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ffmpeg/tests/dnn/dnn-layer-pad-test.c

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/*
* 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;
ff_dnn_execute_layer_pad(operands, input_indexes, 1, &params, NULL);
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;
ff_dnn_execute_layer_pad(operands, input_indexes, 1, &params, NULL);
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;
ff_dnn_execute_layer_pad(operands, input_indexes, 1, &params, NULL);
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;
}
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