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-rw-r--r--tests/validation/reference/SoftmaxLayer.cpp20
1 files changed, 12 insertions, 8 deletions
diff --git a/tests/validation/reference/SoftmaxLayer.cpp b/tests/validation/reference/SoftmaxLayer.cpp
index aa640ad5e6..7f2c36ecef 100644
--- a/tests/validation/reference/SoftmaxLayer.cpp
+++ b/tests/validation/reference/SoftmaxLayer.cpp
@@ -39,21 +39,25 @@ SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta)
// Create reference
SimpleTensor<T> dst{ src.shape(), src.data_type(), 1 };
- // Compute reference
- const int cols = src.shape()[0];
- const int upper_dims = src.num_elements() / cols;
+ const bool is_4D_input = (src.shape().num_dimensions() > 2);
+
+ // Compute reference. Lower dims are
+ // - the number of columns for the 2D case
+ // - the collapsing of the first three dimensions (i.e., the flattened dimension of each batch) in the 4D case
+ const int lower_dims = (is_4D_input ? src.shape()[2] * src.shape()[1] * src.shape()[0] : src.shape()[0]);
+ const int upper_dims = src.num_elements() / lower_dims;
for(int r = 0; r < upper_dims; ++r)
{
- const T *src_row_ptr = src.data() + r * cols;
- T *dst_row_ptr = dst.data() + r * cols;
+ const T *src_row_ptr = src.data() + r * lower_dims;
+ T *dst_row_ptr = dst.data() + r * lower_dims;
// Find max
- const T max = *std::max_element(src_row_ptr, src_row_ptr + cols);
+ const T max = *std::max_element(src_row_ptr, src_row_ptr + lower_dims);
// Regularize
T sum(0.f);
- std::transform(src_row_ptr, src_row_ptr + cols, dst_row_ptr, [&sum, max, beta](T val)
+ std::transform(src_row_ptr, src_row_ptr + lower_dims, dst_row_ptr, [&sum, max, beta](T val)
{
const T res(std::exp((val - max) * beta));
sum += res;
@@ -61,7 +65,7 @@ SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta)
});
// Normalize
- std::transform(dst_row_ptr, dst_row_ptr + cols, dst_row_ptr, [sum](T val)
+ std::transform(dst_row_ptr, dst_row_ptr + lower_dims, dst_row_ptr, [sum](T val)
{
return val / sum;
});