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config.yaml
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#在这里修改生成参数，例如模型路径等。
#因为有的参数训练和生成时都需要，懒得区分就把包括训练参数在内的大部分参数都放这里了（
#只有在web端生成时生效。
model:
DDPM:
#-----------------------------------------------#
# 模型路径
#-----------------------------------------------#
model_path: 'training/my_checkpoint/diffusion_model_last_epoch_weights.pth'
#-----------------------------------------------#
# 卷积通道数的设置
#-----------------------------------------------#
channel: 128
#-----------------------------------------------#
# 输入图像大小的设置
#-----------------------------------------------#
input_shape: [96, 96]
#-----------------------------------------------#
# betas相关参数
#-----------------------------------------------#
schedule: "linear"
num_timesteps: 1000
schedule_low: 1e-4
schedule_high: 0.02
#-------------------------------#
# 是否使用Cuda
# 没有GPU可以设置成False
#-------------------------------#
cuda: true
save_dir: "Web/front_end/static"
DCGAN:
latent_dim: 100
model_name: "DCGAN"
#-----------------------------------------------#
# 模型路径
#-----------------------------------------------#
checkpoint_path: "training/my_checkpoint/DC_GAN_epoch=999.ckpt"
image_size: 128
batch_size: 512
image_channels: 3
num_workers: 8
g_lr: 0.0003
d_lr: 0.0001
epochs: 5000
unrolled_DCGAN:
latent_dim: 100
model_name: "unrolled_DCGAN"
#-----------------------------------------------#
# 模型路径
#-----------------------------------------------#
checkpoint_path: "training/my_checkpoint/unrolled_DCGAN_epoch=1199.ckpt"
image_size: 128
batch_size: 512
image_channels: 3
num_workers: 8
g_lr: 0.0003
d_lr: 0.0001
epochs: 5000
unrolled_step: 5
EBGAN:
latent_dim: 100
model_name: "EBGAN"
#-----------------------------------------------#
# 模型路径
#-----------------------------------------------#
checkpoint_path: "training/my_checkpoint/EBGAN_epoch=1199.ckpt"
image_size: 128
batch_size: 512
image_channels: 3
num_workers: 8
g_lr: 0.0003
d_lr: 0.0001
epochs: 5000
WGAN:
DATALOADER:
BATCH_SIZE: 32
NUM_WORKERS: 0
DATASET:
NAME: dataset/cartoon_color
TRAIN_TEST_RATIO: 1.0
IMAGE:
CHANNEL: 3
HEIGHT: 64
NUMBER: 10
PIXEL_MEAN: (0.5, 0.5, 0.5)
PIXEL_STD: (0.5, 0.5, 0.5)
SAVE_NUMBER: 64
SAVE_PATH: Web/front_end/static
SAVE_ROW_NUMBER: 8
SEPARATE: True
WIDTH: 64
LOG_CONFIGURATION: config/logging.conf
MODEL:
CHECKPOINT_DIR: checkpoints
D:
DIMENSION: 256
PATH:
DEVICE: cuda
G:
DIMENSION: 1024
INPUT_SIZE: 100
#-----------------------------------------------#
# 模型路径
#-----------------------------------------------#
PATH: training\my_checkpoint\WGAN_G_epoch_39999.pth
NAME: WGAN
WGAN:
CRITIC_ITERS: 5
GENERATOR_ITERS: 40000
IC: False
LAMBDA: 10
WEIGHT_CLIPING_LIMIT: 0.01
OUTPUT_DIR: log
PROJECT_NAME: WGAN
SOLVER:
BASE_LR: 0.0001
BETAS: 0.5, 0.999
CHECKPOINT_FREQ: 500
EPOCHS: 300
EVALUATE_BATCH: 128
EVALUATE_ITERATION: 125
WEIGHT_DECAY: 1e-05
WALKING_LATENT_SPACE:
IMAGE_FPS: 10
IMAGE_NUMBER: 16
IMAGE_ROW_NUMBER: 4
STEP: 50
WGAN-GP:
#-----------------------------------------------#
# 模型路径
#-----------------------------------------------#
generator_path: "training/my_checkpoint/WGANGP_gen.pth"
discriminator_path: "training/my_checkpoint/WGANGP_disc.pth"
channels_img: 3
features_gen: 64
features_disc: 64
noise_dim: 100
device: cuda
gen_search_num: 100
gen_num: 10
seed: 42
output_dir: "Web/front_end/static"
GAN:
#-----------------------------------------------#
# 模型路径
#-----------------------------------------------#
generator_path: "training/my_checkpoint/GAN_netg.pth"
discriminator_path: "training/my_checkpoint/GAN_netd.pth"
num_workers: 4
image_size: 96
batch_size: 256
max_epoch: 280
lr1: 2e-4
lr2: 2e-4
beta1: 0.5
gpu: True
d_every: 1
g_every: 5
nz: 100
ngf: 64
ndf: 64