# feat=feat.cpu().detach().numpy())
#%%
from hessian_analysis_tools import scan_hess_npz, average_H, plot_consistentcy_mat, plot_consistency_hist, plot_consistency_example
savedir = r"E:\OneDrive - Washington University in St. Louis\HessNetArchit\StyleGAN\ctrl_Hessians"
figdir = r"E:\OneDrive - Washington University in St. Louis\HessNetArchit\StyleGAN"
realfigdir = r"E:\OneDrive - Washington University in St. Louis\Hessian_summary\StyleGAN"
modelnm = "StyleGAN_shuffle"
# Load the Hessian NPZ
eva_ctrl, evc_ctrl, feat_ctrl, meta = scan_hess_npz(savedir,
"eig_full_trial(\d*).npz",
evakey='eva',
evckey='evc',
featkey="feat")
# compute the Mean Hessian and save
H_avg, eva_avg, evc_avg = average_H(eva_ctrl, evc_ctrl)
np.savez(join(figdir, "H_avg_%s.npz" % modelnm),
H_avg=H_avg,
eva_avg=eva_avg,
evc_avg=evc_avg,
feats=feat_ctrl)
# 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,
eva_BP = data["eva_BP"]
evc_BP = data["evc_BP"]
eva_col.append(eva_BP)
evc_col.append(evc_BP)
eva_col = np.array(eva_col)
#%%
figdir = r"E:\OneDrive - Washington University in St. Louis\Hessian_summary\DCGAN"
savedir = r"E:\Cluster_Backup\DCGAN"
from hessian_analysis_tools import plot_spectra, compute_hess_corr, plot_consistency_example, plot_consistentcy_mat, \
plot_consistency_hist, average_H, scan_hess_npz
eva_col, evc_col, feat_col, meta = scan_hess_npz(savedir,
"Hessian_cmp_(\d*).npz",
featkey="feat")
feat_col = np.array(feat_col).squeeze()
H_avg, eva_avg, evc_avg = average_H(eva_col, evc_col)
np.savez(join(figdir, "H_avg_%s.npz" % "DCGAN"),
H_avg=H_avg,
eva_avg=eva_avg,
evc_avg=evc_avg,
feats=feat_col)
#%%
fig = plot_spectra(
eva_col,
figdir=figdir,
titstr="DCGAN",
)
#%%
corr_mat_log, corr_mat_lin = compute_hess_corr(eva_col,
evc_col,
figdir=figdir,
#%%
def average_H(eigval_col, eigvec_col):
"""Compute the average Hessian over a bunch of positions"""
nH = len(eigvec_col)
dimen = eigval_col.shape[1]
H_avg = np.zeros((dimen, dimen))
for iH in range(nH):
H = (eigvec_col[iH] *
eigval_col[iH][np.newaxis, :]) @ eigvec_col[iH].T
H_avg += H
H_avg /= nH
eva_avg, evc_avg = np.linalg.eigh(H_avg)
return H_avg, eva_avg, evc_avg
H_avg, eva_avg, evc_avg = average_H(eigval_col, eigvec_col)
#%%
maxang = 1.5
figdir = "E:\Cluster_Backup\StyleGAN2_axis\Face256"
for eigid in list(range(20)) + list(range(20, 60, 2)) + list(
range(60, 200, 4)):
mtg = vis_eigen_action(
eigvec=evc_avg[:, -eigid - 1],
ref_codes=feat_arr[[0, 2, 4, 6, 8, 10, 12, 14, 16, 20, 40, 60], :],
G=G,
sphere=True,
maxdist=1.5,
rown=5,
transpose=False,
namestr="SG2_Face256_AVGeig%d_Sph%.1f" % (eigid, maxang),
figdir=figdir)