FFmpeg
dnn-layer-conv2d-test.c
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1 /*
2  * Copyright (c) 2019 Guo Yejun
3  *
4  * This file is part of FFmpeg.
5  *
6  * FFmpeg is free software; you can redistribute it and/or
7  * modify it under the terms of the GNU Lesser General Public
8  * License as published by the Free Software Foundation; either
9  * version 2.1 of the License, or (at your option) any later version.
10  *
11  * FFmpeg is distributed in the hope that it will be useful,
12  * but WITHOUT ANY WARRANTY; without even the implied warranty of
13  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
14  * Lesser General Public License for more details.
15  *
16  * You should have received a copy of the GNU Lesser General Public
17  * License along with FFmpeg; if not, write to the Free Software
18  * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
19  */
20 
21 #include <stdio.h>
22 #include <string.h>
23 #include <math.h>
24 #include "libavfilter/dnn/dnn_backend_native_layer_conv2d.h"
25 
26 #define EPSON 0.00001
27 
28 static int test_with_same_dilate(void)
29 {
30  // the input data and expected data are generated with below python code.
31  /*
32  x = tf.placeholder(tf.float32, shape=[1, None, None, 3])
33  y = tf.layers.conv2d(x, 2, 3, activation=tf.nn.tanh, padding='same', dilation_rate=(2, 2), bias_initializer=tf.keras.initializers.he_normal())
34  data = np.random.rand(1, 5, 6, 3);
35 
36  sess=tf.Session()
37  sess.run(tf.global_variables_initializer())
38 
39  weights = dict([(var.name, sess.run(var)) for var in tf.trainable_variables()])
40  kernel = weights['conv2d/kernel:0']
41  kernel = np.transpose(kernel, [3, 0, 1, 2])
42  print("kernel:")
43  print(kernel.shape)
44  print(list(kernel.flatten()))
45 
46  bias = weights['conv2d/bias:0']
47  print("bias:")
48  print(bias.shape)
49  print(list(bias.flatten()))
50 
51  output = sess.run(y, feed_dict={x: data})
52 
53  print("input:")
54  print(data.shape)
55  print(list(data.flatten()))
56 
57  print("output:")
58  print(output.shape)
59  print(list(output.flatten()))
60  */
61 
62  ConvolutionalParams params;
63  DnnOperand operands[2];
64  int32_t input_indexes[1];
65  float input[1*5*6*3] = {
66  0.7012556460308194, 0.4233847954643357, 0.19515900664313612, 0.16343083004926495, 0.5758261611052848, 0.9510767434014871, 0.11014085055947687,
67  0.906327053637727, 0.8136794715542507, 0.45371764543639526, 0.5768443343523952, 0.19543668786046986, 0.15648326047898609, 0.2099500241141279,
68  0.17658777090552413, 0.059335724777169196, 0.1729991838469117, 0.8150514704819208, 0.4435535466703049, 0.3752188477566878, 0.749936650421431,
69  0.6823494635284907, 0.10776389679424747, 0.34247481674596836, 0.5147867256244629, 0.9063709728129032, 0.12423605800856818, 0.6064872945412728,
70  0.5891681538551459, 0.9865836236466314, 0.9002163879294677, 0.003968273184274618, 0.8628374809643967, 0.1327176268279583, 0.8449799925703798,
71  0.1937671869354366, 0.41524410152707425, 0.02038786604756837, 0.49792466069597496, 0.8881874553848784, 0.9683921035597336, 0.4122972568010813,
72  0.843553550993252, 0.9588482762501964, 0.5190350762645546, 0.4283584264145317, 0.09781496073714646, 0.9501058833776156, 0.8665541760152776,
73  0.31669272550095806, 0.07133074675453632, 0.606438007334886, 0.7007157020538224, 0.4827996264130444, 0.5167615606392761, 0.6385043039312651,
