| /* |
| * Copyright (c) 2017-2019 ARM Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #include "arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h" |
| |
| #include "arm_compute/core/utils/misc/InfoHelpers.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| #include "arm_compute/runtime/NEON/NEScheduler.h" |
| |
| using namespace arm_compute::misc; |
| using namespace arm_compute::misc::shape_calculator; |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| Status validate_arguments_optimized(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, |
| unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); |
| ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN); |
| ARM_COMPUTE_RETURN_ERROR_ON(dilation.x() < 1 || dilation.y() < 1); |
| const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); |
| const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) + (weights->dimension(idx_w) - 1) * (dilation.x() - 1) > input->dimension(idx_w) + conv_info.pad_left() + conv_info.pad_right()); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_h) + (weights->dimension(idx_h) - 1) * (dilation.y() - 1) > input->dimension(idx_h) + conv_info.pad_top() + conv_info.pad_bottom()); |
| |
| if(biases != nullptr) |
| { |
| const unsigned int channel_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL); |
| ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); |
| ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(channel_idx)); |
| } |
| |
| const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); |
| |
| if(is_quantized) |
| { |
| const UniformQuantizationInfo iq_info = input->quantization_info().uniform(); |
| const UniformQuantizationInfo wq_info = weights->quantization_info().uniform(); |
| const UniformQuantizationInfo oq_info = output->quantization_info().uniform(); |
| |
| float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale; |
| ARM_COMPUTE_UNUSED(multiplier); |
| ARM_COMPUTE_RETURN_ERROR_ON(multiplier > 1.0f); |
| } |
| |
| if(!NEDepthwiseConvolutionAssemblyDispatch::is_optimized_supported(input, weights, conv_info, depth_multiplier, dilation)) |
| { |
| TensorInfo accumulator = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseConvolutionLayer3x3Kernel::validate(input, weights, is_quantized ? &accumulator : output, conv_info, depth_multiplier, dilation)); |
| |
| if(is_quantized) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(NEDirectConvolutionLayerOutputStageKernel::validate(&accumulator, biases, output)); |
| } |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseConvolutionAssemblyDispatch::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation)); |
| } |
| |
| //Validate Activation Layer |
| if(act_info.enabled()) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(output, nullptr, act_info)); |
| } |
| |
| return Status{}; |
| } |
| } // namespace |
| |
| NEDepthwiseConvolutionLayerOptimized::NEDepthwiseConvolutionLayerOptimized(std::shared_ptr<IMemoryManager> memory_manager) |
| : _func(std::move(memory_manager)) |
| { |
| } |
| |
| void NEDepthwiseConvolutionLayerOptimized::configure(ITensor *input, |
| const ITensor *weights, |
| const ITensor *biases, |
| ITensor *output, const PadStrideInfo &conv_info, |
| unsigned int depth_multiplier, |
| const ActivationLayerInfo &act_info, |
| const Size2D &dilation) |
| { |
| _func.configure(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); |
| } |
| |
| Status NEDepthwiseConvolutionLayerOptimized::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, |
| unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation) |
| { |
| return validate_arguments_optimized(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); |
| } |
| |
| void NEDepthwiseConvolutionLayerOptimized::run() |
| { |
| _func.run(); |
| } |
| |
| void NEDepthwiseConvolutionLayerOptimized::prepare() |
| { |
| _func.prepare(); |
| } |
| |
| NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal::NEDepthwiseConvolutionLayerOptimizedInternal(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(memory_manager), _dwc_kernel(), _dwc_optimized_func(memory_manager), _output_stage_kernel(), _border_handler(), _permute_input(), _permute_weights(), _permute_output(), |
| _activationlayer_function(), _accumulator(), _permuted_input(), _permuted_weights(), _permuted_output(), _original_weights(nullptr), _has_bias(false), _is_quantized(false), _is_optimized(false), |
