From 5edd9de84178db51c7492da86d76f3468092bde3 Mon Sep 17 00:00:00 2001
From: yunfan <yunfan.shao@outlook.com>
Date: Tue, 4 Dec 2018 15:49:01 +0800
Subject: [PATCH] fix bugs

---
 fastNLP/core/dataset.py                   |  2 +-
 fastNLP/models/cnn_text_classification.py | 23 -----------------------
 2 files changed, 1 insertion(+), 24 deletions(-)

diff --git a/fastNLP/core/dataset.py b/fastNLP/core/dataset.py
index bc4dcf57..cdca4356 100644
--- a/fastNLP/core/dataset.py
+++ b/fastNLP/core/dataset.py
@@ -67,8 +67,8 @@ class DataSet(object):
                 self.dataset = dataset
                 self.idx = idx
             def __getitem__(self, item):
-                assert self.idx < len(self.dataset), "index:{} out of range".format(self.idx)
                 assert item in self.dataset.field_arrays, "no such field:{} in instance {}".format(item, self.dataset[self.idx])
+                assert self.idx < len(self.dataset.field_arrays[item]), "index:{} out of range".format(self.idx)
                 return self.dataset.field_arrays[item][self.idx]
             def __repr__(self):
                 return self.dataset[self.idx].__repr__()
diff --git a/fastNLP/models/cnn_text_classification.py b/fastNLP/models/cnn_text_classification.py
index 9aa07e66..c8fe5181 100644
--- a/fastNLP/models/cnn_text_classification.py
+++ b/fastNLP/models/cnn_text_classification.py
@@ -33,7 +33,6 @@ class CNNText(torch.nn.Module):
             padding=padding)
         self.dropout = nn.Dropout(dropout)
         self.fc = encoder.Linear(sum(kernel_nums), num_classes)
-        self._loss = nn.CrossEntropyLoss()
 
     def forward(self, word_seq):
         """
@@ -56,25 +55,3 @@ class CNNText(torch.nn.Module):
         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()
-        total = label_seq.size(0)
-        return {'acc': 1.0 * correct / total}