1.增加DataSet的__setitem__方法，使得其可以直接random.shuffle(dataset); 2.优化部分log输出显示

fastNLP/core/callbacks/__init__.py

@@ -4,7 +4,8 @@ __all__ = [
     'EventsList',
     'Filter',
     'CallbackManager',
-    'CheckpointCallback',
+    'ModelCheckpointCallback',
+    'TrainerCheckpointCallback',
     'choose_progress_callback',
     'ProgressCallback',
     'RichCallback',
@@ -16,7 +17,7 @@ __all__ = [
 from .callback import Callback
 from .callback_events import EventsList, Events, Filter
 from .callback_manager import CallbackManager
-from .checkpoint_callback import CheckpointCallback
+from .checkpoint_callback import ModelCheckpointCallback, TrainerCheckpointCallback
 from .progress_callback import choose_progress_callback, ProgressCallback, RichCallback
 from .lr_scheduler_callback import LRSchedCallback
 from .load_best_model_callback import LoadBestModelCallback

fastNLP/core/callbacks/checkpoint_callback.py

@@ -1,5 +1,6 @@
 __all__ = [
-    'CheckpointCallback'
+    'ModelCheckpointCallback',
+    'TrainerCheckpointCallback'
 ]
 import os
 from typing import Union, Optional, Callable, Dict, Sequence, Any, Mapping

fastNLP/core/controllers/evaluator.py

@@ -133,17 +133,18 @@ class Evaluator:
 
         self.driver.barrier()
 
-    def run(self, num_eval_batch_per_dl: int = -1) -> Dict:
+    def run(self, num_eval_batch_per_dl: int = -1, **kwargs) -> Dict:
         """
         返回一个字典类型的数据，其中key为metric的名字，value为对应metric的结果。
-        如果存在多个metric，一个dataloader的情况，key的命名规则是
-            metric_indicator_name#metric_name
-        如果存在多个数据集，一个metric的情况，key的命名规则是
-            metric_indicator_name#dataloader_name (其中 # 是默认的 separator ，可以通过 Evaluator 初始化参数修改)。
-        如果存在多个metric，多个dataloader的情况，key的命名规则是
-            metric_indicator_name#metric_name#dataloader_name
-        :param num_eval_batch_per_dl: 每个 dataloader 测试多少个 batch 的数据，-1 为测试所有数据。
+            如果存在多个metric，一个dataloader的情况，key的命名规则是
+                metric_indicator_name#metric_name
+            如果存在多个数据集，一个metric的情况，key的命名规则是
+                metric_indicator_name#metric_name#dataloader_name (其中 # 是默认的 separator ，可以通过 Evaluator 初始化参数修改)。
+            如果存在多个metric，多个dataloader的情况，key的命名规则是
+                metric_indicator_name#metric_name#dataloader_name
+            其中 metric_indicator_name 可能不存在。
 
+        :param num_eval_batch_per_dl: 每个 dataloader 测试多少个 batch 的数据，-1 为测试所有数据。
         :return:
         """
         assert isinstance(num_eval_batch_per_dl, int), "num_eval_batch_per_dl must be of int type."
@@ -157,7 +158,6 @@ class Evaluator:
             assert self.driver.has_test_dataloaders()
 
         metric_results = {}
-
         self.reset()
         evaluate_context = self.driver.get_evaluate_context()
         self.driver.set_model_mode(mode='eval' if self.model_use_eval_mode else 'train')

fastNLP/core/controllers/trainer.py

@@ -291,6 +291,7 @@ class Trainer(TrainerEventTrigger):
                 raise FileNotFoundError("You are using `resume_from`, but we can not find your specific file.")
 
         if self.evaluator is not None and num_eval_sanity_batch > 0:
+            logger.info(f"Running evaluator sanity check for {num_eval_sanity_batch} batches.")
             self.on_sanity_check_begin()
             sanity_check_res = self.evaluator.run(num_eval_batch_per_dl=num_eval_sanity_batch)
             self.on_sanity_check_end(sanity_check_res)

fastNLP/core/dataset/dataset.py

@@ -8,9 +8,8 @@ __all__ = [
 
 import _pickle as pickle
 from copy import deepcopy
-from typing import Optional, List, Callable, Union, Dict, Any
+from typing import Optional, List, Callable, Union, Dict, Any, Mapping
 from functools import partial
-import warnings
 
 import numpy as np
 from threading import Thread
@@ -197,6 +196,20 @@ class DataSet:
         else:
             raise KeyError("Unrecognized type {} for idx in __getitem__ method".format(type(idx)))
 
+    def __setitem__(self, key, value):
+        assert isinstance(key, int) and key<len(self)
+        assert isinstance(value, Instance) or isinstance(value, Mapping)
+        ins_keys = set(value.keys())
+        ds_keys = set(self.get_field_names())
+
+        if len(ins_keys - ds_keys) != 0:
+            raise KeyError(f"The following keys are not found in the Dataset:{list(ins_keys - ds_keys)}.")
+        if len(ds_keys - ins_keys) != 0:
+            raise KeyError(f"The following keys are not found in the Instance:{list(ds_keys - ins_keys)}.")
+
+        for field_name, field in self.field_arrays.items():
+            field[key] = value[field_name]
+
     def __getattribute__(self, item):
         return object.__getattribute__(self, item)
 
