[Bug-fix] fix Tensor.item() to np.array(Tensor).item due to low performance

paddle/fluid/pybind/eager_method.cc

@@ -143,7 +143,7 @@ static PyObject* tensor_method_numpy(TensorObject* self,
              "correct and will be "
              "removed in future. For Tensor contain only one element, Please "
              "modify "
-             " 'Tensor.numpy()[0]' to 'Tensor.item()' as soon as "
+             " 'Tensor.numpy()[0]' to 'float(Tensor)' as soon as "
              "possible, "
              "otherwise 'Tensor.numpy()[0]' will raise error in future.";
       py_rank = 1;

python/paddle/fluid/dygraph/math_op_patch.py

@@ -139,21 +139,21 @@ def _float_(var):
         ), "only one element variable can be converted to float."
         tensor = var.value().get_tensor()
         assert tensor._is_initialized(), "variable's tensor is not initialized"
-        return float(var.item())
+        return float(np.array(var).flatten()[0])
 
     def _long_(var):
         numel = np.prod(var.shape)
         assert numel == 1, "only one element variable can be converted to long."
         tensor = var.value().get_tensor()
         assert tensor._is_initialized(), "variable's tensor is not initialized"
-        return int(var.item())
+        return int(np.array(var).flatten()[0])
 
     def _int_(var):
         numel = np.prod(var.shape)
         assert numel == 1, "only one element variable can be converted to int."
         tensor = var.value().get_tensor()
         assert tensor._is_initialized(), "variable's tensor is not initialized"
-        return int(var.item())
+        return int(np.array(var).flatten()[0])
 
     def _len_(var):
         assert var.ndim > 0, "len() of a 0D tensor is wrong"
@@ -171,7 +171,7 @@ def _index_(var):
         ), "only one element variable can be converted to python index."
         tensor = var.value().get_tensor()
         assert tensor._is_initialized(), "variable's tensor is not initialized"
-        return int(var.item())
+        return int(np.array(var).flatten()[0])
 
     @property
     def _ndim_(var):

python/paddle/fluid/dygraph/varbase_patch_methods.py

@@ -718,11 +718,11 @@ def __nonzero__(self):
         ), "When Variable is used as the condition of if/while , Variable can only contain one element."
         if framework.global_var._in_eager_mode_:
             assert self._is_initialized(), "tensor not initialized"
-            return bool(self.item() > 0)
+            return bool(np.array(self) > 0)
         else:
             tensor = self.value().get_tensor()
             assert tensor._is_initialized(), "tensor not initialized"
-            return bool(self.item() > 0)
+            return bool(np.array(tensor) > 0)
 
     def __bool__(self):
         return self.__nonzero__()

python/paddle/nn/decode.py

@@ -712,7 +712,7 @@ def _maybe_copy(state, new_state, step_mask):
 
     step_idx = 0
     step_idx_tensor = paddle.full(shape=[1], fill_value=step_idx, dtype="int64")
-    while cond.item():
+    while np.array(cond).item():
         (step_outputs, next_states, next_inputs, next_finished) = decoder.step(
             step_idx_tensor, inputs, states, **kwargs
         )

python/paddle/nn/functional/pooling.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import numpy as np
+
 from paddle import _C_ops, _legacy_C_ops, in_dynamic_mode
 from paddle.fluid.framework import Variable, in_dygraph_mode
 
@@ -706,7 +708,7 @@ def _unpool_output_size(x, kernel_size, stride, padding, output_size):
         else:
             for i, var in enumerate(output_size):
                 if isinstance(var, Variable):
-                    output_size[i] = var.item()
+                    output_size[i] = np.array(var).item()
 
     if len(output_size) == len(kernel_size) + 2:
         output_size = output_size[2:]
@@ -1609,7 +1611,7 @@ def adaptive_avg_pool2d(x, output_size, data_format='NCHW', name=None):
 
     if in_dygraph_mode():
         output_size = [
-            item.item(0) if isinstance(item, Variable) else item
+            np.array(item).item(0) if isinstance(item, Variable) else item
             for item in output_size
         ]
     # output_size support Variable in static graph mode

python/paddle/static/nn/metric.py

@@ -14,6 +14,8 @@
 """
 All layers just related to metric.
 """
+import numpy as np
+
 import paddle
 from paddle import _legacy_C_ops
 from paddle.fluid.data_feeder import check_variable_and_dtype
@@ -76,7 +78,7 @@ def accuracy(input, label, k=1, correct=None, total=None):
         if total is None:
             total = _varbase_creator(dtype="int32")
 
-        _k = k.item(0) if isinstance(k, Variable) else k
+        _k = np.array(k).item(0) if isinstance(k, Variable) else k
         topk_out, topk_indices = _legacy_C_ops.top_k_v2(
             input, 'k', _k, 'sorted', False
         )