292 lines
12 KiB
C++
292 lines
12 KiB
C++
/*
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* Copyright (c) 2016-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "arm_compute/runtime/NEON/NEFunctions.h"
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#include "arm_compute/core/Types.h"
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#include "arm_compute/runtime/Allocator.h"
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#include "arm_compute/runtime/BlobLifetimeManager.h"
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#include "arm_compute/runtime/MemoryManagerOnDemand.h"
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#include "arm_compute/runtime/PoolManager.h"
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#include "utils/Utils.h"
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using namespace arm_compute;
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using namespace utils;
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class NEONCNNExample : public Example
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{
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public:
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bool do_setup(int argc, char **argv) override
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{
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ARM_COMPUTE_UNUSED(argc);
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ARM_COMPUTE_UNUSED(argv);
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// Create memory manager components
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// 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))
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auto lifetime_mgr0 = std::make_shared<BlobLifetimeManager>(); // Create lifetime manager
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auto lifetime_mgr1 = std::make_shared<BlobLifetimeManager>(); // Create lifetime manager
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auto pool_mgr0 = std::make_shared<PoolManager>(); // Create pool manager
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auto pool_mgr1 = std::make_shared<PoolManager>(); // Create pool manager
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auto mm_layers = std::make_shared<MemoryManagerOnDemand>(lifetime_mgr0, pool_mgr0); // Create the memory manager
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auto mm_transitions = std::make_shared<MemoryManagerOnDemand>(lifetime_mgr1, pool_mgr1); // Create the memory manager
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// The weights and biases tensors should be initialized with the values inferred with the training
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// Set memory manager where allowed to manage internal memory requirements
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conv0 = arm_compute::support::cpp14::make_unique<NEConvolutionLayer>(mm_layers);
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conv1 = arm_compute::support::cpp14::make_unique<NEConvolutionLayer>(mm_layers);
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fc0 = arm_compute::support::cpp14::make_unique<NEFullyConnectedLayer>(mm_layers);
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softmax = arm_compute::support::cpp14::make_unique<NESoftmaxLayer>(mm_layers);
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/* [Initialize tensors] */
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// Initialize src tensor
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constexpr unsigned int width_src_image = 32;
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constexpr unsigned int height_src_image = 32;
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constexpr unsigned int ifm_src_img = 1;
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const TensorShape src_shape(width_src_image, height_src_image, ifm_src_img);
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src.allocator()->init(TensorInfo(src_shape, 1, DataType::F32));
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// Initialize tensors of conv0
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constexpr unsigned int kernel_x_conv0 = 5;
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constexpr unsigned int kernel_y_conv0 = 5;
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constexpr unsigned int ofm_conv0 = 8;
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const TensorShape weights_shape_conv0(kernel_x_conv0, kernel_y_conv0, src_shape.z(), ofm_conv0);
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const TensorShape biases_shape_conv0(weights_shape_conv0[3]);
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const TensorShape out_shape_conv0(src_shape.x(), src_shape.y(), weights_shape_conv0[3]);
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weights0.allocator()->init(TensorInfo(weights_shape_conv0, 1, DataType::F32));
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biases0.allocator()->init(TensorInfo(biases_shape_conv0, 1, DataType::F32));
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out_conv0.allocator()->init(TensorInfo(out_shape_conv0, 1, DataType::F32));
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// Initialize tensor of act0
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out_act0.allocator()->init(TensorInfo(out_shape_conv0, 1, DataType::F32));
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// Initialize tensor of pool0
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TensorShape out_shape_pool0 = out_shape_conv0;
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out_shape_pool0.set(0, out_shape_pool0.x() / 2);
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out_shape_pool0.set(1, out_shape_pool0.y() / 2);
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out_pool0.allocator()->init(TensorInfo(out_shape_pool0, 1, DataType::F32));
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// Initialize tensors of conv1
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constexpr unsigned int kernel_x_conv1 = 3;
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constexpr unsigned int kernel_y_conv1 = 3;
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constexpr unsigned int ofm_conv1 = 16;
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const TensorShape weights_shape_conv1(kernel_x_conv1, kernel_y_conv1, out_shape_pool0.z(), ofm_conv1);
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const TensorShape biases_shape_conv1(weights_shape_conv1[3]);
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const TensorShape out_shape_conv1(out_shape_pool0.x(), out_shape_pool0.y(), weights_shape_conv1[3]);
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weights1.allocator()->init(TensorInfo(weights_shape_conv1, 1, DataType::F32));
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biases1.allocator()->init(TensorInfo(biases_shape_conv1, 1, DataType::F32));
