COMPMID-3097 Fuse activation with fully connected layer CL
Change-Id: I447030e69b9e565f2f81529a41af8c5e7ece7ecf
Signed-off-by: Giorgio Arena <giorgio.arena@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2702
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/arm_compute/core/PixelValue.h b/arm_compute/core/PixelValue.h
index c5f6608..31bc550 100644
--- a/arm_compute/core/PixelValue.h
+++ b/arm_compute/core/PixelValue.h
@@ -41,11 +41,11 @@
}
/** Initialize the union with a pixel value of chosen datatype
*
- * @param[in] v int value.
+ * @param[in] v value.
* @param[in] datatype DataType that @p v have to be stored
* @param[in] qinfo (Optional) QuantizationInfo to apply in case of quantized data types to @p v
*/
- PixelValue(int64_t v, DataType datatype, QuantizationInfo qinfo = QuantizationInfo())
+ PixelValue(double v, DataType datatype, QuantizationInfo qinfo = QuantizationInfo())
: PixelValue()
{
switch(datatype)
@@ -57,13 +57,13 @@
value.s8 = static_cast<int8_t>(v);
break;
case DataType::QASYMM8:
- value.u8 = quantize_qasymm8(static_cast<uint8_t>(v), qinfo);
+ value.u8 = quantize_qasymm8(static_cast<float>(v), qinfo);
break;
case DataType::QASYMM8_SIGNED:
- value.s8 = quantize_qasymm8_signed(static_cast<int8_t>(v), qinfo);
+ value.s8 = quantize_qasymm8_signed(static_cast<float>(v), qinfo);
break;
case DataType::QSYMM8:
- value.s8 = quantize_qsymm8(static_cast<int8_t>(v), qinfo);
+ value.s8 = quantize_qsymm8(static_cast<float>(v), qinfo);
break;
case DataType::U16:
value.u16 = static_cast<uint16_t>(v);
@@ -72,10 +72,10 @@
value.s16 = static_cast<int16_t>(v);
break;
case DataType::QASYMM16:
- value.u16 = quantize_qasymm16(static_cast<uint16_t>(v), qinfo);
+ value.u16 = quantize_qasymm16(static_cast<float>(v), qinfo);
break;
case DataType::QSYMM16:
- value.s16 = quantize_qsymm16(static_cast<int16_t>(v), qinfo);
+ value.s16 = quantize_qsymm16(static_cast<float>(v), qinfo);
break;
case DataType::U32:
value.u32 = static_cast<uint32_t>(v);
@@ -96,10 +96,8 @@
value.f32 = static_cast<float>(v);
break;
case DataType::F64:
- value.f64 = static_cast<double>(v);
- break;
default:
- value.s64 = v;
+ value.f64 = v;
break;
}
}
diff --git a/arm_compute/core/Types.h b/arm_compute/core/Types.h
index 2030b17..cf689d7 100644
--- a/arm_compute/core/Types.h
+++ b/arm_compute/core/Types.h
@@ -799,39 +799,6 @@
DimensionRoundingType _round_type;
};
-/** Fully connected layer info */
-struct FullyConnectedLayerInfo
-{
- DataLayout weights_trained_layout{ DataLayout::NCHW }; /**< Layout that the weights have been trained with. */
- bool transpose_weights{ true }; /**< Transpose weights if true. */
- bool are_weights_reshaped{ false }; /**< Reshape the weights tensor if false. */
- bool retain_internal_weights{ false }; /**< Retain internal reshaped weights. */
- bool fp_mixed_precision{ false }; /**< Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy. */
-
- /** Sets the weights trained data layout
- *
- * @param[in] layout Data layout that the weights were trained with
- *
- * @return Updated object
- */
- FullyConnectedLayerInfo &set_weights_trained_layout(DataLayout layout)
- {
- weights_trained_layout = layout;
- return *this;
- }
- /** Sets the transpose weights flag
- *
- * @param[in] should_transpose_weights Boolean flag indicating if weights should be transposed
- *
- * @return Updated object
- */
- FullyConnectedLayerInfo &set_transpose_weights(bool should_transpose_weights)
- {
- transpose_weights = should_transpose_weights;
- return *this;
- }
-};
-
/** PriorBox layer info */
class PriorBoxLayerInfo final
{
@@ -1674,6 +1641,40 @@
bool _enabled = { false };
};
+/** Fully connected layer info */
+struct FullyConnectedLayerInfo
+{
