修改 callback 的测试文件

fastNLP/core/callback.py

@@ -584,7 +584,9 @@ class TensorboardCallback(Callback):
             path = os.path.join(save_dir, 'tensorboard_logs_{}'.format(self.trainer.start_time))
         if tensorboardX_flag:
             self._summary_writer = SummaryWriter(path)
-    
+        else:
+            self._summary_writer = None
+            
     def on_batch_begin(self, batch_x, batch_y, indices):
         if "model" in self.options and self.graph_added is False:
             # tesorboardX 这里有大bug，暂时没法画模型图
@@ -596,10 +598,10 @@ class TensorboardCallback(Callback):
             self.graph_added = True
     
     def on_backward_begin(self, loss):
-        if "loss" in self.options:
+        if "loss" in self.options and self._summary_writer:
             self._summary_writer.add_scalar("loss", loss.item(), global_step=self.trainer.step)
         
-        if "model" in self.options:
+        if "model" in self.options and self._summary_writer:
             for name, param in self.trainer.model.named_parameters():
                 if param.requires_grad:
                     self._summary_writer.add_scalar(name + "_mean", param.mean(), global_step=self.trainer.step)
@@ -608,15 +610,16 @@ class TensorboardCallback(Callback):
                                                     global_step=self.trainer.step)
     
     def on_valid_end(self, eval_result, metric_key, optimizer, is_better_eval):
-        if "metric" in self.options:
+        if "metric" in self.options and self._summary_writer:
             for name, metric in eval_result.items():
                 for metric_key, metric_val in metric.items():
                     self._summary_writer.add_scalar("valid_{}_{}".format(name, metric_key), metric_val,
                                                     global_step=self.trainer.step)
     
     def on_train_end(self):
-        self._summary_writer.close()
-        del self._summary_writer
+        if self._summary_writer:
+            self._summary_writer.close()
+            del self._summary_writer
     
     def on_exception(self, exception):
         if hasattr(self, "_summary_writer"):

test/core/test_dataset.py

@@ -172,7 +172,7 @@ class TestDataSetMethods(unittest.TestCase):
         def split_sent(ins):
             return ins['raw_sentence'].split()
         csv_loader = CSVLoader(headers=['raw_sentence', 'label'],sep='\t')
-        dataset = csv_loader.load('../data_for_tests/tutorial_sample_dataset.csv')
+        dataset = csv_loader.load('test/data_for_tests/tutorial_sample_dataset.csv')
         dataset.drop(lambda x: len(x['raw_sentence'].split()) == 0, inplace=True)
         dataset.apply(split_sent, new_field_name='words', is_input=True)
         # print(dataset)

test/test_tutorials.py

@@ -10,7 +10,7 @@ from fastNLP.core.metrics import AccuracyMetric
 class TestTutorial(unittest.TestCase):
     def test_fastnlp_10min_tutorial(self):
         # 从csv读取数据到DataSet
-        sample_path = "data_for_tests/tutorial_sample_dataset.csv"
+        sample_path = "test/data_for_tests/tutorial_sample_dataset.csv"
         dataset = DataSet.read_csv(sample_path, headers=('raw_sentence', 'label'),
                                    sep='\t')
         print(len(dataset))
@@ -113,14 +113,14 @@ class TestTutorial(unittest.TestCase):
 
     def test_fastnlp_1min_tutorial(self):
         # tutorials/fastnlp_1min_tutorial.ipynb
-        data_path = "tutorials/sample_data/tutorial_sample_dataset.csv"
+        data_path = "test/data_for_tests/tutorial_sample_dataset.csv"
         ds = DataSet.read_csv(data_path, headers=('raw_sentence', 'label'), sep='\t')
         print(ds[1])
 
         # 将所有数字转为小写
         ds.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence')
         # label转int
-        ds.apply(lambda x: int(x['label']), new_field_name='label_seq', is_target=True)
+        ds.apply(lambda x: int(x['label']), new_field_name='target', is_target=True)
 
         def split_sent(ins):
             return ins['raw_sentence'].split()
@@ -137,9 +137,9 @@ class TestTutorial(unittest.TestCase):
         train_data.apply(lambda x: [vocab.add(word) for word in x['words']])
 
