| /* | |
| * Copyright (c) 2017-2021 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 ResNetV1_50 network using the Compute Library's graph API */ | |
| class GraphResNetV1_50Example : public Example | |
| { | |
| public: | |
| GraphResNetV1_50Example() | |
| : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "ResNetV1_50") | |
| { | |
| } | |
| bool do_setup(int argc, char **argv) override | |
| { | |
| // Parse arguments | |
| cmd_parser.parse(argc, argv); | |
| cmd_parser.validate(); | |
| // 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; | |
| } | |
| // Print parameter values | |
| std::cout << common_params << std::endl; | |
| // Get trainable parameters data path | |
| std::string data_path = common_params.data_path; | |
| // Create a preprocessor object | |
| const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } }; | |
| std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<CaffePreproccessor>(mean_rgb, | |
| false /* Do not convert to BGR */); | |
| // Create input descriptor | |
| const auto operation_layout = common_params.data_layout; | |
| const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, common_params.batches), DataLayout::NCHW, operation_layout); | |
| TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_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, "/cnn_data/resnet50_model/conv1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(2, 2, 3, 3)) | |
| .set_name("conv1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_variance.npy"), | |
| get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_gamma.npy"), | |
| get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_beta.npy"), | |
| 0.0000100099996416f) | |
| .set_name("conv1/BatchNorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1/Relu") | |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, 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 << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("pool5") | |
| << ConvolutionLayer( | |
| 1U, 1U, 1000U, | |
| get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_weights.npy", weights_layout), | |
| get_weights_accessor(data_path, "/cnn_data/resnet50_model/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; | |
| config.tuner_mode = common_params.tuner_mode; | |
| config.tuner_file = common_params.tuner_file; | |
| config.mlgo_file = common_params.mlgo_file; | |
| config.use_synthetic_type = arm_compute::is_data_type_quantized(common_params.data_type); | |
| config.synthetic_type = common_params.data_type; | |
| 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) | |
| { | |
| std::stringstream unit_path_ss; | |
| unit_path_ss << "/cnn_data/resnet50_model/" << name << "_unit_" << (i + 1) << "_bottleneck_v1_"; | |
| std::stringstream unit_name_ss; | |
| unit_name_ss << name << "/unit" << (i + 1) << "/bottleneck_v1/"; | |
| std::string unit_path = unit_path_ss.str(); | |
| std::string unit_name = unit_name_ss.str(); | |
| unsigned int middle_stride = 1; | |
| if(i == (num_units - 1)) | |
| { | |
| middle_stride = stride; | |
| } | |
| SubStream right(graph); | |
| right << ConvolutionLayer( | |
| 1U, 1U, base_depth, | |
| get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(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"), | |
| 0.0000100099996416f) | |
| .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<arm_compute::graph::ITensorAccessor>(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"), | |
| 0.0000100099996416f) | |
| .set_name(unit_name + "conv2/BatchNorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu") | |
| << ConvolutionLayer( | |
| 1U, 1U, base_depth * 4, | |
| get_weights_accessor(data_path, unit_path + "conv3_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 0)) | |
| .set_name(unit_name + "conv3/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, unit_path + "conv3_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, unit_path + "conv3_BatchNorm_moving_variance.npy"), | |
| get_weights_accessor(data_path, unit_path + "conv3_BatchNorm_gamma.npy"), | |
| get_weights_accessor(data_path, unit_path + "conv3_BatchNorm_beta.npy"), | |
| 0.0000100099996416f) | |
| .set_name(unit_name + "conv2/BatchNorm"); | |
| if(i == 0) | |
| { | |
| SubStream left(graph); | |
| left << ConvolutionLayer( | |
| 1U, 1U, base_depth * 4, | |
| get_weights_accessor(data_path, unit_path + "shortcut_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 0)) | |
| .set_name(unit_name + "shortcut/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, unit_path + "shortcut_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, unit_path + "shortcut_BatchNorm_moving_variance.npy"), | |
| get_weights_accessor(data_path, unit_path + "shortcut_BatchNorm_gamma.npy"), | |
| get_weights_accessor(data_path, unit_path + "shortcut_BatchNorm_beta.npy"), | |
| 0.0000100099996416f) | |
| .set_name(unit_name + "shortcut/BatchNorm"); | |
| graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(unit_name + "add"); | |
| } | |
| else if(middle_stride > 1) | |
| { | |
| SubStream left(graph); | |
| left << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, common_params.data_layout, PadStrideInfo(middle_stride, middle_stride, 0, 0), true)).set_name(unit_name + "shortcut/MaxPool"); | |
| graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(unit_name + "add"); | |
| } | |
| else | |
| { | |
| SubStream left(graph); | |
| graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(unit_name + "add"); | |
| } | |
| graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu"); | |
| } | |
| } | |
| }; | |
| /** Main program for ResNetV1_50 | |
| * | |
| * Model is based on: | |
| * https://arxiv.org/abs/1512.03385 | |
| * "Deep Residual Learning for Image Recognition" | |
| * Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun | |
| * | |
| * Provenance: download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz | |
| * | |
| * @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<GraphResNetV1_50Example>(argc, argv); | |
| } |