diff options
Diffstat (limited to 'arm_compute/core')
-rw-r--r-- | arm_compute/core/Types.h | 6 | ||||
-rw-r--r-- | arm_compute/core/utils/misc/ShapeCalculator.h | 24 |
2 files changed, 0 insertions, 30 deletions
diff --git a/arm_compute/core/Types.h b/arm_compute/core/Types.h index b1f340d18e..b5fd21d29d 100644 --- a/arm_compute/core/Types.h +++ b/arm_compute/core/Types.h @@ -1874,12 +1874,6 @@ struct ConvolutionInfo Size2D dilation{ Size2D(1, 1) }; /**< Dilation, in elements, across x and y. Defaults to (1, 1). */ }; -struct DepthwiseConvolutionReshapeInfo -{ - unsigned int c0{ 1 }; /**< Number of channels processed by the depth-wise convolution */ - bool transpose{ false }; /**< True if the block MxC0 (where M is the area of the filter i.e. KwxKh) has to be transposed */ -}; - /** GEMMLowp output stage type */ enum class GEMMLowpOutputStageType { diff --git a/arm_compute/core/utils/misc/ShapeCalculator.h b/arm_compute/core/utils/misc/ShapeCalculator.h index ba37f9a61e..8e49c068af 100644 --- a/arm_compute/core/utils/misc/ShapeCalculator.h +++ b/arm_compute/core/utils/misc/ShapeCalculator.h @@ -287,30 +287,6 @@ inline TensorShape compute_interleaved_shape(const ITensorInfo &a, int mult_inte return shape_interleaved_a; } -/** Calculate the reshaped shape of the weights to use in depthwise convolution - * - * @param[in] input Input tensor info - * @param[in] info Depthwise convolution information to be used for reshaping. - * - * @return the calculated shape - */ -inline TensorShape compute_reshaped_depthwise_weights_shape(const ITensorInfo &input, const DepthwiseConvolutionReshapeInfo &info) -{ - const auto data_layout = input.data_layout(); - TensorShape weights_shape{}; - - const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); - const int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); - const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); - const size_t num_channels = input.dimension(channel_idx); - const size_t num_rows = input.dimension(height_idx); - const size_t num_cols = input.dimension(width_idx); - - weights_shape.set(0, num_rows * num_cols * info.c0); - weights_shape.set(1, DIV_CEIL(num_channels, info.c0)); - return weights_shape; -} - /** Calculate the transposed 1xW shape * * @param[in] b Input tensor info |