| /* |
| * 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/CL/functions/CLDepthwiseConvolutionLayer.h" |
| |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h" |
| #include "arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/PixelValue.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| #include "arm_compute/runtime/CL/CLScheduler.h" |
| #include "support/ToolchainSupport.h" |
| |
| namespace arm_compute |
| { |
| using namespace arm_compute::misc; |
| using namespace arm_compute::misc::shape_calculator; |
| |
| CLDepthwiseConvolutionLayer3x3::CLDepthwiseConvolutionLayer3x3(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(std::move(memory_manager)), _kernel(nullptr), _border_handler(), _permute_input_to_nchw(), _permute_weights_to_nchw(), _permute_output_to_nhwc(), _reshape_weights(), _permuted_input(), |
| _permuted_weights(), _permuted_output(), _original_weights(nullptr), _needs_permute(false), _needs_weights_reshape(false), _is_prepared(false) |
| { |
| } |
| |
| void CLDepthwiseConvolutionLayer3x3::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, |
| ActivationLayerInfo act_info, const Size2D &dilation) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); |
| // idx_w and idx_h only used for validation |
| const size_t idx_w = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH); |
| const size_t idx_h = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT); |
| ARM_COMPUTE_UNUSED(idx_w); |
| ARM_COMPUTE_UNUSED(idx_h); |
| |
| ARM_COMPUTE_ERROR_ON(weights->info()->dimension(idx_w) + (weights->info()->dimension(idx_w) - 1) * (dilation.x() - 1) > input->info()->dimension(idx_w) + conv_info.pad_left() + conv_info.pad_right()); |
| ARM_COMPUTE_ERROR_ON(weights->info()->dimension(idx_h) + (weights->info()->dimension(idx_h) - 1) * (dilation.y() - 1) > input->info()->dimension(idx_h) + conv_info.pad_top() + conv_info.pad_bottom()); |
| |
| const bool is_nhwc = input->info()->data_layout() == DataLayout::NHWC; |
| |
| _needs_permute = is_nhwc && (depth_multiplier > 1); |
| _needs_weights_reshape = is_nhwc && (depth_multiplier == 1) |
| && is_data_type_quantized_asymmetric(input->info()->data_type()); |
| _is_prepared = false; |
| _original_weights = weights; |
| |
| ICLTensor *input_to_use = input; |
| const ICLTensor *weights_to_use = weights; |
| ICLTensor *output_to_use = output; |
| |
| const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1)); |
| const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()); |
| const bool is_stride_1_dilation_1 = (is_stride_1 && dilation.x() == 1 && dilation.y() == 1); |
| |
| DepthwiseConvolutionReshapeInfo info; |
| info.c0 = 4; |
| info.transpose = is_stride_1_dilation_1 && is_dot8_supported; |
| |
| if(_needs_permute) |
| { |
| _memory_group.manage(&_permuted_input); |
| _memory_group.manage(&_permuted_output); |
| |
| // Configure the function to transform the input tensor from NHWC -> NCHW |
| _permute_input_to_nchw.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_to_nchw.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()); |
| |
| input_to_use = &_permuted_input; |
| weights_to_use = &_permuted_weights; |
| output_to_use = &_permuted_output; |
| |
| _kernel = arm_compute::support::cpp14::make_unique<CLDepthwiseConvolutionLayer3x3NCHWKernel>(); |
| } |
| else if(is_nhwc) |
| { |
| if(_needs_weights_reshape) |
| { |
| _reshape_weights.configure(weights, &_permuted_weights, info); |
| weights_to_use = &_permuted_weights; |
| } |
| _kernel = arm_compute::support::cpp14::make_unique<CLDepthwiseConvolutionLayer3x3NHWCKernel>(); |
| } |
| else |
| { |
| _kernel = arm_compute::support::cpp14::make_unique<CLDepthwiseConvolutionLayer3x3NCHWKernel>(); |
| } |
| |
| // Configure kernel |
| _kernel->set_target(CLScheduler::get().target()); |
| _kernel->configure(input_to_use, weights_to_use, biases, output_to_use, conv_info, depth_multiplier, act_info, dilation); |
| |
| // Permute output if needed |
| if(_needs_permute) |
| { |
| // Configure the function to transform the convoluted output to ACL's native ordering format NCHW |
| _permuted_output.info()->set_data_layout(DataLayout::NCHW); |
| _permute_output_to_nhwc.configure(&_permuted_output, output, PermutationVector(2U, 0U, 1U)); |
| |
| // Allocate tensors |
| _permuted_input.allocator()->allocate(); |
| _permuted_output.allocator()->allocate(); |
| } |
| // Configure border handler |
| PixelValue &&zero_value(0.f); |
| if(is_data_type_quantized_asymmetric(input->info()->data_type())) |
| { |
| zero_value = PixelValue(static_cast<uint8_t>(input->info()->quantization_info().uniform().offset)); |
| } |
| _border_handler.configure(input_to_use, _kernel->border_size(), BorderMode::CONSTANT, zero_value); |
| } |
| |
| Status CLDepthwiseConvolutionLayer3x3::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, |
| unsigned int depth_multiplier, ActivationLayerInfo act_info, GPUTarget gpu_target, const Size2D &dilation) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); |
| ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN); |
| |
| const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); |
| const bool is_nhwc = input->data_layout() == DataLayout::NHWC; |
| const bool needs_permute = is_nhwc && (depth_multiplier > 1); |
| const bool needs_weights_reshape = is_nhwc && (depth_multiplier == 1) && is_quantized; |
| const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1)); |
| const bool is_stride_1_dilation_1 = (is_stride_1 && dilation.x() == 1 && dilation.y() == 1); |
| const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()); |
| DepthwiseConvolutionReshapeInfo info; |
| info.c0 = 4; |
| info.transpose = is_stride_1_dilation_1 && is_dot8_supported; |
| |
| if(is_quantized) |
| { |
| const UniformQuantizationInfo iq_info = input->quantization_info().uniform(); |
| const UniformQuantizationInfo wq_info = weights->quantization_info().uniform(); |
| const UniformQuantizationInfo oq_info = (output->total_size() == 0) ? iq_info : output->quantization_info().uniform(); |
| |
| const float multiplier = iq_info.scale * wq_info.scale / oq_info.scale; |
| ARM_COMPUTE_UNUSED(multiplier); |
| ARM_COMPUTE_RETURN_ERROR_ON(multiplier > 1.0f); |
| } |
| |
| if(needs_permute) |
| { |
| TensorShape permuted_input_shape = input->tensor_shape(); |
| TensorShape permuted_weights_shape = weights->tensor_shape(); |
| TensorShape permuted_output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); |
| |
| permute(permuted_input_shape, PermutationVector(1U, 2U, 0U)); |
| permute(permuted_weights_shape, PermutationVector(1U, 2U, 0U)); |
| permute(permuted_output_shape, PermutationVector(1U, 2U, 0U)); |
| |
| const TensorInfo permuted_input = input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_input_shape).set_data_layout(DataLayout::NCHW); |
| const TensorInfo permuted_weights = weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_weights_shape).set_data_layout(DataLayout::NCHW); |
| const TensorInfo permuted_output = output->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_output_shape).set_data_layout(DataLayout::NCHW); |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(&permuted_input, &permuted_weights, biases, &permuted_output, conv_info, depth_multiplier, act_info, gpu_target, |
| dilation)); |
| } |
| else if(is_nhwc) |
| { |
| if(needs_weights_reshape) |
| { |
| auto reshaped_weights_shape = arm_compute::misc::shape_calculator::compute_reshaped_depthwise_weights_shape(*weights, info); |
| ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(input, &weights->clone()->set_tensor_shape(reshaped_weights_shape), biases, output, conv_info, depth_multiplier, |
| act_info, dilation)); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation)); |
| } |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, gpu_target, dilation)); |
| } |
| |
| return Status{}; |
| } |
| |
| void CLDepthwiseConvolutionLayer3x3::run() |
| { |
| prepare(); |
| |
| MemoryGroupResourceScope scope_mg(_memory_group); |
| |
| if(_needs_permute) |
| { |
| _permute_input_to_nchw.run(); |
| } |
| CLScheduler::get().enqueue(_border_handler); |
| CLScheduler::get().enqueue(*_kernel); |
| |
| if(_needs_permute) |
| { |
| _permute_output_to_nhwc.run(); |
| } |
| } |
| |
| void CLDepthwiseConvolutionLayer3x3::prepare() |
| { |
| if(!_is_prepared) |
| { |
| if(_needs_permute) |
| { |
| ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); |
| |
| _permuted_weights.allocator()->allocate(); |
| _permute_weights_to_nchw.run(); |
| _original_weights->mark_as_unused(); |
| } |
| |
| if(_needs_weights_reshape) |
| { |
| ARM_COMPUTE_ERROR_ON(_needs_permute); |
| ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); |
| _permuted_weights.allocator()->allocate(); |
| CLScheduler::get().enqueue(_reshape_weights); |
| _original_weights->mark_as_unused(); |
| } |
| _is_prepared = true; |
| } |
| } |
| |
| CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(std::move(memory_manager)), |
| _optimised_function(nullptr), |
| _dwc_native_kernel(), |
| _permute_input_to_nhwc(), |
| _permute_weights_to_nhwc(), |
| _permute_output_to_nchw(), |
| _permuted_input(), |
| _permuted_weights(), |
| _permuted_output(), |
| _original_weights(), |
| _needs_permute(false), |
| _is_prepared(false) |
| { |
| } |
| |
| void CLDepthwiseConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *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(CLDepthwiseConvolutionLayer::validate(input->info(), |
| weights->info(), |
| biases != nullptr ? biases->info() : nullptr, |
| output->info(), |
| conv_info, |
| depth_multiplier, |
| act_info, |
| dilation)); |
| |
| const size_t idx_w = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH); |
| const size_t idx_h = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT); |
| |
| const GPUTarget gpu_target = CLScheduler::get().target(); |
| const bool can_run_optimised_3x3_kernel = (weights->info()->dimension(idx_w) == 3) && (weights->info()->dimension(idx_h) == 3) && (is_data_type_float(input->info()->data_type()) |
| || (get_arch_from_target(gpu_target) == GPUTarget::MIDGARD)); |
| |
| _needs_permute = false; |
| _is_prepared = false; |
| _original_weights = weights; |
| |
| if(bool(can_run_optimised_3x3_kernel)) |
| { |
| auto f = arm_compute::support::cpp14::make_unique<CLDepthwiseConvolutionLayer3x3>(); |
| f->configure(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); |
| _optimised_function = std::move(f); |
| } |
| else |
| { |
| _needs_permute = input->info()->data_layout() == DataLayout::NCHW; |
| |
| ICLTensor *input_to_use = input; |
| const ICLTensor *weights_to_use = weights; |
| ICLTensor *output_to_use = output; |
| if(_needs_permute) |
| { |
| _memory_group.manage(&_permuted_input); |
| _memory_group.manage(&_permuted_output); |
| |
| // Configure the function to transform the input tensor from NCHW -> NHWC |
| _permute_input_to_nhwc.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_to_nhwc.configure(weights, &_permuted_weights, PermutationVector(2U, 0U, 1U)); |
| _permuted_weights.info()->set_data_layout(DataLayout::NHWC); |
| |
| // Set output quantization info before dwc kernel configure |
| _permuted_output.info()->set_quantization_info(output->info()->quantization_info()); |
| |
| input_to_use = &_permuted_input; |
| weights_to_use = &_permuted_weights; |
| output_to_use = &_permuted_output; |
| } |
| |
| DWCWeightsKernelInfo dwc_weights_info; |
| dwc_weights_info.n0 = (depth_multiplier == 1) ? 8 : 1; |
