import lpips
# import models # from PerceptualSimilarity folder
# ImDist = models.PerceptualLoss(model='net-lin', net='squeeze', use_gpu=1, gpu_ids=[0])
ImDist = lpips.LPIPS(net='squeeze', )
# model_vgg = models.PerceptualLoss(model='net-lin', net='vgg', use_gpu=1, gpu_ids=[0])
ImDist.cuda()
for param in ImDist.parameters():
param.requires_grad_(False)
#%%
# BGAN = BigGAN.from_pretrained("biggan-deep-256")
# for param in BGAN.parameters():
# param.requires_grad_(False)
# embed_mat = BGAN.embeddings.parameters().__next__().data
# BGAN.cuda()
from GAN_utils import BigGAN_wrapper, loadBigGAN
BGAN = loadBigGAN()
G = BigGAN_wrapper(BGAN)
#%%
def LExpMap(refvect, tangvect, ticks=11, lims=(-1, 1)):
refvect, tangvect = refvect.reshape(1, -1), tangvect.reshape(1, -1)
steps = np.linspace(lims[0], lims[1], ticks)[:, np.newaxis]
interp_vects = steps @ tangvect + refvect
return interp_vects
def SExpMap(refvect, tangvect, ticks=11, lims=(-1, 1)):
refvect, tangvect = refvect.reshape(1, -1), tangvect.reshape(1, -1)
steps = np.linspace(lims[0], lims[1], ticks)[:, np.newaxis] * np.pi / 2
interp_vects = steps @ tangvect + refvect
#%%
ImDist = LPIPS(net="squeeze")
datadir = r"E:\OneDrive - Washington University in St. Louis\HessNetArchit\BigGAN"
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
#%%
BGAN = loadBigGAN()
SD = BGAN.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, "BigGAN_shuffle.pt"))
# print(name, Weight.shape, Weight.mean().item(), Weight.std().item())
#%%
BGAN_sf = loadBigGAN()
BGAN_sf.load_state_dict(torch.load(join(datadir, "BigGAN_shuffle.pt")))
G_sf = BigGAN_wrapper(BGAN_sf)
#%%
rown=5,
transpose=False)
#%%
vis_eigen_action(eigvect_avg[:, -5],
None,
figdir=figdir,
page_B=50,
maxdist=20,
rown=5,
transpose=False)
#%% BigGAN on ImageNet Class Specific
from GAN_utils import BigGAN_wrapper, loadBigGAN
from pytorch_pretrained_biggan import BigGAN
from torchvision.transforms import ToPILImage
BGAN = loadBigGAN("biggan-deep-256").cuda()
BG = BigGAN_wrapper(BGAN)
EmbedMat = BG.BigGAN.embeddings.weight.cpu().numpy()
#%%
figdir = r"E:\OneDrive - Washington University in St. Louis\Hessian_summary\BigGAN"
Hessdir = r"E:\OneDrive - Washington University in St. Louis\Hessian_summary\BigGAN"
data = np.load(join(Hessdir, "H_avg_1000cls.npz"))
eva_BG = data['eigvals_avg']
evc_BG = data['eigvects_avg']
evc_nois = data['eigvects_nois_avg']
evc_clas = data['eigvects_clas_avg']
#%%
imgs = BG.render(np.random.randn(1, 256) * 0.06)
#%%
eigi = 5
refvecs = np.vstack((EmbedMat[:, np.random.randint(0, 1000, 10)],
