原文：Stylegan2-Ada-Google-Colab-Starter-Notebook/Stylegan2_Ada_Colab_Starter.ipynb
1. 准备阶段
1.1. 项目路径
挂载硬盘，其主要目的：
[1] - 保存 results 和 checkpoints，以用于重用.
[2] - 加载自定义数据集.
假如硬盘路径为：/content/drive.
将自定义数据集放置在 my_data 路径.
1.2. TF 环境
tensorflow 环境：
#tensorflow_version 1.x
import tensorflow as tf
print('Tensorflow version: {}'.format(tf.__version__) )
#Tensorflow version: 1.15.2
#
#显卡信息
#nvidia-smi -L
print('GPU Identified at: {}'.format(tf.test.gpu_device_name()))1.3. stylegan2-ada 源码
stylegan2-ada 源码下载:
git clone https://github.com/NVlabs/stylegan2-ada.git
cd stylegan2-ada
nvcc test_nvcc.cu -o test_nvcc -run1.4. 迁移学习权重下载
建议：采用迁移学习，训练收敛的速度更快.
stylegan2 ada 论文里的一些模型，这里采用的是 FFHQ 的 1024x1024 版本. 如：
256x256 : http://d36zk2xti64re0.cloudfront.net/stylegan2/networks/stylegan2-horse-config-f.pkl
512x384 : http://d36zk2xti64re0.cloudfront.net/stylegan2/networks/stylegan2-car-config-f.pkl
1024x1024 : http://d36zk2xti64re0.cloudfront.net/stylegan2/networks/stylegan2-ffhq-config-f.pkl
下载 FFHQ 权重模型：
wget http://d36zk2xti64re0.cloudfront.net/stylegan2/networks/stylegan2-ffhq-config-f.pkl1.4.1. 需求分辨率的预训练权重不存在时
当没有适合指定分辨率的预训练模型时，则需要进行一些 stylegan2 的处理.
[1] - 自行创建适合指定分辨率的预训练 stylegan2 网络模型权重.
git clone https://github.com/aydao/stylegan2-surgery.git
cd stylegan2-surgery
#创建一个新的、未训练的指定分辨率的网络
#1.改变 width 和 height
python create_initial_network_pkl.py --width 256 --height 256log 信息如：
Local submit - run_dir: .
dnnlib: Running training.diagnostic.create_initial_pkl() on localhost...
Constructing networks...
Setting up TensorFlow plugin "fused_bias_act.cu": Preprocessing... Loading... Done.
Setting up TensorFlow plugin "upfirdn_2d.cu": Preprocessing... Loading... Done.
G Params OutputShape WeightShape
--- --- --- ---
latents_in - (?, 512) -
labels_in - (?, 0) -
lod - () -
dlatent_avg - (512,) -
G_mapping/latents_in - (?, 512) -
G_mapping/labels_in - (?, 0) -
G_mapping/Normalize - (?, 512) -
G_mapping/Dense0 262656 (?, 512) (512, 512)
G_mapping/Dense1 262656 (?, 512) (512, 512)
G_mapping/Dense2 262656 (?, 512) (512, 512)
G_mapping/Dense3 262656 (?, 512) (512, 512)
G_mapping/Dense4 262656 (?, 512) (512, 512)
G_mapping/Dense5 262656 (?, 512) (512, 512)
G_mapping/Dense6 262656 (?, 512) (512, 512)
G_mapping/Dense7 262656 (?, 512) (512, 512)
G_mapping/Broadcast - (?, 14, 512) -
G_mapping/dlatents_out - (?, 14, 512) -
Truncation/Lerp - (?, 14, 512) -
G_synthesis/dlatents_in - (?, 14, 512) -
G_synthesis/4x4/Const 8192 (?, 512, 4, 4) (1, 512, 4, 4)
G_synthesis/4x4/Conv 2622465 (?, 512, 4, 4) (3, 3, 512, 512)
G_synthesis/4x4/ToRGB 264195 (?, 3, 4, 4) (1, 1, 512, 3)
G_synthesis/8x8/Conv0_up 2622465 (?, 512, 8, 8) (3, 3, 512, 512)
G_synthesis/8x8/Conv1 2622465 (?, 512, 8, 8) (3, 3, 512, 512)
G_synthesis/8x8/Upsample - (?, 3, 8, 8) -
G_synthesis/8x8/ToRGB 264195 (?, 3, 8, 8) (1, 1, 512, 3)
G_synthesis/16x16/Conv0_up 2622465 (?, 512, 16, 16) (3, 3, 512, 512)
G_synthesis/16x16/Conv1 2622465 (?, 512, 16, 16) (3, 3, 512, 512)
G_synthesis/16x16/Upsample - (?, 3, 16, 16) -
G_synthesis/16x16/ToRGB 264195 (?, 3, 16, 16) (1, 1, 512, 3)
G_synthesis/32x32/Conv0_up 2622465 (?, 512, 32, 32) (3, 3, 512, 512)
G_synthesis/32x32/Conv1 2622465 (?, 512, 32, 32) (3, 3, 512, 512)
G_synthesis/32x32/Upsample - (?, 3, 32, 32) -
G_synthesis/32x32/ToRGB 264195 (?, 3, 32, 32) (1, 1, 512, 3)
G_synthesis/64x64/Conv0_up 2622465 (?, 512, 64, 64) (3, 3, 512, 512)
G_synthesis/64x64/Conv1 2622465 (?, 512, 64, 64) (3, 3, 512, 512)
G_synthesis/64x64/Upsample - (?, 3, 64, 64) -
G_synthesis/64x64/ToRGB 264195 (?, 3, 64, 64) (1, 1, 512, 3)
G_synthesis/128x128/Conv0_up 1442561 (?, 256, 128, 128) (3, 3, 512, 256)
G_synthesis/128x128/Conv1 721409 (?, 256, 128, 128) (3, 3, 256, 256)
G_synthesis/128x128/Upsample - (?, 3, 128, 128) -
G_synthesis/128x128/ToRGB 132099 (?, 3, 128, 128) (1, 1, 256, 3)
G_synthesis/256x256/Conv0_up 426369 (?, 128, 256, 256) (3, 3, 256, 128)
G_synthesis/256x256/Conv1 213249 (?, 128, 256, 256) (3, 3, 128, 128)
G_synthesis/256x256/Upsample - (?, 3, 256, 256) -
G_synthesis/256x256/ToRGB 66051 (?, 3, 256, 256) (1, 1, 128, 3)
G_synthesis/images_out - (?, 3, 256, 256) -
G_synthesis/noise0 - (1, 1, 4, 4) -
G_synthesis/noise1 - (1, 1, 8, 8) -
G_synthesis/noise2 - (1, 1, 8, 8) -
G_synthesis/noise3 - (1, 1, 16, 16) -
G_synthesis/noise4 - (1, 1, 16, 16) -
G_synthesis/noise5 - (1, 1, 32, 32) -
G_synthesis/noise6 - (1, 1, 32, 32) -
G_synthesis/noise7 - (1, 1, 64, 64) -
G_synthesis/noise8 - (1, 1, 64, 64) -
G_synthesis/noise9 - (1, 1, 128, 128) -
G_synthesis/noise10 - (1, 1, 128, 128) -
G_synthesis/noise11 - (1, 1, 256, 256) -
G_synthesis/noise12 - (1, 1, 256, 256) -
images_out - (?, 3, 256, 256) -
--- --- --- ---
Total 30034338
D Params OutputShape WeightShape
--- --- --- ---
images_in - (?, 3, 256, 256) -
labels_in - (?, 0) -
256x256/FromRGB 512 (?, 128, 256, 256) (1, 1, 3, 128)
256x256/Conv0 147584 (?, 128, 256, 256) (3, 3, 128, 128)
256x256/Conv1_down 295168 (?, 256, 128, 128) (3, 3, 128, 256)
256x256/Skip 32768 (?, 256, 128, 128) (1, 1, 128, 256)
128x128/Conv0 590080 (?, 256, 128, 128) (3, 3, 256, 256)
128x128/Conv1_down 1180160 (?, 512, 64, 64) (3, 3, 256, 512)
128x128/Skip 131072 (?, 512, 64, 64) (1, 1, 256, 512)
64x64/Conv0 2359808 (?, 512, 64, 64) (3, 3, 512, 512)
64x64/Conv1_down 2359808 (?, 512, 32, 32) (3, 3, 512, 512)
64x64/Skip 262144 (?, 512, 32, 32) (1, 1, 512, 512)
32x32/Conv0 2359808 (?, 512, 32, 32) (3, 3, 512, 512)
32x32/Conv1_down 2359808 (?, 512, 16, 16) (3, 3, 512, 512)
32x32/Skip 262144 (?, 512, 16, 16) (1, 1, 512, 512)
16x16/Conv0 2359808 (?, 512, 16, 16) (3, 3, 512, 512)
16x16/Conv1_down 2359808 (?, 512, 8, 8) (3, 3, 512, 512)
16x16/Skip 262144 (?, 512, 8, 8) (1, 1, 512, 512)
8x8/Conv0 2359808 (?, 512, 8, 8) (3, 3, 512, 512)
8x8/Conv1_down 2359808 (?, 512, 4, 4) (3, 3, 512, 512)
8x8/Skip 262144 (?, 512, 4, 4) (1, 1, 512, 512)
4x4/MinibatchStddev - (?, 513, 4, 4) -
4x4/Conv 2364416 (?, 512, 4, 4) (3, 3, 513, 512)
4x4/Dense0 4194816 (?, 512) (8192, 512)
Output 513 (?, 1) (512, 1)
scores_out - (?, 1) -
--- --- --- ---
Total 28864129
Saving network-initial-config-f-256x256-0.pkl
dnnlib: Finished training.diagnostic.create_initial_pkl() in 9s.即可得到没有权重参数的空网络.
[2] - 复制权重到空网络
如：由 stylegan2-ffhq-config-f.pkl 权重参数复制到 network-initial-config-f-256x256-0.pkl.
python copy_weights.py /content/stylegan2-ffhq-config-f.pkl /content/stylegan2-surgery/network-initial-config-f-256x256-0.pkl --output_pkl /content/surgery_output.pkllog 信息如：
Setting up TensorFlow plugin "fused_bias_act.cu": Preprocessing... Compiling... Loading... Done.
Setting up TensorFlow plugin "upfirdn_2d.cu": Preprocessing... Compiling... Loading... Done.
