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import torch
import numpy as np
from tqdm import tqdm
def edit(generator, latents, indices, semantics=1, start_distance=-15.0, end_distance=15.0, num_samples=1, step=11):
layers, boundaries, values = factorize_weight(generator, indices)
codes = latents.detach().cpu().numpy() # (1,18,512)
# Generate visualization pages.
distances = np.linspace(start_distance, end_distance, step)
num_sam = num_samples
num_sem = semantics
edited_latents = []
for sem_id in tqdm(range(num_sem), desc='Semantic ', leave=False):
boundary = boundaries[sem_id:sem_id + 1]
for sam_id in tqdm(range(num_sam), desc='Sample ', leave=False):
code = codes[sam_id:sam_id + 1]
for col_id, d in enumerate(distances, start=1):
temp_code = code.copy()
temp_code[:, layers, :] += boundary * d
edited_latents.append(torch.from_numpy(temp_code).float().cuda())
return torch.cat(edited_latents)
def factorize_weight(g_ema, layers='all'):
weights = []
if layers == 'all' or 0 in layers:
weight = g_ema.conv1.conv.modulation.weight.T
weights.append(weight.cpu().detach().numpy())
if layers == 'all':
layers = list(range(g_ema.num_layers - 1))
else:
layers = [l - 1 for l in layers if l != 0]
for idx in layers:
weight = g_ema.convs[idx].conv.modulation.weight.T
weights.append(weight.cpu().detach().numpy())
weight = np.concatenate(weights, axis=1).astype(np.float32)
weight = weight / np.linalg.norm(weight, axis=0, keepdims=True)
eigen_values, eigen_vectors = np.linalg.eig(weight.dot(weight.T))
return layers, eigen_vectors.T, eigen_values
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