COMPMID-549 Create a Logger for GraphAPI
Change-Id: If912d8232e12cd496923d55d386898450dac09e2
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/89897
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/src/graph/nodes/ConvolutionLayer.cpp b/src/graph/nodes/ConvolutionLayer.cpp
index a992095..b47be8d 100644
--- a/src/graph/nodes/ConvolutionLayer.cpp
+++ b/src/graph/nodes/ConvolutionLayer.cpp
@@ -23,6 +23,7 @@
*/
#include "arm_compute/graph/nodes/ConvolutionLayer.h"
+#include "arm_compute/core/Logger.h"
#include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h"
#include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h"
#include "arm_compute/runtime/IFunction.h"
@@ -184,8 +185,6 @@
std::unique_ptr<arm_compute::IFunction> func;
_target_hint = ctx.hints().target_hint();
- _input = input;
- _output = output;
const ConvolutionMethodHint conv_method_hint = ctx.hints().convolution_method_hint();
// Check if the weights and biases are loaded
@@ -197,19 +196,21 @@
_biases.set_target(_target_hint);
// Calculate output shape
- TensorShape output_shape = calculate_convolution_layer_output_shape(_input->info()->tensor_shape(), _weights.info().tensor_shape(), _conv_info);
+ TensorShape output_shape = calculate_convolution_layer_output_shape(input->info()->tensor_shape(), _weights.info().tensor_shape(), _conv_info);
// Output auto inizialitation if not yet initialized
- arm_compute::auto_init_if_empty(*_output->info(), output_shape, 1, _input->info()->data_type(), _input->info()->fixed_point_position());
+ arm_compute::auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
// Create appropriate convolution function
if(_num_groups == 1)
{
- func = instantiate_convolution(conv_method_hint);
+ func = instantiate_convolution(input, output, conv_method_hint);
+ ARM_COMPUTE_LOG("Instantiating CLConvolutionLayer");
}
else
{
- func = instantiate_grouped_convolution(conv_method_hint);
+ func = instantiate_grouped_convolution(input, output, conv_method_hint);
+ ARM_COMPUTE_LOG("Instantiating NEConvolutionLayer");
}
// Fill weights
@@ -223,49 +224,38 @@
_biases.allocate_and_fill_if_needed();
}
+ ARM_COMPUTE_LOG(" Data Type: " << input->info()->data_type()
+ << " Input Shape: " << input->info()->tensor_shape()
+ << " Weights shape: " << _weights.info().tensor_shape()
+ << " Biases Shape: " << _biases.info().tensor_shape()
+ << " Output Shape: " << output->info()->tensor_shape()
+ << " PadStrideInfo: " << _conv_info
+ << " Groups: " << _num_groups
+ << " WeightsInfo: " << _weights_info
+ << std::endl);
+
return func;
}
-void ConvolutionLayer::print_info()
-{
- if(_target_hint == TargetHint::OPENCL)
- {
- std::cout << "Instantiating CLConvolutionLayer";
- }
- else
- {
- std::cout << "Instantiating NEConvolutionLayer";
- }
- std::cout << " Data Type: " << _input->info()->data_type()
- << " Input Shape: " << _input->info()->tensor_shape()
- << " Weights shape: " << _weights.info().tensor_shape()
- << " Biases Shape: " << _biases.info().tensor_shape()
- << " Output Shape: " << _output->info()->tensor_shape()
- << " PadStrideInfo: " << _conv_info
- << " Groups: " << _num_groups
- << " WeightsInfo: " << _weights_info
- << std::endl;
-}
-
-std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_convolution(ConvolutionMethodHint conv_method_hint)
+std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_convolution(ITensor *input, ITensor *output, ConvolutionMethodHint conv_method_hint)
{
std::unique_ptr<arm_compute::IFunction> func;
if(_target_hint == TargetHint::OPENCL)
{
- func = instantiate<TargetHint::OPENCL>(_input, _weights.tensor(), _biases.tensor(), _output, _conv_info, _weights_info, conv_method_hint);
+ func = instantiate<TargetHint::OPENCL>(input, _weights.tensor(), _biases.tensor(), output, _conv_info, _weights_info, conv_method_hint);
}
else
{
- func = instantiate<TargetHint::NEON>(_input, _weights.tensor(), _biases.tensor(), _output, _conv_info, _weights_info, conv_method_hint);
+ func = instantiate<TargetHint::NEON>(input, _weights.tensor(), _biases.tensor(), output, _conv_info, _weights_info, conv_method_hint);
}
return func;
}
-std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_grouped_convolution(ConvolutionMethodHint conv_method_hint)
+std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_grouped_convolution(ITensor *input, ITensor *output, ConvolutionMethodHint conv_method_hint)
{
// Get tensor shapes
- TensorShape input_shape = _input->info()->tensor_shape();
- TensorShape output_shape = _output->info()->tensor_shape();
+ TensorShape input_shape = input->info()->tensor_shape();
+ TensorShape output_shape = output->info()->tensor_shape();
TensorShape weights_shape = _weights.info().tensor_shape();
TensorShape biases_shape = _biases.info().tensor_shape();
@@ -309,8 +299,8 @@
// Create sub-tensors for input, output, weights and bias
auto hint_to_use = (_target_hint == TargetHint::OPENCL) ? TargetHint::OPENCL : TargetHint::NEON;
- _is[i] = SubTensor(_input, input_shape, input_coord, hint_to_use);
- _os[i] = SubTensor(_output, output_shape, output_coord, hint_to_use);
+ _is[i] = SubTensor(input, input_shape, input_coord, hint_to_use);
+ _os[i] = SubTensor(output, output_shape, output_coord, hint_to_use);
_ws[i] = SubTensor(_weights.tensor(), weights_shape, weights_coord, hint_to_use);
_bs[i] = SubTensor(_biases.tensor(), biases_shape, biases_coord, hint_to_use);