修改element,计算值转为非tensor形式

fastNLP/core/metrics/element.py

@@ -3,6 +3,7 @@ __all__ = [
 ]
 
 import os
+import functools
 
 from .backend import Backend, AutoBackend
 from fastNLP.core.log import logger
@@ -10,6 +11,17 @@ from .utils import AggregateMethodError
 from fastNLP.envs.env import FASTNLP_GLOBAL_RANK
 
 
+def _wrap_cal_value(func):
+    @functools.wraps(func)
+    def _wrap_cal(*args, **kwargs):
+        self = args[0]
+        value = func(*args, **kwargs)
+        value = self.backend.get_scalar(value)
+        return value
+
+    return _wrap_cal
+
+
 class Element:
     def __init__(self, name, value: float, aggregate_method, backend: Backend):
         """
@@ -107,6 +119,7 @@ class Element:
         对元素进行 fill_value， 会执行队友 backend 的 fill_value 方法
 
         """
+        self._check_value_initialized()
         self._value = self.backend.fill_value(self._value, value)
 
     def to(self, device):
@@ -138,163 +151,148 @@ class Element:
             raise RuntimeError(prefix + " is not initialized. Please either specify backend when creating this "
                                         "element, or use it after it being used by the `Metric.update()` method.")
 
+    @_wrap_cal_value
     def __add__(self, other):
         self._check_value_when_call()
         if isinstance(other, Element):
             other = other.value
         return self.value + other
 
+    @_wrap_cal_value
     def __radd__(self, other):
         self._check_value_when_call()
         if isinstance(other, Element):
-            self.value += other.value
-        else:
-            self.value += other
-        return self
+            other = other.value
+        return self.value + other
 
+    @_wrap_cal_value
     def __sub__(self, other):
         self._check_value_when_call()
         if isinstance(other, Element):
-            self.value -= other.value
-        else:
-            self.value -= other
-        return self
+            other = other.value
+        return self.value - other
 
+    @_wrap_cal_value
     def __rsub__(self, other):
         self._check_value_when_call()
         if isinstance(other, Element):
-            self.value -= other.value
-        else:
-            self.value -= other
-        return self
+            other = other.value
+        return self.value - other
 
+    @_wrap_cal_value
     def __mul__(self, other):
         self._check_value_when_call()
         if isinstance(other, Element):
-            self.value *= other.value
-        else:
-            self.value *= other
-        return self
+            other = other.value
+        return self.value * other
 
+    @_wrap_cal_value
     def __imul__(self, other):
         self._check_value_when_call()
         if isinstance(other, Element):
-            self.value *= other.value
-        else:
-            self.value *= other
-        return self
+            other = other.value
+        return self.value * other
 
+    @_wrap_cal_value
     def __floordiv__(self, other):
         self._check_value_when_call()
         if isinstance(other, Element):
-            self.value //= other.value
-        else:
-            self.value //= other
-        return self
+            other = other.value
+        return self.value // other
 
+    @_wrap_cal_value
     def __rfloordiv__(self, other):
         self._check_value_when_call()
         if isinstance(other, Element):
-            self.value //= other.value
-        else:
-            self.value //= other
-        return self
+            other = other.value
+        return self.value // other
 
+    @_wrap_cal_value
     def __truediv__(self, other):
         self._check_value_when_call()
         if isinstance(other, Element):
-            self.value /= other.value
-        else:
-            self.value /= other
-        return self
+            other = other.value
+        return self.value / other
 
+    @_wrap_cal_value
     def __rtruediv__(self, other):
         self._check_value_when_call()
         if isinstance(other, Element):
-            self.value /= other.value
-        else:
-            self.value /= other
-        return self
+            other = other.value
+        return self.value / other
 
+    @_wrap_cal_value
     def __mod__(self, other):
         self._check_value_when_call()
         if isinstance(other, Element):
-            self.value %= other.value
-        else:
-            self.value %= other
-        return self
+            other = other.value
+        return self.value % other
 