         # index句子, Vocabulary.to_index(word)
-        train_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq',
+        train_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='words',
                          is_input=True)
-        dev_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq',
+        dev_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='words',
                        is_input=True)
 
         from fastNLP.models import CNNText
@@ -152,14 +152,14 @@ class TestTutorial(unittest.TestCase):
                           dev_data=dev_data,
                           loss=CrossEntropyLoss(),
                           optimizer= Adam(),
-                          metrics=AccuracyMetric(target='label_seq')
+                          metrics=AccuracyMetric(target='target')
                           )
         trainer.train()
         print('Train finished!')
 
     def test_fastnlp_advanced_tutorial(self):
         import os
-        os.chdir("tutorials/fastnlp_advanced_tutorial")
+        os.chdir("test/tutorials/fastnlp_advanced_tutorial")
 
         from fastNLP import DataSet
         from fastNLP import Instance

tutorials/fastnlp_advanced_tutorial/advance_tutorial.ipynb

@@ -170,11 +170,11 @@
     {
      "data": {
       "text/plain": [
-       "DataSet({'image': tensor([[ 4.7106e-01, -1.2246e+00,  3.1234e-01, -1.6781e+00, -8.7967e-01],\n",
-       "        [ 1.1454e+00,  1.2236e-01,  3.0258e-01, -1.5454e+00,  8.9201e-01],\n",
-       "        [-5.7143e-03,  3.9488e-01,  2.0287e-01, -1.5726e+00,  9.3171e-01],\n",
-       "        [ 6.8914e-01, -2.6302e-01, -8.2694e-01,  9.5942e-01, -5.2589e-01],\n",
-       "        [-5.7798e-03, -9.1621e-03,  1.0077e-03,  9.1716e-02,  1.0565e+00]]) type=torch.Tensor,\n",
+       "DataSet({'image': tensor([[ 0.3582, -1.0358,  1.4785, -1.5288, -0.9982],\n",
+       "        [-0.3973, -0.4294,  0.9215, -1.9631, -1.6556],\n",
+       "        [ 0.3313, -1.7714,  0.8729,  0.6976, -1.3172],\n",
+       "        [-0.6403,  0.5023, -0.9919,  1.1178, -0.3710],\n",
+       "        [-0.3692,  1.8631, -1.3646, -0.7290, -1.0774]]) type=torch.Tensor,\n",
        "'label': 0 type=int})"
       ]
      },
@@ -524,7 +524,11 @@
    "outputs": [],
    "source": [
     "# 设定特征域、标签域\n",
-    "data_set.set_input(\"premise\", \"premise_len\", \"hypothesis\", \"hypothesis_len\")\n",
+    "data_set.rename_field(\"premise\",\"words1\")\n",
+    "data_set.rename_field(\"premise_len\",\"seq_len1\")\n",
+    "data_set.rename_field(\"hypothesis\",\"words2\")\n",
+    "data_set.rename_field(\"hypothesis_len\",\"seq_len2\")\n",
+    "data_set.set_input(\"words1\", \"seq_len1\", \"words2\", \"seq_len2\")\n",
     "data_set.set_target(\"truth\")"
    ]
   },
@@ -536,10 +540,10 @@
     {
      "data": {
       "text/plain": [
-       "{'premise': ['a', 'woman', 'is', 'walking', 'across', 'the', 'street', 'eating', 'a', 'banana', ',', 'while', 'a', 'man', 'is', 'following', 'with', 'his', 'briefcase', '.'] type=list,\n",
-       "'hypothesis': ['a', 'woman', 'eating', 'a', 'banana', 'crosses', 'a', 'street'] type=list,\n",
-       "'premise_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
-       "'hypothesis_len': [1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
+       "{'words1': ['a', 'woman', 'is', 'walking', 'across', 'the', 'street', 'eating', 'a', 'banana', ',', 'while', 'a', 'man', 'is', 'following', 'with', 'his', 'briefcase', '.'] type=list,\n",
+       "'seq_len1': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
+       "'words2': ['a', 'woman', 'eating', 'a', 'banana', 'crosses', 'a', 'street'] type=list,\n",
+       "'seq_len2': [1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
        "'label': 0 type=int}"
       ]
      },
@@ -613,7 +617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -622,49 +626,49 @@
     "vocab = Vocabulary(max_size=10000, min_freq=2, unknown='<unk>', padding='<pad>')\n",
     "\n",
     "# 构建词表\n",
-    "train_data.apply(lambda x: [vocab.add(word) for word in x['premise']])\n",
-    "train_data.apply(lambda x: [vocab.add(word) for word in x['hypothesis']])\n",
