diff options
Diffstat (limited to 'tests/dnn')
| -rw-r--r-- | tests/dnn/.gitignore | 8 | ||||
| -rw-r--r-- | tests/dnn/Makefile | 18 | ||||
| -rw-r--r-- | tests/dnn/dnn-layer-avgpool-test.c | 197 | ||||
| -rw-r--r-- | tests/dnn/dnn-layer-conv2d-test.c | 248 | ||||
| -rw-r--r-- | tests/dnn/dnn-layer-dense-test.c | 131 | ||||
| -rw-r--r-- | tests/dnn/dnn-layer-depth2space-test.c | 102 | ||||
| -rw-r--r-- | tests/dnn/dnn-layer-mathbinary-test.c | 214 | ||||
| -rw-r--r-- | tests/dnn/dnn-layer-mathunary-test.c | 148 | ||||
| -rw-r--r-- | tests/dnn/dnn-layer-maximum-test.c | 71 | ||||
| -rw-r--r-- | tests/dnn/dnn-layer-pad-test.c | 239 |
10 files changed, 0 insertions, 1376 deletions
diff --git a/tests/dnn/.gitignore b/tests/dnn/.gitignore deleted file mode 100644 index 03b04d6653..0000000000 --- a/tests/dnn/.gitignore +++ /dev/null @@ -1,8 +0,0 @@ -/dnn-layer-conv2d-test -/dnn-layer-depth2space-test -/dnn-layer-maximum-test -/dnn-layer-pad-test -/dnn-layer-mathbinary-test -/dnn-layer-mathunary-test -/dnn-layer-avgpool-test -/dnn-layer-dense-test diff --git a/tests/dnn/Makefile b/tests/dnn/Makefile deleted file mode 100644 index ef827520de..0000000000 --- a/tests/dnn/Makefile +++ /dev/null @@ -1,18 +0,0 @@ -DNNTESTPROGS += dnn-layer-pad -DNNTESTPROGS += dnn-layer-conv2d -DNNTESTPROGS += dnn-layer-depth2space -DNNTESTPROGS += dnn-layer-dense -DNNTESTPROGS += dnn-layer-mathbinary -DNNTESTPROGS += dnn-layer-maximum -DNNTESTPROGS += dnn-layer-mathunary -DNNTESTPROGS += dnn-layer-avgpool - -DNNTESTOBJS := $(DNNTESTOBJS:%=$(DNNTESTSDIR)%) $(DNNTESTPROGS:%=$(DNNTESTSDIR)/%-test.o) -DNNTESTPROGS := $(DNNTESTPROGS:%=$(DNNTESTSDIR)/%-test$(EXESUF)) --include $(wildcard $(DNNTESTOBJS:.o=.d)) - -$(DNNTESTPROGS): %$(EXESUF): %.o $(FF_STATIC_DEP_LIBS) - $(LD) $(LDFLAGS) $(LDEXEFLAGS) $(LD_O) $(filter %.o,$^) $(FF_STATIC_DEP_LIBS) $(EXTRALIBS-avcodec) $(EXTRALIBS-avfilter) $(EXTRALIBS-avformat) $(EXTRALIBS-avutil) $(EXTRALIBS-swresample) $(EXTRALIBS) - -testclean:: - $(RM) $(addprefix $(DNNTESTSDIR)/,$(CLEANSUFFIXES) *-test$(EXESUF)) diff --git a/tests/dnn/dnn-layer-avgpool-test.c b/tests/dnn/dnn-layer-avgpool-test.c deleted file mode 100644 index 4a925ea22a..0000000000 --- a/tests/dnn/dnn-layer-avgpool-test.c +++ /dev/null @@ -1,197 +0,0 @@ -/* - * Copyright (c) 2020 - * - * 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 "libavfilter/dnn/dnn_backend_native_layer_avgpool.h" - -#define EPSON 0.00001 - -static int test_with_same(void) -{ - // the input data and expected data are generated with below python code. - /* - import tensorflow as tf - import numpy as np - - x = tf.placeholder(tf.float32, shape=[1, None, None, 3]) - y = tf.layers.average_pooling2d(x, pool_size=[2,2], strides=[1,1], padding='VALID') - data = np.random.rand(1, 5, 6, 3); - - sess=tf.Session() - sess.run(tf.global_variables_initializer()) - - output = sess.run(y, feed_dict={x: data}) - - print("input:") - print(data.shape) - print(list(data.flatten())) - - print("output:") - print(output.shape) - print(list(output.flatten())) - */ - - AvgPoolParams params; - DnnOperand operands[2]; - int32_t input_indexes[1]; - float input[1*5*6*3] = { - 0.7461309859908424, 0.7567538372797069, 0.07662743569678687, 0.8882112610336333, 0.9720443314026668, 0.3337200343220823, 0.4421032129780248, - 0.14940809044964876, 0.6773177061961277, 0.9778844630669781, 0.6522650522626998, 0.0317651530878591, 0.31259897552911364, 0.6235936821891896, - 0.40016094349542775, 0.4599222930032276, 0.7893807222960093, 0.8475986363538283, 0.5058802717647394, 0.7827005363222633, 0.3032188123727916, - 0.8983728631302361, 0.20622408444965523, 0.22966072303869878, 0.09535751273161308, 0.8760709100995375, 0.9982324154558745, 0.7904595468621013, - 0.13883671508879347, 0.9332751439533138, 0.0010861680752152214, 0.3607210449251048, 0.6600652759586171, 0.7629572058138805, 0.29441975810476106, - 0.2683471432889405, 0.22574580829831536, 0.8893251976212904, 0.3907737043801005, 0.6421829842863968, 0.6670373870457297, 0.9383850793160277, - 0.4120458907436003, 0.3589847212711481, 0.48047736550128983, 0.6428192648418949, 0.0313661686292348, 0.429357100401472, 0.5123413386514056, - 0.8492446404097114, 0.9045286128486804, 0.8123708563814285, 0.3943245008451698, 0.9576713003177785, 0.5985610965938726, 0.9350833279543561, - 0.8010079897491659, 0.45882114217642866, 0.35275037908941487, 0.4555844661432271, 0.12352455940255314, 0.37801756635035544, 0.2824056214573083, - 0.6229462823245029, 0.7235305681391472, 0.5408259266122064, 0.12142224381781208, 0.34431198802873686, 0.7112823816321276, 0.6307144385115417, - 0.8136734589018082, 0.842095618140585, 0.8602767724004784, 0.6649236853766185, 0.5184782829419623, 0.9119607270982825, 0.3084111974561645, - 