mirror of
https://github.com/xenia-project/FFmpeg.git
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240 lines
8.5 KiB
C
240 lines
8.5 KiB
C
/*
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* Copyright (c) 2019 Guo Yejun
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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_pad.h"
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#define EPSON 0.00001
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static int test_with_mode_symmetric(void)
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{
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// the input data and expected data are generated with below python code.
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/*
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x = tf.placeholder(tf.float32, shape=[1, None, None, 3])
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y = tf.pad(x, [[0, 0], [2, 3], [3, 2], [0, 0]], 'SYMMETRIC')
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data = np.arange(48).reshape(1, 4, 4, 3);
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sess=tf.Session()
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sess.run(tf.global_variables_initializer())
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output = sess.run(y, feed_dict={x: data})
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print(list(data.flatten()))
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print(list(output.flatten()))
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print(data.shape)
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print(output.shape)
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*/
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LayerPadParams params;
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DnnOperand operands[2];
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int32_t input_indexes[1];
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float input[1*4*4*3] = {
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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
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};
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float expected_output[1*9*9*3] = {
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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
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};
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float *output;
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params.mode = LPMP_SYMMETRIC;
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params.paddings[0][0] = 0;
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params.paddings[0][1] = 0;
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params.paddings[1][0] = 2;
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params.paddings[1][1] = 3;
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params.paddings[2][0] = 3;
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params.paddings[2][1] = 2;
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params.paddings[3][0] = 0;
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params.paddings[3][1] = 0;
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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] = 4;
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operands[0].dims[2] = 4;
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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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dnn_execute_layer_pad(operands, input_indexes, 1, ¶ms);
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output = operands[1].data;
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for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
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if (fabs(output[i] - expected_output[i]) > EPSON) {
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printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output[i]);
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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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static int test_with_mode_reflect(void)
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{
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// the input data and expected data are generated with below python code.
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/*
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x = tf.placeholder(tf.float32, shape=[3, None, None, 3])
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y = tf.pad(x, [[1, 2], [0, 0], [0, 0], [0, 0]], 'REFLECT')
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data = np.arange(36).reshape(3, 2, 2, 3);
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sess=tf.Session()
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sess.run(tf.global_variables_initializer())
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output = sess.run(y, feed_dict={x: data})
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print(list(data.flatten()))
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print(list(output.flatten()))
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print(data.shape)
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print(output.shape)
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*/
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LayerPadParams params;
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DnnOperand operands[2];
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int32_t input_indexes[1];
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float input[3*2*2*3] = {
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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
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};
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float expected_output[6*2*2*3] = {
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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,
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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,
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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
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};
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float *output;
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params.mode = LPMP_REFLECT;
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params.paddings[0][0] = 1;
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params.paddings[0][1] = 2;
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params.paddings[1][0] = 0;
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params.paddings[1][1] = 0;
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params.paddings[2][0] = 0;
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params.paddings[2][1] = 0;
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params.paddings[3][0] = 0;
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params.paddings[3][1] = 0;
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operands[0].data = input;
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operands[0].dims[0] = 3;
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operands[0].dims[1] = 2;
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operands[0].dims[2] = 2;
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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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dnn_execute_layer_pad(operands, input_indexes, 1, ¶ms);
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output = operands[1].data;
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for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
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if (fabs(output[i] - expected_output[i]) > EPSON) {
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printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output[i]);
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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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static int test_with_mode_constant(void)
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{
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// the input data and expected data are generated with below python code.
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/*
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x = tf.placeholder(tf.float32, shape=[1, None, None, 3])
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y = tf.pad(x, [[0, 0], [1, 0], [0, 0], [1, 2]], 'CONSTANT', constant_values=728)
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data = np.arange(12).reshape(1, 2, 2, 3);
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sess=tf.Session()
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sess.run(tf.global_variables_initializer())
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output = sess.run(y, feed_dict={x: data})
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print(list(data.flatten()))
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print(list(output.flatten()))
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print(data.shape)
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print(output.shape)
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*/
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LayerPadParams params;
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DnnOperand operands[2];
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int32_t input_indexes[1];
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float input[1*2*2*3] = {
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
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};
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float expected_output[1*3*2*6] = {
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728.0, 728.0, 728.0, 728.0, 728.0, 728.0, 728.0, 728.0, 728.0, 728.0, 728.0,
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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,
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728.0, 6.0, 7.0, 8.0, 728.0, 728.0, 728.0, 9.0, 10.0, 11.0, 728.0, 728.0
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};
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float *output;
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params.mode = LPMP_CONSTANT;
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params.constant_values = 728;
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params.paddings[0][0] = 0;
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params.paddings[0][1] = 0;
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params.paddings[1][0] = 1;
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params.paddings[1][1] = 0;
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params.paddings[2][0] = 0;
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params.paddings[2][1] = 0;
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params.paddings[3][0] = 1;
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params.paddings[3][1] = 2;
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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] = 2;
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operands[0].dims[2] = 2;
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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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dnn_execute_layer_pad(operands, input_indexes, 1, ¶ms);
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output = operands[1].data;
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for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
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if (fabs(output[i] - expected_output[i]) > EPSON) {
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printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output[i]);
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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 argc, char **argv)
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{
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if (test_with_mode_symmetric())
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return 1;
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if (test_with_mode_reflect())
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return 1;
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if (test_with_mode_constant())
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return 1;
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}
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