74  0.23069664707810555, 0.058233497329354456, 0.06323892961591071, 0.24816458893245974, 0.8646369065257812, 0.24742185893094837, 0.09991225948167437,
75  0.625700606979606, 0.7678541502111257, 0.6215834594679912, 0.5623003956582483, 0.07389123942681242, 0.7659100715711249, 0.486061471642225,
76  0.9947455699829012, 0.9094911797643259, 0.7644355876253265, 0.05384315321492239, 0.13565394382783613, 0.9810628204953316, 0.007386389078887889,
77  0.226182754156241, 0.2609021390764772, 0.24182802076928933, 0.13264782451941648, 0.2035816485767682, 0.005504188177612557, 0.7014619934040155,
78  0.956215988391991, 0.5670398541013633, 0.9809764721750784, 0.6886338100487461, 0.5758152317218274, 0.7137823176776179
79  };
80  float expected_output[1*5*6*2] = {
81  -0.9480655, -0.7169147, -0.9404794, -0.5567385, -0.8991124, -0.8306558, -0.94487447, -0.8932543, -0.88238764, -0.7301602,
82  -0.8974813, -0.7026703, -0.8858988, -0.53203243, -0.92881465, -0.5648504, -0.8871471, -0.7000097, -0.91754407, -0.79684794,
83  -0.760465, -0.117928326, -0.88302773, -0.8975289, -0.70615053, 0.19231977, -0.8318776, -0.386184, -0.80698484, -0.8556624,
84  -0.7336671, -0.6168619, -0.7658234, -0.63449603, -0.73314047, -0.87502456, -0.58158904, -0.4184259, -0.52618927, -0.13613208,
85  -0.5093187, -0.21027721, -0.39455596, -0.44507834, -0.22269244, -0.73400885, -0.77655095, -0.74408925, -0.57313335, -0.15333457,
86  -0.74620694, -0.34858236, -0.42586932, -0.5240488, 0.1634339, -0.2447881, -0.57927346, -0.62732303, -0.82287043, -0.8474058
87  };
88  float *output;
89  float kernel[2*3*3*3] = {
90  0.26025516, 0.16536498, -0.24351254, 0.33892477, -0.34005195, 0.35202783, 0.34056443, 0.01422739, 0.13799345, 0.29489166,
91  0.2781723, 0.178585, 0.22122234, 0.044115514, 0.13134438, 0.31705368, 0.22527462, -0.021323413, 0.115134746, -0.18216397,
92  -0.21197563, -0.027848959, -0.01704529, -0.12401503, -0.23415318, -0.12661739, -0.35338148, 0.20049328, -0.076153606,
93  -0.23642601, -0.3125769, -0.025851756, -0.30006272, 0.050762743, 0.32003498, 0.3052225, -0.0017385483, 0.25337684, -0.25664508,
94  0.27846587, -0.3112659, 0.2066065, 0.31499845, 0.113178134, 0.09449363, -0.11828774, -0.12671001, -0.36259216, 0.2710235,
95  -0.19676702, 0.023612618, -0.2596915, -0.34949252, -0.108270735
96  };
97  float bias[2] = { -1.6574852, -0.72915393 };
98 
99  params.activation = TANH;
100  params.has_bias = 1;
101  params.biases = bias;
102  params.dilation = 2;
103  params.input_num = 3;
104  params.kernel = kernel;
105  params.kernel_size = 3;
106  params.output_num = 2;
107  params.padding_method = SAME;
108 
109  operands[0].data = input;
110  operands[0].dims[0] = 1;
111  operands[0].dims[1] = 5;
112  operands[0].dims[2] = 6;
113  operands[0].dims[3] = 3;
114  operands[1].data = NULL;
115 
116  input_indexes[0] = 0;
117  dnn_execute_layer_conv2d(operands, input_indexes, 1, &params);
118 
119  output = operands[1].data;
120  for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
121  if (fabs(output[i] - expected_output[i]) > EPSON) {
122  printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output[i]);
123  av_freep(&output);
124  return 1;
125  }
126  }
127 
128  av_freep(&output);
129  return 0;
130 }
131 
132 static int test_with_valid(void)
133 {
134  // the input data and expected data are generated with below python code.
