From e2c82fee3b6d38f6e79412c78176792b817defd0 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Mon, 2 Oct 2017 18:51:47 +0100 Subject: COMPMID-550: Adds support for branches. Change-Id: I778007c9221ce3156400284c4039b90245eb2b7f Reviewed-on: http://mpd-gerrit.cambridge.arm.com/90043 Tested-by: Kaizen Reviewed-by: Anthony Barbier --- src/graph/nodes/ConvolutionLayer.cpp | 46 +++++++++++++++++++++++------------- 1 file changed, 29 insertions(+), 17 deletions(-) (limited to 'src/graph/nodes/ConvolutionLayer.cpp') diff --git a/src/graph/nodes/ConvolutionLayer.cpp b/src/graph/nodes/ConvolutionLayer.cpp index b47be8dc33..303780ff35 100644 --- a/src/graph/nodes/ConvolutionLayer.cpp +++ b/src/graph/nodes/ConvolutionLayer.cpp @@ -67,7 +67,8 @@ TensorShape calculate_convolution_layer_output_shape(const TensorShape &input_sh // Instantiate GEMM based convolution layer template -std::unique_ptr instantiate_function(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info) +std::unique_ptr 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(); conv->configure( @@ -81,7 +82,8 @@ std::unique_ptr instantiate_function(ITensor *input, ITe // Instantiate direct convolution layer template -std::unique_ptr instantiate_direct_function(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info) +std::unique_ptr 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(); conv->configure( @@ -94,11 +96,13 @@ std::unique_ptr instantiate_direct_function(ITensor *inp } template -std::unique_ptr instantiate(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, +std::unique_ptr 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 instantiate(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, +std::unique_ptr 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) { @@ -113,7 +117,8 @@ std::unique_ptr instantiate(ITensor } template <> -std::unique_ptr instantiate(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, +std::unique_ptr 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) { @@ -169,18 +174,25 @@ private: std::vector> _convolutions; }; -std::unique_ptr ConvolutionLayer::instantiate_node(GraphContext &ctx, ITensor *input, ITensor *output) +std::unique_ptr 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 func; @@ -196,20 +208,20 @@ std::unique_ptr ConvolutionLayer::instantiate_node(Graph _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 @@ std::unique_ptr ConvolutionLayer::instantiate_node(Graph _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 -- cgit v1.2.1