2020-05-25 16:46:27 +02:00
|
|
|
/*
|
|
|
|
* Copyright (c) 2020
|
|
|
|
*
|
|
|
|
* This file is part of FFmpeg.
|
|
|
|
*
|
|
|
|
* FFmpeg is free software; you can redistribute it and/or
|
|
|
|
* modify it under the terms of the GNU Lesser General Public
|
|
|
|
* License as published by the Free Software Foundation; either
|
|
|
|
* version 2.1 of the License, or (at your option) any later version.
|
|
|
|
*
|
|
|
|
* FFmpeg is distributed in the hope that it will be useful,
|
|
|
|
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
|
|
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
|
|
|
|
* Lesser General Public License for more details.
|
|
|
|
*
|
|
|
|
* You should have received a copy of the GNU Lesser General Public
|
|
|
|
* License along with FFmpeg; if not, write to the Free Software
|
|
|
|
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
|
|
|
|
*/
|
|
|
|
|
|
|
|
#include <stdio.h>
|
|
|
|
#include <string.h>
|
|
|
|
#include <math.h>
|
|
|
|
#include "libavfilter/dnn/dnn_backend_native_layer_mathunary.h"
|
|
|
|
#include "libavutil/avassert.h"
|
|
|
|
|
|
|
|
#define EPS 0.00001
|
|
|
|
|
|
|
|
static float get_expected(float f, DNNMathUnaryOperation op)
|
|
|
|
{
|
|
|
|
switch (op)
|
|
|
|
{
|
|
|
|
case DMUO_ABS:
|
|
|
|
return (f >= 0) ? f : -f;
|
2020-06-06 14:12:47 +02:00
|
|
|
case DMUO_SIN:
|
|
|
|
return sin(f);
|
2020-06-06 14:12:49 +02:00
|
|
|
case DMUO_COS:
|
|
|
|
return cos(f);
|
2020-06-06 14:12:51 +02:00
|
|
|
case DMUO_TAN:
|
|
|
|
return tan(f);
|
2020-06-18 11:15:32 +02:00
|
|
|
case DMUO_ASIN:
|
|
|
|
return asin(f);
|
2020-06-18 11:15:34 +02:00
|
|
|
case DMUO_ACOS:
|
|
|
|
return acos(f);
|
2020-06-18 11:15:36 +02:00
|
|
|
case DMUO_ATAN:
|
|
|
|
return atan(f);
|
2020-06-29 16:54:01 +02:00
|
|
|
case DMUO_SINH:
|
|
|
|
return sinh(f);
|
2020-06-29 16:54:03 +02:00
|
|
|
case DMUO_COSH:
|
|
|
|
return cosh(f);
|
2020-06-29 16:54:05 +02:00
|
|
|
case DMUO_TANH:
|
|
|
|
return tanh(f);
|
2020-06-29 16:54:07 +02:00
|
|
|
case DMUO_ASINH:
|
|
|
|
return asinh(f);
|
2020-06-29 16:54:09 +02:00
|
|
|
case DMUO_ACOSH:
|
|
|
|
return acosh(f);
|
2020-06-29 16:54:11 +02:00
|
|
|
case DMUO_ATANH:
|
|
|
|
return atanh(f);
|
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, 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 \n \
to 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 \n \
to 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 \n \
to generate output result of native model\n")
Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
2020-07-31 09:41:24 +02:00
|
|
|
case DMUO_CEIL:
|
|
|
|
return ceil(f);
|
dnn_backend_native_layer_mathunary: add floor 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 = 'floor'
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.floor(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 {}_savemodel/model.pb --outdir={}_savemodel/ \n \nto generate model.model\n".format(name,name))
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={}_savemodel/model.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow -f framemd5 {}_savemodel/tensorflow_out.md5\n \
or\n \
./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow {}_savemodel/out_tensorflow.jpg\n \nto generate output result of tensorflow model\n".format(name, name, name, name))
print("To verify, please ffmpeg path use\n \n \
./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.model:input=dnn_in:output=dnn_out:dnn_backend=native -f framemd5 {}_savemodel/native_out.md5\n \
or \n \
./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.model:input=dnn_in:output=dnn_out:dnn_backend=native {}_savemodel/out_native.jpg\n \nto generate output result of native model\n".format(name, name, name, name))
Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
2020-08-06 08:47:16 +02:00
|
|
|
case DMUO_FLOOR:
|
|
|
|
return floor(f);
|
2020-08-10 13:05:42 +02:00
|
|
|
case DMUO_ROUND:
|
|
|
|
return round(f);
|
2021-03-22 10:20:12 +02:00
|
|
|
case DMUO_EXP:
|
|
|
|
return exp(f);
|
2020-05-25 16:46:27 +02:00
|
|
|
default:
|
|
|
|
av_assert0(!"not supported yet");
|
|
|
|
return 0.f;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
static int test(DNNMathUnaryOperation op)
|
|
|
|
{
|
|
|
|
DnnLayerMathUnaryParams params;
