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
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
|
/*
* Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "NodeExecution.h"
#include "locomotiv/NodeData.h"
#include "NodeDataImpl.h"
#include "NodeDomain.h"
#include <nncc/core/ADT/tensor/Shape.h>
#include <nncc/core/ADT/tensor/Buffer.h>
#include <nncc/core/ADT/tensor/Overlay.h>
#include <nncc/core/ADT/tensor/LexicalLayout.h>
#include "nncc/core/ADT/tensor/IndexEnumerator.h"
#include <gtest/gtest.h>
namespace
{
using nncc::core::ADT::tensor::Shape;
using nncc::core::ADT::tensor::LexicalLayout;
using nncc::core::ADT::tensor::make_buffer;
using nncc::core::ADT::tensor::make_overlay;
void run_test(const float *ifm, const float *ker, const float *expected_ofm, const Shape &ifm_shape,
const Shape ker_shape, const Shape ofm_shape, const uint32_t stride_v,
const uint32_t stride_h, const uint32_t pad_top = 0, const uint32_t pad_bottom = 0,
const uint32_t pad_left = 0, const uint32_t pad_right = 0)
{
auto g = loco::make_graph();
// Fill output data of FeatureEncode from ifm
auto ifm_enc = g->nodes()->create<loco::FeatureEncode>();
{
auto ifm_enc_buf = make_buffer<float, LexicalLayout>(ifm_shape);
auto ifm_overlay = make_overlay<float, LexicalLayout>(ifm_shape, const_cast<float *>(ifm));
for (nncc::core::ADT::tensor::IndexEnumerator e{ifm_shape}; e.valid(); e.advance())
{
const auto &ind = e.current();
ifm_enc_buf.at(ind) = ifm_overlay.at(ind);
}
auto enc_data = locomotiv::make_data(ifm_enc_buf);
locomotiv::annot_data(ifm_enc, std::move(enc_data));
locomotiv::annot_domain(ifm_enc, loco::Domain::Feature);
}
// Fill output data of FilterEncode from ker
auto ker_enc = g->nodes()->create<loco::FilterEncode>();
{
auto ker_enc_buf = make_buffer<float, LexicalLayout>(ker_shape);
auto ker_overlay = make_overlay<float, LexicalLayout>(ker_shape, const_cast<float *>(ker));
for (nncc::core::ADT::tensor::IndexEnumerator e{ker_shape}; e.valid(); e.advance())
{
const auto &ind = e.current();
ker_enc_buf.at(ind) = ker_overlay.at(ind);
}
auto enc_data = locomotiv::make_data(ker_enc_buf);
locomotiv::annot_data(ker_enc, std::move(enc_data));
locomotiv::annot_domain(ker_enc, loco::Domain::Filter);
}
// build Conv2D
auto conv2d = g->nodes()->create<loco::Conv2D>();
conv2d->ifm(ifm_enc);
conv2d->ker(ker_enc);
conv2d->stride()->vertical(stride_v);
conv2d->stride()->horizontal(stride_h);
conv2d->pad()->top(pad_top);
conv2d->pad()->bottom(pad_bottom);
conv2d->pad()->left(pad_left);
conv2d->pad()->right(pad_right);
// run interpreter
locomotiv::NodeExecution::get().run(conv2d);
// get result of calculation
auto conv2d_result = locomotiv::annot_data(conv2d);
// check the result
ASSERT_NE(conv2d_result, nullptr);
ASSERT_TRUE(conv2d_result->dtype() == loco::DataType::FLOAT32);
ASSERT_TRUE(*(conv2d_result->shape()) == ofm_shape);
auto ofm_overlay =
make_overlay<float, LexicalLayout>(ofm_shape, const_cast<float *>(expected_ofm));
for (nncc::core::ADT::tensor::IndexEnumerator e{ofm_shape}; e.valid(); e.advance())
{
const auto &ind = e.current();
ASSERT_FLOAT_EQ(ofm_overlay.at(ind), conv2d_result->as_f32_bufptr()->at(ind));
}
ASSERT_EQ(loco::Domain::Feature, locomotiv::annot_domain(conv2d));
