potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-29 07:13:02 +04:00
Code Packages Releases Wiki Activity

dnn: add the CANN backend (#22634)

Browse Source 
* cann backend impl v1

* cann backend impl v2: use opencv parsers to build models for cann

* adjust fc according to the new transA and transB

* put cann net in cann backend node and reuse forwardLayer

* use fork() to create a child process and compile cann model

* remove legacy code

* remove debug code

* fall bcak to CPU backend if there is one layer not supoorted by CANN backend

* fix netInput forward
This commit is contained in:
Yuantao Feng
2022-12-21 14:04:41 +08:00
committed by GitHub
parent a08c98cdfb
commit a2b3acfc6e
34 changed files with 2208 additions and 28 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/src/layers/convolution_layer.cpp
+74
View File
@@ -48,6 +48,7 @@
#include "../ie_ngraph.hpp"
#include "../op_vkcom.hpp"
#include "../op_webnn.hpp"
#include "../op_cann.hpp"
#include <opencv2/core/utils/configuration.private.hpp>
#include <opencv2/core/utils/logger.hpp>
@@ -369,6 +370,17 @@ public:
return true;
}
#endif
#ifdef HAVE_CANN
if (backendId == DNN_BACKEND_CANN)
{
if (ksize != 2)
{
CV_LOG_WARNING(NULL, "CANN supports Conv2D for now");
return false;
}
return true;
}
#endif // HAVE_CANN
return false;
}
@@ -768,6 +780,68 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_CANN
virtual Ptr<BackendNode> initCann(const std::vector<Ptr<BackendWrapper> > &inputsWrapper, const int index, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert(!blobs.empty());
CV_Assert(inputsWrapper.size() == 1);
CV_Assert(nodes.size() == 1);
bool has_bias = hasBias() || fusedBias;
auto x = inputsWrapper[0].dynamicCast<CannBackendWrapper>();
const int x_in_channel = x->host->size[1];
const int filter_out_channel = blobs[0].size[1];
const int groups = x_in_channel / filter_out_channel;
// create operator
std::string op_name = cv::format("conv2d_%d", index);
auto op = std::make_shared<ge::op::Conv2D>(op_name);
// set attributes
op->set_attr_strides(ge::Operator::OpListInt(
{1, 1, (int64_t)strides[0], (int64_t)strides[1]}
));
op->set_attr_pads(ge::Operator::OpListInt(
{(int64_t)pads_begin[1], (int64_t)pads_end[1], (int64_t)pads_begin[0], (int64_t)pads_end[0]}
));
op->set_attr_dilations(ge::Operator::OpListInt(
{1, 1, (int64_t)dilations[0], (int64_t)dilations[1]}
));
op->set_attr_groups(groups);
op->set_attr_data_format("NCHW");
// set inputs
// set inputs : x
auto op_x = nodes[0].dynamicCast<CannBackendNode>()->getOp();
op->set_input_x_by_name(*op_x, "y");
auto x_desc = x->getTensorDesc();
op->update_input_desc_x(*x_desc);
// set inputs : weight
const Mat& w_mat = blobs[0];
auto op_const_weight = std::make_shared<CannConstOp>(w_mat.data, w_mat.type(), shape(w_mat), cv::format("%s_w", op_name.c_str()));
op->set_input_filter(*(op_const_weight->getOp()));
op->update_input_desc_filter(*(op_const_weight->getTensorDesc()));
// set inputs : bias
if (has_bias)
{
int out_channel = blobs[0].size[0];
Mat b_mat({out_channel}, CV_32F, &biasvec[0]);
std::vector<int> bias_shape{out_channel};
auto op_const_bias = std::make_shared<CannConstOp>(b_mat.data, b_mat.type(), bias_shape, cv::format("%s_b", op_name.c_str()));
op->set_input_bias(*(op_const_bias->getOp()));
op->update_input_desc_bias(*(op_const_bias->getTensorDesc()));
}
// set outputs
auto output_desc = std::make_shared<ge::TensorDesc>(ge::Shape(), ge::FORMAT_NCHW, ge::DT_FLOAT);
op->update_output_desc_y(*output_desc);
return Ptr<BackendNode>(new CannBackendNode(op));
}
#endif
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,