[FFmpeg-devel] [v5] dnn_backend_native_layer_mathunary: add ceil support

[Guo, Yejun]

> -----Original Message-----
> From: ffmpeg-devel <ffmpeg-devel-bounces at ffmpeg.org> On Behalf Of Mingyu
> Yin
> Sent: 2020年8月3日 19:17
> To: ffmpeg-devel at ffmpeg.org
> Subject: [FFmpeg-devel] [v5] dnn_backend_native_layer_mathunary: add ceil
> support
> 
> It can be tested with the model generated with below python script:
> 
> import tensorflow as tf
> import os
> import numpy as np
> import imageio
> from tensorflow.python.framework import graph_util name = 'ceil'
> 
> pb_file_path = os.getcwd()
> if not os.path.exists(pb_file_path+'/{}_savemodel/'.format(name)):
>     os.mkdir(pb_file_path+'/{}_savemodel/'.format(name))
> 
> with tf.Session(graph=tf.Graph()) as sess:
>     in_img = imageio.imread('detection.jpg')
>     in_img = in_img.astype(np.float32)
>     in_data = in_img[np.newaxis, :]
>     input_x = tf.placeholder(tf.float32, shape=[1, None, None, 3],
> name='dnn_in')
>     y_ = tf.math.ceil(input_x*255)/255
>     y = tf.identity(y_, name='dnn_out')
>     sess.run(tf.global_variables_initializer())
>     constant_graph = graph_util.convert_variables_to_constants(sess,
> sess.graph_def, ['dnn_out'])
> 
>     with
> tf.gfile.FastGFile(pb_file_path+'/{}_savemodel/model.pb'.format(name),
> mode='wb') as f:
>         f.write(constant_graph.SerializeToString())
> 
>     print("model.pb generated, please in ffmpeg path use\n \n \
>     python tools/python/convert.py ceil_savemodel/model.pb
> --outdir=ceil_savemodel/ \n \nto generate model.model\n")
> 
>     output = sess.run(y, feed_dict={ input_x: in_data})
>     imageio.imsave("out.jpg", np.squeeze(output))
> 
>     print("To verify, please ffmpeg path use\n \n \
>     ./ffmpeg -i detection.jpg -vf
> format=rgb24,dnn_processing=model=ceil_savemodel/model.pb:input=dnn_in:
> output=dnn_out:dnn_backend=tensorflow -f framemd5
> ceil_savemodel/tensorflow_out.md5\n  \
>     or\n \
>     ./ffmpeg -i detection.jpg -vf
> format=rgb24,dnn_processing=model=ceil_savemodel/model.pb:input=dnn_in:
> output=dnn_out:dnn_backend=tensorflow ceil_savemodel/out_tensorflow.jpg\n
> \nto generate output result of tensorflow model\n")
> 
>     print("To verify, please ffmpeg path use\n \n \
>     ./ffmpeg -i detection.jpg -vf
> format=rgb24,dnn_processing=model=ceil_savemodel/model.model:input=dnn
> _in:output=dnn_out:dnn_backend=native -f framemd5
> ceil_savemodel/native_out.md5\n  \
>     or \n \
>     ./ffmpeg -i detection.jpg -vf
> format=rgb24,dnn_processing=model=ceil_savemodel/model.model:input=dnn
> _in:output=dnn_out:dnn_backend=native ceil_savemodel/out_native.jpg\n \nto
> generate output result of native model\n")
> 
> Signed-off-by: Mingyu Yin <mingyu.yin at intel.com>
> ---
>  libavfilter/dnn/dnn_backend_native_layer_mathunary.c | 4 ++++
> libavfilter/dnn/dnn_backend_native_layer_mathunary.h | 1 +
>  tests/dnn/dnn-layer-mathunary-test.c                 | 4 ++++
>  tools/python/convert_from_tensorflow.py              | 4 +++-
>  tools/python/convert_header.py                       | 2 +-
>  5 files changed, 13 insertions(+), 2 deletions(-)
LGTM, will push soon, thanks.