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
savedir = r"E:\OneDrive - Washington University in St. Louis\HessNetArchit\StyleGAN\ctrl_Hessians"
os.makedirs(savedir, exist_ok=True)
for triali in tqdm(range(38, 50)):
feat = torch.randn(1, 512).cuda()
eigvals, eigvects, H = hessian_compute(
G_sf,
feat,
ImDist,
hessian_method="BP",
)
np.savez(join(savedir, "eig_full_trial%d.npz" % (triali)),
H=H,
eva=eigvals,
evc=eigvects,
feat=feat.cpu().detach().numpy())
# feat.requires_grad_(True)
# for blocki in [0, 3, 5, 8, 10, 12]:
# L2dist_col = []
# torch.cuda.empty_cache()
# H1 = SGAN_sf.convs[blocki].register_forward_hook(Hess_hook)
# img = SGAN_sf([feat], truncation=1)
# H1.remove()
# T0 = time()
DCGAN,
):
self.DCGAN = DCGAN
def visualize(self, code, scale=1.0):
imgs = self.DCGAN(code, ) # Matlab version default to 0.7
return torch.clamp((imgs + 1.0) / 2.0, 0, 1) * scale
G = DCGAN_wrapper(model.avgG)
#%%
noise, _ = model.buildNoiseData(1)
feat = noise.detach().clone().cuda()
T0 = time()
eva_BI, evc_BI, H_BI = hessian_compute(G,
feat,
ImDist,
hessian_method="BackwardIter")
print("%.2f sec" % (time() - T0)) # 13.40 sec
T0 = time()
eva_FI, evc_FI, H_FI = hessian_compute(G,
feat,
ImDist,
hessian_method="ForwardIter")
print("%.2f sec" % (time() - T0)) # 6.89 sec
T0 = time()
eva_BP, evc_BP, H_BP = hessian_compute(G, feat, ImDist, hessian_method="BP")
print("%.2f sec" % (time() - T0)) # 12.5 sec
print("Correlation of Flattened Hessian matrix BP vs BackwardIter %.3f" %
np.corrcoef(H_BP.flatten(), H_BI.flatten())[0, 1])
print("Correlation of Flattened Hessian matrix BP vs ForwardIter %.3f" %
np.corrcoef(H_BP.flatten(), H_FI.flatten())[0, 1])
# np.savez(join("E:\OneDrive - Washington University in St. Louis\Hessian_summary\StyleGAN2", "Hess_cmp_%d.npz"%triali), eva_BI=eva_BI, evc_BI=evc_BI, H_BI=H_BI,
# eva_FI=eva_FI, evc_FI=evc_FI, H_FI=H_FI, H_col=H_col,
# eva_BP=eva_BP, evc_BP=evc_BP, H_BP=H_BP, feat=feat.detach().cpu().numpy())
# print("Save finished")
#%%
"""Compute spectrum for different models through CMD interface. """
modelname = "ffhq-256-config-e-003810" # 109 sec
SGAN = loadStyleGAN2(modelname+".pt", size=256, channel_multiplier=1) # 491 sec per BP
G = StyleGAN2_wrapper(SGAN)
savedir = join(rootdir, modelname)
os.makedirs(savedir, exist_ok=True)
for triali in range(150):
feat = torch.randn(1, 512).detach().clone().cuda()
T0 = time()
eva_BP, evc_BP, H_BP = hessian_compute(G, feat, ImDist, hessian_method="BP",
preprocess=lambda img: F.interpolate(img, (256, 256), mode='bilinear', align_corners=True))
print("%.2f sec" % (time() - T0)) # 109 sec
np.savez(join(savedir, "Hess_BP_%d.npz"%triali), eva_BP=eva_BP, evc_BP=evc_BP, H_BP=H_BP, feat=feat.detach().cpu().numpy())
modelname = "stylegan2-cat-config-f"
SGAN = loadStyleGAN2(modelname+".pt", size=256, channel_multiplier=2)
G = StyleGAN2_wrapper(SGAN)
savedir = join(rootdir, modelname)
os.makedirs(savedir, exist_ok=True)
for triali in range(150):
feat = torch.randn(1, 512).detach().clone().cuda()
T0 = time()
