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fate: add unit test for dnn-layer-pad

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'make fate-dnn-layer-pad' to run the test

Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
This commit is contained in:
Guo, Yejun 2019-07-29 09:56:40 +08:00 committed by Pedro Arthur
parent df8db34552
commit 3805aae479
4 changed files with 226 additions and 1 deletions
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5
tests/Makefile
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@ -10,7 +10,8 @@ FFMPEG=ffmpeg$(PROGSSUF)$(EXESUF)
$(AREF): CMP= $(AREF): CMP=
APITESTSDIR := tests/api APITESTSDIR := tests/api
FATE_OUTDIRS = tests/data tests/data/fate tests/data/filtergraphs tests/data/lavf tests/data/lavf-fate tests/data/pixfmt tests/vsynth1 $(APITESTSDIR) DNNTESTSDIR := tests/dnn
FATE_OUTDIRS = tests/data tests/data/fate tests/data/filtergraphs tests/data/lavf tests/data/lavf-fate tests/data/pixfmt tests/vsynth1 $(APITESTSDIR) $(DNNTESTSDIR)
OUTDIRS += $(FATE_OUTDIRS) OUTDIRS += $(FATE_OUTDIRS)
$(VREF): tests/videogen$(HOSTEXESUF) | tests/vsynth1 $(VREF): tests/videogen$(HOSTEXESUF) | tests/vsynth1
@ -85,6 +86,7 @@ FILTERDEMDECENCMUX = $(call ALLYES, $(1:%=%_FILTER) $(2)_DEMUXER $(3)_DECODER $(
PARSERDEMDEC = $(call ALLYES, $(1)_PARSER $(2)_DEMUXER $(3)_DECODER) PARSERDEMDEC = $(call ALLYES, $(1)_PARSER $(2)_DEMUXER $(3)_DECODER)
include $(SRC_PATH)/$(APITESTSDIR)/Makefile include $(SRC_PATH)/$(APITESTSDIR)/Makefile
include $(SRC_PATH)/$(DNNTESTSDIR)/Makefile
include $(SRC_PATH)/tests/fate/acodec.mak include $(SRC_PATH)/tests/fate/acodec.mak
include $(SRC_PATH)/tests/fate/vcodec.mak include $(SRC_PATH)/tests/fate/vcodec.mak
@ -118,6 +120,7 @@ include $(SRC_PATH)/tests/fate/cover-art.mak
include $(SRC_PATH)/tests/fate/dca.mak include $(SRC_PATH)/tests/fate/dca.mak
include $(SRC_PATH)/tests/fate/demux.mak include $(SRC_PATH)/tests/fate/demux.mak
include $(SRC_PATH)/tests/fate/dfa.mak include $(SRC_PATH)/tests/fate/dfa.mak
include $(SRC_PATH)/tests/fate/dnn.mak
include $(SRC_PATH)/tests/fate/dnxhd.mak include $(SRC_PATH)/tests/fate/dnxhd.mak
include $(SRC_PATH)/tests/fate/dpcm.mak include $(SRC_PATH)/tests/fate/dpcm.mak
include $(SRC_PATH)/tests/fate/ea.mak include $(SRC_PATH)/tests/fate/ea.mak

11
tests/dnn/Makefile Normal file
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@ -0,0 +1,11 @@
DNNTESTPROGS += dnn-layer-pad
DNNTESTOBJS := $(DNNTESTOBJS:%=$(DNNTESTSDIR)%) $(DNNTESTPROGS:%=$(DNNTESTSDIR)/%-test.o)
DNNTESTPROGS := $(DNNTESTPROGS:%=$(DNNTESTSDIR)/%-test$(EXESUF))
-include $(wildcard $(DNNTESTOBJS:.o=.d))
$(DNNTESTPROGS): %$(EXESUF): %.o $(FF_DEP_LIBS)
$(LD) $(LDFLAGS) $(LDEXEFLAGS) $(LD_O) $(filter %.o,$^) $(FF_EXTRALIBS) $(ELIBS)
testclean::
$(RM) $(addprefix $(DNNTESTSDIR)/,$(CLEANSUFFIXES) *-test$(EXESUF))

203
tests/dnn/dnn-layer-pad-test.c Normal file
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@ -0,0 +1,203 @@
/*
* 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;
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[1*9*9*3];
memset(output, 0, sizeof(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;
dnn_execute_layer_pad(input, output, &params, 1, 4, 4, 3);
for (int i = 0; i < sizeof(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]);
return 1;
}
}
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;
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[6*2*2*3];
memset(output, 0, sizeof(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;
dnn_execute_layer_pad(input, output, &params, 3, 2, 2, 3);
for (int i = 0; i < sizeof(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]);
return 1;
}
}
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;
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[1*3*2*6];
memset(output, 0, sizeof(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;
dnn_execute_layer_pad(input, output, &params, 1, 2, 2, 3);
for (int i = 0; i < sizeof(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]);
return 1;
}
}
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;
}

8
tests/fate/dnn.mak Normal file
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@ -0,0 +1,8 @@
FATE_DNN += fate-dnn-layer-pad
fate-dnn-layer-pad: $(DNNTESTSDIR)/dnn-layer-pad-test$(EXESUF)
fate-dnn-layer-pad: CMD = run $(DNNTESTSDIR)/dnn-layer-pad-test$(EXESUF)
fate-dnn-layer-pad: CMP = null
FATE-yes += $(FATE_DNN)
fate-dnn: $(FATE_DNN)