+    @_wrap_cal_value
     def __rmod__(self, other):
         self._check_value_when_call()
         if isinstance(other, Element):
-            self.value /= other.value
-        else:
-            self.value /= other
-        return self
+            other = other.value
+        return self.value % other
 
+    @_wrap_cal_value
     def __pow__(self, other, modulo=None):
         self._check_value_when_call()
+        if isinstance(other, Element):
+            other = other.value
         if modulo is None:
-            if isinstance(other, Element):
-                self.value **= other.value
-            else:
-                self.value **= other
+            return self.value ** other
         else:
-            if isinstance(other, Element):
-                self.value = pow(self.value, other.value, modulo)
-            else:
-                self.value = pow(self.value, other, modulo)
-        return self
+            return pow(self.value, other, modulo)
 
+    @_wrap_cal_value
     def __rpow__(self, other):
         self._check_value_when_call()
         if isinstance(other, Element):
-            self.value **= other.value
-        else:
-            self.value **= other
-        return self
+            other = other.value
+        return self.value ** other
 
+    @_wrap_cal_value
     def __lt__(self, other) -> bool:
         self._check_value_when_call()
         if isinstance(other, Element):
-            return self.value < other.value
-        else:
-            return self.value < other
+            other = other.value
+        return self.value < other
 
+    @_wrap_cal_value
     def __le__(self, other) -> bool:
         self._check_value_when_call()
         if isinstance(other, Element):
-            return self.value <= other.value
-        else:
-            return self.value <= other
+            other = other.value
+        return self.value <= other
 
+    @_wrap_cal_value
     def __eq__(self, other):
         self._check_value_when_call()
         if isinstance(other, Element):
-            return self.value == other.value
-        else:
-            return self.value == other
+            other = other.value
+        return self.value == other
 
+    @_wrap_cal_value
     def __ne__(self, other) -> bool:
         self._check_value_when_call()
         if isinstance(other, Element):
-            return self.value != other.value
-        else:
-            return self.value != other
+            other = other.value
+        return self.value != other
 
+    @_wrap_cal_value
     def __ge__(self, other) -> bool:
         self._check_value_when_call()
         if isinstance(other, Element):
-            return self.value >= other.value
-        else:
-            return self.value >= other
+            other = other.value
+        return self.value >= other
 
+    @_wrap_cal_value
     def __gt__(self, other) -> bool:
         self._check_value_when_call()
         if isinstance(other, Element):
-            return self.value > other.value
-        else:
-            return self.value > other
+            other = other.value
+        return self.value > other
 
     def __str__(self):
         return str(self.value)

tests/core/metrics/test_element_cal_element.py

@@ -0,0 +1,35 @@
+import pytest
+
+from fastNLP.core.metrics import Metric
+from fastNLP.envs.imports import _NEED_IMPORT_TORCH
+from .utils import find_free_network_port, setup_ddp
+if _NEED_IMPORT_TORCH:
+    import torch
+
+
+class MyMetric(Metric):
+    def __init__(self):
+        super(MyMetric, self).__init__()
+        self.register_element(name="t1", value=0)
+        self.register_element(name="t2", value=0)
+        self.register_element(name="t3", value=0)
+
+    def update(self, pred):
+        self.t1 = len(pred)
+        self.t2 = len(pred)
+        temp = self.t1 + self.t2
+        self.t3 = temp
+        self.t1 = self.t3 / self.t2
+
+    def get_metric(self) -> dict:
+        return {"t1": self.t1.get_scalar(), "t2": self.t2.get_scalar(), "t3": self.t3.get_scalar()}
+
+
+class TestElemnt:
+
+    def test_case_v1(self):
+        pred = torch.tensor([1, 1, 1, 1])
+        metric = MyMetric()
+        metric.update(pred)
+        res = metric.get_metric()
+        print(res)