| /* |
| * Copyright (c) 2016-2021 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/NEFunctions.h" |
| |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/runtime/Allocator.h" |
| #include "arm_compute/runtime/BlobLifetimeManager.h" |
| #include "arm_compute/runtime/MemoryManagerOnDemand.h" |
| #include "arm_compute/runtime/PoolManager.h" |
| #include "utils/Utils.h" |
| |
| using namespace arm_compute; |
| using namespace utils; |
| |
| class NEONCNNExample : public Example |
| { |
| public: |
| bool do_setup(int argc, char **argv) override |
| { |
| ARM_COMPUTE_UNUSED(argc); |
| ARM_COMPUTE_UNUSED(argv); |
| |
| // Create memory manager components |
| // We need 2 memory managers: 1 for handling the tensors within the functions (mm_layers) and 1 for handling the input and output tensors of the functions (mm_transitions)) |
| auto lifetime_mgr0 = std::make_shared<BlobLifetimeManager>(); // Create lifetime manager |
| auto lifetime_mgr1 = std::make_shared<BlobLifetimeManager>(); // Create lifetime manager |
| auto pool_mgr0 = std::make_shared<PoolManager>(); // Create pool manager |
| auto pool_mgr1 = std::make_shared<PoolManager>(); // Create pool manager |
| auto mm_layers = std::make_shared<MemoryManagerOnDemand>(lifetime_mgr0, pool_mgr0); // Create the memory manager |
| auto mm_transitions = std::make_shared<MemoryManagerOnDemand>(lifetime_mgr1, pool_mgr1); // Create the memory manager |
| |
| // The weights and biases tensors should be initialized with the values inferred with the training |
| |
| // Set memory manager where allowed to manage internal memory requirements |
| conv0 = std::make_unique<NEConvolutionLayer>(mm_layers); |
| conv1 = std::make_unique<NEConvolutionLayer>(mm_layers); |
| fc0 = std::make_unique<NEFullyConnectedLayer>(mm_layers); |
| softmax = std::make_unique<NESoftmaxLayer>(mm_layers); |
| |
| /* [Initialize tensors] */ |
| |
| // Initialize src tensor |
| constexpr unsigned int width_src_image = 32; |
| constexpr unsigned int height_src_image = 32; |
| constexpr unsigned int ifm_src_img = 1; |
| |
| const TensorShape src_shape(width_src_image, height_src_image, ifm_src_img); |
| src.allocator()->init(TensorInfo(src_shape, 1, DataType::F32)); |
| |
| // Initialize tensors of conv0 |
| constexpr unsigned int kernel_x_conv0 = 5; |
| constexpr unsigned int kernel_y_conv0 = 5; |
| constexpr unsigned int ofm_conv0 = 8; |
| |
| const TensorShape weights_shape_conv0(kernel_x_conv0, kernel_y_conv0, src_shape.z(), ofm_conv0); |
| const TensorShape biases_shape_conv0(weights_shape_conv0[3]); |
| const TensorShape out_shape_conv0(src_shape.x(), src_shape.y(), weights_shape_conv0[3]); |
| |
| weights0.allocator()->init(TensorInfo(weights_shape_conv0, 1, DataType::F32)); |
| biases0.allocator()->init(TensorInfo(biases_shape_conv0, 1, DataType::F32)); |
| out_conv0.allocator()->init(TensorInfo(out_shape_conv0, 1, DataType::F32)); |
| |
| // Initialize tensor of act0 |
| out_act0.allocator()->init(TensorInfo(out_shape_conv0, 1, DataType::F32)); |
| |
| // Initialize tensor of pool0 |
| TensorShape out_shape_pool0 = out_shape_conv0; |
| out_shape_pool0.set(0, out_shape_pool0.x() / 2); |
| out_shape_pool0.set(1, out_shape_pool0.y() / 2); |
| out_pool0.allocator()->init(TensorInfo(out_shape_pool0, 1, DataType::F32)); |
| |
| // Initialize tensors of conv1 |
| constexpr unsigned int kernel_x_conv1 = 3; |
| constexpr unsigned int kernel_y_conv1 = 3; |
| constexpr unsigned int ofm_conv1 = 16; |
| |
| const TensorShape weights_shape_conv1(kernel_x_conv1, kernel_y_conv1, out_shape_pool0.z(), ofm_conv1); |
