Comments (1)
You can refer to the following code in the discover_subspace.ipynb
.
if task == 'eye_photo_act_mp_sup_mp':
for space, magnitude in zip(['s'], [20]): # zip(['s', 'w+', 'w', 'z'], [8, 2, 2, 1]):
dataloader = dataloader_to(zs0[:2], space)
nose_segmenter = FaceSegmenter(segmenter_path, mask_labels=[4, 5, 6], morphology='dilation', ks_ratio=0.1).to(device)
f_photo = MaskedMSE(generator, space, nose_segmenter, (48, 48), return_complementary=True)
JtJs_photo = load_or_compute(cache_path % (space, task, 'photo'), lambda: find_jtj_direct(f_photo, dataloader), recompute=False, map_location=device)
with torch.no_grad():
s_act, v_act = compute_early_projected_pca(JtJs_photo[0],[JtJs_photo[1]], sup_ratio=[1e-3])
torch.save([s_act, v_act], span_path % (space, task))
visualize(zs0[[21, 30]], space, magnitude, v_act, steps=[ -1, 1], selector=range(10))
Basically this code changes the region marked by nose_segmenter
(wrong name, should be eye_segmenter) while preserving the rest region (as you need). For W+ space, just assign "w+"
to space
.
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