| _is_nchw(true), _permute(false), _is_activationlayer_enabled(false), _is_prepared(false) |
| { |
| } |
| |
| void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal::configure_generic(ITensor *input, |
| const ITensor *weights, |
| const ITensor *biases, |
| ITensor *output, |
| const PadStrideInfo &conv_info, |
| unsigned int depth_multiplier, |
| const ActivationLayerInfo &act_info, |
| const Size2D &dilation) |
| { |
| ARM_COMPUTE_UNUSED(act_info); |
| |
| PixelValue zero_value(0.f); |
| |
| // Initialize the intermediate accumulator tensor in case of quantized input |
| if(_is_quantized) |
| { |
| TensorShape accum_shape = output->info()->tensor_shape(); |
| DataLayout accum_layout = output->info()->data_layout(); |
| if(!_is_nchw) |
| { |
| permute(accum_shape, PermutationVector(1U, 2U, 0U)); |
| accum_layout = DataLayout::NCHW; |
| } |
| |
| _memory_group.manage(&_accumulator); |
| _accumulator.allocator()->init(TensorInfo(accum_shape, 1, DataType::S32, output->info()->quantization_info())); |
| _accumulator.info()->set_data_layout(accum_layout); |
| zero_value = PixelValue(static_cast<uint32_t>(input->info()->quantization_info().uniform().offset)); |
| } |
| |
| if(!_is_nchw) |
| { |
| _memory_group.manage(&_permuted_input); |
| _memory_group.manage(&_permuted_output); |
| |
| // Configure the function to transform the input tensor from NHWC -> NCHW |
| _permute_input.configure(input, &_permuted_input, PermutationVector(1U, 2U, 0U)); |
| _permuted_input.info()->set_data_layout(DataLayout::NCHW); |
| |
| // Configure the function to transform the weights tensor from HWI -> IHW |
| _permute_weights.configure(weights, &_permuted_weights, PermutationVector(1U, 2U, 0U)); |
| _permuted_weights.info()->set_data_layout(DataLayout::NCHW); |
| _permuted_output.info()->set_quantization_info(output->info()->quantization_info()); |
| |
| // Configure depthwise |
| _dwc_kernel.configure(&_permuted_input, &_permuted_weights, (_is_quantized) ? &_accumulator : &_permuted_output, conv_info, depth_multiplier, dilation); |
| |
| // Configure border handler |
| _border_handler.configure(&_permuted_input, _dwc_kernel.border_size(), BorderMode::CONSTANT, zero_value); |
| |
| // Allocate tensors |
| _permuted_input.allocator()->allocate(); |
| } |
| else |
| { |
| // Configure depthwise convolution kernel |
| _dwc_kernel.configure(input, weights, (_is_quantized) ? &_accumulator : output, conv_info, depth_multiplier, dilation); |
| |
| // Configure border handler |
| _border_handler.configure(input, _dwc_kernel.border_size(), BorderMode::CONSTANT, zero_value); |
| } |
| |
| // Configure biases accumulation |
| if(_is_quantized) |
| { |
| const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo wq_info = weights->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo oq_info = (output->info()->total_size() == 0) ? iq_info : output->info()->quantization_info().uniform(); |
| |
| float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale; |
| int output_multiplier; |
| int output_shift; |
| quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); |
| _output_stage_kernel.configure(&_accumulator, biases, _is_nchw ? output : &_permuted_output, output_multiplier, output_shift, oq_info.offset); |
| _accumulator.allocator()->allocate(); |
| } |
| else if(_has_bias) |
| { |
| _output_stage_kernel.configure(_is_nchw ? output : &_permuted_output, biases); |
| } |
| |
| // Permute output |
| if(!_is_nchw) |
| { |
| // Configure the function to transform the convoluted output to NHWC |
| _permute_output.configure(&_permuted_output, output, PermutationVector(2U, 0U, 1U)); |
| _permuted_output.allocator()->allocate(); |
| } |
| } |
| |
| void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal::configure_optimized(const ITensor *input, |
| const ITensor *weights, |
| const ITensor *biases, |
| ITensor *output, |
| const PadStrideInfo &conv_info, |
| unsigned int depth_multiplier, |
| const ActivationLayerInfo &act_info, |
| const Size2D &dilation) |
| { |
| ActivationLayerInfo act_info_to_use = ActivationLayerInfo(); |
| const bool is_relu = arm_compute::utils::info_helpers::is_relu(act_info); |
| const bool is_relu6 = arm_compute::utils::info_helpers::is_relu6(act_info); |
| _is_activationlayer_enabled = act_info.enabled() && !(is_relu || is_relu6); |
| if(!_is_activationlayer_enabled) |
| { |
| act_info_to_use = act_info; |
| } |
| |
| if(_is_nchw) |
| { |