| |
| const TensorShape biases_shape_conv1(weights_shape_conv1[3]); |
| const TensorShape out_shape_conv1(out_shape_pool0.x(), out_shape_pool0.y(), weights_shape_conv1[3]); |
| |
| weights1.allocator()->init(TensorInfo(weights_shape_conv1, 1, DataType::F32)); |
| biases1.allocator()->init(TensorInfo(biases_shape_conv1, 1, DataType::F32)); |
| out_conv1.allocator()->init(TensorInfo(out_shape_conv1, 1, DataType::F32)); |
| |
| // Initialize tensor of act1 |
| out_act1.allocator()->init(TensorInfo(out_shape_conv1, 1, DataType::F32)); |
| |
| // Initialize tensor of pool1 |
| TensorShape out_shape_pool1 = out_shape_conv1; |
| out_shape_pool1.set(0, out_shape_pool1.x() / 2); |
| out_shape_pool1.set(1, out_shape_pool1.y() / 2); |
| out_pool1.allocator()->init(TensorInfo(out_shape_pool1, 1, DataType::F32)); |
| |
| // Initialize tensor of fc0 |
| constexpr unsigned int num_labels = 128; |
| |
| const TensorShape weights_shape_fc0(out_shape_pool1.x() * out_shape_pool1.y() * out_shape_pool1.z(), num_labels); |
| const TensorShape biases_shape_fc0(num_labels); |
| const TensorShape out_shape_fc0(num_labels); |
| |
| weights2.allocator()->init(TensorInfo(weights_shape_fc0, 1, DataType::F32)); |
| biases2.allocator()->init(TensorInfo(biases_shape_fc0, 1, DataType::F32)); |
| out_fc0.allocator()->init(TensorInfo(out_shape_fc0, 1, DataType::F32)); |
| |
| // Initialize tensor of act2 |
| out_act2.allocator()->init(TensorInfo(out_shape_fc0, 1, DataType::F32)); |
| |
| // Initialize tensor of softmax |
| const TensorShape out_shape_softmax(out_shape_fc0.x()); |
| out_softmax.allocator()->init(TensorInfo(out_shape_softmax, 1, DataType::F32)); |
| |
| constexpr auto data_layout = DataLayout::NCHW; |
| |
| /* -----------------------End: [Initialize tensors] */ |
| |
| /* [Configure functions] */ |
| |
| // in:32x32x1: 5x5 convolution, 8 output features maps (OFM) |
| conv0->configure(&src, &weights0, &biases0, &out_conv0, PadStrideInfo(1 /* stride_x */, 1 /* stride_y */, 2 /* pad_x */, 2 /* pad_y */)); |
| |
| // in:32x32x8, out:32x32x8, Activation function: relu |
| act0.configure(&out_conv0, &out_act0, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); |
| |
| // in:32x32x8, out:16x16x8 (2x2 pooling), Pool type function: Max |
| pool0.configure(&out_act0, &out_pool0, PoolingLayerInfo(PoolingType::MAX, 2, data_layout, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */))); |
| |
| // in:16x16x8: 3x3 convolution, 16 output features maps (OFM) |
| conv1->configure(&out_pool0, &weights1, &biases1, &out_conv1, PadStrideInfo(1 /* stride_x */, 1 /* stride_y */, 1 /* pad_x */, 1 /* pad_y */)); |
| |
| // in:16x16x16, out:16x16x16, Activation function: relu |
| act1.configure(&out_conv1, &out_act1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); |
| |
| // in:16x16x16, out:8x8x16 (2x2 pooling), Pool type function: Average |
| pool1.configure(&out_act1, &out_pool1, PoolingLayerInfo(PoolingType::AVG, 2, data_layout, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */))); |
| |
| // in:8x8x16, out:128 |
| fc0->configure(&out_pool1, &weights2, &biases2, &out_fc0); |
| |
| // in:128, out:128, Activation function: relu |
| act2.configure(&out_fc0, &out_act2, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); |
| |
| // in:128, out:128 |
| softmax->configure(&out_act2, &out_softmax); |
| |
| /* -----------------------End: [Configure functions] */ |
| |
| /*[ Add tensors to memory manager ]*/ |
| |
| // We need 2 memory groups for handling the input and output |
