| /* |
| * Copyright (c) 2018-2021 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 ShuffleNet network using the Compute Library's graph API */ |
| class ShuffleNetExample : public Example |
| { |
| public: |
| ShuffleNetExample() : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "ShuffleNet") |
| { |
| } |
| 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; |
| } |
| |
| // Set default layout if needed (Single kernel grouped convolution not yet supported int NHWC) |
| if (!common_opts.data_layout->is_set()) |
| { |
| common_params.data_layout = DataLayout::NHWC; |
| } |
| |
| // Checks |
| ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), |
| "QASYMM8 not supported for this graph"); |
| |
| // Print parameter values |
| std::cout << common_params << std::endl; |
| std::cout << "Model: Shufflenet_1_g4" << std::endl; |
| |
| // Create model path |
| std::string model_path = "/cnn_data/shufflenet_model/"; |
| |
| // Get trainable parameters data path |
| std::string data_path = common_params.data_path; |
| |
| // Add model path to data path |
| if (!data_path.empty()) |
| { |
| data_path += model_path; |
| } |
| |
| // 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; |
| |
| // Create preprocessor |
| std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>(0); |
| |
| 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(3U, 3U, 24U, get_weights_accessor(data_path, "conv3_0_w_0.npy", weights_layout), |
| get_weights_accessor(data_path, "conv3_0_b_0.npy", weights_layout), |
| PadStrideInfo(2, 2, 1, 1)) |
| .set_name("Conv1/convolution") |
| << BatchNormalizationLayer(get_weights_accessor(data_path, "conv3_0_bn_rm_0.npy"), |
| get_weights_accessor(data_path, "conv3_0_bn_riv_0.npy"), |
| get_weights_accessor(data_path, "conv3_0_bn_s_0.npy"), |
| get_weights_accessor(data_path, "conv3_0_bn_b_0.npy"), 1e-5f) |
| .set_name("Conv1/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) |
| .set_name("Conv1/Relu") |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 1, 1))) |
| .set_name("pool1/MaxPool"); |
| |
| // Stage 2 |
| add_residual_block(data_path, DataLayout::NCHW, 0U /* unit */, 112U /* depth */, 2U /* stride */); |
| add_residual_block(data_path, DataLayout::NCHW, 1U /* unit */, 136U /* depth */, 1U /* stride */); |
| add_residual_block(data_path, DataLayout::NCHW, 2U /* unit */, 136U /* depth */, 1U /* stride */); |
| add_residual_block(data_path, DataLayout::NCHW, 3U /* unit */, 136U /* depth */, 1U /* stride */); |
| |
| // Stage 3 |
| add_residual_block(data_path, DataLayout::NCHW, 4U /* unit */, 136U /* depth */, 2U /* stride */); |
| add_residual_block(data_path, DataLayout::NCHW, 5U /* unit */, 272U /* depth */, 1U /* stride */); |
| add_residual_block(data_path, DataLayout::NCHW, 6U /* unit */, 272U /* depth */, 1U /* stride */); |
| add_residual_block(data_path, DataLayout::NCHW, 7U /* unit */, 272U /* depth */, 1U /* stride */); |
| add_residual_block(data_path, DataLayout::NCHW, 8U /* unit */, 272U /* depth */, 1U /* stride */); |
| add_residual_block(data_path, DataLayout::NCHW, 9U /* unit */, 272U /* depth */, 1U /* stride */); |
| add_residual_block(data_path, DataLayout::NCHW, 10U /* unit */, 272U /* depth */, 1U /* stride */); |
| add_residual_block(data_path, DataLayout::NCHW, 11U /* unit */, 272U /* depth */, 1U /* stride */); |
| |
| // Stage 4 |
| add_residual_block(data_path, DataLayout::NCHW, 12U /* unit */, 272U /* depth */, 2U /* stride */); |
| add_residual_block(data_path, DataLayout::NCHW, 13U /* unit */, 544U /* depth */, 1U /* stride */); |
| add_residual_block(data_path, DataLayout::NCHW, 14U /* unit */, 544U /* depth */, 1U /* stride */); |
| add_residual_block(data_path, DataLayout::NCHW, 15U /* unit */, 544U /* depth */, 1U /* stride */); |
| |
| graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("predictions/AvgPool") |
| << FlattenLayer().set_name("predictions/Reshape") |
| << FullyConnectedLayer(1000U, get_weights_accessor(data_path, "pred_w_0.npy", weights_layout), |
| get_weights_accessor(data_path, "pred_b_0.npy")) |
| .set_name("predictions/FC") |
