[ERCBCM] Optimize the model interfaces and prints.

ercbcm/ERCBCM.py

@@ -6,7 +6,6 @@ class ERCBCM(nn.Module):
     def __init__(self):
         super(ERCBCM, self).__init__()
         print('>>> ERCBCM Init!')
-
         self.bert_base = BertForSequenceClassification.from_pretrained('bert-base-uncased')
 
     def forward(self, text, label):

ercbcm/__init__.py

@@ -7,17 +7,16 @@ sys.path.insert(0, myPath + '/../')
 
 import torch
 
-from ercbcm.model_loader import load_checkpoint
+from ercbcm.model_loader import load
 from ercbcm.ERCBCM import ERCBCM
 from modules.tokenizer import tokenizer, normalize_v2, PAD_TOKEN_ID
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-print('>>> GPU Available?', torch.cuda.is_available())
 
 # ==========
 
 model_for_predict = ERCBCM().to(device)
-load_checkpoint('ercbcm/model.pt', model_for_predict, device)
+load('ercbcm/model.pt', model_for_predict, device)
 
 def predict(sentence, name):
     label = torch.tensor([0])

ercbcm/model_loader.py

@@ -1,35 +1,8 @@
 import torch
 
-# Save and Load Functions.
-
-def save_checkpoint(save_path, model, valid_loss):
-    if save_path == None:
-        return
-    state_dict = {'model_state_dict': model.state_dict(),
-                  'valid_loss': valid_loss}
-    torch.save(state_dict, save_path)
-    print('[SAVE] Model has been saved successfully to \'{}\''.format(save_path))
-
-def load_checkpoint(load_path, model, device):
-    if load_path == None:
-        return
+def load(load_path, model, device):
+    if load_path == None: return
     state_dict = torch.load(load_path, map_location=device)
-    print('[LOAD] Model has been loaded successfully from \'{}\''.format(load_path))
     model.load_state_dict(state_dict['model_state_dict'])
-    return state_dict['valid_loss']
-
-def save_metrics(save_path, train_loss_list, valid_loss_list, global_steps_list):
-    if save_path == None:
-        return
-    state_dict = {'train_loss_list': train_loss_list,
-                  'valid_loss_list': valid_loss_list,
-                  'global_steps_list': global_steps_list}
-    torch.save(state_dict, save_path)
-    print('[SAVE] Model with matrics has been saved successfully to \'{}\''.format(save_path))
-
-def load_metrics(load_path, device):
-    if load_path == None:
-        return
-    state_dict = torch.load(load_path, map_location=device)
-    print('[LOAD] Model with matrics has been loaded successfully from \'{}\''.format(load_path))
-    return state_dict['train_loss_list'], state_dict['valid_loss_list'], state_dict['global_steps_list']
+    print('[LOAD] Model has been loaded successfully from \'{}\''.format(load_path))
+    return state_dict['valid_loss']