From 7b2f02685f38f0a25af77635757e33c5f63b0cde Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Tue, 14 Aug 2018 16:40:18 +0100 Subject: COMPMID-145 : Create ResNet v2 graph example Change-Id: I6ff3d227321d8c3914f90ba4fc496b2fc122845c Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/144070 Tested-by: Jenkins Reviewed-by: Pablo Tello --- examples/graph_mobilenet_v2.cpp | 4 +- examples/graph_resnet50.cpp | 12 +- examples/graph_resnet_v2_50.cpp | 245 ++++++++++++++++++++++++++++++++++++++++ 3 files changed, 253 insertions(+), 8 deletions(-) create mode 100644 examples/graph_resnet_v2_50.cpp (limited to 'examples') diff --git a/examples/graph_mobilenet_v2.cpp b/examples/graph_mobilenet_v2.cpp index 1e50947c67..63dcba6950 100644 --- a/examples/graph_mobilenet_v2.cpp +++ b/examples/graph_mobilenet_v2.cpp @@ -64,8 +64,8 @@ public: // Print parameter values std::cout << common_params << std::endl; - // Create core of graph - std::string model_path = "/mobilenet_v2_1.0_224_model/"; + // Create model path + std::string model_path = "/cnn_data/mobilenet_v2_1.0_224_model/"; // Create input descriptor const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, common_params.data_layout); diff --git a/examples/graph_resnet50.cpp b/examples/graph_resnet50.cpp index 0ad719a2ca..4221d98048 100644 --- a/examples/graph_resnet50.cpp +++ b/examples/graph_resnet50.cpp @@ -31,16 +31,16 @@ using namespace arm_compute::utils; using namespace arm_compute::graph::frontend; using namespace arm_compute::graph_utils; -/** Example demonstrating how to implement ResNet50 network using the Compute Library's graph API +/** Example demonstrating how to implement ResNetV1_50 network using the Compute Library's graph API * * @param[in] argc Number of arguments * @param[in] argv Arguments */ -class GraphResNet50Example : public Example +class GraphResNetV1_50Example : public Example { public: - GraphResNet50Example() - : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "ResNet50") + GraphResNetV1_50Example() + : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "ResNetV1_50") { } bool do_setup(int argc, char **argv) override @@ -237,7 +237,7 @@ private: } }; -/** Main program for ResNet50 +/** Main program for ResNetV1_50 * * @note To list all the possible arguments execute the binary appended with the --help option * @@ -246,5 +246,5 @@ private: */ int main(int argc, char **argv) { - return arm_compute::utils::run_example(argc, argv); + return arm_compute::utils::run_example(argc, argv); } diff --git a/examples/graph_resnet_v2_50.cpp b/examples/graph_resnet_v2_50.cpp new file mode 100644 index 0000000000..3db08c44d3 --- /dev/null +++ b/examples/graph_resnet_v2_50.cpp @@ -0,0 +1,245 @@ +/* + * Copyright (c) 2018 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.h" +#include "support/ToolchainSupport.h" +#include "utils/CommonGraphOptions.h" +#include "utils/GraphUtils.h" +#include "utils/Utils.h" + +using namespace arm_compute::utils; +using namespace arm_compute::graph::frontend; +using namespace arm_compute::graph_utils; + +/** Example demonstrating how to implement ResNetV2_50 network using the Compute Library's graph API + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments + */ +class GraphResNetV2_50Example : public Example +{ +public: + GraphResNetV2_50Example() + : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "ResNetV2_50") + { + } + bool do_setup(int argc, char **argv) override + { + // Parse arguments + cmd_parser.parse(argc, argv); + + // Consume common parameters + common_params = consume_common_graph_parameters(common_opts); + + // Return when help menu is requested + if(common_params.help) + { + cmd_parser.print_help(argv[0]); + return false; + } + + // Checks + ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!"); + + // Print parameter values + std::cout << common_params << std::endl; + + // Get trainable parameters data path + std::string data_path = common_params.data_path; + std::string model_path = "/cnn_data/resnet_v2_50_model/"; + if(!data_path.empty()) + { + data_path += model_path; + } + + // Create a preprocessor object + std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(); + + // Create input descriptor + const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, common_params.data_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout); + + // Set weights trained layout + const DataLayout weights_layout = DataLayout::NCHW; + + graph << common_params.target + << common_params.fast_math_hint + << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false /* Do not convert to BGR */)) + << ConvolutionLayer( + 7U, 7U, 64U, + get_weights_accessor(data_path, "conv1_weights.npy", weights_layout), + get_weights_accessor(data_path, "conv1_biases.npy", weights_layout), + PadStrideInfo(2, 2, 3, 3)) + .set_name("conv1/convolution") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool1/MaxPool"); + + add_residual_block(data_path, "block1", weights_layout, 64, 3, 2); + add_residual_block(data_path, "block2", weights_layout, 