| /* |
| * 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; |
| using namespace arm_compute::utils; |
| using namespace arm_compute::graph::frontend; |
| using namespace arm_compute::graph_utils; |
| |
| /** Example demonstrating how to implement MobileNetV2's network using the Compute Library's graph API */ |
| class GraphMobilenetV2Example : public Example |
| { |
| public: |
| GraphMobilenetV2Example() |
| : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "MobileNetV2") |
| { |
| } |
| GraphMobilenetV2Example(const GraphMobilenetV2Example &) = delete; |
| GraphMobilenetV2Example &operator=(const GraphMobilenetV2Example &) = delete; |
| GraphMobilenetV2Example(GraphMobilenetV2Example &&) = default; // NOLINT |
| GraphMobilenetV2Example &operator=(GraphMobilenetV2Example &&) = default; // NOLINT |
| ~GraphMobilenetV2Example() override = default; |
| |
| 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; |
| } |
| |
| // Print parameter values |
| std::cout << common_params << std::endl; |
| |
| // 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); |
| TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout); |
| |
| // Create a preprocessor object |
| std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(); |
| |
| // 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 graph |
| graph << common_params.target |
| << DepthwiseConvolutionMethod::Optimized3x3 // FIXME(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method |
| << common_params.fast_math_hint |
| << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false)) |
| << ConvolutionLayer(3U, 3U, 32U, |
| get_weights_accessor(data_path, "Conv_weights.npy", DataLayout::NCHW), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL)) |
| .set_name("Conv") |
| << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, "Conv_BatchNorm_moving_variance.npy"), |
| get_weights_accessor(data_path, "Conv_BatchNorm_gamma.npy"), |
| get_weights_accessor(data_path, "Conv_BatchNorm_beta.npy"), |
| 0.0010000000474974513f) |
| .set_name("Conv/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) |
| .set_name("Conv/Relu6"); |
| |
| get_expanded_conv(data_path, "expanded_conv", 32U, 16U, PadStrideInfo(1, 1, 1, 1)); |
| get_expanded_conv(data_path, "expanded_conv_1", 16U, 24U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true); |
| get_expanded_conv(data_path, "expanded_conv_2", 24U, 24U, PadStrideInfo(1, 1, 1, 1), true, true); |
| get_expanded_conv(data_path, "expanded_conv_3", 24U, 32U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true); |
| get_expanded_conv(data_path, "expanded_conv_4", 32U, 32U, PadStrideInfo(1, 1, 1, 1), true, true); |
| get_expanded_conv(data_path, "expanded_conv_5", 32U, 32U, PadStrideInfo(1, 1, 1, 1), true, true); |
| get_expanded_conv(data_path, "expanded_conv_6", 32U, 64U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true); |
| get_expanded_conv(data_path, "expanded_conv_7", 64U, 64U, PadStrideInfo(1, 1, 1, 1), true, true); |
| get_expanded_conv(data_path, "expanded_conv_8", 64U, 64U, PadStrideInfo(1, 1, 1, 1), true, true); |
| get_expanded_conv(data_path, "expanded_conv_9", 64U, 64U, PadStrideInfo(1, 1, 1, 1), true, true); |
| get_expanded_conv(data_path, "expanded_conv_10", 64U, 96U, PadStrideInfo(1, 1, 1, 1), true); |
| get_expanded_conv(data_path, "expanded_conv_11", 96U, 96U, PadStrideInfo(1, 1, 1, 1), true, true); |
| get_expanded_conv(data_path, "expanded_conv_12", 96U, 96U, PadStrideInfo(1, 1, 1, 1), true, true); |
| get_expanded_conv(data_path, "expanded_conv_13", 96U, 160U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true); |
| get_expanded_conv(data_path, "expanded_conv_14", 160U, 160U, PadStrideInfo(1, 1, 1, 1), true, true); |
| get_expanded_conv(data_path, "expanded_conv_15", 160U, 160U, PadStrideInfo(1, 1, 1, 1), true, true); |
| get_expanded_conv(data_path, "expanded_conv_16", 160U, 320U, PadStrideInfo(1, 1, 1, 1), true); |
| |
| graph << ConvolutionLayer(1U, 1U, 1280U, |
