mirror of
https://github.com/FFmpeg/FFmpeg.git
synced 2024-11-26 19:01:44 +02:00
240 lines
8.5 KiB
C
240 lines
8.5 KiB
C
/*
|
|
* Copyright (c) 2019 Guo Yejun
|
|
*
|
|
* 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_pad.h"
|
|
|
|
#define EPSON 0.00001
|
|
|
|
static int test_with_mode_symmetric(void)
|
|
{
|
|
// the input data and expected data are generated with below python code.
|
|
/*
|
|
x = tf.placeholder(tf.float32, shape=[1, None, None, 3])
|
|
y = tf.pad(x, [[0, 0], [2, 3], [3, 2], [0, 0]], 'SYMMETRIC')
|
|
data = np.arange(48).reshape(1, 4, 4, 3);
|
|
|
|
sess=tf.Session()
|
|
sess.run(tf.global_variables_initializer())
|
|
output = sess.run(y, feed_dict={x: data})
|
|
|
|
print(list(data.flatten()))
|
|
print(list(output.flatten()))
|
|
print(data.shape)
|
|
print(output.shape)
|
|
*/
|
|
|
|
LayerPadParams params;
|
|
DnnOperand operands[2];
|
|
int32_t input_indexes[1];
|
|
float input[1*4*4*3] = {
|
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47
|
|
};
|
|
float expected_output[1*9*9*3] = {
|
|
18.0, 19.0, 20.0, 15.0, 16.0, 17.0, 12.0, 13.0, 14.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 21.0, 22.0, 23.0, 18.0, 19.0, 20.0, 6.0, 7.0, 8.0, 3.0,
|
|
4.0, 5.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 9.0, 10.0, 11.0, 6.0, 7.0, 8.0, 6.0, 7.0, 8.0, 3.0, 4.0, 5.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 3.0,
|
|
4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 9.0, 10.0, 11.0, 6.0, 7.0, 8.0, 18.0, 19.0, 20.0, 15.0, 16.0, 17.0, 12.0, 13.0, 14.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0,
|
|
21.0, 22.0, 23.0, 21.0, 22.0, 23.0, 18.0, 19.0, 20.0, 30.0, 31.0, 32.0, 27.0, 28.0, 29.0, 24.0, 25.0, 26.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 33.0,
|
|
34.0, 35.0, 30.0, 31.0, 32.0, 42.0, 43.0, 44.0, 39.0, 40.0, 41.0, 36.0, 37.0, 38.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 45.0, 46.0, 47.0, 42.0, 43.0,
|
|
44.0, 42.0, 43.0, 44.0, 39.0, 40.0, 41.0, 36.0, 37.0, 38.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 45.0, 46.0, 47.0, 42.0, 43.0, 44.0, 30.0, 31.0, 32.0,
|
|
27.0, 28.0, 29.0, 24.0, 25.0, 26.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 33.0, 34.0, 35.0, 30.0, 31.0, 32.0, 18.0, 19.0, 20.0, 15.0, 16.0, 17.0, 12.0,
|
|
13.0, 14.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 21.0, 22.0, 23.0, 18.0, 19.0, 20.0
|
|
};
|
|
float *output;
|
|
|
|
params.mode = LPMP_SYMMETRIC;
|
|
params.paddings[0][0] = 0;
|
|
params.paddings[0][1] = 0;
|
|
params.paddings[1][0] = 2;
|
|
params.paddings[1][1] = 3;
|
|
params.paddings[2][0] = 3;
|
|
params.paddings[2][1] = 2;
|
|
params.paddings[3][0] = 0;
|
|
params.paddings[3][1] = 0;
|
|
|
|
operands[0].data = input;
|
|
operands[0].dims[0] = 1;
|
|
operands[0].dims[1] = 4;
|
|
operands[0].dims[2] = 4;
|
|
operands[0].dims[3] = 3;
|
|
operands[1].data = NULL;
|
|
|
|
input_indexes[0] = 0;
|
|
dnn_execute_layer_pad(operands, input_indexes, 1, ¶ms);
|
|
|
|
output = operands[1].data;
|
|
for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
|
|
if (fabs(output[i] - expected_output[i]) > EPSON) {
|
|
printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output[i]);
|
|
av_freep(&output);
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
av_freep(&output);
|
|
return 0;
|
|
|
|
}
|
|
|
|
static int test_with_mode_reflect(void)
|
|
{
|
|
// the input data and expected data are generated with below python code.
