2020-05-25 16:46:27 +02:00
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/*
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* Copyright (c) 2020
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*
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* This file is part of FFmpeg.
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*
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* FFmpeg is free software; you can redistribute it and/or
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* modify it under the terms of the GNU Lesser General Public
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* License as published by the Free Software Foundation; either
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* version 2.1 of the License, or (at your option) any later version.
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*
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* FFmpeg is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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* Lesser General Public License for more details.
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*
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* You should have received a copy of the GNU Lesser General Public
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* License along with FFmpeg; if not, write to the Free Software
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* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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*/
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#include <stdio.h>
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#include <string.h>
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#include <math.h>
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#include "libavfilter/dnn/dnn_backend_native_layer_mathunary.h"
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#include "libavutil/avassert.h"
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#define EPS 0.00001
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static float get_expected(float f, DNNMathUnaryOperation op)
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{
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switch (op)
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{
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case DMUO_ABS:
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return (f >= 0) ? f : -f;
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2020-06-06 14:12:47 +02:00
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case DMUO_SIN:
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return sin(f);
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2020-06-06 14:12:49 +02:00
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case DMUO_COS:
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return cos(f);
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2020-06-06 14:12:51 +02:00
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case DMUO_TAN:
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return tan(f);
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2020-06-18 11:15:32 +02:00
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case DMUO_ASIN:
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return asin(f);
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2020-06-18 11:15:34 +02:00
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case DMUO_ACOS:
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return acos(f);
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2020-06-18 11:15:36 +02:00
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case DMUO_ATAN:
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return atan(f);
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2020-06-29 16:54:01 +02:00
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case DMUO_SINH:
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return sinh(f);
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2020-06-29 16:54:03 +02:00
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case DMUO_COSH:
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return cosh(f);
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2020-06-29 16:54:05 +02:00
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case DMUO_TANH:
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return tanh(f);
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2020-06-29 16:54:07 +02:00
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case DMUO_ASINH:
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return asinh(f);
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2020-06-29 16:54:09 +02:00
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case DMUO_ACOSH:
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return acosh(f);
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2020-06-29 16:54:11 +02:00
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case DMUO_ATANH:
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return atanh(f);
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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
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case DMUO_CEIL:
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return ceil(f);
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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
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case DMUO_FLOOR:
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return floor(f);
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2020-08-10 13:05:42 +02:00
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case DMUO_ROUND:
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return round(f);
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2020-05-25 16:46:27 +02:00
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default:
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av_assert0(!"not supported yet");
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return 0.f;
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}
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}
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static int test(DNNMathUnaryOperation op)
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{
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DnnLayerMathUnaryParams params;
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DnnOperand operands[2];
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int32_t input_indexes[1];
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2020-06-29 16:54:11 +02:00
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float input[1*1*3*3] = {
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0.1, 0.5, 0.75, -3, 2.5, 2, -2.1, 7.8, 100};
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2020-05-25 16:46:27 +02:00
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float *output;
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params.un_op = op;
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operands[0].data = input;
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operands[0].dims[0] = 1;
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operands[0].dims[1] = 1;
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2020-06-29 16:54:11 +02:00
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operands[0].dims[2] = 3;
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2020-05-25 16:46:27 +02:00
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operands[0].dims[3] = 3;
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operands[1].data = NULL;
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input_indexes[0] = 0;
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2020-08-25 05:47:50 +02:00
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dnn_execute_layer_math_unary(operands, input_indexes, 1, ¶ms, NULL);
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2020-05-25 16:46:27 +02:00
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output = operands[1].data;
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for (int i = 0; i < sizeof(input) / sizeof(float); ++i) {
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float expected_output = get_expected(input[i], op);
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2020-07-08 08:09:51 +02:00
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int output_nan = isnan(output[i]);
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int expected_nan = isnan(expected_output);
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if ((!output_nan && !expected_nan && fabs(output[i] - expected_output) > EPS) ||
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(output_nan && !expected_nan) || (!output_nan && expected_nan)) {
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2020-05-25 16:46:27 +02:00
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printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output);
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av_freep(&output);
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return 1;
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}
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}
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av_freep(&output);
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return 0;
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}
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int main(int agrc, char **argv)
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{
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if (test(DMUO_ABS))
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return 1;
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2020-06-06 14:12:47 +02:00
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if (test(DMUO_SIN))
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return 1;
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2020-06-06 14:12:49 +02:00
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if (test(DMUO_COS))
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return 1;
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2020-06-06 14:12:51 +02:00
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if (test(DMUO_TAN))
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return 1;
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2020-06-18 11:15:32 +02:00
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if (test(DMUO_ASIN))
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return 1;
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2020-06-18 11:15:34 +02:00
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if (test(DMUO_ACOS))
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return 1;
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2020-06-18 11:15:36 +02:00
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if (test(DMUO_ATAN))
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return 1;
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2020-06-29 16:54:01 +02:00
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if (test(DMUO_SINH))
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return 1;
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2020-06-29 16:54:03 +02:00
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if (test(DMUO_COSH))
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return 1;
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2020-06-29 16:54:05 +02:00
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if (test(DMUO_TANH))
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return 1;
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2020-06-29 16:54:07 +02:00
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if (test(DMUO_ASINH))
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return 1;
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2020-06-29 16:54:09 +02:00
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if (test(DMUO_ACOSH))
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return 1;
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2020-06-29 16:54:11 +02:00
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if (test(DMUO_ATANH))
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return 1;
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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
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if (test(DMUO_CEIL))
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return 1;
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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
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if (test(DMUO_FLOOR))
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return 1;
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2020-08-10 13:05:42 +02:00
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if (test(DMUO_ROUND))
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return 1;
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2020-05-25 16:46:27 +02:00
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return 0;
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}
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