COMPMID-1918: Different qinfos support in NEConcatLayer.
Added support in NEDepthConcatenateLayerKernel and NEWidthConcatenateLayer for
different quantization arguments both for the input and output.
If input's quantization infos are not homogeneous the input values are requantized using
the output's quantization info.
Change-Id: I2daa638361947eb3ec848d5425d0a5bbfea1936d
Reviewed-on: https://review.mlplatform.org/627
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Isabella Gottardi <isabella.gottardi@arm.com>
diff --git a/tests/validation/reference/WidthConcatenateLayer.cpp b/tests/validation/reference/WidthConcatenateLayer.cpp
index 6be171b..3854339 100644
--- a/tests/validation/reference/WidthConcatenateLayer.cpp
+++ b/tests/validation/reference/WidthConcatenateLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -34,7 +34,7 @@
namespace reference
{
template <typename T>
-SimpleTensor<T> widthconcatenate_layer(const std::vector<SimpleTensor<T>> &srcs)
+SimpleTensor<T> widthconcatenate_layer(const std::vector<SimpleTensor<T>> &srcs, SimpleTensor<T> &dst)
{
// Create reference
std::vector<TensorShape> shapes;
@@ -44,10 +44,6 @@
shapes.emplace_back(src.shape());
}
- DataType dst_type = srcs.empty() ? DataType::UNKNOWN : srcs[0].data_type();
- TensorShape dst_shape = calculate_width_concatenate_shape(shapes);
- SimpleTensor<T> dst(dst_shape, dst_type);
-
// Compute reference
int width_offset = 0;
const int width_out = dst.shape().x();
@@ -74,21 +70,32 @@
for(int r = 0; r < height; ++r)
{
const int offset = u * height * depth + d * height + r;
- std::copy(src_ptr, src_ptr + width, dst_ptr + width_offset + offset * width_out);
- src_ptr += width;
+ if(src.data_type() == DataType::QASYMM8 && src.quantization_info() != dst.quantization_info())
+ {
+ std::transform(src_ptr, src_ptr + width, dst_ptr + width_offset + offset * width_out, [src, dst](T t)
+ {
+ const float dequantized_input = src.quantization_info().dequantize(t);
+ return dst.quantization_info().quantize(dequantized_input, RoundingPolicy::TO_NEAREST_UP);
+ });
+ src_ptr += width;
+ }
+ else
+ {
+ std::copy(src_ptr, src_ptr + width, dst_ptr + width_offset + offset * width_out);
+ src_ptr += width;
+ }
}
}
}
-
width_offset += width;
}
return dst;
}
-template SimpleTensor<float> widthconcatenate_layer(const std::vector<SimpleTensor<float>> &srcs);
-template SimpleTensor<half> widthconcatenate_layer(const std::vector<SimpleTensor<half>> &srcs);
-template SimpleTensor<uint8_t> widthconcatenate_layer(const std::vector<SimpleTensor<uint8_t>> &srcs);
+template SimpleTensor<float> widthconcatenate_layer(const std::vector<SimpleTensor<float>> &srcs, SimpleTensor<float> &dst);
+template SimpleTensor<half> widthconcatenate_layer(const std::vector<SimpleTensor<half>> &srcs, SimpleTensor<half> &dst);
+template SimpleTensor<uint8_t> widthconcatenate_layer(const std::vector<SimpleTensor<uint8_t>> &srcs, SimpleTensor<uint8_t> &dst);
} // namespace reference
} // namespace validation
} // namespace test