falkTX
/
FFmpeg
mirror of https://github.com/falkTX/FFmpeg.git
1
0
Fork 0
Code Issues 0 Releases 338 Wiki Activity
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
94418 Commits
32 Branches
293MB
Tree: 0e66e1b61e
Branches Tags
${ item.name }
Create branch ${ searchTerm }
from '0e66e1b61e'
${ noResults }
FFmpeg/tests/dnn/dnn-layer-pad-test.c

204 lines
7.6KB
Raw Blame History 

  1. /*

  2.  * Copyright (c) 2019 Guo Yejun

  3.  *

  4.  * This file is part of FFmpeg.

  5.  *

  6.  * FFmpeg is free software; you can redistribute it and/or

  7.  * modify it under the terms of the GNU Lesser General Public

  8.  * License as published by the Free Software Foundation; either

  9.  * version 2.1 of the License, or (at your option) any later version.

  10.  *

  11.  * FFmpeg is distributed in the hope that it will be useful,

  12.  * but WITHOUT ANY WARRANTY; without even the implied warranty of

  13.  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU

  14.  * Lesser General Public License for more details.

  15.  *

  16.  * You should have received a copy of the GNU Lesser General Public

  17.  * License along with FFmpeg; if not, write to the Free Software

  18.  * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA

  19.  */

  20. 

  21. #include <stdio.h>

  22. #include <string.h>

  23. #include <math.h>

  24. #include "libavfilter/dnn/dnn_backend_native_layer_pad.h"

  25. 

  26. #define EPSON 0.00001

  27. 

  28. static int test_with_mode_symmetric(void)

  29. {

  30.     // the input data and expected data are generated with below python code.

  31.     /*

  32.     x = tf.placeholder(tf.float32, shape=[1, None, None, 3])

  33.     y = tf.pad(x, [[0, 0], [2, 3], [3, 2], [0, 0]], 'SYMMETRIC')

  34.     data = np.arange(48).reshape(1, 4, 4, 3);

  35. 

  36.     sess=tf.Session()

  37.     sess.run(tf.global_variables_initializer())

  38.     output = sess.run(y, feed_dict={x: data})

  39. 

  40.     print(list(data.flatten()))

  41.     print(list(output.flatten()))

  42.     print(data.shape)

  43.     print(output.shape)

  44.     */

  45. 

  46.     LayerPadParams params;

  47.     float input[1*4*4*3] = {

  48.         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

  49.     };

  50.     float expected_output[1*9*9*3] = {

  51.         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,

  52.         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,

  53.         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,

  54.         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,

  55.         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,

  56.         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,

  57.         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,

  58.         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

  59.     };

  60.     float output[1*9*9*3];

  61.     memset(output, 0, sizeof(output));

  62. 

  63.     params.mode = LPMP_SYMMETRIC;

  64.     params.paddings[0][0] = 0;

  65.     params.paddings[0][1] = 0;

  66.     params.paddings[1][0] = 2;

  67.     params.paddings[1][1] = 3;

  68.     params.paddings[2][0] = 3;

  69.     params.paddings[2][1] = 2;

  70.     params.paddings[3][0] = 0;

  71.     params.paddings[3][1] = 0;

  72. 

  73.     dnn_execute_layer_pad(input, output, &params, 1, 4, 4, 3);

  74. 

  75.     for (int i = 0; i < sizeof(output) / sizeof(float); i++) {

  76.         if (fabs(output[i] - expected_output[i]) > EPSON) {

  77.             printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output[i]);

  78.             return 1;

  79.         }

  80.     }

  81. 

  82.     return 0;

  83. 

  84. }

  85. 

  86. static int test_with_mode_reflect(void)

  87. {

  88.     // the input data and expected data are generated with below python code.

  89.     /*

  90.     x = tf.placeholder(tf.float32, shape=[3, None, None, 3])

  91.     y = tf.pad(x, [[1, 2], [0, 0], [0, 0], [0, 0]], 'REFLECT')

  92.     data = np.arange(36).reshape(3, 2, 2, 3);

  93. 

