 'data/activitynet/rawframes_val'
+ann_file_val = 'data/activitynet/activitynet_val_list_rawframes.txt'
 
 file_client_args = dict(io_backend='disk')
 

tools/data/activitynet/tsn_extract_video_feat_config.py

@@ -5,8 +5,8 @@
 
 # dataset settings
 dataset_type = 'VideoDataset'
-data_root_val = 'data/kinetics400/videos_val'
-ann_file_val = 'data/kinetics400/kinetics400_val_list_videos.txt'
+data_root_val = 'data/activitynet/videos_val'
+ann_file_val = 'data/activitynet/activitynet_val_list_videos.txt'
 
 file_client_args = dict(io_backend='disk')
 

tools/data/hacs/write_feature_csv.py

@@ -11,7 +11,7 @@
 for feature, video in zip(features, video_list):
     video_id = video.split()[0].split('/')[1]
     csv_file = video_id.replace('mp4', 'csv')
-    feat = feature['pred_scores']['item'].numpy()
+    feat = feature['pred_score'].numpy()
     feat = feat.tolist()
     csv_path = f'{feature_dir}/{csv_file}'
     with open(csv_path, 'w') as f:

tools/misc/clip_feature_extraction.py

@@ -5,6 +5,7 @@
 
 from mmengine import dump, list_from_file, load
 from mmengine.config import Config, DictAction
+from mmengine.dist.utils import is_main_process
 from mmengine.runner import Runner
 
 
@@ -133,8 +134,7 @@ def merge_args(cfg, args):
         for idx, transform in enumerate(test_pipeline):
             if transform.type == 'SampleFrames':
                 test_pipeline[idx]['twice_sample'] = False
-            # if transform.type in ['ThreeCrop', 'TenCrop']:
-            if transform.type == 'TenCrop':
+            if transform.type in ['ThreeCrop', 'TenCrop']:
                 test_pipeline[idx].type = 'CenterCrop'
 
     # -------------------- pipeline settings  --------------------
@@ -231,7 +231,7 @@ def preprocess(self, inputs, data_samples, training=False):
 def split_feats(args):
     total_feats = load(args.dump)
     if args.dump_score:
-        total_feats = [sample['pred_scores']['item'] for sample in total_feats]
+        total_feats = [sample['pred_score'] for sample in total_feats]
 
     video_list = list_from_file(args.video_list)
     video_list = [line.split(' ')[0] for line in video_list]
@@ -258,8 +258,8 @@ def main():
 
     # start testing
     runner.test()
-
-    split_feats(args)
+    if is_main_process():
+        split_feats(args)
 
 
 if __name__ == '__main__':

tools/misc/dist_clip_feature_extraction.sh

@@ -3,10 +3,11 @@
 CONFIG=$1
 CHECKPOINT=$2
 GPUS=$3
+OUTPUT_DIR=$4
 PORT=${PORT:-29500}
 
 PYTHONPATH="$(dirname $0)/../..":$PYTHONPATH \
 # Arguments starting from the forth one are captured by ${@:4}
 python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
-    $(dirname "$0")/clip_feature_extraction.py $CONFIG $CHECKPOINT \
-    --launcher pytorch ${@:4}
+    $(dirname "$0")/clip_feature_extraction.py $CONFIG $CHECKPOINT $OUTPUT_DIR \
+    --launcher pytorch ${@:5}