config.yaml

TASK: localization

dali: True

DATA:
  dataset_name: SoccerNet
  data_dir: /home/vorajv/opensportslib/SoccerNet/annotations/
  classes:
    - PASS
    - DRIVE

  epoch_num_frames: 500000
  mixup: true
  modality: rgb
  crop_dim: -1
  dilate_len: 0        # Dilate ground truth labels
  clip_len: 100
  input_fps: 25
  extract_fps: 2
  imagenet_mean: [0.485, 0.456, 0.406]
  imagenet_std: [0.229, 0.224, 0.225]
  target_height: 224
  target_width: 398

  train:
    type: VideoGameWithDali
    classes: ${DATA.classes}
    output_map: [data, label]
    video_path: ${DATA.data_dir}/train/
    path: ${DATA.train.video_path}/annotations-2023-224p-train.json
    dataloader:
      batch_size: 8
      shuffle: true
      num_workers: 4
      pin_memory: true

  valid:
    type: VideoGameWithDali
    classes: ${DATA.classes}
    output_map: [data, label]
    video_path: ${DATA.data_dir}/valid/
    path: ${DATA.valid.video_path}/annotations-2023-224p-valid.json
    dataloader:
      batch_size: 8
      shuffle: true

  valid_data_frames:
    type: VideoGameWithDaliVideo
    classes: ${DATA.classes}
    output_map: [data, label]
    video_path: ${DATA.valid.video_path}
    path: ${DATA.valid.path}
    overlap_len: 0
    dataloader:
      batch_size: 4
      shuffle: false

  test:
    type: VideoGameWithDaliVideo
    classes: ${DATA.classes}
    output_map: [data, label]
    video_path: ${DATA.data_dir}/test/
    path: ${DATA.test.video_path}/annotations-2023-224p-test.json
    results: results_spotting_test
    nms_window: 2 
    metric: tight
    overlap_len: 50
    dataloader:
      batch_size: 4
      shuffle: false

  challenge:
    type: VideoGameWithDaliVideo
    overlap_len: 50
    output_map: [data, label]
    path: ${DATA.data_dir}/challenge/annotations.json
    dataloader:
      batch_size: 4
      shuffle: false

MODEL:
    type: E2E
    runner:
      type: runner_e2e
    backbone:
      type: rny008_gsm
    head:
      type: gru
    multi_gpu: true
    load_weights: null

TRAIN:
  type: trainer_e2e
  num_epochs: 10
  acc_grad_iter: 1
  base_num_valid_epochs: 4
  start_valid_epoch: 4
  valid_map_every: 1
  criterion_valid: map

  criterion:
    type: CrossEntropyLoss

  optimizer:
    type: AdamWithScaler
    lr: 0.01

  scheduler:
    type: ChainedSchedulerE2E
    acc_grad_iter: 1
    num_epochs: ${TRAIN.num_epochs}
    warm_up_epochs: 3

SYSTEM:
  log_dir: ./logs
  save_dir: ./checkpoints
  work_dir: ${SYSTEM.save_dir}
  seed: 42
  GPU: 4         # number of gpus to use
  device: cuda   # auto | cuda | cpu
  gpu_id: 0      # device id for single gpu training