eva_BP, evc_BP, H_BP = hessian_compute(G, feat, ImDist, hessian_method="BP",
preprocess=lambda img: F.interpolate(img, (256, 256), mode='bilinear', align_corners=True))
print("%.2f sec" % (time() - T0)) # 109 sec
np.savez(join(savedir, "Hess_BP_%d.npz"%triali), eva_BP=eva_BP, evc_BP=evc_BP, H_BP=H_BP, feat=feat.detach().cpu().numpy())
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
savedir = r"E:\OneDrive - Washington University in St. Louis\HessNetArchit\BigBiGAN\ctrl_Hessians" # join(datadir, "ctrl_Hessians")
os.makedirs(savedir, exist_ok=True)
for triali in tqdm(range(0, 200)):
feat = torch.randn(1, 120).cuda()
eigvals, eigvects, H = hessian_compute(
G_sf,
feat,
ImDist,
hessian_method="BP",
)
np.savez(join(savedir, "eig_full_trial%d.npz" % (triali)),
H=H,
eva=eigvals,
evc=eigvects,
feat=feat.cpu().detach().numpy())
# feat.requires_grad_(True)
# for blocki in [0, 3, 5, 8, 10, 12]:
# L2dist_col = []
# torch.cuda.empty_cache()
# H1 = SGAN_sf.convs[blocki].register_forward_hook(Hess_hook)
# img = SGAN_sf([feat], truncation=1)
# H1.remove()
# T0 = time()
return cc_12, cclog_12, cc_21, cclog_21
#%%
saveroot = r"E:\Cluster_Backup"
#%%
BGAN = loadBigGAN()
G = BigGAN_wrapper(BGAN)
savedir = join(saveroot, "ImDist_cmp\\BigGAN")
os.makedirs(savedir, exist_ok=True)
SSIM_stat_col = []
MSE_stat_col = []
for idx in range(100):
refvec = G.sample_vector(1, device="cuda") # 0.1 * torch.randn(1, 256)
eigvals_PS, eigvects_PS, H_PS = hessian_compute(G,
refvec,
ImDist,
hessian_method="BP")
eigvals_SSIM, eigvects_SSIM, H_SSIM = hessian_compute(G,
refvec,
D,
hessian_method="BP")
eigvals_MSE, eigvects_MSE, H_MSE = hessian_compute(G,
refvec,
MSE,
hessian_method="BP")
#% eigvals_L1, eigvects_L1, H_L1 = hessian_compute(G, refvec, L1, hessian_method="BP")
print("SSIM - LPIPS comparison")
cc_SSIM, logcc_SSIM, reg_coef_SSIM, logreg_coef_SSIM = spectra_cmp(
-eigvals_SSIM[::-1], eigvals_PS, show=True)
H_cc_SSIM, logH_cc_SSIM = Hessian_cmp(-eigvals_SSIM[::-1],
eigvects_SSIM,
return torch.clamp((imgs + 1.0) / 2.0, 0, 1) * scale
G = StyleGAN_wrapper(model.cuda())
#%%
savedir = r"E:\OneDrive - Washington University in St. Louis\Hessian_summary\StyleGAN_hr"
#%%
data = np.load(join(savedir, "Hessian_EPS_BP.npz"))
H_BP = data["H_BP"]
feat = torch.tensor(data['feat']).detach().cuda()
#%%
# noise = torch.randn(1, 512)
# feat = noise.detach().clone().cuda()
# G.StyleGAN.cuda()
H_col = []
for EPS in [1E-6, 1E-5, 1E-4, 3E-4, 1E-3, 3E-3, 1E-2, 3E-2, 1E-1, ]:
T0 = time()
eva_FI, evc_FI, H_FI = hessian_compute(G, feat, ImDist, hessian_method="ForwardIter", EPS=EPS,
preprocess=lambda img: F.interpolate(img, (256, 256), mode='bilinear', align_corners=True))
print("%.2f sec" % (time() - T0)) # 260.5 sec
print("EPS %.1e Correlation of Flattened Hessian matrix BP vs ForwardIter %.3f" % (
EPS, np.corrcoef(H_BP.flatten(), H_FI.flatten())[0, 1]))
H_col.append((eva_FI, evc_FI, H_FI))
np.savez(join(savedir, "Hessian_EPS_accuracy.npz"), H_col=H_col, feat=feat.detach().cpu().numpy())
print("Save Completed. ")
#%%