| _memory_group.manage(&_permuted_input); |
| _memory_group.manage(&_permuted_output); |
| |
| // Configure the function to transform the input tensor from NCHW -> NHWC |
| _permute_input.configure(input, &_permuted_input, PermutationVector(2U, 0U, 1U)); |
| _permuted_input.info()->set_data_layout(DataLayout::NHWC); |
| |
| // Configure the function to transform the weights tensor from IHW -> HWI |
| _permute_weights.configure(weights, &_permuted_weights, PermutationVector(2U, 0U, 1U)); |
| _permuted_weights.info()->set_data_layout(DataLayout::NHWC); |
| |
| _permuted_output.info()->set_data_layout(DataLayout::NHWC); |
| _permuted_output.info()->set_quantization_info(output->info()->quantization_info()); |
| |
| // Configure optimized depthwise |
| _dwc_optimized_func.configure(&_permuted_input, &_permuted_weights, biases, &_permuted_output, conv_info, depth_multiplier, act_info_to_use, dilation); |
| |
| // Configure the function to transform the convoluted output to ACL's native ordering format NCHW |
| _permuted_output.info()->set_data_layout(DataLayout::NHWC); |
| _permute_output.configure(&_permuted_output, output, PermutationVector(1U, 2U, 0U)); |
| |
| // Allocate tensors |
| _permuted_input.allocator()->allocate(); |
| _permuted_output.allocator()->allocate(); |
| } |
| else |
| { |
| _dwc_optimized_func.configure(input, weights, biases, output, conv_info, depth_multiplier, act_info_to_use, dilation); |
| } |
| } |
| |
| void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal::configure(ITensor *input, |
| const ITensor *weights, |
| const ITensor *biases, |
| ITensor *output, const PadStrideInfo &conv_info, |
| unsigned int depth_multiplier, |
| const ActivationLayerInfo &act_info, |
| const Size2D &dilation) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); |
| // Perform validation step |
| ARM_COMPUTE_ERROR_THROW_ON(NEDepthwiseConvolutionLayerOptimizedInternal::validate(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), |
| output->info(), conv_info, depth_multiplier, act_info, dilation)); |
| |
| _original_weights = weights; |
| _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); |
| _has_bias = biases != nullptr; |
| _is_optimized = NEDepthwiseConvolutionAssemblyDispatch::is_optimized_supported(input->info(), |
| weights->info(), |
| conv_info, |
| depth_multiplier, |
| dilation); |
| _is_nchw = input->info()->data_layout() == DataLayout::NCHW; |
| _permute = _is_optimized == _is_nchw; |
| _is_prepared = false; |
| _is_activationlayer_enabled = act_info.enabled(); |
| |
| // Configure appropriate pipeline |
| if(_is_optimized) |
| { |
| configure_optimized(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); |
| } |
| else |
| { |
| configure_generic(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); |
| } |
| |
| // Configure activation |
| if(_is_activationlayer_enabled) |
| { |
| _activationlayer_function.configure(output, nullptr, act_info); |
| } |
| } |
| |
| Status NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal::validate(const ITensorInfo *input, |
| const ITensorInfo *weights, |
| const ITensorInfo *biases, |
| const ITensorInfo *output, |
| const PadStrideInfo &conv_info, |
| unsigned int depth_multiplier, |
| const ActivationLayerInfo &act_info, |
| const Size2D &dilation) |
| { |
| return validate_arguments_optimized(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); |
| } |
| |
| void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal::run_generic() |
| { |
| // Fill border |
| NEScheduler::get().schedule(&_border_handler, Window::DimX); |
| |
| // Execute depthwise convolution |
| NEScheduler::get().schedule(&_dwc_kernel, Window::DimX); |
| |
| // Add biases |
| if(_has_bias || _is_quantized) |
| { |
| NEScheduler::get().schedule(&_output_stage_kernel, Window::DimX); |
| } |
| |
| // Permute output |
| if(!_is_nchw) |
| { |
| _permute_output.run(); |
| } |
| } |
| |
| void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal::run_optimized() |
| { |
| // Run assembly function |
| _dwc_optimized_func.run(); |
| |
| // Permute output |
| if(_is_nchw) |
| { |
| _permute_output.run(); |
| } |
| } |
| |
| void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal::run() |
| { |
| prepare(); |
| |
| MemoryGroupResourceScope scope_mg(_memory_group); |
| |
| // Permute input |
| if(_permute) |
| { |
| _permute_input.run(); |
| } |
| |
| _is_optimized ? run_optimized() : run_generic(); |
