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Running on Zero
| """Diagnostic replay of saved tensors; capture or substitute pre-simplification remesh.""" | |
| from pathlib import Path | |
| import json | |
| import numpy as np | |
| def replay(raw_path, layout_path, output, filtered_path=None): | |
| import torch | |
| import cumesh | |
| import o_voxel | |
| output = Path(output) | |
| output.mkdir(parents=True, exist_ok=True) | |
| with np.load(raw_path, allow_pickle=False) as data: | |
| arrays = {key: data[key].copy() for key in data.files} | |
| layout = {key: slice(*value) for key, value in json.loads(Path(layout_path).read_text()).items()} | |
| original = cumesh.remeshing.remesh_narrow_band_dc | |
| class Captured(Exception): | |
| pass | |
| def interception(*args, **kwargs): | |
| if filtered_path: | |
| with np.load(filtered_path, allow_pickle=False) as data: | |
| return (torch.from_numpy(data['vertices'].astype(np.float32)).cuda(), | |
| torch.from_numpy(data['faces'].astype(np.int32)).cuda()) | |
| vertices, faces = original(*args, **kwargs) | |
| np.savez_compressed(output / 'pre-simplify.npz', vertices=vertices.cpu().numpy(), faces=faces.cpu().numpy()) | |
| raise Captured() | |
| cumesh.remeshing.remesh_narrow_band_dc = interception | |
| try: | |
| result = o_voxel.postprocess.to_glb( | |
| vertices=torch.from_numpy(arrays['vertices']).cuda(), faces=torch.from_numpy(arrays['faces']).cuda(), | |
| attr_volume=torch.from_numpy(arrays['attrs']).cuda(), coords=torch.from_numpy(arrays['coords']).cuda(), | |
| attr_layout=layout, grid_size=int(arrays['grid_size']) if arrays['grid_size'].ndim == 0 | |
| else arrays['grid_size'].tolist(), aabb=[[-.5, -.5, -.5], [.5, .5, .5]], | |
| decimation_target=50000, texture_size=1024, remesh=True, remesh_band=1, remesh_project=0) | |
| result.export(output / 'filtered-remesh.glb') | |
| except Captured: | |
| return str(output / 'pre-simplify.npz') | |
| finally: | |
| cumesh.remeshing.remesh_narrow_band_dc = original | |
| return str(output / 'filtered-remesh.glb') | |