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您好,非常感谢您将模型开源! 在使用过程中,我发现模型在进行重建时,对于输入的前两张人脸平均重建时间达到了15秒左右,而后续人脸的重建时间只需要1到2秒,与Readme中提到的一致,具体可见这一个log(我已对结果存储部分代码进行修改,没有选择存储全部结果,所以存储时间很快):
0%| | 0/19 [00:00<?, ?it/s]save results 0.21760940551757812 5%|█████▌ | 1/19 [00:15<04:45, 15.87s/it]save results 0.2519359588623047 11%|███████████ | 2/19 [00:30<04:19, 15.24s/it]save results 0.21797394752502441 16%|████████████████▌ | 3/19 [00:32<02:25, 9.11s/it]save results 0.2182457447052002 21%|██████████████████████ | 4/19 [00:34<01:33, 6.21s/it]save results 0.22073841094970703 26%|███████████████████████████▋ | 5/19 [00:36<01:04, 4.63s/it]
请问具体是什么原因造成的?有什么方法可以避免这一点吗?
The text was updated successfully, but these errors were encountered:
遇到了同样的问题,有没有可能进一步压缩重建效率?
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主要耗时在face_alignment库里面,有一些不必要的numba编译,第一次在编译numba算子,把所有带@jit(nopython=True)的装饰器去掉可以提升效率。另外在util/render.py里面把GLContext换成CudaContext可进一步加速。极限可以到0.3s每张图
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您好,非常感谢您将模型开源!
在使用过程中,我发现模型在进行重建时,对于输入的前两张人脸平均重建时间达到了15秒左右,而后续人脸的重建时间只需要1到2秒,与Readme中提到的一致,具体可见这一个log(我已对结果存储部分代码进行修改,没有选择存储全部结果,所以存储时间很快):
请问具体是什么原因造成的?有什么方法可以避免这一点吗?
The text was updated successfully, but these errors were encountered: