COMPIMID-553: MobileNet use case.

Change-Id: I1181abbd5785065f3d57e91844376a4b110938a9
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/110701
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 170 insertions, 0 deletions

diff --git a/examples/graph_mobilenet.cpp b/examples/graph_mobilenet.cpp
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+++ b/examples/graph_mobilenet.cpp
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+/*
+ * Copyright (c) 2017 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/Graph.h"
+#include "arm_compute/graph/Nodes.h"
+#include "support/ToolchainSupport.h"
+#include "utils/GraphUtils.h"
+#include "utils/Utils.h"
+
+#include <cstdlib>
+
+using namespace arm_compute::graph;
+using namespace arm_compute::graph_utils;
+
+BranchLayer get_dwsc_node(const std::string &data_path, std::string &&param_path,
+                          unsigned int  conv_filt,
+                          PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info)
+{
+    std::string total_path = "/cnn_data/mobilenet_v1_model/" + param_path + "_";
+    SubGraph    sg;
+    sg << DepthwiseConvolutionLayer(
+           3U, 3U,
+           get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy"),
+           std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+           dwc_pad_stride_info,
+           true)
+       << 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_beta.npy"),
+           get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"),
+           0.001f)
+       << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
+       << ConvolutionLayer(
+           1U, 1U, conv_filt,
+           get_weights_accessor(data_path, total_path + "pointwise_weights.npy"),
+           std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+           conv_pad_stride_info)
+       << BatchNormalizationLayer(
+           get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_mean.npy"),
+           get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_variance.npy"),
+           get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_beta.npy"),
+           get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_gamma.npy"),
+           0.001f)
+       << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));
+
+    return BranchLayer(std::move(sg));
+}
+
+/** Example demonstrating how to implement MobileNet's network using the Compute Library's graph API
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] image, [optional] labels )
+ */
+void main_graph_mobilenet(int argc, const char **argv)
+{
+    std::string data_path; /* Path to the trainable data */
+    std::string image;     /* Image data */
+    std::string label;     /* Label data */
+
+    constexpr float mean_r = 122.68f; /* Mean value to subtract from red channel */
+    constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */
+    constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */
+
+    // Parse arguments
+    if(argc < 2)
+    {
+        // Print help
+        std::cout << "Usage: " << argv[0] << " [path_to_data] [image] [labels]\n\n";
+        std::cout << "No data folder provided: using random values\n\n";
+    }
+    else if(argc == 2)
+    {
+        data_path = argv[1];
+        std::cout << "Usage: " << argv[0] << " " << argv[1] << " [image] [labels]\n\n";
+        std::cout << "No image provided: using random values\n\n";
+    }
+    else if(argc == 3)
+    {
+        data_path = argv[1];
+        image     = argv[2];
+        std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [labels]\n\n";
+        std::cout << "No text file with labels provided: skipping output accessor\n\n";
+    }
+    else
+    {
+        data_path = argv[1];
+        image     = argv[2];
+        label     = argv[3];
+    }
+
+    // Check if OpenCL is available and initialize the scheduler
+    TargetHint hint = TargetHint::NEON;
+    if(Graph::opencl_is_available())
+    {
+        hint = TargetHint::OPENCL;
+    }
+
+    Graph graph;
+    graph << hint
+          << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
+                    get_input_accessor(image, mean_r, mean_g, mean_b))
+          << ConvolutionLayer(
+              3U, 3U, 32U,
+              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_weights.npy"),
+              std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+              PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))
+          << BatchNormalizationLayer(
+              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_moving_mean.npy"),
+              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_moving_variance.npy"),
+              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_beta.npy"),
+              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_gamma.npy"),
+              0.001f)
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
+          << get_dwsc_node(data_path, "Conv2d_1", 64, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0))
+          << get_dwsc_node(data_path, "Conv2d_2", 128, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+          << get_dwsc_node(data_path, "Conv2d_3", 128, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+          << get_dwsc_node(data_path, "Conv2d_4", 256, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+          << get_dwsc_node(data_path, "Conv2d_5", 256, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+          << get_dwsc_node(data_path, "Conv2d_6", 512, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+          << get_dwsc_node(data_path, "Conv2d_7", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+          << get_dwsc_node(data_path, "Conv2d_8", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+          << get_dwsc_node(data_path, "Conv2d_9", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+          << get_dwsc_node(data_path, "Conv2d_10", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+          << get_dwsc_node(data_path, "Conv2d_11", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+          << get_dwsc_node(data_path, "Conv2d_12", 1024, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+          << get_dwsc_node(data_path, "Conv2d_13", 1024, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+          << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
+          << ConvolutionLayer(
+              1U, 1U, 1001U,
+              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Logits_Conv2d_1c_1x1_weights.npy"),
+              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Logits_Conv2d_1c_1x1_biases.npy"),
+              PadStrideInfo(1, 1, 0, 0))
+          << ReshapeLayer(TensorShape(1001U))
+          << SoftmaxLayer()
+          << Tensor(get_output_accessor(label, 5));
+
+    graph.run();
+}
+
+/** Main program for MobileNetV1
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] image, [optional] labels )
+ */
+int main(int argc, const char **argv)
+{
+    return arm_compute::utils::run_example(argc, argv, main_graph_mobilenet);
+}