| << 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; |
| |
| 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, |
| DataLayout weights_layout, |
| unsigned int unit, |
| unsigned int depth, |
| unsigned int stride) |
| { |
| PadStrideInfo dwc_info = PadStrideInfo(1, 1, 1, 1); |
| const unsigned int gconv_id = unit * 2; |
| const unsigned int num_groups = 4; |
| const std::string unit_id_name = arm_compute::support::cpp11::to_string(unit); |
| const std::string gconv_id_name = arm_compute::support::cpp11::to_string(gconv_id); |
| const std::string gconv_id_1_name = arm_compute::support::cpp11::to_string(gconv_id + 1); |
| const std::string unit_name = "unit" + unit_id_name; |
| |
| SubStream left_ss(graph); |
| SubStream right_ss(graph); |
| |
| if (stride == 2) |
| { |
| right_ss << PoolingLayer( |
| PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(2, 2, 1, 1))) |
| .set_name(unit_name + "/pool_1/AveragePool"); |
| dwc_info = PadStrideInfo(2, 2, 1, 1); |
| } |
| |
| left_ss |
| << ConvolutionLayer(1U, 1U, depth, |
| get_weights_accessor(data_path, "gconv1_" + gconv_id_name + "_w_0.npy", weights_layout), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| PadStrideInfo(1, 1, 0, 0), num_groups) |
| .set_name(unit_name + "/gconv1_" + gconv_id_name + "/convolution") |
| << BatchNormalizationLayer(get_weights_accessor(data_path, "gconv1_" + gconv_id_name + "_bn_rm_0.npy"), |
| get_weights_accessor(data_path, "gconv1_" + gconv_id_name + "_bn_riv_0.npy"), |
| get_weights_accessor(data_path, "gconv1_" + gconv_id_name + "_bn_s_0.npy"), |
| get_weights_accessor(data_path, "gconv1_" + gconv_id_name + "_bn_b_0.npy"), |
| 1e-5f) |
| .set_name(unit_name + "/gconv1_" + gconv_id_name + "/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) |
| .set_name(unit_name + "/gconv1_" + gconv_id_name + "/Relu") |
| << ChannelShuffleLayer(num_groups).set_name(unit_name + "/shuffle_0/ChannelShufle") |
| << DepthwiseConvolutionLayer( |
| 3U, 3U, get_weights_accessor(data_path, "gconv3_" + unit_id_name + "_w_0.npy", weights_layout), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), dwc_info) |
| .set_name(unit_name + "/gconv3_" + unit_id_name + "/depthwise") |
| << BatchNormalizationLayer(get_weights_accessor(data_path, "gconv3_" + unit_id_name + "_bn_rm_0.npy"), |
| get_weights_accessor(data_path, "gconv3_" + unit_id_name + "_bn_riv_0.npy"), |
| get_weights_accessor(data_path, "gconv3_" + unit_id_name + "_bn_s_0.npy"), |
| get_weights_accessor(data_path, "gconv3_" + unit_id_name + "_bn_b_0.npy"), 1e-5f) |
| .set_name(unit_name + "/gconv3_" + unit_id_name + "/BatchNorm") |
| << ConvolutionLayer( |
| 1U, 1U, depth, |
| get_weights_accessor(data_path, "gconv1_" + gconv_id_1_name + "_w_0.npy", weights_layout), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0), num_groups) |
| .set_name(unit_name + "/gconv1_" + gconv_id_1_name + "/convolution") |
| << BatchNormalizationLayer(get_weights_accessor(data_path, "gconv1_" + gconv_id_1_name + "_bn_rm_0.npy"), |
| get_weights_accessor(data_path, "gconv1_" + gconv_id_1_name + "_bn_riv_0.npy"), |
| get_weights_accessor(data_path, "gconv1_" + gconv_id_1_name + "_bn_s_0.npy"), |
| get_weights_accessor(data_path, "gconv1_" + gconv_id_1_name + "_bn_b_0.npy"), |
| 1e-5f) |
| .set_name(unit_name + "/gconv1_" + gconv_id_1_name + "/BatchNorm"); |
| |
| if (stride == 2) |
| { |
| graph << ConcatLayer(std::move(left_ss), std::move(right_ss)).set_name(unit_name + "/Concat"); |
| } |
| else |
| { |
| graph << EltwiseLayer(std::move(left_ss), std::move(right_ss), EltwiseOperation::Add) |
| .set_name(unit_name + "/Add"); |
| } |
| graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) |
| .set_name(unit_name + "/Relu"); |
| } |
| }; |
| |
| /** Main program for ShuffleNet |
| * |
| * Model is based on: |
| * https://arxiv.org/abs/1707.01083 |
| * "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices" |
| * Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, Jian Sun |
| * |
| * Provenance: https://s3.amazonaws.com/download.onnx/models/opset_9/shufflenet.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<ShuffleNetExample>(argc, argv); |
| } |