修改代码以适配新embeddings模块

reproduction/text_classification/model/awd_lstm.py

@@ -2,7 +2,7 @@ import torch
 import torch.nn as nn
 from fastNLP.core.const import Const as C
 from .awdlstm_module import LSTM
-from fastNLP.modules import encoder
+from fastNLP.embeddings.utils import get_embeddings
 from fastNLP.modules.decoder.mlp import MLP
 
 
@@ -14,7 +14,7 @@ class AWDLSTMSentiment(nn.Module):
                  nfc=128,
                  wdrop=0.5):
         super(AWDLSTMSentiment,self).__init__()
-        self.embed = encoder.Embedding(init_embed)
+        self.embed = get_embeddings(init_embed)
         self.lstm = LSTM(input_size=self.embed.embedding_dim, hidden_size=hidden_dim, num_layers=num_layers, bidirectional=True, wdrop=wdrop)
         self.mlp = MLP(size_layer=[hidden_dim* 2, nfc, num_classes])
 

reproduction/text_classification/model/lstm.py

@@ -2,7 +2,7 @@ import torch
 import torch.nn as nn
 from fastNLP.core.const import Const as C
 from fastNLP.modules.encoder.lstm import LSTM
-from fastNLP.modules import encoder
+from fastNLP.embeddings.utils import get_embeddings
 from fastNLP.modules.decoder.mlp import MLP
 
 
@@ -13,7 +13,7 @@ class BiLSTMSentiment(nn.Module):
                  num_layers=1,
                  nfc=128):
         super(BiLSTMSentiment,self).__init__()
-        self.embed = encoder.Embedding(init_embed)
+        self.embed = get_embeddings(init_embed)
         self.lstm = LSTM(input_size=self.embed.embedding_dim, hidden_size=hidden_dim, num_layers=num_layers, bidirectional=True)
         self.mlp = MLP(size_layer=[hidden_dim* 2, nfc, num_classes])
 

reproduction/text_classification/model/lstm_self_attention.py

@@ -2,8 +2,8 @@ import torch
 import torch.nn as nn
 from fastNLP.core.const import Const as C
 from fastNLP.modules.encoder.lstm import LSTM
-from fastNLP.modules import encoder
-from fastNLP.modules.aggregator.attention import SelfAttention
+from fastNLP.embeddings.utils import get_embeddings
+from fastNLP.modules.encoder.attention import SelfAttention
 from fastNLP.modules.decoder.mlp import MLP
 
 
@@ -16,7 +16,7 @@ class BiLSTM_SELF_ATTENTION(nn.Module):
                  attention_hops=1,
                  nfc=128):
         super(BiLSTM_SELF_ATTENTION,self).__init__()
-        self.embed = encoder.Embedding(init_embed)
+        self.embed = get_embeddings(init_embed)
         self.lstm = LSTM(input_size=self.embed.embedding_dim, hidden_size=hidden_dim, num_layers=num_layers, bidirectional=True)
         self.attention = SelfAttention(input_size=hidden_dim * 2 , attention_unit=attention_unit, attention_hops=attention_hops)
         self.mlp = MLP(size_layer=[hidden_dim* 2*attention_hops, nfc, num_classes])

reproduction/text_classification/train_awdlstm.py

@@ -5,7 +5,7 @@ import os
 os.environ['FASTNLP_BASE_URL'] = 'http://10.141.222.118:8888/file/download/'
 os.environ['FASTNLP_CACHE_DIR'] = '/remote-home/hyan01/fastnlp_caches'
 
-from data.IMDBLoader import IMDBLoader
+from fastNLP.io.data_loader import IMDBLoader
 from fastNLP.embeddings import StaticEmbedding
 from model.awd_lstm import AWDLSTMSentiment