const_layer.cpp
4.22 KB
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
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
// Copyright (C) 2018, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
#include "../precomp.hpp"
#include "../op_inf_engine.hpp"
#include "../op_cuda.hpp"
#include "layers_common.hpp"
#include "../ie_ngraph.hpp"
#ifdef HAVE_OPENCL
#include "opencl_kernels_dnn.hpp"
#endif
#ifdef HAVE_CUDA
#include "../cuda4dnn/primitives/const.hpp"
using namespace cv::dnn::cuda4dnn;
#endif
namespace cv { namespace dnn {
class ConstLayerImpl CV_FINAL : public ConstLayer
{
public:
ConstLayerImpl(const LayerParams& params)
{
setParamsFrom(params);
CV_Assert(blobs.size() == 1);
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
backendId == DNN_BACKEND_CUDA;
}
virtual bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert(inputs.empty());
outputs.assign(1, shape(blobs[0]));
return false;
}
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
std::vector<UMat> outputs;
outs.getUMatVector(outputs);
if (outs.depth() == CV_16S)
convertFp16(blobs[0], outputs[0]);
else
blobs[0].copyTo(outputs[0]);
return true;
}
#endif
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
std::vector<Mat> outputs;
outputs_arr.getMatVector(outputs);
blobs[0].copyTo(outputs[0]);
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::ConstLayer ieLayer(name);
ieLayer.setData(wrapToInfEngineBlob(blobs[0]));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
getShape<size_t>(blobs[0]),
blobs[0].data);
return Ptr<BackendNode>(new InfEngineNgraphNode(node));
}
#endif // HAVE_DNN_NGRAPH
#ifdef HAVE_CUDA
Ptr<BackendNode> initCUDA(
void *context_,
const std::vector<Ptr<BackendWrapper>>& inputs,
const std::vector<Ptr<BackendWrapper>>& outputs
) override
{
auto context = reinterpret_cast<csl::CSLContext*>(context_);
CV_Assert(blobs.size() == 1);
return make_cuda_node<cuda4dnn::ConstOp>(preferableTarget, std::move(context->stream), blobs[0]);
}
#endif
virtual bool tryQuantize(const std::vector<std::vector<float> > &scales,
const std::vector<std::vector<int> > &zeropoints, LayerParams& params) CV_OVERRIDE
{
Mat quantizedBlob;
blobs[0].convertTo(quantizedBlob, CV_8S, 1.f/scales[1][0], zeropoints[1][0]);
params.blobs.clear();
params.blobs.push_back(quantizedBlob);
return true;
}
};
Ptr<Layer> ConstLayer::create(const LayerParams& params)
{
return Ptr<Layer>(new ConstLayerImpl(params));
}
}} // namespace cv::dnn