+ DataLayout weights_trained_layout{ DataLayout::NCHW }; /**< Layout that the weights have been trained with. */
+ bool transpose_weights{ true }; /**< Transpose weights if true. */
+ bool are_weights_reshaped{ false }; /**< Reshape the weights tensor if false. */
+ bool retain_internal_weights{ false }; /**< Retain internal reshaped weights. */
+ bool fp_mixed_precision{ false }; /**< Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy. */
+ ActivationLayerInfo activation_info{}; /**< Fused activation to apply after the matrix multiplication. */
+
+ /** Sets the weights trained data layout
+ *
+ * @param[in] layout Data layout that the weights were trained with
+ *
+ * @return Updated object
+ */
+ FullyConnectedLayerInfo &set_weights_trained_layout(DataLayout layout)
+ {
+ weights_trained_layout = layout;
+ return *this;
+ }
+ /** Sets the transpose weights flag
+ *
+ * @param[in] should_transpose_weights Boolean flag indicating if weights should be transposed
+ *
+ * @return Updated object
+ */
+ FullyConnectedLayerInfo &set_transpose_weights(bool should_transpose_weights)
+ {
+ transpose_weights = should_transpose_weights;
+ return *this;
+ }
+};
+
/** Normalization Layer Information class */
class NormalizationLayerInfo
{
@@ -1944,16 +1945,16 @@
/** GEMMLowp output stage info */
struct GEMMLowpOutputStageInfo
{
- GEMMLowpOutputStageType type{ GEMMLowpOutputStageType::NONE }; /**< GEMMLowp output stage type */
- int32_t gemmlowp_offset{ 0 }; /**< GEMMLowp output stage offset used for quantizing to QASYMM8 */
- int32_t gemmlowp_multiplier{ 0 }; /**< GEMMLowp output stage multiplier used for quantizing to QASYMM8 */
- int32_t gemmlowp_shift{ 0 }; /**< GEMMLowp output stage shift used for quantizing to uint8 */
- int32_t gemmlowp_min_bound{ 0 }; /**< GEMMLowp min value used to saturate down the output result before converting back to QASYMM8 */
- int32_t gemmlowp_max_bound{ 0 }; /**< GEMMLowp max value used to saturate down the output result before converting back to QASYMM8 */
- std::vector<int32_t> gemmlowp_multipliers{}; /**< GEMMLowp output stage multiplier used for quantizing to QASYMM8 */
- std::vector<int32_t> gemmlowp_shifts{}; /**< GEMMLowp output stage multiplier used for quantizing to QASYMM8 */
- bool is_quantized_per_channel{ false }; /**< GEMMLowp quantized per-channel flag */
- DataType output_data_type{ DataType::UNKNOWN }; /**< Output tensor data type to use if the output is not initialized */
+ GEMMLowpOutputStageType type{ GEMMLowpOutputStageType::NONE }; /**< GEMMLowp output stage type */
+ int32_t gemmlowp_offset{ 0 }; /**< GEMMLowp output stage offset used for quantizing to QASYMM8 */
+ int32_t gemmlowp_multiplier{ 0 }; /**< GEMMLowp output stage multiplier used for quantizing to QASYMM8 */
+ int32_t gemmlowp_shift{ 0 }; /**< GEMMLowp output stage shift used for quantizing to uint8 */
+ int32_t gemmlowp_min_bound{ std::numeric_limits<int32_t>::lowest() }; /**< GEMMLowp min value used to saturate down the output result before converting back to QASYMM8 */
+ int32_t gemmlowp_max_bound{ std::numeric_limits<int32_t>::max() }; /**< GEMMLowp max value used to saturate down the output result before converting back to QASYMM8 */
+ std::vector<int32_t> gemmlowp_multipliers{}; /**< GEMMLowp output stage multiplier used for quantizing to QASYMM8 */
+ std::vector<int32_t> gemmlowp_shifts{}; /**< GEMMLowp output stage multiplier used for quantizing to QASYMM8 */
+ bool is_quantized_per_channel{ false }; /**< GEMMLowp quantized per-channel flag */
+ DataType output_data_type{ DataType::UNKNOWN }; /**< Output tensor data type to use if the output is not initialized */
};
/** GEMM LHS (Left Hand Side) matrix information */