COMPMID-550: Adds support for branches.
Change-Id: I778007c9221ce3156400284c4039b90245eb2b7f
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/90043
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 b47be8d..303780f 100644
--- a/src/graph/nodes/ConvolutionLayer.cpp
+++ b/src/graph/nodes/ConvolutionLayer.cpp
@@ -67,7 +67,8 @@
// Instantiate GEMM based convolution layer
template <typename ConvolutionType, typename TensorType, TargetHint target_hint>
-std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+std::unique_ptr<arm_compute::IFunction> instantiate_function(arm_compute::ITensor *input, arm_compute::ITensor *weights, arm_compute::ITensor *biases, arm_compute::ITensor *output,
+ const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
{
auto conv = arm_compute::support::cpp14::make_unique<ConvolutionType>();
conv->configure(
@@ -81,7 +82,8 @@
// Instantiate direct convolution layer
template <typename ConvolutionType, typename TensorType, TargetHint target_hint>
-std::unique_ptr<arm_compute::IFunction> instantiate_direct_function(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info)
+std::unique_ptr<arm_compute::IFunction> instantiate_direct_function(arm_compute::ITensor *input, arm_compute::ITensor *weights, arm_compute::ITensor *biases, arm_compute::ITensor *output,
+ const PadStrideInfo &conv_info)
{
auto conv = arm_compute::support::cpp14::make_unique<ConvolutionType>();
conv->configure(
@@ -94,11 +96,13 @@
}
template <TargetHint target_hint>
-std::unique_ptr<arm_compute::IFunction> instantiate(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
+std::unique_ptr<arm_compute::IFunction> instantiate(arm_compute::ITensor *input, arm_compute::ITensor *weights, arm_compute::ITensor *biases, arm_compute::ITensor *output,
+ const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
ConvolutionMethodHint conv_method);
template <>
-std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
+std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(arm_compute::ITensor *input, arm_compute::ITensor *weights, arm_compute::ITensor *biases, arm_compute::ITensor *output,
+ const PadStrideInfo &conv_info,
const WeightsInfo &weights_info,
ConvolutionMethodHint conv_method)
{
@@ -113,7 +117,8 @@
}
template <>
-std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
+std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(arm_compute::ITensor *input, arm_compute::ITensor *weights, arm_compute::ITensor *biases, arm_compute::ITensor *output,
+ const PadStrideInfo &conv_info,
const WeightsInfo &weights_info,
ConvolutionMethodHint conv_method)
{
@@ -169,18 +174,25 @@
std::vector<std::unique_ptr<IFunction>> _convolutions;
};
-std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_node(GraphContext &ctx, ITensor *input, ITensor *output)
+std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_node(GraphContext &ctx, ITensorObject *input, ITensorObject *output)
{
+ ARM_COMPUTE_ERROR_ON(input == nullptr || input->tensor() == nullptr);
+ ARM_COMPUTE_ERROR_ON(output == nullptr || output->tensor() == nullptr);
+
+ arm_compute::ITensor *in = input->tensor();
+ arm_compute::ITensor *out = output->tensor();
+
// Set weights and biases info
if(_weights.tensor() == nullptr)
{
- _weights.set_info(TensorInfo(TensorShape(_conv_width, _conv_height, input->info()->dimension(2) / _num_groups, _ofm),
- input->info()->num_channels(), input->info()->data_type(),
- input->info()->fixed_point_position()));
+ _weights.set_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()));
}
if(_biases.tensor() == nullptr)
{
- _biases.set_info(TensorInfo(TensorShape(_ofm), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position()));
+ _biases.set_info(TensorInfo(TensorShape(_ofm), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position()));
}
std::unique_ptr<arm_compute::IFunction> func;
@@ -196,20 +208,20 @@
_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(in->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(*out->info(), output_shape, 1, in->info()->data_type(), in->info()->fixed_point_position());
// Create appropriate convolution function
if(_num_groups == 1)
{
- func = instantiate_convolution(input, output, conv_method_hint);
+ func = instantiate_convolution(in, out, conv_method_hint);
ARM_COMPUTE_LOG("Instantiating CLConvolutionLayer");
}
else
{
- func = instantiate_grouped_convolution(input, output, conv_method_hint);
+ func = instantiate_grouped_convolution(in, out, conv_method_hint);
ARM_COMPUTE_LOG("Instantiating NEConvolutionLayer");
}
@@ -224,11 +236,11 @@
_biases.allocate_and_fill_if_needed();
}
- ARM_COMPUTE_LOG(" Data Type: " << input->info()->data_type()
- << " Input Shape: " << input->info()->tensor_shape()
+ ARM_COMPUTE_LOG(" Data Type: " << in->info()->data_type()
+ << " Input Shape: " << in->info()->tensor_shape()
<< " Weights shape: " << _weights.info().tensor_shape()
<< " Biases Shape: " << _biases.info().tensor_shape()
- << " Output Shape: " << output->info()->tensor_shape()
+ << " Output Shape: " << out->info()->tensor_shape()
<< " PadStrideInfo: " << _conv_info
<< " Groups: " << _num_groups
<< " WeightsInfo: " << _weights_info