Preprocessing time about avod HOT 3 CLOSED

BEV generation normally takes 12-18 ms, so this seems like an issue with your CPU. For faster anchor filtering with integral images, the voxel size can also be increased, but only for the voxel grid used for filtering. Ideally, these preprocessing operations could be moved onto the GPU to run faster, and pull requests are welcome. We have also noticed that Tensorflow will be much slower for the first 3-5 samples inferenced, so timing should take place after several samples have been inferenced already. GPU inference time should not take that long if you are using one of the latest GPU models.

from avod.

It might relate to my numpy configuration:

In [8]: numpy.show_config()
lapack_opt_info:
libraries = ['openblas', 'openblas']
library_dirs = ['/usr/local/lib']
define_macros = [('HAVE_CBLAS', None)]
language = c
blas_opt_info:
libraries = ['openblas', 'openblas']
library_dirs = ['/usr/local/lib']
define_macros = [('HAVE_CBLAS', None)]
language = c
openblas_info:
libraries = ['openblas', 'openblas']
library_dirs = ['/usr/local/lib']
define_macros = [('HAVE_CBLAS', None)]
language = c
blis_info:
NOT AVAILABLE
openblas_lapack_info:
libraries = ['openblas', 'openblas']
library_dirs = ['/usr/local/lib']
define_macros = [('HAVE_CBLAS', None)]
language = c
lapack_mkl_info:
NOT AVAILABLE
blas_mkl_info:
NOT AVAILABLE

Could you help print this info on your machine ? It would help a lot.

from avod.

It is rather clear now: the preprocessing time takes roughly 0.1s
create_bev_map: 0.02s
the np.dstack: 0..02s
fill empty anchor: 0.06s:

np.stack could be optimized, when you report 0.02s, do you mean fill empty anchor is throw away ?

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