COMPMID-797 Integrate Mobilenet QASYMM8 with new graph.

Change-Id: I4df63ec2f4eb27a8a6eec2bea27741bf8dec6910
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/126966
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

6 files changed, 75 insertions, 27 deletions

diff --git a/src/graph/backends/CL/CLDeviceBackend.cpp b/src/graph/backends/CL/CLDeviceBackend.cpp
index f10eb33a98..92cb6936c3 100644
--- a/src/graph/backends/CL/CLDeviceBackend.cpp
+++ b/src/graph/backends/CL/CLDeviceBackend.cpp
@@ -126,7 +126,7 @@ std::unique_ptr<ITensorHandle> CLDeviceBackend::create_tensor(const Tensor &tens
     ARM_COMPUTE_ERROR_ON(tensor_desc.target != Target::CL);
 
     // Create backend tensor handle
-    TensorInfo info(tensor_desc.shape, 1, tensor_desc.data_type);
+    TensorInfo info(tensor_desc.shape, 1, tensor_desc.data_type, tensor_desc.quant_info);
     auto       backend_tensor_handle = support::cpp14::make_unique<CLTensorHandle>(info);
 
     return std::move(backend_tensor_handle);
diff --git a/src/graph/backends/CL/CLFunctionsFactory.cpp b/src/graph/backends/CL/CLFunctionsFactory.cpp
index 1b448fefd2..ad73a797e3 100644
--- a/src/graph/backends/CL/CLFunctionsFactory.cpp
+++ b/src/graph/backends/CL/CLFunctionsFactory.cpp
@@ -154,10 +154,16 @@ std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node,
     ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
 
     // Extract IO and info
-    ICLTensor              *input          = get_backing_tensor(node.input(0));
-    ICLTensor              *weights        = get_backing_tensor(node.input(1));
-    ICLTensor              *biases         = get_backing_tensor(node.input(2));
-    ICLTensor              *output         = get_backing_tensor(node.output(0));
+    ICLTensor *input   = get_backing_tensor(node.input(0));
+    ICLTensor *weights = get_backing_tensor(node.input(1));
+    ICLTensor *biases  = get_backing_tensor(node.input(2));
+    ICLTensor *output  = get_backing_tensor(node.output(0));
+
+    if(is_data_type_quantized_asymmetric(input->info()->data_type()))
+    {
+        biases->info()->set_data_type(DataType::S32);
+    }
+
     const PadStrideInfo     conv_info      = node.convolution_info();
     const ConvolutionMethod conv_algorithm = node.convolution_method();
 
@@ -190,6 +196,8 @@ std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node,
     // Log info
     ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name
                                << " Data Type: " << input->info()->data_type()
+                               << " Input QuantInfo: " << input->info()->quantization_info()
+                               << " Weights QuantInfo: " << weights->info()->quantization_info()
                                << " Input shape: " << input->info()->tensor_shape()
                                << " Weights shape: " << weights->info()->tensor_shape()
                                << " Output shape: " << output->info()->tensor_shape()
@@ -251,10 +259,16 @@ std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvoluti
     ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
 
     // Extract IO and info
-    ICLTensor                       *input         = get_backing_tensor(node.input(0));
-    ICLTensor                       *weights       = get_backing_tensor(node.input(1));
-    ICLTensor                       *biases        = get_backing_tensor(node.input(2));
-    ICLTensor                       *output        = get_backing_tensor(node.output(0));
+    ICLTensor *input   = get_backing_tensor(node.input(0));
+    ICLTensor *weights = get_backing_tensor(node.input(1));
+    ICLTensor *biases  = get_backing_tensor(node.input(2));
+    ICLTensor *output  = get_backing_tensor(node.output(0));
+
+    if(is_data_type_quantized_asymmetric(input->info()->data_type()))
+    {
+        biases->info()->set_data_type(DataType::S32);
+    }
+
     const PadStrideInfo              conv_info     = node.convolution_info();
     const DepthwiseConvolutionMethod dwc_algorithm = node.depthwise_convolution_method();
 
