examples/neon_cnn.cpp - platform/external/ComputeLibrary - Git at Google

 /*
  * Copyright (c) 2016, 2017 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;

 void main_cnn(int argc, const char **argv)
 {
     ARM_COMPUTE_UNUSED(argc);
     ARM_COMPUTE_UNUSED(argv);

     // Create NEON allocator
     Allocator allocator;

     // 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 src tensor should contain the input image
     Tensor src;

     // The weights and biases tensors should be initialized with the values inferred with the training
     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;

     // Create layers and set memory manager where allowed to manage internal memory requirements
     NEConvolutionLayer    conv0(mm_layers);
     NEConvolutionLayer    conv1(mm_layers);
     NEPoolingLayer        pool0;
     NEPoolingLayer        pool1;
     NEFullyConnectedLayer fc0(mm_layers);
     NEActivationLayer     act0;
     NEActivationLayer     act1;
     NEActivationLayer     act2;
     NESoftmaxLayer        softmax(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));

     /* -----------------------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, 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, 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
     MemoryGroup memory_group0(mm_transitions);
     MemoryGroup memory_group1(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] */

     // Finalize layers memory manager

     // Set allocator that the memory manager will use
     mm_layers->set_allocator(&allocator);

     // Number of pools that the manager will create. This specifies how many layers you want to run in parallel
     mm_layers->set_num_pools(1);

     // Finalize the manager. (Validity checks, memory allocations etc)
     mm_layers->finalize();

     // Finalize transitions memory manager

     // Set allocator that the memory manager will use
     mm_transitions->set_allocator(&allocator);

     // Number of pools that the manager will create. This specifies how many models we can run in parallel.
     // Setting to 2 as we need one for the input and one for the output at any given time
     mm_transitions->set_num_pools(2);

     // Finalize the manager. (Validity checks, memory allocations etc)
     mm_transitions->finalize();

     /* [Initialize weights and biases tensors] */

     // Once the tensors have been allocated, the src, weights and biases tensors can be initialized
     // ...

     /* -----------------------[Initialize weights and biases tensors] */

     /* [Execute the functions] */

     // 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();

     /* -----------------------End: [Execute the functions] */
 }

 /** 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, const char **argv)
 {
     return utils::run_example(argc, argv, main_cnn);
 }
	/*
	* Copyright (c) 2016, 2017 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;

	void main_cnn(int argc, const char **argv)
	{
	ARM_COMPUTE_UNUSED(argc);
	ARM_COMPUTE_UNUSED(argv);

	// Create NEON allocator
	Allocator allocator;

	// 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 src tensor should contain the input image
	Tensor src;

	// The weights and biases tensors should be initialized with the values inferred with the training
	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;

	// Create layers and set memory manager where allowed to manage internal memory requirements
	NEConvolutionLayer conv0(mm_layers);
	NEConvolutionLayer conv1(mm_layers);
	NEPoolingLayer pool0;
	NEPoolingLayer pool1;
	NEFullyConnectedLayer fc0(mm_layers);
	NEActivationLayer act0;
	NEActivationLayer act1;
	NEActivationLayer act2;
	NESoftmaxLayer softmax(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));

	/* -----------------------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, 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, 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
	MemoryGroup memory_group0(mm_transitions);
	MemoryGroup memory_group1(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] */

	// Finalize layers memory manager

	// Set allocator that the memory manager will use
	mm_layers->set_allocator(&allocator);

	// Number of pools that the manager will create. This specifies how many layers you want to run in parallel
	mm_layers->set_num_pools(1);

	// Finalize the manager. (Validity checks, memory allocations etc)
	mm_layers->finalize();

	// Finalize transitions memory manager

	// Set allocator that the memory manager will use
	mm_transitions->set_allocator(&allocator);

	// Number of pools that the manager will create. This specifies how many models we can run in parallel.
	// Setting to 2 as we need one for the input and one for the output at any given time
	mm_transitions->set_num_pools(2);

	// Finalize the manager. (Validity checks, memory allocations etc)
	mm_transitions->finalize();

	/* [Initialize weights and biases tensors] */

	// Once the tensors have been allocated, the src, weights and biases tensors can be initialized
	// ...

	/* -----------------------[Initialize weights and biases tensors] */

	/* [Execute the functions] */

	// 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();

	/* -----------------------End: [Execute the functions] */
	}

	/** 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, const char **argv)
	{
	return utils::run_example(argc, argv, main_cnn);
	}