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Merge pull request #23319 from fengyuentau:fix_zoo_issue_136

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Related issue: https://github.com/opencv/opencv_zoo/issues/136

Features added:

- Support operators with multiple output: ONNX Split.
- Support Slice without steps.

Bugs fixed:

- Wrong settings in ClipByValue (Relu6).
- Wrong calculation of pads in convolution layer (It is wrong generally but only fixed specifically for CANN for now).

### 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
2023-03-14 02:46:33 +08:00
committed by GitHub
parent e03e2e7f94
commit b94e13c8ae
23 changed files with 317 additions and 202 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/src/layers/convolution_layer.cpp
+26 -8
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@@ -782,7 +782,8 @@ public:
}
#ifdef HAVE_CANN
virtual Ptr<BackendNode> initCann(const std::vector<Ptr<BackendWrapper> > &inputsWrapper, const int index, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
virtual Ptr<BackendNode> initCann(const std::vector<Ptr<BackendWrapper> > &inputsWrapper,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert(!blobs.empty());
CV_Assert(inputsWrapper.size() == 1);
@@ -791,18 +792,35 @@ public:
bool has_bias = hasBias() || fusedBias;
auto x = inputsWrapper[0].dynamicCast<CannBackendWrapper>();
const int x_in_channel = x->host->size[1];
const auto shape_x = x->host->size; // [b, c, h, w]
const int filter_out_channel = blobs[0].size[1];
const int groups = x_in_channel / filter_out_channel;
const int groups = shape_x[1] / filter_out_channel;
// create operator
std::string op_name = cv::format("conv2d_%d", index);
auto op = std::make_shared<ge::op::Conv2D>(op_name);
auto op = std::make_shared<ge::op::Conv2D>(name);
// set attributes
op->set_attr_strides(ge::Operator::OpListInt(
{1, 1, (int64_t)strides[0], (int64_t)strides[1]}
));
// recalculate pads in case of "SAME" padMode with odd pads
// since in 'getConvPoolPaddings' pads are divided equally
// leading to the loss of one pad
if (padMode == "SAME")
{
for (int i = 0; i < pads_begin.size(); i++) {
if (strides[i] <= kernel_size[i])
{
int pads_at_i = kernel_size[i] - 1 - (shape_x[i+2] - 1 + strides[i]) % strides[i];
pads_begin[i] = pads_at_i / 2;
// if odd, add extra padding to the end for SAME_UPPER
// or to the beginning for SAME_LOWER. Since here we cannot
// identity SAME_UPPER and SAME_LOWER, extra padding is always
// added to the end.
pads_end[i] = pads_at_i - pads_begin[i];
}
}
}
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]}
));
@@ -815,12 +833,12 @@ public:
// set inputs
// set inputs : x
auto op_x = nodes[0].dynamicCast<CannBackendNode>()->getOp();
op->set_input_x_by_name(*op_x, "y");
op->set_input_x_by_name(*op_x, x->name.c_str());
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()));
auto op_const_weight = std::make_shared<CannConstOp>(w_mat.data, w_mat.type(), shape(w_mat), cv::format("%s_w", name.c_str()));
op->set_input_filter(*(op_const_weight->getOp()));
op->update_input_desc_filter(*(op_const_weight->getTensorDesc()));
// set inputs : bias
@@ -830,7 +848,7 @@ public:
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()));
auto op_const_bias = std::make_shared<CannConstOp>(b_mat.data, b_mat.type(), bias_shape, cv::format("%s_b", name.c_str()));
op->set_input_bias(*(op_const_bias->getOp()));
op->update_input_desc_bias(*(op_const_bias->getTensorDesc()));
}