| |
| // Run activation |
| if(_is_activationlayer_enabled) |
| { |
| _activationlayer_function.run(); |
| } |
| } |
| |
| void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerOptimizedInternal::prepare() |
| { |
| if(!_is_prepared) |
| { |
| // Permute weights |
| if(_permute) |
| { |
| _permuted_weights.allocator()->allocate(); |
| _permute_weights.run(); |
| _original_weights->mark_as_unused(); |
| } |
| |
| // Prepare optimized function |
| if(_is_optimized) |
| { |
| _dwc_optimized_func.prepare(); |
| if(!_permuted_weights.is_used()) |
| { |
| _permuted_weights.allocator()->free(); |
| } |
| } |
| |
| _is_prepared = true; |
| } |
| } |
| |
| NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerGeneric::NEDepthwiseConvolutionLayerGeneric() |
| : _depthwise_conv_kernel(), _fill_border(), _permute_input(), _permute_weights(), _permute_output(), _activationlayer_function(), _permuted_input(), _permuted_weights(), _permuted_output(), |
| _is_prepared(false), _is_nchw(false), _is_activationlayer_enabled(false), _original_weights(nullptr) |
| { |
| } |
| |
| void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerGeneric::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, |
| unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); |
| ARM_COMPUTE_ERROR_THROW_ON(NEDepthwiseConvolutionLayer::validate(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), |
| output->info(), conv_info, depth_multiplier, act_info, dilation)); |
| |
| _is_nchw = input->info()->data_layout() == DataLayout::NCHW; |
| _is_prepared = !_is_nchw; |
| |
| ITensor *input_to_use = input; |
| const ITensor *weights_to_use = weights; |
| ITensor *output_to_use = output; |
| if(_is_nchw) |
| { |
| _permute_input.configure(input, &_permuted_input, PermutationVector(2U, 0U, 1U)); |
| _permuted_input.info()->set_data_layout(DataLayout::NHWC); |
| input_to_use = &_permuted_input; |
| |
| _permute_weights.configure(weights, &_permuted_weights, PermutationVector(2U, 0U, 1U)); |
| _permuted_weights.info()->set_data_layout(DataLayout::NHWC); |
| weights_to_use = &_permuted_weights; |
| |
| _permuted_output.allocator()->init(output->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(TensorShape())); |
| output_to_use = &_permuted_output; |
| } |
| _original_weights = weights_to_use; |
| |
| _depthwise_conv_kernel.configure(input_to_use, weights_to_use, biases, output_to_use, conv_info, depth_multiplier, dilation); |
| _fill_border.configure(input_to_use, _depthwise_conv_kernel.border_size(), BorderMode::CONSTANT, PixelValue(static_cast<uint64_t>(0), input->info()->data_type(), input->info()->quantization_info())); |
| |
| if(_is_nchw) |
| { |
| _permute_output.configure(&_permuted_output, output, PermutationVector(1U, 2U, 0U)); |
| _permuted_output.info()->set_data_layout(DataLayout::NHWC); |
| |
| _permuted_input.allocator()->allocate(); |
| _permuted_weights.allocator()->allocate(); |
| _permuted_output.allocator()->allocate(); |
| } |
| |
| //Configure Activation Layer |
| _is_activationlayer_enabled = act_info.enabled(); |
| if(_is_activationlayer_enabled) |
| { |
| _activationlayer_function.configure(output, nullptr, act_info); |
| } |
| } |
| |
| Status NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerGeneric::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, |
| const PadStrideInfo &conv_info, |
| unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); |
| if(input->data_layout() == DataLayout::NCHW) |
| { |
| TensorShape permuted_input_shape = input->tensor_shape(); |
| TensorShape permuted_weights_shape = weights->tensor_shape(); |
| TensorShape permuted_output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); |
| permute(permuted_input_shape, PermutationVector(2U, 0U, 1U)); |
| permute(permuted_weights_shape, PermutationVector(2U, 0U, 1U)); |
| permute(permuted_output_shape, PermutationVector(2U, 0U, 1U)); |
| |
| const TensorInfo permuted_input = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_input_shape).set_data_layout(DataLayout::NHWC)); |
| const TensorInfo permuted_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_weights_shape).set_data_layout(DataLayout::NHWC)); |
