diff options
Diffstat (limited to 'examples/neon_cnn.cpp')
| -rw-r--r-- | examples/neon_cnn.cpp | 50 |
1 files changed, 29 insertions, 21 deletions
diff --git a/examples/neon_cnn.cpp b/examples/neon_cnn.cpp index ee6f46d28b..1f7a1ea6ca 100644 --- a/examples/neon_cnn.cpp +++ b/examples/neon_cnn.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2016-2020 ARM Limited. + * Copyright (c) 2016-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,13 +21,13 @@ * 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/NEON/NEFunctions.h" #include "arm_compute/runtime/PoolManager.h" + #include "utils/Utils.h" using namespace arm_compute; @@ -43,20 +43,21 @@ public: // 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 + 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 = 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); + 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] */ @@ -116,7 +117,8 @@ public: // 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 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); @@ -138,22 +140,28 @@ public: /* [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 */)); + 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 */))); + 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 */)); + 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 */))); + 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); @@ -170,8 +178,8 @@ public: // 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); + 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(); @@ -257,7 +265,7 @@ private: Tensor out_fc0{}; Tensor out_softmax{}; - // NEON allocator + // Allocator Allocator allocator{}; // Memory groups |