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>
diff --git a/src/graph/backends/CL/CLDeviceBackend.cpp b/src/graph/backends/CL/CLDeviceBackend.cpp
index f10eb33..92cb693 100644
--- a/src/graph/backends/CL/CLDeviceBackend.cpp
+++ b/src/graph/backends/CL/CLDeviceBackend.cpp
@@ -126,7 +126,7 @@
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 1b448fe..ad73a79 100644
--- a/src/graph/backends/CL/CLFunctionsFactory.cpp
+++ b/src/graph/backends/CL/CLFunctionsFactory.cpp
@@ -154,10 +154,16 @@
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 @@
// 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 @@
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 @@
// 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 8cd9994..a55215f 100644
--- a/src/graph/backends/GLES/GCDeviceBackend.cpp
+++ b/src/graph/backends/GLES/GCDeviceBackend.cpp
@@ -87,7 +87,7 @@
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 12e7c04..d3c5737 100644
--- a/src/graph/backends/GLES/GCFunctionsFactory.cpp
+++ b/src/graph/backends/GLES/GCFunctionsFactory.cpp
@@ -154,10 +154,16 @@
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 @@
// 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 @@
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 @@
// 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 aaf0582..9123196 100644
--- a/src/graph/backends/NEON/NEDeviceBackend.cpp
+++ b/src/graph/backends/NEON/NEDeviceBackend.cpp
@@ -93,7 +93,7 @@
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 228af9c..906378c 100644
--- a/src/graph/backends/NEON/NEFunctionFactory.cpp
+++ b/src/graph/backends/NEON/NEFunctionFactory.cpp
@@ -140,10 +140,16 @@
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 @@
// 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 @@
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 @@
// 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()