COMPMID-2273: Fuse Batch Normalization with Depthwise Convolution layer at graph level (only for CL)
Change-Id: I1d941c6e66722f39583bf68148c980bb28ff89a1
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1423
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/arm_compute/graph/backends/FusedConvolutionBatchNormalizationFunction.h b/arm_compute/graph/backends/FusedConvolutionBatchNormalizationFunction.h
index 92af17b..a6da76b 100644
--- a/arm_compute/graph/backends/FusedConvolutionBatchNormalizationFunction.h
+++ b/arm_compute/graph/backends/FusedConvolutionBatchNormalizationFunction.h
@@ -54,7 +54,7 @@
* Data types supported: QASYMM8/F16/F32.
* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
* @param[in] bias Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
- * Data type supported: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type.
+ * Data type supported: Should match @p input data type.
* @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
* Data types supported: Same as @p input.
* @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input