COMPMID-1807: Implement ResNet12
Change-Id: I10696b7835eb8ab74ddd5611a278ac0b39d879ca
Reviewed-on: https://review.mlplatform.org/333
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <Anthony.barbier@arm.com>
diff --git a/examples/graph_resnet12.cpp b/examples/graph_resnet12.cpp
new file mode 100644
index 0000000..334e708
--- /dev/null
+++ b/examples/graph_resnet12.cpp
@@ -0,0 +1,219 @@
+/*
+ * 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 ResNet12 network using the Compute Library's graph API */
+class GraphResNet12Example : public Example
+{
+public:
+ GraphResNet12Example()
+ : cmd_parser(), common_opts(cmd_parser), model_input_width(nullptr), model_input_height(nullptr), common_params(), graph(0, "ResNet12")
+ {
+ model_input_width = cmd_parser.add_option<SimpleOption<unsigned int>>("image-width", 192);
+ model_input_height = cmd_parser.add_option<SimpleOption<unsigned int>>("image-height", 128);
+
+ // Add model id option
+ model_input_width->set_help("Input image width.");
+ model_input_height->set_help("Input image height.");
+ }
+ GraphResNet12Example(const GraphResNet12Example &) = delete;
+ GraphResNet12Example &operator=(const GraphResNet12Example &) = delete;
+ GraphResNet12Example(GraphResNet12Example &&) = default; // NOLINT
+ GraphResNet12Example &operator=(GraphResNet12Example &&) = default; // NOLINT
+ ~GraphResNet12Example() override = default;
+ 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;
+ }
+
+ // Get input image width and height
+ const unsigned int image_width = model_input_width->value();
+ const unsigned int image_height = model_input_height->value();
+
+ // Checks
+ ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph");
+
+ // Print parameter values
+ std::cout << common_params << std::endl;
+ std::cout << "Image width: " << image_width << std::endl;
+ std::cout << "Image height: " << image_height << std::endl;
+
+ // Get trainable parameters data path
+ const std::string data_path = common_params.data_path;
+ const std::string model_path = "/cnn_data/resnet12_model/";
+
+ // Create a preprocessor object
+ std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
+
+ // Create input descriptor
+ const TensorShape tensor_shape = permute_shape(TensorShape(image_width, image_height, 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;
+
+ 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(
+ 9U, 9U, 64U,
+ get_weights_accessor(data_path, "conv1_weights.npy", weights_layout),
+ get_weights_accessor(data_path, "conv1_biases.npy", weights_layout),
+ PadStrideInfo(1, 1, 4, 4))
+ .set_name("conv1/convolution")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1/Relu");
+
+ add_residual_block(data_path, "block1", weights_layout);
+ add_residual_block(data_path, "block2", weights_layout);
+ add_residual_block(data_path, "block3", weights_layout);
+ add_residual_block(data_path, "block4", weights_layout);
+
+ graph << ConvolutionLayer(
+ 3U, 3U, 64U,
+ get_weights_accessor(data_path, "conv10_weights.npy", weights_layout),
+ get_weights_accessor(data_path, "conv10_biases.npy"),
+ PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv10/convolution")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv10/Relu")
+ << ConvolutionLayer(
+ 3U, 3U, 64U,
+ get_weights_accessor(data_path, "conv11_weights.npy", weights_layout),
+ get_weights_accessor(data_path, "conv11_biases.npy"),
+ PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv11/convolution")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv11/Relu")
+ << ConvolutionLayer(
+ 9U, 9U, 3U,
+ get_weights_accessor(data_path, "conv12_weights.npy", weights_layout),
+ get_weights_accessor(data_path, "conv12_biases.npy"),
+ PadStrideInfo(1, 1, 4, 4))
+ .set_name("conv12/convolution")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH)).set_name("conv12/Tanh")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 0.58f, 0.5f)).set_name("conv12/Linear")
+ << OutputLayer(arm_compute::support::cpp14::make_unique<DummyAccessor>(0));
+
+ // Finalize graph
+ GraphConfig config;
+ config.num_threads = common_params.threads;
+ config.use_tuner = common_params.enable_tuner;
+ graph.finalize(common_params.target, config);
+
+ return true;
+ }
+
+ void do_run() override
+ {
+ // Run graph
+ graph.run();
+ }
+
+private:
+ CommandLineParser cmd_parser;
+ CommonGraphOptions common_opts;
+ SimpleOption<unsigned int> *model_input_width{ nullptr };
+ SimpleOption<unsigned int> *model_input_height{ nullptr };
+ CommonGraphParams common_params;
+ Stream graph;
+
+ void add_residual_block(const std::string &data_path, const std::string &name, DataLayout weights_layout)
+ {
+ std::stringstream unit_path_ss;
+ unit_path_ss << data_path << name << "_";
+ std::stringstream unit_name_ss;
+ unit_name_ss << name << "/";
+
+ std::string unit_path = unit_path_ss.str();
+ std::string unit_name = unit_name_ss.str();
+
+ SubStream left(graph);
+ SubStream right(graph);
+
+ right << ConvolutionLayer(
+ 3U, 3U, 64U,
+ get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout),
+ get_weights_accessor(data_path, unit_path + "conv1_biases.npy", weights_layout),
+ PadStrideInfo(1, 1, 1, 1))
+ .set_name(unit_name + "conv1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_moving_variance.npy"),
+ get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_gamma.npy"),
+ get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_beta.npy"),
+ 0.0000100099996416f)
+ .set_name(unit_name + "conv1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu")
+
+ << ConvolutionLayer(
+ 3U, 3U, 64U,
+ get_weights_accessor(data_path, unit_path + "conv2_weights.npy", weights_layout),
+ get_weights_accessor(data_path, unit_path + "conv2_biases.npy", weights_layout),
+ PadStrideInfo(1, 1, 1, 1))
+ .set_name(unit_name + "conv2/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_moving_variance.npy"),
+ get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_gamma.npy"),
+ get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_beta.npy"),
+ 0.0000100099996416f)
+ .set_name(unit_name + "conv2/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv2/Relu");
+
+ graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(unit_name + "add");
+ }
+};
+
+/** Main program for ResNet12
+ *
+ * Model is based on:
+ * https://arxiv.org/pdf/1709.01118.pdf
+ * "WESPE: Weakly Supervised Photo Enhancer for Digital Cameras"
+ * Andrey Ignatov, Nikolay Kobyshev, Kenneth Vanhoey, Radu Timofte, Luc Van Gool
+ *
+ * @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<GraphResNet12Example>(argc, argv);
+}