| | 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() |
| |
|
| | |
| | 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 |
| |
|
