From fe105fa2c2dea4841c621b8fb57a1f5804e312be Mon Sep 17 00:00:00 2001
From: Symisc Systems <chm@symisc.net>
Date: Tue, 5 Jun 2018 08:37:52 +0200
Subject: [PATCH] Train your Realnets models on the CPU!

---
 samples/realnet_train_model.c | 75 +++++++++++++++++++++++++++++++++++
 1 file changed, 75 insertions(+)
 create mode 100644 samples/realnet_train_model.c

diff --git a/samples/realnet_train_model.c b/samples/realnet_train_model.c
new file mode 100644
index 0000000..ed89ca1
--- /dev/null
+++ b/samples/realnet_train_model.c
@@ -0,0 +1,75 @@
+/*
+ * Programming introduction with the SOD Embedded RealNets Model Training API.
+ * Training must be enabled via the compile-time directive SOD_ENABLE_NET_TRAIN.
+ *
+ * Copyright (C) PixLab | Symisc Systems, https://sod.pixlab.io
+ */
+/*
+* Compile this file together with the SOD embedded source code to generate
+* the executable. For example:
+*
+*  gcc sod.c realnet_train_model.c -D SOD_ENABLE_NET_TRAIN -lm -Ofast -march=native -Wall -std=c99 -o sod_realnet_train_intro
+*  
+* Under Microsoft Visual Studio (>= 2015), just drop `sod.c` and its accompanying
+* header files on your source tree and you're done. If you have any trouble
+* integrating SOD in your project, please submit a support request at:
+* https://sod.pixlab.io/support.html
+*/
+/*
+* This simple program is a quick introduction on how to embed and start
+* experimenting with SOD without having to do a lot of tedious
+* reading and configuration.
+*
+* Make sure you have the latest release of SOD from:
+*  https://pixlab.io/downloads
+* The SOD Embedded C/C++ documentation is available at:
+*  https://sod.pixlab.io/api.html
+*/
+#include <stdio.h>
+#include "sod.h"
+/*
+ * Training log consumer callback that should be called
+ * by the Realnet trainer to report training progress.
+ */
+void log_consumer_callback(const char *zText, size_t text_len, void *pUserdata)
+{
+	/* Simply redirect to stdout */
+	puts(zText);
+}
+int main(int argc, char *argv[])
+{
+	/* Training instructions (i.e. where positive and negative samples
+	 * are located, tree minimal depth, max trees, model copyright notice and so on).
+	 * Pass a path or download one from https://pixlab.io/downloads
+	 */
+	const char *zTrainFile = argc > 1 ? argv[1] : "train.txt";
+	/*
+	 * Relanet trainer handle 
+	 */
+	sod_realnet_trainer *pNet;
+	int rc;
+	/* Allocate a new Realnet Trainer handle */
+	rc = sod_realnet_train_init(&pNet);
+	if (rc != SOD_OK) return rc;
+	/*
+	 * Install our training progress log consumer callback.
+	 */
+	rc = sod_realnet_train_config(pNet, SOD_REALNET_TR_LOG_CALLBACK, log_consumer_callback, 0);
+	if (rc != SOD_OK) return rc;
+	/*
+	 * Where to store the output model.
+	 */
+	rc = sod_realnet_train_config(pNet, SOD_REALNET_TR_OUTPUT_MODEL, "./pedestrian_detetcor.realnet");
+	if (rc != SOD_OK) return rc;
+	/*
+	 * Start the heavy training process on your CPU driven by
+	 * the Realnet instructions found on `zTrainFile`.
+	 */
+	rc = sod_realnet_train_start(pNet, zTrainFile);
+	/* Wait some days...*/
+	sod_realnet_train_release(pNet);
+	/* check the progress log and you should find
+	* a working model on the path you specified earlier.
+	*/
+	return rc;
+}
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