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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 | # PyTorch StudioGAN: https://github.com/POSTECH-CVLab/PyTorch-StudioGAN
# The MIT License (MIT)
# See license file or visit https://github.com/POSTECH-CVLab/PyTorch-StudioGAN for details
# src/utils/sefa.py
import torch
import utils.misc as misc
def apply_sefa(generator, backbone, z, fake_label, num_semantic_axis, maximum_variations, num_cols):
generator = misc.peel_model(generator)
w = generator.linear0.weight
if backbone == "big_resnet":
zs = z
z = torch.split(zs, generator.chunk_size, 0)[0]
eigen_vectors = torch.svd(w).V.to(z.device)[:, :num_semantic_axis]
z_dim = len(z)
zs_start = z.repeat(num_semantic_axis).view(-1, 1, z_dim)
zs_end = (z.unsqueeze(1) + maximum_variations * eigen_vectors).T.view(-1, 1, z_dim)
if backbone == "big_resnet":
zs_shard = zs[z_dim:].expand([1, 1, -1]).repeat(num_semantic_axis, 1, 1)
zs_start = torch.cat([zs_start, zs_shard], axis=2)
zs_end = torch.cat([zs_end, zs_shard], axis=2)
zs_canvas = misc.interpolate(x0=zs_start, x1=zs_end, num_midpoints=num_cols - 2).view(-1, zs_start.shape[-1])
images_canvas = generator(zs_canvas, fake_label.repeat(len(zs_canvas)), eval=True)
return images_canvas
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