Move scheduler step position after optimizer step following pytorch c…

…onvention. And other minor formatting issue.

run_networks.py

@@ -14,7 +14,7 @@
 class model ():
 
     def __init__(self, config, data, test=False):
-        
+
         self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
         self.config = config
         self.training_opt = self.config['training_opt']
@@ -52,7 +52,7 @@ def __init__(self, config, data, test=False):
             os.remove(self.log_file)
 
     def init_models(self, optimizer=True):
-        
+
         networks_defs = self.config['networks']
         self.networks = {}
         self.model_optim_params_list = []
@@ -79,9 +79,9 @@ def init_models(self, optimizer=True):
             # Optimizer list
             optim_params = val['optim_params']
             self.model_optim_params_list.append({'params': self.networks[key].parameters(),
-                                                'lr': optim_params['lr'],
-                                                'momentum': optim_params['momentum'],
-                                                'weight_decay': optim_params['weight_decay']})
+                                                 'lr': optim_params['lr'],
+                                                 'momentum': optim_params['momentum'],
+                                                 'weight_decay': optim_params['weight_decay']})
 
     def init_criterions(self):
 
@@ -188,12 +188,6 @@ def train(self):
 
             torch.cuda.empty_cache()
 
-            # Set model modes and set scheduler
-            # In training, step optimizer scheduler and set model to train() 
-            self.model_optimizer_scheduler.step()
-            if self.criterion_optimizer:
-                self.criterion_optimizer_scheduler.step()
-
             # Iterate over dataset
             for step, (inputs, labels, _) in enumerate(self.data['train']):
 
@@ -234,6 +228,12 @@ def train(self):
                                       % (minibatch_acc)]
                         print_write(print_str, self.log_file)
 
+            # Set model modes and set scheduler
+            # In training, step optimizer scheduler and set model to train()
+            self.model_optimizer_scheduler.step()
+            if self.criterion_optimizer:
+                self.criterion_optimizer_scheduler.step()
+
             # After every epoch, validation
             self.eval(phase='val')
 

utils.py

@@ -33,7 +33,7 @@ def init_weights(model, weights_path, caffe=False, classifier=False):
     """Initialize weights"""
     print('Pretrained %s weights path: %s' % ('classifier' if classifier else 'feature model',
                                               weights_path))    
-    weights = torch.load(weights_path)   
+    weights = torch.load(weights_path)
     if not classifier:
         if caffe:
             weights = {k: weights[k] if k in weights else model.state_dict()[k]