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author | Сергей Баранников/AI Tools Lab /SRR/Engineer/삼성전자 <s.barannikov@samsung.com> | 2019-08-21 17:22:51 +0900 |
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committer | Alexander Efimov/AI Tools Lab/./Samsung Electronics <a.efimov@samsung.com> | 2019-08-21 11:22:51 +0300 |
commit | 291566d19eb1164fe0f0e296523ba66a4dd663c0 (patch) | |
tree | d3474a4482ae637363885f13251ab56162463a66 /compiler/mir-tflite-importer | |
parent | 55b84a2363af5f113ee5fd4d0821019878774381 (diff) | |
download | nnfw-291566d19eb1164fe0f0e296523ba66a4dd663c0.tar.gz nnfw-291566d19eb1164fe0f0e296523ba66a4dd663c0.tar.bz2 nnfw-291566d19eb1164fe0f0e296523ba66a4dd663c0.zip |
[mir_tflite] Use new PadOp constructor (#6752)
Switch to the new `PadOp` constructor.
Signed-off-by: Sergei Barannikov <s.barannikov@samsung.com>
Diffstat (limited to 'compiler/mir-tflite-importer')
-rw-r--r-- | compiler/mir-tflite-importer/tflite_op_creator.cpp | 22 |
1 files changed, 10 insertions, 12 deletions
diff --git a/compiler/mir-tflite-importer/tflite_op_creator.cpp b/compiler/mir-tflite-importer/tflite_op_creator.cpp index 3283ab607..4474ca941 100644 --- a/compiler/mir-tflite-importer/tflite_op_creator.cpp +++ b/compiler/mir-tflite-importer/tflite_op_creator.cpp @@ -445,23 +445,21 @@ TFLiteOpCreator::convertPad(const ::tflite::PadOptions * /*opts*/, mir::Tensor<int32_t> paddings_tensor(extractTensor(inputs.at(1))); const auto &input_shape = input->getShape(); - int32_t num_dims = input_shape.rank(); + const int num_dims = input_shape.rank(); - std::vector<std::pair<int32_t, int32_t>> paddings; - paddings.reserve(static_cast<uint64_t>(num_dims)); - for (int axis = 0; axis < num_dims; axis++) + std::vector<std::int32_t> padding_before(num_dims); + std::vector<std::int32_t> padding_after(num_dims); + for (int i = 0; i < num_dims; i++) { - paddings.emplace_back(paddings_tensor.at(mir::Index({axis, 0})), - paddings_tensor.at(mir::Index({axis, 1}))); + padding_before[i] = paddings_tensor.at(mir::Index({i, 0})); + padding_after[i] = paddings_tensor.at(mir::Index({i, 1})); } - float filler_value = 0.0; - mir::Scalar filler(reinterpret_cast<char *>(&filler_value), mir::DataType::FLOAT32, - sizeof(filler_value)); + const float padding_value = 0.0f; - // FIXME Do we really need num_dims as an argument? It looks redundant. - auto result = createOp<ops::PadOp>(input, num_dims, paddings, filler); - return {result->getOutput(0)}; + auto result = + createOp<ops::PadOp>(input, padding_before, padding_after, padding_value)->getOutput(0); + return {result}; } std::vector<mir::Operation::Output *> |