How to finetune SLOWFAST_8x8_R50_DETECTION.pyth ?

[sleepyMMp]

❓ Questions on how to use PyTorchVideo

Hi, please help me to fintune SLOWFAST_8x8_R50_DETECTION.pyth which is used in " from pytorchvideo.models.hub import slowfast_r50_detection"
I wrote a yaml for training slowfast_r50_detection based on the conbination of SLOWFAST_32x2_R50_SHORT.yaml and SLOWFAST_8x8_R50.yaml. CHECKPOINT_FILE_PATH use SLOWFAST_8x8_R50_DETECTION.pyth
It can work when I dont use CHECKPOINT_FILE_PATH, however the result cannot be loaded by slowfast_r50_detection()
It doesnt work when I use SLOWFAST_8x8_R50_DETECTION.pyth as CHECKPOINT_FILE_PATH and the error is "RuntimeError: The size of tensor a (2304) must match the size of tensor b (7) at non-singleton dimension 4" Maybe there is something wrong with the configuration, please help me.
Thank You!

Here is my yaml.

TRAIN:
ENABLE: True
DATASET: ava
BATCH_SIZE: 2
EVAL_PERIOD: 1
CHECKPOINT_PERIOD: 1
AUTO_RESUME: True
CHECKPOINT_FILE_PATH: "xxx/SLOWFAST_8x8_R50_DETECTION.pyth"
CHECKPOINT_TYPE: pytorch
DATA:
NUM_FRAMES: 32
SAMPLING_RATE: 2
TRAIN_JITTER_SCALES: [256, 320]
TRAIN_CROP_SIZE: 224
TEST_CROP_SIZE: 256
INPUT_CHANNEL_NUM: [3, 3]
PATH_TO_DATA_DIR: '/data/lijh56/SlowFast-main/dataset/traindata'
DETECTION:
ENABLE: True
ALIGNED: True
AVA:
FRAME_DIR: 'xxxx'
FRAME_LIST_DIR: 'xxxx'
ANNOTATION_DIR: 'xxxx'
DETECTION_SCORE_THRESH: 0.8
TRAIN_PREDICT_BOX_LISTS: [
"ava_train_v2.2.csv",
"person_box_67091280_iou90/ava_detection_train_boxes_and_labels_include_negative_v2.2.csv",
]
TEST_PREDICT_BOX_LISTS: ["person_box_67091280_iou90/ava_detection_val_boxes_and_labels.csv"]

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_STRIDES: [[1, 1], [2, 2], [2, 2], [2, 2]]
SPATIAL_DILATIONS: [[1, 1], [1, 1], [1, 1], [1, 1]]
NONLOCAL:
LOCATION: [[[], []], [[], []], [[], []], [[], []]]
GROUP: [[1, 1], [1, 1], [1, 1], [1, 1]]
INSTANTIATION: dot_product
BN:
USE_PRECISE_STATS: 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: 25
MOMENTUM: 0.9
WEIGHT_DECAY: 1e-7
WARMUP_EPOCHS: 5.0
WARMUP_START_LR: 0.000125
OPTIMIZING_METHOD: sgd
MODEL:
NUM_CLASSES: 2
ARCH: slowfast
MODEL_NAME: SlowFast
LOSS_FUNC: bce
DROPOUT_RATE: 0.5
HEAD_ACT: sigmoid
TEST:
ENABLE: False
DATASET: ava
BATCH_SIZE: 8
DATA_LOADER:
NUM_WORKERS: 2
PIN_MEMORY: True
NUM_GPUS: 1
NUM_SHARDS: 1
RNG_SEED: 0
OUTPUT_DIR: "xxxx"