arm_compute v19.11
diff --git a/examples/graph_mnist.cpp b/examples/graph_mnist.cpp
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+/*
+ * Copyright (c) 2019 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 TensorShape tensor_shape = permute_shape(TensorShape(28U, 28U, 1U), DataLayout::NCHW, common_params.data_layout);
+ TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_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, 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, 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;
+
+ 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);
+}