diff options
Diffstat (limited to 'src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp')
| -rw-r--r-- | src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp | 205 |
1 files changed, 124 insertions, 81 deletions
diff --git a/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp index d5d1b5f41e..7d40cf1829 100644 --- a/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2021 Arm Limited. + * Copyright (c) 2019-2021, 2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -24,19 +24,15 @@ #include "arm_compute/runtime/CL/functions/CLGEMMDeconvolutionLayer.h" #include "arm_compute/core/Helpers.h" -#include "arm_compute/core/Validate.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/core/utils/quantization/AsymmHelpers.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/function_info/ActivationLayerInfo.h" #include "arm_compute/runtime/CL/CLScheduler.h" + +#include "src/common/utils/Log.h" #include "src/core/CL/kernels/CLDeconvolutionReshapeOutputKernel.h" #include "src/core/CL/kernels/CLFillBorderKernel.h" -#include "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.h" -#include "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h" -#include "src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h" -#include "src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h" -#include "src/core/CL/kernels/CLGEMMLowpReductionKernel.h" -#include "src/core/CL/kernels/CLIm2ColKernel.h" -#include "src/core/CL/kernels/CLWeightsReshapeKernel.h" #include <tuple> @@ -44,12 +40,13 @@ namespace arm_compute { namespace { -std::pair<Coordinates, Coordinates> compute_start_end_slice_coordinates(const ITensorInfo &output_info, const PadStrideInfo &deconv_info, bool is_nchw) +std::pair<Coordinates, Coordinates> +compute_start_end_slice_coordinates(const ITensorInfo &output_info, const PadStrideInfo &deconv_info, bool is_nchw) { Coordinates start; Coordinates end; - if(is_nchw) + if (is_nchw) { start.set(0, deconv_info.pad_left()); start.set(1, deconv_info.pad_top()); @@ -67,13 +64,16 @@ std::pair<Coordinates, Coordinates> compute_start_end_slice_coordinates(const IT end.set(2, output_info.dimension(2) - deconv_info.pad_bottom()); } - return { start, end }; + return {start, end}; } -Status construct_gemmlowp_output_stage(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, GEMMLowpOutputStageInfo &output_stage_info) +Status construct_gemmlowp_output_stage(const ITensorInfo *input, + const ITensorInfo *weights, + const ITensorInfo *output, + GEMMLowpOutputStageInfo &output_stage_info) { const auto data_type = input->data_type(); - if(is_data_type_quantized_asymmetric(data_type)) + if (is_data_type_quantized_asymmetric(data_type)) { const UniformQuantizationInfo iq_info = input->quantization_info().uniform(); const UniformQuantizationInfo wq_info = weights->quantization_info().uniform(); @@ -82,7 +82,8 @@ Status construct_gemmlowp_output_stage(const ITensorInfo *input, const ITensorIn float multiplier = iq_info.scale * wq_info.scale / oq_info.scale; int output_multiplier(0); int output_shift(0); - ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); + ARM_COMPUTE_RETURN_ON_ERROR( + quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; output_stage_info.gemmlowp_multiplier = output_multiplier; @@ -126,15 +127,21 @@ CLGEMMDeconvolutionLayer::CLGEMMDeconvolutionLayer(std::shared_ptr<IMemoryManage CLGEMMDeconvolutionLayer::~CLGEMMDeconvolutionLayer() = default; -Status CLGEMMDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, const ITensorInfo *output, const PadStrideInfo &deconv_info) +Status CLGEMMDeconvolutionLayer::validate(const ITensorInfo *input, + const ITensorInfo *weights, + const ITensorInfo *bias, + const ITensorInfo *output, + const PadStrideInfo &deconv_info) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16, DataType::QASYMM8, DataType::QASYMM8_SIGNED); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16, DataType::QASYMM8, + DataType::QASYMM8_SIGNED); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights); DataLayout data_layout = input->data_layout(); - const