sat3density / options /sat2density_cvact.yaml
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_parent_: options/base.yaml
gpu_ids: '0'
## config for wandb
project: 'sat2pano'
Group: 'craft_feature'
model: craft_feature # model/craft_feature
arch:
gen: ## config for generator
netG: imaginaire.generators.craft_2stage_add_style
weight_norm_type: spectral
activation_norm_type: instance
padding_mode: reflect
transform_mode: volum_rendering
feature_model:
style_inject: histo # use histogram to inject illumination, chose list [histo, perspective]
cat_PE:
cat_opa: true
cat_depth: true
depth_arch: # Density Net
name: depth
num_filters: 32
num_downsamples: 4
num_res_blocks: 6
output_nc: 64
render_arch: # Render Net
name: render
num_filters: 64
num_downsamples: 4
num_res_blocks: 9
output_nc: 3
style_enc_cfg: # style injection
input_image_channels: 3
num_filters: 256
kernel_size: 3
style_dims: 128
interm_style_dims: 256
hidden_channel: 256
weight_norm_type: spectral
dis: # discriminator
netD: imaginaire.discriminators.multires_patch_pano
num_filters: 64
max_num_filters: 512
num_discriminators: 3
num_layers: 3
weight_norm_type: spectral
activation_norm_type: instance
data: # data options
dataset: CVACT_Shi # dataset name
root: ./dataset/CVACT/
sat_size: [256,256]
pano_size: [512, 128]
sample_number: 100 # points per ray
max_height: 8 # pre-defined density space in height axis
sky_mask: true
histo_mode: rgb
# val:
# sub: 500
optim:
lr_gen: 0.00005
lr_dis: 0.00005
gan_mode: non_saturated #'hinge', 'least_square', 'non_saturated', 'wasserstein'
loss_weight:
L1: 1
L2: 10
GaussianKL: 0.1
feature_matching: 10.0
Perceptual: 10
sky_inner: 1
GAN: 1
lr_policy:
iteration_mode: False # iteration or epoch
type: step
step_size: 45
gamma: 0.1
ground_prior: true
######## for test, if only style, will random choice one style for save dir
only_style:
only_img:
save_dir: