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| import torch |
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| import utils.misc as misc |
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| 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] |
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| 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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