np.savez(
join(figdir, "spectra_col_%s.npz" % modelnm),
eigval_col=eva_ctrl,
)
# compute and plot the correlation between hessian at different points
"""Note the spectra of shuffled BigBiGAN doesn't make sense we use randomly initialized one instead"""
#%%
corr_mat_log_ctrl, corr_mat_lin_ctrl = compute_hess_corr(eva_ctrl + 1E-7,
evc_ctrl,
figdir=figdir,
use_cuda=True,
savelabel=modelnm +
"shift1E-7")
corr_mat_vec_ctrl = compute_vector_hess_corr(eva_ctrl + 1E-7,
evc_ctrl,
figdir=figdir,
use_cuda=True,
savelabel=modelnm + "shift1E-7")
fig1, fig2 = plot_consistentcy_mat(corr_mat_log_ctrl,
corr_mat_lin_ctrl,
figdir=figdir,
titstr="%s" % modelnm + "shift1E-7",
savelabel=modelnm)
fig11, fig22 = plot_consistency_hist(corr_mat_log_ctrl,
corr_mat_lin_ctrl,
figdir=figdir,
titstr="%s" % modelnm + "shift1E-7",
savelabel=modelnm)
fig3 = plot_consistency_example(eva_ctrl + 1E-7,
evc_ctrl,
figdir=figdir,
feat=feat.detach().cpu().numpy(), feat_z=feat_z.detach().cpu().numpy())
#%%
#%
figdir = r"E:\OneDrive - Washington University in St. Louis\Hessian_summary\StyleGAN2_wspace"
os.makedirs(figdir, exist_ok=True)
# Load the Hessian NPZ
eva_col, evc_col, feat_col, meta = scan_hess_npz(savedir, "Hess_BP_(\d*).npz", featkey="feat")
# compute the Mean Hessian and save
H_avg, eva_avg, evc_avg = average_H(eva_col, evc_col, )
np.savez(join(figdir, "H_avg_%s.npz"%modelnm), H_avg=H_avg, eva_avg=eva_avg, evc_avg=evc_avg, feats=feat_col)
# compute and plot spectra
fig0 = plot_spectra(eigval_col=eva_col, savename="%s_spectrum"%modelnm, figdir=figdir)
np.savez(join(figdir, "spectra_col_%s.npz"%modelnm), eigval_col=eva_col, )
# compute and plot the correlation between hessian at different points
corr_mat_log, corr_mat_lin = compute_hess_corr(eva_col, evc_col, figdir=figdir, use_cuda=False, savelabel=modelnm)
corr_mat_vec = compute_vector_hess_corr(eva_col, evc_col, figdir=figdir, use_cuda=False, savelabel=modelnm)
fig1, fig2 = plot_consistentcy_mat(corr_mat_log, corr_mat_lin, figdir=figdir, titstr="%s"%modelnm, savelabel=modelnm)
fig11, fig22 = plot_consistency_hist(corr_mat_log, corr_mat_lin, figdir=figdir, titstr="%s"%modelnm,
savelabel=modelnm)
fig3 = plot_consistency_example(eva_col, evc_col, figdir=figdir, nsamp=5, titstr="%s"%modelnm, savelabel=modelnm)
fig3 = plot_consistency_example(eva_col, evc_col, figdir=figdir, nsamp=3, titstr="%s"%modelnm, savelabel=modelnm)
#%% "stylegan2-cat-config-f"
modelname = "stylegan2-cat-config-f"
modelnm = "stylegan2-cat-config-f"+"_wspace"
modelsnm = "Cat256"
SGAN = loadStyleGAN2(modelname+".pt", size=256, channel_multiplier=2)
G = StyleGAN2_wrapper(SGAN)
G.use_wspace(True)
savedir = join(rootdir, modelname+"_wspace")
os.makedirs(savedir, exist_ok=True)
from hessian_analysis_tools import plot_consistentcy_mat, compute_hess_corr, compute_vector_hess_corr, plot_layer_consistency_mat
datadir = r"E:\OneDrive - Washington University in St. Louis\HessNetArchit\StyleGAN2"
layernames = [("StyleBlock%02d" % blocki) for blocki in range(12)] # if blocki!=8 else "SelfAttention"
eva_col, evc_col = [], []
for blocki in range(12):
data = np.load(join(datadir, "eig_genBlock%02d_latent.npz"%blocki))
# data = np.load(join(datadir, "eig_ConvBlock%02d.npz"%blocki))
eva_col.append(data["eva"])
evc_col.append(data["evc"])
fig0 = plot_layer_spectra(eva_col, layernames=layernames, figdir=datadir, titstr="StyleGAN2",
namestr="wspace_all_block")
fig0 = plot_layer_spectra(eva_col, layernames=layernames, figdir=datadir, titstr="StyleGAN2", normalize=True,
namestr="wspace_all_block_norm")
corr_mat_log, corr_mat_lin = compute_hess_corr(eva_col, evc_col, savelabel="StyleGAN2_wspace", figdir=datadir)
corr_mat_vec = compute_vector_hess_corr(eva_col, evc_col, savelabel="StyleGAN2_wspace", figdir=datadir)
fig1, fig2, fig3 = plot_layer_consistency_mat(corr_mat_log, corr_mat_lin, corr_mat_vec, savelabel="StyleGAN2_wspace",
figdir=datadir, titstr="StyleGAN2", layernames=layernames)
#%%
datadir = r"E:\OneDrive - Washington University in St. Louis\HessNetArchit\StyleGAN2"
layernames = [("StyleBlock%02d" % blocki) for blocki in range(12)] # if blocki!=8 else "SelfAttention"
eva_col, evc_col = [], []
for blocki in range(12):
# data = np.load(join(datadir, "eig_genBlock%02d_latent.npz"%blocki))
data = np.load(join(datadir, "eig_genBlock%02d.npz"%blocki))
eva_col.append(data["eva"])
evc_col.append(data["evc"])
fig0 = plot_layer_spectra(eva_col, layernames=layernames, figdir=datadir, titstr="StyleGAN2",
namestr="zspace_all_block")
fig0 = plot_layer_spectra(eva_col, layernames=layernames, figdir=datadir, titstr="StyleGAN2", normalize=True,
namestr="zspace_all_block_norm")
# compute and plot spectra
fig0 = plot_spectra(eigval_col=eva_ctrl,
savename="%s_spectrum" % modelnm,
figdir=figdir)
np.savez(
join(figdir, "spectra_col_%s.npz" % modelnm),
eigval_col=eva_ctrl,
)
# compute and plot the correlation between hessian at different points
corr_mat_log_ctrl, corr_mat_lin_ctrl = compute_hess_corr(eva_ctrl,
evc_ctrl,
figdir=figdir,
use_cuda=True,
savelabel=modelnm)
corr_mat_vec_ctrl = compute_vector_hess_corr(eva_ctrl,
evc_ctrl,
figdir=figdir,
savelabel=modelnm)
fig1, fig2 = plot_consistentcy_mat(corr_mat_log_ctrl,
corr_mat_lin_ctrl,
figdir=figdir,
titstr="%s" % modelnm,
savelabel=modelnm)
fig11, fig22 = plot_consistency_hist(corr_mat_log_ctrl,
corr_mat_lin_ctrl,
figdir=figdir,
titstr="%s" % modelnm,
savelabel=modelnm)
fig3 = plot_consistency_example(eva_ctrl,
evc_ctrl,
figdir=figdir,
nsamp=5,