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import time
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import torch
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import vox2seq
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if __name__ == "__main__":
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stats = {
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'z_order_cuda': [],
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'z_order_pytorch': [],
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'hilbert_cuda': [],
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'hilbert_pytorch': [],
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}
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RES = [16, 32, 64, 128, 256]
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for res in RES:
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coords = torch.meshgrid(torch.arange(res), torch.arange(res), torch.arange(res))
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coords = torch.stack(coords, dim=-1).reshape(-1, 3).int().cuda()
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start = time.time()
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for _ in range(100):
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code_z_cuda = vox2seq.encode(coords, mode='z_order').cuda()
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torch.cuda.synchronize()
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stats['z_order_cuda'].append((time.time() - start) / 100)
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start = time.time()
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for _ in range(100):
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code_z_pytorch = vox2seq.pytorch.encode(coords, mode='z_order').cuda()
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torch.cuda.synchronize()
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stats['z_order_pytorch'].append((time.time() - start) / 100)
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start = time.time()
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for _ in range(100):
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code_h_cuda = vox2seq.encode(coords, mode='hilbert').cuda()
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torch.cuda.synchronize()
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stats['hilbert_cuda'].append((time.time() - start) / 100)
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start = time.time()
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for _ in range(100):
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code_h_pytorch = vox2seq.pytorch.encode(coords, mode='hilbert').cuda()
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torch.cuda.synchronize()
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stats['hilbert_pytorch'].append((time.time() - start) / 100)
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print(f"{'Resolution':<12}{'Z-Order (CUDA)':<24}{'Z-Order (PyTorch)':<24}{'Hilbert (CUDA)':<24}{'Hilbert (PyTorch)':<24}")
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for res, z_order_cuda, z_order_pytorch, hilbert_cuda, hilbert_pytorch in zip(RES, stats['z_order_cuda'], stats['z_order_pytorch'], stats['hilbert_cuda'], stats['hilbert_pytorch']):
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print(f"{res:<12}{z_order_cuda:<24.6f}{z_order_pytorch:<24.6f}{hilbert_cuda:<24.6f}{hilbert_pytorch:<24.6f}")
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