def __init__(self, opt, emb_matrix=None):
print(">> Current Model: StudentNetwork")
super(StudentNetwork, self).__init__()
if opt['base_mode'] == 0: # start from scratch
print(" Do not use base model.")
self.inst_encoder = BaseNetwork(opt=opt, emb_matrix=emb_matrix)
else:
self.base_model_file = opt['save_dir'] + '/' + opt['base_id'] + '/best_model.pt'
self.base_opt = torch_utils.load_config(self.base_model_file)
if opt['base_mode'] == 1: # load & fine tune
print(" Fine-tune base model.")
inst_base_model = BaseModel(self.base_opt)
inst_base_model.load(self.base_model_file)
self.inst_encoder = inst_base_model.model
elif opt['base_mode'] == 2: # load & fix pre-trained
print(" Fix pre-trained base model.")
inst_base_model = BaseModel(self.base_opt)
inst_base_model.load(self.base_model_file)
inst_base_model = inst_base_model.model
for param in inst_base_model.parameters():
param.requires_grad = False
inst_base_model.eval()
self.inst_encoder = inst_base_model
else:
print('Illegal Parameter (base_mode).')
assert False
self.pe_emb = nn.Embedding(constant.MAX_LEN * 2 + 1, opt['pe_dim_attn'])
self.ner_emb = nn.Embedding(constant.NER_NUM, opt['ner_dim_attn'])
self.attn_layer = MultiAspectAttention(opt)
self.final_linear = nn.Linear(2 * opt['hidden_dim'], opt['num_class'])
self.opt = opt
self.init_weights()
def __init__(self, opt, emb_matrix=None):
print(">> Current Model: TeacherNetwork")
super(TeacherNetwork, self).__init__()
if opt['base_mode'] == 0: # start from scratch
print(" Do not use base model.")
self.inst_encoder = BaseNetwork(opt=opt, emb_matrix=emb_matrix)
else:
self.base_model_file = opt['save_dir'] + '/' + opt[
'base_id'] + '/best_model.pt'
self.base_opt = torch_utils.load_config(self.base_model_file)
if opt['base_mode'] == 1: # load & fine tune
print(" Fine-tune base model.")
inst_base_model = BaseModel(self.base_opt)
inst_base_model.load(self.base_model_file)
self.inst_encoder = inst_base_model.model
elif opt['base_mode'] == 2: # load & fix pre-trained
print(" Fix pre-trained base model.")
inst_base_model = BaseModel(self.base_opt)
inst_base_model.load(self.base_model_file)
inst_base_model = inst_base_model.model
for param in inst_base_model.parameters():
param.requires_grad = False
inst_base_model.eval()
self.inst_encoder = inst_base_model
else:
print('Illegal Parameter (base_mode).')
assert False
self.rel_matrix = nn.Embedding(
opt['num_class'],
opt['num_class'],
padding_idx=constant.LABEL_TO_ID['no_relation'])
self.opt = opt
self.init_weights()
file_logger = helper.FileLogger(
model_save_dir + '/' + opt['log'],
header="# epoch\ttrain_loss\tdev_loss\tdev_f1\ttest_loss\ttest_f1")
# print model info
helper.print_config(opt)
# model
base_outputs = ['placeholder' for _ in range(0, len(train_batch))]
if opt['base_mode'] == 3:
base_outputs = []
base_model_file = opt['save_dir'] + '/' + opt['base_id'] + '/best_model.pt'
print("Loading base model from {}".format(base_model_file))
base_opt = torch_utils.load_config(base_model_file)
base_model = BaseModel(opt=base_opt)
base_model.load(base_model_file)
base_model.model.eval()
for _, batch in enumerate(train_batch):
inputs = [b.cuda() for b in batch[:10]
] if opt['cuda'] else [b for b in batch[:10]]
base_logits, _, _ = base_model.model(inputs)
base_outputs.append([base_logits.data.cpu().numpy()])
teacher_outputs = []
if opt['use_teacher']:
teacher_model_file = opt['save_dir'] + '/' + opt[
'teacher_id'] + '/best_model.pt'
print("Loading teacher model from {}".format(teacher_model_file))
teacher_opt = torch_utils.load_config(teacher_model_file)
teacher_model = TeacherModel(opt=teacher_opt)
teacher_model.load(teacher_model_file)
