- fix callback & tests

fastNLP/core/callback.py

@@ -29,7 +29,7 @@ class Callback(object):
     @property
     def n_steps(self):
         """total number of steps for training"""
-        return self.n_steps
+        return self._trainer.n_steps
 
     @property
     def batch_size(self):
@@ -124,6 +124,21 @@ class Callback(object):
         pass
 
 
+def transfer(func):
+    """装饰器，将对CallbackManager的调用转发到各个Callback子类.
+    :param func:
+    :return:
+    """
+
+    def wrapper(manager, *arg):
+        returns = []
+        for callback in manager.callbacks:
+            returns.append(getattr(callback, func.__name__)(*arg))
+        return returns
+
+    return wrapper
+
+
 class CallbackManager(Callback):
     """A manager for all callbacks passed into Trainer.
     It collects resources inside Trainer and raise callbacks.
@@ -150,42 +165,59 @@ class CallbackManager(Callback):
             else:
                 raise TypeError(f"Expect callbacks in CallbackManager(callbacks) to be list. Got {type(callbacks)}.")
 
+        for env_name, env_val in env.items():
+            for callback in self.callbacks:
+                setattr(callback, '_'+env_name, env_val)  # Callback.trainer
+
+    @transfer
     def on_train_begin(self):
         pass
 
+    @transfer
     def on_epoch_begin(self):
         pass
 
+    @transfer
     def on_batch_begin(self, batch_x, batch_y, indices):
         pass
 
+    @transfer
     def on_loss_begin(self, batch_y, predict_y):
         pass
 
+    @transfer
     def on_backward_begin(self, loss):
         pass
 
+    @transfer
     def on_backward_end(self):
         pass
 
+    @transfer
     def on_step_end(self):
         pass
 
+    @transfer
     def on_batch_end(self):
         pass
 
+    @transfer
     def on_valid_begin(self):
         pass
 
+    @transfer
     def on_valid_end(self, eval_result, metric_key):
         pass
 
+    @transfer
     def on_epoch_end(self):
         pass
 
+    @transfer
     def on_train_end(self):
         pass
 
+    @transfer
     def on_exception(self, exception):
         pass
 

test/core/test_callbacks.py

@@ -139,11 +139,14 @@ class TestCallback(unittest.TestCase):
 
     def test_readonly_property(self):
         from fastNLP.core.callback import Callback
+        passed_epochs = []
+        total_epochs = 5
         class MyCallback(Callback):
             def __init__(self):
                 super(MyCallback, self).__init__()
 
-            def on_epoch_begin(self, cur_epoch, total_epoch):
+            def on_epoch_begin(self):
+                passed_epochs.append(self.epoch)
                 print(self.n_epochs, self.n_steps, self.batch_size)
                 print(self.model)
                 print(self.optimizer)
@@ -151,7 +154,7 @@ class TestCallback(unittest.TestCase):
         data_set, model = prepare_env()
         trainer = Trainer(data_set, model,
                           loss=BCELoss(pred="predict", target="y"),
-                          n_epochs=5,
+                          n_epochs=total_epochs,
                           batch_size=32,
                           print_every=50,
                           optimizer=SGD(lr=0.1),
@@ -161,3 +164,4 @@ class TestCallback(unittest.TestCase):
                           metrics=AccuracyMetric(pred="predict", target="y"),
                           callbacks=[MyCallback()])
         trainer.train()
+        assert passed_epochs == list(range(1, total_epochs+1))

test/core/test_dataset.py

@@ -217,6 +217,7 @@ class TestDataSetMethods(unittest.TestCase):
         self.assertTrue(len(ds) > 0)
 
     def test_add_null(self):
+        # TODO test failed because 'fastNLP\core\fieldarray.py:143: RuntimeError'
         ds = DataSet()
         ds.add_field('test', [])
         ds.set_target('test')

test/models/test_enas.py

@@ -1,112 +0,0 @@
-import unittest
-
-from fastNLP import DataSet
-from fastNLP import Instance
-from fastNLP import Vocabulary
-from fastNLP.core.losses import CrossEntropyLoss
-from fastNLP.core.metrics import AccuracyMetric
-
-
-class TestENAS(unittest.TestCase):
-    def testENAS(self):
-        # 从csv读取数据到DataSet
-        sample_path = "tutorials/sample_data/tutorial_sample_dataset.csv"
-        dataset = DataSet.read_csv(sample_path, headers=('raw_sentence', 'label'),
-                                   sep='\t')
-        print(len(dataset))
-        print(dataset[0])
-        print(dataset[-3])
-
-        dataset.append(Instance(raw_sentence='fake data', label='0'))
-        # 将所有数字转为小写
-        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", "seq_len")
-
-        # 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])
-
-        # 如果你们需要做强化学习或者GAN之类的项目，你们也可以使用这些数据预处理的工具
-        from fastNLP.core.batch import Batch
-        from fastNLP.core.sampler import RandomSampler
-
-        batch_iterator = Batch(dataset=train_data, batch_size=2, sampler=RandomSampler())
-        for batch_x, batch_y in batch_iterator:
-            print("batch_x has: ", batch_x)
-            print("batch_y has: ", batch_y)
-            break
-
-        from fastNLP.models.enas_model import ENASModel
-        from fastNLP.models.enas_controller import Controller
-        model = ENASModel(embed_num=len(vocab), num_classes=5)
-        controller = Controller()
-
-        from fastNLP.models.enas_trainer import ENASTrainer
-        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')
-
-        loss = CrossEntropyLoss(pred="output", target="label_seq")
-        metric = AccuracyMetric(pred="predict", target="label_seq")
-
-        trainer = ENASTrainer(model=model, controller=controller, train_data=train_data, dev_data=test_data,
-                          loss=CrossEntropyLoss(pred="output", target="label_seq"),
-                          metrics=AccuracyMetric(pred="predict", target="label_seq"),
-                          check_code_level=-1,
-                          save_path=None,
-                          batch_size=32,
-                          print_every=1,
-                          n_epochs=3,
-                          final_epochs=1)
-        trainer.train()
-        print('Train finished!')
-
-        # 调用Tester在test_data上评价效果
-        from fastNLP import Tester
-
-        tester = Tester(data=test_data, model=model, metrics=AccuracyMetric(pred="predict", target="label_seq"),
-                        batch_size=4)
-
-        acc = tester.test()
-        print(acc)
-
-
-if __name__ == '__main__':
-    unittest.main()