Source:
G Params OutputShape WeightShape
--- --- --- ---
latents_in - (?, 512) -
labels_in - (?, 0) -
lod - () -
dlatent_avg - (512,) -
G_mapping/latents_in - (?, 512) -
G_mapping/labels_in - (?, 0) -
G_mapping/Normalize - (?, 512) -
G_mapping/Dense0 262656 (?, 512) (512, 512)
G_mapping/Dense1 262656 (?, 512) (512, 512)
G_mapping/Dense2 262656 (?, 512) (512, 512)
G_mapping/Dense3 262656 (?, 512) (512, 512)
G_mapping/Dense4 262656 (?, 512) (512, 512)
G_mapping/Dense5 262656 (?, 512) (512, 512)
G_mapping/Dense6 262656 (?, 512) (512, 512)
G_mapping/Dense7 262656 (?, 512) (512, 512)
G_mapping/Broadcast - (?, 18, 512) -
G_mapping/dlatents_out - (?, 18, 512) -
Truncation/Lerp - (?, 18, 512) -
G_synthesis/dlatents_in - (?, 18, 512) -
G_synthesis/4x4/Const 8192 (?, 512, 4, 4) (1, 512, 4, 4)
G_synthesis/4x4/Conv 2622465 (?, 512, 4, 4) (3, 3, 512, 512)
G_synthesis/4x4/ToRGB 264195 (?, 3, 4, 4) (1, 1, 512, 3)
G_synthesis/8x8/Conv0_up 2622465 (?, 512, 8, 8) (3, 3, 512, 512)
G_synthesis/8x8/Conv1 2622465 (?, 512, 8, 8) (3, 3, 512, 512)
G_synthesis/8x8/Upsample - (?, 3, 8, 8) -
G_synthesis/8x8/ToRGB 264195 (?, 3, 8, 8) (1, 1, 512, 3)
G_synthesis/16x16/Conv0_up 2622465 (?, 512, 16, 16) (3, 3, 512, 512)
G_synthesis/16x16/Conv1 2622465 (?, 512, 16, 16) (3, 3, 512, 512)
G_synthesis/16x16/Upsample - (?, 3, 16, 16) -
G_synthesis/16x16/ToRGB 264195 (?, 3, 16, 16) (1, 1, 512, 3)
G_synthesis/32x32/Conv0_up 2622465 (?, 512, 32, 32) (3, 3, 512, 512)
G_synthesis/32x32/Conv1 2622465 (?, 512, 32, 32) (3, 3, 512, 512)
G_synthesis/32x32/Upsample - (?, 3, 32, 32) -
G_synthesis/32x32/ToRGB 264195 (?, 3, 32, 32) (1, 1, 512, 3)
G_synthesis/64x64/Conv0_up 2622465 (?, 512, 64, 64) (3, 3, 512, 512)
G_synthesis/64x64/Conv1 2622465 (?, 512, 64, 64) (3, 3, 512, 512)
G_synthesis/64x64/Upsample - (?, 3, 64, 64) -
G_synthesis/64x64/ToRGB 264195 (?, 3, 64, 64) (1, 1, 512, 3)
G_synthesis/128x128/Conv0_up 1442561 (?, 256, 128, 128) (3, 3, 512, 256)
G_synthesis/128x128/Conv1 721409 (?, 256, 128, 128) (3, 3, 256, 256)
G_synthesis/128x128/Upsample - (?, 3, 128, 128) -
G_synthesis/128x128/ToRGB 132099 (?, 3, 128, 128) (1, 1, 256, 3)
G_synthesis/256x256/Conv0_up 426369 (?, 128, 256, 256) (3, 3, 256, 128)
G_synthesis/256x256/Conv1 213249 (?, 128, 256, 256) (3, 3, 128, 128)
G_synthesis/256x256/Upsample - (?, 3, 256, 256) -
G_synthesis/256x256/ToRGB 66051 (?, 3, 256, 256) (1, 1, 128, 3)
G_synthesis/512x512/Conv0_up 139457 (?, 64, 512, 512) (3, 3, 128, 64)
G_synthesis/512x512/Conv1 69761 (?, 64, 512, 512) (3, 3, 64, 64)
G_synthesis/512x512/Upsample - (?, 3, 512, 512) -
G_synthesis/512x512/ToRGB 33027 (?, 3, 512, 512) (1, 1, 64, 3)
G_synthesis/1024x1024/Conv0_up 51297 (?, 32, 1024, 1024) (3, 3, 64, 32)
G_synthesis/1024x1024/Conv1 25665 (?, 32, 1024, 1024) (3, 3, 32, 32)
G_synthesis/1024x1024/Upsample - (?, 3, 1024, 1024) -
G_synthesis/1024x1024/ToRGB 16515 (?, 3, 1024, 1024) (1, 1, 32, 3)
G_synthesis/images_out - (?, 3, 1024, 1024) -
G_synthesis/noise0 - (1, 1, 4, 4) -
G_synthesis/noise1 - (1, 1, 8, 8) -
G_synthesis/noise2 - (1, 1, 8, 8) -
G_synthesis/noise3 - (1, 1, 16, 16) -
G_synthesis/noise4 - (1, 1, 16, 16) -
G_synthesis/noise5 - (1, 1, 32, 32) -
G_synthesis/noise6 - (1, 1, 32, 32) -
G_synthesis/noise7 - (1, 1, 64, 64) -
G_synthesis/noise8 - (1, 1, 64, 64) -
G_synthesis/noise9 - (1, 1, 128, 128) -
G_synthesis/noise10 - (1, 1, 128, 128) -
G_synthesis/noise11 - (1, 1, 256, 256) -
G_synthesis/noise12 - (1, 1, 256, 256) -
G_synthesis/noise13 - (1, 1, 512, 512) -
G_synthesis/noise14 - (1, 1, 512, 512) -
G_synthesis/noise15 - (1, 1, 1024, 1024) -
G_synthesis/noise16 - (1, 1, 1024, 1024) -
images_out - (?, 3, 1024, 1024) -
--- --- --- ---
Total 30370060
D Params OutputShape WeightShape
--- --- --- ---
images_in - (?, 3, 1024, 1024) -
labels_in - (?, 0) -
1024x1024/FromRGB 128 (?, 32, 1024, 1024) (1, 1, 3, 32)
1024x1024/Conv0 9248 (?, 32, 1024, 1024) (3, 3, 32, 32)
1024x1024/Conv1_down 18496 (?, 64, 512, 512) (3, 3, 32, 64)
1024x1024/Skip 2048 (?, 64, 512, 512) (1, 1, 32, 64)
512x512/Conv0 36928 (?, 64, 512, 512) (3, 3, 64, 64)
512x512/Conv1_down 73856 (?, 128, 256, 256) (3, 3, 64, 128)
512x512/Skip 8192 (?, 128, 256, 256) (1, 1, 64, 128)
256x256/Conv0 147584 (?, 128, 256, 256) (3, 3, 128, 128)
256x256/Conv1_down 295168 (?, 256, 128, 128) (3, 3, 128, 256)
256x256/Skip 32768 (?, 256, 128, 128) (1, 1, 128, 256)
128x128/Conv0 590080 (?, 256, 128, 128) (3, 3, 256, 256)
128x128/Conv1_down 1180160 (?, 512, 64, 64) (3, 3, 256, 512)
128x128/Skip 131072 (?, 512, 64, 64) (1, 1, 256, 512)
64x64/Conv0 2359808 (?, 512, 64, 64) (3, 3, 512, 512)
64x64/Conv1_down 2359808 (?, 512, 32, 32) (3, 3, 512, 512)
64x64/Skip 262144 (?, 512, 32, 32) (1, 1, 512, 512)
32x32/Conv0 2359808 (?, 512, 32, 32) (3, 3, 512, 512)
32x32/Conv1_down 2359808 (?, 512, 16, 16) (3, 3, 512, 512)
32x32/Skip 262144 (?, 512, 16, 16) (1, 1, 512, 512)
16x16/Conv0 2359808 (?, 512, 16, 16) (3, 3, 512, 512)
16x16/Conv1_down 2359808 (?, 512, 8, 8) (3, 3, 512, 512)
16x16/Skip 262144 (?, 512, 8, 8) (1, 1, 512, 512)
8x8/Conv0 2359808 (?, 512, 8, 8) (3, 3, 512, 512)
8x8/Conv1_down 2359808 (?, 512, 4, 4) (3, 3, 512, 512)
8x8/Skip 262144 (?, 512, 4, 4) (1, 1, 512, 512)
4x4/MinibatchStddev - (?, 513, 4, 4) -
4x4/Conv 2364416 (?, 512, 4, 4) (3, 3, 513, 512)
4x4/Dense0 4194816 (?, 512) (8192, 512)
Output 513 (?, 1) (512, 1)
scores_out - (?, 1) -
--- --- --- ---
Total 29012513
Gs Params OutputShape WeightShape
--- --- --- ---
latents_in - (?, 512) -
labels_in - (?, 0) -
lod - () -
dlatent_avg - (512,) -
G_mapping/latents_in - (?, 512) -
G_mapping/labels_in - (?, 0) -
G_mapping/Normalize - (?, 512) -
G_mapping/Dense0 262656 (?, 512) (512, 512)
G_mapping/Dense1 262656 (?, 512) (512, 512)
G_mapping/Dense2 262656 (?, 512) (512, 512)
G_mapping/Dense3 262656 (?, 512) (512, 512)
G_mapping/Dense4 262656 (?, 512) (512, 512)
G_mapping/Dense5 262656 (?, 512) (512, 512)
G_mapping/Dense6 262656 (?, 512) (512, 512)
G_mapping/Dense7 262656 (?, 512) (512, 512)
G_mapping/Broadcast - (?, 18, 512) -
G_mapping/dlatents_out - (?, 18, 512) -
Truncation/Lerp - (?, 18, 512) -
G_synthesis/dlatents_in - (?, 18, 512) -
G_synthesis/4x4/Const 8192 (?, 512, 4, 4) (1, 512, 4, 4)
G_synthesis/4x4/Conv 2622465 (?, 512, 4, 4) (3, 3, 512, 512)
G_synthesis/4x4/ToRGB 264195 (?, 3, 4, 4) (1, 1, 512, 3)
G_synthesis/8x8/Conv0_up 2622465 (?, 512, 8, 8) (3, 3, 512, 512)
G_synthesis/8x8/Conv1 2622465 (?, 512, 8, 8) (3, 3, 512, 512)
G_synthesis/8x8/Upsample - (?, 3, 8, 8) -
G_synthesis/8x8/ToRGB 264195 (?, 3, 8, 8) (1, 1, 512, 3)
G_synthesis/16x16/Conv0_up 2622465 (?, 512, 16, 16) (3, 3, 512, 512)
G_synthesis/16x16/Conv1 2622465 (?, 512, 16, 16) (3, 3, 512, 512)
G_synthesis/16x16/Upsample - (?, 3, 16, 16) -
G_synthesis/16x16/ToRGB 264195 (?, 3, 16, 16) (1, 1, 512, 3)
G_synthesis/32x32/Conv0_up 2622465 (?, 512, 32, 32) (3, 3, 512, 512)
G_synthesis/32x32/Conv1 2622465 (?, 512, 32, 32) (3, 3, 512, 512)
G_synthesis/32x32/Upsample - (?, 3, 32, 32) -
G_synthesis/32x32/ToRGB 264195 (?, 3, 32, 32) (1, 1, 512, 3)
G_synthesis/64x64/Conv0_up 2622465 (?, 512, 64, 64) (3, 3, 512, 512)
G_synthesis/64x64/Conv1 2622465 (?, 512, 64, 64) (3, 3, 512, 512)
G_synthesis/64x64/Upsample - (?, 3, 64, 64) -
G_synthesis/64x64/ToRGB 264195 (?, 3, 64, 64) (1, 1, 512, 3)
G_synthesis/128x128/Conv0_up 1442561 (?, 256, 128, 128) (3, 3, 512, 256)
G_synthesis/128x128/Conv1 721409 (?, 256, 128, 128) (3, 3, 256, 256)
G_synthesis/128x128/Upsample - (?, 3, 128, 128) -
G_synthesis/128x128/ToRGB 132099 (?, 3, 128, 128) (1, 1, 256, 3)
G_synthesis/256x256/Conv0_up 426369 (?, 128, 256, 256) (3, 3, 256, 128)
G_synthesis/256x256/Conv1 213249 (?, 128, 256, 256) (3, 3, 128, 128)
G_synthesis/256x256/Upsample - (?, 3, 256, 256) -
G_synthesis/256x256/ToRGB 66051 (?, 3, 256, 256) (1, 1, 128, 3)
G_synthesis/512x512/Conv0_up 139457 (?, 64, 512, 512) (3, 3, 128, 64)
G_synthesis/512x512/Conv1 69761 (?, 64, 512, 512) (3, 3, 64, 64)
G_synthesis/512x512/Upsample - (?, 3, 512, 512) -
G_synthesis/512x512/ToRGB 33027 (?, 3, 512, 512) (1, 1, 64, 3)
G_synthesis/1024x1024/Conv0_up 51297 (?, 32, 1024, 1024) (3, 3, 64, 32)
G_synthesis/1024x1024/Conv1 25665 (?, 32, 1024, 1024) (3, 3, 32, 32)
G_synthesis/1024x1024/Upsample - (?, 3, 1024, 1024) -
G_synthesis/1024x1024/ToRGB 16515 (?, 3, 1024, 1024) (1, 1, 32, 3)
G_synthesis/images_out - (?, 3, 1024, 1024) -
G_synthesis/noise0 - (1, 1, 4, 4) -
G_synthesis/noise1 - (1, 1, 8, 8) -
G_synthesis/noise2 - (1, 1, 8, 8) -
G_synthesis/noise3 - (1, 1, 16, 16) -