|
|
|
|
DnnOperand operands[2];
|
|
|
|
int32_t input_indexes[1];
|
2020-06-29 16:54:11 +02:00
|
|
|
float input[1*1*3*3] = {
|
|
|
|
0.1, 0.5, 0.75, -3, 2.5, 2, -2.1, 7.8, 100};
|
2020-05-25 16:46:27 +02:00
|
|
|
float *output;
|
|
|
|
|
|
|
|
params.un_op = op;
|
|
|
|
|
|
|
|
operands[0].data = input;
|
|
|
|
operands[0].dims[0] = 1;
|
|
|
|
operands[0].dims[1] = 1;
|
2020-06-29 16:54:11 +02:00
|
|
|
operands[0].dims[2] = 3;
|
2020-05-25 16:46:27 +02:00
|
|
|
operands[0].dims[3] = 3;
|
|
|
|
operands[1].data = NULL;
|
|
|
|
|
|
|
|
input_indexes[0] = 0;
|
2021-01-22 13:28:29 +02:00
|
|
|
ff_dnn_execute_layer_math_unary(operands, input_indexes, 1, ¶ms, NULL);
|
2020-05-25 16:46:27 +02:00
|
|
|
|
|
|
|
output = operands[1].data;
|
|
|
|
for (int i = 0; i < sizeof(input) / sizeof(float); ++i) {
|
|
|
|
float expected_output = get_expected(input[i], op);
|
2020-07-08 08:09:51 +02:00
|
|
|
int output_nan = isnan(output[i]);
|
|
|
|
int expected_nan = isnan(expected_output);
|
|
|
|
if ((!output_nan && !expected_nan && fabs(output[i] - expected_output) > EPS) ||
|
|
|
|
(output_nan && !expected_nan) || (!output_nan && expected_nan)) {
|
2020-05-25 16:46:27 +02:00
|
|
|
printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output);
|
|
|
|
av_freep(&output);
|
|
|
|
return 1;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
av_freep(&output);
|
|
|
|
return 0;
|
|
|
|
}
|
|
|
|
|
|
|
|
int main(int agrc, char **argv)
|
|
|
|
{
|
|
|
|
if (test(DMUO_ABS))
|
|
|
|
return 1;
|
2020-06-06 14:12:47 +02:00
|
|
|
if (test(DMUO_SIN))
|
|
|
|
return 1;
|
2020-06-06 14:12:49 +02:00
|
|
|
if (test(DMUO_COS))
|
|
|
|
return 1;
|
2020-06-06 14:12:51 +02:00
|
|
|
if (test(DMUO_TAN))
|
|
|
|
return 1;
|
2020-06-18 11:15:32 +02:00
|
|
|
if (test(DMUO_ASIN))
|
|
|
|
return 1;
|
2020-06-18 11:15:34 +02:00
|
|
|
if (test(DMUO_ACOS))
|
|
|
|
return 1;
|
2020-06-18 11:15:36 +02:00
|
|
|
if (test(DMUO_ATAN))
|
|
|
|
return 1;
|
2020-06-29 16:54:01 +02:00
|
|
|
if (test(DMUO_SINH))
|
|
|
|
return 1;
|
2020-06-29 16:54:03 +02:00
|
|
|
if (test(DMUO_COSH))
|
|
|
|
return 1;
|
2020-06-29 16:54:05 +02:00
|
|
|
if (test(DMUO_TANH))
|
|
|
|
return 1;
|
2020-06-29 16:54:07 +02:00
|
|
|
if (test(DMUO_ASINH))
|
|
|
|
return 1;
|
2020-06-29 16:54:09 +02:00
|
|
|
if (test(DMUO_ACOSH))
|
|
|
|
return 1;
|
2020-06-29 16:54:11 +02:00
|
|
|
if (test(DMUO_ATANH))
|
|
|
|
return 1;
|
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, 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 \n \
to 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 \n \
to 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 \n \
to generate output result of native model\n")
Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
2020-07-31 09:41:24 +02:00
|
|
|
if (test(DMUO_CEIL))
|
|
|
|
return 1;
|
dnn_backend_native_layer_mathunary: add floor 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 = 'floor'
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.floor(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 {}_savemodel/model.pb --outdir={}_savemodel/ \n \nto generate model.model\n".format(name,name))
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={}_savemodel/model.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow -f framemd5 {}_savemodel/tensorflow_out.md5\n \
or\n \
./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow {}_savemodel/out_tensorflow.jpg\n \nto generate output result of tensorflow model\n".format(name, name, name, name))
print("To verify, please ffmpeg path use\n \n \
./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.model:input=dnn_in:output=dnn_out:dnn_backend=native -f framemd5 {}_savemodel/native_out.md5\n \
or \n \
./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.model:input=dnn_in:output=dnn_out:dnn_backend=native {}_savemodel/out_native.jpg\n \nto generate output result of native model\n".format(name, name, name, name))
Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
2020-08-06 08:47:16 +02:00
|
|
|
if (test(DMUO_FLOOR))
|
|
|
|
return 1;
|
2020-08-10 13:05:42 +02:00
|
|
|
if (test(DMUO_ROUND))
|
|
|
|
return 1;
|
2021-03-22 10:20:12 +02:00
|
|
|
if (test(DMUO_EXP))
|
|
|
|
return 1;
|
2020-05-25 16:46:27 +02:00
|
|
|
return 0;
|
|
|
|
}
|