0.39460705638161364, 0.17710447526170836, 0.1715485945814199, 0.17277563576521882, 0.40188232428735704, 0.22847985411491878, 0.4135361701550696, - 0.24621846601980057, 0.6576588108454774, 0.6063336087333997, 0.6452342242996931, 0.7071689702737508, 0.1973416063225648 - }; - float expected_output[] = { - 0.75964886, 0.6794307, 0.23580676, 0.5810112, 0.5509369, 0.55973274, 0.5764512, 0.45414522, 0.6601476, 0.52050734, 0.44385415, - 0.50631666, 0.38414115, 0.5170288, 0.544043, 0.61143976, 0.5419003, 0.5579729, 0.5680455, 0.6363218, 0.4655096, 0.51198983, - 0.5270792, 0.66168886, 0.48517057, 0.3513146, 0.7103355, 0.48667657, 0.34504217, 0.7318065, 0.5221889, 0.4746775, 0.69765306, - 0.78766406, 0.34437215, 0.6130092, 0.48132777, 0.7110491, 0.6464378, 0.40914366, 0.4391975, 0.5392131, 0.45033398, 0.37297475, - 0.43326652, 0.4748823, 0.48711336, 0.64649844, 0.51921225, 0.60038865, 0.8538945, 0.7215426, 0.60399896, 0.89988345, 0.707405, - 0.5652921, 0.54241943, 0.41785273, 0.30268195, 0.3263432, 0.3313644, 0.37539417, 0.35238582, 0.34811732, 0.48849532, 0.56799453, - 0.41089734, 0.63070333, 0.5892633, 0.6379743, 0.7604212, 0.5197186, 0.88611877, 0.48666745, 0.45654267, 0.5445326, 0.2399799, - 0.28369135, 0.28949338, 0.20001422, 0.2931559, 0.3240504, 0.44306934, 0.5099349, 0.44572634, 0.68241394, 0.40183762, 0.6452342, - 0.707169, 0.1973416 - }; - float *output; - - params.strides = 1; - params.kernel_size = 2; - params.padding_method = SAME; - - operands[0].data = input; - operands[0].dims[0] = 1; - operands[0].dims[1] = 5; - operands[0].dims[2] = 6; - operands[0].dims[3] = 3; - operands[1].data = NULL; - - input_indexes[0] = 0; - ff_dnn_execute_layer_avg_pool(operands, input_indexes, 1, ¶ms, 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_valid(void) -{ - // the input data and expected data are generated with below python code. - /* - import tensorflow as tf - import numpy as np - - x = tf.placeholder(tf.float32, shape=[1, None, None, 3]) - y = tf.layers.average_pooling2d(x, pool_size=[2,2], strides=[1,1], padding='VALID') - data = np.random.rand(1, 5, 6, 3); - - sess=tf.Session() - sess.run(tf.global_variables_initializer()) - - output = sess.run(y, feed_dict={x: data}) - - print("input:") - print(data.shape) - print(list(data.flatten())) - - print("output:") - print(output.shape) - print(list(output.flatten())) - */ - - AvgPoolParams params; - DnnOperand operands[2]; - int32_t input_indexes[1]; - float input[1*5*6*3] = { - 0.5046741692941682, 0.9273653202485155, 0.8193878359859937, 0.1904059431360905, 0.8664919633253656, 0.7484625128286059, 0.984534184632278, - 0.31900804890072254, 0.3259426099940872, 0.05388974903570376, 0.7356610151331133, 0.46710858713311965, 0.718553768817036, 0.062478421853278676, - 0.7813224786584609, 0.4826837517658389, 0.9748095400220147, 0.8078547703898341, 0.11976750668368585, 0.8713586777195065, 0.41447321551284355, - 0.9818788239089807, 0.4335715767584073, 0.4059793452147419, 0.3677205907204525, 0.47919995923571, 0.8341395256258882, 0.7059726374074609, - 0.5478504551919791, 0.8622900484790175, 0.8343709722511167, 0.05089827275068537, 0.6465283980840416, 0.544539116066677, 0.39812057257884337, - 0.9578115576866337, 0.25012888117580145, 0.579333516024662, 0.5556732133051457, 0.6119862111181243, 0.0018736758772316398, 0.9795490254040474, - 0.4488085008883018, 0.28947489777011737, 0.4834108668633247, 0.9280490084385024, 0.9895821458049648, 0.31777618554697606, 0.42679693258977847, - 0.74447844466923, 0.9752225305081498, 0.17564130841849335, 0.22382692067314292, 0.009602884447469373, 0.5144884415025782, 0.031622570708844555, - 0.8277532752502512, 0.4111593210409763, 0.5272084646575664, 0.28856508082905297, 0.11317726946036655, 0.7203328275540273, 0.8310055019972384, - 0.8535951508685228, 0.40230347305233227, 0.2819703265132867, 0.6243143957791139, 0.7512463693822311, 0.7523056340495644, 0.8838077258040928, - 0.5472240664033092, 0.2550538284454935, 0.5560317774456567, 0.8966847087518931, 0.6728358284165321, 0.30361297147530875, 0.464343925441822, - 0.34507695659461224, 0.6333175615390685, 0.26661369038523497, 0.9926748632253231, 0.9994267301382666, 0.8684917986974414, 0.3598754806113009, - 0.49550268625464666, 0.03652458679973214, 0.13469081713137177, 0.4579424049273835, 0.48641107969110353, 0.9670250266945365 - }; - float expected_output[1*4*5*3] = { - 0.44918162, 0.7746969, 0.5970757, 0.63113487, 0.5245679, 0.578631, 0.52802926, 0.52042985, 0.6223702, 0.57819676, 0.34922206, - 0.6893124, 0.64503694, 0.37157673, 0.7983793, 0.49094033, 0.47153437, 0.5889187, 0.6025985, 0.30103004, 0.6757697, 0.6126377, - 0.5765268, 0.62440413, 0.7237974, 0.5832023, 0.7004543, 0.49533707, 0.35433105, 0.6472913, 0.44694072, 0.28500956, 