| DWCKernelInfo dwc_info; |
| dwc_info.activation_info = act_info; |
| _dwc_native_kernel.configure(input_to_use, weights_to_use, biases, output_to_use, dwc_weights_info, dwc_info, conv_info, depth_multiplier, dilation); |
| |
| if(_needs_permute) |
| { |
| _permuted_input.allocator()->allocate(); |
| |
| // Configure the function to transform the convoluted output to NCHW format |
| _permuted_output.info()->set_data_layout(DataLayout::NCHW); |
| _permute_output_to_nchw.configure(&_permuted_output, output, PermutationVector(1U, 2U, 0U)); |
| _permuted_output.allocator()->allocate(); |
| } |
| } |
| } |
| |
| Status CLDepthwiseConvolutionLayer::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_MISMATCHING_DATA_LAYOUT(input, output); |
| |
| 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()); |
| |
| const GPUTarget gpu_target = CLScheduler::get().target(); |
| const bool can_run_optimised_3x3_kernel = (weights->dimension(idx_w) == 3) && (weights->dimension(idx_h) == 3) && (is_data_type_float(input->data_type()) |
| || (get_arch_from_target(gpu_target) == GPUTarget::MIDGARD)); |
| |
| if(!can_run_optimised_3x3_kernel) |
| { |
| DWCWeightsKernelInfo dwc_weights_info; |
| dwc_weights_info.n0 = (depth_multiplier == 1) ? 8 : 1; |
| DWCKernelInfo dwc_info; |
| dwc_info.activation_info = act_info; |
| |
| const bool needs_permute = input->data_layout() == DataLayout::NCHW; |
| |
| if(needs_permute) |
| { |
| TensorShape permuted_input_shape = input->tensor_shape(); |
| TensorShape permuted_weights_shape = weights->tensor_shape(); |
| TensorShape permuted_output_shape = 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 = input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_input_shape).set_data_layout(DataLayout::NHWC); |
| const TensorInfo permuted_weights = weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_weights_shape).set_data_layout(DataLayout::NHWC); |
| const TensorInfo permuted_output = output->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_output_shape).set_data_layout(DataLayout::NHWC); |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(input, &permuted_input, PermutationVector(2U, 0U, 1U))); |
| ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(weights, &permuted_weights, PermutationVector(2U, 0U, 1U))); |
| ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayerNativeKernel::validate(&permuted_input, &permuted_weights, biases, &permuted_output, dwc_weights_info, |
| dwc_info, conv_info, depth_multiplier, dilation)); |
| ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(&permuted_output, output, PermutationVector(1U, 2U, 0U))); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayerNativeKernel::validate(input, weights, biases, output, dwc_weights_info, dwc_info, conv_info, depth_multiplier, dilation)); |
| } |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, GPUTarget::MIDGARD, dilation)); |
| } |
| return Status{}; |
| } |
| |
| void CLDepthwiseConvolutionLayer::run() |
| { |
| prepare(); |
| |
| MemoryGroupResourceScope scope_mg(_memory_group); |
| |
| if(_optimised_function != nullptr) |
| { |
| _optimised_function->run(); |
| } |
| else |
| { |
| if(_needs_permute) |
| { |
| _permute_input_to_nhwc.run(); |
| } |
| CLScheduler::get().enqueue(_dwc_native_kernel); |
| if(_needs_permute) |
| { |
| _permute_output_to_nchw.run(); |
| } |
| } |
| } |
| |
| void CLDepthwiseConvolutionLayer::prepare() |
| { |
| if(_optimised_function != nullptr) |
| { |
| _optimised_function->prepare(); |
| } |
| else if(!_is_prepared) |
| { |
| if(_needs_permute) |
| { |
| ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); |
| |
| _permuted_weights.allocator()->allocate(); |
| _permute_weights_to_nhwc.run(); |
| _original_weights->mark_as_unused(); |
| } |
| _is_prepared = true; |
| } |
| } |
| } // namespace arm_compute |