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https://github.com/open-mmlab/mmdeploy.git
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823ca38646
* first * fix0 * fix1 * dirty work * wip * add allocator * finally done! * lint * fix lint * better gather * better onnx2ncnn * fix expand * [Fix] NCNN TensorSlice op bugs (#42) * fix custom ops support, fix multiple mark bug, add name mapping * check if the value_info need to be added * remove unnecessary print * add nms implement * two stage split wip * add two stage split * add split retinanet visualize * add two stage split (wip) * finish two stage split * fix lint * move parse string to mmdeploy.utils * add calib data generator * create calib dataset * finish end2end int8 * add split two stage tensorrt visualize * fix tensorslice bugs * fix lint * fix clang-format * remove comments * int param * fix lint Co-authored-by: grimoire <yaoqian@sensetime.com> * add two stage ncnn support * remove unused ops * git unused config * remove no_grad, should add in refactor * add ncnn wrapper * fix lint * size return tuple * Resolve grammar error * Fix lint * Trim Trailing Whitespace * fix trim * update wrapper * remove logs * remove * csrc optimize Co-authored-by: hanrui1sensetime <83800577+hanrui1sensetime@users.noreply.github.com>
55 lines
1.4 KiB
C++
Executable File
55 lines
1.4 KiB
C++
Executable File
#include "constantofshape.h"
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#include "../ncnn_ops_definer.h"
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namespace mmlab {
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using namespace ncnn;
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DEFINE_LAYER_CREATOR(ConstantOfShape)
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DEFINE_NCNN_OPS(ConstantOfShape, ConstantOfShape)
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ConstantOfShape::ConstantOfShape() {
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one_blob_only = true;
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support_inplace = false;
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}
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int ConstantOfShape::load_param(const ParamDict& pd) {
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val = pd.get(0, 0.f);
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return 0;
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}
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int ConstantOfShape::forward(const Mat& bottom_blob, Mat& top_blob,
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const Option& opt) const {
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int dims = bottom_blob.w - 1;
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const float* bottom_ptr = bottom_blob;
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const float* shape_ptr = bottom_ptr + 1;
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if (dims == 1) {
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int w = (int)(shape_ptr[0] + 0.5);
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size_t elemsize = sizeof(val);
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top_blob.create(w, elemsize, opt.blob_allocator);
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if (top_blob.empty()) return -100;
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top_blob.fill(val);
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return 0;
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}
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if (dims == 2) {
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int h = (int)(shape_ptr[0] + 0.5);
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int w = (int)(shape_ptr[1] + 0.5);
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size_t elemsize = sizeof(val);
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top_blob.create(w, h, elemsize, opt.blob_allocator);
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if (top_blob.empty()) return -100;
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top_blob.fill(val);
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return 0;
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}
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if (dims == 3) {
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int channels = (int)(shape_ptr[0] + 0.5);
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int h = (int)(shape_ptr[1] + 0.5);
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int w = (int)(shape_ptr[2] + 0.5);
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size_t elemsize = sizeof(val);
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top_blob.create(w, h, channels, elemsize, opt.blob_allocator);
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if (top_blob.empty()) return -100;
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top_blob.fill(val);
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return 0;
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}
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}
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} // namespace mmlab
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