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train_aug_bg.toml
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train_aug_bg.toml
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######################################### 0. Base Info ###############################################
gpu_ids = "0" # GPU setups
local_rank = 0 # DDP if uses, otherwise ignore it
use_cudnn = false
image_size = 512 # the image size.
time_step = 1 # the time step for temporal attention.
num_source = 2 # fix the number of sources.
MAX_NUM_SOURCE = 8 # The max number of source images for query operation in the attention module.
share_bg = true # whether share background or not, if not, then all images need to use inpainting.
bg_ks = 11
ft_ks = 1
use_inpaintor = false # use addtional background inpaintor or not.
# dataset
batch_size = 1
intervals = 1
serial_batches = false
# visualization
verbose = true
tb_visual = true
ip = ""
port = 0
# Set the number of cores for Image Cropper, and 3D-Pose Estimation.
# If num_workers is 0, then we will not use multi-processing.
num_workers = 1
######################################### 1. Body Model ##############################################
NUMBER_VERTS = 6890
NUMBER_FACES = 13776
digital_type = "cloth_smpl_link" # the type of digitalizing human 3D mesh, it could be ["smpl", "cloth_smpl_link", "sil2smpl"]
smpl_model = "./assets/checkpoints/pose3d/smpl_model.pkl"
smpl_model_hand = "./assets/checkpoints/pose3d/smpl_model_with_hand_v2.pkl"
###################################### 2. FlowComposition ############################################
tex_size = 3
only_vis = false
temporal = false
conf_erode_ks = 3
out_dilate_ks = 9
map_name = "uv_seg"
face_path = "./assets/checkpoints/pose3d/smpl_faces.npy"
fim_enc_path = "./assets/configs/pose3d/mapper_fim_enc.txt"
uv_map_path = "./assets/configs/pose3d/mapper_uv.txt"
part_path = "./assets/configs/pose3d/smpl_part_info.json"
front_path = "./assets/configs/pose3d/front_body.json"
head_path = "./assets/configs/pose3d/head.json"
facial_path = "./assets/configs/pose3d/front_facial.json"
###################### 3. LiquidWarping GAN, generator and discriminator #############################
train_name = "LWGAugBGTrainer"
gen_name = "AttLWB-SPADE"
dis_name = "patch_global_body_head"
# detials of the network architecture, it contains the configurations of the generator and the discriminator.
neural_render_cfg_path = "./assets/configs/neural_renders/AttLWB-SPADE.toml"
load_path_G = "None"
load_path_D = "None"
load_iter = -1
[Train]
# visualization
num_iters_validate = 1
tb_visual = true
print_freq_s = 180 # print errors/loss on terminal per 2 min
display_freq_s = 600 # show images on tensorboard per 10 min
save_latest_freq_s = 7200 # save a checkpoint per 2 hours
# loss
use_face = true
face_factor = 1.0
face_loss_path = "./assets/checkpoints/losses/sphere20a_20171020.pth"
use_vgg = "VGG19"
vgg_loss_path = "./assets/checkpoints/losses/vgg19-dcbb9e9d.pth"
#vgg_loss_path = ""
lambda_rec = 10.0
lambda_tsf = 10.0
lambda_face = 5.0
lambda_mask = 5.0
lambda_mask_smooth = 1.0
lambda_D_prob = 1.0
opti = "Adam"
train_G_every_n_iterations = 1
G_adam_b1 = 0.9
G_adam_b2 = 0.999
D_adam_b1 = 0.9
D_adam_b2 = 0.999
lr_G = 0.0001
lr_D = 0.0001
final_lr = 0.00001
niters_or_epochs_no_decay = 400000 # fixing learning rate at the first niters_or_epochs_no_decay
niters_or_epochs_decay = 0 # then, decreasing the learning rate
aug_bg = true # background