[FFmpeg-devel] [PATCH 2/2] convert_from_tensorflow.py: support conv2d with dilation

[Pedro Arthur]

Pushed.

Em qua, 14 de ago de 2019 às 03:37, Guo, Yejun <yejun.guo at intel.com> escreveu:
>
>
>
> > -----Original Message-----
> > From: ffmpeg-devel [mailto:ffmpeg-devel-bounces at ffmpeg.org] On Behalf Of
> > Pedro Arthur
> > Sent: Wednesday, August 14, 2019 12:09 AM
> > To: FFmpeg development discussions and patches <ffmpeg-devel at ffmpeg.org>
> > Subject: Re: [FFmpeg-devel] [PATCH 2/2] convert_from_tensorflow.py: support
> > conv2d with dilation
> >
> > LGTM.
> > Should push soon.
>
> thanks.
>
> >
> > BTW I just noticed that the tensorflow backend is failling to load SR
> > filter models.
> >
> > $ python tools/python/convert.py sr_models/srcnn.pb
> > $ ./ffmpeg -i input.jpg -vf
> > sr=model=srcnn.model:dnn_backend=tensorflow out_srcnn_tf.png
> >
> > The above command fails.
> > It seems commit ccbab41039af424237eaac5c302c293ab97540f8 is the
> > problem. I thought I had tested it but clearly I made a mistake
> > somewhere in the process.
> > I suppose you have the .pb files to test it, but let me know if you need them.
>
> yes, I have the .pb files. I missed the patch for such support. Will refine and send out soon.
>
>
> >
> > Em sex, 9 de ago de 2019 às 12:25, Guo, Yejun <yejun.guo at intel.com>
> > escreveu:
> > >
> > >
> > >
> > > > -----Original Message-----
> > > > From: Guo, Yejun
> > > > Sent: Tuesday, July 30, 2019 9:26 AM
> > > > To: ffmpeg-devel at ffmpeg.org
> > > > Cc: Guo, Yejun <yejun.guo at intel.com>
> > > > Subject: [PATCH 2/2] convert_from_tensorflow.py: support conv2d with
> > dilation
> > > >
> > > > conv2d with dilation > 1 generates tens of nodes in graph, it is not
> > > > easy to parse each node one by one, so we do special tricks to parse
> > > > the conv2d layer.
> > > >
> > > > Signed-off-by: Guo, Yejun <yejun.guo at intel.com>
> > > > ---
> > > >  tools/python/convert_from_tensorflow.py | 80
> > > > ++++++++++++++++++++++++---------
> > > >  1 file changed, 59 insertions(+), 21 deletions(-)
> > >
> > > this patch set asks for review, thanks.
> > >
> > > I've locally finished more patches to improve dnn module, plan to send more
> > them set by set, since the patches have dependency.
> > >
> > > Just in case you are interested in these new patches, I've uploaded to
> > https://github.com/guoyejun/ffmpeg/tree/dnn0809.
> > > for your convenient, I also copy the oneline log here for each patch (from
> > newer to older) with 4 patch sets.
> > >
> > > 7eced90 libavfilter/dnn: support multiple outputs for native mode
> > > 28a7054 libavfilter/dnn/dnn_backend_native: find the input operand
> > according to input name
> > >
> > > 256e657 FATE/dnn: add unit test for layer maximum
> > > 8c616a0 libavfilter/dnn: add layer maximum for native mode.
> > >
> > > 8ec6c0c FATE/dnn: add unit test for dnn depth_to_space layer
> > > 09ef108 libavfilter/dnn: separate depth_to_space layer from
> > dnn_backend_native.c to a new file
> > > c65b59d FATE/dnn: add unit test for dnn conv2d layer
> > > a5d69a7 libavfilter/dnn: separate conv2d layer from dnn_backend_native.c to
> > a new file
> > >
> > > 202d323 dnn: export operand info in python script and load in c code
> > > 3c706a0 dnn: change .model file format to put layer number at the end of file
> > > 0256731 dnn: introduce dnn operand (in c code) to hold operand infos within
> > network
> > >
> > >
> > > Besides continuous dnn improvement, I also plan to add two generic video
> > filters for dnn.
> > > - a generic filter to process the content of AVFrame with different dnn
> > networks.
> > > and so the current specific filters such as vf_sr (some changes needed) and
> > vf_derain are no longer needed, since they can be
> > > included in this specific filter. And of course, in practice I'll not remove them.
> > >
> > > - a generic filter to analyze the content of AVFrame to generate some side
> > data with different dnn networks. The content of AVFrame does not change.
> > > The application, which invokes the filter with a given dnn network, has the
> > responsibility/knowledge to parse the side data (analyze result).
> > >
> > > _______________________________________________
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> > >
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