0.6628852, - 0.39628282, 0.38472247, 0.6456326, 0.58590746, 0.60042334, 0.47854072, 0.7081889, 0.7219026, 0.5818187, 0.5276401, 0.56669396, - 0.49804622, 0.4463231, 0.4799649, 0.5335578, 0.36531678, 0.4946247, 0.6143306, 0.6498792, 0.5644355, 0.6163815, 0.7432098, - 0.5146416, 0.38221055, 0.6153918, 0.45535153, 0.5272688 - }; - float *output; - - params.strides = 1; - params.kernel_size = 2; - params.padding_method = VALID; - - operands[0].data = input; - operands[0].dims[0] = 1; - operands[0].dims[1] = 5; - operands[0].dims[2] = 6; - operands[0].dims[3] = 3; - operands[1].data = NULL; - - input_indexes[0] = 0; - ff_dnn_execute_layer_avg_pool(operands, input_indexes, 1, ¶ms, 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_same()) - return 1; - if (test_with_valid()) - return 1; - - return 0; -} diff --git a/tests/dnn/dnn-layer-conv2d-test.c b/tests/dnn/dnn-layer-conv2d-test.c deleted file mode 100644 index 5ee60eeaf0..0000000000 --- a/tests/dnn/dnn-layer-conv2d-test.c +++ /dev/null @@ -1,248 +0,0 @@ -/* - * 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_conv2d.h" - -#define EPSON 0.00001 - -static int test_with_same_dilate(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.layers.conv2d(x, 2, 3, activation=tf.nn.tanh, padding='same', dilation_rate=(2, 2), bias_initializer=tf.keras.initializers.he_normal()) - data = np.random.rand(1, 5, 6, 3); - - sess=tf.Session() - sess.run(tf.global_variables_initializer()) - - weights = dict([(var.name, sess.run(var)) for var in tf.trainable_variables()]) - kernel = weights['conv2d/kernel:0'] - kernel = np.transpose(kernel, [3, 0, 1, 2]) - print("kernel:") - print(kernel.shape) - print(list(kernel.flatten())) - - bias = weights['conv2d/bias:0'] - print("bias:") - print(bias.shape) - print(list(bias.flatten())) - - output = sess.run(y, feed_dict={x: data}) - - print("input:") - print(data.shape) - print(list(data.flatten())) - - print("output:") - print(output.shape) - print(list(output.flatten())) - */ - - ConvolutionalParams params; - DnnOperand operands[2]; - int32_t input_indexes[1]; - float input[1*5*6*3] = { - 0.7012556460308194, 0.4233847954643357, 0.19515900664313612, 0.16343083004926495, 0.5758261611052848, 0.9510767434014871, 0.11014085055947687, - 0.906327053637727, 0.8136794715542507, 0.45371764543639526, 0.5768443343523952, 0.19543668786046986, 0.15648326047898609, 0.2099500241141279, - 0.17658777090552413, 0.059335724777169196, 0.1729991838469117, 0.8150514704819208, 0.4435535466703049, 0.3752188477566878, 0.749936650421431, - 0.6823494635284907, 0.10776389679424747, 0.34247481674596836, 0.5147867256244629, 0.9063709728129032, 0.12423605800856818, 0.6064872945412728, - 0.5891681538551459, 0.9865836236466314, 0.9002163879294677, 0.003968273184274618, 0.8628374809643967, 0.1327176268279583, 0.8449799925703798, - 0.1937671869354366, 0.41524410152707425, 0.02038786604756837, 0.49792466069597496, 0.8881874553848784, 0.9683921035597336, 0.4122972568010813, - 0.843553550993252, 0.9588482762501964, 0.5190350762645546, 0.4283584264145317, 0.09781496073714646, 0.9501058833776156, 0.8665541760152776, - 0.31669272550095806, 0.07133074675453632, 0.606438007334886, 0.7007157020538224, 0.4827996264130444, 0.5167615606392761, 0.6385043039312651, - 0.23069664707810555, 0.058233497329354456, 0.06323892961591071, 0.24816458893245974, 0.8646369065257812, 0.24742185893094837, 0.09991225948167437, - 0.625700606979606, 0.7678541502111257, 0.6215834594679912, 0.5623003956582483, 0.07389123942681242, 0.7659100715711249, 0.486061471642225, - 0.9947455699829012, 0.9094911797643259, 0.7644355876253265, 0.05384315321492239, 0.13565394382783613, 0.9810628204953316, 0.007386389078887889, - 0.226182754156241, 0.2609021390764772, 0.24182802076928933, 0.13264782451941648, 0.2035816485767682, 0.005504188177612557, 0.7014619934040155, - 0.956215988391991, 0.5670398541013633, 0.9809764721750784, 0.6886338100487461, 0.5758152317218274, 0.7137823176776179 - }; - float expected_output[1*5*6*2] = { - -0.9480655, -0.7169147, -0.9404794, -0.5567385, -0.8991124, -0.8306558, -0.94487447, -0.8932543, -0.88238764, -0.7301602, - -0.8974813, -0.7026703, -0.8858988, -0.53203243, -0.92881465, -0.5648504, -0.8871471, -0.7000097, -0.91754407, -0.79684794, - -0.760465, -0.117928326, -0.88302773, -0.8975289, -0.70615053, 0.19231977, -0.8318776, -0.386184, -0.80698484, -0.8556624, - -0.7336671, -0.6168619, -0.7658234, -0.63449603, -0.73314047, -0.87502456, -0.58158904, -0.4184259, -0.52618927, -0.13613208, - -0.5093187, -0.21027721, -0.39455596, -0.44507834, -0.22269244, -0.73400885, -0.77655095, -0.74408925, -0.57313335, -0.15333457, - -0.74620694, -0.34858236, -0.42586932, -0.5240488, 0.1634339, -0.2447881, -0.57927346, -0.62732303, -0.82287043, -0.8474058 - }; - float *output; - float kernel[2*3*3*3] = { - 0.26025516, 0.16536498, -0.24351254, 0.33892477, -0.34005195, 0.35202783, 0.34056443, 0.01422739, 0.13799345, 0.29489166, - 0.2781723, 0.178585, 0.22122234, 0.044115514, 0.13134438, 0.31705368, 0.22527462, -0.021323413, 0.115134746, -0.18216397, - -0.21197563, -0.027848959, -0.01704529, -0.12401503, -0.23415318, -0.12661739, -0.35338148, 0.20049328, -0.076153606, - -0.23642601, -0.3125769, -0.025851756, -0.30006272, 0.050762743, 0.32003498, 0.3052225, -0.0017385483, 0.25337684, -0.25664508, - 0.27846587, -0.3112659, 0.2066065, 0.31499845, 0.113178134, 0.09449363, -0.11828774, -0.12671001, -0.36259216, 0.2710235, - -0.19676702, 0.023612618, -0.2596915, -0.34949252, -0.108270735 - }; - float bias[2] = { -1.6574852, -0.72915393 }; - - NativeContext ctx; - ctx.class = NULL; - ctx.options.conv2d_threads = 1; - - params.activation = TANH; - params.has_bias = 1; - params.biases = bias; - params.dilation = 2; - params.input_num = 3; - params.kernel = kernel; - params.kernel_size = 3; - params.output_num = 2; - params.padding_method = SAME; - - operands[0].data = input; - operands[0].dims[0] = 1; - operands[0].dims[1] = 5; - operands[0].dims[2] = 6; - operands[0].dims[3] = 3; - operands[1].data = NULL; - - input_indexes[0] = 0; - ff_dnn_execute_layer_conv2d(operands, input_indexes, 1, ¶ms, &ctx); - - 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_valid(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.layers.conv2d(x, 2, 3, activation=tf.nn.tanh, padding='valid', bias_initializer=tf.keras.initializers.he_normal()) - data = np.random.rand(1, 5, 6, 3); - - sess=tf.Session() - sess.run(tf.global_variables_initializer()) - - weights = dict([(var.name, sess.run(var)) for var in tf.trainable_variables()]) - kernel = weights['conv2d/kernel:0'] - kernel = np.transpose(kernel, [3, 0, 1, 2]) - print("kernel:") - print(kernel.shape) - print(list(kernel.flatten())) - - bias = weights['conv2d/bias:0'] - print("bias:") - print(bias.shape) - print(list(bias.flatten())) - - output = sess.run(y, feed_dict={x: data}) - - print("input:") - print(data.shape) - print(list(data.flatten())) - - print("output:") - print(output.shape) - print(list(output.flatten())) - */ - - ConvolutionalParams params; - DnnOperand operands[2]; - int32_t input_indexes[1]; - float input[1*5*6*3] = { - 0.26126657468269665, 0.42762216215337556, 0.7466274030131497, 0.802550266787863, 0.3709323443076644, 0.5919817068197668, 0.49274512279324967, - 0.7170132295090351, 0.0911793215410649, 0.5134213878288361, 0.670132600785118, 0.49417034512633484, 0.03887389460089885, 0.436785102836845, - 0.1490231658611978, 0.6413606121498127, 0.8595987991375995, 0.9132593077586231, 0.7075959004873255, 0.17754995944845464, 0.5212507214937141, - 0.35379732738215475, 0.25205107358505296, 0.3928792840544273, 0.09485294189485782, 0.8685115437448666, 0.6489046799288605, 0.509253797582924, - 0.8993255536791972, 0.18740056466602373, 0.34237617336313986, 0.3871438962989183, 0.1488532571774911, 0.5187002331293636, 0.8137098818752955, - 0.521761863717401, 0.4622312310118274, 0.29038411334638825, 0.16194915718170566, 0.5175999923925211, 0.8852230040101133, 0.0218263385047206, - 0.08482355352852367, 0.3463638568376264, 0.28627127120619733, 0.9553293378948409, 0.4803391055970835, 0.841635695030805, 0.3556828280031952, - 0.06778527221541808, 0.28193560357091596, 0.8399957619031576, 0.03305536359456385, 0.6625039162109645, 0.9300552020023897, 0.8551529138204146, - 0.6133216915522418, 0.222427800857393, 0.1315422686800336, 0.6189144989185527, 0.5346184916866876, 0.8348888624532548, 0.6544834567840291, - 0.2844062293389934, 0.28780026600883324, 0.5372272015684924, 0.6250226011503823, 0.28119106062279453, 0.49655812908420094, 0.6451488959145951, - 0.7362580606834843, 0.44815578616664087, 0.6454760235835586, 0.6794062414265861, 0.045378883014935756, 0.9008388543865096, 0.7949752851269782, - 0.4179928876222264, 0.28733419007048644, 0.996902319501908, 0.5690851338677467, 0.9511814013279738, 0.025323788678181636, 0.5594359732604794, - 0.1213732595086251, 0.7172624313368294, 0.6759328959074691, 0.07252138454885071, 0.17557735158403442, 0.5988895455048769 - }; - float expected_output[1*3*4*2] = { - -0.556947, -0.42143887, -0.092070885, 0.27404794, -0.41886684, 0.0862887, -0.25001016, -0.342721, 0.020730592, 0.04016919, -0.69839877, - -0.06136704, 0.14186388, -0.11655602, -0.23489095, -0.3845829, -0.19017771, 0.1595885, -0.18308741, -0.3071209, -0.5848686, -0.22509028, - -0.6023201, -0.14448485 - }; - float *output; - float kernel[2*3*3*3] = { - -0.25291282, 0.22402048, 0.028642118, -0.14615723, -0.27362752, -0.34801802, -0.2759148, 0.19594926, -0.25029412, 