| /* |
| * 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 LeNet's network using the Compute Library's graph API */ |
| class GraphLenetExample : public Example |
| { |
| public: |
| GraphLenetExample() |
| : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "LeNet") |
| { |
| } |
| 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; |
| } |
| |
| // 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; |
| |
| // Get trainable parameters data path |
| std::string data_path = common_params.data_path; |
| unsigned int batches = 4; /** Number of batches */ |
| |
| // Create input descriptor |
| const auto operation_layout = common_params.data_layout; |
| const TensorShape tensor_shape = permute_shape(TensorShape(28U, 28U, 1U, 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; |
| |
| //conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx |
| graph << common_params.target |
| << common_params.fast_math_hint |
| << InputLayer(input_descriptor, get_input_accessor(common_params)) |
| << ConvolutionLayer( |
| 5U, 5U, 20U, |
| get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_b.npy"), |
| PadStrideInfo(1, 1, 0, 0)) |
| .set_name("conv1") |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool1") |
| << ConvolutionLayer( |
| 5U, 5U, 50U, |
| get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_b.npy"), |
| PadStrideInfo(1, 1, 0, 0)) |
| .set_name("conv2") |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool2") |
| << FullyConnectedLayer( |
| 500U, |
| get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_b.npy")) |
| .set_name("ip1") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu") |
| << FullyConnectedLayer( |
| 10U, |
| get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_b.npy")) |
| .set_name("ip2") |
| << SoftmaxLayer().set_name("prob") |
| << OutputLayer(get_output_accessor(common_params)); |
| |
| // 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; |
| }; |
| |
| /** Main program for LeNet |
| * |
| * Model is based on: |
| * http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf |
| * "Gradient-Based Learning Applied to Document Recognition" |
| * Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner |
| * |
| * The original model uses tanh instead of relu activations. However the use of relu activations in lenet has been |
| * widely adopted to improve accuracy.* |
| * |
| * @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<GraphLenetExample>(argc, argv); |
| } |