clean some codes and fix some bugs

fastNLP/core/dataset.py

@@ -30,8 +30,18 @@ class DataSet(list):
         return self
 
     def index_field(self, field_name, vocab):
-        for ins in self:
-            ins.index_field(field_name, vocab)
+        if isinstance(field_name, str) and isinstance(vocab, Vocabulary):
+            field_list = [field_name]
+            vocab_list = [vocab]
+        else:
+            classes = (list, tuple)
+            assert isinstance(field_name, classes) and isinstance(vocab, classes) and len(field_name) == len(vocab)
+            field_list = field_name
+            vocab_list = vocab
+
+        for name, vocabs in zip(field_list, vocab_list):
+            for ins in self:
+                ins.index_field(name, vocabs)
         return self
 
     def to_tensor(self, idx: int, padding_length: dict):

fastNLP/core/metrics.py

@@ -57,6 +57,20 @@ class SeqLabelEvaluator(Evaluator):
         return {"accuracy": float(accuracy)}
 
 
+class SNLIEvaluator(Evaluator):
+    def __init__(self):
+        super(SNLIEvaluator, self).__init__()
+
+    def __call__(self, predict, truth):
+        y_prob = [torch.nn.functional.softmax(y_logit, dim=-1) for y_logit in predict]
+        y_prob = torch.cat(y_prob, dim=0)
+        y_pred = torch.argmax(y_prob, dim=-1)
+        truth = [t['truth'] for t in truth]
+        y_true = torch.cat(truth, dim=0).view(-1)
+        acc = float(torch.sum(y_pred == y_true)) / y_true.size(0)
+        return {"accuracy": acc}
+
+
 def _conver_numpy(x):
     """convert input data to numpy array
 

fastNLP/core/tester.py

@@ -83,6 +83,7 @@ class Tester(object):
             truth_list.append(batch_y)
         eval_results = self.evaluate(output_list, truth_list)
         print("[tester] {}".format(self.print_eval_results(eval_results)))
+        logger.info("[tester] {}".format(self.print_eval_results(eval_results)))
 
     def mode(self, model, is_test=False):
         """Train mode or Test mode. This is for PyTorch currently.
@@ -131,3 +132,10 @@ class ClassificationTester(Tester):
         print(
             "[FastNLP Warning] ClassificationTester will be deprecated. Please use Tester directly.")
         super(ClassificationTester, self).__init__(**test_args)
+
+
+class SNLITester(Tester):
+    def __init__(self, **test_args):
+        print(
+            "[FastNLP Warning] SNLITester will be deprecated. Please use Tester directly.")
+        super(SNLITester, self).__init__(**test_args)

fastNLP/core/vocabulary.py

@@ -18,6 +18,7 @@ def isiterable(p_object):
         return False
     return True
 
+
 def check_build_vocab(func):
     def _wrapper(self, *args, **kwargs):
         if self.word2idx is None:
@@ -28,6 +29,7 @@ def check_build_vocab(func):
         return func(self, *args, **kwargs)
     return _wrapper
 
+
 class Vocabulary(object):
     """Use for word and index one to one mapping
 
@@ -52,7 +54,6 @@ class Vocabulary(object):
         self.word2idx = None
         self.idx2word = None
 
-
     def update(self, word):
         """add word or list of words into Vocabulary
 
@@ -70,7 +71,6 @@ class Vocabulary(object):
                 self.word_count[word] += 1
             self.word2idx = None
 
-
     def build_vocab(self):
         """build 'word to index' dict, and filter the word using `max_size` and `min_freq`
         """
@@ -163,3 +163,11 @@ class Vocabulary(object):
         """
         self.__dict__.update(state)
         self.idx2word = None
+
+    def __contains__(self, item):
+        """Check if a word in vocabulary.
+
+        :param item: the word
+        :return: True or False
+        """
+        return self.has_word(item)

fastNLP/loader/dataset_loader.py

@@ -5,6 +5,7 @@ from fastNLP.core.dataset import DataSet
 from fastNLP.core.instance import Instance
 from fastNLP.core.field import *
 
+
 def convert_seq_dataset(data):
     """Create an DataSet instance that contains no labels.
 
@@ -23,6 +24,7 @@ def convert_seq_dataset(data):
         dataset.append(Instance(word_seq=x))
     return dataset
 
+
 def convert_seq2tag_dataset(data):
     """Convert list of data into DataSet
 
@@ -45,6 +47,7 @@ def convert_seq2tag_dataset(data):
         dataset.append(ins)
     return dataset
 
+
 def convert_seq2seq_dataset(data):
     """Convert list of data into DataSet
 
@@ -84,6 +87,7 @@ class DataSetLoader(BaseLoader):
         """
         raise NotImplementedError
 
+
 class RawDataSetLoader(DataSetLoader):
     def __init__(self):
         super(RawDataSetLoader, self).__init__()
@@ -98,6 +102,7 @@ class RawDataSetLoader(DataSetLoader):
     def convert(self, data):
         return convert_seq_dataset(data)
 
+
 class POSDataSetLoader(DataSetLoader):
     """Dataset Loader for POS Tag datasets.
 
