COMPMID-1579: Add support for ChannelShuffle operator in NEON

Change-Id: I6d5f91579850906e1eb973ff6c5612195255e631

1 files changed, 256 insertions, 0 deletions

diff --git a/examples/graph_shufflenet.cpp b/examples/graph_shufflenet.cpp
new file mode 100644
index 0000000000..af8d11beb9
--- /dev/null
+++ b/examples/graph_shufflenet.cpp
@@ -0,0 +1,256 @@
+/*
+ * 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);
+}