| /* |
| * Copyright (c) 2018-2020 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 */ |
| 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); |
| 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; |
| 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<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(); |
| |
| // Create input descriptor |
| const auto operation_layout = common_params.data_layout; |
| const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, 1U), 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, "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, 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 << 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, operation_layout)).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; |
| config.tuner_mode = common_params.tuner_mode; |
| config.tuner_file = common_params.tuner_file; |
| config.convert_to_uint8 = (common_params.data_type == DataType::QASYMM8); |
| |
| 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, common_params.data_layout, 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<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"), |
| 0.000009999999747378752f) |
| .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<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"), |
| 0.000009999999747378752f) |
| .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 << EltwiseLayer(std::move(shortcut), std::move(residual), EltwiseOperation::Add).set_name(unit_name + "add"); |
| } |
| } |
| }; |
| |
| /** Main program for ResNetV2_50 |
| * |
| * Model is based on: |
| * https://arxiv.org/abs/1603.05027 |
| * "Identity Mappings in Deep Residual Networks" |
| * Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun |
| * |
| * Provenance: download.tensorflow.org/models/resnet_v2_50_2017_04_14.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<GraphResNetV2_50Example>(argc, argv); |
| } |
