I like your paper very much. Can you tell me how to get the feature bank (LMDB)? I want to apply your work to my own data set. Do you run the script extract_feature.py directly or extract it like LFB?

How to extract the features of custom data sets? Can you give me some advice? I ran the script and added yaml configuration file, and there was an error that the dimensions could not be spliced. How did you extract the features at that time? Do you need to resize the images? Can the resized features still be used as feature bank?

Hello, when I want to extract the feature bank of the data set, I encountered a new problem. My yaml configuration file and the error I encountered are like this. Is the pre-training weight wrong? I hope you can give me an answer.
SLOWFAST_32x2_BANK1.yaml:
TRAIN:
ENABLE: False
DATASET: ava
BATCH_SIZE: 64
EVAL_PERIOD: 20
CHECKPOINT_PERIOD: 1
AUTO_RESUME: True
CHECKPOINT_FILE_PATH: "/home/tuxiangone/bang/Behavior_Model/LSTC/pretrained/lstc-resnet50.pyth"
DATA:
NUM_FRAMES: 32
SAMPLING_RATE: 2
TRAIN_JITTER_SCALES: [256, 320]
TRAIN_CROP_SIZE: 224
TEST_CROP_SIZE: 224
INPUT_CHANNEL_NUM: [3, 3]
DETECTION:
ENABLE: True
ALIGNED: True
AVA:
FEATURE_EXTRACTION: True
FRAME_DIR: '/home/tuxiangone/bang/Behavior_Model/SlowFast/data/ava/frames'
FRAME_LIST_DIR: '/home/tuxiangone/bang/Behavior_Model/SlowFast/data/ava/frame_lists'
ANNOTATION_DIR: '/home/tuxiangone/bang/Behavior_Model/SlowFast/data/ava/annotations'
DETECTION_SCORE_THRESH: 0.8
TRAIN_PREDICT_BOX_LISTS: [
"ava_train_v2.2.csv",
"ava_train_predicted_boxes.csv",
]
TEST_PREDICT_BOX_LISTS: ["ava_val_predicted_boxes.csv"]
TEST_GT_BOX_LISTS: ["ava_val_v2.2.csv"]
FEATURE_BANK_PATH: "output/feature_bank"
SLIDING_WINDOW_SIZE: 15
GATHER_BANK: False
SLOWFAST:
ALPHA: 4
BETA_INV: 8
FUSION_CONV_CHANNEL_RATIO: 2
FUSION_KERNEL_SZ: 7
RESNET:
ZERO_INIT_FINAL_BN: True
WIDTH_PER_GROUP: 64
NUM_GROUPS: 1
DEPTH: 50
TRANS_FUNC: bottleneck_transform
STRIDE_1X1: False
NUM_BLOCK_TEMP_KERNEL: [[3, 3], [4, 4], [6, 6], [3, 3]]
SPATIAL_DILATIONS: [[1, 1], [1, 1], [1, 1], [2, 2]]
SPATIAL_STRIDES: [[1, 1], [2, 2], [2, 2], [1, 1]]
NONLOCAL:
LOCATION: [[[], []], [[], []], [[], []], [[], []]]
GROUP: [[1, 1], [1, 1], [1, 1], [1, 1]]
INSTANTIATION: dot_product
POOL: [[[1, 2, 2], [1, 2, 2]], [[1, 2, 2], [1, 2, 2]], [[1, 2, 2], [1, 2, 2]], [[1, 2, 2], [1, 2, 2]]]
BN:
USE_PRECISE_STATS: False
FREEZE: False
NUM_BATCHES_PRECISE: 200
SOLVER:
BASE_LR: 0.1
LR_POLICY: steps_with_relative_lrs
STEPS: [0, 10, 15, 20]
LRS: [1, 0.1, 0.01, 0.001]
MAX_EPOCH: 20
MOMENTUM: 0.9
WEIGHT_DECAY: 1e-7
WARMUP_EPOCHS: 5.0
WARMUP_START_LR: 0.000125
OPTIMIZING_METHOD: sgd
MODEL:
NUM_CLASSES: 80
ARCH: slowfast
MODEL_NAME: BankContext
LOSS_FUNC: bce
DROPOUT_RATE: 0.5
HEAD_ACT: sigmoid
TEST:
ENABLE: True
DATASET: ava
BATCH_SIZE: 8
CHECKPOINT_FILE_PATH: "/home/tuxiangone/bang/Behavior_Model/LSTC/pretrained/SLOWFAST_8x8_R50_KINETICS.pkl"
DATA_LOADER:
NUM_WORKERS: 2
PIN_MEMORY: True
NUM_GPUS: 1
NUM_SHARDS: 1
RNG_SEED: 0
OUTPUT_DIR: "output/raw_bank"

CACHE:

ENABLE: True

LOG_MODEL_INFO: False
My error:
[INFO: checkpoint.py: 401]: loading checkpoint from /home/tuxiangone/bang/Behavior_Model/LSTC/pretrained/SLOWFAST_8x8_R50_KINETICS.pkl
[09/01 16:48:34][INFO] slowfast.utils.checkpoint: 401: loading checkpoint from /home/tuxiangone/bang/Behavior_Model/LSTC/pretrained/SLOWFAST_8x8_R50_KINETICS.pkl
Traceback (most recent call last):
File "tools/extract_feature.py", line 175, in
launch_job(
File "/home/tuxiangone/bang/Behavior_Model/LSTC/build/lib/slowfast/utils/misc.py", line 307, in launch_job
func(cfg=cfg)
File "tools/extract_feature.py", line 144, in extract_feature
cu.load_test_checkpoint(cfg, model)
File "/home/tuxiangone/bang/Behavior_Model/LSTC/build/lib/slowfast/utils/checkpoint.py", line 405, in load_test_checkpoint
load_checkpoint(
File "/home/tuxiangone/bang/Behavior_Model/LSTC/build/lib/slowfast/utils/checkpoint.py", line 268, in load_checkpoint
checkpoint = torch.load(f, map_location="cpu")
File "/home/tuxiangone/anaconda3/envs/pytorch/lib/python3.8/site-packages/torch/serialization.py", line 608, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "/home/tuxiangone/anaconda3/envs/pytorch/lib/python3.8/site-packages/torch/serialization.py", line 777, in _legacy_load
magic_number = pickle_module.load(f, **pickle_load_args)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xd4 in position 1: invalid continuation byte

Hello, I reported an error during training, no module name"lmdb ".Could you please send me this script? I carefully read setup.py, which is basically the same as that of SlowFast, and there is no information about the generation of lmdb package, so it can't be trained normally. Can you update this library?