COMPMID-782 Port examples to the new format

Change-Id: Ib178a97c080ff650094d02ee49e2a0aa22376dd0
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/115717
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>

1 files changed, 211 insertions, 204 deletions

diff --git a/examples/neon_cnn.cpp b/examples/neon_cnn.cpp
index 2be5acfbaf..05b6c832bc 100644
--- a/examples/neon_cnn.cpp
+++ b/examples/neon_cnn.cpp
@@ -33,254 +33,261 @@
 using namespace arm_compute;
 using namespace utils;
 
-void main_cnn(int argc, char **argv)
+class NEONCNNExample : public Example
 {
-    ARM_COMPUTE_UNUSED(argc);
-    ARM_COMPUTE_UNUSED(argv);
+public:
+    void do_setup(int argc, char **argv) override
+    {
+        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
 
-    // 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
 
-    // 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);
+        // Set memory manager where allowed to manage internal memory requirements
+        conv0   = arm_compute::support::cpp14::make_unique<NEConvolutionLayer>(mm_layers);
+        conv1   = arm_compute::support::cpp14::make_unique<NEConvolutionLayer>(mm_layers);
+        fc0     = arm_compute::support::cpp14::make_unique<NEFullyConnectedLayer>(mm_layers);
+        softmax = arm_compute::support::cpp14::make_unique<NESoftmaxLayer>(mm_layers);
 
-    /* [Initialize tensors] */
+        /* [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;
+        // 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));
+        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;
+        // 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]);
+        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));
+        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));
 
-    /* -----------------------End: [Initialize tensors] */
+        // Initialize tensor of act0
+        out_act0.allocator()->init(TensorInfo(out_shape_conv0, 1, DataType::F32));
 
-    /* [Configure functions] */
+        // 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));
 
-    // 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 */));
+        // Initialize tensors of conv1
+        constexpr unsigned int kernel_x_conv1 = 3;
+        constexpr unsigned int kernel_y_conv1 = 3;
+        constexpr unsigned int ofm_conv1      = 16;
 
-    // in:32x32x8, out:32x32x8, Activation function: relu
-    act0.configure(&out_conv0, &out_act0, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        const TensorShape weights_shape_conv1(kernel_x_conv1, kernel_y_conv1, out_shape_pool0.z(), ofm_conv1);
 
-    // 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 */)));
+        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));
 
-    // 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 */));
+        // 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));
 
-    // in:16x16x16, out:16x16x16, Activation function: relu
-    act1.configure(&out_conv1, &out_act1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        // Initialize tensor of fc0
+        constexpr unsigned int num_labels = 128;
 
-    // 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 */)));
+        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);
 
-    // in:8x8x16, out:128
-    fc0.configure(&out_pool1, &weights2, &biases2, &out_fc0);
+        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));
 
-    // in:128, out:128, Activation function: relu
-    act2.configure(&out_fc0, &out_act2, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        // Initialize tensor of act2
+        out_act2.allocator()->init(TensorInfo(out_shape_fc0, 1, DataType::F32));
 
-    // in:128, out:128
-    softmax.configure(&out_act2, &out_softmax);
+        // 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: [Configure functions] */
+        /* -----------------------End: [Initialize tensors] */
 
-    /*[ Add tensors to memory manager ]*/
+        /* [Configure functions] */
 
-    // 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);
+        // 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 */));
 
-    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();
+        // in:32x32x8, out:32x32x8, Activation function: relu
+        act0.configure(&out_conv0, &out_act0, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
-    /* -----------------------End: [ Add tensors to memory manager ] */
+        // 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 */)));
 
-    /* [Allocate tensors] */
+        // 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 */));
 
-    // 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();
+        // in:16x16x16, out:16x16x16, Activation function: relu
+        act1.configure(&out_conv1, &out_act1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
-    /* -----------------------End: [Allocate tensors] */
+        // 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 */)));
 
-    // Finalize layers memory manager
+        // in:8x8x16, out:128
+        fc0->configure(&out_pool1, &weights2, &biases2, &out_fc0);
 
-    // Set allocator that the memory manager will use
-    mm_layers->set_allocator(&allocator);
+        // in:128, out:128, Activation function: relu
+        act2.configure(&out_fc0, &out_act2, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
-    // 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);
+        // in:128, out:128
+        softmax->configure(&out_act2, &out_softmax);
 
-    // Finalize the manager. (Validity checks, memory allocations etc)
-    mm_layers->finalize();
+        /* -----------------------End: [Configure functions] */
 
-    // Finalize transitions memory manager
+        /*[ Add tensors to memory manager ]*/
 
-    // Set allocator that the memory manager will use
-    mm_transitions->set_allocator(&allocator);
+        // 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 = arm_compute::support::cpp14::make_unique<MemoryGroup>(mm_transitions);
+        memory_group1 = arm_compute::support::cpp14::make_unique<MemoryGroup>(mm_transitions);
 
-    // 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);
+        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();
 
-    // Finalize the manager. (Validity checks, memory allocations etc)
-    mm_transitions->finalize();
+        /* -----------------------End: [ Add tensors to memory manager ] */
 
-    /* [Initialize weights and biases tensors] */
+        /* [Allocate tensors] */
 
-    // Once the tensors have been allocated, the src, weights and biases tensors can be initialized
-    // ...
+        // 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();
 
-    /* -----------------------[Initialize weights and biases tensors] */
+        /* -----------------------End: [Allocate tensors] */
 
-    /* [Execute the functions] */
+        // Finalize layers memory manager
 
-    // Acquire memory for the memory groups
-    memory_group0.acquire();
-    memory_group1.acquire();
+        // Set allocator that the memory manager will use
+        mm_layers->set_allocator(&allocator);
 
-    conv0.run();
-    act0.run();
-    pool0.run();
-    conv1.run();
-    act1.run();
-    pool1.run();
-    fc0.run();
-    act2.run();
-    softmax.run();
+        // 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);
 
-    // Release memory
-    memory_group0.release();
-    memory_group1.release();
+        // Finalize the manager. (Validity checks, memory allocations etc)
+        mm_layers->finalize();
 
-    /* -----------------------End: [Execute the functions] */
-}
+        // 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();
+    }
+    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{};
+
+    // NEON 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
  *
@@ -293,5 +300,5 @@ void main_cnn(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return utils::run_example(argc, argv, main_cnn);
+    return utils::run_example<NEONCNNExample>(argc, argv);
 }