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Merge pull request #23401 from fengyuentau:fix_cann_layer_support

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dnn: Support more operators in CANN backend #23401

This PR adds the support of following layers:

- [x] Sub
- [x] PRelu
- [x] DeConv
- [x] Also warn users if backend is switched back to default if some of the layers are not supported.
- [ ] [Dropped] LSTM: some hacks (adding layers) were introduced which makes it even harder to build the graph for CANN backend.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Yuantao Feng
2023-04-20 15:18:35 +08:00
committed by GitHub
parent b3a2444bcf
commit 3c1fcd5deb
21 changed files with 178 additions and 80 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/src/layers/convolution_layer.cpp
+79 -4
View File
@@ -782,16 +782,17 @@ public:
}
#ifdef HAVE_CANN
virtual Ptr<BackendNode> initCann(const std::vector<Ptr<BackendWrapper> > &inputsWrapper,
virtual Ptr<BackendNode> initCann(const std::vector<Ptr<BackendWrapper> > &inputs,
const std::vector<Ptr<BackendWrapper> > &outputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert(!blobs.empty());
CV_Assert(inputsWrapper.size() == 1);
CV_Assert(inputs.size() == 1);
CV_Assert(nodes.size() == 1);
bool has_bias = hasBias() || fusedBias;
auto x = inputsWrapper[0].dynamicCast<CannBackendWrapper>();
auto x = inputs[0].dynamicCast<CannBackendWrapper>();
const auto shape_x = x->host->size; // [b, c, h, w]
const int filter_out_channel = blobs[0].size[1];
const int groups = shape_x[1] / filter_out_channel;
@@ -1611,7 +1612,8 @@ public:
#endif // HAVE_INF_ENGINE
{
return backendId == DNN_BACKEND_CUDA ||
(kernel_size.size() == 2 && (backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE));
(kernel_size.size() == 2 && (backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE)) ||
(kernel_size.size() == 2 && backendId == DNN_BACKEND_CANN);
}
}
@@ -2272,6 +2274,79 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_CANN
virtual Ptr<BackendNode> initCann(const std::vector<Ptr<BackendWrapper> > &inputs,
const std::vector<Ptr<BackendWrapper> > &outputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert(!blobs.empty());
CV_Assert(inputs.size() == 1);
CV_Assert(nodes.size() == 1);
bool has_bias = hasBias() || fusedBias;
auto x = inputs[0].dynamicCast<CannBackendWrapper>();
auto y = outputs[0].dynamicCast<CannBackendWrapper>();
const auto shape_x = x->host->size; // [N, C, H, W]
const auto shape_y = y->host->size; // [N, C, H, W]
const int filter_out_channel = blobs[0].size[0];
const int groups = shape_x[1] / filter_out_channel;
// create operator
auto op = std::make_shared<ge::op::Conv2DTransposeD>(name);
// set attributes
op->set_attr_input_size(
ge::Operator::OpListInt({(int64_t)shape_y[0],
(int64_t)shape_y[1],
(int64_t)shape_y[2],
(int64_t)shape_y[3],})
);
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");
op->set_attr_output_padding(
ge::Operator::OpListInt({0, 0, (int64_t)adjust_pads[0], (int64_t)adjust_pads[1]}) // adjust_pads: [height, width]
);
// set inputs
// set inputs : x
auto op_x = nodes[0].dynamicCast<CannBackendNode>()->getOp();
op->set_input_x_by_name(*op_x, x->name.c_str());
auto desc_x = x->getTensorDesc();
op->update_input_desc_x(*desc_x);
// set inputs : weight
const Mat& mat_w = blobs[0];
auto op_const_w = std::make_shared<CannConstOp>(mat_w.data, mat_w.type(), shape(mat_w), cv::format("%s_w", name.c_str()));
op->set_input_filter(*(op_const_w->getOp()));
op->update_input_desc_filter(*(op_const_w->getTensorDesc()));
// set inputs : bias
if (has_bias)
{
int out_channel = blobs[0].size[0];
const Mat& mat_b = blobs[1];
std::vector<int> shape_b{out_channel};
auto op_const_b = std::make_shared<CannConstOp>(mat_b.data, mat_b.type(), shape_b, cv::format("%s_b", name.c_str()));
op->set_input_bias(*(op_const_b->getOp()));
op->update_input_desc_bias(*(op_const_b->getTensorDesc()));
}
// set outputs
auto desc_output = std::make_shared<ge::TensorDesc>(ge::Shape(), ge::FORMAT_NCHW, ge::DT_FLOAT);
op->update_output_desc_y(*desc_output);
return Ptr<BackendNode>(new CannBackendNode(op));
}
#endif // HAVE_CANN
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,