@@ -813,6 +826,3 @@ class DataSet:
         self.collate_fns.set_input(*field_names)
 
 
-class IterableDataset:
-    pass
-

fastNLP/core/dataset/field.py

@@ -46,9 +46,6 @@ class FieldArray:
 
     def __setitem__(self, idx: int, val: Any):
         assert isinstance(idx, int)
-        if idx == -1:
-            idx = len(self) - 1
-        assert 0 <= idx < len(self), f"0<= idx <{len(self)}, but idx is {idx}"
         self.content[idx] = val
 
     def get(self, indices: Union[int, List[int]]):
@@ -79,7 +76,7 @@ class FieldArray:
 
     def split(self, sep: str = None, inplace: bool = True):
         r"""
-        依次对自身的元素使用.split()方法，应该只有当本field的元素为str时，该方法才有用。将返回值
+        依次对自身的元素使用.split()方法，应该只有当本field的元素为str时，该方法才有用。
 
         :param sep: 分割符，如果为None则直接调用str.split()。
         :param inplace: 如果为True，则将新生成值替换本field。否则返回list。

fastNLP/core/drivers/driver.py

@@ -6,6 +6,7 @@ from abc import ABC, abstractmethod
 from datetime import datetime
 from pathlib import Path
 from io import BytesIO
+import json
 
 __all__ = [
     'Driver'
@@ -447,13 +448,14 @@ class Driver(ABC):
 
             exc_type, exc_value, exc_traceback_obj = sys.exc_info()
             _write_exc_info = {
-                'exc_type': exc_type,
-                'exc_value': exc_value,
-                'time': str(datetime.now().strftime('%Y-%m-%d-%H:%M:%S')),
-                'global_rank': getattr(self, "global_rank", None),
-                'rank': self.get_local_rank(),
+                'exc_type': str(exc_type.__name__),
+                'exc_value': str(exc_value),
+                'exc_time': str(datetime.now().strftime('%Y-%m-%d-%H:%M:%S')),
+                'exc_global_rank': getattr(self, "global_rank", None),
+                'exc_local_rank': self.get_local_rank(),
             }
-            sys.stderr.write(str(_write_exc_info)+"\n")
+            sys.stderr.write("\nException info:\n")
+            sys.stderr.write(json.dumps(_write_exc_info, indent=2)+"\n")
 
             sys.stderr.write(f"Start to stop these pids:{self._pids}, please wait several seconds.\n")
             for pid in self._pids:

fastNLP/core/drivers/torch_driver/initialize_torch_driver.py

@@ -27,7 +27,7 @@ def initialize_torch_driver(driver: str, device: Optional[Union[str, torch.devic
     # world_size 和 rank
     if FASTNLP_BACKEND_LAUNCH in os.environ:
         if device is not None:
-            logger.warning("Parameter `device` would be ignored when you are using `torch.distributed.run` to pull "
+            logger.info("Parameter `device` would be ignored when you are using `torch.distributed.run` to pull "
                            "up your script. And we will directly get the local device via "
                            "`os.environ['LOCAL_RANK']`.")
         return TorchDDPDriver(model, torch.device(f"cuda:{os.environ['LOCAL_RANK']}"), True, **kwargs)
@@ -65,7 +65,7 @@ def initialize_torch_driver(driver: str, device: Optional[Union[str, torch.devic
         if not isinstance(device, List):
             return TorchSingleDriver(model, device, **kwargs)
         else:
-            logger.warning("Notice you are using `torch` driver but your chosen `device` are multi gpus, we will use "
+            logger.info("Notice you are using `torch` driver but your chosen `device` are multi gpus, we will use "
                            "`TorchDDPDriver` by default. But if you mean using `TorchDDPDriver`, you should choose parameter"
                            "`driver` as `TorchDDPDriver`.")
             return TorchDDPDriver(model, device, **kwargs)

tests/core/dataset/test_dataset.py

@@ -105,6 +105,20 @@ class TestDataSetMethods(unittest.TestCase):
         self.assertTrue(isinstance(field_array, FieldArray))
         self.assertEqual(len(field_array), 40)
 
+    def test_setitem(self):
+        ds = DataSet({"x": [[1, 2, 3, 4]] * 40, "y": [[5, 6]] * 40})
+        ds.add_field('i', list(range(len(ds))))
+        assert ds.get_field('i').content == list(range(len(ds)))
+        import random
+        random.shuffle(ds)
+        import numpy as np
+        np.random.shuffle(ds)
+        assert ds.get_field('i').content != list(range(len(ds)))
+
+        ins1 = ds[1]
+        ds[2] = ds[1]
+        assert ds[2]['x'] == ins1['x'] and ds[2]['y'] == ins1['y']
+
     def test_get_item_error(self):
         with self.assertRaises(RuntimeError):
             ds = DataSet({"x": [[1, 2, 3, 4]] * 10, "y": [[5, 6]] * 10})