FGT_codes/FGT/config/train.yaml

### General settings
name: FGT_train
use_tb_logger: true
outputdir: /myData/ret/experiments
datadir: /myData
record_iter: 16

### Calling definition
model: model
datasetName_train: train_dataset
network: network

### datasets
datasets:
  train:
    name: youtubevos
    type: video
    mode: train
    dataInfo_config: ./config/data_info.yaml
    use_shuffle: True
    n_workers: 0
    batch_size: 2

  val:
    name: youtubevos
    type: video
    mode: val
    use_shuffle: False
    n_workers: 1
    batch_size: 1
    val_config: ./config/valid_config.yaml

### train settings
train:
  lr: 0.0001
  lr_decay: 0.1
  manual_seed: 10
  BETA1: 0.9
  BETA2: 0.999
  MAX_ITERS: 500000
  UPDATE_INTERVAL: 300000 # 400000 is also OK
  WARMUP: ~
  val_freq: 1  # Set to 1 is for debug, you can enlarge it to 50 in regular training
  TEMPORAL_GAN: ~  # without temporal GAN

### logger
logger:
  PRINT_FREQ: 16
  SAVE_CHECKPOINT_FREQ: 4000   # 100 is for debug consideration

### Data related parameters
flow2rgb: 1
flow_direction: for
num_frames: 5
sample: random
max_val: 0.01

### Model related parameters
res_h: 240
res_w: 432
in_channel: 4
cnum: 64
flow_inChannel: 2
flow_cnum: 64
dist_cnum: 32
frame_hidden: 512
flow_hidden: 256
PASSMASK: 1
num_blocks: 8
kernel_size_w: 7
kernel_size_h: 7
stride_h: 3
stride_w: 3
num_head: 4
conv_type: vanilla
norm: None
use_bias: 1
ape: 1
pos_mode: single
mlp_ratio: 40
drop: 0
init_weights: 1
tw: 2
sw: 8
gd: 4

### Loss weights
L1M: 1
L1V: 1
adv: 0.01

### inference parameters
ref_length: 10