From 3143b2801330743e891de6184776c472cd9035ff Mon Sep 17 00:00:00 2001
From: Joshua Friedrich <joshua.friedrich@stud-mail.uni-wuerzburg.de>
Date: Thu, 1 Apr 2021 17:11:35 +0200
Subject: [PATCH] add fitness tracking, STILL working on it

---
 .gitignore |  1 +
 train.py   | 21 +++++++++++++++------
 2 files changed, 16 insertions(+), 6 deletions(-)

diff --git a/.gitignore b/.gitignore
index 91ce33fb931e..caea448b0156 100644
--- a/.gitignore
+++ b/.gitignore
@@ -250,3 +250,4 @@ com_crashlytics_export_strings.xml
 crashlytics.properties
 crashlytics-build.properties
 fabric.properties
+evolve.txt
diff --git a/train.py b/train.py
index 207fcb5b0730..21c6529dfb9e 100644
--- a/train.py
+++ b/train.py
@@ -404,28 +404,35 @@ def train(hyp, opt, device, tb_writer=None):
                 os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
 
             # Log
+            fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
             tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss',  # train loss
                     'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
                     'val/box_loss', 'val/obj_loss', 'val/cls_loss',  # val loss
-                    'x/lr0', 'x/lr1', 'x/lr2']  # params
-            for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
+                    'x/lr0', 'x/lr1', 'x/lr2',
+                    # 'metrics/fitness'
+                    ]  # params
+            for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):# + list(fi), tags):
                 if tb_writer:
                     tb_writer.add_scalar(tag, x, epoch)  # tensorboard
                 if wandb_logger.wandb:
                     wandb_logger.log({tag: x})  # W&B
 
             # Update best mAP
-            fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
+            # fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
             if fi > best_fitness:
                 best_fitness = fi
             wandb_logger.end_epoch(best_result=best_fitness == fi)
+            # wandb_logger.log({"metrics/fitness": str(fi)})  # W&B
 
 
 
             ### Ray Tune report Metric
-            print("fitness: ")
-            print(fi)
-            tune.report(fitness=fi)
+            if(opt.raytune):
+                print("fitness: ")
+                print(fi)
+                tune.report(fitness=fi)
+
+
 
 
 
@@ -495,6 +502,8 @@ def train(hyp, opt, device, tb_writer=None):
     return results
 
 
+
+
 ''' Add Raytune-Hyp-Opt functionality'''
 global_hyp = None
 global_opt = None