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
|
/*
* Copyright 2019 Xilinx Inc.
*
* 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 "common.h"
#include <cassert>
#include <numeric>
int getTensorShape(vart::Runner* runner, GraphInfo* shapes, int cntin,
int cntout) {
auto outputTensors = runner->get_output_tensors();
auto inputTensors = runner->get_input_tensors();
if (shapes->output_mapping.empty()) {
shapes->output_mapping.resize((unsigned)cntout);
std::iota(shapes->output_mapping.begin(), shapes->output_mapping.end(), 0);
}
for (int i = 0; i < cntin; i++) {
auto dim_num = inputTensors[i]->get_shape().size();
if (dim_num == 4) {
shapes->inTensorList[i].channel = inputTensors[i]->get_shape().at(3);
shapes->inTensorList[i].width = inputTensors[i]->get_shape().at(2);
shapes->inTensorList[i].height = inputTensors[i]->get_shape().at(1);
shapes->inTensorList[i].size =
inputTensors[i]->get_element_num() / inputTensors[0]->get_shape().at(0);
} else if (dim_num == 2) {
shapes->inTensorList[i].channel = inputTensors[i]->get_shape().at(1);
shapes->inTensorList[i].width = 1;
shapes->inTensorList[i].height = 1;
shapes->inTensorList[i].size =
inputTensors[i]->get_element_num() / inputTensors[0]->get_shape().at(0);
}
}
for (int i = 0; i < cntout; i++) {
auto dim_num = outputTensors[shapes->output_mapping[i]]->get_shape().size();
if (dim_num == 4) {
shapes->outTensorList[i].channel =
outputTensors[shapes->output_mapping[i]]->get_shape().at(3);
shapes->outTensorList[i].width =
outputTensors[shapes->output_mapping[i]]->get_shape().at(2);
shapes->outTensorList[i].height =
outputTensors[shapes->output_mapping[i]]->get_shape().at(1);
shapes->outTensorList[i].size =
outputTensors[shapes->output_mapping[i]]->get_element_num() /
outputTensors[shapes->output_mapping[0]]->get_shape().at(0);
} else if (dim_num == 2) {
shapes->outTensorList[i].channel =
outputTensors[shapes->output_mapping[i]]->get_shape().at(1);
shapes->outTensorList[i].width = 1;
shapes->outTensorList[i].height = 1;
shapes->outTensorList[i].size =
outputTensors[shapes->output_mapping[i]]->get_element_num() /
outputTensors[shapes->output_mapping[0]]->get_shape().at(0);
}
}
return 0;
}
static int find_tensor(std::vector<const xir::Tensor*> tensors,
const std::string& name) {
int ret = -1;
for (auto i = 0u; i < tensors.size(); ++i) {
if (tensors[i]->get_name().find(name) != std::string::npos) {
ret = (int)i;
break;
}
}
assert(ret != -1);
return ret;
}
int getTensorShape(vart::Runner* runner, GraphInfo* shapes, int cntin,
std::vector<std::string> output_names) {
for (auto i = 0u; i < output_names.size(); ++i) {
auto idx = find_tensor(runner->get_output_tensors(), output_names[i]);
shapes->output_mapping.push_back(idx);
}
getTensorShape(runner, shapes, cntin, (int)output_names.size());
return 0;
}
|