+    "train_data.apply(lambda x: [vocab.add(word) for word in x['words1']])\n",
+    "train_data.apply(lambda x: [vocab.add(word) for word in x['words2']])\n",
     "vocab.build_vocab()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "({'premise': [2, 10, 9, 2, 15, 115, 6, 11, 5, 132, 17, 2, 76, 9, 77, 55, 3] type=list,\n",
-       " 'hypothesis': [1, 2, 56, 17, 1, 4, 13, 49, 123, 12, 6, 11, 3] type=list,\n",
-       " 'premise_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
-       " 'hypothesis_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
-       " 'label': 0 type=int},\n",
-       " {'premise': [50, 124, 10, 7, 68, 91, 92, 38, 2, 55, 3] type=list,\n",
-       " 'hypothesis': [21, 10, 5, 2, 55, 7, 99, 64, 48, 1, 22, 1, 3] type=list,\n",
-       " 'premise_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
-       " 'hypothesis_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
+       "({'words1': [2, 9, 4, 2, 75, 85, 7, 86, 76, 77, 87, 88, 89, 2, 90, 3] type=list,\n",
+       " 'seq_len1': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
+       " 'words2': [18, 9, 10, 1, 3] type=list,\n",
+       " 'seq_len2': [1, 1, 1, 1, 1] type=list,\n",
        " 'label': 1 type=int},\n",
-       " {'premise': [13, 24, 4, 14, 29, 5, 25, 4, 8, 39, 9, 14, 34, 4, 40, 41, 4, 16, 12, 2, 11, 4, 30, 28, 2, 42, 8, 2, 43, 44, 17, 2, 45, 35, 26, 31, 27, 5, 6, 32, 3] type=list,\n",
-       " 'hypothesis': [37, 49, 123, 30, 28, 2, 55, 12, 2, 11, 3] type=list,\n",
-       " 'premise_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
-       " 'hypothesis_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
-       " 'label': 0 type=int})"
+       " {'words1': [22, 32, 5, 110, 81, 111, 112, 5, 82, 3] type=list,\n",
+       " 'seq_len1': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
+       " 'words2': [64, 32, 82, 133, 84, 3] type=list,\n",
+       " 'seq_len2': [1, 1, 1, 1, 1, 1] type=list,\n",
+       " 'label': 0 type=int},\n",
+       " {'words1': [2, 9, 97, 1, 20, 7, 54, 5, 1, 1, 70, 2, 11, 110, 2, 62, 3] type=list,\n",
+       " 'seq_len1': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
+       " 'words2': [23, 1, 58, 10, 12, 1, 70, 133, 84, 3] type=list,\n",
+       " 'seq_len2': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
+       " 'label': 1 type=int})"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# 根据词表index句子\n",
-    "train_data.apply(lambda x: [vocab.to_index(word) for word in x['premise']], new_field_name='premise')\n",
-    "train_data.apply(lambda x: [vocab.to_index(word) for word in x['hypothesis']], new_field_name='hypothesis')\n",
-    "dev_data.apply(lambda x: [vocab.to_index(word) for word in x['premise']], new_field_name='premise')\n",
-    "dev_data.apply(lambda x: [vocab.to_index(word) for word in x['hypothesis']], new_field_name='hypothesis')\n",
-    "test_data.apply(lambda x: [vocab.to_index(word) for word in x['premise']], new_field_name='premise')\n",
-    "test_data.apply(lambda x: [vocab.to_index(word) for word in x['hypothesis']], new_field_name='hypothesis')\n",
+    "train_data.apply(lambda x: [vocab.to_index(word) for word in x['words1']], new_field_name='words1')\n",
+    "train_data.apply(lambda x: [vocab.to_index(word) for word in x['words2']], new_field_name='words2')\n",
+    "dev_data.apply(lambda x: [vocab.to_index(word) for word in x['words1']], new_field_name='words1')\n",
+    "dev_data.apply(lambda x: [vocab.to_index(word) for word in x['words2']], new_field_name='words2')\n",
+    "test_data.apply(lambda x: [vocab.to_index(word) for word in x['words1']], new_field_name='words1')\n",
+    "test_data.apply(lambda x: [vocab.to_index(word) for word in x['words2']], new_field_name='words2')\n",
     "train_data[-1], dev_data[-1], test_data[-1]"
    ]
   },
@@ -679,7 +683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -703,35 +707,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "({'premise': [1037, 2158, 1998, 1037, 2450, 2892, 1996, 2395, 1999, 2392, 1997, 1037, 10733, 1998, 100, 4825, 1012] type=list,\n",
