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3ce19a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | import torch
import numpy as np
import imageio
def slerp(low, high, val):
low_norm = low/torch.norm(low, dim=1, keepdim=True)
high_norm = high/torch.norm(high, dim=1, keepdim=True)
omega = torch.acos((low_norm*high_norm).sum(1))
so = torch.sin(omega)
res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
return res
def random_interp(H, sampler, shape, imle, fname, logprint, lat1=None, lat2=None, sn1=None, sn2=None):
num_lin = 1
mb = 15
batches = []
# step = (-f_latent + s_latent) / num_lin
for t in range(num_lin):
f_latent = torch.randn([1, H.latent_dim], dtype=torch.float32).cuda()
s_latent = torch.randn([1, H.latent_dim], dtype=torch.float32).cuda()
if lat1 is not None:
print('loading from input')
f_latent = lat1
s_latent = lat2
sample_w = torch.cat([slerp(f_latent, s_latent, v) for v in torch.linspace(0, 1, mb).cuda()], dim=0)
# out = imle(sample_w, spatial_noise=snoise, input_is_w=True)
out = imle(sample_w, spatial_noise=None)
batches.append(sampler.sample_from_out(out))
n_rows = len(batches)
im = np.concatenate(batches, axis=0).reshape((n_rows, mb, *shape[1:])).transpose([0, 2, 1, 3, 4]).reshape(
[n_rows * shape[1], mb * shape[2], 3])
logprint(f'printing samples to {fname}')
imageio.imwrite(fname, im) |