修改dataset.py的apply signature; batch当中增加num_batches属性; tester的format_eval_results修改; metric增加fast_evaluate_call机制

fastNLP/core/batch.py

@@ -26,6 +26,7 @@ class Batch(object):
         self.as_numpy = as_numpy
         self.idx_list = None
         self.curidx = 0
+        self.num_batches = len(dataset)//batch_size + int(len(dataset)%batch_size!=0)
 
     def __iter__(self):
         self.idx_list = self.sampler(self.dataset)
@@ -56,6 +57,9 @@ class Batch(object):
 
             return batch_x, batch_y
 
+    def __len__(self):
+        return self.num_batches
+
 def to_tensor(batch, dtype):
     if dtype in (np.int8, np.int16, np.int32, np.int64):
         batch = torch.LongTensor(batch)

fastNLP/core/dataset.py

@@ -168,6 +168,7 @@ class DataSet(object):
         """
         if old_name in self.field_arrays:
             self.field_arrays[new_name] = self.field_arrays.pop(old_name)
+            self.field_arrays[new_name].name = new_name
         else:
             raise KeyError("{} is not a valid name. ".format(old_name))
 
@@ -213,12 +214,12 @@ class DataSet(object):
 
         return wrapper
 
-    def apply(self, func, new_field_name=None):
+    def apply(self, func, new_field_name=None, is_input=False, is_target=False):
         """Apply a function to every instance of the DataSet.
 
         :param func: a function that takes an instance as input.
         :param str new_field_name: If not None, results of the function will be stored as a new field.
-        :return results: returned values of the function over all instances.
+        :return results: if new_field_name is not passed, returned values of the function over all instances.
         """
         results = [func(ins) for ins in self]
         if new_field_name is not None:
@@ -231,7 +232,7 @@ class DataSet(object):
                                is_input=old_field.is_input,
                                is_target=old_field.is_target)
             else:
-                self.add_field(name=new_field_name, fields=results)
+                self.add_field(name=new_field_name, fields=results, is_input=is_input, is_target=is_target)
         else:
             return results
 

fastNLP/core/metrics.py

@@ -245,7 +245,7 @@ class AccuracyMetric(MetricBase):
             self.total += np.prod(list(pred.size()))
 
     def get_metric(self, reset=True):
-        evaluate_result = {'acc': self.acc_count/self.total}
+        evaluate_result = {'acc': round(self.acc_count/self.total, 6)}
         if reset:
             self.acc_count = 0
             self.total = 0

fastNLP/core/tester.py

@@ -17,7 +17,7 @@ from fastNLP.core.utils import get_func_signature
 class Tester(object):
     """An collection of model inference and evaluation of performance, used over validation/dev set and test set. """
 
-    def __init__(self, data, model, metrics, batch_size=16, use_cuda=False, verbose=0):
+    def __init__(self, data, model, metrics, batch_size=16, use_cuda=False, verbose=1):
         super(Tester, self).__init__()
 
         if not isinstance(data, DataSet):
@@ -76,7 +76,7 @@ class Tester(object):
             _check_loss_evaluate(prev_func_signature=prev_func_signature, func_signature=e.func_signature,
                                  check_res=e.check_res, output=output, batch_y=truths, check_level=0)
 
-        if self.verbose >= 0:
+        if self.verbose >= 1:
             print("[tester] \n{}".format(self._format_eval_results(eval_results)))
         self._mode(network, is_test=False)
         return eval_results
@@ -107,7 +107,7 @@ class Tester(object):
         """
         _str = ''
         for metric_name, metric_result in results.items():
-            _str += metric_name + '\n\t'
-            _str += ", ".join([str(key) + "=" + str(value) for key, value in results.items()])
-        _str += '\n'
-        return _str
+            _str += metric_name + ': '
+            _str += ", ".join([str(key) + "=" + str(value) for key, value in metric_result.items()])
+            _str += '\n'
+        return _str[:-1]

fastNLP/core/trainer.py

@@ -28,9 +28,9 @@ class Trainer(object):
 
     """
 
-    def __init__(self, train_data, model, losser=None, metrics=None, n_epochs=3, batch_size=32, print_every=-1,
+    def __init__(self, train_data, model, losser=None, metrics=None, n_epochs=3, batch_size=32, print_every=50,
                  validate_every=-1,
-                 dev_data=None, use_cuda=False, save_path="./save",
+                 dev_data=None, use_cuda=False, save_path=None,
                  optimizer=Adam(lr=0.01, weight_decay=0), check_code_level=0,
                  metric_key=None,
                  **kwargs):
@@ -307,8 +307,8 @@ def _check_code(dataset, model, losser, metrics, batch_size=DEFAULT_CHECK_BATCH_
         _move_dict_value_to_device(batch_x, batch_y, device=model_devcie)
         # forward check
         if batch_count==0:
-            _check_forward_error(forward_func=model.forward, check_level=check_level,
-                                 batch_x=batch_x)
+            _check_forward_error(forward_func=model.forward, dataset=dataset,
+                                 batch_x=batch_x, check_level=check_level)
 
         refined_batch_x = _build_args(model.forward, **batch_x)
         output = model(**refined_batch_x)

fastNLP/core/utils.py

@@ -207,7 +207,7 @@ class CheckError(Exception):
     CheckError. Used in losses.LossBase, metrics.MetricBase.
     """
     def __init__(self, check_res:CheckRes, func_signature:str):
-        errs = [f'The following problems occurred when calling {func_signature}']
+        errs = [f'The following problems occurred when calling `{func_signature}`']
 
         if check_res.varargs:
             errs.append(f"\tvarargs: {check_res.varargs}(Does not support pass positional arguments, please delete it)")
@@ -255,7 +255,7 @@ def _check_loss_evaluate(prev_func_signature:str, func_signature:str, check_res:
             warnings.warn(message=_unused_warn)
 
 
-def _check_forward_error(forward_func, batch_x, check_level):
+def _check_forward_error(forward_func, batch_x, dataset, check_level):
     check_res = _check_arg_dict_list(forward_func, batch_x)
     func_signature = get_func_signature(forward_func)