G_synthesis/noise4 - (1, 1, 16, 16) -
G_synthesis/noise5 - (1, 1, 32, 32) -
G_synthesis/noise6 - (1, 1, 32, 32) -
G_synthesis/noise7 - (1, 1, 64, 64) -
G_synthesis/noise8 - (1, 1, 64, 64) -
G_synthesis/noise9 - (1, 1, 128, 128) -
G_synthesis/noise10 - (1, 1, 128, 128) -
G_synthesis/noise11 - (1, 1, 256, 256) -
G_synthesis/noise12 - (1, 1, 256, 256) -
G_synthesis/noise13 - (1, 1, 512, 512) -
G_synthesis/noise14 - (1, 1, 512, 512) -
G_synthesis/noise15 - (1, 1, 1024, 1024) -
G_synthesis/noise16 - (1, 1, 1024, 1024) -
images_out - (?, 3, 1024, 1024) -
--- --- --- ---
Total 30370060
Target:
G Params OutputShape WeightShape
--- --- --- ---
latents_in - (?, 512) -
labels_in - (?, 0) -
lod - () -
dlatent_avg - (512,) -
G_mapping/latents_in - (?, 512) -
G_mapping/labels_in - (?, 0) -
G_mapping/Normalize - (?, 512) -
G_mapping/Dense0 262656 (?, 512) (512, 512)
G_mapping/Dense1 262656 (?, 512) (512, 512)
G_mapping/Dense2 262656 (?, 512) (512, 512)
G_mapping/Dense3 262656 (?, 512) (512, 512)
G_mapping/Dense4 262656 (?, 512) (512, 512)
G_mapping/Dense5 262656 (?, 512) (512, 512)
G_mapping/Dense6 262656 (?, 512) (512, 512)
G_mapping/Dense7 262656 (?, 512) (512, 512)
G_mapping/Broadcast - (?, 14, 512) -
G_mapping/dlatents_out - (?, 14, 512) -
Truncation/Lerp - (?, 14, 512) -
G_synthesis/dlatents_in - (?, 14, 512) -
G_synthesis/4x4/Const 8192 (?, 512, 4, 4) (1, 512, 4, 4)
G_synthesis/4x4/Conv 2622465 (?, 512, 4, 4) (3, 3, 512, 512)
G_synthesis/4x4/ToRGB 264195 (?, 3, 4, 4) (1, 1, 512, 3)
G_synthesis/8x8/Conv0_up 2622465 (?, 512, 8, 8) (3, 3, 512, 512)
G_synthesis/8x8/Conv1 2622465 (?, 512, 8, 8) (3, 3, 512, 512)
G_synthesis/8x8/Upsample - (?, 3, 8, 8) -
G_synthesis/8x8/ToRGB 264195 (?, 3, 8, 8) (1, 1, 512, 3)
G_synthesis/16x16/Conv0_up 2622465 (?, 512, 16, 16) (3, 3, 512, 512)
G_synthesis/16x16/Conv1 2622465 (?, 512, 16, 16) (3, 3, 512, 512)
G_synthesis/16x16/Upsample - (?, 3, 16, 16) -
G_synthesis/16x16/ToRGB 264195 (?, 3, 16, 16) (1, 1, 512, 3)
G_synthesis/32x32/Conv0_up 2622465 (?, 512, 32, 32) (3, 3, 512, 512)
G_synthesis/32x32/Conv1 2622465 (?, 512, 32, 32) (3, 3, 512, 512)
G_synthesis/32x32/Upsample - (?, 3, 32, 32) -
G_synthesis/32x32/ToRGB 264195 (?, 3, 32, 32) (1, 1, 512, 3)
G_synthesis/64x64/Conv0_up 2622465 (?, 512, 64, 64) (3, 3, 512, 512)
G_synthesis/64x64/Conv1 2622465 (?, 512, 64, 64) (3, 3, 512, 512)
G_synthesis/64x64/Upsample - (?, 3, 64, 64) -
G_synthesis/64x64/ToRGB 264195 (?, 3, 64, 64) (1, 1, 512, 3)
G_synthesis/128x128/Conv0_up 1442561 (?, 256, 128, 128) (3, 3, 512, 256)
G_synthesis/128x128/Conv1 721409 (?, 256, 128, 128) (3, 3, 256, 256)
G_synthesis/128x128/Upsample - (?, 3, 128, 128) -
G_synthesis/128x128/ToRGB 132099 (?, 3, 128, 128) (1, 1, 256, 3)
G_synthesis/256x256/Conv0_up 426369 (?, 128, 256, 256) (3, 3, 256, 128)
G_synthesis/256x256/Conv1 213249 (?, 128, 256, 256) (3, 3, 128, 128)
G_synthesis/256x256/Upsample - (?, 3, 256, 256) -
G_synthesis/256x256/ToRGB 66051 (?, 3, 256, 256) (1, 1, 128, 3)
G_synthesis/images_out - (?, 3, 256, 256) -
G_synthesis/noise0 - (1, 1, 4, 4) -
G_synthesis/noise1 - (1, 1, 8, 8) -
G_synthesis/noise2 - (1, 1, 8, 8) -
G_synthesis/noise3 - (1, 1, 16, 16) -
G_synthesis/noise4 - (1, 1, 16, 16) -
G_synthesis/noise5 - (1, 1, 32, 32) -
G_synthesis/noise6 - (1, 1, 32, 32) -
G_synthesis/noise7 - (1, 1, 64, 64) -
G_synthesis/noise8 - (1, 1, 64, 64) -
G_synthesis/noise9 - (1, 1, 128, 128) -
G_synthesis/noise10 - (1, 1, 128, 128) -
G_synthesis/noise11 - (1, 1, 256, 256) -
G_synthesis/noise12 - (1, 1, 256, 256) -
images_out - (?, 3, 256, 256) -
--- --- --- ---
Total 30034338
D Params OutputShape WeightShape
--- --- --- ---
images_in - (?, 3, 256, 256) -
labels_in - (?, 0) -
256x256/FromRGB 512 (?, 128, 256, 256) (1, 1, 3, 128)
256x256/Conv0 147584 (?, 128, 256, 256) (3, 3, 128, 128)
256x256/Conv1_down 295168 (?, 256, 128, 128) (3, 3, 128, 256)
256x256/Skip 32768 (?, 256, 128, 128) (1, 1, 128, 256)
128x128/Conv0 590080 (?, 256, 128, 128) (3, 3, 256, 256)
128x128/Conv1_down 1180160 (?, 512, 64, 64) (3, 3, 256, 512)
128x128/Skip 131072 (?, 512, 64, 64) (1, 1, 256, 512)
64x64/Conv0 2359808 (?, 512, 64, 64) (3, 3, 512, 512)
64x64/Conv1_down 2359808 (?, 512, 32, 32) (3, 3, 512, 512)
64x64/Skip 262144 (?, 512, 32, 32) (1, 1, 512, 512)
32x32/Conv0 2359808 (?, 512, 32, 32) (3, 3, 512, 512)
32x32/Conv1_down 2359808 (?, 512, 16, 16) (3, 3, 512, 512)
32x32/Skip 262144 (?, 512, 16, 16) (1, 1, 512, 512)
16x16/Conv0 2359808 (?, 512, 16, 16) (3, 3, 512, 512)
16x16/Conv1_down 2359808 (?, 512, 8, 8) (3, 3, 512, 512)
16x16/Skip 262144 (?, 512, 8, 8) (1, 1, 512, 512)
8x8/Conv0 2359808 (?, 512, 8, 8) (3, 3, 512, 512)
8x8/Conv1_down 2359808 (?, 512, 4, 4) (3, 3, 512, 512)
8x8/Skip 262144 (?, 512, 4, 4) (1, 1, 512, 512)
4x4/MinibatchStddev - (?, 513, 4, 4) -
4x4/Conv 2364416 (?, 512, 4, 4) (3, 3, 513, 512)
4x4/Dense0 4194816 (?, 512) (8192, 512)
Output 513 (?, 1) (512, 1)
scores_out - (?, 1) -
--- --- --- ---
Total 28864129
Gs Params OutputShape WeightShape
--- --- --- ---
latents_in - (?, 512) -
labels_in - (?, 0) -
lod - () -
dlatent_avg - (512,) -
G_mapping/latents_in - (?, 512) -
G_mapping/labels_in - (?, 0) -
G_mapping/Normalize - (?, 512) -
G_mapping/Dense0 262656 (?, 512) (512, 512)
G_mapping/Dense1 262656 (?, 512) (512, 512)
G_mapping/Dense2 262656 (?, 512) (512, 512)
G_mapping/Dense3 262656 (?, 512) (512, 512)
G_mapping/Dense4 262656 (?, 512) (512, 512)
G_mapping/Dense5 262656 (?, 512) (512, 512)
G_mapping/Dense6 262656 (?, 512) (512, 512)
G_mapping/Dense7 262656 (?, 512) (512, 512)
G_mapping/Broadcast - (?, 14, 512) -
G_mapping/dlatents_out - (?, 14, 512) -
Truncation/Lerp - (?, 14, 512) -
G_synthesis/dlatents_in - (?, 14, 512) -
G_synthesis/4x4/Const 8192 (?, 512, 4, 4) (1, 512, 4, 4)
G_synthesis/4x4/Conv 2622465 (?, 512, 4, 4) (3, 3, 512, 512)
G_synthesis/4x4/ToRGB 264195 (?, 3, 4, 4) (1, 1, 512, 3)
G_synthesis/8x8/Conv0_up 2622465 (?, 512, 8, 8) (3, 3, 512, 512)
G_synthesis/8x8/Conv1 2622465 (?, 512, 8, 8) (3, 3, 512, 512)
G_synthesis/8x8/Upsample - (?, 3, 8, 8) -
G_synthesis/8x8/ToRGB 264195 (?, 3, 8, 8) (1, 1, 512, 3)
G_synthesis/16x16/Conv0_up 2622465 (?, 512, 16, 16) (3, 3, 512, 512)
G_synthesis/16x16/Conv1 2622465 (?, 512, 16, 16) (3, 3, 512, 512)
G_synthesis/16x16/Upsample - (?, 3, 16, 16) -
G_synthesis/16x16/ToRGB 264195 (?, 3, 16, 16) (1, 1, 512, 3)
G_synthesis/32x32/Conv0_up 2622465 (?, 512, 32, 32) (3, 3, 512, 512)
G_synthesis/32x32/Conv1 2622465 (?, 512, 32, 32) (3, 3, 512, 512)
G_synthesis/32x32/Upsample - (?, 3, 32, 32) -
G_synthesis/32x32/ToRGB 264195 (?, 3, 32, 32) (1, 1, 512, 3)
G_synthesis/64x64/Conv0_up 2622465 (?, 512, 64, 64) (3, 3, 512, 512)
G_synthesis/64x64/Conv1 2622465 (?, 512, 64, 64) (3, 3, 512, 512)
G_synthesis/64x64/Upsample - (?, 3, 64, 64) -
G_synthesis/64x64/ToRGB 264195 (?, 3, 64, 64) (1, 1, 512, 3)
G_synthesis/128x128/Conv0_up 1442561 (?, 256, 128, 128) (3, 3, 512, 256)
G_synthesis/128x128/Conv1 721409 (?, 256, 128, 128) (3, 3, 256, 256)
G_synthesis/128x128/Upsample - (?, 3, 128, 128) -
G_synthesis/128x128/ToRGB 132099 (?, 3, 128, 128) (1, 1, 256, 3)
G_synthesis/256x256/Conv0_up 426369 (?, 128, 256, 256) (3, 3, 256, 128)
G_synthesis/256x256/Conv1 213249 (?, 128, 256, 256) (3, 3, 128, 128)
G_synthesis/256x256/Upsample - (?, 3, 256, 256) -
G_synthesis/256x256/ToRGB 66051 (?, 3, 256, 256) (1, 1, 128, 3)
G_synthesis/images_out - (?, 3, 256, 256) -
G_synthesis/noise0 - (1, 1, 4, 4) -
G_synthesis/noise1 - (1, 1, 8, 8) -
G_synthesis/noise2 - (1, 1, 8, 8) -
G_synthesis/noise3 - (1, 1, 16, 16) -
G_synthesis/noise4 - (1, 1, 16, 16) -
G_synthesis/noise5 - (1, 1, 32, 32) -
G_synthesis/noise6 - (1, 1, 32, 32) -
G_synthesis/noise7 - (1, 1, 64, 64) -
G_synthesis/noise8 - (1, 1, 64, 64) -
G_synthesis/noise9 - (1, 1, 128, 128) -
G_synthesis/noise10 - (1, 1, 128, 128) -
G_synthesis/noise11 - (1, 1, 256, 256) -
G_synthesis/noise12 - (1, 1, 256, 256) -
images_out - (?, 3, 256, 256) -
--- --- --- ---
Total 30034338
Restoring: G_synthesis/4x4/Const/const
Restoring: G_synthesis/4x4/Conv/weight
Restoring: G_synthesis/4x4/Conv/mod_weight
Restoring: G_synthesis/4x4/Conv/mod_bias
Restoring: G_synthesis/4x4/Conv/noise_strength
Restoring: G_synthesis/4x4/Conv/bias
Restoring: G_synthesis/4x4/ToRGB/weight
Restoring: G_synthesis/4x4/ToRGB/mod_weight
Restoring: G_synthesis/4x4/ToRGB/mod_bias
Restoring: G_synthesis/4x4/ToRGB/bias
Restoring: G_synthesis/8x8/Conv0_up/weight
Restoring: G_synthesis/8x8/Conv0_up/mod_weight
Restoring: G_synthesis/8x8/Conv0_up/mod_bias