@@ -275,6 +289,8 @@ std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvoluti
     // Log info
     ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name
                                << " Data Type: " << input->info()->data_type()
+                               << " Input QuantInfo: " << input->info()->quantization_info()
+                               << " Weights QuantInfo: " << weights->info()->quantization_info()
                                << " Input shape: " << input->info()->tensor_shape()
                                << " Weights shape: " << weights->info()->tensor_shape()
                                << " Output shape: " << output->info()->tensor_shape()
diff --git a/src/graph/backends/GLES/GCDeviceBackend.cpp b/src/graph/backends/GLES/GCDeviceBackend.cpp
index 8cd9994744..a55215f058 100644
--- a/src/graph/backends/GLES/GCDeviceBackend.cpp
+++ b/src/graph/backends/GLES/GCDeviceBackend.cpp
@@ -87,7 +87,7 @@ std::unique_ptr<ITensorHandle> GCDeviceBackend::create_tensor(const Tensor &tens
     ARM_COMPUTE_ERROR_ON(tensor_desc.target != Target::GC);
 
     // Create backend tensor handle
-    TensorInfo info(tensor_desc.shape, 1, tensor_desc.data_type);
+    TensorInfo info(tensor_desc.shape, 1, tensor_desc.data_type, tensor_desc.quant_info);
     auto       backend_tensor_handle = support::cpp14::make_unique<GCTensorHandle>(info);
 
     return std::move(backend_tensor_handle);
diff --git a/src/graph/backends/GLES/GCFunctionsFactory.cpp b/src/graph/backends/GLES/GCFunctionsFactory.cpp
index 12e7c042d4..d3c5737e68 100644
--- a/src/graph/backends/GLES/GCFunctionsFactory.cpp
+++ b/src/graph/backends/GLES/GCFunctionsFactory.cpp
@@ -154,10 +154,16 @@ std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node,
     ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
 
     // Extract IO and info
-    IGCTensor              *input          = get_backing_tensor(node.input(0));
-    IGCTensor              *weights        = get_backing_tensor(node.input(1));
-    IGCTensor              *biases         = get_backing_tensor(node.input(2));
-    IGCTensor              *output         = get_backing_tensor(node.output(0));
+    IGCTensor *input   = get_backing_tensor(node.input(0));
+    IGCTensor *weights = get_backing_tensor(node.input(1));
+    IGCTensor *biases  = get_backing_tensor(node.input(2));
+    IGCTensor *output  = get_backing_tensor(node.output(0));
+
+    if(is_data_type_quantized_asymmetric(input->info()->data_type()))
+    {
+        biases->info()->set_data_type(DataType::S32);
+    }
+
     const PadStrideInfo     conv_info      = node.convolution_info();
     const ConvolutionMethod conv_algorithm = node.convolution_method();
 
@@ -180,6 +186,8 @@ std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node,
     // Log info
     ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name
                                << " Data Type: " << input->info()->data_type()
+                               << " Input QuantInfo: " << input->info()->quantization_info()
+                               << " Weights QuantInfo: " << weights->info()->quantization_info()
                                << " Input shape: " << input->info()->tensor_shape()
                                << " Weights shape: " << weights->info()->tensor_shape()
                                << " Output shape: " << output->info()->tensor_shape()
@@ -241,10 +249,16 @@ std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvoluti
     ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
 
     // Extract IO and info
-    IGCTensor                       *input         = get_backing_tensor(node.input(0));
-    IGCTensor                       *weights       = get_backing_tensor(node.input(1));
-    IGCTensor                       *biases        = get_backing_tensor(node.input(2));
-    IGCTensor                       *output        = get_backing_tensor(node.output(0));
+    IGCTensor *input   = get_backing_tensor(node.input(0));
+    IGCTensor *weights = get_backing_tensor(node.input(1));
+    IGCTensor *biases  = get_backing_tensor(node.input(2));
+    IGCTensor *output  = get_backing_tensor(node.output(0));
+
+    if(is_data_type_quantized_asymmetric(input->info()->data_type()))
+    {
+        biases->info()->set_data_type(DataType::S32);
+    }
+
     const PadStrideInfo              conv_info     = node.convolution_info();
     const DepthwiseConvolutionMethod dwc_algorithm = node.depthwise_convolution_method();
 
