<reponame>Animadversio/Visual_Neuro_InSilico_Exp
import torch
import numpy as np
from tqdm import tqdm
from os.path import join
import os
from Hessian.GAN_hessian_compute import hessian_compute
from torchvision.transforms import ToPILImage
from GAN_utils import loadPGGAN, PGGAN_wrapper
from Hessian.hessian_analysis_tools import plot_spectra, compute_hess_corr
from lpips import LPIPS
ImDist = LPIPS(net="squeeze")
datadir = r"E:\OneDrive - Washington University in St. Louis\HessNetArchit\PGGAN"
os.makedirs(datadir, exist_ok=True)
#%%
PGGAN = loadPGGAN()
SD = PGGAN.state_dict()
#%%
shuffled_SD = {}
for name, Weight in SD.items():
    idx = torch.randperm(Weight.numel())
    W_shuf = Weight.view(-1)[idx].view(Weight.shape)
    shuffled_SD[name] = W_shuf
#%%
torch.save(shuffled_SD, join(datadir, "PGGAN_shuffle.pt"))
    # print(name, Weight.shape, Weight.mean().item(), Weight.std().item())
#%%
PGGAN_sf = loadPGGAN()
PGGAN_sf.load_state_dict(torch.load(join(datadir, "PGGAN_shuffle.pt")))
G_sf = PGGAN_wrapper(PGGAN_sf)
#%%
img = G_sf.visualize(torch.randn(1,512).cuda()).cpu()
ToPILImage()(img[0,:].cpu()).show()
#%%
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\PGGAN\ctrl_Hessians"
os.makedirs(savedir, exist_ok=True)
for triali in tqdm(range(0, 100)):
    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()
    #     H00 = get_full_hessian(L2dist_col[0], feat)
    #     eva00, evc00 = np.linalg.eigh(H00)
    #     print("Spent %.2f sec computing" % (time() - T0))
    #     np.savez(join(savedir, "eig_genBlock%02d_trial%d.npz"%(blocki, triali)), H=H00, eva=eva00, evc=evc00,
    #              feat=feat.cpu().detach().numpy())
#%%
from Hessian.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\PGGAN\ctrl_Hessians"
figdir = r"E:\OneDrive - Washington University in St. Louis\HessNetArchit\PGGAN"
realfigdir = r"E:\OneDrive - Washington University in St. Louis\Hessian_summary\PGGAN"

modelnm = "PGGAN_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, 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:
    eva_ctrl = data["eigval_col"]
with np.load(join(realfigdir, "spectra_col_BP.npz")) as data:
    eva_real = data["eigval_col"]
fig0 = plot_spectra(eva_real, savename="PGGAN_shuffle_spectrum_cmp", figdir=figdir, abs=True,
            titstr="PGGAN cmp", label="real", fig=None)
fig0 = plot_spectra(eva_ctrl, savename="PGGAN_shuffle_spectrum_cmp", figdir=figdir, abs=True,
            titstr="PGGAN cmp", label="shuffled", fig=fig0)
#%%
with np.load(join(realfigdir, "Hess__corr_mat.npz")) as data:
    corr_mat_log, corr_mat_lin = data["corr_mat_log"], data["corr_mat_lin"]
fig11, fig22 = plot_consistency_hist(corr_mat_log, corr_mat_lin, figdir=figdir, titstr="%s"%"real",
                                    savelabel="PGGAN_shuffle_cmp")
fig11, fig22 = plot_consistency_hist(corr_mat_log_ctrl, corr_mat_lin_ctrl, figdir=figdir, titstr="%s"%"shuffle",
                                    savelabel="PGGAN_shuffle_cmp", figs=(fig11, fig22))
#%%
# eva_col, evc_col, meta = scan_hess_npz(r"E:\Cluster_Backup\StyleGAN2\ffhq-512-avg-tpurun1")
# np.savez(join(realfigdir, "spectra_col_%s.npz"%"ffhq-512-avg-tpurun1"), eigval_col=eva_col)
