COMPMID-1691: Optimize CLDepthwiseConvolutionKernel (QASYMM8/NHWC) for 3x3 kernels (stride=1 and stride=2)
Change-Id: I7d0d2dc350feeb40d253d17f9ffd5051a8fb42ef
Reviewed-on: https://review.mlplatform.org/511
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/arm_compute/core/utils/misc/ShapeCalculator.h b/arm_compute/core/utils/misc/ShapeCalculator.h
index 35e2167..b256e73 100644
--- a/arm_compute/core/utils/misc/ShapeCalculator.h
+++ b/arm_compute/core/utils/misc/ShapeCalculator.h
@@ -250,6 +250,30 @@
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
@@ -405,6 +429,38 @@
return output_shape;
}
+/** Calculate the depthwise convolution output shape of a tensor
+ *
+ * @param[in] input Input tensor info
+ * @param[in] weights_width Weights width
+ * @param[in] weights_height Weights height
+ * @param[in] conv_info Padding and stride information to use for the convolution.
+ * @param[in] depth_multiplier Multiplier to apply to the input's depth in order to retrieve the output's depth.
+ *
+ * @return the calculated shape
+ */
+inline TensorShape compute_depthwise_convolution_shape(const ITensorInfo &input, int weights_width, int weights_height, PadStrideInfo conv_info, unsigned int depth_multiplier)
+{
+ const TensorShape input_shape{ input.tensor_shape() };
+
+ const DataLayout data_layout = input.data_layout();
+ 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);
+
+ unsigned int output_width = 0;
+ unsigned int output_height = 0;
+ std::tie(output_width, output_height) = scaled_dimensions(input_shape[width_idx], input_shape[height_idx],
+ weights_width, weights_width, conv_info);
+
+ TensorShape output_shape{ input_shape };
+ output_shape.set(width_idx, output_width);
+ output_shape.set(height_idx, output_height);
+ output_shape.set(channel_idx, input_shape[channel_idx] * depth_multiplier);
+
+ return output_shape;
+}
+
/** Calculate the upsampled output shape used for deconvolution
*
* @param[in] input Input tensor info