| /* |
| * Copyright (c) 2017-2018 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/graph/nodes/DepthwiseConvolutionLayer.h" |
| |
| #include "arm_compute/graph/Error.h" |
| #include "arm_compute/graph/NodeContext.h" |
| #include "arm_compute/graph/OperationRegistry.h" |
| #include "support/ToolchainSupport.h" |
| |
| using namespace arm_compute::graph; |
| |
| std::unique_ptr<arm_compute::IFunction> DepthwiseConvolutionLayer::instantiate_node(GraphContext &ctx, ITensorObject *input, ITensorObject *output) |
| { |
| ARM_COMPUTE_ERROR_ON_UNALLOCATED_TENSOR_OBJECT(input, output); |
| |
| arm_compute::ITensor *in = input->tensor(); |
| arm_compute::ITensor *out = output->tensor(); |
| _target_hint = ctx.hints().target_hint(); |
| |
| if(_weights.tensor() == nullptr) |
| { |
| TensorShape weights_shape(_conv_width, _conv_height, input->tensor()->info()->tensor_shape().z()); |
| TensorInfo info = TensorInfo(TensorShape(weights_shape), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position()); |
| info.set_quantization_info(_quant_info); |
| _weights.set_info(std::move(info)); |
| } |
| if(_biases.has_accessor() && _biases.tensor() == nullptr) |
| { |
| DataType dt = in->info()->data_type(); |
| _biases.set_info(TensorInfo(TensorShape(in->info()->dimension(2)), in->info()->num_channels(), is_data_type_quantized_asymmetric(dt) ? DataType::S32 : dt, in->info()->fixed_point_position())); |
| } |
| |
| bool weights_is_loaded = _weights.tensor() != nullptr; |
| bool biases_is_loaded = _biases.has_accessor() ? _biases.tensor() != nullptr : true; |
| |
| _weights.set_target(_target_hint); |
| if(_biases.has_accessor()) |
| { |
| _biases.set_target(_target_hint); |
| } |
| |
| // Create node context |
| NodeContext node_ctx(OperationType::DepthwiseConvolutionLayer); |
| node_ctx.set_target(_target_hint); |
| node_ctx.add_input(in); |
| node_ctx.add_input(_weights.tensor()); |
| if(_biases.has_accessor()) |
| { |
| node_ctx.add_input(_biases.tensor()); |
| } |
| node_ctx.add_output(out); |
| node_ctx.add_parameter<PadStrideInfo>("ConvolutionInfo", _conv_info); |
| node_ctx.add_parameter<bool>("Optimized3x3", _opt3x3); |
| |
| // Configure operation |
| auto func = OperationRegistry::get().find_operation(OperationType::DepthwiseConvolutionLayer, _target_hint)->configure(node_ctx); |
| |
| // Fill tensors |
| if(!weights_is_loaded) |
| { |
| _weights.allocate_and_fill_if_needed(); |
| } |
| if(!biases_is_loaded) |
| { |
| _biases.allocate_and_fill_if_needed(); |
| } |
| |
| // Get function |
| return func; |
| } |