Mode all optimizer settings to 'hyp.yaml', integrate proper momentum with Adam optimizer

train.py

@@ -20,8 +20,9 @@ except:
 
 
 # Hyperparameters
-hyp = {'lr0': 0.01,  # initial learning rate (SGD=1E-2, Adam=1E-3)
+hyp = {'optimizer': 'adam' #if none, default is SGD
-       'momentum': 0.937,  # SGD momentum
+       'lr0': 0.01,  # initial learning rate (SGD=1E-2, Adam=1E-3)
+       'momentum': 0.937,  # SGD momentum/Adam beta1
        'weight_decay': 5e-4,  # optimizer weight decay
        'giou': 0.05,  # giou loss gain
        'cls': 0.58,  # cls loss gain
@@ -90,8 +91,11 @@ def train(hyp):
             else:
                 pg0.append(v)  # all else
 
-    optimizer = optim.Adam(pg0, lr=hyp['lr0']) if opt.adam else \
+    if hyp.optimizer =='adam':
-        optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
+        optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) #use default beta2, adjust beta1 for Adam momentum per momentum adjustments in https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
+    else:
+        optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
+
     optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']})  # add pg1 with weight_decay
     optimizer.add_param_group({'params': pg2})  # add pg2 (biases)
     print('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
@@ -380,7 +384,6 @@ if __name__ == '__main__':
     parser.add_argument('--weights', type=str, default='', help='initial weights path')
     parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied')
     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    parser.add_argument('--adam', action='store_true', help='use adam optimizer')
     parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%')
     parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
     parser.add_argument('--hyp', type=str, default='', help ='hyp cfg path [*.yaml].')