Revert "COMPMID-915: Create ResNet50 example"

This reverts commit 2e8c7ee2ecebd9783c97bbd602a61989e1247d6b.

Change-Id: Id90691f427a68d01480889f8d5fff190fd72c5a3
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/121176
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>

1 files changed, 0 insertions, 231 deletions

diff --git a/examples/graph_resnet50.cpp b/examples/graph_resnet50.cpp
deleted file mode 100644
index 23432b821b..0000000000
--- a/examples/graph_resnet50.cpp
+++ /dev/null
@@ -1,231 +0,0 @@
-/*
- * Copyright (c) 2017-2018 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/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::utils;
-using namespace arm_compute::graph;
-using namespace arm_compute::graph_utils;
-
-/** Example demonstrating how to implement Microsoft's ResNet50 network using the Compute Library's graph API
- *
- * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
- */
-class GraphResNet50Example : public Example
-{
-public:
-    void do_setup(int argc, char **argv) override
-    {
-        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 */
-        constexpr float std    = 1.f;     /* Standard deviation value to divide from the channels */
-
-        // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
-        TargetHint target_hint = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
-
-        // Parse arguments
-        if(argc < 2)
-        {
-            // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 2)
-        {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 3)
-        {
-            data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n";
-            std::cout << "No image provided: using random values\n\n";
-        }
-        else if(argc == 4)
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
-            std::cout << "No text file with labels provided: skipping output accessor\n\n";
-        }
-        else
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            label     = argv[4];
-        }
-
-        graph << target_hint
-              << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
-                        get_input_accessor(image,
-                                           mean_r, mean_g, mean_b,
-                                           std, std, std, false /* Do not convert to BGR */))
-              << ConvolutionLayer(
-                  7U, 7U, 64U,
-                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_weights.npy"),
-                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                  PadStrideInfo(2, 2, 3, 3))
-              << BatchNormalizationLayer(
-                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_mean.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_variance.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_gamma.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_beta.npy"),
-                  0.001f)
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)));
-
-        add_residual_block(data_path, "block1", 64, 3, 2);
-        add_residual_block(data_path, "block2", 128, 4, 2);
-        add_residual_block(data_path, "block3", 256, 6, 2);
-        add_residual_block(data_path, "block4", 512, 3, 1);
-
-        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
-              << ConvolutionLayer(
-                  1U, 1U, 1000U,
-                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_weights.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_biases.npy"),
-                  PadStrideInfo(1, 1, 0, 0))
-              << FlattenLayer()
-              << SoftmaxLayer()
-              << Tensor(get_output_accessor(label, 5));
-    }
-    void do_run() override
-    {
-        // Run graph
-        graph.run();
-    }
-
-private:
-    Graph graph{};
-
-    void add_residual_block(const std::string &data_path, const std::string &name, unsigned int base_depth, unsigned int num_units, unsigned int stride)
-    {
-        for(unsigned int i = 0; i < num_units; ++i)
-        {
-            std::stringstream unit;
-            unit << "/cnn_data/resnet50_model/" << name << "_unit_" << (i + 1) << "_bottleneck_v1_";
-            std::string unit_name = unit.str();
-
-            unsigned int middle_stride = 1;
-
-            if(i == num_units - 1)
-            {
-                middle_stride = stride;
-            }
-
-            SubGraph right;
-            right << ConvolutionLayer(
-                      1U, 1U, base_depth,
-                      get_weights_accessor(data_path, unit_name + "conv1_weights.npy"),
-                      std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                      PadStrideInfo(1, 1, 0, 0))
-                  << BatchNormalizationLayer(
-                      get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_moving_mean.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_moving_variance.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_gamma.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_beta.npy"),
-                      0.001f)
-                  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-
-                  << ConvolutionLayer(
-                      3U, 3U, base_depth,
-                      get_weights_accessor(data_path, unit_name + "conv2_weights.npy"),
-                      std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                      PadStrideInfo(middle_stride, middle_stride, 1, 1))
-                  << BatchNormalizationLayer(
-                      get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_moving_mean.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_moving_variance.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_gamma.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_beta.npy"),
-                      0.001f)
-                  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-
-                  << ConvolutionLayer(
-                      1U, 1U, base_depth * 4,
-                      get_weights_accessor(data_path, unit_name + "conv3_weights.npy"),
-                      std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                      PadStrideInfo(1, 1, 0, 0))
-                  << BatchNormalizationLayer(
-                      get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_moving_mean.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_moving_variance.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_gamma.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_beta.npy"),
-                      0.001f);
-
-            if(i == 0)
-            {
-                SubGraph left;
-                left << ConvolutionLayer(
-                         1U, 1U, base_depth * 4,
-                         get_weights_accessor(data_path, unit_name + "shortcut_weights.npy"),
-                         std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                         PadStrideInfo(1, 1, 0, 0))
-                     << BatchNormalizationLayer(
-                         get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_moving_mean.npy"),
-                         get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_moving_variance.npy"),
-                         get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_gamma.npy"),
-                         get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_beta.npy"),
-                         0.001f);
-
-                graph << ResidualLayer(std::move(left), std::move(right));
-            }
-            else if(middle_stride > 1)
-            {
-                SubGraph left;
-                left << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, PadStrideInfo(middle_stride, middle_stride, 0, 0)))
-                     // TODO (alegil01) : Remove once we understand why a single node graph does not run in CL
-                     << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f, 0.f));
-
-                graph << ResidualLayer(std::move(left), std::move(right));
-            }
-            else
-            {
-                graph << ResidualLayer(std::move(right));
-            }
-
-            graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
-        }
-    }
-};
-
-/** Main program for ResNet50
- *
- * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
- */
-int main(int argc, char **argv)
-{
-    return arm_compute::utils::run_example<GraphResNet50Example>(argc, argv);
-}