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authorСергей Баранников/AI Tools Lab /SRR/Engineer/삼성전자 <s.barannikov@samsung.com>2019-08-01 21:11:53 +0300
committerEfimov Alexander/AI Tools Lab/./Samsung Electronics <a.efimov@samsung.com>2019-08-01 21:11:53 +0300
commit0d173e13318f801ec1991f41263aa6456a051349 (patch)
treeb1f3f1ccc6b14345e3fcc02fd93fee4e1f909db3 /compiler/nnc
parentd53495a7397d71c17550db9666070ce3cbf85892 (diff)
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[nnc] Support 0-dimensional tensors in x86-c++ backend (#6121)
Fixes compilation error of TFLIte StyleTransfer nework. Signed-off-by: Sergei Barannikov <s.barannikov@samsung.com>
Diffstat (limited to 'compiler/nnc')
-rw-r--r--compiler/nnc/passes/soft_backend/code_snippets/cpp_header_types.def6
-rw-r--r--compiler/nnc/unittests/soft_backend/CPPHeaderTypes.cpp2
2 files changed, 2 insertions, 6 deletions
diff --git a/compiler/nnc/passes/soft_backend/code_snippets/cpp_header_types.def b/compiler/nnc/passes/soft_backend/code_snippets/cpp_header_types.def
index 77efd9ad8..771329cdd 100644
--- a/compiler/nnc/passes/soft_backend/code_snippets/cpp_header_types.def
+++ b/compiler/nnc/passes/soft_backend/code_snippets/cpp_header_types.def
@@ -92,10 +92,6 @@ public:
/** Returns number of elements in table*/
index_t getNumElems() const
{
- if (_dims == 0)
- {
- return 0;
- }
index_t volume = 1;
for (index_t i = 0; i < _dims; ++i)
{
@@ -120,7 +116,7 @@ using Index = Shape;
class Tensor
{
public:
- Tensor(): _shape(), _data(nullptr), _managed(true){}
+ Tensor(): Tensor(Shape{}){}
Tensor(Tensor &&orig): _shape(orig._shape), _data(orig._data), _managed(orig._managed)
{
diff --git a/compiler/nnc/unittests/soft_backend/CPPHeaderTypes.cpp b/compiler/nnc/unittests/soft_backend/CPPHeaderTypes.cpp
index f474664d8..bd81d565d 100644
--- a/compiler/nnc/unittests/soft_backend/CPPHeaderTypes.cpp
+++ b/compiler/nnc/unittests/soft_backend/CPPHeaderTypes.cpp
@@ -64,7 +64,7 @@ TEST(SOFT_BACKEND, shape_and_index) {
TEST(SOFT_BACKEND, tensor) {
// test reshape
Tensor t1;
- ASSERT_EQ(t1.getShape().getNumElems(), 0);
+ ASSERT_EQ(t1.getShape().getNumElems(), 1);
const index_t tensor1_height = 2;
const index_t tensor1_width = 4;
t1.reshape(Shape{tensor1_height, tensor1_width});