default.yaml

dataset: actnet
train_list:
val_list:
root_path:
data_dir: 
resume:

pretrained_glancer: 
pretrained_focuser: 
train_stage: 

pretrain_glancer: true
arch: resnet
num_segments: 16
k: 3
dropout: 0.5
num_classes: 200
evaluate: false
eval_freq: 5

dense_sample: false
partial_fcvid_eval: false
partial_ratio: 0.2
ada_reso_skip: false
reso_list: 224
random_crop: false
center_crop: false
ada_crop_list:
rescale_to: 224
policy_input_offset: 0
save_meta: false

epochs: 50
batch_size: 64
backbone_lr: 0.01
fc_lr: 0.005
lr_type: cos # support step or cos
lr_steps: 50-100
momentum: 0.9
weight_decay: 0.0001
clip_grad: 20
npb: true

input_size: 224
patch_size: 96
glance_size: 224
random_patch: false
feature_map_channels: 1280
action_dim: 49
hidden_state_dim: 1024 #for policy network, focuser
policy_conv: true
hidden_dim: 1024 #for gru
penalty: 0.5
consensus: gru
reward: random
gamma: 0.7
policy_lr: 0.0003
with_glancer: true
continuous: false

seed: 1007
gpus: 0
gpu: 
workers: 16
world_size: 1
rank: 0
dist_url: tcp://127.0.0.1:8888
dist_backend: nccl
multiprocessing_distributed: true
distributed:
amp: true