0.34606284, 0.10376671, - -0.1015394, 0.23616093, 0.2134214, 0.35285157, 0.05893758, 0.0024731457, -0.17143056, 0.35758412, 0.2186206, -0.28384736, -0.21206513, - -0.20871592, 0.27070445, 0.25878823, 0.11136332, -0.33737376, 0.08353335, -0.34290665, 0.041805506, -0.09738535, 0.3284936, -0.16838405, - -0.032494456, -0.29193437, 0.033259362, -0.09272635, -0.2802651, -0.28648436, 0.3542878, 0.2432127, -0.24551713, 0.27813476, 0.21024024, - -0.013690501, -0.1350077, -0.07826337, -0.34563828, 0.3220685, -0.07571727, 0.19420576, 0.20783454, 0.18738335, 0.16672492 - }; - float bias[2] = { -0.4773722, -0.19620377 }; - - NativeContext ctx; - ctx.class = NULL; - ctx.options.conv2d_threads = 1; - - params.activation = TANH; - params.has_bias = 1; - params.biases = bias; - params.dilation = 1; - params.input_num = 3; - params.kernel = kernel; - params.kernel_size = 3; - params.output_num = 2; - params.padding_method = VALID; - - operands[0].data = input; - operands[0].dims[0] = 1; - operands[0].dims[1] = 5; - operands[0].dims[2] = 6; - operands[0].dims[3] = 3; - operands[1].data = NULL; - - input_indexes[0] = 0; - ff_dnn_execute_layer_conv2d(operands, input_indexes, 1, ¶ms, &ctx); - - 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_valid()) - return 1; - if (test_with_same_dilate()) - return 1; - - return 0; -} diff --git a/tests/dnn/dnn-layer-dense-test.c b/tests/dnn/dnn-layer-dense-test.c deleted file mode 100644 index 696f7505e5..0000000000 --- a/tests/dnn/dnn-layer-dense-test.c +++ /dev/null @@ -1,131 +0,0 @@ -/* - * Copyright (c) 2020 - * - * 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_dense.h" - -#define EPSON 0.00001 - -static int test(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.layers.dense(input_x, 3, activation=tf.nn.sigmoid, bias_initializer=tf.keras.initializers.he_normal()) - data = np.random.rand(1, 5, 6, 3); - - sess=tf.Session() - sess.run(tf.global_variables_initializer()) - - weights = dict([(var.name, sess.run(var)) for var in tf.trainable_variables()]) - kernel = weights['dense/kernel:0'] - kernel = np.transpose(kernel, [1, 0]) - print("kernel:") - print(kernel.shape) - print(list(kernel.flatten())) - - bias = weights['dense/bias:0'] - print("bias:") - print(bias.shape) - print(list(bias.flatten())) - - output = sess.run(y, feed_dict={x: data}) - - print("input:") - print(data.shape) - print(list(data.flatten())) - - print("output:") - print(output.shape) - print(list(output.flatten())) - */ - - DenseParams params; - DnnOperand operands[2]; - int32_t input_indexes[1]; - float input[1*5*6*3] = { - 0.5552418686576308, 0.20653189262022464, 0.31115120939398877, 0.5897014433221428, 0.37340078861060655, 0.6470921693941893, 0.8039950367872679, 0.8762700891949274, - 0.6556655583829558, 0.5911096107039339, 0.18640250865290997, 0.2803248779238966, 0.31586613136402053, 0.9447300740056483, 0.9443980824873418, 0.8158851991115941, - 0.5631010340387631, 0.9407402251929046, 0.6485434876551682, 0.5631376966470001, 0.17581924875609634, 0.7033802439103178, 0.04802402495561675, 0.9183681450194972, - 0.46059317944364, 0.07964160481596883, 0.871787076270302, 0.973743142324361, 0.15923146943258415, 0.8212946080584571, 0.5415954459227064, 0.9552813822803975, - 0.4908552668172057, 0.33723691635292274, 0.46588057864910026, 0.8994239961321776, 0.09845220457674186, 0.1713400292123486, 0.39570294912818826, 0.08018956486392803, - 0.5290478278169032, 0.7141906125920976, 0.0320878067840098, 0.6412406575332606, 0.0075712007102423096, 0.7150828462386156, 0.1311989216968138, 0.4706847944253756, - 0.5447610794883336, 0.3430923933318001, 0.536082357943209, 0.4371629342483694, 0.40227962985019927, 0.3553806249465469, 0.031806622424259245, 0.7053916426174, - 0.3261570237309813, 0.419500213292063, 0.3155691223480851, 0.05664028113178088, 0.3636491555914486, 0.8502419746667123, 0.9836596530684955, 0.1628681802975801, - 0.09410832912479894, 0.28407218939480294, 0.7983417928813697, 0.24132158596506748, 0.8154729498062224, 0.29173768373895637, 0.13407102008052096, 0.18705786678800385, - 0.7167943621295573, 0.09222004247174376, 0.2319220738766018, 0.17708964382285064, 0.1391440370249517, 0.3254088083499256, 0.4013916894718289, 0.4819742663322323, - 0.15080103744648077, 0.9302407847555013, 0.9397597961319524, 0.5719200825550793, 0.9538938024682824, 0.9583882089203861, 0.5168861091262276, 0.1926396841842669, - 0.6781176744337578, 0.719366447288566 - }; - float