Restoring: G_synthesis/8x8/Conv0_up/noise_strength
Restoring: G_synthesis/8x8/Conv0_up/bias
Restoring: G_synthesis/8x8/Conv1/weight
Restoring: G_synthesis/8x8/Conv1/mod_weight
Restoring: G_synthesis/8x8/Conv1/mod_bias
Restoring: G_synthesis/8x8/Conv1/noise_strength
Restoring: G_synthesis/8x8/Conv1/bias
Restoring: G_synthesis/8x8/ToRGB/weight
Restoring: G_synthesis/8x8/ToRGB/mod_weight
Restoring: G_synthesis/8x8/ToRGB/mod_bias
Restoring: G_synthesis/8x8/ToRGB/bias
Restoring: G_synthesis/16x16/Conv0_up/weight
Restoring: G_synthesis/16x16/Conv0_up/mod_weight
Restoring: G_synthesis/16x16/Conv0_up/mod_bias
Restoring: G_synthesis/16x16/Conv0_up/noise_strength
Restoring: G_synthesis/16x16/Conv0_up/bias
Restoring: G_synthesis/16x16/Conv1/weight
Restoring: G_synthesis/16x16/Conv1/mod_weight
Restoring: G_synthesis/16x16/Conv1/mod_bias
Restoring: G_synthesis/16x16/Conv1/noise_strength
Restoring: G_synthesis/16x16/Conv1/bias
Restoring: G_synthesis/16x16/ToRGB/weight
Restoring: G_synthesis/16x16/ToRGB/mod_weight
Restoring: G_synthesis/16x16/ToRGB/mod_bias
Restoring: G_synthesis/16x16/ToRGB/bias
Restoring: G_synthesis/32x32/Conv0_up/weight
Restoring: G_synthesis/32x32/Conv0_up/mod_weight
Restoring: G_synthesis/32x32/Conv0_up/mod_bias
Restoring: G_synthesis/32x32/Conv0_up/noise_strength
Restoring: G_synthesis/32x32/Conv0_up/bias
Restoring: G_synthesis/32x32/Conv1/weight
Restoring: G_synthesis/32x32/Conv1/mod_weight
Restoring: G_synthesis/32x32/Conv1/mod_bias
Restoring: G_synthesis/32x32/Conv1/noise_strength
Restoring: G_synthesis/32x32/Conv1/bias
Restoring: G_synthesis/32x32/ToRGB/weight
Restoring: G_synthesis/32x32/ToRGB/mod_weight
Restoring: G_synthesis/32x32/ToRGB/mod_bias
Restoring: G_synthesis/32x32/ToRGB/bias
Restoring: G_synthesis/64x64/Conv0_up/weight
Restoring: G_synthesis/64x64/Conv0_up/mod_weight
Restoring: G_synthesis/64x64/Conv0_up/mod_bias
Restoring: G_synthesis/64x64/Conv0_up/noise_strength
Restoring: G_synthesis/64x64/Conv0_up/bias
Restoring: G_synthesis/64x64/Conv1/weight
Restoring: G_synthesis/64x64/Conv1/mod_weight
Restoring: G_synthesis/64x64/Conv1/mod_bias
Restoring: G_synthesis/64x64/Conv1/noise_strength
Restoring: G_synthesis/64x64/Conv1/bias
Restoring: G_synthesis/64x64/ToRGB/weight
Restoring: G_synthesis/64x64/ToRGB/mod_weight
Restoring: G_synthesis/64x64/ToRGB/mod_bias
Restoring: G_synthesis/64x64/ToRGB/bias
Restoring: G_synthesis/128x128/Conv0_up/weight
Restoring: G_synthesis/128x128/Conv0_up/mod_weight
Restoring: G_synthesis/128x128/Conv0_up/mod_bias
Restoring: G_synthesis/128x128/Conv0_up/noise_strength
Restoring: G_synthesis/128x128/Conv0_up/bias
Restoring: G_synthesis/128x128/Conv1/weight
Restoring: G_synthesis/128x128/Conv1/mod_weight
Restoring: G_synthesis/128x128/Conv1/mod_bias
Restoring: G_synthesis/128x128/Conv1/noise_strength
Restoring: G_synthesis/128x128/Conv1/bias
Restoring: G_synthesis/128x128/ToRGB/weight
Restoring: G_synthesis/128x128/ToRGB/mod_weight
Restoring: G_synthesis/128x128/ToRGB/mod_bias
Restoring: G_synthesis/128x128/ToRGB/bias
Restoring: G_synthesis/256x256/Conv0_up/weight
Restoring: G_synthesis/256x256/Conv0_up/mod_weight
Restoring: G_synthesis/256x256/Conv0_up/mod_bias
Restoring: G_synthesis/256x256/Conv0_up/noise_strength
Restoring: G_synthesis/256x256/Conv0_up/bias
Restoring: G_synthesis/256x256/Conv1/weight
Restoring: G_synthesis/256x256/Conv1/mod_weight
Restoring: G_synthesis/256x256/Conv1/mod_bias
Restoring: G_synthesis/256x256/Conv1/noise_strength
Restoring: G_synthesis/256x256/Conv1/bias
Restoring: G_synthesis/256x256/ToRGB/weight
Restoring: G_synthesis/256x256/ToRGB/mod_weight
Restoring: G_synthesis/256x256/ToRGB/mod_bias
Restoring: G_synthesis/256x256/ToRGB/bias
Restoring: G_mapping/Dense0/weight
Restoring: G_mapping/Dense0/bias
Restoring: G_mapping/Dense1/weight
Restoring: G_mapping/Dense1/bias
Restoring: G_mapping/Dense2/weight
Restoring: G_mapping/Dense2/bias
Restoring: G_mapping/Dense3/weight
Restoring: G_mapping/Dense3/bias
Restoring: G_mapping/Dense4/weight
Restoring: G_mapping/Dense4/bias
Restoring: G_mapping/Dense5/weight
Restoring: G_mapping/Dense5/bias
Restoring: G_mapping/Dense6/weight
Restoring: G_mapping/Dense6/bias
Restoring: G_mapping/Dense7/weight
Restoring: G_mapping/Dense7/bias
Not restoring (not present): 256x256/FromRGB/weight
Not restoring (not present): 256x256/FromRGB/bias
Restoring: 256x256/Conv0/weight
Restoring: 256x256/Conv0/bias
Restoring: 256x256/Conv1_down/weight
Restoring: 256x256/Conv1_down/bias
Restoring: 256x256/Skip/weight
Restoring: 128x128/Conv0/weight
Restoring: 128x128/Conv0/bias
Restoring: 128x128/Conv1_down/weight
Restoring: 128x128/Conv1_down/bias
Restoring: 128x128/Skip/weight
Restoring: 64x64/Conv0/weight
Restoring: 64x64/Conv0/bias
Restoring: 64x64/Conv1_down/weight
Restoring: 64x64/Conv1_down/bias
Restoring: 64x64/Skip/weight
Restoring: 32x32/Conv0/weight
Restoring: 32x32/Conv0/bias
Restoring: 32x32/Conv1_down/weight
Restoring: 32x32/Conv1_down/bias
Restoring: 32x32/Skip/weight
Restoring: 16x16/Conv0/weight
Restoring: 16x16/Conv0/bias
Restoring: 16x16/Conv1_down/weight
Restoring: 16x16/Conv1_down/bias
Restoring: 16x16/Skip/weight
Restoring: 8x8/Conv0/weight
Restoring: 8x8/Conv0/bias
Restoring: 8x8/Conv1_down/weight
Restoring: 8x8/Conv1_down/bias
Restoring: 8x8/Skip/weight
Restoring: 4x4/Conv/weight
Restoring: 4x4/Conv/bias
Restoring: 4x4/Dense0/weight
Restoring: 4x4/Dense0/bias
Restoring: Output/weight
Restoring: Output/bias
Restoring: G_synthesis/4x4/Const/const
Restoring: G_synthesis/4x4/Conv/weight
Restoring: G_synthesis/4x4/Conv/mod_weight
Restoring: G_synthesis/4x4/Conv/mod_bias
Restoring: G_synthesis/4x4/Conv/noise_strength
Restoring: G_synthesis/4x4/Conv/bias
Restoring: G_synthesis/4x4/ToRGB/weight
Restoring: G_synthesis/4x4/ToRGB/mod_weight
Restoring: G_synthesis/4x4/ToRGB/mod_bias
Restoring: G_synthesis/4x4/ToRGB/bias
Restoring: G_synthesis/8x8/Conv0_up/weight
Restoring: G_synthesis/8x8/Conv0_up/mod_weight
Restoring: G_synthesis/8x8/Conv0_up/mod_bias
Restoring: G_synthesis/8x8/Conv0_up/noise_strength
Restoring: G_synthesis/8x8/Conv0_up/bias
Restoring: G_synthesis/8x8/Conv1/weight
Restoring: G_synthesis/8x8/Conv1/mod_weight
Restoring: G_synthesis/8x8/Conv1/mod_bias
Restoring: G_synthesis/8x8/Conv1/noise_strength
Restoring: G_synthesis/8x8/Conv1/bias
Restoring: G_synthesis/8x8/ToRGB/weight
Restoring: G_synthesis/8x8/ToRGB/mod_weight
Restoring: G_synthesis/8x8/ToRGB/mod_bias
Restoring: G_synthesis/8x8/ToRGB/bias
Restoring: G_synthesis/16x16/Conv0_up/weight
Restoring: G_synthesis/16x16/Conv0_up/mod_weight
Restoring: G_synthesis/16x16/Conv0_up/mod_bias
Restoring: G_synthesis/16x16/Conv0_up/noise_strength
Restoring: G_synthesis/16x16/Conv0_up/bias
Restoring: G_synthesis/16x16/Conv1/weight
Restoring: G_synthesis/16x16/Conv1/mod_weight
Restoring: G_synthesis/16x16/Conv1/mod_bias
Restoring: G_synthesis/16x16/Conv1/noise_strength
Restoring: G_synthesis/16x16/Conv1/bias
Restoring: G_synthesis/16x16/ToRGB/weight
Restoring: G_synthesis/16x16/ToRGB/mod_weight
Restoring: G_synthesis/16x16/ToRGB/mod_bias
Restoring: G_synthesis/16x16/ToRGB/bias
Restoring: G_synthesis/32x32/Conv0_up/weight
Restoring: G_synthesis/32x32/Conv0_up/mod_weight
Restoring: G_synthesis/32x32/Conv0_up/mod_bias
Restoring: G_synthesis/32x32/Conv0_up/noise_strength
Restoring: G_synthesis/32x32/Conv0_up/bias
Restoring: G_synthesis/32x32/Conv1/weight
Restoring: G_synthesis/32x32/Conv1/mod_weight
Restoring: G_synthesis/32x32/Conv1/mod_bias
Restoring: G_synthesis/32x32/Conv1/noise_strength
Restoring: G_synthesis/32x32/Conv1/bias
Restoring: G_synthesis/32x32/ToRGB/weight
Restoring: G_synthesis/32x32/ToRGB/mod_weight
Restoring: G_synthesis/32x32/ToRGB/mod_bias
Restoring: G_synthesis/32x32/ToRGB/bias
Restoring: G_synthesis/64x64/Conv0_up/weight
Restoring: G_synthesis/64x64/Conv0_up/mod_weight
Restoring: G_synthesis/64x64/Conv0_up/mod_bias
Restoring: G_synthesis/64x64/Conv0_up/noise_strength
Restoring: G_synthesis/64x64/Conv0_up/bias
Restoring: G_synthesis/64x64/Conv1/weight
Restoring: G_synthesis/64x64/Conv1/mod_weight
Restoring: G_synthesis/64x64/Conv1/mod_bias
Restoring: G_synthesis/64x64/Conv1/noise_strength
Restoring: G_synthesis/64x64/Conv1/bias
Restoring: G_synthesis/64x64/ToRGB/weight
Restoring: G_synthesis/64x64/ToRGB/mod_weight
Restoring: G_synthesis/64x64/ToRGB/mod_bias
Restoring: G_synthesis/64x64/ToRGB/bias
Restoring: G_synthesis/128x128/Conv0_up/weight
Restoring: G_synthesis/128x128/Conv0_up/mod_weight