-       " 'hypothesis': [100, 1037, 3232, 1997, 7884, 1010, 2048, 2111, 3328, 2408, 1996, 2395, 1012] type=list,\n",
-       " 'premise_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
-       " 'hypothesis_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
-       " 'label': 0 type=int},\n",
-       " {'premise': [2019, 3080, 2158, 2003, 5948, 4589, 10869, 2012, 1037, 4825, 1012] type=list,\n",
-       " 'hypothesis': [100, 2158, 1999, 1037, 4825, 2003, 3403, 2005, 2010, 7954, 2000, 7180, 1012] type=list,\n",
-       " 'premise_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
-       " 'hypothesis_len': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
-       " 'label': 1 type=int})"
+       "({'words1': [1037, 2450, 1999, 1037, 2665, 6598, 1998, 7415, 2058, 2014, 2132, 2559, 2875, 1037, 3028, 1012] type=list,\n",
+       " 'seq_len1': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
+       " 'words2': [100, 2450, 2003, 3147, 1012] type=list,\n",
+       " 'seq_len2': [1, 1, 1, 1, 1] type=list,\n",
+       " 'label': 1 type=int},\n",
+       " {'words1': [2048, 2308, 1010, 3173, 2833, 100, 16143, 1010, 8549, 1012] type=list,\n",
+       " 'seq_len1': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1] type=list,\n",
+       " 'words2': [100, 2308, 8549, 2169, 2060, 1012] type=list,\n",
+       " 'seq_len2': [1, 1, 1, 1, 1, 1] type=list,\n",
+       " 'label': 0 type=int})"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# 根据词表index句子\n",
-    "train_data_2.apply(lambda x: [vocab_bert.to_index(word) for word in x['premise']], new_field_name='premise')\n",
-    "train_data_2.apply(lambda x: [vocab_bert.to_index(word) for word in x['hypothesis']], new_field_name='hypothesis')\n",
-    "dev_data_2.apply(lambda x: [vocab_bert.to_index(word) for word in x['premise']], new_field_name='premise')\n",
-    "dev_data_2.apply(lambda x: [vocab_bert.to_index(word) for word in x['hypothesis']], new_field_name='hypothesis')\n",
+    "train_data_2.apply(lambda x: [vocab_bert.to_index(word) for word in x['words1']], new_field_name='words1')\n",
+    "train_data_2.apply(lambda x: [vocab_bert.to_index(word) for word in x['words2']], new_field_name='words2')\n",
+    "dev_data_2.apply(lambda x: [vocab_bert.to_index(word) for word in x['words1']], new_field_name='words1')\n",
+    "dev_data_2.apply(lambda x: [vocab_bert.to_index(word) for word in x['words2']], new_field_name='words2')\n",
     "train_data_2[-1], dev_data_2[-1]"
    ]
   },
@@ -747,7 +751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -760,10 +764,10 @@
        " 'num_classes': 3,\n",
        " 'gpu': True,\n",
        " 'batch_size': 32,\n",
-       " 'vocab_size': 156}"
+       " 'vocab_size': 143}"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -779,7 +783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -788,21 +792,17 @@
        "ESIM(\n",
        "  (drop): Dropout(p=0.3)\n",
        "  (embedding): Embedding(\n",
-       "    (embed): Embedding(156, 300, padding_idx=0)\n",
+       "    143, 300\n",
        "    (dropout): Dropout(p=0.3)\n",
        "  )\n",
-       "  (embedding_layer): Linear(\n",
-       "    (linear): Linear(in_features=300, out_features=300, bias=True)\n",
-       "  )\n",
+       "  (embedding_layer): Linear(in_features=300, out_features=300, bias=True)\n",
        "  (encoder): LSTM(\n",
        "    (lstm): LSTM(300, 300, batch_first=True, bidirectional=True)\n",
        "  )\n",
-       "  (bi_attention): Bi_Attention()\n",
+       "  (bi_attention): BiAttention()\n",
        "  (mean_pooling): MeanPoolWithMask()\n",
        "  (max_pooling): MaxPoolWithMask()\n",
-       "  (inference_layer): Linear(\n",
-       "    (linear): Linear(in_features=1200, out_features=300, bias=True)\n",
-       "  )\n",
+       "  (inference_layer): Linear(in_features=1200, out_features=300, bias=True)\n",
        "  (decoder): LSTM(\n",
        "    (lstm): LSTM(300, 300, batch_first=True, bidirectional=True)\n",
        "  )\n",
@@ -816,7 +816,7 @@
        ")"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -824,49 +824,10 @@
