| from selectors import BaseSelector
|
| import torch
|
| import numpy as np
|
| import imageio
|
|
|
| def spatial_vissual(H, sampler, shape, imle, fname, logprint, lat1=None, lat2=None, sn1=None, sn2=None):
|
| num_lin = 1
|
| mb = 8
|
|
|
| batches = []
|
|
|
| base_latent = torch.randn([1, H.latent_dim], dtype=torch.float32).cuda()
|
| base_snosie = [torch.randn([1, 1, s, s], dtype=torch.float32).cuda() for s in sampler.res]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| base_latent = [torch.zeros([1, H.latent_dim], dtype=torch.float32).cuda() for i in range(10)]
|
| base_snosie = [torch.zeros([1, 1, s, s], dtype=torch.float32).cuda() for s in sampler.res]
|
| out = imle(base_latent[0], spatial_noise=base_snosie)
|
| batches.append(sampler.sample_from_out(out))
|
|
|
|
|
| base_latent = [torch.randn([1, H.latent_dim], dtype=torch.float32).cuda() for i in range(10)]
|
| base_snosie = [torch.randn([1, 1, s, s], dtype=torch.float32).cuda() for s in sampler.res]
|
| for i in range(10):
|
| out = imle(base_latent[i], spatial_noise=base_snosie)
|
| batches.append(sampler.sample_from_out(out))
|
|
|
| lat_dim = base_latent[0].shape[1]
|
| print(lat_dim)
|
| for i in range(10):
|
| out = imle(base_latent[i], spatial_noise=base_snosie)
|
| batches.append(sampler.sample_from_out(out))
|
| lat = base_latent[i]
|
| for j in range(10):
|
| lat2 = base_latent[j]
|
| cur = torch.cat((lat[:, 0:lat_dim//2], lat2[:, lat_dim//2:]), dim=1)
|
| out = imle(cur, spatial_noise=base_snosie)
|
| batches.append(sampler.sample_from_out(out))
|
|
|
| print(len(batches))
|
| mb = 11
|
| n_rows = 11
|
| 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) |