Restoring: G_synthesis/128x128/Conv0_up/mod_bias
Restoring: G_synthesis/128x128/Conv0_up/noise_strength
Restoring: G_synthesis/128x128/Conv0_up/bias
Restoring: G_synthesis/128x128/Conv1/weight
Restoring: G_synthesis/128x128/Conv1/mod_weight
Restoring: G_synthesis/128x128/Conv1/mod_bias
Restoring: G_synthesis/128x128/Conv1/noise_strength
Restoring: G_synthesis/128x128/Conv1/bias
Restoring: G_synthesis/128x128/ToRGB/weight
Restoring: G_synthesis/128x128/ToRGB/mod_weight
Restoring: G_synthesis/128x128/ToRGB/mod_bias
Restoring: G_synthesis/128x128/ToRGB/bias
Restoring: G_synthesis/256x256/Conv0_up/weight
Restoring: G_synthesis/256x256/Conv0_up/mod_weight
Restoring: G_synthesis/256x256/Conv0_up/mod_bias
Restoring: G_synthesis/256x256/Conv0_up/noise_strength
Restoring: G_synthesis/256x256/Conv0_up/bias
Restoring: G_synthesis/256x256/Conv1/weight
Restoring: G_synthesis/256x256/Conv1/mod_weight
Restoring: G_synthesis/256x256/Conv1/mod_bias
Restoring: G_synthesis/256x256/Conv1/noise_strength
Restoring: G_synthesis/256x256/Conv1/bias
Restoring: G_synthesis/256x256/ToRGB/weight
Restoring: G_synthesis/256x256/ToRGB/mod_weight
Restoring: G_synthesis/256x256/ToRGB/mod_bias
Restoring: G_synthesis/256x256/ToRGB/bias
Restoring: G_mapping/Dense0/weight
Restoring: G_mapping/Dense0/bias
Restoring: G_mapping/Dense1/weight
Restoring: G_mapping/Dense1/bias
Restoring: G_mapping/Dense2/weight
Restoring: G_mapping/Dense2/bias
Restoring: G_mapping/Dense3/weight
Restoring: G_mapping/Dense3/bias
Restoring: G_mapping/Dense4/weight
Restoring: G_mapping/Dense4/bias
Restoring: G_mapping/Dense5/weight
Restoring: G_mapping/Dense5/bias
Restoring: G_mapping/Dense6/weight
Restoring: G_mapping/Dense6/bias
Restoring: G_mapping/Dense7/weight
Restoring: G_mapping/Dense7/bias2. 数据准备
图片数据清洗：
from fastai.vision import verify_images
local_path = "my_data/"
verify_images(local_path, delete=True)
#
import os
from PIL import Image
from tqdm import tqdm
import os
from os import mkdir
import shutil
for p in ["datasets/", 'datasets/custom']:
try:
os.mkdir(p)
except:
pass创建 stylegan2 训练所需的 TF records：
cd stylegan2-ada/
python dataset_tool.py create_from_images /content/datasets/custom/ /content/my_data/256/3. 训练
cd /content/stylegan2-ada/training3.1. training_loop
训练循环，每 10000 张图像创建一个 checkpoint.
training_loop.py
"""
Main training loop.
"""
import os
import pickle
import time
import PIL.Image
import numpy as np
import tensorflow as tf
import dnnlib
import dnnlib.tflib as tflib
from dnnlib.tflib.autosummary import autosummary
from training import dataset
#----------------------------------------------------------------------------
# Select size and contents of the image snapshot grids that are exported
# periodically during training.
def setup_snapshot_image_grid(training_set):
gw = np.clip(7680 // training_set.shape[2], 7, 32)
gh = np.clip(4320 // training_set.shape[1], 4, 32)
# Unconditional.
if training_set.label_size == 0:
reals, labels = training_set.get_minibatch_np(gw * gh)
return (gw, gh), reals, labels
# Row per class.
cw, ch = (gw, 1)
nw = (gw - 1) // cw + 1
nh = (gh - 1) // ch + 1
# Collect images.
blocks = [[] for _i in range(nw * nh)]
for _iter in range(1000000):
real, label = training_set.get_minibatch_np(1)
idx = np.argmax(label[0])
while idx < len(blocks) and len(blocks[idx]) >= cw * ch:
idx += training_set.label_size
if idx < len(blocks):
blocks[idx].append((real, label))
if all(len(block) >= cw * ch for block in blocks):
break
# Layout grid.
reals = np.zeros([gw * gh] + training_set.shape, dtype=training_set.dtype)
labels = np.zeros([gw * gh, training_set.label_size], dtype=training_set.label_dtype)
for i, block in enumerate(blocks):
for j, (real, label) in enumerate(block):
x = (i % nw) * cw + j % cw
y = (i // nw) * ch + j // cw
if x < gw and y < gh:
reals[x + y * gw] = real[0]
labels[x + y * gw] = label[0]
return (gw, gh), reals, labels
#----------------------------------------------------------------------------
def save_image_grid(images, filename, drange, grid_size):
lo, hi = drange
gw, gh = grid_size
images = np.asarray(images, dtype=np.float32)
images = (images - lo) * (255 / (hi - lo))
images = np.rint(images).clip(0, 255).astype(np.uint8)
_N, C, H, W = images.shape
images = images.reshape(gh, gw, C, H, W)
images = images.transpose(0, 3, 1, 4, 2)
images = images.reshape(gh * H, gw * W, C)
PIL.Image.fromarray(images, {3: 'RGB', 1: 'L'}[C]).save(filename)
#----------------------------------------------------------------------------
# Main training script.
def training_loop(
run_dir = '.', # Output directory.
G_args = {}, # Options for generator network.
D_args = {}, # Options for discriminator network.
G_opt_args = {}, # Options for generator optimizer.
D_opt_args = {}, # Options for discriminator optimizer.
loss_args = {}, # Options for loss function.
train_dataset_args = {}, # Options for dataset to train with.
metric_dataset_args = {}, # Options for dataset to evaluate metrics against.
augment_args = {}, # Options for adaptive augmentations.
metric_arg_list = [], # Metrics to evaluate during training.
num_gpus = 1, # Number of GPUs to use.
minibatch_size = 32, # Global minibatch size.
minibatch_gpu = 4, # Number of samples processed at a time by one GPU.
G_smoothing_kimg = 10, # Half-life of the exponential moving average (EMA) of generator weights.
G_smoothing_rampup = None, # EMA ramp-up coefficient.
minibatch_repeats = 4, # Number of minibatches to run in the inner loop.
lazy_regularization = True, # Perform regularization as a separate training step?
G_reg_interval = 4, # How often the perform regularization for G? Ignored if lazy_regularization=False.
D_reg_interval = 16, # How often the perform regularization for D? Ignored if lazy_regularization=False.
total_kimg = 25000, # Total length of the training, measured in thousands of real images.
kimg_per_tick = 10, # Progress snapshot interval.
image_snapshot_ticks = 1, # How often to save image snapshots? None = only save 'reals.png' and 'fakes-init.png'.
network_snapshot_ticks = 1, # How often to save network snapshots? None = only save 'networks-final.pkl'.
resume_pkl = None, # Network pickle to resume training from, None = train from scratch.
resume_kimg = 15000, # Assumed training progress at the beginning. Affects reporting and training schedule.
resume_time = 0.0, # Assumed wallclock time at the beginning. Affects reporting.
abort_fn = None, # Callback function for determining whether to abort training.
progress_fn = None, # Callback function for updating training progress.
):
assert minibatch_size % (num_gpus * minibatch_gpu) == 0
start_time = time.time()
print('Loading training set...')
training_set = dataset.load_dataset(**train_dataset_args)
print('Image shape:', np.int32(training_set.shape).tolist())
print('Label shape:', [training_set.label_size])
print()
print('Constructing networks...')
with tf.device('/gpu:0'):
G = tflib.Network('G', num_channels=training_set.shape[0], resolution=training_set.shape[1], label_size=training_set.label_size, **G_args)
D = tflib.Network('D', num_channels=training_set.shape[0], resolution=training_set.shape[1], label_size=training_set.label_size, **D_args)
Gs = G.clone('Gs')
if resume_pkl is not None:
print(f'Resuming from "{resume_pkl}"')
with dnnlib.util.open_url(resume_pkl) as f:
rG, rD, rGs = pickle.load(f)
G.copy_vars_from(rG)
D.copy_vars_from(rD)
Gs.copy_vars_from(rGs)
G.print_layers()
D.print_layers()
print('Exporting sample images...')
grid_size, grid_reals, grid_labels = setup_snapshot_image_grid(training_set)
save_image_grid(grid_reals, os.path.join(run_dir, 'reals.png'), drange=[0,255], grid_size=grid_size)
grid_latents = np.random.randn(np.prod(grid_size), *G.input_shape[1:])
grid_fakes = Gs.run(grid_latents, grid_labels, is_validation=True, minibatch_size=minibatch_gpu)
save_image_grid(grid_fakes, os.path.join(run_dir, 'fakes_init.png'), drange=[-1,1], grid_size=grid_size)
print(f'Replicating networks across {num_gpus} GPUs...')
G_gpus = [G]
D_gpus = [D]
for gpu in range(1, num_gpus):
with tf.device(f'/gpu:{gpu}'):
G_gpus.append(G.clone(f'{G.name}_gpu{gpu}'))
D_gpus.append(D.clone(f'{D.name}_gpu{gpu}'))
print('Initializing augmentations...')
aug = None
if augment_args.get('class_name', None) is not None:
aug = dnnlib.util.construct_class_by_name(**augment_args)
aug.init_validation_set(D_gpus=D_gpus, training_set=training_set)
print('Setting up optimizers...')