}
} // namespace
// clang-format off
/* ifm and ofm are from the code below:
ifm = tf.random_normal([1, 5, 5, 1], stddev=1)
ker = tf.random_normal([3, 3, 1, 1], stddev=1)
out = tf.nn.conv2d(ifm, ker, strides = [1, 2, 2, 1], padding= 'VALID')
with tf.Session() as sess:
print(sess.run(out))
*/
TEST(NodeExecution_Conv2D, f32_1x5x5x1_calculation)
{
using nncc::core::ADT::tensor::Shape;
const float ifm[] =
{
-0.48850584, 1.4292705, -1.3424522, -0.7441476, -1.8964586,
1.7021934, -0.39246717, 0.6248314, 0.12724274, 1.3915083,
0.382255, 0.7725081, 0.9171561, -1.1847119, 0.61858755,
1.1530193, -0.476239, -0.9038663, -0.48764458, 0.339963,
2.2817912, -0.8464133, -1.0598192, 0.8361126, 1.2344601
};
const float ker[] =
{
-0.0830195, 0.21088193, -0.11781317,
0.07755677, 1.6337638, 1.0792778,
-1.6922939, -1.5437212, 0.96667504
};
const float ofm[] =
{
-0.28752697, 2.8108592,
-5.220376 , 0.7973861
};
run_test(ifm, ker, ofm,
Shape{1, 5, 5, 1}, Shape{1, 3, 3, 1}, Shape{1, 2, 2, 1}, // shapes of input, ker, output
2, 2 // stride
);
}
TEST(NodeExecution_Conv2D, f32_multiple_channel)
{
// testing channel != 1, stride = [1,1]
using nncc::core::ADT::tensor::Shape;
float ifm[1*5*5*3];
for (int n = 0; n < 5*5*3; n++) ifm[n] = 2.2;
float ker[2*2*2*3]; // nhwc
for (int n = 0; n < 2*2*2*3; n++) ker[n] = 1.1;
float ofm[1*4*4*2];
for (int n = 0; n < 1*4*4*2; n++) ofm[n] = 29.04;
run_test(ifm, ker, ofm,
Shape{1, 5, 5, 3}, Shape{2, 2, 2, 3}, Shape{1, 4, 4, 2}, // shapes of input, ker, output
1, 1 // stride
);
}
/* ifm and ofm are from the code below:
tensorflow version : 1.12.0
import tensorflow as tf
ifm = tf.constant([-1.3653529, 0.4160791, 0.5059157, 0.7649683, 0.39364856,
-1.0164733, 1.506766, -1.1413091, 1.2766701, -0.9253511,
1.3570246, 0.32089928, -0.9898171, 1.983792, -0.3423274,
-1.1901658, 1.2288222, -0.47401968, -0.01369802, 0.4136331,
0.06960588, -0.16537654, -0.65015996, -0.555224, 0.7140603
], shape=[1, 5, 5, 1])
ker = tf.constant([2.3490515, -0.4572366, 0.05790535,
0.3672005, 0.52679914, 0.74607974,
-1.7211207, 1.1174419, -0.59663385
], shape=[3, 3, 1, 1])
ofm = tf.nn.conv2d(ifm, ker, strides=[1, 1, 1, 1], padding='SAME')
with tf.Session() as sess:
print(sess.run(ofm))
*/
TEST(NodeExecution_Conv2D, with_padding)
{
using nncc::core::ADT::tensor::Shape;
const float ifm[] =
{
-1.3653529, 0.4160791, 0.5059157, 0.7649683, 0.39364856,
-1.0164733, 1.506766, -1.1413091, 1.2766701, -0.9253511,
1.3570246, 0.32089928, -0.9898171, 1.983792, -0.3423274,
-1.1901658, 1.2288222, -0.47401968, -0.01369802, 0.4136331,
0.06960588, -0.16537654, -0.65015996, -0.555224, 0.7140603
};
const float ker[] =
{
2.3490515, -0.4572366, 0.05790535,
0.3672005, 0.52679914, 0.74607974,
-1.7211207, 1.1174419, -0.59663385
};
const float ofm[] =
{
-2.443676, 4.2094254, -3.6403496, 4.8254814, -2.743059,
2.5620093, -5.185688, -1.1470609, 4.54913, -2.1985974,
-0.5567835, 0.49045527, 2.5752437, -2.3383713, 4.455967,
-0.13562866, 2.9236434, 1.4019353, -3.0521483, 6.782954,
0.5286269, -3.9317036, 2.285041, -1.0817666, -0.04901773
};
run_test(ifm, ker, ofm,
Shape{1, 5, 5, 1}, Shape{1, 3, 3, 1}, Shape{1, 5, 5, 1}, // shapes of input, ker, output
1, 1, // stride
1, 1, 1, 1 // padding
);
}
// clang-format on
|
