tests/dnn/dnn-layer-pad-test.c - manifest_repos/ffmpeg - Git at Google

 /*
  * 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, &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;
     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;
     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;
 }
	/*
	* 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[144*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[199*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, &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[322*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[622*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, &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[122*3] = {
	0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
	};
	float expected_output[132*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, &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;
	}