| const TensorInfo permuted_output = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_output_shape).set_data_layout(DataLayout::NCHW)); |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPermute::validate(input, &permuted_input, PermutationVector(2U, 0U, 1U))); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPermute::validate(weights, &permuted_weights, PermutationVector(2U, 0U, 1U))); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPermute::validate(&permuted_output, output, PermutationVector(1U, 2U, 0U))); |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseConvolutionLayerNativeKernel::validate(&permuted_input, &permuted_weights, biases, &permuted_output, conv_info, depth_multiplier, dilation)); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseConvolutionLayerNativeKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, dilation)); |
| } |
| |
| // Validate Activation Layer |
| if(act_info.enabled()) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(output, nullptr, act_info)); |
| } |
| |
| return Status{}; |
| } |
| |
| void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerGeneric::run() |
| { |
| if(_is_nchw) |
| { |
| prepare(); |
| _permute_input.run(); |
| } |
| |
| NEScheduler::get().schedule(&_fill_border, Window::DimX); |
| NEScheduler::get().schedule(&_depthwise_conv_kernel, Window::DimY); |
| |
| if(_is_nchw) |
| { |
| _permute_output.run(); |
| } |
| |
| if(_is_activationlayer_enabled) |
| { |
| _activationlayer_function.run(); |
| } |
| } |
| |
| void NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayerGeneric::prepare() |
| { |
| if(!_is_prepared) |
| { |
| ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); |
| |
| _permute_weights.run(); |
| _original_weights->mark_as_unused(); |
| _is_prepared = true; |
| } |
| } |
| |
| NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) |
| : _depth_conv_func(DepthwiseConvolutionFunction::GENERIC), _func_optimized(std::move(memory_manager)), _func_generic() |
| { |
| } |
| |
| void NEDepthwiseConvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, |
| const ActivationLayerInfo &act_info, const Size2D &dilation) |
| { |
| _depth_conv_func = get_depthwiseconvolution_function(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info, depth_multiplier, act_info, dilation); |
| switch(_depth_conv_func) |
| { |
| case DepthwiseConvolutionFunction::OPTIMIZED: |
| _func_optimized.configure(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); |
| break; |
| case DepthwiseConvolutionFunction::GENERIC: |
| _func_generic.configure(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Unsupported DepthwiseConvolutionFunction"); |
| } |
| } |
| |
| Status NEDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, |
| unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation) |
| { |
| DepthwiseConvolutionFunction depth_conv_func = get_depthwiseconvolution_function(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); |
| switch(depth_conv_func) |
| { |
| case DepthwiseConvolutionFunction::OPTIMIZED: |
| return NEDepthwiseConvolutionLayerOptimized::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); |
| break; |
| case DepthwiseConvolutionFunction::GENERIC: |
| return NEDepthwiseConvolutionLayerGeneric::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Unsupported DepthwiseConvolutionFunction"); |
| } |
| } |
| |
| DepthwiseConvolutionFunction NEDepthwiseConvolutionLayer::get_depthwiseconvolution_function(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, |
| const PadStrideInfo &conv_info, |
| unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation) |
| { |
| if(bool(NEDepthwiseConvolutionLayerOptimized::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation))) |
| { |
| return DepthwiseConvolutionFunction::OPTIMIZED; |
| } |
| else |
| { |
| return DepthwiseConvolutionFunction::GENERIC; |
| } |
| } |
| |
| void NEDepthwiseConvolutionLayer::run() |
| { |
| switch(_depth_conv_func) |
| { |
| case DepthwiseConvolutionFunction::OPTIMIZED: |
| _func_optimized.run(); |
| break; |
| case DepthwiseConvolutionFunction::GENERIC: |
| _func_generic.run(); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("DepthwiseConvolutionFunction not properly configured"); |
| } |
| } |
| |
| void NEDepthwiseConvolutionLayer::prepare() |
| { |
| switch(_depth_conv_func) |
| { |
| case DepthwiseConvolutionFunction::OPTIMIZED: |
| _func_optimized.prepare(); |
| break; |
| case DepthwiseConvolutionFunction::GENERIC: |
| _func_generic.prepare(); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("DepthwiseConvolutionFunction not properly configured"); |
| } |
| } |
| } // namespace arm_compute |