Fix missing network references

* Add missing references to LeNet
* Remove mnist example, which is an internally developed network

Resolves: COMPMID-4559

Signed-off-by: SiCongLi <sicong.li@arm.com>
Change-Id: I139354890f1a9acb54a87add6895262102b3b8de
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5782
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Georgios Pinitas <georgios.pinitas@arm.com>

2 files changed, 8 insertions, 172 deletions

diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp
index 6560a980cc..1bcd95fb58 100644
--- a/examples/graph_lenet.cpp
+++ b/examples/graph_lenet.cpp
@@ -132,6 +132,14 @@ private:
 
 /** Main program for LeNet
  *
+ * Model is based on:
+ *      http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf
+ *      "Gradient-Based Learning Applied to Document Recognition"
+ *      Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner
+ *
+ * The original model uses tanh instead of relu activations. However the use of relu activations in lenet has been
+ * widely adopted to improve accuracy.*
+ *
  * @note To list all the possible arguments execute the binary appended with the --help option
  *
  * @param[in] argc Number of arguments
diff --git a/examples/graph_mnist.cpp b/examples/graph_mnist.cpp
deleted file mode 100644
index 4ef96cc596..0000000000
--- a/examples/graph_mnist.cpp
+++ /dev/null
@@ -1,172 +0,0 @@
-/*
- * Copyright (c) 2019-2021 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;
-using namespace arm_compute::utils;
-using namespace arm_compute::graph::frontend;
-using namespace arm_compute::graph_utils;
-
-/** Example demonstrating how to implement Mnist's network using the Compute Library's graph API */
-class GraphMnistExample : public Example
-{
-public:
-    GraphMnistExample()
-        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "LeNet")
-    {
-    }
-    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;
-        }
-
-        // Print parameter values
-        std::cout << common_params << std::endl;
-
-        // Get trainable parameters data path
-        std::string data_path = common_params.data_path;
-
-        // Add model path to data path
-        if(!data_path.empty() && arm_compute::is_data_type_quantized_asymmetric(common_params.data_type))
-        {
-            data_path += "/cnn_data/mnist_qasymm8_model/";
-        }
-
-        // Create input descriptor
-        const auto        operation_layout = common_params.data_layout;
-        const TensorShape tensor_shape     = permute_shape(TensorShape(28U, 28U, 1U), DataLayout::NCHW, operation_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
-
-        const QuantizationInfo in_quant_info = QuantizationInfo(0.003921568859368563f, 0);
-
-        const std::vector<std::pair<QuantizationInfo, QuantizationInfo>> conv_quant_info =
-        {
-            { QuantizationInfo(0.004083447158336639f, 138), QuantizationInfo(0.0046257381327450275f, 0) }, // conv0
-            { QuantizationInfo(0.0048590428195893764f, 149), QuantizationInfo(0.03558270260691643f, 0) },  // conv1
-            { QuantizationInfo(0.004008443560451269f, 146), QuantizationInfo(0.09117382764816284f, 0) },   // conv2
-            { QuantizationInfo(0.004344311077147722f, 160), QuantizationInfo(0.5494495034217834f, 167) },  // fc
-        };
-
-        // Set weights trained layout
-        const DataLayout        weights_layout = DataLayout::NHWC;
-        FullyConnectedLayerInfo fc_info        = FullyConnectedLayerInfo();
-        fc_info.set_weights_trained_layout(weights_layout);
-
-        graph << common_params.target
-              << common_params.fast_math_hint
-              << InputLayer(input_descriptor.set_quantization_info(in_quant_info),
-                            get_input_accessor(common_params))
-              << ConvolutionLayer(
-                  3U, 3U, 32U,
-                  get_weights_accessor(data_path, "conv2d_weights_quant_FakeQuantWithMinMaxVars.npy", weights_layout),
-                  get_weights_accessor(data_path, "conv2d_Conv2D_bias.npy"),
-                  PadStrideInfo(1U, 1U, 1U, 1U), 1, conv_quant_info.at(0).first, conv_quant_info.at(0).second)
-              .set_name("Conv0")
-
-              << ConvolutionLayer(
-                  3U, 3U, 32U,
-                  get_weights_accessor(data_path, "conv2d_1_weights_quant_FakeQuantWithMinMaxVars.npy", weights_layout),
-                  get_weights_accessor(data_path, "conv2d_1_Conv2D_bias.npy"),
-                  PadStrideInfo(1U, 1U, 1U, 1U), 1, conv_quant_info.at(1).first, conv_quant_info.at(1).second)
-              .set_name("conv1")
-
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("maxpool1")
-
-              << ConvolutionLayer(
-                  3U, 3U, 32U,
-                  get_weights_accessor(data_path, "conv2d_2_weights_quant_FakeQuantWithMinMaxVars.npy", weights_layout),
-                  get_weights_accessor(data_path, "conv2d_2_Conv2D_bias.npy"),
-                  PadStrideInfo(1U, 1U, 1U, 1U), 1, conv_quant_info.at(2).first, conv_quant_info.at(2).second)
-              .set_name("conv2")
-
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("maxpool2")
-
-              << FullyConnectedLayer(
-                  10U,
-                  get_weights_accessor(data_path, "dense_weights_quant_FakeQuantWithMinMaxVars_transpose.npy", weights_layout),
-                  get_weights_accessor(data_path, "dense_MatMul_bias.npy"),
-                  fc_info, conv_quant_info.at(3).first, conv_quant_info.at(3).second)
-              .set_name("fc")
-
-              << SoftmaxLayer().set_name("prob");
-
-        if(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type))
-        {
-            graph << DequantizationLayer().set_name("dequantize");
-        }
-
-        graph << 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;
-        config.mlgo_file   = common_params.mlgo_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;
-};
-
-/** Main program for Mnist Example
- *
- * @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<GraphMnistExample>(argc, argv);
-}