c26730ed8f
They test libavfilter internal API, so they should be libavfilter test programs (which implies: linked statically to libavfilter to access internal APIs and linked normally (statically or dynamically depending upon the build configuration) against all the other libs). Right now, they are always linked statically against all libs, which is a significant size waste compared to shared libs as all of libavcodec has been pulled in despite not being really used. This also leads to linking failures on systems for which av_export_avutil is intended: libavcodec does not expect to be linked statically against the library providing avpriv_(cga|vga16)_font in this case. This is fixed by this commit. Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
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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ff_dnn_execute_layer_pad(operands, input_indexes, 1, ¶ms, NULL);
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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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ff_dnn_execute_layer_pad(operands, input_indexes, 1, ¶ms, NULL);
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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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ff_dnn_execute_layer_pad(operands, input_indexes, 1, ¶ms, NULL);
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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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