135  /*
136  x = tf.placeholder(tf.float32, shape=[1, None, None, 3])
137  y = tf.layers.conv2d(x, 2, 3, activation=tf.nn.tanh, padding='valid', bias_initializer=tf.keras.initializers.he_normal())
138  data = np.random.rand(1, 5, 6, 3);
139 
140  sess=tf.Session()
141  sess.run(tf.global_variables_initializer())
142 
143  weights = dict([(var.name, sess.run(var)) for var in tf.trainable_variables()])
144  kernel = weights['conv2d/kernel:0']
145  kernel = np.transpose(kernel, [3, 0, 1, 2])
146  print("kernel:")
147  print(kernel.shape)
148  print(list(kernel.flatten()))
149 
150  bias = weights['conv2d/bias:0']
151  print("bias:")
152  print(bias.shape)
153  print(list(bias.flatten()))
154 
155  output = sess.run(y, feed_dict={x: data})
156 
157  print("input:")
158  print(data.shape)
159  print(list(data.flatten()))
160 
161  print("output:")
162  print(output.shape)
163  print(list(output.flatten()))
164  */
165 
166  ConvolutionalParams params;
167  DnnOperand operands[2];
168  int32_t input_indexes[1];
169  float input[1*5*6*3] = {
170  0.26126657468269665, 0.42762216215337556, 0.7466274030131497, 0.802550266787863, 0.3709323443076644, 0.5919817068197668, 0.49274512279324967,
171  0.7170132295090351, 0.0911793215410649, 0.5134213878288361, 0.670132600785118, 0.49417034512633484, 0.03887389460089885, 0.436785102836845,
172  0.1490231658611978, 0.6413606121498127, 0.8595987991375995, 0.9132593077586231, 0.7075959004873255, 0.17754995944845464, 0.5212507214937141,
173  0.35379732738215475, 0.25205107358505296, 0.3928792840544273, 0.09485294189485782, 0.8685115437448666, 0.6489046799288605, 0.509253797582924,
174  0.8993255536791972, 0.18740056466602373, 0.34237617336313986, 0.3871438962989183, 0.1488532571774911, 0.5187002331293636, 0.8137098818752955,
175  0.521761863717401, 0.4622312310118274, 0.29038411334638825, 0.16194915718170566, 0.5175999923925211, 0.8852230040101133, 0.0218263385047206,
176  0.08482355352852367, 0.3463638568376264, 0.28627127120619733, 0.9553293378948409, 0.4803391055970835, 0.841635695030805, 0.3556828280031952,
177  0.06778527221541808, 0.28193560357091596, 0.8399957619031576, 0.03305536359456385, 0.6625039162109645, 0.9300552020023897, 0.8551529138204146,
178  0.6133216915522418, 0.222427800857393, 0.1315422686800336, 0.6189144989185527, 0.5346184916866876, 0.8348888624532548, 0.6544834567840291,
179  0.2844062293389934, 0.28780026600883324, 0.5372272015684924, 0.6250226011503823, 0.28119106062279453, 0.49655812908420094, 0.6451488959145951,
180  0.7362580606834843, 0.44815578616664087, 0.6454760235835586, 0.6794062414265861, 0.045378883014935756, 0.9008388543865096, 0.7949752851269782,
181  0.4179928876222264, 0.28733419007048644, 0.996902319501908, 0.5690851338677467, 0.9511814013279738, 0.025323788678181636, 0.5594359732604794,
182  0.1213732595086251, 0.7172624313368294, 0.6759328959074691, 0.07252138454885071, 0.17557735158403442, 0.5988895455048769
183  };
184  float expected_output[1*3*4*2] = {
185  -0.556947, -0.42143887, -0.092070885, 0.27404794, -0.41886684, 0.0862887, -0.25001016, -0.342721, 0.020730592, 0.04016919, -0.69839877,
186  -0.06136704, 0.14186388, -0.11655602, -0.23489095, -0.3845829, -0.19017771, 0.1595885, -0.18308741, -0.3071209, -0.5848686, -0.22509028,
187  -0.6023201, -0.14448485
188  };
189  float *output;
190  float kernel[2*3*3*3] = {
191  -0.25291282, 0.22402048, 0.028642118, -0.14615723, -0.27362752, -0.34801802, -0.2759148, 0.19594926, -0.25029412, 0.34606284, 0.10376671,
192  -0.1015394, 0.23616093, 0.2134214, 0.35285157, 0.05893758, 0.0024731457, -0.17143056, 0.35758412, 0.2186206, -0.28384736, -0.21206513,
193  -0.20871592, 0.27070445, 0.25878823, 0.11136332, -0.33737376, 0.08353335, -0.34290665, 0.041805506, -0.09738535, 0.3284936, -0.16838405,
194  -0.032494456, -0.29193437, 0.033259362, -0.09272635, -0.2802651, -0.28648436, 0.3542878, 0.2432127, -0.24551713, 0.27813476, 0.21024024,
