/* * 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(); // Create lifetime manager auto lifetime_mgr1 = std::make_shared(); // Create lifetime manager auto pool_mgr0 = std::make_shared(); // Create pool manager auto pool_mgr1 = std::make_shared(); // Create pool manager auto mm_layers = std::make_shared(lifetime_mgr0, pool_mgr0); // Create the memory manager auto mm_transitions = std::make_shared(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(mm_layers); conv1 = std::make_unique(mm_layers); fc0 = std::make_unique(mm_layers); softmax = std::make_unique(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(mm_transitions); memory_group1 = std::make_unique(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 memory_group0{}; std::unique_ptr memory_group1{}; // Layers std::unique_ptr conv0{}; std::unique_ptr conv1{}; std::unique_ptr fc0{}; std::unique_ptr 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(argc, argv); }