bool padded_input = deconv_info.pad_bottom() > 0 || deconv_info.pad_left() > 0 || deconv_info.pad_right() > 0 || deconv_info.pad_top() > 0; + const bool padded_input = deconv_info.pad_bottom() > 0 || deconv_info.pad_left() > 0 || + deconv_info.pad_right() > 0 || deconv_info.pad_top() > 0; const bool is_nchw = input->data_layout() == DataLayout::NCHW; const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); @@ -148,21 +155,31 @@ Status CLGEMMDeconvolutionLayer::validate(const ITensorInfo *input, const ITenso TensorShape nhwc_weights_shape = weights->tensor_shape(); TensorShape nhwc_input_shape = input->tensor_shape(); - if(is_nchw) + if (is_nchw) { permute(nhwc_weights_shape, PermutationVector(2, 0, 1)); permute(nhwc_input_shape, PermutationVector(2, 0, 1)); - TensorInfo nhwc_input_info = input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(nhwc_input_shape).set_data_layout(DataLayout::NCHW); + TensorInfo nhwc_input_info = input->clone() + ->set_is_resizable(true) + .reset_padding() + .set_tensor_shape(nhwc_input_shape) + .set_data_layout(DataLayout::NCHW); - TensorInfo nhwc_weights_info = weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(nhwc_weights_shape).set_data_layout(DataLayout::NCHW); + TensorInfo nhwc_weights_info = weights->clone() + ->set_is_resizable(true) + .reset_padding() + .set_tensor_shape(nhwc_weights_shape) + .set_data_layout(DataLayout::NCHW); CLPermute::validate(weights, &nhwc_weights_info, PermutationVector(2, 0, 1)); CLPermute::validate(input, &nhwc_input_info, PermutationVector(2, 0, 1)); } - const TensorShape reshaped_shape = TensorShape(nhwc_weights_shape[0], nhwc_weights_shape[1] * nhwc_weights_shape[2] * nhwc_weights_shape[3]); - const TensorInfo reshaped_info = weights->clone()->set_tensor_shape(reshaped_shape).set_data_layout(DataLayout::NCHW).set_is_resizable(true); + const TensorShape reshaped_shape = + TensorShape(nhwc_weights_shape[0], nhwc_weights_shape[1] * nhwc_weights_shape[2] * nhwc_weights_shape[3]); + const TensorInfo reshaped_info = + weights->clone()->set_tensor_shape(reshaped_shape).set_data_layout(DataLayout::NCHW).set_is_resizable(true); ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayer::validate(weights, &reshaped_info)); TensorShape transposed_shape(reshaped_shape[1], reshaped_shape[0]); @@ -170,76 +187,95 @@ Status CLGEMMDeconvolutionLayer::validate(const ITensorInfo *input, const ITenso ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(&reshaped_info, &reshaped_t_info)); TensorShape gemm_output_shape(weights->dimension(idx_w) * weights->dimension(idx_h) * weights->dimension(idx_b), - input->dimension(idx_w), - input->dimension(idx_h), - input->dimension(idx_b)); + input->dimension(idx_w), input->dimension(idx_h), input->dimension(idx_b)); TensorInfo gemm_output_info = reshaped_t_info.clone()->set_tensor_shape(gemm_output_shape).set_is_resizable(true); GEMMInfo gemm_info(false, false, true, input->dimension(idx_h), true); GEMMLowpOutputStageInfo output_stage_info; - if(is_quantized) + if (is_quantized) { - ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyCore::validate(&input->clone()->set_tensor_shape(nhwc_input_shape), &reshaped_t_info, nullptr, &gemm_output_info.set_data_type(DataType::S32), - gemm_info)); + ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyCore::validate( + &input->clone()->set_tensor_shape(nhwc_input_shape), &reshaped_t_info, nullptr, + &gemm_output_info.set_data_type(DataType::S32), gemm_info)); ARM_COMPUTE_RETURN_ON_ERROR(construct_gemmlowp_output_stage(input, weights, output, output_stage_info)); } else { - ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(&input->clone()->set_tensor_shape(nhwc_input_shape).set_is_resizable(true), &reshaped_t_info, nullptr, &gemm_output_info, 1.0f, 0.0f, gemm_info)); + ARM_COMPUTE_RETURN_ON_ERROR( + CLGEMM::validate(&input->clone()->set_tensor_shape(nhwc_input_shape).set_is_resizable(true), + &reshaped_t_info, nullptr, &gemm_output_info, 1.0f, 0.0f, gemm_info)); } const PadStrideInfo stride_info(deconv_info.stride().first, deconv_info.stride().second); - auto