add update default hyp dict with provided yaml

train.py

@@ -42,17 +42,6 @@ hyp = {'lr0': 0.01,  # initial learning rate (SGD=1E-2, Adam=1E-3)
 # Don't need to be printing every time
 #print(hyp)
 
-# Overwrite hyp with hyp*.txt (optional)
-if f:
-    print('Using %s' % f[0])
-    for k, v in zip(hyp.keys(), np.loadtxt(f[0])):
-        hyp[k] = v
-
-# Print focal loss if gamma > 0
-if hyp['fl_gamma']:
-    print('Using FocalLoss(gamma=%g)' % hyp['fl_gamma'])
-
-
 def train(hyp):
     #write all results to the tb log_dir, so all data from one run is together
     log_dir = tb_writer.log_dir
@@ -410,7 +399,7 @@ if __name__ == '__main__':
         print(f'WARNING: No run provided to resume from. Resuming from most recent run found at {last}')
     else:
         last = ''
-        
+
     # if resuming, check for hyp file
     if last:
         last_hyp = last.replace('last.pt', 'hyp.yaml')
@@ -430,7 +419,16 @@ if __name__ == '__main__':
     # Train
     if not opt.evolve:
         tb_writer = SummaryWriter(comment=opt.name)
+        
+        #updates hyp defaults from hyp.yaml
+        if opt.hyp: hyp.update(opt.hyp)
+
+        # Print focal loss if gamma > 0
+        if hyp['fl_gamma']:
+            print('Using FocalLoss(gamma=%g)' % hyp['fl_gamma'])
+        print(f'Beginning training with {hyp}\n\n')
         print('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/')
+        
         train(hyp)
 
     # Evolve hyperparameters (optional)