Fine Tuning AVA model on custom dataset

Hi, Thank you for sharing your great code

I'm trying to fine tune the AVA model on my custom dataset with 6 classes but I'm not sure about the format of my dataset.
I named my dataset ava and tried to prepare the annotation files as mentioned in [DATASET.md](https://github.com/facebookresearch/SlowFast/blob/master/slowfast/datasets/DATASET.md); however, I couldn't understand how can I provide the action labels for each frame automatically because it is a bit hard to label the action in all frames manually.
However, I tried to label a small portion of my dataset manually only to check if I can make the code work but I faced the following error:

> Traceback (most recent call last):
File "tools/run_net.py", line 44, in
main()
File "tools/run_net.py", line 25, in main
launch_job(cfg=cfg, init_method=args.init_method, func=train)
File "/media/adminadmin/New Volume/Vaziri/SlowFast/slowfast/utils/misc.py", line 297, in launch_job
func(cfg=cfg)
File "/media/adminadmin/New Volume/Vaziri/SlowFast/tools/train_net.py", line 450, in train
train_epoch(
File "/media/adminadmin/New Volume/Vaziri/SlowFast/tools/train_net.py", line 88, in train_epoch
optimizer.step()
File "/usr/local/lib/python3.8/dist-packages/torch/optim/optimizer.py", line 89, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/optim/sgd.py", line 110, in step
F.sgd(params_with_grad,
File "/usr/local/lib/python3.8/dist-packages/torch/optim/_functional.py", line 169, in sgd
buf.mul_(momentum).add_(d_p, alpha=1 - dampening)
RuntimeError: The size of tensor a (80) must match the size of tensor b (6) at non-singleton dimension 0

I guess the code is somehow hardcoded on the AVA dataset and its 80 classes but I don't know how can I use it on my own dataset!
Also my config file is like:

> TRAIN:
ENABLE: True
DATASET: ava
BATCH_SIZE: 2
EVAL_PERIOD: 1
CHECKPOINT_PERIOD: 1
AUTO_RESUME: True
CHECKPOINT_FILE_PATH: /media/adminadmin/New Volume/Vaziri/SlowFast/Models/SLOWFAST_32x2_R101_50_50.pkl
CHECKPOINT_TYPE: pytorch
DATA:
PATH_TO_DATA_DIR: /media/adminadmin/New Volume/Vaziri/SlowFast/data/ava
NUM_FRAMES: 32
SAMPLING_RATE: 1
TRAIN_JITTER_SCALES: [256, 320]
TRAIN_CROP_SIZE: 224
TEST_CROP_SIZE: 256
INPUT_CHANNEL_NUM: [3, 3]
DETECTION:
ENABLE: True
ALIGNED: False
AVA:
BGR: False
DETECTION_SCORE_THRESH: 0.8
#TEST_PREDICT_BOX_LISTS: ["person_box_67091280_iou90/ava_detection_val_boxes_and_labels.csv"]
FRAME_LIST_DIR: /media/adminadmin/New Volume/Vaziri/SlowFast/data/ava/frames
TRAIN_LISTS: ["/media/adminadmin/New Volume/Vaziri/SlowFast/data/ava/frame_lists/train.csv"]
TEST_LISTS: ["/media/adminadmin/New Volume/Vaziri/SlowFast/data/ava/frame_lists/val.csv"]
ANNOTATION_DIR: /media/adminadmin/New Volume/Vaziri/SlowFast/data/ava/annotations
SLOWFAST:
ALPHA: 4
BETA_INV: 8
FUSION_CONV_CHANNEL_RATIO: 2
FUSION_KERNEL_SZ: 5
RESNET:
ZERO_INIT_FINAL_BN: True
WIDTH_PER_GROUP: 64
NUM_GROUPS: 1
DEPTH: 101
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: [[[], []], [[], []], [[6, 13, 20], []], [[], []]]
GROUP: [[1, 1], [1, 1], [1, 1], [1, 1]]
INSTANTIATION: dot_product
POOL: [[[2, 2, 2], [2, 2, 2]], [[2, 2, 2], [2, 2, 2]], [[2, 2, 2], [2, 2, 2]], [[2, 2, 2], [2, 2, 2]]]
BN:
USE_PRECISE_STATS: False
NUM_BATCHES_PRECISE: 200
SOLVER:
MOMENTUM: 0.9
WEIGHT_DECAY: 1e-7
OPTIMIZING_METHOD: sgd
MODEL:
NUM_CLASSES: 6
ARCH: slowfast
MODEL_NAME: SlowFast
LOSS_FUNC: bce
DROPOUT_RATE: 0.5
HEAD_ACT: sigmoid
TEST:
ENABLE: True
DATASET: ava
BATCH_SIZE: 2
DATA_LOADER:
NUM_WORKERS: 2
PIN_MEMORY: True
NUM_GPUS: 1
NUM_SHARDS: 1
RNG_SEED: 0
OUTPUT_DIR: .