add doc comments

fastNLP/core/fieldarray.py

@@ -20,6 +20,7 @@ class FieldArray(object):
         self.padding_val = padding_val
         self.is_target = is_target
         self.is_input = is_input
+        # TODO: auto detect dtype
         self.dtype = None
 
     def __repr__(self):

fastNLP/io/dataset_loader.py

@@ -1,3 +1,4 @@
+#TODO: need fix for current DataSet
 import os
 
 from fastNLP.core.dataset import DataSet

fastNLP/models/cnn_text_classification.py

@@ -37,8 +37,9 @@ class CNNText(torch.nn.Module):
 
     def forward(self, word_seq):
         """
+
         :param word_seq: torch.LongTensor, [batch_size, seq_len]
-        :return x: torch.LongTensor, [batch_size, num_classes]
+        :return output: dict of torch.LongTensor, [batch_size, num_classes]
         """
         x = self.embed(word_seq)  # [N,L] -> [N,L,C]
         x = self.conv_pool(x)  # [N,L,C] -> [N,C]
@@ -47,14 +48,31 @@ class CNNText(torch.nn.Module):
         return {'output':x}
 
     def predict(self, word_seq):
+        """
+
+        :param word_seq: torch.LongTensor, [batch_size, seq_len]
+        :return predict: dict of torch.LongTensor, [batch_size, seq_len]
+        """
         output = self(word_seq)
         _, predict = output['output'].max(dim=1)
         return {'predict': predict}
 
     def get_loss(self, output, label_seq):
+        """
+
+        :param output: output of forward(), [batch_size, seq_len]
+        :param label_seq: true label in DataSet, [batch_size, seq_len]
+        :return loss: torch.Tensor
+        """
         return self._loss(output, label_seq)
 
     def evaluate(self, predict, label_seq):
+        """
+
+        :param predict: iterable predict tensors
+        :param label_seq: iterable true label tensors
+        :return accuracy: dict of float
+        """
         predict, label_seq = torch.stack(tuple(predict), dim=0), torch.stack(tuple(label_seq), dim=0)
         predict, label_seq = predict.squeeze(), label_seq.squeeze()
         correct = (predict == label_seq).long().sum().item()