From 236bfe7033a313ab98ff436d85f38a58b0738ed1 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Thu, 23 Nov 2017 15:59:55 +0000 Subject: COMPIMID-553: MobileNet use case. Change-Id: I1181abbd5785065f3d57e91844376a4b110938a9 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/110701 Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com Reviewed-by: Anthony Barbier --- examples/graph_mobilenet.cpp | 170 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 170 insertions(+) create mode 100644 examples/graph_mobilenet.cpp (limited to 'examples/graph_mobilenet.cpp') diff --git a/examples/graph_mobilenet.cpp b/examples/graph_mobilenet.cpp new file mode 100644 index 0000000000..2b2da9e517 --- /dev/null +++ b/examples/graph_mobilenet.cpp @@ -0,0 +1,170 @@ +/* + * Copyright (c) 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/graph/Graph.h" +#include "arm_compute/graph/Nodes.h" +#include "support/ToolchainSupport.h" +#include "utils/GraphUtils.h" +#include "utils/Utils.h" + +#include + +using namespace arm_compute::graph; +using namespace arm_compute::graph_utils; + +BranchLayer get_dwsc_node(const std::string &data_path, std::string &¶m_path, + unsigned int conv_filt, + PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info) +{ + std::string total_path = "/cnn_data/mobilenet_v1_model/" + param_path + "_"; + SubGraph sg; + sg << DepthwiseConvolutionLayer( + 3U, 3U, + get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy"), + std::unique_ptr(nullptr), + dwc_pad_stride_info, + true) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"), + get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"), + 0.001f) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) + << ConvolutionLayer( + 1U, 1U, conv_filt, + get_weights_accessor(data_path, total_path + "pointwise_weights.npy"), + std::unique_ptr(nullptr), + conv_pad_stride_info) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_beta.npy"), + get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_gamma.npy"), + 0.001f) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)); + + return BranchLayer(std::move(sg)); +} + +/** Example demonstrating how to implement MobileNet's network using the Compute Library's graph API + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] image, [optional] labels ) + */ +void main_graph_mobilenet(int argc, const char **argv) +{ + std::string data_path; /* Path to the trainable data */ + std::string image; /* Image data */ + std::string label; /* Label data */ + + constexpr float mean_r = 122.68f; /* Mean value to subtract from red channel */ + constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */ + constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */ + + // Parse arguments + if(argc < 2) + { + // Print help + std::cout << "Usage: " << argv[0] << " [path_to_data] [image] [labels]\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 2) + { + data_path = argv[1]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " [image] [labels]\n\n"; + std::cout << "No image provided: using random values\n\n"; + } + else if(argc == 3) + { + data_path = argv[1]; + image = argv[2]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [labels]\n\n"; + std::cout << "No text file with labels provided: skipping output accessor\n\n"; + } + else + { + data_path = argv[1]; + image = argv[2]; + label = argv[3]; + } + + // Check if OpenCL is available and initialize the scheduler + TargetHint hint = TargetHint::NEON; + if(Graph::opencl_is_available()) + { + hint = TargetHint::OPENCL; + } + + Graph graph; + graph << hint + << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32), + get_input_accessor(image, mean_r, mean_g, mean_b)) + << ConvolutionLayer( + 3U, 3U, 32U, + get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_beta.npy"), + get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_gamma.npy"), + 0.001f) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) + << get_dwsc_node(data_path, "Conv2d_1", 64, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)) + << get_dwsc_node(data_path, "Conv2d_2", 128, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)) + << get_dwsc_node(data_path, "Conv2d_3", 128, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)) + << get_dwsc_node(data_path, "Conv2d_4", 256, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)) + << get_dwsc_node(data_path, "Conv2d_5", 256, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)) + << get_dwsc_node(data_path, "Conv2d_6", 512, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)) + << get_dwsc_node(data_path, "Conv2d_7", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)) + << get_dwsc_node(data_path, "Conv2d_8", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)) + << get_dwsc_node(data_path, "Conv2d_9", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)) + << get_dwsc_node(data_path, "Conv2d_10", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)) + << get_dwsc_node(data_path, "Conv2d_11", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)) + << get_dwsc_node(data_path, "Conv2d_12", 1024, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)) + << get_dwsc_node(data_path, "Conv2d_13", 1024, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)) + << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)) + << ConvolutionLayer( + 1U, 1U, 1001U, + get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Logits_Conv2d_1c_1x1_weights.npy"), + get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Logits_Conv2d_1c_1x1_biases.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ReshapeLayer(TensorShape(1001U)) + << SoftmaxLayer() + << Tensor(get_output_accessor(label, 5)); + + graph.run(); +} + +/** Main program for MobileNetV1 + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] image, [optional] labels ) + */ +int main(int argc, const char **argv) +{ + return arm_compute::utils::run_example(argc, argv, main_graph_mobilenet); +} -- cgit v1.2.1