| 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 = []
|
|
|
| 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=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) |
