# log dir 
log_dir: ../debug_logs/ucf

# model setting
pretrained: ../weights/xception_best.pth   # path to a pre-trained model, if using one
model_name: ucf   # model name
backbone_name: xception  # backbone name
encoder_feat_dim: 512  # feature dimension of the backbone
  
#backbone setting
backbone_config:
  mode: adjust_channel
  num_classes: 2
  inc: 3
  dropout: false

compression: c23  # compression-level for videos
train_batchSize: 32   # training batch size
test_batchSize: 32   # test batch size
workers: 8   # number of data loading workers
frame_num: {'train': 32, 'test': 32}   # number of frames to use per video in training and testing
resolution: 256   # resolution of output image to network
with_mask: false   # whether to include mask information in the input
with_landmark: false   # whether to include facial landmark information in the input
save_ckpt: true   # whether to save checkpoint
save_feat: true   # whether to save features
specific_task_number: 2 # default num datasets in FF++ used by DFB, overwritten in training

# mean and std for normalization
mean: [0.5, 0.5, 0.5]
std: [0.5, 0.5, 0.5]

# optimizer config
optimizer:
  # choose between 'adam' and 'sgd'
  type: adam
  adam:
    lr: 0.0002  # learning rate
    beta1: 0.9  # beta1 for Adam optimizer
    beta2: 0.999 # beta2 for Adam optimizer
    eps: 0.00000001  # epsilon for Adam optimizer
    weight_decay: 0.0005  # weight decay for regularization
    amsgrad: false
  sgd:
    lr: 0.0002  # learning rate
    momentum: 0.9  # momentum for SGD optimizer
    weight_decay: 0.0005  # weight decay for regularization

# training config
lr_scheduler: null   # learning rate scheduler
nEpochs: 20   # number of epochs to train for
start_epoch: 0   # manual epoch number (useful for restarts)
save_epoch: 1   # interval epochs for saving models
rec_iter: 100   # interval iterations for recording
logdir: ./logs   # folder to output images and logs
manualSeed: 1024   # manual seed for random number generation
save_ckpt: false   # whether to save checkpoint

# loss function
loss_func:
 cls_loss: cross_entropy   # loss function to use
 spe_loss: cross_entropy
 con_loss: contrastive_regularization
 rec_loss: l1loss
losstype: null

# metric
metric_scoring: auc   # metric for evaluation (auc, acc, eer, ap)

# cuda

cuda: true   # whether to use CUDA acceleration
cudnn: true   # whether to use CuDNN for convolution operations