np.savez(join(savedir, "Hessian_EPS_BP.npz"), eva_BP=eva_BP, evc_BP=evc_BP, H_BP=H_BP, feat=feat.detach().cpu().numpy())
#%%
G.StyleGAN.to("cpu")
feat.cpu()
T0 = time()
eva_BI, evc_BI, H_BI = hessian_compute(G, feat, ImDist, hessian_method="BackwardIter", preprocess=lambda img: F.interpolate(img, (256, 256), mode='bilinear', align_corners=True), device="cpu")
print("%.2f sec" % (time() - T0)) # this will exceed gpu memory
csr_end = min(csr + B, imgn)
imgs = self.visualize(codes[csr:csr_end, :], scale=scale,
res=res).to(device)
img_all = imgs if img_all is None else torch.cat(
(img_all, imgs), 0)
csr = csr_end
return img_all
Gw = PGGAN_wrapper2(G)
#%%
T0 = time()
eigvals, eigvects, H = hessian_compute(
Gw,
refvec,
ImDist,
hessian_method="BackwardIter",
cutoff=50,
preprocess=lambda img: F.interpolate(img, size=(256, 256)),
device="cuda") # EPS=1E-5,
print("%.3f sec" % (time() - T0)) # 77sec
#%%
T0 = time()
eigvals_BP, eigvects_BP, H_BP = hessian_compute(
Gw,
refvec,
ImDist,
hessian_method="BP",
preprocess=lambda img: F.interpolate(img, size=(256, 256)),
device="cuda") # EPS=1E-5,
print("%.3f sec" % (time() - T0)) # 377sec
#%%
"E:\OneDrive - Washington University in St. Louis\HessNetArchit\StyleGAN",
"StyleGAN_shuffle.pt")))
savedir = join(saveroot, label)
os.makedirs(savedir, exist_ok=True)
print(savedir)
for triali in range(0, 80):
if args.wspace:
feat_z = torch.randn(1, 512).cuda()
feat = G.StyleGAN.style(feat_z)
else:
feat = torch.randn(1, 512).cuda()
T0 = time()
eva_BP, evc_BP, H_BP = hessian_compute(G,
feat,
ImDist,
hessian_method="BP",
preprocess=lambda img: img)
# preprocess=lambda img: F.interpolate(img, (256, 256), mode='bilinear', align_corners=True))
print("%.2f sec" % (time() - T0)) # 109 sec
np.savez(join(savedir, "Hess_BP_%d.npz" % triali),
eva_BP=eva_BP,
evc_BP=evc_BP,
H_BP=H_BP,
feat=feat.detach().cpu().numpy())
figdir = join(saveroot, "summary", label) # saveroot
os.makedirs(figdir, exist_ok=True)
# modelnm = "StyleGAN_wspace_shuffle"
# Load the Hessian NPZ
eva_ctrl, evc_ctrl, feat_ctrl, meta = scan_hess_npz(savedir,
#%%
SG.random = False
vec = torch.randn(1, 512).cuda()
img1 = SG.visualize(vec)
img2 = SG.visualize(vec)
L1max = (img1 - img2).abs().max()
dsim = ImDist(img1, img2)
print("Maximal difference between trial L1max %.3f Dsim %.3f" % (L1max, dsim))
# random Maximal difference between trial L1max 0.610 Dsim 0.069
# fixednoise Maximal difference between trial L1max 0.000 Dsim 0.000
#%% Effect of allowing noise in Z space spectra
SG.use_wspace(False)
SG.random = False
feat = torch.randn(1, 512)
eva_BP, evc_BP, H_BP = hessian_compute(SG, feat, ImDist, hessian_method="BP")
SG.random = True
eva_BP_rnd, evc_BP_rnd, H_BP_rnd = hessian_compute(SG,
feat,
ImDist,
hessian_method="BP")
#%%
figdir = r"E:\OneDrive - Washington University in St. Louis\Hessian_summary\StyleGAN"
plt.figure(figsize=[8, 4])
plt.subplot(121)
plt.plot(eva_BP)
plt.plot(eva_BP_rnd)
plt.ylabel("eig")
plt.subplot(122)
plt.plot(np.log10(np.abs(eva_BP)), label="fixednoise")
plt.plot(np.log10(np.abs(eva_BP_rnd)), label="random")