| get_weights_accessor(data_path, "Conv_1_weights.npy", DataLayout::NCHW), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| PadStrideInfo(1, 1, 0, 0)) |
| .set_name("Conv_1") |
| << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv_1_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, "Conv_1_BatchNorm_moving_variance.npy"), |
| get_weights_accessor(data_path, "Conv_1_BatchNorm_gamma.npy"), |
| get_weights_accessor(data_path, "Conv_1_BatchNorm_beta.npy"), |
| 0.0010000000474974513f) |
| .set_name("Conv_1/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) |
| .set_name("Conv_1/Relu6") |
| << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool") |
| << ConvolutionLayer(1U, 1U, 1001U, |
| get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW), |
| get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"), |
| PadStrideInfo(1, 1, 0, 0)) |
| .set_name("Logits/Conv2d_1c_1x1") |
| << ReshapeLayer(TensorShape(1001U)).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_file = common_params.tuner_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 get_expanded_conv(const std::string &data_path, std::string &¶m_path, |
| unsigned int input_channels, unsigned int output_channels, |
| PadStrideInfo dwc_pad_stride_info, |
| bool has_expand = false, bool is_residual = false, unsigned int expansion_size = 6) |
| { |
| std::string total_path = param_path + "_"; |
| SubStream left(graph); |
| |
| // Add expand node |
| if(has_expand) |
| { |
| left << ConvolutionLayer(1U, 1U, input_channels * expansion_size, |
| get_weights_accessor(data_path, total_path + "expand_weights.npy", DataLayout::NCHW), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) |
| .set_name(param_path + "/expand/Conv2D") |
| << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "expand_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, total_path + "expand_BatchNorm_moving_variance.npy"), |
| get_weights_accessor(data_path, total_path + "expand_BatchNorm_gamma.npy"), |
| get_weights_accessor(data_path, total_path + "expand_BatchNorm_beta.npy"), |
| 0.0010000000474974513f) |
| .set_name(param_path + "/expand/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) |
| .set_name(param_path + "/expand/Relu6"); |
| } |
| |
| // Add depthwise node |
| left << DepthwiseConvolutionLayer(3U, 3U, |
| get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy", DataLayout::NCHW), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| dwc_pad_stride_info) |
| .set_name(param_path + "/depthwise/depthwise") |
| << 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_gamma.npy"), |
| get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"), |
| 0.0010000000474974513f) |
| .set_name(param_path + "/depthwise/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) |
| .set_name(param_path + "/depthwise/Relu6"); |
| |
| // Add project node |
| left << ConvolutionLayer(1U, 1U, output_channels, |
| get_weights_accessor(data_path, total_path + "project_weights.npy", DataLayout::NCHW), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) |
| .set_name(param_path + "/project/Conv2D") |
| << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "project_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, total_path + "project_BatchNorm_moving_variance.npy"), |
| get_weights_accessor(data_path, total_path + "project_BatchNorm_gamma.npy"), |
| get_weights_accessor(data_path, total_path + "project_BatchNorm_beta.npy"), |
| 0.0010000000474974513) |
| .set_name(param_path + "/project/BatchNorm"); |
| |
| if(is_residual) |
| { |
| // Add residual node |
| SubStream right(graph); |
| graph << BranchLayer(BranchMergeMethod::ADD, std::move(left), std::move(right)).set_name(param_path + "/add"); |
| } |
| else |
| { |
| graph.forward_tail(left.tail_node()); |
| } |
| } |
| }; |
| |
| /** Main program for MobileNetV2 |
| * |
| * @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<GraphMobilenetV2Example>(argc, argv); |
| } |