Merge branch 'trainer' of github.com:FengZiYjun/fastNLP into trainer

fastNLP/core/dataset.py

@@ -87,6 +87,8 @@ class DataSet(object):
         if isinstance(idx, int):
             return Instance(**{name: self.field_arrays[name][idx] for name in self.field_arrays})
         elif isinstance(idx, slice):
+            if idx.start is not None and (idx.start >= len(self) or idx.start <= -len(self)):
+                raise RuntimeError(f"Start index {idx.start} out of range 0-{len(self)-1}")
             data_set = DataSet()
             for field in self.field_arrays.values():
                 data_set.add_field(name=field.name,
@@ -135,7 +137,9 @@ class DataSet(object):
         :param bool is_target: whether this field is label or target.
         """
         if len(self.field_arrays) != 0:
-            assert len(self) == len(fields)
+            if len(self) != len(fields):
+                raise RuntimeError(f"The field to append must have the same size as dataset. "
+                                   f"Dataset size {len(self)} != field size {len(fields)}")
         self.field_arrays[name] = FieldArray(name, fields, padding_val=padding_val, is_target=is_target,
                                              is_input=is_input)
 

fastNLP/core/fieldarray.py

@@ -33,7 +33,10 @@ class FieldArray(object):
             type_set = set([type(item) for item in content[0]])
         else:
             # 1-D list
+            if len(content) == 0:
+                raise RuntimeError("Cannot create FieldArray with an empty list.")
             type_set = set([type(item) for item in content])
+
         if len(type_set) == 1 and any(basic_type in type_set for basic_type in (str, int, float)):
             return type_set.pop()
         elif len(type_set) == 2 and float in type_set and int in type_set:

fastNLP/core/optimizer.py

@@ -42,8 +42,10 @@ class SGD(Optimizer):
 
     def construct_from_pytorch(self, model_params):
         if self.model_params is None:
-            self.model_params = model_params
-        return torch.optim.SGD(self.model_params, **self.settings)
+            # careful! generator cannot be assigned.
+            return torch.optim.SGD(model_params, **self.settings)
+        else:
+            return torch.optim.SGD(self.model_params, **self.settings)
 
 
 class Adam(Optimizer):
@@ -75,5 +77,7 @@ class Adam(Optimizer):
 
     def construct_from_pytorch(self, model_params):
         if self.model_params is None:
-            self.model_params = model_params
-        return torch.optim.Adam(self.model_params, **self.settings)
+            # careful! generator cannot be assigned.
+            return torch.optim.Adam(model_params, **self.settings)
+        else:
+            return torch.optim.Adam(self.model_params, **self.settings)

fastNLP/models/cnn_text_classification.py

@@ -18,8 +18,8 @@ class CNNText(torch.nn.Module):
     def __init__(self, embed_num,
                  embed_dim,
                  num_classes,
-                 kernel_nums=(3,4,5),
-                 kernel_sizes=(3,4,5),
+                 kernel_nums=(3, 4, 5),
+                 kernel_sizes=(3, 4, 5),
                  padding=0,
                  dropout=0.5):
         super(CNNText, self).__init__()
@@ -45,7 +45,7 @@ class CNNText(torch.nn.Module):
         x = self.conv_pool(x)  # [N,L,C] -> [N,C]
         x = self.dropout(x)
         x = self.fc(x)  # [N,C] -> [N, N_class]
-        return {'output':x}
+        return {'output': x}
 
     def predict(self, word_seq):
         """
@@ -78,4 +78,3 @@ class CNNText(torch.nn.Module):
         correct = (predict == label_seq).long().sum().item()
         total = label_seq.size(0)
         return {'acc': 1.0 * correct / total}
-

test/test_tutorial.py

@@ -0,0 +1,95 @@
+import unittest
+
+from fastNLP import DataSet
+from fastNLP import Instance
+from fastNLP import Tester
+from fastNLP import Vocabulary
+from fastNLP.core.losses import CrossEntropyLoss
+from fastNLP.core.metrics import AccuracyMetric
+from fastNLP.models import CNNText
+
+
+class TestTutorial(unittest.TestCase):
+    def test_tutorial(self):
+        # 从csv读取数据到DataSet
+        dataset = DataSet.read_csv("./data_for_tests/tutorial_sample_dataset.csv", headers=('raw_sentence', 'label'),
+                                   sep='\t')
+        print(len(dataset))
+        print(dataset[0])
+
+        dataset.append(Instance(raw_sentence='fake data', label='0'))
+        dataset.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence')
+        # label转int
+        dataset.apply(lambda x: int(x['label']), new_field_name='label')
+
+        # 使用空格分割句子
+        def split_sent(ins):
+            return ins['raw_sentence'].split()
+
+        dataset.apply(split_sent, new_field_name='words')
+        # 增加长度信息
+        dataset.apply(lambda x: len(x['words']), new_field_name='seq_len')
+        print(len(dataset))
+        print(dataset[0])
+
+        # DataSet.drop(func)筛除数据
+        dataset.drop(lambda x: x['seq_len'] <= 3)
+        print(len(dataset))
+
+        # 设置DataSet中，哪些field要转为tensor
+        # set target，loss或evaluate中的golden，计算loss，模型评估时使用
+        dataset.set_target("label")
+        # set input，模型forward时使用
+        dataset.set_input("words")
+
+        # 分出测试集、训练集
+        test_data, train_data = dataset.split(0.5)
+        print(len(test_data))
+        print(len(train_data))
+
+        # 构建词表, Vocabulary.add(word)
+        vocab = Vocabulary(min_freq=2)
+        train_data.apply(lambda x: [vocab.add(word) for word in x['words']])
+        vocab.build_vocab()
+
+        # index句子, Vocabulary.to_index(word)
+        train_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='words')
+        test_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='words')
+        print(test_data[0])
+
+        model = CNNText(embed_num=len(vocab), embed_dim=50, num_classes=5, padding=2, dropout=0.1)
+
+        from fastNLP import Trainer
+        from copy import deepcopy
+
+        # 更改DataSet中对应field的名称，要以模型的forward等参数名一致
+        train_data.rename_field('words', 'word_seq')  # input field 与 forward 参数一致
+        train_data.rename_field('label', 'label_seq')
+        test_data.rename_field('words', 'word_seq')
+        test_data.rename_field('label', 'label_seq')
+
+        # 实例化Trainer，传入模型和数据，进行训练
+        copy_model = deepcopy(model)
+        overfit_trainer = Trainer(model=copy_model, train_data=test_data, dev_data=test_data,
+                                  losser=CrossEntropyLoss(input="output", target="label_seq"),
+                                  metrics=AccuracyMetric(pred="predict", target="label_seq"),
+                                  save_path="./save",
+                                  batch_size=4,
+                                  n_epochs=10)
+        overfit_trainer.train()
+
+        trainer = Trainer(model=model, train_data=train_data, dev_data=test_data,
+                          losser=CrossEntropyLoss(input="output", target="label_seq"),
+                          metrics=AccuracyMetric(pred="predict", target="label_seq"),
+                          save_path="./save",
+                          batch_size=4,
+                          n_epochs=10)
+        trainer.train()
+        print('Train finished!')
+
+        # 使用fastNLP的Tester测试脚本
+
+        tester = Tester(data=test_data, model=model, metrics=AccuracyMetric(pred="predict", target="label_seq"),
+                        batch_size=4)
+        acc = tester.test()
+        print(acc)