|
|
/*
|
|
x = tf.placeholder(tf.float32, shape=[3, None, None, 3])
|
|
y = tf.pad(x, [[1, 2], [0, 0], [0, 0], [0, 0]], 'REFLECT')
|
|
data = np.arange(36).reshape(3, 2, 2, 3);
|
|
|
|
sess=tf.Session()
|
|
sess.run(tf.global_variables_initializer())
|
|
output = sess.run(y, feed_dict={x: data})
|
|
|
|
print(list(data.flatten()))
|
|
print(list(output.flatten()))
|
|
print(data.shape)
|
|
print(output.shape)
|
|
*/
|
|
|
|
LayerPadParams params;
|
|
DnnOperand operands[2];
|
|
int32_t input_indexes[1];
|
|
float input[3*2*2*3] = {
|
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35
|
|
};
|
|
float expected_output[6*2*2*3] = {
|
|
12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0,
|
|
12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0,
|
|
35.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0
|
|
};
|
|
float *output;
|
|
|
|
params.mode = LPMP_REFLECT;
|
|
params.paddings[0][0] = 1;
|
|
params.paddings[0][1] = 2;
|
|
params.paddings[1][0] = 0;
|
|
params.paddings[1][1] = 0;
|
|
params.paddings[2][0] = 0;
|
|
params.paddings[2][1] = 0;
|
|
params.paddings[3][0] = 0;
|
|
params.paddings[3][1] = 0;
|
|
|
|
operands[0].data = input;
|
|
operands[0].dims[0] = 3;
|
|
operands[0].dims[1] = 2;
|
|
operands[0].dims[2] = 2;
|
|
operands[0].dims[3] = 3;
|
|
operands[1].data = NULL;
|
|
|
|
input_indexes[0] = 0;
|
|
dnn_execute_layer_pad(operands, input_indexes, 1, ¶ms);
|
|
|
|
output = operands[1].data;
|
|
for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
|
|
if (fabs(output[i] - expected_output[i]) > EPSON) {
|
|
printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output[i]);
|
|
av_freep(&output);
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
av_freep(&output);
|
|
return 0;
|
|
|
|
}
|
|
|
|
static int test_with_mode_constant(void)
|
|
{
|
|
// the input data and expected data are generated with below python code.
|
|
/*
|
|
x = tf.placeholder(tf.float32, shape=[1, None, None, 3])
|
|
y = tf.pad(x, [[0, 0], [1, 0], [0, 0], [1, 2]], 'CONSTANT', constant_values=728)
|
|
data = np.arange(12).reshape(1, 2, 2, 3);
|
|
|
|
sess=tf.Session()
|
|
sess.run(tf.global_variables_initializer())
|
|
output = sess.run(y, feed_dict={x: data})
|
|
|
|
print(list(data.flatten()))
|
|
print(list(output.flatten()))
|
|
print(data.shape)
|
|
print(output.shape)
|
|
*/
|
|
|
|
LayerPadParams params;
|
|
DnnOperand operands[2];
|
|
int32_t input_indexes[1];
|
|
float input[1*2*2*3] = {
|
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
|
|
};
|
|
float expected_output[1*3*2*6] = {
|
|
728.0, 728.0, 728.0, 728.0, 728.0, 728.0, 728.0, 728.0, 728.0, 728.0, 728.0,
|
|
728.0, 728.0, 0.0, 1.0, 2.0, 728.0, 728.0, 728.0, 3.0, 4.0, 5.0, 728.0, 728.0,
|
|
728.0, 6.0, 7.0, 8.0, 728.0, 728.0, 728.0, 9.0, 10.0, 11.0, 728.0, 728.0
|
|
};
|
|
float *output;
|
|
|
|
params.mode = LPMP_CONSTANT;
|
|
params.constant_values = 728;
|
|
params.paddings[0][0] = 0;
|
|
params.paddings[0][1] = 0;
|
|
params.paddings[1][0] = 1;
|
|
params.paddings[1][1] = 0;
|
|
params.paddings[2][0] = 0;
|
|
params.paddings[2][1] = 0;
|
|
params.paddings[3][0] = 1;
|
|
params.paddings[3][1] = 2;
|
|
|
|
operands[0].data = input;
|
|
operands[0].dims[0] = 1;
|
|
operands[0].dims[1] = 2;
|
|
operands[0].dims[2] = 2;
|
|
operands[0].dims[3] = 3;
|
|
operands[1].data = NULL;
|
|
|
|
input_indexes[0] = 0;
|
|
dnn_execute_layer_pad(operands, input_indexes, 1, ¶ms);
|
|
|
|
output = operands[1].data;
|
|
for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
|
|
if (fabs(output[i] - expected_output[i]) > EPSON) {
|
|
printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output[i]);
|
|
av_freep(&output);
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
av_freep(&output);
|
|
return 0;
|
|
|
|
}
|
|
|
|
int main(int argc, char **argv)
|
|
{
|
|
if (test_with_mode_symmetric())
|
|
return 1;
|
|
|
|
if (test_with_mode_reflect())
|
|
return 1;
|
|
|
|
if (test_with_mode_constant())
|
|
return 1;
|
|
}
|