@@ -166,6 +171,7 @@ class POSDataSetLoader(DataSetLoader):
         """
         return convert_seq2seq_dataset(data)
 
+
 class TokenizeDataSetLoader(DataSetLoader):
     """
     Data set loader for tokenization data sets
@@ -339,6 +345,7 @@ class LMDataSetLoader(DataSetLoader):
     def convert(self, data):
         pass
 
+
 class PeopleDailyCorpusLoader(DataSetLoader):
     """
         People Daily Corpus: Chinese word segmentation, POS tag, NER
@@ -390,3 +397,72 @@ class PeopleDailyCorpusLoader(DataSetLoader):
 
     def convert(self, data):
         pass
+
+
+class SNLIDataSetLoader(DataSetLoader):
+    """A data set loader for SNLI data set.
+
+    """
+
+    def __init__(self):
+        super(SNLIDataSetLoader, self).__init__()
+
+    def load(self, path_list):
+        """
+
+        :param path_list: A list of file name, in the order of premise file, hypothesis file, and label file.
+        :return: data_set: A DataSet object.
+        """
+        assert len(path_list) == 3
+        line_set = []
+        for file in path_list:
+            if not os.path.exists(file):
+                raise FileNotFoundError("file {} NOT found".format(file))
+
+            with open(file, 'r', encoding='utf-8') as f:
+                lines = f.readlines()
+                line_set.append(lines)
+
+        premise_lines, hypothesis_lines, label_lines = line_set
+        assert len(premise_lines) == len(hypothesis_lines) and len(premise_lines) == len(label_lines)
+
+        data_set = []
+        for premise, hypothesis, label in zip(premise_lines, hypothesis_lines, label_lines):
+            p = premise.strip().split()
+            h = hypothesis.strip().split()
+            l = label.strip()
+            data_set.append([p, h, l])
+
+        return self.convert(data_set)
+
+    def convert(self, data):
+        """Convert a 3D list to a DataSet object.
+
+        :param data: A 3D tensor.
+            [
+                [ [premise_word_11, premise_word_12, ...], [hypothesis_word_11, hypothesis_word_12, ...], [label_1] ],
+                [ [premise_word_21, premise_word_22, ...], [hypothesis_word_21, hypothesis_word_22, ...], [label_2] ],
+                ...
+            ]
+        :return: data_set: A DataSet object.
+        """
+
+        data_set = DataSet()
+
+        for example in data:
+            p, h, l = example
+            # list, list, str
+            x1 = TextField(p, is_target=False)
+            x2 = TextField(h, is_target=False)
+            x1_len = TextField([1] * len(p), is_target=False)
+            x2_len = TextField([1] * len(h), is_target=False)
+            y = LabelField(l, is_target=True)
+            instance = Instance()
+            instance.add_field("premise", x1)
+            instance.add_field("hypothesis", x2)
+            instance.add_field("premise_len", x1_len)
+            instance.add_field("hypothesis_len", x2_len)
+            instance.add_field("truth", y)
+            data_set.append(instance)
+
+        return data_set

fastNLP/loader/embed_loader.py

@@ -6,11 +6,12 @@ import torch
 from fastNLP.loader.base_loader import BaseLoader
 from fastNLP.core.vocabulary import Vocabulary
 
+
 class EmbedLoader(BaseLoader):
     """docstring for EmbedLoader"""
 
-    def __init__(self, data_path):
-        super(EmbedLoader, self).__init__(data_path)
+    def __init__(self):
+        super(EmbedLoader, self).__init__()
 
     @staticmethod
     def _load_glove(emb_file):
@@ -55,15 +56,15 @@ class EmbedLoader(BaseLoader):
         :param emb_type: str, the pre-trained embedding format, support glove now
         :param vocab: Vocabulary, a mapping from word to index, can be provided by user or built from pre-trained embedding
         :param emb_pkl: str, the embedding pickle file.
-        :return embedding_np: numpy array of shape (len(word_dict), emb_dim)
+        :return embedding_tensor: Tensor of shape (len(word_dict), emb_dim)
                 vocab: input vocab or vocab built by pre-train
         TODO: fragile code
         """
         # If the embedding pickle exists, load it and return.
         if os.path.exists(emb_pkl):
             with open(emb_pkl, "rb") as f:
-                embedding_np, vocab = _pickle.load(f)
-            return embedding_np, vocab
+                embedding_tensor, vocab = _pickle.load(f)
+            return embedding_tensor, vocab
         # Otherwise, load the pre-trained embedding.
         pretrain = EmbedLoader._load_pretrain(emb_file, emb_type)
         if vocab is None:
@@ -71,14 +72,14 @@ class EmbedLoader(BaseLoader):
             vocab = Vocabulary()
             for w in pretrain.keys():
                 vocab.update(w)
-        embedding_np = torch.randn(len(vocab), emb_dim)
+        embedding_tensor = torch.randn(len(vocab), emb_dim)
         for w, v in pretrain.items():
             if len(v.shape) > 1 or emb_dim != v.shape[0]:
                 raise ValueError('pretrian embedding dim is {}, dismatching required {}'.format(v.shape, (emb_dim,)))
             if vocab.has_word(w):
-                embedding_np[vocab[w]] = v
+                embedding_tensor[vocab[w]] = v
 
