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,
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,
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, corr_mat_lin = compute_hess_corr(eva_ctrl,
evc_ctrl,
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_ctrl,
evc_ctrl,
figdir=figdir,
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,
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,
return fig
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,
plt.savefig(join(figdir, "Hess_%s_corrmat_lin.jpg" % Hlabel))
plt.savefig(join(figdir, "Hess_%s_corrmat_lin.pdf" % Hlabel))
plt.show()
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,
plt.ylabel("density")
plt.title(
"Histogram of Non-Diag Correlation\n %s on %s scale\n mean %.3f median %.3f"
% (GAN, "log" if log else "lin", corr_mean, corr_medi))
plt.show()
return fig
#%%
GANsnm = "StyleGAN2-Face512"
modeldir = join(summarydir, "StyleGAN2")
eigval_col, eigvec_col, feat_col, meta = scan_hess_npz(
r"E:\Cluster_Backup\StyleGAN2\ffhq-512-avg-tpurun1", featkey="feat")
corr_mat_log, corr_mat_lin = compute_hess_corr(
eigval_col,
eigvec_col,
savelabel="ffhq-512-avg-tpurun1",
figdir=modeldir,
)
plot_consistency_example(eigval_col,
eigvec_col,
nsamp=5,
titstr=GANsnm,
figdir=modeldir,
savelabel="ffhq-512-avg-tpurun1")
#%%
corrmat_npz_dict = {
"fc6GAN": "fc6GAN\\evol_hess_corr_mat.npz",
"DCGAN": "DCGAN\\Hess__corr_mat.npz",
"BigGAN": "BigGAN\\Hess_all_consistency_corr_mat.npz",
"BigBiGAN": "BigBiGAN\\evol_hess_corr_mat.npz",
H_i = (evc_i * eva_i[np.newaxis, :]) @ evc_i.T
for eigj in range(posN):
eva_j, evc_j = eigval_col[eigj], eigvec_col[eigj]
H_j = (evc_j * eva_j[np.newaxis, :]) @ evc_j.T
# corr_mat_log[eigi, eigj] = \
# np.corrcoef(ma.masked_invalid(np.log10(vHv_ij)), ma.masked_invalid(np.log10(eva_j)))[0, 1]
corr_mat_lin[eigi, eigj] = np.corrcoef(H_i.flatten(),
H_j.flatten())[0, 1]
print("%.1f sec" % (time() - T0)) #
return corr_mat_lin
corr_mat_vec = compute_vector_hess_corr(eva_col, evc_col, use_cuda=True)
# 576 sec without cuda 92.7 sec with cuda
#%% Older version hess corr
corr_mat_log, corr_mat_lin = compute_hess_corr(eva_col, evc_col, use_cuda=True)
#%%
def plot_layer_consistency_mat(corr_mat_log,
corr_mat_lin,
corr_mat_vec,
savelabel="",
figdir=archdir,
titstr="GAN",
layernames=None):
posN = corr_mat_log.shape[0]
corr_mat_log_nodiag = corr_mat_log.copy()
corr_mat_lin_nodiag = corr_mat_lin.copy()
corr_mat_vec_nodiag = corr_mat_vec.copy()
np.fill_diagonal(corr_mat_log_nodiag, np.nan)
# 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)
fig0 = plot_spectra(eigval_col=eigval_col, savename="%s_spectrum" % modelnm)
corr_mat_log, corr_mat_lin = compute_hess_corr(
eigval_col,
eigvec_col,
figdir=figdir,
use_cuda=False,
savelabel=modelnm,
)
fig1, fig2 = plot_consistentcy_mat(corr_mat_log,
corr_mat_lin,
figdir=figdir,
titstr="StyleGAN2 %s" % modelnm,
savelabel=modelnm)
fig3 = plot_consistency_example(eigval_col,
eigvec_col,
figdir=figdir,
nsamp=5,
titstr="StyleGAN2 %s" % modelnm,
savelabel=modelnm)
#%% "ImageNet512"