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out_conv1.allocator()->init(TensorInfo(out_shape_conv1, 1, DataType::F32));
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// Initialize tensor of act1
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out_act1.allocator()->init(TensorInfo(out_shape_conv1, 1, DataType::F32));
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// Initialize tensor of pool1
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TensorShape out_shape_pool1 = out_shape_conv1;
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out_shape_pool1.set(0, out_shape_pool1.x() / 2);
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out_shape_pool1.set(1, out_shape_pool1.y() / 2);
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out_pool1.allocator()->init(TensorInfo(out_shape_pool1, 1, DataType::F32));
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// Initialize tensor of fc0
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constexpr unsigned int num_labels = 128;
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const TensorShape weights_shape_fc0(out_shape_pool1.x() * out_shape_pool1.y() * out_shape_pool1.z(), num_labels);
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const TensorShape biases_shape_fc0(num_labels);
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const TensorShape out_shape_fc0(num_labels);
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weights2.allocator()->init(TensorInfo(weights_shape_fc0, 1, DataType::F32));
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biases2.allocator()->init(TensorInfo(biases_shape_fc0, 1, DataType::F32));
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out_fc0.allocator()->init(TensorInfo(out_shape_fc0, 1, DataType::F32));
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// Initialize tensor of act2
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out_act2.allocator()->init(TensorInfo(out_shape_fc0, 1, DataType::F32));
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// Initialize tensor of softmax
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const TensorShape out_shape_softmax(out_shape_fc0.x());
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out_softmax.allocator()->init(TensorInfo(out_shape_softmax, 1, DataType::F32));
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constexpr auto data_layout = DataLayout::NCHW;
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/* -----------------------End: [Initialize tensors] */
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/* [Configure functions] */
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// in:32x32x1: 5x5 convolution, 8 output features maps (OFM)
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conv0->configure(&src, &weights0, &biases0, &out_conv0, PadStrideInfo(1 /* stride_x */, 1 /* stride_y */, 2 /* pad_x */, 2 /* pad_y */));
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// in:32x32x8, out:32x32x8, Activation function: relu
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act0.configure(&out_conv0, &out_act0, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
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// in:32x32x8, out:16x16x8 (2x2 pooling), Pool type function: Max
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pool0.configure(&out_act0, &out_pool0, PoolingLayerInfo(PoolingType::MAX, 2, data_layout, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */)));
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// in:16x16x8: 3x3 convolution, 16 output features maps (OFM)
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conv1->configure(&out_pool0, &weights1, &biases1, &out_conv1, PadStrideInfo(1 /* stride_x */, 1 /* stride_y */, 1 /* pad_x */, 1 /* pad_y */));
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// in:16x16x16, out:16x16x16, Activation function: relu
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act1.configure(&out_conv1, &out_act1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
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// in:16x16x16, out:8x8x16 (2x2 pooling), Pool type function: Average
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pool1.configure(&out_act1, &out_pool1, PoolingLayerInfo(PoolingType::AVG, 2, data_layout, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */)));
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// in:8x8x16, out:128
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fc0->configure(&out_pool1, &weights2, &biases2, &out_fc0);
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// in:128, out:128, Activation function: relu
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act2.configure(&out_fc0, &out_act2, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
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// in:128, out:128
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softmax->configure(&out_act2, &out_softmax);
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/* -----------------------End: [Configure functions] */
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/*[ Add tensors to memory manager ]*/
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// We need 2 memory groups for handling the input and output
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// We call explicitly allocate after manage() in order to avoid overlapping lifetimes
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memory_group0 = arm_compute::support::cpp14::make_unique<MemoryGroup>(mm_transitions);
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memory_group1 = arm_compute::support::cpp14::make_unique<MemoryGroup>(mm_transitions);
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memory_group0->manage(&out_conv0);
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out_conv0.allocator()->allocate();
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memory_group1->manage(&out_act0);
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out_act0.allocator()->allocate();
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memory_group0->manage(&out_pool0);
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out_pool0.allocator()->allocate();
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memory_group1->manage(&out_conv1);
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out_conv1.allocator()->allocate();
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memory_group0->manage(&out_act1);
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out_act1.allocator()->allocate();
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memory_group1->manage(&out_pool1);
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out_pool1.allocator()->allocate();
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memory_group0->manage(&out_fc0);
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out_fc0.allocator()->allocate();
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memory_group1->manage(&out_act2);
