[Model] Update ERC Model to v2-1216-03

{model → ercbcm}/model.pt

@@ -1,3 +1,3 @@
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-size 438022701
+oid sha256:3c5e5b3e35d73c471fb1c5ddaddb8f5c81b8b6d99d61f1a94599a03b9689db8c
+size 438023533

model/ERCBCM.py

@@ -1,14 +0,0 @@
-from torch import nn
-from transformers import BertForSequenceClassification
-
-class ERCBCM(nn.Module):
-  
-    def __init__(self):
-        super(ERCBCM, self).__init__()
-        print('>>> ERCBCM Init!')
-
-        self.encoder = BertForSequenceClassification.from_pretrained('bert-base-uncased')
-
-    def forward(self, text, label):
-        loss, text_fea = self.encoder(text, labels=label)[:2]
-        return loss, text_fea

model/__init__.py

@@ -1,38 +0,0 @@
-import os, sys
-
-myPath = os.path.dirname(os.path.abspath(__file__))
-sys.path.insert(0, myPath + '/../../')
-
-# ==========
-
-import torch
-
-from modules.prediction.model_loader import load_checkpoint
-from modules.prediction.ERCBCM import ERCBCM
-from modules.tokenizer import tokenizer, normalize_v2, PAD_TOKEN_ID
-
-erc_root_folder = './model'
-
-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(erc_root_folder + '/model.pt', model_for_predict, device)
-
-def predict(sentence, name):
-    label = torch.tensor([0])
-    label = label.type(torch.LongTensor)
-    label = label.to(device)
-    text = tokenizer.encode(normalize_v2(sentence, name))
-    text += [PAD_TOKEN_ID] * (128 - len(text))
-    text = torch.tensor([text])
-    text = text.type(torch.LongTensor)
-    text = text.to(device)
-    _, output = model_for_predict(text, label)
-    pred = torch.argmax(output, 1).tolist()[0]
-    return 'CALLING' if pred == 1 else 'MENTIONING'
-
-print(predict('are you okay, jimmy', 'jimmy'))
-print(predict('jimmy is good', 'jimmy'))

model/model_loader.py

@@ -1,38 +0,0 @@
-import torch
-
-import torch.nn as nn
-
-# 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
-    state_dict = torch.load(load_path, map_location=device)
-    print('DICT:', state_dict)
-    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']