arm_compute v18.11
diff --git a/examples/graph_shufflenet.cpp b/examples/graph_shufflenet.cpp
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+/*
+ * 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::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);
+
+ // 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::NCHW;
+ }
+
+ // Checks
+ ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph");
+ ARM_COMPUTE_EXIT_ON_MSG(common_params.data_type == DataType::F16 && common_params.target == Target::NEON, "F16 NEON 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 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);
+
+ // Set weights trained layout
+ const DataLayout weights_layout = DataLayout::NCHW;
+
+ // Create preprocessor
+ std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::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, 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)).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_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 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, 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
+ *
+ * @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);
+}