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# dataset-related
raw_data_dir: data/raw/videos
processed_data_dir: data/processed/videos
binary_data_dir: data/binary/videos
video_id: May

# feature-related
cond_type: idexp_lm3d_normalized
smo_win_size: 5
cond_hid_dim: 32
cond_out_dim: 16
# generator_condition_on_pose: false # pose is camera extrinsic and intrinsic
generator_condition_on_pose: true # pose is camera extrinsic and intrinsic
gpc_reg_prob: 0.5
gpc_reg_fade_kimg: 1000

# network-related
task_cls: tasks.eg3ds.eg3d_task.EG3DTask
z_dim: 512
w_dim: 512
neural_rendering_resolution: 128
final_resolution: 512

base_channel: 32768 # Capacity multiplier
max_channel: 512 # Max. feature maps
mapping_network_depth: 2 # num of layers in mapping network
num_fp16_layers_in_super_resolution: 4
num_fp16_layers_in_generator: 0
num_fp16_layers_in_discriminator: 4


# GAN-related
blur_raw_target: true
blur_init_sigma: 10
# blur_fade_kimg: 200 # Fade out the blur during the first N kimg.
blur_fade_kimg: 20 # Fade out the blur during the first N kimg.
# neural rendering-related
num_samples_coarse: 48 # number of uniform samples to take per ray.
num_samples_fine: 48 # number of importance samples to take per ray.
ray_near: 2.25
ray_far: 4.05
box_warp: 1 # the side-length of the bounding box spanned by the tri-planes; box_warp=1 means [-0.5, -0.5, -0.5] -> [0.5, 0.5, 0.5].

# loss related
group_size_for_mini_batch_std: 2 # 4
lambda_gradient_penalty: 5. # gradient penalty to discriminator


lambda_G_supervise_adv: 0.
lambda_G_supervise_mse_raw: 1.0
lambda_G_supervise_mse: 0.
lambda_G_adversarial_adv: 0.

lambda_density_reg: 0.25 # strength of density regularization for Generator
density_reg_p_dist: 0.004 # distance at which to sample perturbed points for density regularization


# trainer related
seed: 9999
lr_g: 0.0025
lr_d: 0.002
optimizer_adam_beta1_g: 0.
optimizer_adam_beta2_g: 0.99
optimizer_adam_beta1_d: 0.
optimizer_adam_beta2_d: 0.99
reg_interval_g: 4
reg_interval_d: 16

batch_size: 4
ema_interval:  400  #  bs * 10 / 32 kimg
max_updates: 25000_000 # 25000 kimg
num_workers: 4
work_dir: ''
load_ckpt: ''
tb_log_interval: 100
num_ckpt_keep: 1
val_check_interval: 2000
valid_infer_interval: 2000
num_sanity_val_steps: 1
num_valid_plots: 25
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
accumulate_grad_batches: 1
clip_grad_norm: 0 #1
clip_grad_value: 0