From 6ff3b19ee6120edf015fad8caab2991faa3070af Mon Sep 17 00:00:00 2001 From: Anthony Barbier Date: Mon, 4 Sep 2017 18:44:23 +0100 Subject: COMPMID-344 Updated doxygen Change-Id: I32f7b84daa560e460b77216add529c8fa8b327ae --- examples/neon_cnn.cpp | 230 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 230 insertions(+) create mode 100644 examples/neon_cnn.cpp (limited to 'examples/neon_cnn.cpp') diff --git a/examples/neon_cnn.cpp b/examples/neon_cnn.cpp new file mode 100644 index 0000000000..952ae4d485 --- /dev/null +++ b/examples/neon_cnn.cpp @@ -0,0 +1,230 @@ +/* + * 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 "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); + + // 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; + + NEConvolutionLayer conv0; + NEConvolutionLayer conv1; + NEPoolingLayer pool0; + NEPoolingLayer pool1; + NEFullyConnectedLayer fc0; + NEActivationLayer act0; + NEActivationLayer act1; + NEActivationLayer act2; + NESoftmaxLayer softmax; + + /* [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()); + + // 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)); + + // in:16x16x8: 3x3 convolution, 16 output features maps (OFM) + conv1.configure(&out_pool0, &weights1, &biases1, &out_conv1, PadStrideInfo()); + + // 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)); + + // 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] */ + + /* [Allocate tensors] */ + + // Now that the padding requirements are known we can allocate the images: + src.allocator()->allocate(); + weights0.allocator()->allocate(); + weights1.allocator()->allocate(); + weights2.allocator()->allocate(); + biases0.allocator()->allocate(); + biases1.allocator()->allocate(); + biases2.allocator()->allocate(); + out_conv0.allocator()->allocate(); + out_conv1.allocator()->allocate(); + out_act0.allocator()->allocate(); + out_act1.allocator()->allocate(); + out_act2.allocator()->allocate(); + out_pool0.allocator()->allocate(); + out_pool1.allocator()->allocate(); + out_fc0.allocator()->allocate(); + out_softmax.allocator()->allocate(); + + /* -----------------------End: [Allocate tensors] */ + + /* [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] */ + + conv0.run(); + act0.run(); + pool0.run(); + conv1.run(); + act1.run(); + pool1.run(); + fc0.run(); + act2.run(); + softmax.run(); + + /* -----------------------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); +} \ No newline at end of file -- cgit v1.2.1