  94.     sess=tf.Session()

  95.     sess.run(tf.global_variables_initializer())

  96.     output = sess.run(y, feed_dict={x: data})

  97. 

  98.     print(list(data.flatten()))

  99.     print(list(output.flatten()))

  100.     print(data.shape)

  101.     print(output.shape)

  102.     */

  103. 

  104.     LayerPadParams params;

  105.     float input[3*2*2*3] = {

  106.         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

  107.     };

  108.     float expected_output[6*2*2*3] = {

  109.         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,

  110.         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,

  111.         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

  112.     };

  113.     float output[6*2*2*3];

  114.     memset(output, 0, sizeof(output));

  115. 

  116.     params.mode = LPMP_REFLECT;

  117.     params.paddings[0][0] = 1;

  118.     params.paddings[0][1] = 2;

  119.     params.paddings[1][0] = 0;

  120.     params.paddings[1][1] = 0;

  121.     params.paddings[2][0] = 0;

  122.     params.paddings[2][1] = 0;

  123.     params.paddings[3][0] = 0;

  124.     params.paddings[3][1] = 0;

  125. 

  126.     dnn_execute_layer_pad(input, output, &params, 3, 2, 2, 3);

  127. 

  128.     for (int i = 0; i < sizeof(output) / sizeof(float); i++) {

  129.         if (fabs(output[i] - expected_output[i]) > EPSON) {

  130.             printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output[i]);

  131.             return 1;

  132.         }

  133.     }

  134. 

  135.     return 0;

  136. 

  137. }

  138. 

  139. static int test_with_mode_constant(void)

  140. {

  141.     // the input data and expected data are generated with below python code.

  142.     /*

  143.     x = tf.placeholder(tf.float32, shape=[1, None, None, 3])

  144.     y = tf.pad(x, [[0, 0], [1, 0], [0, 0], [1, 2]], 'CONSTANT', constant_values=728)

  145.     data = np.arange(12).reshape(1, 2, 2, 3);

  146. 

  147.     sess=tf.Session()

  148.     sess.run(tf.global_variables_initializer())

  149.     output = sess.run(y, feed_dict={x: data})

  150. 

  151.     print(list(data.flatten()))

  152.     print(list(output.flatten()))

  153.     print(data.shape)

  154.     print(output.shape)

  155.     */

  156. 

  157.     LayerPadParams params;

  158.     float input[1*2*2*3] = {

  159.         0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11

  160.     };

  161.     float expected_output[1*3*2*6] = {

  162.         728.0, 728.0, 728.0, 728.0, 728.0, 728.0, 728.0, 728.0, 728.0, 728.0, 728.0,

  163.         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,

  164.         728.0, 6.0, 7.0, 8.0, 728.0, 728.0, 728.0, 9.0, 10.0, 11.0, 728.0, 728.0

  165.     };

  166.     float output[1*3*2*6];

  167.     memset(output, 0, sizeof(output));

  168. 

  169.     params.mode = LPMP_CONSTANT;

  170.     params.constant_values = 728;

  171.     params.paddings[0][0] = 0;

  172.     params.paddings[0][1] = 0;

  173.     params.paddings[1][0] = 1;

  174.     params.paddings[1][1] = 0;

  175.     params.paddings[2][0] = 0;

  176.     params.paddings[2][1] = 0;

  177.     params.paddings[3][0] = 1;

  178.     params.paddings[3][1] = 2;

  179. 

  180.     dnn_execute_layer_pad(input, output, &params, 1, 2, 2, 3);

  181. 

  182.     for (int i = 0; i < sizeof(output) / sizeof(float); i++) {

  183.         if (fabs(output[i] - expected_output[i]) > EPSON) {

  184.             printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output[i]);

  185.             return 1;

  186.         }

  187.     }

  188. 

  189.     return 0;

  190. 

  191. }

  192. 

  193. int main(int argc, char **argv)

  194. {

  195.     if (test_with_mode_symmetric())

  196.         return 1;

  197. 

  198.     if (test_with_mode_reflect())

  199.         return 1;

  200. 

  201.     if (test_with_mode_constant())

  202.         return 1;

  203. }