expected_output[1*5*6*3] = { - -0.3921688, -0.9243112, -0.29659146, -0.64000785, -0.9466343, -0.62125254, -0.71759033, -0.9171336, -0.735589, -0.34365994, - -0.92100817, -0.23903961, -0.8962277, -0.9521279, -0.90962386, -0.7488303, -0.9563761, -0.7701762, -0.40800542, -0.87684774, - -0.3339763, -0.6354543, -0.97068924, -0.6246325, -0.6992075, -0.9706726, -0.6818918, -0.51864433, -0.9592881, -0.51187396, - -0.7423632, -0.89911884, -0.7457824, -0.82009757, -0.96402895, -0.8235518, -0.61980766, -0.94494647, -0.5410502, -0.8281218, - -0.95508635, -0.8201453, -0.5937325, -0.8679507, -0.500767, -0.39430764, -0.93967676, -0.32183182, -0.58913624, -0.939717, - -0.55179894, -0.55004454, -0.9214453, -0.4889004, -0.75294703, -0.9118363, -0.7200309, -0.3248641, -0.8878874, -0.18977344, - -0.8873837, -0.9571257, -0.90145934, -0.50521654, -0.93739635, -0.39051685, -0.61143184, -0.9591179, -0.605999, -0.40008977, - -0.92219675, -0.26732883, -0.19607787, -0.9172511, -0.07068595, -0.5409857, -0.9387041, -0.44181606, -0.4705004, -0.8899935, - -0.37997037, -0.66105115, -0.89754754, -0.68141997, -0.6324047, -0.886776, -0.65066385, -0.8334821, -0.94801456, -0.83297 - }; - float *output; - float kernel[3*3] = { - 0.56611896, -0.5144603, -0.82600045, 0.19219112, 0.3835776, -0.7475352, 0.5209291, -0.6301091, -0.99442935}; - float bias[3] = {-0.3654299, -1.5711838, -0.15546428}; - - params.activation = TANH; - params.has_bias = 1; - params.biases = bias; - params.input_num = 3; - params.kernel = kernel; - params.output_num = 3; - - operands[0].data = input; - operands[0].dims[0] = 1; - operands[0].dims[1] = 5; - operands[0].dims[2] = 6; - operands[0].dims[3] = 3; - operands[1].data = NULL; - - input_indexes[0] = 0; - ff_dnn_execute_layer_dense(operands, input_indexes, 1, ¶ms, 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()) - return 1; - - return 0; -} diff --git a/tests/dnn/dnn-layer-depth2space-test.c b/tests/dnn/dnn-layer-depth2space-test.c deleted file mode 100644 index 958247e675..0000000000 --- a/tests/dnn/dnn-layer-depth2space-test.c +++ /dev/null @@ -1,102 +0,0 @@ -/* - * 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.h" -#include "libavfilter/dnn/dnn_backend_native_layer_depth2space.h" - -#define EPSON 0.00001 - -static int test(void) -{ - // the input data and expected data are generated with below python code. - /* - x = tf.placeholder(tf.float32, shape=[1, None, None, 4]) - y = tf.depth_to_space(x, 2) - data = np.random.rand(1, 5, 3, 4); - - sess=tf.Session() - sess.run(tf.global_variables_initializer()) - - output = sess.run(y, feed_dict={x: data}) - - print("input:") - print(data.shape) - print(list(data.flatten())) - - print("output:") - print(output.shape) - print(list(output.flatten())) - */ - - DepthToSpaceParams params; - DnnOperand operands[2]; - int32_t input_indexes[1]; - float input[1*5*3*4] = { - 0.09771065121566602, 0.6336807372403175, 0.5142416549709786, 0.8027206567330333, 0.2154276025069397, 0.12112878462616772, 0.913936596765778, - 0.38881443647542646, 0.5850447615898835, 0.9311499327398275, 0.3613660929428246, 0.5420722002125493, 0.6002131190230359, 0.44800665702299525, - 0.7271322557896777, 0.3869293511885826, 0.5144404769364138, 0.6910844856987723, 0.6142102742269762, 0.6249991371621018, 0.45663376215836626, - 0.19523477129943423, 0.2483895888532045, 0.64326768256278, 0.5485877602998981, 0.45442067849873546, 0.529374943304256, 0.30439850391811885, - 0.11961343361340993, 0.2909643484561082, 0.9810970344127848, 0.8886928489786549, 0.6112237084436409, 0.8852482695156674, 0.9110868043114374, - 0.21242780027585217, 0.7101536973207572, 0.9709717457443375, 0.2702666770969332, 0.7718295953780221, 0.3957005164588574, 0.24383544252475453, - 0.040143453532367035, 0.26358051835323115, 0.013130251443791319, 0.3016550481482074, 0.03582340459943956, 0.718025513612361, 0.09844204177633753, - 0.04433767496953056, 0.6221895044119757, 0.6190414032940228, 0.8963550834625371, 0.5642449700064629, 0.2482982014723497, 0.17824909294583013, - 0.024401882408643272, 0.21742800875253465, 0.6794724473181843, 0.4814830479242237 - }; - float expected_output[1*10*6*1] = { - 0.097710654, 0.63368076, 0.2154276, 0.12112878, 0.58504474, 0.93114996, 0.51424164, 0.80272067, 0.9139366, 0.38881445, - 0.3613661, 0.5420722, 0.6002131, 0.44800666, 0.5144405, 0.6910845, 0.45663378, 0.19523478, 0.72713226, 0.38692936, - 0.61421025, 0.62499917, 0.24838959, 0.6432677, 0.54858774, 0.4544207, 0.11961343, 0.29096434, 0.6112237, 0.88524824, - 0.52937496, 0.3043985, 0.98109704, 0.88869286, 0.9110868, 0.2124278, 0.7101537, 0.97097176, 0.3957005, 0.24383545, - 0.013130251, 0.30165505, 0.27026668, 0.7718296, 0.040143453, 0.26358053, 0.035823405, 0.7180255, 0.09844204, - 0.044337675, 0.8963551, 0.564245, 0.024401883, 0.21742801, 0.6221895, 0.6190414, 0.2482982, 0.17824909, 0.67947245, 0.48148304 - }; - float *output; - - operands[0].data = input; - operands[0].dims[0] = 1; - operands[0].dims[1] = 5; - operands[0].dims[2] = 3; - operands[0].dims[3] = 