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out_act2.allocator()->allocate();
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memory_group0->manage(&out_softmax);
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out_softmax.allocator()->allocate();
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/* -----------------------End: [ Add tensors to memory manager ] */
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/* [Allocate tensors] */
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// Now that the padding requirements are known we can allocate all tensors
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src.allocator()->allocate();
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weights0.allocator()->allocate();
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weights1.allocator()->allocate();
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weights2.allocator()->allocate();
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biases0.allocator()->allocate();
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biases1.allocator()->allocate();
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biases2.allocator()->allocate();
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/* -----------------------End: [Allocate tensors] */
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// Populate the layers manager. (Validity checks, memory allocations etc)
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mm_layers->populate(allocator, 1 /* num_pools */);
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// Populate the transitions manager. (Validity checks, memory allocations etc)
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mm_transitions->populate(allocator, 2 /* num_pools */);
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return true;
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}
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void do_run() override
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{
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// Acquire memory for the memory groups
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memory_group0->acquire();
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memory_group1->acquire();
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conv0->run();
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act0.run();
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pool0.run();
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conv1->run();
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act1.run();
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pool1.run();
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fc0->run();
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act2.run();
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softmax->run();
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// Release memory
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memory_group0->release();
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memory_group1->release();
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}
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private:
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// The src tensor should contain the input image
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Tensor src{};
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// Intermediate tensors used
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Tensor weights0{};
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Tensor weights1{};
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Tensor weights2{};
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Tensor biases0{};
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Tensor biases1{};
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Tensor biases2{};
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Tensor out_conv0{};
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Tensor out_conv1{};
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Tensor out_act0{};
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Tensor out_act1{};
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Tensor out_act2{};
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Tensor out_pool0{};
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Tensor out_pool1{};
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Tensor out_fc0{};
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Tensor out_softmax{};
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// NEON allocator
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Allocator allocator{};
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// Memory groups
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std::unique_ptr<MemoryGroup> memory_group0{};
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std::unique_ptr<MemoryGroup> memory_group1{};
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// Layers
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std::unique_ptr<NEConvolutionLayer> conv0{};
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std::unique_ptr<NEConvolutionLayer> conv1{};
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std::unique_ptr<NEFullyConnectedLayer> fc0{};
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std::unique_ptr<NESoftmaxLayer> softmax{};
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NEPoolingLayer pool0{};
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NEPoolingLayer pool1{};
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NEActivationLayer act0{};
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NEActivationLayer act1{};
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NEActivationLayer act2{};
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};
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/** Main program for cnn test
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*
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* The example implements the following CNN architecture:
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*
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* Input -> conv0:5x5 -> act0:relu -> pool:2x2 -> conv1:3x3 -> act1:relu -> pool:2x2 -> fc0 -> act2:relu -> softmax
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*
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* @param[in] argc Number of arguments
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* @param[in] argv Arguments
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*/
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int main(int argc, char **argv)
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{
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return utils::run_example<NEONCNNExample>(argc, argv);
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}
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