arm_compute v18.01
Change-Id: I9bfa178c2e38bfd5fc812e62aab6760d87748e05
diff --git a/src/graph/nodes/ConvolutionLayer.cpp b/src/graph/nodes/ConvolutionLayer.cpp
index ae4a8d7..f292b89 100644
--- a/src/graph/nodes/ConvolutionLayer.cpp
+++ b/src/graph/nodes/ConvolutionLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -106,13 +106,16 @@
const WeightsInfo &weights_info,
ConvolutionMethodHint conv_method)
{
- if(conv_method == ConvolutionMethodHint::GEMM)
+ if((conv_method == ConvolutionMethodHint::DIRECT)
+ && arm_compute::CLDirectConvolutionLayer::validate(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), conv_info)) // NOLINT
{
- return instantiate_function<arm_compute::CLConvolutionLayer, arm_compute::ICLTensor, TargetHint::OPENCL>(input, weights, biases, output, conv_info, weights_info);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLDirectConvolutionLayer");
+ return instantiate_direct_function<arm_compute::CLDirectConvolutionLayer, arm_compute::ICLTensor, TargetHint::OPENCL>(input, weights, biases, output, conv_info);
}
else
{
- return instantiate_direct_function<arm_compute::CLDirectConvolutionLayer, arm_compute::ICLTensor, TargetHint::OPENCL>(input, weights, biases, output, conv_info);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLConvolutionLayer");
+ return instantiate_function<arm_compute::CLConvolutionLayer, arm_compute::ICLTensor, TargetHint::OPENCL>(input, weights, biases, output, conv_info, weights_info);
}
}
@@ -122,13 +125,16 @@
const WeightsInfo &weights_info,
ConvolutionMethodHint conv_method)
{
- if(conv_method == ConvolutionMethodHint::GEMM)
+ if((conv_method == ConvolutionMethodHint::DIRECT)
+ && arm_compute::NEDirectConvolutionLayer::validate(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), conv_info)) // NOLINT
{
- return instantiate_function<arm_compute::NEConvolutionLayer, arm_compute::ITensor, TargetHint::NEON>(input, weights, biases, output, conv_info, weights_info);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEDirectConvolutionLayer");
+ return instantiate_direct_function<arm_compute::NEDirectConvolutionLayer, arm_compute::ITensor, TargetHint::NEON>(input, weights, biases, output, conv_info);
}
else
{
- return instantiate_direct_function<arm_compute::NEDirectConvolutionLayer, arm_compute::ITensor, TargetHint::NEON>(input, weights, biases, output, conv_info);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEConvolutionLayer");
+ return instantiate_function<arm_compute::NEConvolutionLayer, arm_compute::ITensor, TargetHint::NEON>(input, weights, biases, output, conv_info, weights_info);
}
}
} // namespace
@@ -184,14 +190,17 @@
// Set weights and biases info
if(_weights.tensor() == nullptr)
{
- _weights.set_info(TensorInfo(TensorShape(_conv_width, _conv_height, in->info()->dimension(2) / _num_groups, _ofm),
+ TensorInfo info = TensorInfo(TensorShape(_conv_width, _conv_height, in->info()->dimension(2) / _num_groups, _ofm),
in->info()->num_channels(),
in->info()->data_type(),
- in->info()->fixed_point_position()));
+ in->info()->fixed_point_position());
+ info.set_quantization_info(_weights_quant_info);
+ _weights.set_info(std::move(info));
}
if(_biases.has_accessor() && _biases.tensor() == nullptr)
{
- _biases.set_info(TensorInfo(TensorShape(_ofm), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position()));
+ DataType dt = in->info()->data_type();
+ _biases.set_info(TensorInfo(TensorShape(_ofm), in->info()->num_channels(), is_data_type_quantized_asymmetric(dt) ? DataType::S32 : dt, in->info()->fixed_point_position()));
}
std::unique_ptr<arm_compute::IFunction> func;
@@ -213,7 +222,8 @@
TensorShape output_shape = calculate_convolution_layer_output_shape(in->info()->tensor_shape(), _weights.info().tensor_shape(), _conv_info);
// Output auto inizialitation if not yet initialized
- arm_compute::auto_init_if_empty(*out->info(), output_shape, 1, in->info()->data_type(), in->info()->fixed_point_position());
+ arm_compute::auto_init_if_empty(*out->info(), output_shape, 1, in->info()->data_type(), in->info()->fixed_point_position(),
+ (_out_quant_info.empty()) ? in->info()->quantization_info() : _out_quant_info);
// Create appropriate convolution function
if(_num_groups == 1)
@@ -254,12 +264,10 @@
std::unique_ptr<arm_compute::IFunction> func;
if(_target_hint == TargetHint::OPENCL)
{
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLConvolutionLayer");
func = instantiate<TargetHint::OPENCL>(input, _weights.tensor(), _biases.tensor(), output, _conv_info, _weights_info, conv_method_hint);
}
else
{
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEConvolutionLayer");
func = instantiate<TargetHint::NEON>(input, _weights.tensor(), _biases.tensor(), output, _conv_info, _weights_info, conv_method_hint);
}
return func;
@@ -321,12 +329,10 @@
// Instantiate convolution function
if(_target_hint == TargetHint::OPENCL)
{
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLConvolutionLayer");
func = instantiate<TargetHint::OPENCL>(_is[i].tensor(), _ws[i].tensor(), _bs[i].tensor(), _os[i].tensor(), _conv_info, _weights_info, conv_method_hint);
}
else
{
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEConvolutionLayer");
func = instantiate<TargetHint::NEON>(_is[i].tensor(), _ws[i].tensor(), _bs[i].tensor(), _os[i].tensor(), _conv_info, _weights_info, conv_method_hint);
}