    "source": [
     "# step 2：加载ESIM模型\n",
     "from fastNLP.models import ESIM\n",
-    "model = ESIM(**args.data)\n",
+    "model = ESIM(args[\"vocab_size\"], args[\"embed_dim\"], args[\"hidden_size\"], args[\"dropout\"], args[\"num_classes\"])\n",
     "model"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "CNNText(\n",
-       "  (embed): Embedding(\n",
-       "    (embed): Embedding(156, 50, padding_idx=0)\n",
-       "    (dropout): Dropout(p=0.0)\n",
-       "  )\n",
-       "  (conv_pool): ConvMaxpool(\n",
-       "    (convs): ModuleList(\n",
-       "      (0): Conv1d(50, 3, kernel_size=(3,), stride=(1,), padding=(2,))\n",
-       "      (1): Conv1d(50, 4, kernel_size=(4,), stride=(1,), padding=(2,))\n",
-       "      (2): Conv1d(50, 5, kernel_size=(5,), stride=(1,), padding=(2,))\n",
-       "    )\n",
-       "  )\n",
-       "  (dropout): Dropout(p=0.1)\n",
-       "  (fc): Linear(\n",
-       "    (linear): Linear(in_features=12, out_features=5, bias=True)\n",
-       "  )\n",
-       ")"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 另一个例子：加载CNN文本分类模型\n",
-    "from fastNLP.models import CNNText\n",
-    "cnn_text_model = CNNText(embed_num=len(vocab), embed_dim=50, num_classes=5, padding=2, dropout=0.1)\n",
-    "cnn_text_model"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1009,54 +970,25 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "training epochs started 2019-04-14-23-22-28\n",
-      "[epoch:   1 step:    1] train loss: 1.51372 time: 0:00:00\n",
-      "[epoch:   1 step:    2] train loss: 1.26874 time: 0:00:00\n",
-      "[epoch:   1 step:    3] train loss: 1.49786 time: 0:00:00\n",
-      "[epoch:   1 step:    4] train loss: 1.37505 time: 0:00:00\n",
-      "Evaluation at Epoch 1/5. Step:4/20. AccuracyMetric: acc=0.344828\n",
-      "\n",
-      "[epoch:   2 step:    5] train loss: 1.21877 time: 0:00:00\n",
-      "[epoch:   2 step:    6] train loss: 1.14183 time: 0:00:00\n",
-      "[epoch:   2 step:    7] train loss: 1.15934 time: 0:00:00\n",
-      "[epoch:   2 step:    8] train loss: 1.55148 time: 0:00:00\n",
-      "Evaluation at Epoch 2/5. Step:8/20. AccuracyMetric: acc=0.344828\n",
-      "\n",
-      "[epoch:   3 step:    9] train loss: 1.1457 time: 0:00:00\n",
-      "[epoch:   3 step:   10] train loss: 1.0547 time: 0:00:00\n",
-      "[epoch:   3 step:   11] train loss: 1.40139 time: 0:00:00\n",
-      "[epoch:   3 step:   12] train loss: 0.551445 time: 0:00:00\n",
-      "Evaluation at Epoch 3/5. Step:12/20. AccuracyMetric: acc=0.275862\n",
-      "\n",
-      "[epoch:   4 step:   13] train loss: 1.07965 time: 0:00:00\n",
-      "[epoch:   4 step:   14] train loss: 1.04118 time: 0:00:00\n",
-      "[epoch:   4 step:   15] train loss: 1.11719 time: 0:00:00\n",
-      "[epoch:   4 step:   16] train loss: 1.09861 time: 0:00:00\n",
-      "Evaluation at Epoch 4/5. Step:16/20. AccuracyMetric: acc=0.275862\n",
-      "\n",
-      "[epoch:   5 step:   17] train loss: 1.10795 time: 0:00:00\n",
-      "[epoch:   5 step:   18] train loss: 1.26715 time: 0:00:00\n",
-      "[epoch:   5 step:   19] train loss: 1.19875 time: 0:00:00\n",
-      "[epoch:   5 step:   20] train loss: 1.09862 time: 0:00:00\n",
-      "Evaluation at Epoch 5/5. Step:20/20. AccuracyMetric: acc=0.37931\n",
-      "\n",
-      "\n",
-      "In Epoch:5/Step:20, got best dev performance:AccuracyMetric: acc=0.37931\n",
-      "Reloaded the best model.\n"
+      "training epochs started 2019-05-14-19-49-25\n"
      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "{'best_eval': {'AccuracyMetric': {'acc': 0.37931}},\n",
-       " 'best_epoch': 5,\n",
-       " 'best_step': 20,\n",
-       " 'seconds': 0.5}"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "AssertionError",