128, 4, 2); + add_residual_block(data_path, "block3", weights_layout, 256, 6, 2); + add_residual_block(data_path, "block4", weights_layout, 512, 3, 1); + + graph << BatchNormalizationLayer( + get_weights_accessor(data_path, "postnorm_moving_mean.npy"), + get_weights_accessor(data_path, "postnorm_moving_variance.npy"), + get_weights_accessor(data_path, "postnorm_gamma.npy"), + get_weights_accessor(data_path, "postnorm_beta.npy"), + 0.000009999999747378752f) + .set_name("postnorm/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("postnorm/Relu") + << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool5") + << ConvolutionLayer( + 1U, 1U, 1001U, + get_weights_accessor(data_path, "logits_weights.npy", weights_layout), + get_weights_accessor(data_path, "logits_biases.npy"), + PadStrideInfo(1, 1, 0, 0)) + .set_name("logits/convolution") + << FlattenLayer().set_name("predictions/Reshape") + << SoftmaxLayer().set_name("predictions/Softmax") + << OutputLayer(get_output_accessor(common_params, 5)); + + // Finalize graph + GraphConfig config; + config.num_threads = common_params.threads; + config.use_tuner = common_params.enable_tuner; + graph.finalize(common_params.target, config); + + return true; + } + + void do_run() override + { + // Run graph + graph.run(); + } + +private: + CommandLineParser cmd_parser; + CommonGraphOptions common_opts; + CommonGraphParams common_params; + Stream graph; + + void add_residual_block(const std::string &data_path, const std::string &name, DataLayout weights_layout, + unsigned int base_depth, unsigned int num_units, unsigned int stride) + { + for(unsigned int i = 0; i < num_units; ++i) + { + // Generate unit names + std::stringstream unit_path_ss; + unit_path_ss << name << "_unit_" << (i + 1) << "_bottleneck_v2_"; + std::stringstream unit_name_ss; + unit_name_ss << name << "/unit" << (i + 1) << "/bottleneck_v2/"; + + std::string unit_path = unit_path_ss.str(); + std::string unit_name = unit_name_ss.str(); + + const TensorShape last_shape = graph.graph().node(graph.tail_node())->output(0)->desc().shape; + unsigned int depth_in = last_shape[arm_compute::get_data_layout_dimension_index(common_params.data_layout, DataLayoutDimension::CHANNEL)]; + unsigned int depth_out = base_depth * 4; + + // All units have stride 1 apart from last one + unsigned int middle_stride = (i == (num_units - 1)) ? stride : 1; + + // Preact + SubStream preact(graph); + preact << BatchNormalizationLayer( + get_weights_accessor(data_path, unit_path + "preact_moving_mean.npy"), + get_weights_accessor(data_path, unit_path + "preact_moving_variance.npy"), + get_weights_accessor(data_path, unit_path + "preact_gamma.npy"), + get_weights_accessor(data_path, unit_path + "preact_beta.npy"), + 0.000009999999747378752f) + .set_name(unit_name + "preact/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "preact/Relu"); + + // Create bottleneck path + SubStream shortcut(graph); + if(depth_in == depth_out) + { + if(middle_stride != 1) + { + shortcut << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, PadStrideInfo(middle_stride, middle_stride, 0, 0), true)).set_name(unit_name + "shortcut/MaxPool"); + } + } + else + { + shortcut.forward_tail(preact.tail_node()); + shortcut << ConvolutionLayer( + 1U, 1U, depth_out, + get_weights_accessor(data_path, unit_path + "shortcut_weights.npy", weights_layout), + get_weights_accessor(data_path, unit_path + "shortcut_biases.npy", weights_layout), + PadStrideInfo(1, 1, 0, 0)) + .set_name(unit_name + "shortcut/convolution"); + } + + // Create residual path + SubStream residual(preact); + residual << ConvolutionLayer( + 1U, 1U, base_depth, + get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(unit_name + "conv1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_beta.npy"), + 0.000009999999747378752f) + .set_name(unit_name + "conv1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu") + << ConvolutionLayer( + 3U, 3U, base_depth, + get_weights_accessor(data_path, unit_path + "conv2_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(middle_stride, middle_stride, 1, 1)) + .set_name(unit_name + "conv2/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_beta.npy"), + 0.000009999999747378752f) + .set_name(unit_name + "conv2/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu") + << ConvolutionLayer( + 1U, 1U, depth_out, + get_weights_accessor(data_path, unit_path + "conv3_weights.npy", weights_layout), + get_weights_accessor(data_path, unit_path + "conv3_biases.npy", weights_layout), + PadStrideInfo(1, 1, 0, 0)) + .set_name(unit_name + "conv3/convolution"); + + graph << BranchLayer(BranchMergeMethod::ADD, std::move(shortcut), std::move(residual)).set_name(unit_name + "add"); + } + } +}; + +/** Main program for ResNetV2_50 + * + * @note To list all the possible arguments execute the binary appended with the --help option + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments + */ +int main(int argc, char **argv) +{ + return arm_compute::utils::run_example(argc, argv); +} -- cgit v1.2.1