         # save and return the result
         with open(emb_pkl, "wb") as f:
-            _pickle.dump((embedding_np, vocab), f)
-        return embedding_np, vocab
+            _pickle.dump((embedding_tensor, vocab), f)
+        return embedding_tensor, vocab

fastNLP/modules/decoder/MLP.py

@@ -1,12 +1,15 @@
 import torch
 import torch.nn as nn
 from fastNLP.modules.utils import initial_parameter
+
+
 class MLP(nn.Module):
-    def __init__(self, size_layer, activation='relu' , initial_method = None):
+    def __init__(self, size_layer, activation='relu', initial_method=None):
         """Multilayer Perceptrons as a decoder
 
-        :param size_layer: list of int, define the size of MLP layers
-        :param activation: str or function, the activation function for hidden layers
+        :param size_layer: list of int, define the size of MLP layers.
+        :param activation: str or function, the activation function for hidden layers.
+        :param initial_method: str, the name of init method.
 
         .. note::
             There is no activation function applying on output layer.
@@ -23,7 +26,7 @@ class MLP(nn.Module):
 
         actives = {
             'relu': nn.ReLU(),
-            'tanh': nn.Tanh()
+            'tanh': nn.Tanh(),
         }
         if activation in actives:
             self.hidden_active = actives[activation]
@@ -31,7 +34,7 @@ class MLP(nn.Module):
             self.hidden_active = activation
         else:
             raise ValueError("should set activation correctly: {}".format(activation))
-        initial_parameter(self, initial_method  )
+        initial_parameter(self, initial_method)
 
     def forward(self, x):
         for layer in self.hiddens:
@@ -40,13 +43,11 @@ class MLP(nn.Module):
         return x
 
 
-
 if __name__ == '__main__':
-    net1 = MLP([5,10,5])
-    net2 = MLP([5,10,5], 'tanh')
+    net1 = MLP([5, 10, 5])
+    net2 = MLP([5, 10, 5], 'tanh')
     for net in [net1, net2]:
         x = torch.randn(5, 5)
         y = net(x)
         print(x)
         print(y)
-    

fastNLP/modules/encoder/linear.py

@@ -1,6 +1,8 @@
 import torch.nn as nn
 
 from fastNLP.modules.utils import initial_parameter
+
+
 class Linear(nn.Module):
     """
     Linear module
@@ -12,10 +14,11 @@ class Linear(nn.Module):
     bidirectional : If True, becomes a bidirectional RNN
     """
 
-    def __init__(self, input_size, output_size, bias=True,initial_method = None        ):
+    def __init__(self, input_size, output_size, bias=True, initial_method=None):
         super(Linear, self).__init__()
         self.linear = nn.Linear(input_size, output_size, bias)
         initial_parameter(self, initial_method)
+
     def forward(self, x):
         x = self.linear(x)
         return x

fastNLP/modules/encoder/lstm.py

@@ -14,16 +14,23 @@ class LSTM(nn.Module):
     bidirectional : If True, becomes a bidirectional RNN. Default: False.
     """
 
-    def __init__(self, input_size, hidden_size=100, num_layers=1, dropout=0.0, bidirectional=False,
-                 initial_method=None):
+    def __init__(self, input_size, hidden_size=100, num_layers=1, dropout=0.0, batch_first=True,
+                 bidirectional=False, bias=True, initial_method=None, get_hidden=False):
         super(LSTM, self).__init__()
-        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, bias=True, batch_first=True,
+        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, bias=bias, batch_first=batch_first,
                             dropout=dropout, bidirectional=bidirectional)
+        self.get_hidden = get_hidden
         initial_parameter(self, initial_method)
 
-    def forward(self, x):
-        x, _ = self.lstm(x)
-        return x
+    def forward(self, x, h0=None, c0=None):
+        if h0 is not None and c0 is not None:
+            x, (ht, ct) = self.lstm(x, (h0, c0))
+        else:
+            x, (ht, ct) = self.lstm(x)
+        if self.get_hidden:
+            return x, (ht, ct)
+        else:
+            return x
 
 
 if __name__ == "__main__":

test/data_for_tests/config

@@ -45,3 +45,28 @@ use_cuda = true
 learn_rate = 1e-3
 momentum = 0.9
 model_name = "class_model.pkl"
+
+[snli_trainer]
+epochs = 5
+batch_size = 32
+validate = true
+save_best_dev = true
+use_cuda = true
+learn_rate = 1e-4
+loss = "cross_entropy"
+print_every_step = 1000
+
+[snli_tester]
+batch_size = 512
+use_cuda = true
+
+[snli_model]
+model_name = "snli_model.pkl"
+embed_dim = 300
+hidden_size = 300
+batch_first = true
+dropout = 0.5
+gpu = true
+embed_file = "./../data_for_tests/glove.840B.300d.txt"
+embed_pkl = "./snli/embed.pkl"
+examples = 0