195  -0.013690501, -0.1350077, -0.07826337, -0.34563828, 0.3220685, -0.07571727, 0.19420576, 0.20783454, 0.18738335, 0.16672492
196  };
197  float bias[2] = { -0.4773722, -0.19620377 };
198 
199  params.activation = TANH;
200  params.has_bias = 1;
201  params.biases = bias;
202  params.dilation = 1;
203  params.input_num = 3;
204  params.kernel = kernel;
205  params.kernel_size = 3;
206  params.output_num = 2;
207  params.padding_method = VALID;
208 
209  operands[0].data = input;
210  operands[0].dims[0] = 1;
211  operands[0].dims[1] = 5;
212  operands[0].dims[2] = 6;
213  operands[0].dims[3] = 3;
214  operands[1].data = NULL;
215 
216  input_indexes[0] = 0;
217  dnn_execute_layer_conv2d(operands, input_indexes, 1, &params);
218 
219  output = operands[1].data;
220  for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
221  if (fabs(output[i] - expected_output[i]) > EPSON) {
222  printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output[i]);
223  av_freep(&output);
224  return 1;
225  }
226  }
227 
228  av_freep(&output);
229  return 0;
230 }
231 
232 int main(int argc, char **argv)
233 {
234  if (test_with_valid())
235  return 1;
236  if (test_with_same_dilate())
237  return 1;
238 
239  return 0;
240 }
dnn_execute_layer_conv2d
int dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_indexes, int32_t output_operand_index, const void *parameters)
Definition: dnn_backend_native_layer_conv2d.c:91
test_with_valid
static int test_with_valid(void)
Definition: dnn-layer-conv2d-test.c:132
output
filter_frame For filters that do not use the this method is called when a frame is pushed to the filter s input It can be called at any time except in a reentrant way If the input frame is enough to produce output
Definition: filter_design.txt:225
ConvolutionalParams::kernel
float * kernel
Definition: dnn_backend_native_layer_conv2d.h:35
TANH
@ TANH
Definition: dnn_backend_native_layer_conv2d.h:26
test_with_same_dilate
static int test_with_same_dilate(void)
Definition: dnn-layer-conv2d-test.c:28
SAME
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Definition: dnn_backend_native_layer_conv2d.h:27
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Definition: dnn_backend_native_layer_conv2d.h:30
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Definition: dnn_backend_native_layer_conv2d.h:31
int32_t
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Definition: dnn_backend_native_layer_conv2d.h:34
NULL
#define NULL
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there are two memory layouts, NHWC or NCHW, so we use dims, dims[0] is Number.
Definition: dnn_backend_native.h:68
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Definition: dnn_backend_native_layer_conv2d.h:30
dnn_backend_native_layer_conv2d.h
printf
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and forward the test the status of outputs and forward it to the corresponding return FFERROR_NOT_READY If the filters stores internally one or a few frame for some input
Definition: filter_design.txt:172
ConvolutionalParams::output_num
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Definition: dnn_backend_native_layer_conv2d.h:30
i
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Definition: dnn-layer-conv2d-test.c:26
VALID
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Definition: dnn_backend_native_layer_conv2d.h:27
main
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Definition: dnn-layer-conv2d-test.c:232
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Definition: dnn_backend_native.h:63
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Definition: dnn_backend_native_layer_conv2d.h:32
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Definition: dnn_backend_native_layer_conv2d.h:29
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Definition: dnn_backend_native_layer_conv2d.h:33
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Definition: dnn_backend_native_layer_conv2d.h:36
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