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Diffstat (limited to 'src/cpu/operators/CpuWinogradConv2d.cpp')
-rw-r--r--src/cpu/operators/CpuWinogradConv2d.cpp6
1 files changed, 3 insertions, 3 deletions
diff --git a/src/cpu/operators/CpuWinogradConv2d.cpp b/src/cpu/operators/CpuWinogradConv2d.cpp
index 1fb6d33a61..e4bcdc0b64 100644
--- a/src/cpu/operators/CpuWinogradConv2d.cpp
+++ b/src/cpu/operators/CpuWinogradConv2d.cpp
@@ -103,7 +103,7 @@ bool get_winograd_kernel_implementation(const ITensorInfo
Tensor4DShape in_shape{internal_get_shape(src)};
Tensor4DShape out_shape{internal_get_shape(dst)};
Tensor4DShape kernel_shape{internal_get_shape(weights)};
- uint32_t nthreads = NEScheduler::num_threads();
+ uint32_t nthreads = NEScheduler::get().num_threads();
// Get configuration arguments for Winograd
winograd_cfg.output_rows = 0;
winograd_cfg.output_cols = 0;
@@ -183,7 +183,7 @@ void CpuWinogradConv2d::configure(const ITensorInfo *src,
ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, conv_info, act_info, enable_fast_math);
ARM_COMPUTE_UNUSED(biases);
const DataType data_type = src->data_type();
- uint32_t nthreads = NEScheduler::num_threads();
+ uint32_t nthreads = NEScheduler::get().num_threads();
_data_layout = src->data_layout();
const Tensor4DShape kernel_shape{internal_get_shape(weights)};
@@ -361,7 +361,7 @@ void CpuWinogradConv2d::run(ITensorPack &tensors)
auto output = tensors.get_tensor(ACL_DST);
Window win;
- const uint32_t nthreads = NEScheduler::num_threads();
+ const uint32_t nthreads = NEScheduler::get().num_threads();
// The Winograd transform implementation does fine-grain threading inside the transforms. Just pass thread_id and nthreads.
win.set(Window::DimX, Window::Dimension(0, nthreads, 1));