File size: 2,009 Bytes
e34aada |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
# dataset-related
raw_data_dir: data/raw/videos
processed_data_dir: data/processed/videos
binary_data_dir: data/binary/videos
video_id: ''
task_cls: ''
# project-related
work_dir: ''
load_ckpt: ''
tb_log_interval: 100
num_ckpt_keep: 1
val_check_interval: 10000
valid_infer_interval: 10000
num_sanity_val_steps: 0
num_valid_plots: 5
eval_max_batches: 100 # num_test_plots
print_nan_grads: false
resume_from_checkpoint: 0 # specify the step, 0 for latest
amp: false
valid_monitor_key: val_loss
valid_monitor_mode: min
save_best: true
debug: false
save_codes:
- tasks
- modules
- egs
# testing related
gen_dir_name: ''
save_gt: true
# training-scheme-related
max_updates: 40_0000
seed: 9999
lr: 0.0005
scheduler: exponential # exponential|rsqrt|warmup|none|step_lr
warmup_updates: 0
optimizer_adam_beta1: 0.9
optimizer_adam_beta2: 0.999
weight_decay: 0
clip_grad_norm: 0 # disable grad clipping
clip_grad_value: 0 # disable grad clipping
rays_sampler_type: uniform
in_rect_percent: 0.95
accumulate_grad_batches: 1
# model-related
use_window_cond: true
with_att: true # only available when use win_cond, use a attention Net in AD-NeRF
cond_type: ''
cond_dim: 64
hidden_size: 256
# NeRF-related
near: 0.3
far: 0.9
n_rays: 1600 # default 2048, 1600 for RTX2080Ti
n_samples_per_ray: 64
n_samples_per_ray_fine: 128
embedding_args:
multi_res_pos: 10 # log2+1 of max freq for positional encoding (3D location)
multi_res_views: 4 # log2+1 of max freq for positional encoding (2D direction)
infer_cond_name: ''
infer_out_video_name: ''
infer_scale_factor: 1.0
infer_smo_std: 0.
infer_audio_source_name: ''
infer_c2w_name: ''
# postprocessing params
infer_lm3d_clamp_std: 1.5
infer_lm3d_lle_percent: 0.25 # percent of lle fused feature to compose the processed lm3d
infer_lm3d_smooth_sigma: 0. # sigma of gaussian kernel to smooth the predicted lm3d
infer_pose_smooth_sigma: 2.
load_imgs_to_memory: false # load uint8 training img to memory, which reduce io costs, at the expense of more memory occupation |