| /* |
| * Copyright (c) 2017-2020 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 AlexNet's network using the Compute Library's graph API */ |
| class GraphAlexnetExample : public Example |
| { |
| public: |
| GraphAlexnetExample() |
| : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "AlexNet") |
| { |
| } |
| 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; |
| |
| // Create a preprocessor object |
| const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } }; |
| std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb); |
| |
| // Create input descriptor |
| const auto operation_layout = common_params.data_layout; |
| const TensorShape tensor_shape = permute_shape(TensorShape(227U, 227U, 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))) |
| // Layer 1 |
| << ConvolutionLayer( |
| 11U, 11U, 96U, |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_b.npy"), |
| PadStrideInfo(4, 4, 0, 0)) |
| .set_name("conv1") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu1") |
| << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("norm1") |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool1") |
| // Layer 2 |
| << ConvolutionLayer( |
| 5U, 5U, 256U, |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_b.npy"), |
| PadStrideInfo(1, 1, 2, 2), 2) |
| .set_name("conv2") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu2") |
| << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("norm2") |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool2") |
| // Layer 3 |
| << ConvolutionLayer( |
| 3U, 3U, 384U, |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_b.npy"), |
| PadStrideInfo(1, 1, 1, 1)) |
| .set_name("conv3") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu3") |
| // Layer 4 |
| << ConvolutionLayer( |
| 3U, 3U, 384U, |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_b.npy"), |
| PadStrideInfo(1, 1, 1, 1), 2) |
| .set_name("conv4") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu4") |
| // Layer 5 |
| << ConvolutionLayer( |
| 3U, 3U, 256U, |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_b.npy"), |
| PadStrideInfo(1, 1, 1, 1), 2) |
| .set_name("conv5") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu5") |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool5") |
| // Layer 6 |
| << FullyConnectedLayer( |
| 4096U, |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_b.npy")) |
| .set_name("fc6") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu6") |
| // Layer 7 |
| << FullyConnectedLayer( |
| 4096U, |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_b.npy")) |
| .set_name("fc7") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu7") |
| // Layer 8 |
| << FullyConnectedLayer( |
| 1000U, |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_w.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_b.npy")) |
| .set_name("fc8") |
| // Softmax |
| << SoftmaxLayer().set_name("prob") |
| << 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; |
| |
| // Load the precompiled kernels from a file into the kernel library, in this way the next time they are needed |
| // compilation won't be required. |
| if(common_params.enable_cl_cache) |
| { |
| restore_program_cache_from_file(); |
| } |
| |
| graph.finalize(common_params.target, config); |
| |
| // Save the opencl kernels to a file |
| if(common_opts.enable_cl_cache) |
| { |
| save_program_cache_to_file(); |
| } |
| |
| 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 AlexNet |
| * |
| * Model is based on: |
| * https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks |
| * "ImageNet Classification with Deep Convolutional Neural Networks" |
| * Alex Krizhevsky and Sutskever, Ilya and Hinton, Geoffrey E |
| * |
| * Provenance: https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet |
| * |
| * @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 |
| * |
| * @return Return code |
| */ |
| int main(int argc, char **argv) |
| { |
| return arm_compute::utils::run_example<GraphAlexnetExample>(argc, argv); |
| } |