out_dims = deconvolution_output_dimensions(input->dimension(idx_w), input->dimension(idx_h), weights->dimension(idx_w), weights->dimension(idx_h), stride_info); - const TensorShape deconv_shape = misc::shape_calculator::compute_deconvolution_output_shape(out_dims, *input, *weights); - TensorInfo col2im_output_info = gemm_output_info.clone()->set_tensor_shape(deconv_shape).set_is_resizable(true); + auto out_dims = deconvolution_output_dimensions(input->dimension(idx_w), input->dimension(idx_h), + weights->dimension(idx_w), weights->dimension(idx_h), stride_info); + const TensorShape deconv_shape = + misc::shape_calculator::compute_deconvolution_output_shape(out_dims, *input, *weights); + TensorInfo col2im_output_info = gemm_output_info.clone()->set_tensor_shape(deconv_shape).set_is_resizable(true); - if(padded_input && is_quantized) + if (padded_input && is_quantized) { const auto start_end = compute_start_end_slice_coordinates(col2im_output_info, deconv_info, is_nchw); - ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionReshapeOutputKernel::validate(&gemm_output_info, bias, &col2im_output_info, input, weights, deconv_info)); - ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpOutputStage::validate(&col2im_output_info, nullptr, &col2im_output_info.clone()->set_is_resizable(true).set_data_type(input->data_type()), output_stage_info)); - ARM_COMPUTE_RETURN_ON_ERROR(CLSlice::validate(&col2im_output_info.clone()->set_is_resizable(true).set_data_type(input->data_type()), output, start_end.first, start_end.second)); + ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionReshapeOutputKernel::validate( + &gemm_output_info, bias, &col2im_output_info, input, weights, deconv_info)); + ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpOutputStage::validate( + &col2im_output_info, nullptr, + &col2im_output_info.clone()->set_is_resizable(true).set_data_type(input->data_type()), output_stage_info)); + ARM_COMPUTE_RETURN_ON_ERROR( + CLSlice::validate(&col2im_output_info.clone()->set_is_resizable(true).set_data_type(input->data_type()), + output, start_end.first, start_end.second)); } - else if(padded_input) + else if (padded_input) { const auto start_end = compute_start_end_slice_coordinates(col2im_output_info, deconv_info, is_nchw); - ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionReshapeOutputKernel::validate(&gemm_output_info, bias, &col2im_output_info, input, weights, deconv_info)); + ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionReshapeOutputKernel::validate( + &gemm_output_info, bias, &col2im_output_info, input, weights, deconv_info)); ARM_COMPUTE_RETURN_ON_ERROR(CLSlice::validate(&col2im_output_info, output, start_end.first, start_end.second)); } - else if(is_quantized) + else if (is_quantized) { - ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionReshapeOutputKernel::validate(&gemm_output_info, bias, &col2im_output_info, input, weights, deconv_info)); - ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpOutputStage::validate(&col2im_output_info, nullptr, output, output_stage_info)); + ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionReshapeOutputKernel::validate( + &gemm_output_info, bias, &col2im_output_info, input, weights, deconv_info)); + ARM_COMPUTE_RETURN_ON_ERROR( + CLGEMMLowpOutputStage::validate(&col2im_output_info, nullptr, output, output_stage_info)); } else { - ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionReshapeOutputKernel::validate(&gemm_output_info, bias, output, input, weights, deconv_info)); + ARM_COMPUTE_RETURN_ON_ERROR( + CLDeconvolutionReshapeOutputKernel::validate(&gemm_output_info, bias, output, input, weights, deconv_info)); } return Status{}; } -void CLGEMMDeconvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &deconv_info) +void CLGEMMDeconvolutionLayer::configure(const ICLTensor *input, + const ICLTensor *weights, + const ICLTensor *bias, + ICLTensor *output, + const PadStrideInfo &deconv_info) { configure(CLKernelLibrary::get().get_compile_context(), input, weights, bias, output, deconv_info); } -void