G_opt_args = dict(G_opt_args)
D_opt_args = dict(D_opt_args)
for args, reg_interval in [(G_opt_args, G_reg_interval), (D_opt_args, D_reg_interval)]:
args['minibatch_multiplier'] = minibatch_size // num_gpus // minibatch_gpu
if lazy_regularization:
mb_ratio = reg_interval / (reg_interval + 1)
args['learning_rate'] *= mb_ratio
if 'beta1' in args: args['beta1'] **= mb_ratio
if 'beta2' in args: args['beta2'] **= mb_ratio
G_opt = tflib.Optimizer(name='TrainG', **G_opt_args)
D_opt = tflib.Optimizer(name='TrainD', **D_opt_args)
G_reg_opt = tflib.Optimizer(name='RegG', share=G_opt, **G_opt_args)
D_reg_opt = tflib.Optimizer(name='RegD', share=D_opt, **D_opt_args)
print('Constructing training graph...')
data_fetch_ops = []
training_set.configure(minibatch_gpu)
for gpu, (G_gpu, D_gpu) in enumerate(zip(G_gpus, D_gpus)):
with tf.name_scope(f'Train_gpu{gpu}'), tf.device(f'/gpu:{gpu}'):
# Fetch training data via temporary variables.
with tf.name_scope('DataFetch'):
real_images_var = tf.Variable(name='images', trainable=False, initial_value=tf.zeros([minibatch_gpu] + training_set.shape))
real_labels_var = tf.Variable(name='labels', trainable=False, initial_value=tf.zeros([minibatch_gpu, training_set.label_size]))
real_images_write, real_labels_write = training_set.get_minibatch_tf()
real_images_write = tflib.convert_images_from_uint8(real_images_write)
data_fetch_ops += [tf.assign(real_images_var, real_images_write)]
data_fetch_ops += [tf.assign(real_labels_var, real_labels_write)]
# Evaluate loss function and register gradients.
fake_labels = training_set.get_random_labels_tf(minibatch_gpu)
terms = dnnlib.util.call_func_by_name(G=G_gpu, D=D_gpu, aug=aug, fake_labels=fake_labels, real_images=real_images_var, real_labels=real_labels_var, **loss_args)
if lazy_regularization:
if terms.G_reg is not None: G_reg_opt.register_gradients(tf.reduce_mean(terms.G_reg * G_reg_interval), G_gpu.trainables)
if terms.D_reg is not None: D_reg_opt.register_gradients(tf.reduce_mean(terms.D_reg * D_reg_interval), D_gpu.trainables)
else:
if terms.G_reg is not None: terms.G_loss += terms.G_reg
if terms.D_reg is not None: terms.D_loss += terms.D_reg
G_opt.register_gradients(tf.reduce_mean(terms.G_loss), G_gpu.trainables)
D_opt.register_gradients(tf.reduce_mean(terms.D_loss), D_gpu.trainables)
print('Finalizing training ops...')
data_fetch_op = tf.group(*data_fetch_ops)
G_train_op = G_opt.apply_updates()
D_train_op = D_opt.apply_updates()
G_reg_op = G_reg_opt.apply_updates(allow_no_op=True)
D_reg_op = D_reg_opt.apply_updates(allow_no_op=True)
Gs_beta_in = tf.placeholder(tf.float32, name='Gs_beta_in', shape=[])
Gs_update_op = Gs.setup_as_moving_average_of(G, beta=Gs_beta_in)
tflib.init_uninitialized_vars()
with tf.device('/gpu:0'):
peak_gpu_mem_op = tf.contrib.memory_stats.MaxBytesInUse()
print('Initializing metrics...')
summary_log = tf.summary.FileWriter(run_dir)
metrics = []
for args in metric_arg_list:
metric = dnnlib.util.construct_class_by_name(**args)
metric.configure(dataset_args=metric_dataset_args, run_dir=run_dir)
metrics.append(metric)
print(f'Training for {total_kimg} kimg...')
print()
if progress_fn is not None:
progress_fn(0, total_kimg)
tick_start_time = time.time()
maintenance_time = tick_start_time - start_time
cur_nimg = 0
cur_tick = -1
tick_start_nimg = cur_nimg
running_mb_counter = 0
done = False
while not done:
# Compute EMA decay parameter.
Gs_nimg = G_smoothing_kimg * 1000.0
if G_smoothing_rampup is not None:
Gs_nimg = min(Gs_nimg, cur_nimg * G_smoothing_rampup)
Gs_beta = 0.5 ** (minibatch_size / max(Gs_nimg, 1e-8))
# Run training ops.
for _repeat_idx in range(minibatch_repeats):
rounds = range(0, minibatch_size, minibatch_gpu * num_gpus)
run_G_reg = (lazy_regularization and running_mb_counter % G_reg_interval == 0)
run_D_reg = (lazy_regularization and running_mb_counter % D_reg_interval == 0)
cur_nimg += minibatch_size
running_mb_counter += 1
# Fast path without gradient accumulation.
if len(rounds) == 1:
tflib.run([G_train_op, data_fetch_op])
if run_G_reg:
tflib.run(G_reg_op)
tflib.run([D_train_op, Gs_update_op], {Gs_beta_in: Gs_beta})
if run_D_reg:
tflib.run(D_reg_op)
# Slow path with gradient accumulation.
else:
for _round in rounds:
tflib.run(G_train_op)
if run_G_reg:
tflib.run(G_reg_op)
tflib.run(Gs_update_op, {Gs_beta_in: Gs_beta})
for _round in rounds:
tflib.run(data_fetch_op)
tflib.run(D_train_op)
if run_D_reg:
tflib.run(D_reg_op)
# Run validation.
if aug is not None:
aug.run_validation(minibatch_size=minibatch_size)
# Tune augmentation parameters.
if aug is not None:
aug.tune(minibatch_size * minibatch_repeats)
# Perform maintenance tasks once per tick.
done = (cur_nimg >= total_kimg * 1000) or (abort_fn is not None and abort_fn())
if done or cur_tick < 0 or cur_nimg >= tick_start_nimg + kimg_per_tick * 1000:
cur_tick += 1
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_end_time = time.time()
total_time = tick_end_time - start_time
tick_time = tick_end_time - tick_start_time
# Report progress.
print(' '.join([
f"tick {autosummary('Progress/tick', cur_tick):<5d}",
f"kimg {autosummary('Progress/kimg', cur_nimg / 1000.0):<8.1f}",
f"time {dnnlib.util.format_time(autosummary('Timing/total_sec', total_time)):<12s}",
f"sec/tick {autosummary('Timing/sec_per_tick', tick_time):<7.1f}",
f"sec/kimg {autosummary('Timing/sec_per_kimg', tick_time / tick_kimg):<7.2f}",
f"maintenance {autosummary('Timing/maintenance_sec', maintenance_time):<6.1f}",
f"gpumem {autosummary('Resources/peak_gpu_mem_gb', peak_gpu_mem_op.eval() / 2**30):<5.1f}",
f"augment {autosummary('Progress/augment', aug.strength if aug is not None else 0):.3f}",
]))
autosummary('Timing/total_hours', total_time / (60.0 * 60.0))
autosummary('Timing/total_days', total_time / (24.0 * 60.0 * 60.0))
if progress_fn is not None:
progress_fn(cur_nimg // 1000, total_kimg)
# Save snapshots.
if image_snapshot_ticks is not None and (done or cur_tick % image_snapshot_ticks == 0):
grid_fakes = Gs.run(grid_latents, grid_labels, is_validation=True, minibatch_size=minibatch_gpu)
save_image_grid(grid_fakes, os.path.join(run_dir, f'fakes{cur_nimg // 1000:06d}.png'), drange=[-1,1], grid_size=grid_size)
if network_snapshot_ticks is not None and (done or cur_tick % network_snapshot_ticks == 0):
pkl = os.path.join(run_dir, f'network-snapshot-{cur_nimg // 1000:06d}.pkl')
with open(pkl, 'wb') as f:
pickle.dump((G, D, Gs), f)
if len(metrics):
print('Evaluating metrics...')
for metric in metrics:
metric.run(pkl, num_gpus=num_gpus)
# Update summaries.
for metric in metrics:
metric.update_autosummaries()
tflib.autosummary.save_summaries(summary_log, cur_nimg)
tick_start_time = time.time()
maintenance_time = tick_start_time - tick_end_time
print()
print('Exiting...')
summary_log.close()
training_set.close() 3.2. 网络训练
网络训练要做的几件事：
[1] - Resume
如果是需要断点训练模型，则将 Resume 修改为自定义的 pickle 文件;
如果是需要迁移学习训练模型，则将 Resume 设置为预训练的模型.
默认是设置为来自 Stylegan2 论文的 FFHQ1024x1024.
如果需要使用自定义创建的权重文件，可以将 Resume 进行对应设置，如：/content/surgery_output.pkl.
[2] - Outdir
grids 和权重保存到路径.
[3] - Cfg
参数配置文件，建议采用 stylegan2 的配置，因为其与原始经验最接近，且能够利用数据增强处理.
[4] - Metrics (不要修改！)
启用 metrics 会减慢训练速度.
如果需要测试模型，建议采用单独进行的方式.
"""
Train a GAN using the techniques described in the paper
"Training Generative Adversarial Networks with Limited Data".
"""
import os
import argparse
import json
import re
import tensorflow as tf
import dnnlib
import dnnlib.tflib as tflib
from training import training_loop
from training import dataset
from metrics import metric_defaults
#------------------------------------------------------------
class UserError(Exception):
pass
#-----------------------------------------------------------
def setup_training_options():
#EDIT THESE!#
outdir = '/content/drive/My Drive/Colab Notebooks/styleganada-results/'
gpus = None # Number of GPUs: <int>, default = 1 gpu
snap = 1 # Snapshot interval: <int>, default = 50 ticks
seed = 1000
data = '/content/datasets/custom/'# Training dataset (required): <path>
res = None# Override dataset resolution: <int>, default = highest available
mirror =True# Augment dataset with x-flips: <bool>, default = False
metrics = []# List of metric names: [], ['fid50k_full'] (default), ...
metricdata = None# Metric dataset (optional): <path>
cfg = 'stylegan2'# Base config: 'auto' (default), 'stylegan2', 'paper256', 'paper512', 'paper1024', 'cifar', 'cifarbaseline'
gamma = None# Override R1 gamma: <float>, default = depends on cfg
kimg = 10000# Override training duration: <int>, default = depends on cfg
aug = 'ada' # Augmentation mode: 'ada' (default), 'noaug', 'fixed', 'adarv'
p = None# Specify p for 'fixed' (required): <float>
target = None # Override ADA target for 'ada' and 'adarv': <float>, default = depends on aug
augpipe = 'bgc'# Augmentation pipeline: 'blit', 'geom', 'color', 'filter', 'noise', 'cutout', 'bg', 'bgc' (default), ..., 'bgcfnc'
cmethod = None # Comparison method: 'nocmethod' (default), 'bcr', 'zcr', 'pagan', 'wgangp', 'auxrot', 'spectralnorm', 'shallowmap', 'adropout'
dcap = None # Multiplier for discriminator capacity: <float>, default = 1
augpipe = 'bgc'
resume = '/content/stylegan2-ffhq-config-f.pkl'# Load previous network: 'noresume' (default), 'ffhq256', 'ffhq512', 'ffhq1024', 'celebahq256', 'lsundog256', <file>, <url>
freezed = None # Freeze-D: <int>, default = 0 discriminator layers
#End of Edit Section #
#---------------------------------------------------
tflib.init_tf({'rnd.np_random_seed': seed})
