ZHIJI_cv_web_ui / NTED /fashion_512.yaml
zejunyang
update model
96ea347
distributed: True
image_to_tensorboard: True
snapshot_save_iter: 50000
snapshot_save_epoch: 20
snapshot_save_start_iter: 20000
snapshot_save_start_epoch: 100
image_save_iter: 1000
max_epoch: 400
logging_iter: 100
amp: False
gen_optimizer:
type: adam
lr: 0.002
adam_beta1: 0.
adam_beta2: 0.99
lr_policy:
iteration_mode: False
type: step
step_size: 1000000
gamma: 1
dis_optimizer:
type: adam
lr: 0.001882
adam_beta1: 0.
adam_beta2: 0.9905
lr_policy:
iteration_mode: False
type: step
step_size: 1000000
gamma: 1
trainer:
type: NTED.extraction_distribution_trainer::Trainer
gan_mode: style_gan2
gan_start_iteration: 1000 # 0
face_crop_method: util.face_crop::crop_face_from_output
hand_crop_method: util.face_crop::crop_hands_from_output
d_reg_every: 16
r1: 10
loss_weight:
weight_perceptual: 1
weight_gan: 1.5
weight_attn_rec: 15
weight_face: 1
weight_hand: 1
weight_l1: 1
weight_l1_hand: 0.8
weight_edge: 100
attn_weights:
8: 1
16: 1
32: 1
64: 1
128: 1
256: 1
vgg_param:
network: vgg19
layers: ['relu_1_1', 'relu_2_1', 'relu_3_1', 'relu_4_1', 'relu_5_1']
num_scales: 3
use_style_loss: True
style_to_perceptual: 1000
vgg_hand_param:
network: vgg19
layers: ['relu_1_1', 'relu_2_1', 'relu_3_1','relu_3_3', 'relu_4_1', 'relu_4_3', 'relu_5_1']
gen:
type: NTED.extraction_distribution_model::Generator
param:
size: 512
semantic_dim: 20
channels:
16: 512
32: 512
64: 512
128: 256
256: 128
512: 64
1024: 32
num_labels:
16: 16
32: 32
64: 32
128: 64
256: 64
512: False
match_kernels:
16: 1
32: 3
64: 3
128: 3
256: 3
512: False
dis:
type: generators.discriminator::Discriminator
param:
size: 512
channels:
4: 512
8: 512
16: 512
32: 512
64: 512
128: 256
256: 128
512: 64
is_square_image: False
data:
type: data.fashion_data::Dataset
preprocess_mode: resize_and_crop # resize_and_crop
path: /apdcephfs/share_1474453/zejunzhang/dataset/pose_trans_dataset_2d
num_workers: 16
sub_path: 512-352
resolution: 512
scale_param: 0.1
train:
batch_size: 4 # real_batch_size: 2 * 2 (source-->target & target --> source) * 4 (GPUs) = 16
distributed: True
val:
batch_size: 4
distributed: True
hand_keypoint: False