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,
use_cuda=False)
# without cuda 2:12 mins, with cuda 6:55
#%%
fig1, fig2 = plot_consistentcy_mat(corr_mat_log,
corr_mat_lin,
figdir=figdir,
titstr="DCGAN")
fig11, fig22 = plot_consistency_hist(corr_mat_log,
corr_mat_lin,
figdir=figdir,
titstr="DCGAN")
#%%
fig3 = plot_consistency_example(
eva_col,
evc_col,
figdir=figdir,
nsamp=5,
titstr="DCGAN",
)
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)
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,
titstr="%s" % modelnm,
savelabel=modelnm)
#%%
with np.load(join(figdir, "spectra_col_%s.npz" % modelnm)) as data:
fig0 = plot_spectra(eigval_col=eva_ctrl,
savename="%s_spectrum" % label,
figdir=figdir)
np.savez(
join(figdir, "spectra_col_%s.npz" % label),
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=label)
fig1, fig2 = plot_consistentcy_mat(corr_mat_log_ctrl,
corr_mat_lin_ctrl,
figdir=figdir,
titstr="%s" % label,
savelabel=label)
fig11, fig22 = plot_consistency_hist(corr_mat_log_ctrl,
corr_mat_lin_ctrl,
figdir=figdir,
titstr="%s" % label,
savelabel=label)
fig3 = plot_consistency_example(eva_ctrl,
evc_ctrl,
figdir=figdir,
nsamp=5,
titstr="%s" % label,
savelabel=label)
fig3 = plot_consistency_example(eva_ctrl,
evc_ctrl,
return fig1, fig2
#%%
subpath = [f.path for f in os.scandir(SGdir) if f.is_dir()]
subfdnm = [f.name for f in os.scandir(SGdir) if f.is_dir()]
failnms2 = []
for fdnm in subfdnm: #["FFHQ512"]:
try:
eigval_col, eigvec_col, meta = scan_hess_npz(join(SGdir, fdnm))
posN = len(eigval_col)
corr_mat_log, corr_mat_lin = compute_hess_corr(eigval_col,
eigvec_col,
fdnm=fdnm)
plot_consistentcy_mat(corr_mat_log,
corr_mat_lin,
Hlabel=fdnm,
posN=posN)
print(fdnm, "folder finished")
except:
print(fdnm, "folder failed, please check")
failnms2.append(fdnm)
#%%
from hessian_analysis_tools import plot_spectra, compute_hess_corr, plot_consistentcy_mat, plot_consistency_example
def scan_hess_npz(
Hdir,
evakey='eva_BP',
evckey='evc_BP',
npzpat="Hess_BP(\d*).npz",
):
datadir = r"E:\Cluster_Backup\StyleGAN"
figdir = r"E:\OneDrive - Washington University in St. Louis\Hessian_summary\StyleGAN"
os.makedirs(figdir, exist_ok=True)
eva_col, evc_col, feat_col, meta = scan_hess_npz(datadir, "Hessian_rand_(\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"%"StyleGAN"), H_avg=H_avg, eva_avg=eva_avg, evc_avg=evc_avg, feats=feat_col)
#%%
fig = plot_spectra(eva_col, figdir=figdir, titstr="StyleGAN", )
#%%
corr_mat_log, corr_mat_lin = compute_hess_corr(eva_col, evc_col, figdir=figdir, use_cuda=True)
# without cuda 12:11 mins, with cuda 8:21
# corr_mat_log, corr_mat_lin = compute_hess_corr(eva_col, evc_col, figdir=figdir, use_cuda=False)
#%
fig1, fig2 = plot_consistentcy_mat(corr_mat_log, corr_mat_lin, posN=300, figdir=figdir, titstr="StyleGAN")
#%
fig3 = plot_consistency_example(eva_col, evc_col, figdir=figdir, nsamp=5, titstr="StyleGAN",)
fig3.show()
#%%
#%% Accuracy plot
figdir = r"E:\OneDrive - Washington University in St. Louis\Hessian_summary\StyleGAN"
datadir = r"E:\Cluster_Data\StyleGAN"
EPS_list = [1E-6, 1E-5, 1E-4, 3E-4, 1E-3, 3E-3, 1E-2, 5E-2, 1E-1]
raw_corr_tab = []
PSD_corr_tab = []
for triali in range(15):
print("Computation trial %d"%triali)
data = np.load(join(savedir, "Hess_accuracy_cmp_%d.npz" % triali), allow_pickle=True)
H_col = data["H_col"]
eva_BP, evc_BP, H_BP = data["eva_BP"], data["evc_BP"], data["H_BP"]