commit 54e98d98fbe082b265b2c4a384eabe0144866bcc
author Pablo Tello <pablo.tello@arm.com> Tue Feb 05 16:16:19 2019 +0000
committer Pablo Marquez <pablo.tello@arm.com> Fri Feb 08 11:15:30 2019 +0000
tree f0367171d8dfcad282523efceb709ec523ef9edd
parent 03bb550b72ebd107a6cbd994008cdcc00597d822

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>

tests/validation/fixtures/WidthConcatenateLayerFixture.h

diff --git a/tests/validation/fixtures/WidthConcatenateLayerFixture.h b/tests/validation/fixtures/WidthConcatenateLayerFixture.h
index 1f79210..47a03ed 100644
--- a/tests/validation/fixtures/WidthConcatenateLayerFixture.h
+++ b/tests/validation/fixtures/WidthConcatenateLayerFixture.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -53,9 +53,20 @@
         // Create input shapes
         std::mt19937                    gen(library->seed());
         std::uniform_int_distribution<> num_dis(2, 8);
-        const int                       num_tensors = num_dis(gen);
+        std::uniform_int_distribution<> offset_dis(0, 20);
 
-        std::vector<TensorShape>         shapes(num_tensors, shape);
+        const int num_tensors = num_dis(gen);
+
+        std::vector<TensorShape> shapes(num_tensors, shape);
+
+        // vector holding the quantization info:
+        //      the last element is the output quantization info
+        //      all other elements are the quantization info for the input tensors
+        std::vector<QuantizationInfo> qinfo(num_tensors + 1, QuantizationInfo());
+        for(auto &qi : qinfo)
+        {
+            qi = QuantizationInfo(1.f / 255.f, offset_dis(gen));
+        }
         std::bernoulli_distribution      mutate_dis(0.5f);
         std::uniform_real_distribution<> change_dis(-0.25f, 0.f);
 
@@ -71,8 +82,8 @@
             }
         }
 
-        _target    = compute_target(shapes, data_type);
-        _reference = compute_reference(shapes, data_type);
+        _target    = compute_target(shapes, qinfo, data_type);
+        _reference = compute_reference(shapes, qinfo, data_type);
     }
 
 protected:
@@ -82,7 +93,7 @@
         library->fill_tensor_uniform(tensor, i);
     }
 
-    TensorType compute_target(std::vector<TensorShape> shapes, DataType data_type)
+    TensorType compute_target(std::vector<TensorShape> shapes, const std::vector<QuantizationInfo> &qinfo, DataType data_type)
     {
         std::vector<TensorType>    srcs;
         std::vector<ITensorType *> src_ptrs;
@@ -90,14 +101,15 @@
         // Create tensors
         srcs.reserve(shapes.size());
 
-        for(const auto &shape : shapes)
+        for(size_t j = 0; j < shapes.size(); ++j)
         {
-            srcs.emplace_back(create_tensor<TensorType>(shape, data_type, 1));
+            srcs.emplace_back(create_tensor<TensorType>(shapes[j], data_type, 1, qinfo[j]));
             src_ptrs.emplace_back(&srcs.back());
         }
 
         TensorShape dst_shape = misc::shape_calculator::calculate_width_concatenate_shape(src_ptrs);
-        TensorType  dst       = create_tensor<TensorType>(dst_shape, data_type, 1);
+
+        TensorType dst = create_tensor<TensorType>(dst_shape, data_type, 1, qinfo[shapes.size()]);
 
         // Create and configure function
         FunctionType width_concat;
@@ -133,19 +145,21 @@
         return dst;
     }
 
-    SimpleTensor<T> compute_reference(std::vector<TensorShape> shapes, DataType data_type)
+    SimpleTensor<T> compute_reference(std::vector<TensorShape> shapes, const std::vector<QuantizationInfo> &qinfo, DataType data_type)
     {
         std::vector<SimpleTensor<T>> srcs;
 
         // Create and fill tensors
-        int i = 0;
-        for(const auto &shape : shapes)
+        for(size_t j = 0; j < shapes.size(); ++j)
         {
-            srcs.emplace_back(shape, data_type, 1);
-            fill(srcs.back(), i++);
+            srcs.emplace_back(shapes[j], data_type, 1, qinfo[j]);
+            fill(srcs.back(), j);
         }
 
-        return reference::widthconcatenate_layer<T>(srcs);
+        const TensorShape dst_shape = calculate_width_concatenate_shape(shapes);
+        SimpleTensor<T>   dst{ dst_shape, data_type, 1, qinfo[shapes.size()] };
+
+        return reference::widthconcatenate_layer<T>(srcs, dst);
     }
 
     TensorType      _target{};