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import os
import pickle
import numpy as np
from dnnlib import tflib
import tensorflow as tf
import argparse
def LoadModel(dataset_name):
# Initialize TensorFlow.
tflib.init_tf()
model_path='./model/'
model_name=dataset_name+'.pkl'
tmp=os.path.join(model_path,model_name)
with open(tmp, 'rb') as f:
_, _, Gs = pickle.load(f)
return Gs
def lerp(a,b,t):
return a + (b - a) * t
#stylegan-ada
def SelectName(layer_name,suffix):
if suffix==None:
tmp1='add:0' in layer_name
tmp2='shape=(?,' in layer_name
tmp4='G_synthesis_1' in layer_name
tmp= tmp1 and tmp2 and tmp4
else:
tmp1=('/Conv0_up'+suffix) in layer_name
tmp2=('/Conv1'+suffix) in layer_name
tmp3=('4x4/Conv'+suffix) in layer_name
tmp4='G_synthesis_1' in layer_name
tmp5=('/ToRGB'+suffix) in layer_name
tmp= (tmp1 or tmp2 or tmp3 or tmp5) and tmp4
return tmp
def GetSNames(suffix):
#get style tensor name
with tf.Session() as sess:
op = sess.graph.get_operations()
layers=[m.values() for m in op]
select_layers=[]
for layer in layers:
layer_name=str(layer)
if SelectName(layer_name,suffix):
select_layers.append(layer[0])
return select_layers
def SelectName2(layer_name):
tmp1='mod_bias' in layer_name
tmp2='mod_weight' in layer_name
tmp3='ToRGB' in layer_name
tmp= (tmp1 or tmp2) and (not tmp3)
return tmp
def GetKName(Gs):
layers=[var for name, var in Gs.components.synthesis.vars.items()]
select_layers=[]
for layer in layers:
layer_name=str(layer)
if SelectName2(layer_name):
select_layers.append(layer)
return select_layers
def GetCode(Gs,random_state,num_img,num_once,dataset_name):
rnd = np.random.RandomState(random_state) #5
truncation_psi=0.7
truncation_cutoff=8
dlatent_avg=Gs.get_var('dlatent_avg')
dlatents=np.zeros((num_img,512),dtype='float32')
for i in range(int(num_img/num_once)):
src_latents = rnd.randn(num_once, Gs.input_shape[1])
src_dlatents = Gs.components.mapping.run(src_latents, None) # [seed, layer, component]
# Apply truncation trick.
if truncation_psi is not None and truncation_cutoff is not None:
layer_idx = np.arange(src_dlatents.shape[1])[np.newaxis, :, np.newaxis]
ones = np.ones(layer_idx.shape, dtype=np.float32)
coefs = np.where(layer_idx < truncation_cutoff, truncation_psi * ones, ones)
src_dlatents_np=lerp(dlatent_avg, src_dlatents, coefs)
src_dlatents=src_dlatents_np[:,0,:].astype('float32')
dlatents[(i*num_once):((i+1)*num_once),:]=src_dlatents
print('get all z and w')
tmp='./npy/'+dataset_name+'/W'
np.save(tmp,dlatents)
def GetImg(Gs,num_img,num_once,dataset_name,save_name='images'):
print('Generate Image')
tmp='./npy/'+dataset_name+'/W.npy'
dlatents=np.load(tmp)
fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
all_images=[]
for i in range(int(num_img/num_once)):
print(i)
images=[]
for k in range(num_once):
tmp=dlatents[i*num_once+k]
tmp=tmp[None,None,:]
tmp=np.tile(tmp,(1,Gs.components.synthesis.input_shape[1],1))
image2= Gs.components.synthesis.run(tmp, randomize_noise=False, output_transform=fmt)
images.append(image2)
images=np.concatenate(images)
all_images.append(images)
all_images=np.concatenate(all_images)
tmp='./npy/'+dataset_name+'/'+save_name
np.save(tmp,all_images)
def GetS(dataset_name,num_img):
print('Generate S')
tmp='./npy/'+dataset_name+'/W.npy'
dlatents=np.load(tmp)[:num_img]
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
Gs=LoadModel(dataset_name)
Gs.print_layers() #for ada
select_layers1=GetSNames(suffix=None) #None,'/mul_1:0','/mod_weight/read:0','/MatMul:0'
dlatents=dlatents[:,None,:]
dlatents=np.tile(dlatents,(1,Gs.components.synthesis.input_shape[1],1))
all_s = sess.run(
select_layers1,
feed_dict={'G_synthesis_1/dlatents_in:0': dlatents})
layer_names=[layer.name for layer in select_layers1]
save_tmp=[layer_names,all_s]
return save_tmp
def convert_images_to_uint8(images, drange=[-1,1], nchw_to_nhwc=False):
"""Convert a minibatch of images from float32 to uint8 with configurable dynamic range.
Can be used as an output transformation for Network.run().
"""
if nchw_to_nhwc:
images = np.transpose(images, [0, 2, 3, 1])
scale = 255 / (drange[1] - drange[0])
images = images * scale + (0.5 - drange[0] * scale)
np.clip(images, 0, 255, out=images)
images=images.astype('uint8')
return images
def GetCodeMS(dlatents):
m=[]
std=[]
for i in range(len(dlatents)):
tmp= dlatents[i]
tmp_mean=tmp.mean(axis=0)
tmp_std=tmp.std(axis=0)
m.append(tmp_mean)
std.append(tmp_std)
return m,std
#%%
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('--dataset_name',type=str,default='ffhq',
help='name of dataset, for example, ffhq')
parser.add_argument('--code_type',choices=['w','s','s_mean_std'],default='w')
args = parser.parse_args()
random_state=5
num_img=100_000
num_once=1_000
dataset_name=args.dataset_name
if not os.path.isfile('./model/'+dataset_name+'.pkl'):
url='https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/'
name='stylegan2-'+dataset_name+'-config-f.pkl'
os.system('wget ' +url+name + ' -P ./model/')
os.system('mv ./model/'+name+' ./model/'+dataset_name+'.pkl')
if not os.path.isdir('./npy/'+dataset_name):
os.system('mkdir ./npy/'+dataset_name)
if args.code_type=='w':
Gs=LoadModel(dataset_name=dataset_name)
GetCode(Gs,random_state,num_img,num_once,dataset_name)
# GetImg(Gs,num_img=num_img,num_once=num_once,dataset_name=dataset_name,save_name='images_100K') #no need
elif args.code_type=='s':
save_name='S'
save_tmp=GetS(dataset_name,num_img=2_000)
tmp='./npy/'+dataset_name+'/'+save_name
with open(tmp, "wb") as fp:
pickle.dump(save_tmp, fp)
elif args.code_type=='s_mean_std':
save_tmp=GetS(dataset_name,num_img=num_img)
dlatents=save_tmp[1]
m,std=GetCodeMS(dlatents)
save_tmp=[m,std]
save_name='S_mean_std'
tmp='./npy/'+dataset_name+'/'+save_name
with open(tmp, "wb") as fp:
pickle.dump(save_tmp, fp)
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