diff options
Diffstat (limited to 'compute/ARMComputeEx/src/runtime/NEON/functions/NEFullyConnectedHybridLayer.cpp')
| -rw-r--r-- | compute/ARMComputeEx/src/runtime/NEON/functions/NEFullyConnectedHybridLayer.cpp | 55 |
1 files changed, 27 insertions, 28 deletions
diff --git a/compute/ARMComputeEx/src/runtime/NEON/functions/NEFullyConnectedHybridLayer.cpp b/compute/ARMComputeEx/src/runtime/NEON/functions/NEFullyConnectedHybridLayer.cpp index a123439d9..fbd88fff0 100644 --- a/compute/ARMComputeEx/src/runtime/NEON/functions/NEFullyConnectedHybridLayer.cpp +++ b/compute/ARMComputeEx/src/runtime/NEON/functions/NEFullyConnectedHybridLayer.cpp @@ -58,7 +58,7 @@ namespace Status validate_mm(const ITensorInfo &input, const ITensorInfo &weights, const ITensorInfo &output) { ARM_COMPUTE_RETURN_ON_ERROR( - NEGEMMLowpMatrixMultiplyCore::validate(&input, &weights, nullptr, &output)); + NEGEMMLowpMatrixMultiplyCore::validate(&input, &weights, nullptr, &output)); return Status{}; } @@ -66,7 +66,7 @@ Status validate_mm(const ITensorInfo &input, const ITensorInfo &weights, const I void NEFullyConnectedHybridLayerReshapeWeights::configure(const ITensor *input, ITensor *output) { - auto k = support::cpp14::make_unique<NETransposeKernel>(); + auto k = std::make_unique<NETransposeKernel>(); k->configure(input, output); _kernel = std::move(k); } @@ -78,11 +78,11 @@ Status NEFullyConnectedHybridLayerReshapeWeights::validate(const ITensorInfo *in } NEFullyConnectedHybridLayer::NEFullyConnectedHybridLayer( - std::shared_ptr<IMemoryManager> memory_manager) - : _memory_group(std::move(memory_manager)), _reshape_weights_function(), _quant_input_kernel(), - _mm_gemmlowp(), _accumulate_biases_kernel(), _reshape_weights_output(), _quantized_input(), - _scale_factor(), _original_weights(nullptr), _are_weights_reshaped(false), - _accumulate_biases(false), _is_prepared(false) + std::shared_ptr<IMemoryManager> memory_manager) + : _memory_group(std::move(memory_manager)), _reshape_weights_function(), _quant_input_kernel(), + _mm_gemmlowp(), _accumulate_biases_kernel(), _reshape_weights_output(), _quantized_input(), + _scale_factor(), _original_weights(nullptr), _are_weights_reshaped(false), + _accumulate_biases(false), _is_prepared(false) { } @@ -103,8 +103,8 @@ void NEFullyConnectedHybridLayer::configure(const ITensor *input, const ITensor // Perform validate step ARM_COMPUTE_ERROR_THROW_ON(NEFullyConnectedHybridLayer::validate( - input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), - fc_info)); + input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), + fc_info)); _are_weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true; _accumulate_biases = false; @@ -132,10 +132,10 @@ void NEFullyConnectedHybridLayer::configure(const ITensor *input, const ITensor bool _is_fc_after_conv; if (is_batched_fc_layer) { - _is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && - (std::equal(input->info()->tensor_shape().cbegin() + 3, - input->info()->tensor_shape().cend(), - output->info()->tensor_shape().cbegin() + 1)); + _is_fc_after_conv = + (TensorShape::num_max_dimensions >= 4) && + (std::equal(input->info()->tensor_shape().cbegin() + 3, input->info()->tensor_shape().cend(), + output->info()->tensor_shape().cbegin() + 1)); } else { @@ -150,23 +150,23 @@ void NEFullyConnectedHybridLayer::configure(const ITensor *input, const ITensor { // Reshape the weights _reshape_weights_output.allocator()->init( - weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape( - compute_transposed_shape(*weights->info()))); + weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape( + compute_transposed_shape(*weights->info()))); _reshape_weights_function.configure(weights_to_use, &_reshape_weights_output); weights_to_use = &_reshape_weights_output; } // Quantize input _quantized_input.allocator()->init( - input->info()->clone()->set_is_resizable(true).reset_padding().set_data_type( - DataType::QASYMM8_SIGNED)); + input->info()->clone()->set_is_resizable(true).reset_padding().set_data_type( + DataType::QASYMM8_SIGNED)); _scale_factor.allocator()->init( - TensorInfo(TensorShape{output->info()->dimension(1)}, 1, DataType::F32)); + TensorInfo(TensorShape{output->info()->dimension(1)}, 1, DataType::F32)); _quant_input_kernel.configure(input, &_quantized_input, &_scale_factor); // GEMM _gemmlowp_output.allocator()->init( - output->info()->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32)); + output->info()->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32)); configure_mm(&_quantized_input, weights_to_use, &_gemmlowp_output); // Multiply scale @@ -195,8 +195,8 @@ Status NEFullyConnectedHybridLayer::validate(const ITensorInfo *input, const ITe bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true; const ITensorInfo &reshaped_weights = - TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape( - compute_transposed_shape(*weights))); + TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape( + compute_transposed_shape(*weights))); // Configure accumulate biases kernel for non quantized asymmetric types if (biases != nullptr) @@ -217,7 +217,7 @@ Status NEFullyConnectedHybridLayer::validate(const ITensorInfo *input, const ITe { // Validate reshape weights kernel ARM_COMPUTE_RETURN_ON_ERROR( - NEFullyConnectedHybridLayerReshapeWeights::validate(weights_to_use, &reshaped_weights)); + NEFullyConnectedHybridLayerReshapeWeights::validate(weights_to_use, &reshaped_weights)); weights_to_use = &reshaped_weights; } @@ -225,20 +225,19 @@ Status NEFullyConnectedHybridLayer::validate(const ITensorInfo *input, const ITe ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != weights_to_use->dimension(1)); // Validate quantization kernel - const ITensorInfo &quantized_input = - TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_data_type( - DataType::QASYMM8_SIGNED)); + const ITensorInfo &quantized_input = TensorInfo( + input->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::QASYMM8_SIGNED)); const ITensorInfo &scale_factor = TensorInfo(TensorShape{output->dimension(1)}, 1, DataType::F32); ARM_COMPUTE_RETURN_ON_ERROR( - NEQuantizationSymmetricKernel::validate(input, &quantized_input, &scale_factor)); + NEQuantizationSymmetricKernel::validate(input, &quantized_input, &scale_factor)); const ITensorInfo &gemmlowp_output = TensorInfo( - output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32)); + output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32)); // Validate matrix multiply kernel ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(quantized_input, *weights_to_use, gemmlowp_output)); ARM_COMPUTE_RETURN_ON_ERROR(NEMultiplyScaleFactorKernel::validate( - &gemmlowp_output, &scale_factor, output, weights->quantization_info().uniform().scale)); + &gemmlowp_output, &scale_factor, output, weights->quantization_info().uniform().scale)); return Status{}; } |