examples/neon_cnn.cpp - ml/ComputeLibrary - Gitiles

 /*
  * 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/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/NEON/NEFunctions.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);
 }
	/*
	* 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/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/NEON/NEFunctions.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);
	}