4; - operands[1].data = NULL; - - input_indexes[0] = 0; - params.block_size = 2; - ff_dnn_execute_layer_depth2space(operands, input_indexes, 1, ¶ms, 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) -{ - return test(); -} diff --git a/tests/dnn/dnn-layer-mathbinary-test.c b/tests/dnn/dnn-layer-mathbinary-test.c deleted file mode 100644 index 2e41dc1ae7..0000000000 --- a/tests/dnn/dnn-layer-mathbinary-test.c +++ /dev/null @@ -1,214 +0,0 @@ -/* - * Copyright (c) 2020 - * - * 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_mathbinary.h" -#include "libavutil/avassert.h" - -#define EPSON 0.00005 - -static float get_expected(float f1, float f2, DNNMathBinaryOperation op) -{ - switch (op) - { - case DMBO_SUB: - return f1 - f2; - case DMBO_ADD: - return f1 + f2; - case DMBO_MUL: - return f1 * f2; - case DMBO_REALDIV: - return f1 / f2; - case DMBO_MINIMUM: - return (f1 < f2) ? f1 : f2; - case DMBO_FLOORMOD: - return (float)((int)(f1) % (int)(f2)); - default: - av_assert0(!"not supported yet"); - return 0.f; - } -} - -static int test_broadcast_input0(DNNMathBinaryOperation op) -{ - DnnLayerMathBinaryParams params; - DnnOperand operands[2]; - int32_t input_indexes[1]; - float input[1*1*2*3] = { - -3, 2.5, 2, -2.1, 7.8, 100 - }; - float *output; - - params.bin_op = op; - params.input0_broadcast = 1; - params.input1_broadcast = 0; - params.v = 7.28; - - operands[0].data = input; - operands[0].dims[0] = 1; - operands[0].dims[1] = 1; - operands[0].dims[2] = 2; - operands[0].dims[3] = 3; - operands[1].data = NULL; - - input_indexes[0] = 0; - ff_dnn_execute_layer_math_binary(operands, input_indexes, 1, ¶ms, NULL); - - output = operands[1].data; - for (int i = 0; i < sizeof(input) / sizeof(float); i++) { - float expected_output = get_expected(params.v, input[i], op); - if (fabs(output[i] - expected_output) > EPSON) { - printf("op %d, at index %d, output: %f, expected_output: %f (%s:%d)\n", - op, i, output[i], expected_output, __FILE__, __LINE__); - av_freep(&output); - return 1; - } - } - - av_freep(&output); - return 0; -} - -static int test_broadcast_input1(DNNMathBinaryOperation op) -{ - DnnLayerMathBinaryParams params; - DnnOperand operands[2]; - int32_t input_indexes[1]; - float input[1*1*2*3] = { - -3, 2.5, 2, -2.1, 7.8, 100 - }; - float *output; - - params.bin_op = op; - params.input0_broadcast = 0; - params.input1_broadcast = 1; - params.v = 7.28; - - operands[0].data = input; - operands[0].dims[0] = 1; - operands[0].dims[1] = 1; - operands[0].dims[2] = 2; - operands[0].dims[3] = 3; - operands[1].data = NULL; - - input_indexes[0] = 0; - ff_dnn_execute_layer_math_binary(operands, input_indexes, 1, ¶ms, NULL); - - output = operands[1].data; - for (int i = 0; i < sizeof(input) / sizeof(float); i++) { - float expected_output = get_expected(input[i], params.v, op); - if (fabs(output[i] - expected_output) > EPSON) { - printf("op %d, at index %d, output: %f, expected_output: %f (%s:%d)\n", - op, i, output[i], expected_output, __FILE__, __LINE__); - av_freep(&output); - return 1; - } - } - - av_freep(&output); - return 0; -} - -static int test_no_broadcast(DNNMathBinaryOperation op) -{ - DnnLayerMathBinaryParams params; - DnnOperand operands[3]; - int32_t input_indexes[2]; - float input0[1*1*2*3] = { - -3, 2.5, 2, -2.1, 7.8, 100 - }; - float input1[1*1*2*3] = { - -1, 2, 3, -21, 8, 10.0 - }; - float *output; - - params.bin_op = op; - params.input0_broadcast = 0; - params.input1_broadcast = 0; - - operands[0].data = input0; - operands[0].dims[0] = 1; - operands[0].dims[1] = 1; - operands[0].dims[2] = 2; - operands[0].dims[3] = 3; - operands[1].data = input1; - operands[1].dims[0] = 1; - operands[1].dims[1] = 1; - operands[1].dims[2] = 2; - operands[1].dims[3] = 3; - operands[2].data = NULL; - - input_indexes[0] = 0; - input_indexes[1] = 1; - ff_dnn_execute_layer_math_binary(operands, input_indexes, 2, ¶ms, NULL); - - output = operands[2].data; - for (int i = 0; i < sizeof(input0) / sizeof(float); i++) { - float expected_output = get_expected(input0[i], input1[i], op); - if (fabs(output[i] - expected_output) > EPSON) { - printf("op %d, at index %d, output: %f, expected_output: %f (%s:%d)\n", - op, i, output[i], expected_output, __FILE__, __LINE__); - av_freep(&output); - return 1; - } - } - - av_freep(&output); - return 0; -} - -static int test(DNNMathBinaryOperation op) -{ - if (test_broadcast_input0(op)) - return 1; - - if (test_broadcast_input1(op)) - return 1; - - if (test_no_broadcast(op)) - return 1; - - return 0; -} - -int main(int argc, char **argv) -{ - if (test(DMBO_SUB)) - return 1; - - if (test(DMBO_ADD)) - return 1; - - if (test(DMBO_MUL)) - return 1; - - if (test(DMBO_REALDIV)) - return 1; - - if (test(DMBO_MINIMUM)) - return 1; - - if (test(DMBO_FLOORMOD)) - return 1; - - return 0; -} diff --git a/tests/dnn/dnn-layer-mathunary-test.c b/tests/dnn/dnn-layer-mathunary-test.c deleted file mode 100644 index 0f84c12960..0000000000 --- a/tests/dnn/dnn-layer-mathunary-test.c +++ /dev/null @@ -1,148 +0,0 @@ -/* - * Copyright (c) 2020 - * - * 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_mathunary.h" -#include "libavutil/avassert.h" - -#define EPS 0.00001 - -static float get_expected(float f, DNNMathUnaryOperation op) -{ - switch (op) - { - case DMUO_ABS: - return (f >= 0) ? f : -f; - case DMUO_SIN: - return sin(f); - case DMUO_COS: - return cos(f); - case DMUO_TAN: - return tan(f); - case DMUO_ASIN: - return asin(f); - case DMUO_ACOS: - return acos(f); - case DMUO_ATAN: - return atan(f); - case DMUO_SINH: - return sinh(f); - case DMUO_COSH: - return cosh(f); - case DMUO_TANH: - return tanh(f); - case DMUO_ASINH: - return asinh(f); - case DMUO_ACOSH: - return acosh(f); - case DMUO_ATANH: - return atanh(f); - case DMUO_CEIL: - return ceil(f); - case DMUO_FLOOR: - return floor(f); - case DMUO_ROUND: - return round(f); - case DMUO_EXP: - return exp(f); - default: - av_assert0(!"not supported yet"); - return 0.f; - } -} - -static int test(DNNMathUnaryOperation op) -{ - DnnLayerMathUnaryParams params; - DnnOperand operands[2]; - int32_t input_indexes[1]; - float input[1*1*3*3] = { - 0.1, 0.5, 0.75, -3, 2.5, 2, -2.1, 7.8, 100}; - float *output; - - params.un_op = op; - - operands[0].data = input; - operands[0].dims[0] = 1; - operands[0].dims[1] = 1; - operands[0].dims[2] = 3; - operands[0].dims[3] = 3; - operands[1].data = NULL; - - input_indexes[0] = 0; - ff_dnn_execute_layer_math_unary(operands, input_indexes, 1, ¶ms, NULL); - - output = operands[1].data; - for (int i = 0; i < sizeof(input) / sizeof(float); ++i) { - float expected_output = get_expected(input[i], op); - int output_nan = isnan(output[i]); - int expected_nan = isnan(expected_output); - if ((!output_nan && !expected_nan && fabs(output[i] - expected_output) > EPS) || - (output_nan && !expected_nan) || (!output_nan && expected_nan)) { - printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output); - av_freep(&output); - return 1; - } - } - - av_freep(&output); - return 0; -} - -int main(int agrc, char **argv) -{ - if (test(DMUO_ABS)) - return 1; - if (test(DMUO_SIN)) - return 1; - if (test(DMUO_COS)) - return 1; - if (test(DMUO_TAN)) - return 1; - if (test(DMUO_ASIN)) - return 1; - if (test(DMUO_ACOS)) - return 1; - if (test(DMUO_ATAN)) - return 1; - if (test(DMUO_SINH)) - return 1; - if (test(DMUO_COSH)) - return 1; - if (test(DMUO_TANH)) - return 1; - if (test(DMUO_ASINH)) - return 1; - if (test(DMUO_ACOSH)) - return 1; - if (test(DMUO_ATANH)) - return 1; - if (test(DMUO_CEIL)) - return 1; - if (test(DMUO_FLOOR)) - return 1; - if (test(DMUO_ROUND)) - return 1; - if (test(DMUO_EXP)) - return 1; - return 0; -} diff --git a/tests/dnn/dnn-layer-maximum-test.c b/tests/dnn/dnn-layer-maximum-test.c deleted file mode 100644 index bf22f3719f..0000000000 --- a/tests/dnn/dnn-layer-maximum-test.c +++ /dev/null @@ -1,71 +0,0 @@ -/* - * 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_maximum.h" - -#define EPSON 0.00001 - -static int test(void) -{ - DnnLayerMaximumParams params; - DnnOperand operands[2]; - int32_t input_indexes[1]; - float input[1*1*2*3] = { - -3, 2.5, 2, -2.1, 7.8, 100 - }; - float *output; - - params.val.y = 2.3; - - operands[0].data = input; - operands[0].dims[0] = 1; - operands[0].dims[1] = 1; - operands[0].dims[2] = 2; - operands[0].dims[3] = 3; - operands[1].data = NULL; - - input_indexes[0] = 0; - ff_dnn_execute_layer_maximum(operands, input_indexes, 1, ¶ms, NULL); - - output = operands[1].data; - for (int i = 0; i < sizeof(input) / sizeof(float); i++) { - float expected_output = input[i] > params.val.y ? input[i] : params.val.y; - if (fabs(output[i] - expected_output) > EPSON) { - printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output); - av_freep(&output); - return 1; - } - } - - av_freep(&output); - return 0; - -} - -int main(int argc, char **argv) -{ - if (test()) - return 1; - - return 0; -} diff --git a/tests/dnn/dnn-layer-pad-test.c b/tests/dnn/dnn-layer-pad-test.c deleted file mode 100644 index a8443ce3be..0000000000 --- a/tests/dnn/dnn-layer-pad-test.c +++ /dev/null @@ -1,239 +0,0 @@ -/* - * 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; - ff_dnn_execute_layer_pad(operands, input_indexes, 1, ¶ms, 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; - ff_dnn_execute_layer_pad(operands, input_indexes, 1, ¶ms, 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; - ff_dnn_execute_layer_pad(operands, input_indexes, 1, ¶ms, 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; -} |