# Initialize dicts.
args = dnnlib.EasyDict()
args.G_args = dnnlib.EasyDict(func_name='training.networks.G_main')
args.D_args = dnnlib.EasyDict(func_name='training.networks.D_main')
args.G_opt_args = dnnlib.EasyDict(beta1=0.0, beta2=0.99)
args.D_opt_args = dnnlib.EasyDict(beta1=0.0, beta2=0.99)
args.loss_args = dnnlib.EasyDict(func_name='training.loss.stylegan2')
args.augment_args = dnnlib.EasyDict(class_name='training.augment.AdaptiveAugment')
# ---------------------------
# General options: gpus, snap
# ---------------------------
if gpus is None:
gpus = 1
assert isinstance(gpus, int)
if not (gpus >= 1 and gpus & (gpus - 1) == 0):
raise UserError('--gpus must be a power of two')
args.num_gpus = gpus
if snap is None:
snap = 50
assert isinstance(snap, int)
if snap < 1:
raise UserError('--snap must be at least 1')
args.image_snapshot_ticks = snap
args.network_snapshot_ticks = snap
# -----------------------------------
# Training dataset: data, res, mirror
# -----------------------------------
assert data is not None
assert isinstance(data, str)
data_name = os.path.basename(os.path.abspath(data))
if not os.path.isdir(data) or len(data_name) == 0:
raise UserError('--data must point to a directory containing *.tfrecords')
desc = data_name
with tf.Graph().as_default(), tflib.create_session().as_default(): # pylint: disable=not-context-manager
args.train_dataset_args = dnnlib.EasyDict(path=data, max_label_size='full')
dataset_obj = dataset.load_dataset(**args.train_dataset_args) # try to load the data and see what comes out
args.train_dataset_args.resolution = dataset_obj.shape[-1] # be explicit about resolution
args.train_dataset_args.max_label_size = dataset_obj.label_size # be explicit about label size
validation_set_available = dataset_obj.has_validation_set
dataset_obj.close()
dataset_obj = None
if res is None:
res = args.train_dataset_args.resolution
else:
assert isinstance(res, int)
if not (res >= 4 and res & (res - 1) == 0):
raise UserError('--res must be a power of two and at least 4')
if res > args.train_dataset_args.resolution:
raise UserError(f'--res cannot exceed maximum available resolution in the dataset ({args.train_dataset_args.resolution})')
desc += f'-res{res:d}'
args.train_dataset_args.resolution = res
if mirror is None:
mirror = False
else:
assert isinstance(mirror, bool)
if mirror:
desc += '-mirror'
args.train_dataset_args.mirror_augment = mirror
# ----------------------------
# Metrics: metrics, metricdata
# ----------------------------
if metrics is None:
metrics = ['fid50k_full']
assert isinstance(metrics, list)
assert all(isinstance(metric, str) for metric in metrics)
args.metric_arg_list = []
for metric in metrics:
if metric not in metric_defaults.metric_defaults:
raise UserError('\n'.join(['--metrics can only contain the following values:', 'none'] + list(metric_defaults.metric_defaults.keys())))
args.metric_arg_list.append(metric_defaults.metric_defaults[metric])
args.metric_dataset_args = dnnlib.EasyDict(args.train_dataset_args)
if metricdata is not None:
assert isinstance(metricdata, str)
if not os.path.isdir(metricdata):
raise UserError('--metricdata must point to a directory containing *.tfrecords')
args.metric_dataset_args.path = metricdata
# -----------------------------
# Base config: cfg, gamma, kimg
# -----------------------------
if cfg is None:
cfg = 'auto'
assert isinstance(cfg, str)
desc += f'-{cfg}'
cfg_specs = {
'auto': dict(ref_gpus=-1, kimg=25000, mb=-1, mbstd=-1, fmaps=-1, lrate=-1, gamma=-1, ema=-1, ramp=0.05, map=2), # populated dynamically based on 'gpus' and 'res'
'stylegan2': dict(ref_gpus=8, kimg=25000, mb=32, mbstd=4, fmaps=1, lrate=0.002, gamma=10, ema=10, ramp=None, map=8), # uses mixed-precision, unlike original StyleGAN2
'paper256': dict(ref_gpus=8, kimg=25000, mb=64, mbstd=8, fmaps=0.5, lrate=0.0025, gamma=1, ema=20, ramp=None, map=8),
'paper512': dict(ref_gpus=8, kimg=25000, mb=64, mbstd=8, fmaps=1, lrate=0.0025, gamma=0.5, ema=20, ramp=None, map=8),
'paper1024': dict(ref_gpus=8, kimg=25000, mb=32, mbstd=4, fmaps=1, lrate=0.002, gamma=2, ema=10, ramp=None, map=8),
'cifar': dict(ref_gpus=2, kimg=100000, mb=64, mbstd=32, fmaps=0.5, lrate=0.0025, gamma=0.01, ema=500, ramp=0.05, map=2),
'cifarbaseline': dict(ref_gpus=2, kimg=100000, mb=64, mbstd=32, fmaps=0.5, lrate=0.0025, gamma=0.01, ema=500, ramp=0.05, map=8),
}
assert cfg in cfg_specs
spec = dnnlib.EasyDict(cfg_specs[cfg])
if cfg == 'auto':
desc += f'{gpus:d}'
spec.ref_gpus = gpus
spec.mb = max(min(gpus * min(4096 // res, 32), 64), gpus) # keep gpu memory consumption at bay
spec.mbstd = min(spec.mb // gpus, 4) # other hyperparams behave more predictably if mbstd group size remains fixed
spec.fmaps = 1 if res >= 512 else 0.5
spec.lrate = 0.002 if res >= 1024 else 0.0025
spec.gamma = 0.0002 * (res ** 2) / spec.mb # heuristic formula
spec.ema = spec.mb * 10 / 32
args.total_kimg = spec.kimg
args.minibatch_size = spec.mb
args.minibatch_gpu = spec.mb // spec.ref_gpus
args.D_args.mbstd_group_size = spec.mbstd
args.G_args.fmap_base = args.D_args.fmap_base = int(spec.fmaps * 16384)
args.G_args.fmap_max = args.D_args.fmap_max = 512
args.G_opt_args.learning_rate = args.D_opt_args.learning_rate = spec.lrate
args.loss_args.r1_gamma = spec.gamma
args.G_smoothing_kimg = spec.ema
args.G_smoothing_rampup = spec.ramp
args.G_args.mapping_layers = spec.map
args.G_args.num_fp16_res = args.D_args.num_fp16_res = 4 # enable mixed-precision training
args.G_args.conv_clamp = args.D_args.conv_clamp = 256 # clamp activations to avoid float16 overflow
if cfg == 'cifar':
args.loss_args.pl_weight = 0 # disable path length regularization
args.G_args.style_mixing_prob = None # disable style mixing
args.D_args.architecture = 'orig' # disable residual skip connections
if gamma is not None:
assert isinstance(gamma, float)
if not gamma >= 0:
raise UserError('--gamma must be non-negative')
desc += f'-gamma{gamma:g}'
args.loss_args.r1_gamma = gamma
if kimg is not None:
assert isinstance(kimg, int)
if not kimg >= 1:
raise UserError('--kimg must be at least 1')
desc += f'-kimg{kimg:d}'
args.total_kimg = kimg
# ---------------------------------------------------
# Discriminator augmentation: aug, p, target, augpipe
# ---------------------------------------------------
if aug is None:
aug = 'ada'
else:
assert isinstance(aug, str)
desc += f'-{aug}'
if aug == 'ada':
args.augment_args.tune_heuristic = 'rt'
args.augment_args.tune_target = 0.6
elif aug == 'noaug':
pass
elif aug == 'fixed':
if p is None:
raise UserError(f'--aug={aug} requires specifying --p')
elif aug == 'adarv':
if not validation_set_available:
raise UserError(f'--aug={aug} requires separate validation set; please see "python dataset_tool.py pack -h"')
args.augment_args.tune_heuristic = 'rv'
args.augment_args.tune_target = 0.5
else:
raise UserError(f'--aug={aug} not supported')
if p is not None:
assert isinstance(p, float)
if aug != 'fixed':
raise UserError('--p can only be specified with --aug=fixed')
if not 0 <= p <= 1:
raise UserError('--p must be between 0 and 1')
desc += f'-p{p:g}'
args.augment_args.initial_strength = p
if target is not None:
assert isinstance(target, float)
if aug not in ['ada', 'adarv']:
raise UserError('--target can only be specified with --aug=ada or --aug=adarv')
if not 0 <= target <= 1:
raise UserError('--target must be between 0 and 1')
desc += f'-target{target:g}'
args.augment_args.tune_target = target
assert augpipe is None or isinstance(augpipe, str)
if augpipe is None:
augpipe = 'bgc'
else:
if aug == 'noaug':
raise UserError('--augpipe cannot be specified with --aug=noaug')
desc += f'-{augpipe}'
augpipe_specs = {
'blit': dict(xflip=1, rotate90=1, xint=1),
'geom': dict(scale=1, rotate=1, aniso=1, xfrac=1),
'color': dict(brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1),
'filter': dict(imgfilter=1),
'noise': dict(noise=1),
'cutout': dict(cutout=1),
'bg': dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1),
'bgc': dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1),
'bgcf': dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1, imgfilter=1),
'bgcfn': dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1, imgfilter=1, noise=1),
'bgcfnc': dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1, imgfilter=1, noise=1, cutout=1),
}
assert augpipe in augpipe_specs
if aug != 'noaug':
args.augment_args.apply_func = 'training.augment.augment_pipeline'
args.augment_args.apply_args = augpipe_specs[augpipe]
# ---------------------------------
# Comparison methods: cmethod, dcap
# ---------------------------------
assert cmethod is None or isinstance(cmethod, str)
if cmethod is None:
cmethod = 'nocmethod'
else:
desc += f'-{cmethod}'
if cmethod == 'nocmethod':
pass
elif cmethod == 'bcr':
args.loss_args.func_name = 'training.loss.cmethods'
args.loss_args.bcr_real_weight = 10
args.loss_args.bcr_fake_weight = 10
args.loss_args.bcr_augment = dnnlib.EasyDict(func_name='training.augment.augment_pipeline', xint=1, xint_max=1/32)
elif cmethod == 'zcr':
args.loss_args.func_name = 'training.loss.cmethods'
args.loss_args.zcr_gen_weight = 0.02
args.loss_args.zcr_dis_weight = 0.2
args.G_args.num_fp16_res = args.D_args.num_fp16_res = 0 # disable mixed-precision training
args.G_args.conv_clamp = args.D_args.conv_clamp = None
elif cmethod == 'pagan':
if aug != 'noaug':
raise UserError(f'--cmethod={cmethod} is not compatible with discriminator augmentation; please specify --aug=noaug')
args.D_args.use_pagan = True
args.augment_args.tune_heuristic = 'rt' # enable ada heuristic
args.augment_args.pop('apply_func', None) # disable discriminator augmentation
args.augment_args.pop('apply_args', None)
args.augment_args.tune_target = 0.95
elif cmethod == 'wgangp':
if aug != 'noaug':
raise UserError(f'--cmethod={cmethod} is not compatible with discriminator augmentation; please specify --aug=noaug')
if gamma is not None:
raise UserError(f'--cmethod={cmethod} is not compatible with --gamma')
args.loss_args = dnnlib.EasyDict(func_name='training.loss.wgangp')
args.G_opt_args.learning_rate = args.D_opt_args.learning_rate = 0.001
args.G_args.num_fp16_res = args.D_args.num_fp16_res = 0 # disable mixed-precision training
args.G_args.conv_clamp = args.D_args.conv_clamp = None
args.lazy_regularization = False
elif cmethod == 'auxrot':
if args.train_dataset_args.max_label_size > 0:
raise UserError(f'--cmethod={cmethod} is not compatible with label conditioning; please specify a dataset without labels')
args.loss_args.func_name = 'training.loss.cmethods'
args.loss_args.auxrot_alpha = 10
args.loss_args.auxrot_beta = 5
args.D_args.score_max = 5 # prepare D to output 5 scalars per image instead of just 1
elif cmethod == 'spectralnorm':
args.D_args.use_spectral_norm = True
elif cmethod == 'shallowmap':
if args.G_args.mapping_layers == 2:
raise UserError(f'--cmethod={cmethod} is a no-op for --cfg={cfg}')
args.G_args.mapping_layers = 2
elif cmethod == 'adropout':
if aug != 'noaug':
raise UserError(f'--cmethod={cmethod} is not compatible with discriminator augmentation; please specify --aug=noaug')
args.D_args.adaptive_dropout = 1
args.augment_args.tune_heuristic = 'rt' # enable ada heuristic
args.augment_args.pop('apply_func', None) # disable discriminator augmentation
args.augment_args.pop('apply_args', None)
args.augment_args.tune_target = 0.6
else:
raise UserError(f'--cmethod={cmethod} not supported')
if dcap is not None:
assert isinstance(dcap, float)
if not dcap > 0:
raise UserError('--dcap must be positive')
desc += f'-dcap{dcap:g}'
args.D_args.fmap_base = max(int(args.D_args.fmap_base * dcap), 1)
args.D_args.fmap_max = max(int(args.D_args.fmap_max * dcap), 1)
# ----------------------------------
# Transfer learning: resume, freezed
# ----------------------------------
resume_specs = {
'ffhq256': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/transfer-learning-source-nets/ffhq-res256-mirror-paper256-noaug.pkl',
'ffhq512': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/transfer-learning-source-nets/ffhq-res512-mirror-stylegan2-noaug.pkl',
'ffhq1024': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/transfer-learning-source-nets/ffhq-res1024-mirror-stylegan2-noaug.pkl',
'celebahq256': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/transfer-learning-source-nets/celebahq-res256-mirror-paper256-kimg100000-ada-target0.5.pkl',
'lsundog256': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/transfer-learning-source-nets/lsundog-res256-paper256-kimg100000-noaug.pkl',
}
assert resume is None or isinstance(resume, str)
if resume is None:
resume = 'noresume'
elif resume == 'noresume':
desc += '-noresume'
elif resume in resume_specs:
desc += f'-resume{resume}'
args.resume_pkl = resume_specs[resume] # predefined url
else:
desc += '-resumecustom'
args.resume_pkl = resume # custom path or url
if resume != 'noresume':
args.augment_args.tune_kimg = 100 # make ADA react faster at the beginning
args.G_smoothing_rampup = None # disable EMA rampup
if freezed is not None:
assert isinstance(freezed, int)
if not freezed >= 0:
raise UserError('--freezed must be non-negative')
desc += f'-freezed{freezed:d}'
args.D_args.freeze_layers = freezed
return desc, args, outdir
#------------------------------------------------------
def run_training():
run_desc, training_options, outdir = setup_training_options()
# Pick output directory.
prev_run_dirs = []
if os.path.isdir(outdir):
prev_run_dirs = [x for x in os.listdir(outdir) if os.path.isdir(os.path.join(outdir, x))]
prev_run_ids = [re.match(r'^\d+', x) for x in prev_run_dirs]
prev_run_ids = [int(x.group()) for x in prev_run_ids if x is not None]
cur_run_id = max(prev_run_ids, default=-1) + 1
training_options.run_dir = os.path.join(outdir, f'{cur_run_id:05d}-{run_desc}')
assert not os.path.exists(training_options.run_dir)
# Print options.
print()
print('Training options:')
print(json.dumps(training_options, indent=2))
print()
print(f'Output directory: {training_options.run_dir}')
print(f'Training data: {training_options.train_dataset_args.path}')
print(f'Training length: {training_options.total_kimg} kimg')
print(f'Resolution: {training_options.train_dataset_args.resolution}')
print(f'Number of GPUs: {training_options.num_gpus}')
print()
# Kick off training.
print('Creating output directory...')
