From: 서상민/동작제어Lab(SR)/Staff Engineer/삼성전자 <sangmin7.seo@samsung.com>
Date: Fri, 8 Jun 2018 08:06:56 +0000 (+0900)
Subject: [PureCL] Support TENSOR_QUANT8_ASYMM type operand (#1623)
X-Git-Tag: 0.2~648
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=ab56911301c425fd5b5912249c844410fe7bcd53;p=platform%2Fcore%2Fml%2Fnnfw.git

[PureCL] Support TENSOR_QUANT8_ASYMM type operand (#1623)

For issue #1481

This patch revises `internal::tflite::operand::Object` to keep `scale`
and `zeroPoint` in order to support operand of
ANEURALNETWORKS_TENSOR_QUANT8_ASYMM type, and then modifies
`ANeuralNetworksModel_addOperand` to pass the scale and zeroPoint values
as well when appending a new operand.

Signed-off-by: Sangmin Seo <sangmin7.seo@samsung.com>
---

diff --git a/runtimes/pure_arm_compute/src/internal/Model.cc b/runtimes/pure_arm_compute/src/internal/Model.cc
index 57cb3d3..87d84f8 100644
--- a/runtimes/pure_arm_compute/src/internal/Model.cc
+++ b/runtimes/pure_arm_compute/src/internal/Model.cc
@@ -67,11 +67,11 @@ namespace tflite
 namespace operand
 {
 
-Index Set::append(const Shape &shape, int32_t type)
+Index Set::append(const Shape &shape, int32_t type, float scale, int32_t zeroPoint)
 {
   int32_t index = _objects.size();
 
-  _objects.emplace_back(new Object{shape, type});
+  _objects.emplace_back(new Object{shape, type, scale, zeroPoint});
 
   return Index{index};
 }
diff --git a/runtimes/pure_arm_compute/src/internal/Model.h b/runtimes/pure_arm_compute/src/internal/Model.h
index 4c2fc5d..746339f 100644
--- a/runtimes/pure_arm_compute/src/internal/Model.h
+++ b/runtimes/pure_arm_compute/src/internal/Model.h
@@ -128,7 +128,9 @@ namespace operand
 class Object
 {
 public:
-  explicit Object(const Shape &shape, const int32_t type) : _shape{shape}, _type{type}
+  explicit Object(const Shape &shape, const int32_t type, const float scale,
+                  const int32_t zeroPoint)
+      : _shape{shape}, _type{type}, _scale{scale}, _zeroPoint{zeroPoint}
   {
     // DO NOTHING
   }
@@ -136,6 +138,8 @@ public:
 public:
   const Shape &shape(void) const { return _shape; }
   const int32_t type(void) const { return _type; }
+  const float scale(void) const { return _scale; }
+  const int32_t zeroPoint(void) const { return _zeroPoint; }
 
 private:
   void data(std::unique_ptr<Data> &&data) { _data = std::move(data); }
@@ -163,6 +167,8 @@ public:
 private:
   const Shape _shape;
   const int32_t _type;
+  const float _scale;
+  const int32_t _zeroPoint;
   std::unique_ptr<Data> _data;
 };
 
@@ -192,7 +198,7 @@ public:
   }
 
 public:
-  Index append(const Shape &, int32_t type);
+  Index append(const Shape &, int32_t type, float scale, int32_t zeroPoint);
 
 public:
   const Object &at(const Index &) const;
diff --git a/runtimes/pure_arm_compute/src/model.cc b/runtimes/pure_arm_compute/src/model.cc
index 74f8107..c1e65ea 100644
--- a/runtimes/pure_arm_compute/src/model.cc
+++ b/runtimes/pure_arm_compute/src/model.cc
@@ -21,8 +21,9 @@ int ANeuralNetworksModel_addOperand(ANeuralNetworksModel *model,
 {
   // ASSUME A tensor operand should consists of fp32 or int32 values.
   // NOTE We do not care about scala operands.
-  assert((type->dimensionCount == 0) || (type->type == 3 /* ANEURALNETWORKS_TENSOR_FLOAT32 */ ||
-                                         type->type == 4 /* ANEURALNETWORKS_TENSOR_INT32 */));
+  assert((type->dimensionCount == 0) || (type->type == ANEURALNETWORKS_TENSOR_FLOAT32 ||
+                                         type->type == ANEURALNETWORKS_TENSOR_INT32 ||
+                                         type->type == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM));
 
   internal::tflite::operand::Shape shape(type->dimensionCount);
 
@@ -31,7 +32,7 @@ int ANeuralNetworksModel_addOperand(ANeuralNetworksModel *model,
     shape.dim(axis) = type->dimensions[axis];
   }
 
-  model->deref().operands().append(shape, type->type);
+  model->deref().operands().append(shape, type->type, type->scale, type->zeroPoint);
 
   // NOTE We do NOT allocate CLTensor here as we do not how to interpret this one.
   //      TensorFlow Lite may interpret a rank-4 tensor either as a feature map (with batch) or