diff options
Diffstat (limited to 'arm_compute/runtime/NEON/functions/NEConvertFullyConnectedWeights.h')
-rw-r--r-- | arm_compute/runtime/NEON/functions/NEConvertFullyConnectedWeights.h | 5 |
1 files changed, 3 insertions, 2 deletions
diff --git a/arm_compute/runtime/NEON/functions/NEConvertFullyConnectedWeights.h b/arm_compute/runtime/NEON/functions/NEConvertFullyConnectedWeights.h index 37b6b6c4dd..42f787090e 100644 --- a/arm_compute/runtime/NEON/functions/NEConvertFullyConnectedWeights.h +++ b/arm_compute/runtime/NEON/functions/NEConvertFullyConnectedWeights.h @@ -32,6 +32,7 @@ namespace arm_compute { +// Forward declarations class ITensor; /** Basic function to run @ref NEConvertFullyConnectedWeightsKernel. */ @@ -42,7 +43,7 @@ public: NEConvertFullyConnectedWeights(); /** Initialize the function. * - * @param[in] input Source weights tensor to convert. Must be 2 dimensional. Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32. + * @param[in] input Source weights tensor to convert. Must be 2 dimensional. Data types supported: All. * @param[out] output The converted weights tensor. Shape and Data Type: Same as @p input. * @param[in] original_input_shape Shape of the original input tensor (the one entering fully connected layer). * @param[in] data_layout The data layout the weights have been trained in. @@ -50,7 +51,7 @@ public: void configure(const ITensor *input, ITensor *output, const TensorShape &original_input_shape, DataLayout data_layout); /** Static function to check if given info will lead to a valid configuration of @ref NEConvertFullyConnectedWeights * - * @param[in] input Source weights tensor info to convert. Must be 2 dimensional. Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32. + * @param[in] input Source weights tensor info to convert. Must be 2 dimensional. Data types supported: All. * @param[in] output The converted weights tensor info. Shape and Data Type: Same as @p input. * @param[in] original_input_shape Shape of the original input tensor (the one entering fully connected layer). * @param[in] data_layout The data layout the weights have been trained in. |