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Diffstat (limited to 'tests/validation_old/TensorOperations.h')
| -rw-r--r-- | tests/validation_old/TensorOperations.h | 64 |
1 files changed, 0 insertions, 64 deletions
diff --git a/tests/validation_old/TensorOperations.h b/tests/validation_old/TensorOperations.h index 0c1ab4134e..04a79f0de3 100644 --- a/tests/validation_old/TensorOperations.h +++ b/tests/validation_old/TensorOperations.h @@ -861,70 +861,6 @@ void warp_perspective(const Tensor<T> &in, Tensor<T> &out, Tensor<T> &valid_mask } } -// Batch Normalization Layer for fixed point type -template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type * = nullptr> -void batch_normalization_layer(const Tensor<T> &in, Tensor<T> &out, const Tensor<T> &mean, const Tensor<T> &var, const Tensor<T> &beta, const Tensor<T> &gamma, float epsilon, int fixed_point_position) -{ - const int cols = static_cast<int>(in.shape()[0]); - const int rows = static_cast<int>(in.shape()[1]); - const int depth = static_cast<int>(in.shape()[2]); - int upper_dims = in.shape().total_size() / (cols * rows * depth); - - for(int r = 0; r < upper_dims; ++r) - { - for(int i = 0; i < depth; ++i) - { - for(int k = 0; k < rows; ++k) - { - for(int l = 0; l < cols; ++l) - { - const int pos = l + k * cols + i * rows * cols + r * cols * rows * depth; - fixed_point_arithmetic::fixed_point<T> in_qs(in[pos], fixed_point_position, true); - fixed_point_arithmetic::fixed_point<T> var_qs(var[i], fixed_point_position, true); - fixed_point_arithmetic::fixed_point<T> mean_qs(mean[i], fixed_point_position, true); - fixed_point_arithmetic::fixed_point<T> beta_qs(beta[i], fixed_point_position, true); - fixed_point_arithmetic::fixed_point<T> gamma_qs(gamma[i], fixed_point_position, true); - fixed_point_arithmetic::fixed_point<T> epsilon_qs(epsilon, fixed_point_position); - - auto denominator = fixed_point_arithmetic::inv_sqrt(var_qs + epsilon_qs); - auto numerator = in_qs - mean_qs; - auto x_bar = numerator * denominator; - x_bar = beta_qs + x_bar * gamma_qs; - out[pos] = x_bar.raw(); - } - } - } - } -} - -// Batch Normalization Layer for floating point type -template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr> -void batch_normalization_layer(const Tensor<T> &in, Tensor<T> &out, const Tensor<T> &mean, const Tensor<T> &var, const Tensor<T> &beta, const Tensor<T> &gamma, float epsilon, int fixed_point_position) -{ - const int cols = static_cast<int>(in.shape()[0]); - const int rows = static_cast<int>(in.shape()[1]); - const int depth = static_cast<int>(in.shape()[2]); - int upper_dims = in.shape().total_size() / (cols * rows * depth); - - for(int r = 0; r < upper_dims; ++r) - { - for(int i = 0; i < depth; ++i) - { - for(int k = 0; k < rows; ++k) - { - for(int l = 0; l < cols; ++l) - { - const int pos = l + k * cols + i * rows * cols + r * cols * rows * depth; - const float denominator = sqrt(var[i] + epsilon); - const float numerator = in[pos] - mean[i]; - const float x_bar = numerator / denominator; - out[pos] = beta[i] + x_bar * gamma[i]; - } - } - } - } -} - // ROI Pooling layer template <typename T> void roi_pooling_layer(const Tensor<T> &in, Tensor<T> &out, const std::vector<ROI> &rois, const ROIPoolingLayerInfo &pool_info) |