CLGEMMDeconvolutionLayer::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, - const PadStrideInfo &deconv_info) +void CLGEMMDeconvolutionLayer::configure(const CLCompileContext &compile_context, + const ICLTensor *input, + const ICLTensor *weights, + const ICLTensor *bias, + ICLTensor *output, + const PadStrideInfo &deconv_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_ERROR_THROW_ON(CLGEMMDeconvolutionLayer::validate(input->info(), - weights->info(), - bias != nullptr ? bias->info() : nullptr, - output->info(), - deconv_info)); + ARM_COMPUTE_ERROR_THROW_ON(CLGEMMDeconvolutionLayer::validate( + input->info(), weights->info(), bias != nullptr ? bias->info() : nullptr, output->info(), deconv_info)); + ARM_COMPUTE_LOG_PARAMS(input, weights, bias, output, deconv_info); _original_weights = weights; - _padded_input = deconv_info.pad_bottom() > 0 || deconv_info.pad_left() > 0 || deconv_info.pad_right() > 0 || deconv_info.pad_top() > 0; - _is_nchw = input->info()->data_layout() == DataLayout::NCHW; - _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); + _padded_input = deconv_info.pad_bottom() > 0 || deconv_info.pad_left() > 0 || deconv_info.pad_right() > 0 || + deconv_info.pad_top() > 0; + _is_nchw = input->info()->data_layout() == DataLayout::NCHW; + _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); const ICLTensor *input_to_use = input; const ICLTensor *weights_to_use = weights; @@ -248,7 +284,7 @@ void CLGEMMDeconvolutionLayer::configure(const CLCompileContext &compile_context // do an outer product in NCHW and then an accumulation through a reduction. This would have two // drawbacks: first, the outer product is less efficient than a full GEMM. Second, the reduction // might be slower than GEMM. - if(_is_nchw) + if (_is_nchw) { _memory_group.manage(&_permuted_input); _permute_input_to_nhwc.configure(compile_context, input, &_permuted_input, PermutationVector(2U, 0U, 1U)); @@ -260,10 +296,11 @@ void CLGEMMDeconvolutionLayer::configure(const CLCompileContext &compile_context } // Reshape the input weights. The weights will be reshaped only once during the call to prepare() - _reshaped_weights.allocator()->init(TensorInfo(TensorShape(weights_to_use->info()->dimension(0), - weights_to_use->info()->dimension(1) * weights_to_use->info()->dimension(2) * weights_to_use->info()->dimension(3)), - 1, - input->info()->data_type(), weights->info()->quantization_info())); + _reshaped_weights.allocator()->init( + TensorInfo(TensorShape(weights_to_use->info()->dimension(0), weights_to_use->info()->dimension(1) * + weights_to_use->info()->dimension(2) * + weights_to_use->info()->dimension(3)), + 1, input->info()->data_type(), weights->info()->quantization_info())); _reshape_weights.configure(compile_context, weights_to_use, &_reshaped_weights); _transpose_weights.configure(compile_context, &_reshaped_weights, &_reshaped_weights_t); @@ -272,15 +309,17 @@ void CLGEMMDeconvolutionLayer::configure(const CLCompileContext &compile_context GEMMInfo gemm_info(false, false, true, input->info()->dimension(idx_h), true); // Configure output stage for asymmetric quantized types - if(_is_quantized) + if (_is_quantized) { // gemmlowp adds the offsets (instead of subtracting them). Thus, we need to negate the original // and restore them back to make it work properly. QuantizationInfo iq_info = input->info()->quantization_info(); QuantizationInfo wq_info = weights->info()->quantization_info(); - input_to_use->info()->set_quantization_info(QuantizationInfo(iq_info.uniform().scale, -iq_info.uniform().offset)); - _reshaped_weights_t.info()->set_quantization_info(QuantizationInfo(wq_info.uniform().scale, -wq_info.uniform().offset)); + input_to_use->info()->set_quantization_info( + QuantizationInfo(iq_info.uniform().scale, -iq_info.uniform().offset)); + _reshaped_weights_t.info()->set_quantization_info( + QuantizationInfo(wq_info.uniform().scale, -wq_info.uniform().offset)); _mm_gemmlowp.configure(compile_context, input_to_use, &_reshaped_weights_t, nullptr, &_gemm_output, gemm_info); @@ -289,10 +328,11 @@ void CLGEMMDeconvolutionLayer::configure(const CLCompileContext &compile_context } else { - _mm_gemm.configure(compile_context, input_to_use, &_reshaped_weights_t, nullptr, &_gemm_output, 1.f, 0.0f, gemm_info); + _mm_gemm.configure(compile_context, input_to_use, &_reshaped_weights_t, nullptr, &_gemm_output, 1.f, 0.0f, + gemm_info); } - if(_is_nchw) + if (_is_nchw) { _permuted_input.allocator()->allocate(); } @@ -301,7 +341,7 @@ void CLGEMMDeconvolutionLayer::configure(const CLCompileContext &compile_context ICLTensor *slice_output = nullptr; ICLTensor *output_stage_output = nullptr; - if(_padded_input && _is_quantized) + if (_padded_input && _is_quantized) { _memory_group.manage(&_slice_gemm_input); _memory_group.manage(&_gemmlowp_final); @@ -309,13 +349,13 @@ void CLGEMMDeconvolutionLayer::configure(const CLCompileContext &compile_context output_stage_output = &_slice_gemm_input; slice_output = output; } - else if(_padded_input) + else if (_padded_input) { _memory_group.manage(&_slice_gemm_input); deconv_reshape_output = &_slice_gemm_input; slice_output = output; } - else if(_is_quantized) + else if (_is_quantized) { _memory_group.manage(&_gemmlowp_final); deconv_reshape_output = &_gemmlowp_final; @@ -327,21 +367,24 @@ void CLGEMMDeconvolutionLayer::configure(const CLCompileContext &compile_context } // Configure a Col2Im call to reshape the output of GEMM - _deconv_reshape->configure(compile_context, &_gemm_output, bias, deconv_reshape_output, input->info(), weights->info(), deconv_info); + _deconv_reshape->configure(compile_context, &_gemm_output, bias, deconv_reshape_output, input->info(), + weights->info(), deconv_info); _gemm_output.allocator()->allocate(); - if(_is_quantized) + if (_is_quantized) { GEMMLowpOutputStageInfo output_stage_info; construct_gemmlowp_output_stage(input->info(), weights->info(), output->info(), output_stage_info); - _gemmlowp_output_stage.configure(compile_context, &_gemmlowp_final, nullptr, output_stage_output, output_stage_info); + _gemmlowp_output_stage.configure(compile_context, &_gemmlowp_final, nullptr, output_stage_output, + output_stage_info); _gemmlowp_final.allocator()->allocate(); } // If the input was padded, the output needs to be sliced. - if(_padded_input) + if (_padded_input) { - const auto start_end = compute_start_end_slice_coordinates(*deconv_reshape_output->info(), deconv_info, _is_nchw); + const auto start_end = + compute_start_end_slice_coordinates(*deconv_reshape_output->info(), deconv_info, _is_nchw); _slice_gemm.configure(compile_context, &_slice_gemm_input, slice_output, start_end.first, start_end.second); _slice_gemm_input.allocator()->allocate(); } @@ -353,12 +396,12 @@ void CLGEMMDeconvolutionLayer::run() MemoryGroupResourceScope scope_mg(_memory_group); - if(_is_nchw) + if (_is_nchw) { _permute_input_to_nhwc.run(); } - if(_is_quantized) + if (_is_quantized) { _mm_gemmlowp.run(); } @@ -369,12 +412,12 @@ void CLGEMMDeconvolutionLayer::run() CLScheduler::get().enqueue(*_deconv_reshape, false); - if(_is_quantized) + if (_is_quantized) { _gemmlowp_output_stage.run(); } - if(_padded_input) + if (_padded_input) { _slice_gemm.run(); } @@ -382,11 +425,11 @@ void CLGEMMDeconvolutionLayer::run() void CLGEMMDeconvolutionLayer::prepare() { - if(!_is_prepared) + if (!_is_prepared) { ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); - if(_is_nchw) + if (_is_nchw) { _permuted_weights.allocator()->allocate(); _permute_weights_to_nhwc.run(); @@ -395,7 +438,7 @@ void CLGEMMDeconvolutionLayer::prepare() _reshaped_weights.allocator()->allocate(); _reshape_weights.run(); - if(_is_nchw) + if (_is_nchw) { _permuted_weights.allocator()->free(); } @@ -404,7 +447,7 @@ void CLGEMMDeconvolutionLayer::prepare() _transpose_weights.run(); // Prepare gemm - if(!_is_quantized) + if (!_is_quantized) { _mm_gemm.prepare(); } @@ -414,7 +457,7 @@ void CLGEMMDeconvolutionLayer::prepare() } // Free resources - if(!_reshaped_weights_t.is_used()) + if (!_reshaped_weights_t.is_used()) { _reshaped_weights_t.allocator()->free(); } |