baddot_imgs_neg,
nrow=5,
padding=2,
)
save_imgrid(
baddot_imgs_neg,
join(savedir, "GAN_baddots_neg.png"),
nrow=5,
padding=2,
)
#%%
modelsnm = "StyleGAN2-Face512_Z"
savedir = join(saveroot, modelsnm)
os.makedirs(savedir, exist_ok=True)
SGAN = loadStyleGAN2("ffhq-512-avg-tpurun1.pt")
G = StyleGAN2_wrapper(SGAN)
H, eva, evc = load_Haverage("StyleGAN2-Face512_Z", descend=True)
randvec = G.sample_vector(10, 'cpu')
distnorm = randvec.norm(dim=1).mean().item()
# distnorm = np.sqrt(randvec.shape[1])
#%%
baddot_imgs_pos = G.visualize_batch_np(distnorm * evc[:, :20].T)
show_imgrid(
baddot_imgs_pos,
nrow=5,
padding=2,
)
save_imgrid(
baddot_imgs_pos,
join(savedir, "GAN_space_baddots_pos.png"),
nrow=5,
from numpy.linalg import norm
import matplotlib.pylab as plt
from time import time
from os.path import join
from imageio import imwrite, imsave
from torchvision.transforms import ToPILImage
from torchvision.utils import make_grid
rootdir = r"E:\OneDrive - Washington University in St. Louis\Hessian_summary"
figsumdir = r"E:\OneDrive - Washington University in St. Louis\Hessian_summary\Figure7"
"""Note the loading and visualization is mostly deterministic, reproducible."""
#%% StyleGAN2 Face 1024
Hessdir = join(rootdir, 'StyleGAN2')
modelnm = "stylegan2-ffhq-config-f"
modelsnm = "Face1024"
SGAN2 = loadStyleGAN2(modelnm+".pt")
SG2 = StyleGAN2_wrapper(SGAN2)
with np.load(join(Hessdir, "H_avg_%s.npz"%modelnm)) as data:
eva_avg, evc_avg = data["eva_avg"], data["evc_avg"]#, data["feat_col"]
#%%
refvec = 0 * torch.randn(1, 512).cuda()
img = SG2.visualize(refvec)
ToPILImage()(make_grid(img).cpu()).show()
#%%
vis_eigen_frame(evc_avg, eva_avg, SG2, ref_code=refvec.cpu(), figdir=figsumdir, namestr="StyleGAN2_%s"%modelsnm, transpose=False,
eiglist=None, eig_rng=(0, 20), maxdist=0.0005, rown=5, sphere=False, )
#%%
from geometry_utils import LExpMap, SExpMap
from build_montages import build_montages
import torch
import torch.optim as optim
import torch.nn.functional as F
from torchvision.transforms import ToPILImage, ToTensor
from hessian_eigenthings.power_iter import Operator, deflated_power_iteration
from hessian_eigenthings.lanczos import lanczos
from lanczos_generalized import lanczos_generalized
from GAN_hvp_operator import GANHVPOperator, GANForwardHVPOperator, compute_hessian_eigenthings, get_full_hessian
import sys
import numpy as np
import matplotlib.pylab as plt
from time import time
from os.path import join
from imageio import imwrite
from build_montages import build_montages, color_framed_montages
import torchvision.models as tv
from GAN_utils import loadStyleGAN2, StyleGAN2_wrapper, ckpt_root
g_ema = loadStyleGAN2("stylegan2-cat-config-f.pt")
G = StyleGAN2_wrapper(g_ema)
#%%
paramnum = 0
for name, param in g_ema.state_dict().items():
paramnum += np.prod(param.shape)
for name, param in g_ema.state_dict().items():
print(name, list(param.shape), "%.1f%%"%(100*np.prod(param.shape) / paramnum))
#%%
#%%
SGAN = loadStyleGAN2('ffhq-512-avg-tpurun1.pt')
#%%
shuffled_SD = shuffle_state_dict(SGAN.state_dict())
#%%
torch.save(shuffled_SD,
join(datadir, "StyleGAN2_ffhq-512-avg-tpurun1_shuffle.pt"))
# print(name, Weight.shape, Weight.mean().item(), Weight.std().item())
#%%
SGAN_sf = loadStyleGAN2('ffhq-512-avg-tpurun1.pt')
SGAN_sf.load_state_dict(
torch.load(join(datadir, "StyleGAN2_ffhq-512-avg-tpurun1_shuffle.pt")))
G_sf = StyleGAN2_wrapper(SGAN_sf)
#%%
img = G_sf.visualize(torch.randn(1, 512).cuda()).cpu()
ToPILImage()(img[0, :].cpu()).show()
#%%
def Hess_hook(module, fea_in, fea_out):
print("hooker on %s" % module.__class__)
ref_feat = fea_out.detach().clone()
ref_feat.requires_grad_(False)
L2dist = torch.pow(fea_out - ref_feat, 2).sum()
L2dist_col.append(L2dist)
return None
maxdist=0.3,
rown=5,
transpose=True)
#%% StyleGAN2
"""StyleGAN2 model"""
from hessian_analysis_tools import scan_hess_npz, average_H, compute_hess_corr, plot_consistentcy_mat, plot_consistency_example
figdir = join(rootdir, 'StyleGAN2')
Hessdir = join(rootdir, 'StyleGAN2')
dataroot = r"E:\Cluster_Backup\StyleGAN2"
#%% Face 512 model
modelnm = "ffhq-512-avg-tpurun1"
modelsnm = "Face512"
SGAN = loadStyleGAN2(modelnm + ".pt")
SG = StyleGAN2_wrapper(SGAN)
eigval_col, eigvec_col, feat_col, meta = scan_hess_npz(join(dataroot, modelnm),
"Hess_BP_(\d*).npz",
featkey="feat")
feat_col = np.array(feat_col).squeeze()
H_avg, eva_avg, evc_avg = average_H(eigval_col, eigvec_col)
np.savez(join(Hessdir, "H_avg_%s.npz" % modelnm),
H_avg=H_avg,
eva_avg=eva_avg,
evc_avg=evc_avg,
feats=feat_col)
#%%
mtg, codes_all = vis_eigen_frame(
evc_avg,
eva_avg,
SG,