@@ -264,6 +278,8 @@ std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvoluti
     // Log info
     ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name
                                << " Data Type: " << input->info()->data_type()
+                               << " Input QuantInfo: " << input->info()->quantization_info()
+                               << " Weights QuantInfo: " << weights->info()->quantization_info()
                                << " Input shape: " << input->info()->tensor_shape()
                                << " Weights shape: " << weights->info()->tensor_shape()
                                << " Output shape: " << output->info()->tensor_shape()
diff --git a/src/graph/backends/NEON/NEDeviceBackend.cpp b/src/graph/backends/NEON/NEDeviceBackend.cpp
index aaf05829bb..9123196540 100644
--- a/src/graph/backends/NEON/NEDeviceBackend.cpp
+++ b/src/graph/backends/NEON/NEDeviceBackend.cpp
@@ -93,7 +93,7 @@ std::unique_ptr<ITensorHandle> NEDeviceBackend::create_tensor(const Tensor &tens
     ARM_COMPUTE_ERROR_ON(tensor_desc.target != Target::NEON);
 
     // Create backend tensor handle
-    TensorInfo info(tensor_desc.shape, 1, tensor_desc.data_type);
+    TensorInfo info(tensor_desc.shape, 1, tensor_desc.data_type, tensor_desc.quant_info);
     auto       backend_tensor_handle = support::cpp14::make_unique<NETensorHandle>(info);
 
     return std::move(backend_tensor_handle);
diff --git a/src/graph/backends/NEON/NEFunctionFactory.cpp b/src/graph/backends/NEON/NEFunctionFactory.cpp
index 228af9ca6f..906378c565 100644
--- a/src/graph/backends/NEON/NEFunctionFactory.cpp
+++ b/src/graph/backends/NEON/NEFunctionFactory.cpp
@@ -140,10 +140,16 @@ std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node,
     ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
 
     // Extract IO and info
-    ITensor                *input          = get_backing_tensor(node.input(0));
-    ITensor                *weights        = get_backing_tensor(node.input(1));
-    ITensor                *biases         = get_backing_tensor(node.input(2));
-    ITensor                *output         = get_backing_tensor(node.output(0));
+    ITensor *input   = get_backing_tensor(node.input(0));
+    ITensor *weights = get_backing_tensor(node.input(1));
+    ITensor *biases  = get_backing_tensor(node.input(2));
+    ITensor *output  = get_backing_tensor(node.output(0));
+
+    if(is_data_type_quantized_asymmetric(input->info()->data_type()))
+    {
+        biases->info()->set_data_type(DataType::S32);
+    }
+
     const PadStrideInfo     conv_info      = node.convolution_info();
     const ConvolutionMethod conv_algorithm = node.convolution_method();
 
@@ -175,6 +181,8 @@ std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node,
     // Log info
     ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name
                                << " Data Type: " << input->info()->data_type()
+                               << " Input QuantInfo: " << input->info()->quantization_info()
+                               << " Weights QuantInfo: " << weights->info()->quantization_info()
                                << " Input shape: " << input->info()->tensor_shape()
                                << " Weights shape: " << weights->info()->tensor_shape()
                                << " Output shape: " << output->info()->tensor_shape()
@@ -234,10 +242,16 @@ std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvoluti
     ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
 
     // Extract IO and info
-    ITensor                         *input         = get_backing_tensor(node.input(0));
-    ITensor                         *weights       = get_backing_tensor(node.input(1));
-    ITensor                         *biases        = get_backing_tensor(node.input(2));
-    ITensor                         *output        = get_backing_tensor(node.output(0));
+    ITensor *input   = get_backing_tensor(node.input(0));
+    ITensor *weights = get_backing_tensor(node.input(1));
+    ITensor *biases  = get_backing_tensor(node.input(2));
+    ITensor *output  = get_backing_tensor(node.output(0));
+
+    if(is_data_type_quantized_asymmetric(input->info()->data_type()))
+    {
+        biases->info()->set_data_type(DataType::S32);
+    }
+
     const PadStrideInfo              conv_info     = node.convolution_info();
     const DepthwiseConvolutionMethod dwc_algorithm = node.depthwise_convolution_method();
 
@@ -258,6 +272,8 @@ std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvoluti
     // Log info
     ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name
                                << " Data Type: " << input->info()->data_type()
+                               << " Input QuantInfo: " << input->info()->quantization_info()
+                               << " Weights QuantInfo: " << weights->info()->quantization_info()
                                << " Input shape: " << input->info()->tensor_shape()
                                << " Weights shape: " << weights->info()->tensor_shape()
                                << " Output shape: " << output->info()->tensor_shape()