+     "evalue": "seq_len can only have one dimension, got False.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-29-a3d2740dc8ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0muse_tqdm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m )\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/Users/fdujyn/anaconda3/lib/python3.6/site-packages/fastNLP/core/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, load_best_model)\u001b[0m\n\u001b[1;32m    522\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 524\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    525\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    526\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mCallbackException\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Users/fdujyn/anaconda3/lib/python3.6/site-packages/fastNLP/core/trainer.py\u001b[0m in \u001b[0;36m_train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    573\u001b[0m                     \u001b[0;31m# negative sampling; replace unknown; re-weight batch_y\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 575\u001b[0;31m                     \u001b[0mprediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m                     \u001b[0;31m# edit prediction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Users/fdujyn/anaconda3/lib/python3.6/site-packages/fastNLP/core/trainer.py\u001b[0m in \u001b[0;36m_data_forward\u001b[0;34m(self, network, x)\u001b[0m\n\u001b[1;32m    661\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_data_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnetwork\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    662\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_build_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnetwork\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 663\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnetwork\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    664\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    665\u001b[0m             raise TypeError(\n",
+      "\u001b[0;32m/Users/fdujyn/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    489\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 491\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    492\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    493\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Users/fdujyn/anaconda3/lib/python3.6/site-packages/fastNLP/models/snli.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, words1, words2, seq_len1, seq_len2, target)\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mseq_len1\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m             \u001b[0mseq_len1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseq_len_to_mask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseq_len1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     79\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m             \u001b[0mseq_len1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpremise0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpremise0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Users/fdujyn/anaconda3/lib/python3.6/site-packages/fastNLP/core/utils.py\u001b[0m in \u001b[0;36mseq_len_to_mask\u001b[0;34m(seq_len)\u001b[0m\n\u001b[1;32m    626\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseq_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 628\u001b[0;31m         \u001b[0;32massert\u001b[0m \u001b[0mseq_len\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"seq_len can only have one dimension, got {seq_len.dim() == 1}.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    629\u001b[0m         \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseq_len\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    630\u001b[0m         \u001b[0mmax_len\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseq_len\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAssertionError\u001b[0m: seq_len can only have one dimension, got False."
+     ]
     }
    ],
    "source": [
@@ -1073,7 +1005,6 @@
     "    print_every=-1,\n",
     "    validate_every=-1,\n",
     "    dev_data=dev_data,\n",
-    "    use_cuda=True,\n",
     "    optimizer=Adam(lr=1e-3, weight_decay=0),\n",
     "    check_code_level=-1,\n",
     "    metric_key='acc',\n",
@@ -1178,7 +1109,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,