os.makedirs(training_options.run_dir)
with open(os.path.join(training_options.run_dir, 'training_options.json'), 'wt') as f:
json.dump(training_options, f, indent=2)
with dnnlib.util.Logger(os.path.join(training_options.run_dir, 'log.txt')):
training_loop.training_loop(**training_options)
#--------------------------------------------------
def _str_to_bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
if v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
raise argparse.ArgumentTypeError('Boolean value expected.')
def _parse_comma_sep(s):
if s is None or s.lower() == 'none' or s == '':
return []
return s.split(',')
def main():
run_training()
#--------------------------------------------------
if __name__ == "__main__":
main()
print('Done.')3.3. 可视化训练过程
可以比较简单的采用创建 timelapse 来可视化训练过程.
import cv2
from os import listdir
from os.path import isfile, join
from google.colab import files
from PIL import Image, ImageDraw
import numpy as np
from tqdm import tqdm
import imageio
import os
#结果保存路径
result_path = '/content/drive/My Drive/Colab Notebooks/styleganada-results/'
image_files = [f for f in listdir(result_path)]
image_files = [f for f in image_files if '.png' in f]
#[0,0] -> [8, 4]
images = []
width = 256
width_images = 8
width_offset = 0 * width
end_width = np.max(2048, width_images * width)
height = 256
height_images = 4
height_offset = 0 * height
end_height = np.max(1280, height_images * height)
for f in tqdm(image_files[:4]):
name = "{}{}".format(result_path, f)
print(name)
img = Image.open(name)
img = img.crop((0, 0, end_width+ width_offset, end_height + height_offset))
img = img.resize((1920,1080))
images.append(img)
#
video_output_path = '/content/out/'
video_name = 'timelapse_movie.mp4'
movie_name = video_output_path + video_name
with imageio.get_writer(movie_name, mode='I') as writer:
for image in tqdm(list(images)):
writer.append_data(np.array(image))4. 采用 Stylegan2-ada 生成图像
import argparse
import numpy as np
import PIL.Image
import dnnlib
import dnnlib.tflib as tflib
import re
import sys
from io import BytesIO
import IPython.display
import numpy as np
from math import ceil
from PIL import Image, ImageDraw
import imageio
import os
import pickle
from google.colab import files
#
dnnlib.tflib.init_tf()
network_pkl = '/content/stylegan2-ffhq-config-f.pkl'
print('Loading networks from "%s"...' % network_pkl)
with dnnlib.util.open_url(network_pkl) as fp:
_G, _D, Gs = pickle.load(fp)
noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]
# Generates a list of images, based on a list of latent vectors (Z), and a list (or a single constant) of truncation_psi's.
def generate_images_in_w_space(dlatents, truncation_psi):
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs.randomize_noise = False
Gs_kwargs.truncation_psi = truncation_psi
dlatent_avg = Gs.get_var('dlatent_avg') # [component]
imgs = []
for row, dlatent in log_progress(enumerate(dlatents), name = "Generating images"):
#row_dlatents = (dlatent[np.newaxis] - dlatent_avg) * np.reshape(truncation_psi, [-1, 1, 1]) + dlatent_avg
dl = (dlatent-dlatent_avg)*truncation_psi + dlatent_avg
row_images = Gs.components.synthesis.run(dlatent, **Gs_kwargs)
imgs.append(PIL.Image.fromarray(row_images[0], 'RGB'))
return imgs
def generate_images(zs, truncation_psi):
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs.randomize_noise = False
if not isinstance(truncation_psi, list):
truncation_psi = [truncation_psi] * len(zs)
imgs = []
for z_idx, z in log_progress(enumerate(zs), size = len(zs), name = "Generating images"):
Gs_kwargs.truncation_psi = truncation_psi[z_idx]
noise_rnd = np.random.RandomState(1) # fix noise
tflib.set_vars({var: noise_rnd.randn(*var.shape.as_list()) for var in noise_vars}) # [height, width]
images = Gs.run(z, None, **Gs_kwargs) # [minibatch, height, width, channel]
imgs.append(PIL.Image.fromarray(images[0], 'RGB'))
return imgs
def generate_zs_from_seeds(seeds):
zs = []
for seed_idx, seed in enumerate(seeds):
rnd = np.random.RandomState(seed)
z = rnd.randn(1, *Gs.input_shape[1:]) # [minibatch, component]
zs.append(z)
return zs
# Generates a list of images, based on a list of seed for latent vectors (Z),
# and a list (or a single constant) of truncation_psi's.
def generate_images_from_seeds(seeds, truncation_psi):
return generate_images(generate_zs_from_seeds(seeds), truncation_psi)
def saveImgs(imgs, location):
for idx, img in log_progress(enumerate(imgs), size = len(imgs), name="Saving images"):
file = location+ str(idx) + ".png"
img.save(file)
def imshow(a, format='png', jpeg_fallback=True):
a = np.asarray(a, dtype=np.uint8)
str_file = BytesIO()
PIL.Image.fromarray(a).save(str_file, format)
im_data = str_file.getvalue()
plt.imshow(im_data)
plt.show()
def showarray(a, fmt='png'):
a = np.uint8(a)
f = StringIO()
PIL.Image.fromarray(a).save(f, fmt)
def clamp(x, minimum, maximum):
return max(minimum, min(x, maximum))
def drawLatent(image,latents,x,y,x2,y2, color=(255,0,0,100)):
buffer = PIL.Image.new('RGBA', image.size, (0,0,0,0))
draw = ImageDraw.Draw(buffer)
cy = (y+y2)/2
draw.rectangle([x,y,x2,y2],fill=(255,255,255,180), outline=(0,0,0,180))
for i in range(len(latents)):
mx = x + (x2-x)*(float(i)/len(latents))
h = (y2-y)*latents[i]*0.1
h = clamp(h,cy-y2,y2-cy)
draw.line((mx,cy,mx,cy+h),fill=color)
return PIL.Image.alpha_composite(image,buffer)
def createImageGrid(images, scale=0.25, rows=1):
w,h = images[0].size
w = int(w*scale)
h = int(h*scale)
height = rows*h
cols = ceil(len(images) / rows)
width = cols*w
canvas = PIL.Image.new('RGBA', (width,height), 'white')
for i,img in enumerate(images):
img = img.resize((w,h), PIL.Image.ANTIALIAS)
canvas.paste(img, (w*(i % cols), h*(i // cols)))
return canvas
def convertZtoW(latent, truncation_psi=0.7, truncation_cutoff=9):
dlatent = Gs.components.mapping.run(latent, None) # [seed, layer, component]
dlatent_avg = Gs.get_var('dlatent_avg') # [component]
for i in range(truncation_cutoff):
dlatent[0][i] = (dlatent[0][i]-dlatent_avg)*truncation_psi + dlatent_avg
return dlatent
def interpolate(zs, steps):
out = []
for i in range(len(zs)-1):
for index in range(steps):
fraction = index/float(steps)
out.append(zs[i+1]*fraction + zs[i]*(1-fraction))
return out
#
# Taken from https://github.com/alexanderkuk/log-progress
def log_progress(sequence, every=1, size=None, name='Items'):
from ipywidgets import IntProgress, HTML, VBox
from IPython.display import display
is_iterator = False
if size is None:
try:
size = len(sequence)
except TypeError:
is_iterator = True
if size is not None:
if every is None:
if size <= 200:
every = 1
else:
every = int(size / 200) # every 0.5%
else:
assert every is not None, 'sequence is iterator, set every'
if is_iterator:
progress = IntProgress(min=0, max=1, value=1)
progress.bar_style = 'info'
else:
progress = IntProgress(min=0, max=size, value=0)
label = HTML()
box = VBox(children=[label, progress])
display(box)
index = 0
try:
for index, record in enumerate(sequence, 1):
if index == 1 or index % every == 0:
if is_iterator:
label.value = '{name}: {index} / ?'.format(
name=name,
index=index
)
else:
progress.value = index
label.value = u'{name}: {index} / {size}'.format(
name=name,
index=index,
size=size
)
yield record
except:
progress.bar_style = 'danger'
raise
else:
progress.bar_style = 'success'
progress.value = index
label.value = "{name}: {index}".format(
name=name,
index=str(index or '?')
)4.1. 生成单张图像
imshow(generate_images_from_seeds(np.random.randint(4294967295, size=1), truncation_psi=0.5)[0])如：
4.2. 生成随机 seeds
seeds = np.random.randint((2**32 - 1), size=9)
imshow(createImageGrid(generate_images_from_seeds(seeds, 0.7), 0.7 , 3))如：
在 10 个 seeds 中间插值，
video_output_path = '/content/out/'
video_name = 'interpolation_movie.mp4'
movie_name = video_output_path + video_name
seeds = list(np.random.randint((2**32) - 1, size=9))
seeds = seeds + [seeds[0]]
zs = generate_zs_from_seeds(seeds)
number_of_steps = 10
trunc_psi = 0.5
imgs = generate_images(interpolate(zs,number_of_steps), trunc_psi)
with imageio.get_writer(movie_name, mode='I') as writer:
for image in log_progress(list(imgs), name = "Creating animation"):
writer.append_data(np.array(image))投影到 w 空间，而不是采用插值方法，
video_name = 'interpolation_ws_movie.mp4'
movie_name = video_output_path + video_name
trunc_psi = 0.5
dls = []
for z in zs:
dls.append(convertZtoW(z ,truncation_psi=trunc_psi))
number_of_steps = 10
imgs = generate_images_in_w_space(interpolate(dls,number_of_steps), trunc_psi)
with imageio.get_writer(movie_name, mode='I') as writer:
for image in log_progress(list(imgs), name = "Creating animation"):
writer.append_data(np.array(image))5. 相关项目应用
GAN 生成马赛克图案.
3 comments
我想问一下，训练过程中输入图片a,b图片，生成x.pkl模型， 那么使用这个模型的时候就是生成阶段，生成阶段就成生成的a,b嘛，那么就是说输入和输出的图片都是一样的，唯一的区别是，模型生成出来的seed是在原图基础上可编辑的对象，然后通过修改用户表情，编辑之类的，就是说输出是输入的图片，这个图片可以进行编辑
你说的图片可编辑是指什么，输出的生成图片可以二次编辑？
你好问一下，输入的图片和输出的图片是一样的嘛，这个stylegan，目前我还没训练，我想知道，他能不能将原图变成可编辑的图片，基于stylegan来说的话