| // We call explicitly allocate after manage() in order to avoid overlapping lifetimes |
| memory_group0 = std::make_unique<MemoryGroup>(mm_transitions); |
| memory_group1 = std::make_unique<MemoryGroup>(mm_transitions); |
| |
| memory_group0->manage(&out_conv0); |
| out_conv0.allocator()->allocate(); |
| memory_group1->manage(&out_act0); |
| out_act0.allocator()->allocate(); |
| memory_group0->manage(&out_pool0); |
| out_pool0.allocator()->allocate(); |
| memory_group1->manage(&out_conv1); |
| out_conv1.allocator()->allocate(); |
| memory_group0->manage(&out_act1); |
| out_act1.allocator()->allocate(); |
| memory_group1->manage(&out_pool1); |
| out_pool1.allocator()->allocate(); |
| memory_group0->manage(&out_fc0); |
| out_fc0.allocator()->allocate(); |
| memory_group1->manage(&out_act2); |
| out_act2.allocator()->allocate(); |
| memory_group0->manage(&out_softmax); |
| out_softmax.allocator()->allocate(); |
| |
| /* -----------------------End: [ Add tensors to memory manager ] */ |
| |
| /* [Allocate tensors] */ |
| |
| // Now that the padding requirements are known we can allocate all tensors |
| src.allocator()->allocate(); |
| weights0.allocator()->allocate(); |
| weights1.allocator()->allocate(); |
| weights2.allocator()->allocate(); |
| biases0.allocator()->allocate(); |
| biases1.allocator()->allocate(); |
| biases2.allocator()->allocate(); |
| |
| /* -----------------------End: [Allocate tensors] */ |
| |
| // Populate the layers manager. (Validity checks, memory allocations etc) |
| mm_layers->populate(allocator, 1 /* num_pools */); |
| |
| // Populate the transitions manager. (Validity checks, memory allocations etc) |
| mm_transitions->populate(allocator, 2 /* num_pools */); |
| |
| return true; |
| } |
| void do_run() override |
| { |
| // Acquire memory for the memory groups |
| memory_group0->acquire(); |
| memory_group1->acquire(); |
| |
| conv0->run(); |
| act0.run(); |
| pool0.run(); |
| conv1->run(); |
| act1.run(); |
| pool1.run(); |
| fc0->run(); |
| act2.run(); |
| softmax->run(); |
| |
| // Release memory |
| memory_group0->release(); |
| memory_group1->release(); |
| } |
| |
| private: |
| // The src tensor should contain the input image |
| Tensor src{}; |
| |
| // Intermediate tensors used |
| Tensor weights0{}; |
| Tensor weights1{}; |
| Tensor weights2{}; |
| Tensor biases0{}; |
| Tensor biases1{}; |
| Tensor biases2{}; |
| Tensor out_conv0{}; |
| Tensor out_conv1{}; |
| Tensor out_act0{}; |
| Tensor out_act1{}; |
| Tensor out_act2{}; |
| Tensor out_pool0{}; |
| Tensor out_pool1{}; |
| Tensor out_fc0{}; |
| Tensor out_softmax{}; |
| |
| // Allocator |
| Allocator allocator{}; |
| |
| // Memory groups |
| std::unique_ptr<MemoryGroup> memory_group0{}; |
| std::unique_ptr<MemoryGroup> memory_group1{}; |
| |
| // Layers |
| std::unique_ptr<NEConvolutionLayer> conv0{}; |
| std::unique_ptr<NEConvolutionLayer> conv1{}; |
| std::unique_ptr<NEFullyConnectedLayer> fc0{}; |
| std::unique_ptr<NESoftmaxLayer> softmax{}; |
| NEPoolingLayer pool0{}; |
| NEPoolingLayer pool1{}; |
| NEActivationLayer act0{}; |
| NEActivationLayer act1{}; |
| NEActivationLayer act2{}; |
| }; |
| |
| /** Main program for cnn test |
| * |
| * The example implements the following CNN architecture: |
| * |
| * Input -> conv0:5x5 -> act0:relu -> pool:2x2 -> conv1:3x3 -> act1:relu -> pool:2x2 -> fc0 -> act2:relu -> softmax |
| * |
| * @param[in] argc Number of arguments |
| * @param[in] argv Arguments |
| */ |
| int main(int argc, char **argv) |
| { |
| return utils::run_example<NEONCNNExample>(argc, argv); |
| } |